From 701812efed6742915d9cf57aef985127b3cc449d Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Wed, 9 Nov 2022 19:52:08 -0500 Subject: [PATCH 01/15] feat!: disentangle threshold selection from the main model (#89) * threshold estimators as separate models * remove threshold estimating from autoencoders * simplify mlflow model saving * mlflow now only supports saving per artifact * registry load function now returns a dataclass instead of dict * replace mlflow with mlflow-skinny to reduce unwanted dependencies Signed-off-by: Avik Basu --- docs/post-processing.md | 3 +- docs/quick-start.md | 6 +- .../Dockerfile.mlflow | 0 .../src/udf/postprocess.py | 2 +- numalogic/models/autoencoder/pipeline.py | 110 +- numalogic/models/forecast/variants/naive.py | 2 +- numalogic/models/threshold/__init__.py | 0 numalogic/models/threshold/_std.py | 41 + numalogic/{scores.py => postprocess.py} | 0 numalogic/registry/__init__.py | 5 +- numalogic/registry/artifact.py | 16 +- numalogic/registry/mlflow_registry.py | 151 +- .../tests/models/autoencoder/test_pipeline.py | 63 +- .../tests/registry/test_mlflow_registry.py | 14 +- numalogic/tests/test_scores.py | 2 +- poetry.lock | 1217 +++++------------ pyproject.toml | 6 +- 17 files changed, 528 insertions(+), 1110 deletions(-) create mode 100644 examples/numalogic-simple-pipeline/Dockerfile.mlflow create mode 100644 numalogic/models/threshold/__init__.py create mode 100644 numalogic/models/threshold/_std.py rename numalogic/{scores.py => postprocess.py} (100%) diff --git a/docs/post-processing.md b/docs/post-processing.md index 048ff8f8..dc1a36af 100644 --- a/docs/post-processing.md +++ b/docs/post-processing.md @@ -3,6 +3,7 @@ Post-processing step is again an optional step, where we normalize the anomalies between 0-10. This is mostly to make the scores more understandable. ```python -from numalogic.scores import tanh_norm +from numalogic.postprocess import tanh_norm + test_anomaly_score_norm = tanh_norm(test_anomaly_score) ``` \ No newline at end of file diff --git a/docs/quick-start.md b/docs/quick-start.md index 6e58ff76..181015dc 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -18,13 +18,13 @@ In this example, the train data set has numbers ranging from 1-10. Whereas in th import numpy as np from numalogic.models.autoencoder import AutoencoderPipeline from numalogic.models.autoencoder.variants import Conv1dAE -from numalogic.scores import tanh_norm +from numalogic.postprocess import tanh_norm X_train = np.array([1, 3, 5, 2, 5, 1, 4, 5, 1, 4, 5, 8, 9, 1, 2, 4, 5, 1, 3]).reshape(-1, 1) -X_test = np.array([-20, 3, 5, 40, 5, 10, 4, 5, 100]).reshape(-1,1) +X_test = np.array([-20, 3, 5, 40, 5, 10, 4, 5, 100]).reshape(-1, 1) model = AutoencoderPipeline( - model=Conv1dAE(in_channels=1, enc_channels=4), seq_len=8, num_epochs=30 + model=Conv1dAE(in_channels=1, enc_channels=4), seq_len=8, num_epochs=30 ) # fit method trains the model on train data set model.fit(X_train) diff --git a/examples/numalogic-simple-pipeline/Dockerfile.mlflow b/examples/numalogic-simple-pipeline/Dockerfile.mlflow new file mode 100644 index 00000000..e69de29b diff --git a/examples/numalogic-simple-pipeline/src/udf/postprocess.py b/examples/numalogic-simple-pipeline/src/udf/postprocess.py index f3113933..df056ea7 100644 --- a/examples/numalogic-simple-pipeline/src/udf/postprocess.py +++ b/examples/numalogic-simple-pipeline/src/udf/postprocess.py @@ -1,7 +1,7 @@ import logging import numpy as np -from numalogic.scores import tanh_norm +from numalogic.postprocess import tanh_norm from pynumaflow.function import Messages, Message, Datum from src.utils import Payload diff --git a/numalogic/models/autoencoder/pipeline.py b/numalogic/models/autoencoder/pipeline.py index 255c07fb..cf102dc6 100644 --- a/numalogic/models/autoencoder/pipeline.py +++ b/numalogic/models/autoencoder/pipeline.py @@ -1,12 +1,12 @@ import io import logging from copy import copy -from typing import Optional, Dict, Tuple, BinaryIO, Union, Callable +from typing import Optional, BinaryIO, Union import numpy as np import torch from numpy.typing import NDArray -from sklearn.base import OutlierMixin +from sklearn.base import TransformerMixin, BaseEstimator from torch import nn, optim, Tensor from torch.utils.data import DataLoader @@ -15,7 +15,7 @@ _LOGGER = logging.getLogger(__name__) -class AutoencoderPipeline(OutlierMixin): +class AutoencoderPipeline(TransformerMixin, BaseEstimator): r""" Class to simplify training, inference, loading and saving of time-series autoencoders. @@ -25,17 +25,12 @@ class AutoencoderPipeline(OutlierMixin): model: model instance seq_len: sequence length loss_fn: loss function used for training - supported values include {"huber", "l1", "mse"} + supported values include {"huber", "l1", "mse"} optimizer: optimizer to used for training. supported values include {"adam", "adagrad", "rmsprop"} lr: learning rate batch_size: batch size for training num_epochs: number of epochs for training - std_tolerance: determines how many times the standard deviation to be used for threshold - reconerr_method: method used to calculate the distance - between the original and the reconstucted data - supported values include {"absolute", "squared"} - threshold_min: the minimum threshold to use; can be used when the threshold calculated is too low resume_train: parameter to decide if resume training is needed. Also, based on this parameter the optimizer state dict @@ -59,10 +54,7 @@ def __init__( lr: float = 0.001, batch_size: int = 256, num_epochs: int = 100, - std_tolerance: float = 3.0, - reconerr_method: str = "absolute", - threshold_min: float = None, - resume_train: bool = False, + resume_train: bool = False ): if not (model and seq_len): raise ValueError("No model and seq len provided!") @@ -75,19 +67,15 @@ def __init__( self.optimizer = self.init_optimizer(optimizer, lr) self.batch_size = batch_size self.num_epochs = num_epochs - - self._thresholds = None - self._stats: Dict[str, Optional[float]] = dict(mean=None, std=None) - self.stdtol = std_tolerance - self.reconerr_func = self.get_reconerr_func(reconerr_method) - self.threshold_min = threshold_min self.resume_train = resume_train + self._epochs_elapsed = 0 @property def model_properties(self): model_properties_dict = { - "thresholds": self._thresholds, - "err_stats": self._stats, + "batch_size": self.batch_size, + "num_epochs": self.num_epochs, + "epochs_elapsed": self._epochs_elapsed } if self.resume_train: model_properties_dict["optimizer_state_dict"] = self.optimizer.state_dict() @@ -97,22 +85,6 @@ def model_properties(self): def model(self) -> AutoencoderModel: return self._model - @property - def thresholds(self) -> Optional[NDArray[float]]: - return self._thresholds - - @property - def err_stats(self) -> Dict[str, Optional[NDArray[float]]]: - return self._stats - - @staticmethod - def get_reconerr_func(method: str) -> Callable: - if method == "squared": - return np.square - if method == "absolute": - return np.abs - raise ValueError(f"Unrecognized reconstruction error method specified: {method}") - @staticmethod def init_criterion(loss_fn: str): if loss_fn == "huber": @@ -161,11 +133,7 @@ def fit(self, X: NDArray[float], y=None, log_freq: int = 5) -> "AutoencoderPipel if epoch % log_freq == 0: _LOGGER.info(f"epoch : {epoch}, loss_mean : {np.mean(losses):.7f}") losses = [] - - self._thresholds, _mean, _std = self.find_thresholds(X) - self._stats["mean"] = _mean - self._stats["std"] = _std - + self._epochs_elapsed += 1 return self def predict(self, X: NDArray[float], seq_len: int = None) -> NDArray[float]: @@ -187,59 +155,22 @@ def predict(self, X: NDArray[float], seq_len: int = None) -> NDArray[float]: _, pred = self._model(dataset.data) return dataset.recover_shape(pred) - def score(self, X: NDArray[float], seq_len: int = None) -> NDArray[float]: - r""" - Return anomaly score using the calculated threshold - - Args: - X: training dataset - seq_len: sequence length / window length - - Returns: - numpy array with anomaly scores - """ - if self._thresholds is None: - raise RuntimeError("Thresholds not present!!!") - thresh = self._thresholds.reshape(1, -1) - if not seq_len: - seq_len = self.seq_len or len(X) - recon_err = self.recon_err(X, seq_len=seq_len) - anomaly_scores = recon_err / thresh - return anomaly_scores - - def recon_err(self, X: NDArray[float], seq_len: int) -> NDArray: + def score(self, X: NDArray[float]) -> NDArray: r""" Returns the reconstruction error. Args: - X: training dataset - seq_len: sequence length / window length + X: data Returns: numpy array with anomaly scores """ - x_recon = self.predict(X, seq_len=seq_len) - recon_err = self.reconerr_func(X - x_recon) + x_recon = self.predict(X, seq_len=self.seq_len) + recon_err = np.abs(X - x_recon) return recon_err - def find_thresholds( - self, X: NDArray[float] - ) -> Tuple[NDArray[float], NDArray[float], NDArray[float]]: - r""" - Calculate threshold for the anomaly model - Args: - X: training dataset - - Returns: - Tuple consisting of thresholds, reconstruction error mean, reconstruction error std - """ - recon_err = self.recon_err(X, seq_len=self.seq_len) - recon_err_mean = np.mean(recon_err, axis=0) - recon_err_std = np.std(recon_err, axis=0) - thresholds = recon_err_mean + (self.stdtol * recon_err_std) - if self.threshold_min: - thresholds[thresholds < self.threshold_min] = self.threshold_min - return thresholds, recon_err_mean, recon_err_std + def transform(self, X: NDArray[float]) -> NDArray: + return self.score(X) def save(self, path: Optional[str] = None) -> Optional[BinaryIO]: r""" @@ -263,8 +194,9 @@ def save(self, path: Optional[str] = None) -> Optional[BinaryIO]: def __load_metadata(self, **metadata) -> None: if self.resume_train: self.optimizer.load_state_dict(metadata["optimizer_state_dict"]) - self._thresholds = metadata["thresholds"] - self._stats = metadata["err_stats"] + self._epochs_elapsed = metadata["epochs_elapsed"] + self.num_epochs = metadata["num_epochs"] + self.batch_size = metadata["batch_size"] def load(self, path: Union[str, BinaryIO] = None, model=None, **metadata) -> None: r""" @@ -466,7 +398,3 @@ def fit(self, X: NDArray[float], y=None, log_freq: int = 5) -> None: if epoch % log_freq == 0: _LOGGER.info(f"epoch : {epoch}, penalty: {penalty} loss_mean : {loss.item():.7f}") - - self._thresholds, _mean, _std = self.find_thresholds(X) - self._stats["mean"] = _mean - self._stats["std"] = _std diff --git a/numalogic/models/forecast/variants/naive.py b/numalogic/models/forecast/variants/naive.py index 4bb21182..d85a1eca 100644 --- a/numalogic/models/forecast/variants/naive.py +++ b/numalogic/models/forecast/variants/naive.py @@ -6,7 +6,7 @@ from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler, FunctionTransformer -from numalogic.scores import tanh_norm +from numalogic.postprocess import tanh_norm class BaselineForecaster: diff --git a/numalogic/models/threshold/__init__.py b/numalogic/models/threshold/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/numalogic/models/threshold/_std.py b/numalogic/models/threshold/_std.py new file mode 100644 index 00000000..edc79b25 --- /dev/null +++ b/numalogic/models/threshold/_std.py @@ -0,0 +1,41 @@ +import numpy as np +from numpy.typing import NDArray +from sklearn.base import BaseEstimator + + +class StdDevThreshold(BaseEstimator): + def __init__( + self, + std_factor: float = 3.0, + min_threshold: float = 0.1 + ): + self.std_factor = std_factor + self.min_threshold = min_threshold + + self._std = None + self._mean = None + self._threshold = None + + @property + def mean(self): + return self._mean + + @property + def std(self): + return self._std + + @property + def threshold(self): + return self._threshold + + def fit(self, X, y=None): + self._std = np.std(X, axis=0) + self._mean = np.mean(X, axis=0) + self._threshold = self._mean + (self.std_factor * self._std) + self._threshold[self._threshold < self.min_threshold] = self.min_threshold + + return self + + def predict(self, X: NDArray[float]) -> NDArray[float]: + anomaly_scores = X / self.threshold + return anomaly_scores diff --git a/numalogic/scores.py b/numalogic/postprocess.py similarity index 100% rename from numalogic/scores.py rename to numalogic/postprocess.py diff --git a/numalogic/registry/__init__.py b/numalogic/registry/__init__.py index f8c4ebcf..d9d72261 100644 --- a/numalogic/registry/__init__.py +++ b/numalogic/registry/__init__.py @@ -1,8 +1,9 @@ from numalogic.registry.artifact import ArtifactManager +from numalogic.registry.artifact import ArtifactData try: from numalogic.registry.mlflow_registry import MLflowRegistrar except ImportError: - __all__ = ["ArtifactManager"] + __all__ = ["ArtifactManager", "ArtifactData"] else: - __all__ = ["ArtifactManager", "MLflowRegistrar"] + __all__ = ["ArtifactManager", "ArtifactData", "MLflowRegistrar"] diff --git a/numalogic/registry/artifact.py b/numalogic/registry/artifact.py index 9e255b93..5933778f 100644 --- a/numalogic/registry/artifact.py +++ b/numalogic/registry/artifact.py @@ -1,9 +1,17 @@ from abc import ABCMeta, abstractmethod +from dataclasses import dataclass from typing import Sequence, Any, Union, Dict from numalogic.tools.types import Artifact +@dataclass +class ArtifactData: + artifact: Artifact + metadata: Dict[str, Any] + extras: Dict[str, Any] + + class ArtifactManager(metaclass=ABCMeta): """ Abstract base class for artifact save, load and delete. @@ -17,7 +25,7 @@ def __init__(self, uri: str): @abstractmethod def load( self, skeys: Sequence[str], dkeys: Sequence[str], latest: bool = True, version: str = None - ) -> Artifact: + ) -> ArtifactData: """ Loads the desired artifact from mlflow registry and returns it. Args: @@ -33,8 +41,7 @@ def save( self, skeys: Sequence[str], dkeys: Sequence[str], - primary_artifact: Artifact, - secondary_artifacts: Union[Sequence[Artifact], Dict[str, Artifact], None] = None, + artifact: Artifact, **metadata ) -> Any: r""" @@ -42,8 +49,7 @@ def save( Args: skeys: static key fields as list/tuple of strings dkeys: dynamic key fields as list/tuple of strings - primary_artifact: primary artifact to be saved - secondary_artifacts: secondary artifact to be saved + artifact: primary artifact to be saved metadata: additional metadata surrounding the artifact that needs to be saved """ pass diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py index 7cb70049..cd835870 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/numalogic/registry/mlflow_registry.py @@ -1,6 +1,4 @@ -import codecs import logging -import pickle from enum import Enum from typing import Optional, Sequence, Union, Dict @@ -10,8 +8,8 @@ from mlflow.exceptions import RestException from mlflow.tracking import MlflowClient -from numalogic.registry import ArtifactManager -from numalogic.tools.types import Artifact, ArtifactDict +from numalogic.registry import ArtifactManager, ArtifactData +from numalogic.tools.types import Artifact _LOGGER = logging.getLogger() @@ -20,7 +18,6 @@ class ModelStage(str, Enum): """ Defines different stages the model state can be in mlflow """ - STAGE = "Staging" ARCHIVE = "Archived" PRODUCTION = "Production" @@ -42,54 +39,38 @@ class MLflowRegistrar(ArtifactManager): Examples -------- >>> from numalogic.models.autoencoder.variants.vanilla import VanillaAE - >>> from numalogic.preprocess.transformer import LogTransformer >>> from numalogic.registry.mlflow_registry import MLflowRegistrar - >>> from sklearn.preprocessing import StandardScaler, Normalizer >>> from sklearn.pipeline import make_pipeline >>> >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] >>> scaler = StandardScaler.fit(data) - >>> ml = MLflowRegistrar(tracking_uri="http://0.0.0.0:8080", artifact_type="pytorch") - >>> ml.save(skeys=["model"],dkeys=["AE"], primary_artifact=VanillaAE(10), - >>> ... secondary_artifacts={"preproc": make_pipeline(scaler)}) - >>> data = ml.load(skeys=["model"],dkeys=["AE"]) + >>> registry = MLflowRegistrar(tracking_uri="http://0.0.0.0:8080", artifact_type="pytorch") + >>> registry.save(skeys=["model"], dkeys=["AE"], artifact=VanillaAE(10)) + >>> artifact_data = registry.load(skeys=["model"], dkeys=["AE"]) """ + _TRACKING_URI = None + + def __new__( + cls, + tracking_uri: Optional[str], + artifact_type: str = "pytorch", + models_to_retain: int = 5, + *args, + **kwargs, + ): + instance = super().__new__(cls, *args, **kwargs) + if (not cls._TRACKING_URI) or (cls._TRACKING_URI != tracking_uri): + cls._TRACKING_URI = tracking_uri + return instance def __init__( self, tracking_uri: str, artifact_type: str = "pytorch", models_to_retain: int = 5 ): super().__init__(tracking_uri) - mlflow.set_tracking_uri(tracking_uri) self.client = MlflowClient() self.handler = self.mlflow_handler(artifact_type) self.models_to_retain = models_to_retain - @staticmethod - def __as_dict( - primary_artifact: Optional[Artifact], - secondary_artifacts: Union[Sequence[Artifact], Dict[str, Artifact], None], - metadata: Optional[dict], - model_properties: Optional[ModelVersion], - ) -> ArtifactDict: - """ - Returns a dictionary comprising information on model, metadata, model_properties - Args: - primary_artifact: main artifact to be saved - secondary_artifacts: secondary artifacts to be saved - metadata: ML models metadata - model_properties: ML model properties (information like time "model_created", - "model_updated_time", "model_name", "tags" , "current stage", - "version" etc.) - - Returns: ArtifactDict type object - """ - return { - "primary_artifact": primary_artifact, - "secondary_artifacts": secondary_artifacts, - "metadata": metadata, - "model_properties": model_properties, - } - @staticmethod def construct_key(skeys: Sequence[str], dkeys: Sequence[str]) -> str: """ @@ -121,68 +102,54 @@ def mlflow_handler(artifact_type: str): raise NotImplementedError("Artifact Type not Implemented") def load( - self, skeys: Sequence[str], dkeys: Sequence[str], latest: bool = True, version: str = None - ) -> ArtifactDict: - """ - Loads the desired artifact from mlflow registry and returns it. - Args: - skeys: static key fields as list/tuple of strings - dkeys: dynamic key fields as list/tuple of strings - latest: boolean field to determine if latest version is desired or not - version: explicit artifact version - - Returns: - A dictionary containing primary_artifact, secondary_artifacts, metadata and - model_properties - """ - + self, + skeys: Sequence[str], + dkeys: Sequence[str], + latest: bool = True, + version: str = None, + ) -> Optional[ArtifactData]: model_key = self.construct_key(skeys, dkeys) try: if latest: - stage = "Production" - model = self.handler.load_model(model_uri=f"models:/{model_key}/{stage}") + model = self.handler.load_model( + model_uri=f"models:/{model_key}/{ModelStage.PRODUCTION}" + ) + version_info = self.client.get_latest_versions( + model_key, stages=[ModelStage.PRODUCTION] + )[-1] elif version is not None: - model = self.handler.load_model(model_uri=f"models:/{model_key}/{version}") + model = self.handler.load_model( + model_uri=f"models:/{model_key}/{version}" + ) + version_info = self.client.get_model_version(model_key, version) else: - _LOGGER.warning("Version not provided in the load mlflow model function call") - return {} + raise ValueError("One of 'latest' or 'version' needed in load method call") _LOGGER.info("Successfully loaded model %s from Mlflow", model_key) - metadata = None - secondary_artifacts = None - model_properties = self.client.get_latest_versions(model_key, stages=["Production"])[-1] - if model_properties.run_id: - run_id = model_properties.run_id - run_data = self.client.get_run(run_id).data.to_dictionary() - if run_data["params"]: - data = run_data["params"] - if "secondary_artifacts" in data: - secondary_artifacts = pickle.loads( - codecs.decode(data["secondary_artifacts"].encode(), "base64") - ) - _LOGGER.info("Successfully loaded secondary_artifacts from Mlflow") - if "metadata" in data: - metadata = pickle.loads(codecs.decode(data["metadata"].encode(), "base64")) - _LOGGER.info("Successfully loaded model metadata from Mlflow") - return self.__as_dict(model, secondary_artifacts, metadata, model_properties) + + run_info = mlflow.get_run(version_info.run_id) + metadata = run_info.data.params or None + _LOGGER.info("Successfully loaded model metadata from Mlflow!") + + return ArtifactData(artifact=model, metadata=metadata, extras=dict(version_info)) except Exception as ex: - _LOGGER.exception("Error when loading a model with key: %s: %r", model_key, ex) - return {} + _LOGGER.exception( + "Error when loading a model with key: %s: %r", model_key, ex + ) + return None def save( self, skeys: Sequence[str], dkeys: Sequence[str], - primary_artifact: Artifact, - secondary_artifacts: Union[Sequence[Artifact], Dict[str, Artifact], None] = None, - **metadata, + artifact: Artifact, + **metadata: str, ) -> Optional[ModelVersion]: """ Saves the artifact into mlflow registry and updates version. Args: skeys: static key fields as list/tuple of strings dkeys: dynamic key fields as list/tuple of strings - primary_artifact: primary artifact to be saved - secondary_artifacts: secondary artifact to be saved + artifact: primary artifact to be saved metadata: additional metadata surrounding the artifact that needs to be saved Returns: @@ -190,22 +157,22 @@ def save( """ model_key = self.construct_key(skeys, dkeys) try: - self.handler.log_model(primary_artifact, "model", registered_model_name=model_key) - if secondary_artifacts: - secondary_artifacts_data = codecs.encode( - pickle.dumps(secondary_artifacts), "base64" - ).decode() - mlflow.log_param(key="secondary_artifacts", value=secondary_artifacts_data) + mlflow.start_run() + self.handler.log_model( + artifact, "model", registered_model_name=model_key + ) if metadata: - data = codecs.encode(pickle.dumps(metadata), "base64").decode() - mlflow.log_param(key="metadata", value=data) - mlflow.log_param(key="model_key", value=model_key) + mlflow.log_params(metadata) model_version = self.transition_stage(skeys=skeys, dkeys=dkeys) _LOGGER.info("Successfully inserted model %s to Mlflow", model_key) return model_version except Exception as ex: - _LOGGER.exception("Error when saving a model with key: %s: %r", model_key, ex) + _LOGGER.exception( + "Error when saving a model with key: %s: %r", model_key, ex + ) return None + finally: + mlflow.end_run() def delete(self, skeys: Sequence[str], dkeys: Sequence[str], version: str) -> None: """ diff --git a/numalogic/tests/models/autoencoder/test_pipeline.py b/numalogic/tests/models/autoencoder/test_pipeline.py index 92e9ebc3..7e3ed59f 100644 --- a/numalogic/tests/models/autoencoder/test_pipeline.py +++ b/numalogic/tests/models/autoencoder/test_pipeline.py @@ -45,11 +45,11 @@ def test_fit_vanilla(self): trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) trainer.fit(self.X_train) - def test_threshold_min(self): - self.model = Conv1dAE(self.X_train.shape[1], 8) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5, threshold_min=1) - trainer.fit(self.X_train) - self.assertTrue(all(i >= 1 for i in trainer.thresholds)) + # def test_threshold_min(self): + # self.model = Conv1dAE(self.X_train.shape[1], 8) + # trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) + # trainer.fit(self.X_train) + # self.assertTrue(all(i >= 1 for i in trainer.thresholds)) def test_predict_01(self): self.model = Conv1dAE(self.X_train.shape[1], 8) @@ -81,11 +81,11 @@ def test_predict_02(self): self.assertEqual(stream_data.shape, pred.shape) - def test_fit_predict(self): + def test_fit_transform(self): trainer = AutoencoderPipeline.with_model( VanillaAE, SEQ_LEN, num_epochs=5, signal_len=SEQ_LEN, n_features=self.X_train.shape[1] ) - pred = trainer.fit_predict(self.X_train) + pred = trainer.fit_transform(self.X_train) self.assertEqual(self.X_train.shape, pred.shape) def test_score_01(self): @@ -98,22 +98,15 @@ def test_score_01(self): score = trainer.score(self.X_val) self.assertEqual(score.shape, pred.shape) - self.assertEqual(trainer.reconerr_func, np.abs) def test_resume_training_01(self): model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=10, resume_train=True) trainer.fit(self.X_train) - thresholds_old = trainer.thresholds.tolist() - err_mean_old = trainer.err_stats["mean"].tolist() - err_std_old = trainer.err_stats["std"].tolist() + self.assertEqual(10, trainer.model_properties["epochs_elapsed"]) + trainer.fit(self.X_train) - thresholds_new = trainer.thresholds.tolist() - err_mean_new = trainer.err_stats["mean"].tolist() - err_std_new = trainer.err_stats["std"].tolist() - self.assertNotEqual(thresholds_new, thresholds_old) - self.assertNotEqual(err_std_new, err_std_old) - self.assertNotEqual(err_mean_new, err_mean_old) + self.assertEqual(20, trainer.model_properties["epochs_elapsed"]) def test_score_02(self): stream_data = self.X_val[:12] @@ -127,13 +120,12 @@ def test_score_02(self): def test_score_03(self): model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5, reconerr_method="absolute") + trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5) trainer.fit(self.X_train) pred = trainer.predict(self.X_val) score = trainer.score(self.X_val) self.assertEqual(score.shape, pred.shape) - self.assertEqual(trainer.reconerr_func, np.abs) def test_score_04(self): model = TransformerAE( @@ -144,18 +136,12 @@ def test_score_04(self): num_decoder_layers=1, ) print(self.X_train.shape) - trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5, reconerr_method="absolute") + trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5) trainer.fit(self.X_train) pred = trainer.predict(self.X_val) score = trainer.score(self.X_val) self.assertEqual(score.shape, pred.shape) - self.assertEqual(trainer.reconerr_func, np.abs) - - def test_score_05(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - with self.assertRaises(ValueError): - AutoencoderPipeline(model, SEQ_LEN, num_epochs=5, reconerr_method="noidea") def test_non_implemented_loss(self): model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) @@ -182,13 +168,6 @@ def test_save_load_path(self): trainer_2 = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) trainer_2.load(path) - self.assertListEqual(trainer_1.thresholds.tolist(), trainer_2.thresholds.tolist()) - self.assertListEqual( - trainer_1.err_stats["mean"].tolist(), trainer_2.err_stats["mean"].tolist() - ) - self.assertListEqual( - trainer_1.err_stats["std"].tolist(), trainer_2.err_stats["std"].tolist() - ) # Check if both model's weights are equal _mean_wts_1, _mean_wts_2 = [], [] @@ -211,13 +190,6 @@ def test_save_load_buf(self): trainer_2 = AutoencoderPipeline(model, SEQ_LEN, num_epochs=3) trainer_2.load(buf) - self.assertListEqual(trainer_1.thresholds.tolist(), trainer_2.thresholds.tolist()) - self.assertListEqual( - trainer_1.err_stats["mean"].tolist(), trainer_2.err_stats["mean"].tolist() - ) - self.assertListEqual( - trainer_1.err_stats["std"].tolist(), trainer_2.err_stats["std"].tolist() - ) # Check if both model's weights are equal _mean_wts_1, _mean_wts_2 = [], [] @@ -255,8 +227,7 @@ def test_load_model_without_resume_train_01(self): model_pl1.fit(X) model_pl2 = AutoencoderPipeline(model, 10) model_pl2.load(model=model_pl1.model, **model_pl1.model_properties) - self.assertEqual(model_pl2.err_stats["std"], model_pl1.err_stats["std"]) - self.assertEqual(list(model_pl1.model_properties.keys()), ["thresholds", "err_stats"]) + self.assertEqual(list(model_pl1.model_properties.keys()), ["batch_size", "num_epochs", "epochs_elapsed"]) def test_load_model_resume_train_01(self): X = np.random.randn(10, 1) @@ -265,10 +236,9 @@ def test_load_model_resume_train_01(self): model_pl1.fit(X) model_pl2 = AutoencoderPipeline(model, 10, resume_train=True) model_pl2.load(model=model_pl1.model, **model_pl1.model_properties) - self.assertEqual(model_pl2.err_stats["std"], model_pl1.err_stats["std"]) self.assertEqual( list(model_pl1.model_properties.keys()), - ["thresholds", "err_stats", "optimizer_state_dict"], + ["batch_size", "num_epochs", "epochs_elapsed", "optimizer_state_dict"], ) def test_load_model_with_resume_train_02(self): @@ -277,8 +247,8 @@ def test_load_model_with_resume_train_02(self): model_pl1 = AutoencoderPipeline(model, 10, resume_train=True) model_pl1.fit(X) self.assertEqual( + ["batch_size", "num_epochs", "epochs_elapsed", "optimizer_state_dict"], list(model_pl1.model_properties.keys()), - ["thresholds", "err_stats", "optimizer_state_dict"], ) def test_load_model_without_resume_train_02(self): @@ -286,7 +256,7 @@ def test_load_model_without_resume_train_02(self): model = VanillaAE(10) model_pl1 = AutoencoderPipeline(model, 10, resume_train=False) model_pl1.fit(X) - self.assertEqual(list(model_pl1.model_properties.keys()), ["thresholds", "err_stats"]) + self.assertEqual(["batch_size", "num_epochs", "epochs_elapsed"], list(model_pl1.model_properties.keys())) def test_exception_in_load_model(self): X = np.random.randn(10, 1) @@ -298,7 +268,6 @@ def test_exception_in_load_model(self): model_pl2.load( path="checkpoint.pth", model=model_pl1.model, **model_pl1.model_properties ) - self.assertEqual(model_pl2.err_stats["std"], model_pl1.err_stats["std"]) def test_exception_invalid_epoch(self): model = VanillaAE(10) diff --git a/numalogic/tests/registry/test_mlflow_registry.py b/numalogic/tests/registry/test_mlflow_registry.py index b0f73c67..b9d3b119 100644 --- a/numalogic/tests/registry/test_mlflow_registry.py +++ b/numalogic/tests/registry/test_mlflow_registry.py @@ -48,6 +48,7 @@ def test_construct_key(self): key = MLflowRegistrar.construct_key(skeys, dkeys) self.assertEqual("model_:nnet::error1", key) + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_param", mock_log_state_dict) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @@ -69,6 +70,7 @@ def test_insert_model(self): mock_status = "READY" self.assertEqual(mock_status, status.status) + @unittest.skip("Needs fixing") @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @@ -86,6 +88,7 @@ def test_insert_model_sklearn(self): mock_status = "READY" self.assertEqual(mock_status, status.status) + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) @patch("mlflow.log_param", OrderedDict({"a": 1})) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @@ -107,10 +110,11 @@ def test_select_model_when_pytorch_model_exist1(self): }, ) data = ml.load(skeys=skeys, dkeys=dkeys) - self.assertIsInstance(data["primary_artifact"], VanillaAE) + self.assertIsInstance(data.artifact, VanillaAE) self.assertIsInstance(data["secondary_artifacts"]["preproc"], Pipeline) self.assertIsInstance(data["secondary_artifacts"]["postproc"], Pipeline) + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) @patch("mlflow.log_param", OrderedDict({"a": 1})) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @@ -135,6 +139,7 @@ def test_select_model_when_pytorch_model_exist2(self): self.assertIsInstance(data["primary_artifact"], VanillaAE) self.assertIsInstance(data["secondary_artifacts"], list) + @unittest.skip("Needs fixing") @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @@ -155,6 +160,7 @@ def test_select_model_when_sklearn_model_exist(self): self.assertIsInstance(data["primary_artifact"], RandomForestRegressor) self.assertEqual(data["metadata"], None) + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) @patch("mlflow.log_param", OrderedDict({"a": 1})) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @@ -172,8 +178,8 @@ def test_select_model_with_version(self): primary_artifact=model, ) data = ml.load(skeys=skeys, dkeys=dkeys, version="1", latest=False) - self.assertIsInstance(data["primary_artifact"], VanillaAE) - self.assertEqual(data["metadata"], None) + self.assertIsInstance(data.artifact, VanillaAE) + self.assertEqual(data.metadata, None) @patch("mlflow.pyfunc.load_model", Mock(side_effect=RuntimeError)) def test_select_model_when_no_model_01(self): @@ -210,6 +216,7 @@ def test_no_implementation(self): with self.assertRaises(NotImplementedError): MLflowRegistrar(TRACKING_URI, artifact_type="some_random") + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_param", mock_log_state_dict) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) @@ -237,6 +244,7 @@ def test_delete_model_when_no_model(self): ml.delete(skeys=fake_skeys, dkeys=fake_dkeys, version="1") self.assertTrue(log.output) + @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", Mock(side_effect=RuntimeError)) def test_insertion_failed(self): fake_skeys = ["Fakemodel_"] diff --git a/numalogic/tests/test_scores.py b/numalogic/tests/test_scores.py index 745db0d1..070409e5 100644 --- a/numalogic/tests/test_scores.py +++ b/numalogic/tests/test_scores.py @@ -2,7 +2,7 @@ import numpy as np -from numalogic.scores import tanh_norm +from numalogic.postprocess import tanh_norm class TestScores(unittest.TestCase): diff --git a/poetry.lock b/poetry.lock index a97d588a..6b49aef3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,23 +1,6 @@ -[[package]] -name = "alembic" -version = "1.8.1" -description = "A database migration tool for SQLAlchemy." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -importlib-resources = {version = "*", markers = "python_version < \"3.9\""} -Mako = "*" -SQLAlchemy = ">=1.3.0" - -[package.extras] -tz = ["python-dateutil"] - [[package]] name = "astroid" -version = "2.12.11" +version = "2.12.12" description = "An abstract syntax tree for Python with inference support." category = "dev" optional = false @@ -104,15 +87,15 @@ python-versions = ">=3.6" [[package]] name = "colorama" -version = "0.4.5" +version = "0.4.6" description = "Cross-platform colored terminal text." category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" [[package]] name = "contourpy" -version = "1.0.5" +version = "1.0.6" description = "Python library for calculating contours of 2D quadrilateral grids" category = "dev" optional = false @@ -123,7 +106,7 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "selenium"] -docs = ["docutils (<0.18)", "sphinx", "sphinx-rtd-theme"] +docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] test = ["Pillow", "flake8", "isort", "matplotlib", "pytest"] test-minimal = ["pytest"] test-no-codebase = ["Pillow", "matplotlib", "pytest"] @@ -168,32 +151,14 @@ tabulate = ">=0.7.7" [[package]] name = "dill" -version = "0.3.5.1" +version = "0.3.6" description = "serialize all of python" category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" - -[package.extras] -graph = ["objgraph (>=1.7.2)"] - -[[package]] -name = "docker" -version = "6.0.0" -description = "A Python library for the Docker Engine API." -category = "main" -optional = true python-versions = ">=3.7" -[package.dependencies] -packaging = ">=14.0" -pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""} -requests = ">=2.26.0" -urllib3 = ">=1.26.0" -websocket-client = ">=0.32.0" - [package.extras] -ssh = ["paramiko (>=2.4.3)"] +graph = ["objgraph (>=1.7.2)"] [[package]] name = "entrypoints" @@ -203,6 +168,17 @@ category = "main" optional = true python-versions = ">=3.6" +[[package]] +name = "exceptiongroup" +version = "1.0.1" +description = "Backport of PEP 654 (exception groups)" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.extras] +test = ["pytest (>=6)"] + [[package]] name = "flake8" version = "5.0.4" @@ -216,28 +192,9 @@ mccabe = ">=0.7.0,<0.8.0" pycodestyle = ">=2.9.0,<2.10.0" pyflakes = ">=2.5.0,<2.6.0" -[[package]] -name = "flask" -version = "2.2.2" -description = "A simple framework for building complex web applications." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -click = ">=8.0" -importlib-metadata = {version = ">=3.6.0", markers = "python_version < \"3.10\""} -itsdangerous = ">=2.0" -Jinja2 = ">=3.0" -Werkzeug = ">=2.2.2" - -[package.extras] -async = ["asgiref (>=3.2)"] -dotenv = ["python-dotenv"] - [[package]] name = "fonttools" -version = "4.37.4" +version = "4.38.0" description = "Tools to manipulate font files" category = "dev" optional = false @@ -290,34 +247,6 @@ python-versions = ">=3.7" [package.dependencies] gitdb = ">=4.0.1,<5" -[[package]] -name = "greenlet" -version = "1.1.3.post0" -description = "Lightweight in-process concurrent programming" -category = "main" -optional = true -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" - -[package.extras] -docs = ["Sphinx"] - -[[package]] -name = "gunicorn" -version = "20.1.0" -description = "WSGI HTTP Server for UNIX" -category = "main" -optional = true -python-versions = ">=3.5" - -[package.dependencies] -setuptools = ">=3.0" - -[package.extras] -eventlet = ["eventlet (>=0.24.1)"] -gevent = ["gevent (>=1.4.0)"] -setproctitle = ["setproctitle"] -tornado = ["tornado (>=0.2)"] - [[package]] name = "idna" version = "3.4" @@ -328,7 +257,7 @@ python-versions = ">=3.5" [[package]] name = "importlib-metadata" -version = "4.13.0" +version = "5.0.0" description = "Read metadata from Python packages" category = "main" optional = true @@ -342,21 +271,6 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker perf = ["ipython"] testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] -[[package]] -name = "importlib-resources" -version = "5.10.0" -description = "Read resources from Python packages" -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] -testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] - [[package]] name = "iniconfig" version = "1.1.1" @@ -379,28 +293,6 @@ pipfile-deprecated-finder = ["pipreqs", "requirementslib"] plugins = ["setuptools"] requirements-deprecated-finder = ["pip-api", "pipreqs"] -[[package]] -name = "itsdangerous" -version = "2.1.2" -description = "Safely pass data to untrusted environments and back." -category = "main" -optional = true -python-versions = ">=3.7" - -[[package]] -name = "jinja2" -version = "3.1.2" -description = "A very fast and expressive template engine." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -MarkupSafe = ">=2.0" - -[package.extras] -i18n = ["Babel (>=2.7)"] - [[package]] name = "joblib" version = "1.2.0" @@ -419,39 +311,15 @@ python-versions = ">=3.7" [[package]] name = "lazy-object-proxy" -version = "1.7.1" +version = "1.8.0" description = "A fast and thorough lazy object proxy." category = "dev" optional = false -python-versions = ">=3.6" - -[[package]] -name = "mako" -version = "1.2.3" -description = "A super-fast templating language that borrows the best ideas from the existing templating languages." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -MarkupSafe = ">=0.9.2" - -[package.extras] -babel = ["Babel"] -lingua = ["lingua"] -testing = ["pytest"] - -[[package]] -name = "markupsafe" -version = "2.1.1" -description = "Safely add untrusted strings to HTML/XML markup." -category = "main" -optional = true python-versions = ">=3.7" [[package]] name = "matplotlib" -version = "3.6.1" +version = "3.6.2" description = "Python plotting package" category = "dev" optional = false @@ -478,42 +346,31 @@ optional = false python-versions = ">=3.6" [[package]] -name = "mlflow" -version = "1.29.0" +name = "mlflow-skinny" +version = "1.30.0" description = "MLflow: A Platform for ML Development and Productionization" category = "main" optional = true python-versions = ">=3.7" [package.dependencies] -alembic = "<2" click = ">=7.0,<9" cloudpickle = "<3" databricks-cli = ">=0.8.7,<1" -docker = ">=4.0.0,<7" entrypoints = "<1" -Flask = "<3" gitpython = ">=2.1.0,<4" -gunicorn = {version = "<21", markers = "platform_system != \"Windows\""} -importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<5" -numpy = "<2" +importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<6" packaging = "<22" -pandas = "<2" -prometheus-flask-exporter = "<1" protobuf = ">=3.12.0,<5" pytz = "<2023" pyyaml = ">=5.1,<7" -querystring-parser = "<2" requests = ">=2.17.3,<3" -scipy = "<2" -sqlalchemy = ">=1.4.0,<2" sqlparse = ">=0.4.0,<1" -waitress = {version = "<3", markers = "platform_system == \"Windows\""} [package.extras] aliyun-oss = ["aliyunstoreplugin"] -extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=0.5.3)", "mlserver-mlflow (>=0.5.3)", "pyarrow", "pysftp", "scikit-learn", "virtualenv"] -pipelines = ["Jinja2 (>=3.0)", "ipython (>=7.0)", "markdown (>=3.3)", "pandas-profiling (>=3.1)", "pyarrow (>=7.0)", "scikit-learn (>=1.0)", "shap (>=0.40)"] +extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=0.5.3)", "mlserver-mlflow (>=0.5.3)", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"] +pipelines = ["Jinja2 (>=2.11)", "Jinja2 (>=3.0)", "ipython (>=7.0)", "markdown (>=3.3)", "pandas-profiling (>=3.1)", "pyarrow (>=7.0)", "scikit-learn (>=1.0)", "shap (>=0.40)"] sqlserver = ["mlflow-dbstore"] [[package]] @@ -558,7 +415,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" [[package]] name = "pandas" -version = "1.5.0" +version = "1.5.1" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false @@ -585,7 +442,7 @@ python-versions = ">=3.7" [[package]] name = "pillow" -version = "9.2.0" +version = "9.3.0" description = "Python Imaging Library (Fork)" category = "dev" optional = false @@ -597,15 +454,15 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa [[package]] name = "platformdirs" -version = "2.5.2" -description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +version = "2.5.3" +description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx (>=4)", "sphinx-autodoc-typehints (>=1.12)"] -test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"] +docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.4)"] +test = ["appdirs (==1.4.4)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] [[package]] name = "pluggy" @@ -619,45 +476,14 @@ python-versions = ">=3.6" dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] -[[package]] -name = "prometheus-client" -version = "0.15.0" -description = "Python client for the Prometheus monitoring system." -category = "main" -optional = true -python-versions = ">=3.6" - -[package.extras] -twisted = ["twisted"] - -[[package]] -name = "prometheus-flask-exporter" -version = "0.20.3" -description = "Prometheus metrics exporter for Flask" -category = "main" -optional = true -python-versions = "*" - -[package.dependencies] -flask = "*" -prometheus-client = "*" - [[package]] name = "protobuf" -version 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709553f4ae879f7e786634b02867966756706174 Mon Sep 17 00:00:00 2001 From: Kushal Batra <34571348+s0nicboOm@users.noreply.github.com> Date: Tue, 29 Nov 2022 22:42:33 +0530 Subject: [PATCH 02/15] fix: fix mlflow test cases (#98) Signed-off-by: s0nicboOm --- .flake8 | 3 +- examples/numalogic-simple-pipeline/Dockerfile | 46 +- .../Dockerfile.mlflow | 0 .../numalogic-simple-pipeline/poetry.lock | 1959 +++++++++++++++++ .../numalogic-simple-pipeline/pyproject.toml | 4 +- .../src/udf/inference.py | 6 +- .../numalogic-simple-pipeline/src/utils.py | 7 +- numalogic/registry/mlflow_registry.py | 1 + numalogic/tests/registry/_mlflow_utils.py | 4 +- .../tests/registry/test_mlflow_registry.py | 119 +- poetry.lock | 1076 +++++++-- pyproject.toml | 6 +- 12 files changed, 2925 insertions(+), 306 deletions(-) delete mode 100644 examples/numalogic-simple-pipeline/Dockerfile.mlflow create mode 100644 examples/numalogic-simple-pipeline/poetry.lock diff --git a/.flake8 b/.flake8 index 51e7f9c6..462dc96e 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,5 @@ [flake8] -ignore = | - E203, F821 +ignore = E203, F821 exclude = .git,__pycache__,docs/source/conf.py,old,build,dist max-complexity = 10 max-line-length = 100 \ No newline at end of file diff --git a/examples/numalogic-simple-pipeline/Dockerfile b/examples/numalogic-simple-pipeline/Dockerfile index 16f2788e..5bf864f5 100644 --- a/examples/numalogic-simple-pipeline/Dockerfile +++ b/examples/numalogic-simple-pipeline/Dockerfile @@ -1,8 +1,9 @@ -FROM python:3.9.12-slim +FROM python:3.10-slim-bullseye AS builder + ENV PYTHONFAULTHANDLER=1 \ PYTHONUNBUFFERED=1 \ PYTHONHASHSEED=random \ - PIP_NO_CACHE_DIR=off \ + PIP_NO_CACHE_DIR=on \ PIP_DISABLE_PIP_VERSION_CHECK=on \ PIP_DEFAULT_TIMEOUT=100 \ POETRY_VERSION=1.2.2 \ @@ -13,28 +14,49 @@ ENV PYTHONFAULTHANDLER=1 \ VENV_PATH="/opt/pysetup/.venv" ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH" + RUN apt-get update \ && apt-get install --no-install-recommends -y \ curl \ wget \ # deps for building python deps - build-essential -RUN apt-get install -y git -# install poetry - respects $POETRY_VERSION & $POETRY_HOME -RUN curl -sSL https://install.python-poetry.org | python3 - + build-essential \ + && apt-get install -y git \ + && apt-get clean && rm -rf /var/lib/apt/lists/* \ + \ + # install dumb-init + && wget -O /dumb-init https://github.com/Yelp/dumb-init/releases/download/v1.2.5/dumb-init_1.2.5_x86_64 \ + && chmod +x /dumb-init \ + && curl -sSL https://install.python-poetry.org | python3 - + +FROM builder AS mlflow WORKDIR $PYSETUP_PATH -ADD . $PYSETUP_PATH -WORKDIR $PYSETUP_PATH -RUN poetry install --all-extras +COPY ./pyproject.toml ./poetry.lock ./ +RUN poetry install --only mlflow --no-cache --no-root && \ + rm -rf ~/.cache/pypoetry/ ADD . /app +WORKDIR /app + +RUN chmod +x entry.sh + +ENTRYPOINT ["/dumb-init", "--"] +CMD ["/app/entry.sh"] + +EXPOSE 5000 + -# install dumb-init -RUN wget -O /dumb-init https://github.com/Yelp/dumb-init/releases/download/v1.2.5/dumb-init_1.2.5_x86_64 -RUN chmod +x /dumb-init +FROM builder AS udf +WORKDIR $PYSETUP_PATH +COPY ./pyproject.toml ./poetry.lock ./ +RUN poetry install --with mlflow-skinny --no-cache --no-root && \ + rm -rf ~/.cache/pypoetry/ + +ADD . /app WORKDIR /app + RUN chmod +x entry.sh ENTRYPOINT ["/dumb-init", "--"] diff --git a/examples/numalogic-simple-pipeline/Dockerfile.mlflow b/examples/numalogic-simple-pipeline/Dockerfile.mlflow deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/numalogic-simple-pipeline/poetry.lock b/examples/numalogic-simple-pipeline/poetry.lock new file mode 100644 index 00000000..f00b00be --- /dev/null +++ b/examples/numalogic-simple-pipeline/poetry.lock @@ -0,0 +1,1959 @@ +[[package]] +name = "alembic" +version = "1.8.1" +description = "A database migration tool for SQLAlchemy." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata 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b/examples/numalogic-simple-pipeline/src/udf/inference.py index 38a99f74..4f270171 100644 --- a/examples/numalogic-simple-pipeline/src/udf/inference.py +++ b/examples/numalogic-simple-pipeline/src/udf/inference.py @@ -28,13 +28,13 @@ def inference(key: str, datum: Datum) -> Messages: messages = Messages() # - artifact = load_model(skeys=["ae"], dkeys=["model"]) + artifact_data = load_model(skeys=["ae"], dkeys=["model"]) # Check if model exists for inference - if artifact: + if artifact_data: # load model from registry pl = AutoencoderPipeline(model=Conv1dAE(in_channels=1, enc_channels=12), seq_len=WIN_SIZE) - pl.load(model=artifact["primary_artifact"], **artifact["metadata"]) + pl.load(model=artifact_data.artifact, **artifact_data.metadata) LOGGER.info("%s - Model found!", payload.uuid) diff --git a/examples/numalogic-simple-pipeline/src/utils.py b/examples/numalogic-simple-pipeline/src/utils.py index d9560a09..9ebc4899 100644 --- a/examples/numalogic-simple-pipeline/src/utils.py +++ b/examples/numalogic-simple-pipeline/src/utils.py @@ -3,7 +3,6 @@ from dataclasses import dataclass from typing import Sequence -import mlflow from dataclasses_json import dataclass_json from numalogic.models.autoencoder import AutoencoderPipeline from numalogic.registry import MLflowRegistrar @@ -13,7 +12,7 @@ DIR = os.path.dirname(__file__) ROOT_DIR = os.path.split(DIR)[0] TRAIN_DATA_PATH = os.path.join(ROOT_DIR, "src/resources/train_data.csv") -TRACKING_URI = "http://mlflow-service.numaflow-system.svc.cluster.local:5000" +TRACKING_URI = "http://mlflow-service.default.svc.cluster.local:5000" LOGGER = logging.getLogger(__name__) @@ -27,9 +26,7 @@ class Payload: def save_model(pl: AutoencoderPipeline, skeys: Sequence[str], dkeys: Sequence[str]) -> None: ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") - mlflow.start_run() - ml_registry.save(skeys=skeys, dkeys=dkeys, primary_artifact=pl.model, **pl.model_properties) - mlflow.end_run() + ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=pl.model, **pl.model_properties) def load_model(skeys: Sequence[str], dkeys: Sequence[str]) -> ArtifactDict: diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py index cd835870..1ca60c7f 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/numalogic/registry/mlflow_registry.py @@ -67,6 +67,7 @@ def __init__( self, tracking_uri: str, artifact_type: str = "pytorch", models_to_retain: int = 5 ): super().__init__(tracking_uri) + mlflow.set_tracking_uri(tracking_uri) self.client = MlflowClient() self.handler = self.mlflow_handler(artifact_type) self.models_to_retain = models_to_retain diff --git a/numalogic/tests/registry/_mlflow_utils.py b/numalogic/tests/registry/_mlflow_utils.py index 4e8d0744..3235b1c7 100644 --- a/numalogic/tests/registry/_mlflow_utils.py +++ b/numalogic/tests/registry/_mlflow_utils.py @@ -109,7 +109,7 @@ def mock_log_model_pytorch(*_, **__): saved_input_example_info=None, signature_dict=None, utc_time_created="2022-05-23 22:35:59.557372", - mlflow_version="1.26.0", + mlflow_version="2.0.1", signature=None, ) @@ -133,7 +133,7 @@ def mock_log_model_sklearn(*_, **__): saved_input_example_info=None, signature_dict=None, utc_time_created="2022-05-23 22:35:59.557372", - mlflow_version="1.26.0", + mlflow_version="2.0.1", signature=None, ) diff --git a/numalogic/tests/registry/test_mlflow_registry.py b/numalogic/tests/registry/test_mlflow_registry.py index b9d3b119..1959d026 100644 --- a/numalogic/tests/registry/test_mlflow_registry.py +++ b/numalogic/tests/registry/test_mlflow_registry.py @@ -1,14 +1,11 @@ import unittest -from collections import OrderedDict from contextlib import contextmanager from unittest.mock import patch, Mock +from mlflow import ActiveRun from sklearn.ensemble import RandomForestRegressor -from sklearn.pipeline import make_pipeline, Pipeline -from sklearn.preprocessing import StandardScaler, Normalizer from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.preprocess.transformer import LogTransformer from numalogic.registry import MLflowRegistrar from numalogic.tests.registry._mlflow_utils import ( model_sklearn, @@ -17,13 +14,14 @@ mock_log_state_dict, mock_get_model_version, mock_transition_stage, - return_scaler, mock_log_model_sklearn, return_pytorch_rundata_dict, return_empty_rundata, - return_pytorch_rundata_list, mock_list_of_model_version, mock_list_of_model_version2, + return_sklearn_rundata, + return_pytorch_rundata_dict_no_metadata, + mock_get_latest_model_version, ) TRACKING_URI = "http://0.0.0.0:5009" @@ -48,32 +46,32 @@ def test_construct_key(self): key = MLflowRegistrar.construct_key(skeys, dkeys) self.assertEqual("model_:nnet::error1", key) - @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_param", mock_log_state_dict) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version) def test_insert_model(self): - model = self.model - ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch", models_to_retain=2) + + ml = MLflowRegistrar(TRACKING_URI) skeys = ["model_", "nnet"] dkeys = ["error1"] - status = ml.save( skeys=skeys, dkeys=dkeys, - primary_artifact=model, - secondary_artifacts=[make_pipeline(return_scaler)], - **model.state_dict(), + artifact=self.model, ) + print(status) mock_status = "READY" self.assertEqual(mock_status, status.status) - @unittest.skip("Needs fixing") @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) + @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) def test_insert_model_sklearn(self): model = self.model_sklearn @@ -83,16 +81,17 @@ def test_insert_model_sklearn(self): status = ml.save( skeys=skeys, dkeys=dkeys, - primary_artifact=model, + artifact=model, ) mock_status = "READY" self.assertEqual(mock_status, status.status) - @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.log_param", OrderedDict({"a": 1})) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) + @patch("mlflow.log_params", {"lr": 0.01}) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) def test_select_model_when_pytorch_model_exist1(self): @@ -100,49 +99,40 @@ def test_select_model_when_pytorch_model_exist1(self): ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch") skeys = ["model_", "nnet"] dkeys = ["error1"] - ml.save( - skeys=skeys, - dkeys=dkeys, - primary_artifact=model, - secondary_artifacts={ - "preproc": make_pipeline(StandardScaler(), LogTransformer()), - "postproc": make_pipeline(Normalizer()), - }, - ) + ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) data = ml.load(skeys=skeys, dkeys=dkeys) + self.assertIsNotNone(data.metadata) self.assertIsInstance(data.artifact, VanillaAE) - self.assertIsInstance(data["secondary_artifacts"]["preproc"], Pipeline) - self.assertIsInstance(data["secondary_artifacts"]["postproc"], Pipeline) - @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.log_param", OrderedDict({"a": 1})) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_list())) + @patch( + "mlflow.tracking.MlflowClient.get_run", + Mock(return_value=return_pytorch_rundata_dict_no_metadata()), + ) def test_select_model_when_pytorch_model_exist2(self): model = self.model - ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch") + ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch", models_to_retain=2) skeys = ["model_", "nnet"] dkeys = ["error1"] ml.save( skeys=skeys, dkeys=dkeys, - primary_artifact=model, - secondary_artifacts=[ - make_pipeline(StandardScaler(), LogTransformer()), - make_pipeline(Normalizer()), - ], + artifact=model, ) data = ml.load(skeys=skeys, dkeys=dkeys) - self.assertIsInstance(data["primary_artifact"], VanillaAE) - self.assertIsInstance(data["secondary_artifacts"], list) + self.assertIsNone(data.metadata) + self.assertIsInstance(data.artifact, VanillaAE) - @unittest.skip("Needs fixing") @patch("mlflow.sklearn.log_model", mock_log_model_sklearn) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) + @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) @patch("mlflow.sklearn.load_model", Mock(return_value=RandomForestRegressor())) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) @@ -154,17 +144,19 @@ def test_select_model_when_sklearn_model_exist(self): ml.save( skeys=skeys, dkeys=dkeys, - primary_artifact=model, + artifact=model, ) data = ml.load(skeys=skeys, dkeys=dkeys) - self.assertIsInstance(data["primary_artifact"], RandomForestRegressor) - self.assertEqual(data["metadata"], None) + self.assertIsInstance(data.artifact, RandomForestRegressor) + self.assertIsNone(data.metadata) - @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch("mlflow.log_param", OrderedDict({"a": 1})) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict_no_metadata())) + ) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict_no_metadata())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_model_version", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_with_version(self): @@ -175,11 +167,11 @@ def test_select_model_with_version(self): ml.save( skeys=skeys, dkeys=dkeys, - primary_artifact=model, + artifact=model, ) - data = ml.load(skeys=skeys, dkeys=dkeys, version="1", latest=False) + data = ml.load(skeys=skeys, dkeys=dkeys, version="5", latest=False) self.assertIsInstance(data.artifact, VanillaAE) - self.assertEqual(data.metadata, None) + self.assertIsNone(data.metadata) @patch("mlflow.pyfunc.load_model", Mock(side_effect=RuntimeError)) def test_select_model_when_no_model_01(self): @@ -216,11 +208,12 @@ def test_no_implementation(self): with self.assertRaises(NotImplementedError): MLflowRegistrar(TRACKING_URI, artifact_type="some_random") - @unittest.skip("Needs fixing") + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) - @patch("mlflow.log_param", mock_log_state_dict) + @patch("mlflow.log_params", mock_log_state_dict) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) @patch("mlflow.tracking.MlflowClient.delete_model_version", None) @patch("mlflow.pytorch.load_model", Mock(side_effect=RuntimeError)) @@ -229,8 +222,8 @@ def test_delete_model_when_model_exist(self): ml = MLflowRegistrar(TRACKING_URI) skeys = ["model_", "nnet"] dkeys = ["error1"] - ml.save(skeys=skeys, dkeys=dkeys, primary_artifact=model, **model.state_dict()) - ml.delete(skeys=skeys, dkeys=dkeys, version="1") + ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) + ml.delete(skeys=skeys, dkeys=dkeys, version="5") with self.assertLogs(level="ERROR") as log: ml.load(skeys=skeys, dkeys=dkeys) self.assertTrue(log.output) @@ -242,17 +235,21 @@ def test_delete_model_when_no_model(self): ml = MLflowRegistrar(TRACKING_URI) with self.assertLogs(level="ERROR") as log: ml.delete(skeys=fake_skeys, dkeys=fake_dkeys, version="1") + print(log.output) self.assertTrue(log.output) - @unittest.skip("Needs fixing") @patch("mlflow.pytorch.log_model", Mock(side_effect=RuntimeError)) + @patch( + "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict_no_metadata())) + ) + @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict_no_metadata())) def test_insertion_failed(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] ml = MLflowRegistrar(TRACKING_URI) with self.assertLogs(level="ERROR") as log: - ml.save(skeys=fake_skeys, dkeys=fake_dkeys, primary_artifact=self.model) + ml.save(skeys=fake_skeys, dkeys=fake_dkeys, artifact=self.model) self.assertTrue(log.output) diff --git a/poetry.lock b/poetry.lock index 6b49aef3..a3f9a22a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,6 +1,23 @@ +[[package]] +name = "alembic" +version = "1.8.1" +description = "A database migration tool for SQLAlchemy." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} +importlib-resources = {version = "*", markers = "python_version < \"3.9\""} +Mako = "*" +SQLAlchemy = ">=1.3.0" + +[package.extras] +tz = ["python-dateutil"] + [[package]] name = "astroid" -version = "2.12.12" +version = "2.12.13" description = "An abstract syntax tree for Python with inference support." category = "dev" optional = false @@ -20,10 +37,10 @@ optional = false python-versions = ">=3.5" [package.extras] -dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"] -docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"] -tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"] -tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] +docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] +tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] [[package]] name = "black" @@ -64,7 +81,7 @@ optional = true python-versions = ">=3.6.0" [package.extras] -unicode-backport = ["unicodedata2"] +unicode_backport = ["unicodedata2"] [[package]] name = "click" @@ -97,7 +114,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7 name = "contourpy" version = "1.0.6" description = "Python library for calculating contours of 2D quadrilateral grids" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -107,9 +124,9 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "selenium"] docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["Pillow", "flake8", "isort", "matplotlib", "pytest"] +test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] test-minimal = ["pytest"] -test-no-codebase = ["Pillow", "matplotlib", "pytest"] +test-no-codebase = ["pytest", "matplotlib", "pillow"] [[package]] name = "coverage" @@ -129,7 +146,7 @@ toml = ["tomli"] name = "cycler" version = "0.11.0" description = "Composable style cycles" -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -160,6 +177,24 @@ python-versions = ">=3.7" [package.extras] graph = ["objgraph (>=1.7.2)"] +[[package]] +name = "docker" +version = "6.0.1" +description = "A Python library for the Docker Engine API." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +packaging = ">=14.0" +pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""} +requests = ">=2.26.0" +urllib3 = ">=1.26.0" +websocket-client = ">=0.32.0" + +[package.extras] +ssh = ["paramiko (>=2.4.3)"] + [[package]] name = "entrypoints" version = "0.4" @@ -170,7 +205,7 @@ python-versions = ">=3.6" [[package]] name = "exceptiongroup" -version = "1.0.1" +version = "1.0.4" description = "Backport of PEP 654 (exception groups)" category = "dev" optional = false @@ -192,18 +227,37 @@ mccabe = ">=0.7.0,<0.8.0" pycodestyle = ">=2.9.0,<2.10.0" pyflakes = ">=2.5.0,<2.6.0" +[[package]] +name = "flask" +version = "2.2.2" +description = "A simple framework for building complex web applications." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +click = ">=8.0" +importlib-metadata = {version = ">=3.6.0", markers = "python_version < \"3.10\""} +itsdangerous = ">=2.0" +Jinja2 = ">=3.0" +Werkzeug = ">=2.2.2" + +[package.extras] +async = ["asgiref (>=3.2)"] +dotenv = ["python-dotenv"] + [[package]] name = "fonttools" version = "4.38.0" description = "Tools to manipulate font files" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "scipy"] +interpolatable = ["scipy", "munkres"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -212,7 +266,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=14.0.0)"] -woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] [[package]] name = "freezegun" @@ -227,11 +281,11 @@ python-dateutil = ">=2.7" [[package]] name = "gitdb" -version = "4.0.9" +version = "4.0.10" description = "Git Object Database" category = "main" optional = true -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] smmap = ">=3.0.1,<6" @@ -247,6 +301,32 @@ python-versions = ">=3.7" [package.dependencies] gitdb = ">=4.0.1,<5" +[[package]] +name = "greenlet" +version = "2.0.1" +description = "Lightweight in-process concurrent programming" +category = "main" +optional = true +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" + +[package.extras] +docs = ["sphinx", "docutils (<0.18)"] +test = ["objgraph", "psutil", "faulthandler"] + +[[package]] +name = "gunicorn" +version = "20.1.0" +description = "WSGI HTTP Server for UNIX" +category = "main" +optional = true +python-versions = ">=3.5" + +[package.extras] +eventlet = ["eventlet (>=0.24.1)"] +gevent = ["gevent (>=1.4.0)"] +setproctitle = ["setproctitle"] +tornado = ["tornado (>=0.2)"] + [[package]] name = "idna" version = "3.4" @@ -257,7 +337,7 @@ python-versions = ">=3.5" [[package]] name = "importlib-metadata" -version = "5.0.0" +version = "5.1.0" description = "Read metadata from Python packages" category = "main" optional = true @@ -267,9 +347,24 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] -testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] + +[[package]] +name = "importlib-resources" +version = "5.10.0" +description = "Read resources from Python packages" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} + +[package.extras] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"] [[package]] name = "iniconfig" @@ -288,10 +383,32 @@ optional = false python-versions = ">=3.6.1,<4.0" [package.extras] +pipfile_deprecated_finder = ["pipreqs", "requirementslib"] +requirements_deprecated_finder = ["pipreqs", "pip-api"] colors = ["colorama (>=0.4.3,<0.5.0)"] -pipfile-deprecated-finder = ["pipreqs", "requirementslib"] plugins = ["setuptools"] -requirements-deprecated-finder = ["pip-api", "pipreqs"] + +[[package]] +name = "itsdangerous" +version = "2.1.2" +description = "Safely pass data to untrusted environments and back." +category = "main" +optional = true +python-versions = ">=3.7" + +[[package]] +name = "jinja2" +version = "3.1.2" +description = "A very fast and expressive template engine." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +MarkupSafe = ">=2.0" + +[package.extras] +i18n = ["Babel (>=2.7)"] [[package]] name = "joblib" @@ -305,7 +422,7 @@ python-versions = ">=3.7" name = "kiwisolver" version = "1.4.4" description = "A fast implementation of the Cassowary constraint solver" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -317,11 +434,57 @@ category = "dev" optional = false python-versions = ">=3.7" +[[package]] +name = "llvmlite" +version = "0.39.1" +description = "lightweight wrapper around basic LLVM functionality" +category = "main" +optional = true +python-versions = ">=3.7" + +[[package]] +name = "mako" +version = "1.2.4" +description = "A super-fast templating language that borrows the best ideas from the existing templating languages." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +MarkupSafe = ">=0.9.2" + +[package.extras] +babel = ["babel"] +lingua = ["lingua"] +testing = ["pytest"] + +[[package]] +name = "markdown" +version = "3.4.1" +description = "Python implementation of Markdown." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} + +[package.extras] +testing = ["coverage", "pyyaml"] + +[[package]] +name = "markupsafe" +version = "2.1.1" +description = "Safely add untrusted strings to HTML/XML markup." +category = "main" +optional = true +python-versions = ">=3.7" + [[package]] name = "matplotlib" version = "3.6.2" description = "Python plotting package" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -345,13 +508,59 @@ category = "dev" optional = false python-versions = ">=3.6" +[[package]] +name = "mlflow" +version = "2.0.1" +description = "MLflow: A Platform for ML Development and Productionization" +category = "main" +optional = true +python-versions = ">=3.8" + +[package.dependencies] +alembic = "<2" +click = ">=7.0,<9" +cloudpickle = "<3" +databricks-cli = ">=0.8.7,<1" +docker = ">=4.0.0,<7" +entrypoints = "<1" +Flask = "<3" +gitpython = ">=2.1.0,<4" +gunicorn = {version = "<21", markers = "platform_system != \"Windows\""} +importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<6" +Jinja2 = [ + {version = ">=2.11,<4", markers = "platform_system != \"Windows\""}, + {version = ">=3.0,<4", markers = "platform_system == \"Windows\""}, +] +markdown = ">=3.3,<4" +matplotlib = "<4" +numpy = "<2" +packaging = "<22" +pandas = "<2" +protobuf = ">=3.12.0,<5" +pyarrow = ">=4.0.0,<11" +pytz = "<2023" +pyyaml = ">=5.1,<7" +querystring-parser = "<2" +requests = ">=2.17.3,<3" +scikit-learn = "<2" +scipy = "<2" +shap = ">=0.40,<1" +sqlalchemy = ">=1.4.0,<2" +sqlparse = ">=0.4.0,<1" +waitress = {version = "<3", markers = "platform_system == \"Windows\""} + +[package.extras] +aliyun-oss = ["aliyunstoreplugin"] +extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] +sqlserver = ["mlflow-dbstore"] + [[package]] name = "mlflow-skinny" -version = "1.30.0" +version = "2.0.1" description = "MLflow: A Platform for ML Development and Productionization" category = "main" optional = true -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] click = ">=7.0,<9" @@ -369,8 +578,7 @@ sqlparse = ">=0.4.0,<1" [package.extras] aliyun-oss = ["aliyunstoreplugin"] -extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=0.5.3)", "mlserver-mlflow (>=0.5.3)", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"] -pipelines = ["Jinja2 (>=2.11)", "Jinja2 (>=3.0)", "ipython (>=7.0)", "markdown (>=3.3)", "pandas-profiling (>=3.1)", "pyarrow (>=7.0)", "scikit-learn (>=1.0)", "shap (>=0.40)"] +extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] sqlserver = ["mlflow-dbstore"] [[package]] @@ -381,9 +589,22 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "numba" +version = "0.56.4" +description = "compiling Python code using LLVM" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} +llvmlite = ">=0.39.0dev0,<0.40" +numpy = ">=1.18,<1.24" + [[package]] name = "numpy" -version = "1.23.4" +version = "1.23.5" description = "NumPy is the fundamental package for array computing with Python." category = "main" optional = false @@ -415,7 +636,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" [[package]] name = "pandas" -version = "1.5.1" +version = "1.5.2" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false @@ -423,8 +644,8 @@ python-versions = ">=3.8" [package.dependencies] numpy = [ - {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, {version = ">=1.20.3", markers = "python_version < \"3.10\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, ] python-dateutil = ">=2.8.1" pytz = ">=2020.1" @@ -434,7 +655,7 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] [[package]] name = "pathspec" -version = "0.10.1" +version = "0.10.2" description = "Utility library for gitignore style pattern matching of file paths." category = "dev" optional = false @@ -444,7 +665,7 @@ python-versions = ">=3.7" name = "pillow" version = "9.3.0" description = "Python Imaging Library (Fork)" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -454,15 +675,15 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa [[package]] name = "platformdirs" -version = "2.5.3" +version = "2.5.4" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.4)"] -test = ["appdirs (==1.4.4)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] +docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx-autodoc-typehints (>=1.19.4)", "sphinx (>=5.3)"] +test = ["appdirs (==1.4.4)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest (>=7.2)"] [[package]] name = "pluggy" @@ -484,6 +705,17 @@ category = "main" optional = true python-versions = ">=3.7" +[[package]] +name = "pyarrow" +version = "10.0.1" +description = "Python library for Apache Arrow" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +numpy = ">=1.16.6" + [[package]] name = "pycodestyle" version = "2.9.1" @@ -510,13 +742,13 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] +tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] [[package]] name = "pylint" -version = "2.15.5" +version = "2.15.6" description = "python code static checker" category = "dev" optional = false @@ -546,7 +778,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pytest" @@ -581,7 +813,7 @@ coverage = {version = ">=5.2.1", extras = ["toml"]} pytest = ">=4.6" [package.extras] -testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"] +testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"] [[package]] name = "python-dateutil" @@ -602,6 +834,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "pywin32" +version = "305" +description = "Python for Window Extensions" +category = "main" +optional = true +python-versions = "*" + [[package]] name = "pyyaml" version = "6.0" @@ -610,6 +850,17 @@ category = "main" optional = true python-versions = ">=3.6" +[[package]] +name = "querystring-parser" +version = "1.2.4" +description = "QueryString parser for Python/Django that correctly handles nested dictionaries" +category = "main" +optional = true +python-versions = "*" + +[package.dependencies] +six = "*" + [[package]] name = "requests" version = "2.28.1" @@ -626,7 +877,7 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] +use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] [[package]] name = "scikit-learn" @@ -643,10 +894,10 @@ scipy = ">=1.3.2" threadpoolctl = ">=2.0.0" [package.extras] -benchmark = ["matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"] -docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] -examples = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] -tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.2)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pyamg (>=4.0.0)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"] +benchmark = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "memory-profiler (>=0.57.0)"] +docs = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.2.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] +examples = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)"] +tests = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=22.3.0)", "mypy (>=0.961)", "pyamg (>=4.0.0)", "numpydoc (>=1.2.0)"] [[package]] name = "scipy" @@ -660,34 +911,20 @@ python-versions = ">=3.8" numpy = ">=1.18.5,<1.26.0" [package.extras] -dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] -doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] -test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] - -[[package]] -name = "setuptools" -version = "65.5.1" -description = "Easily download, build, install, upgrade, and uninstall Python packages" -category = "dev" -optional = false -python-versions = ">=3.7" - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] -testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-panels (>=0.5.2)", "matplotlib (>2)", "numpydoc", "sphinx-tabs"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8"] [[package]] name = "setuptools-scm" version = "7.0.5" description = "the blessed package to manage your versions by scm tags" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" [package.dependencies] packaging = ">=20.0" -setuptools = "*" tomli = ">=1.0.0" typing-extensions = "*" @@ -695,6 +932,32 @@ typing-extensions = "*" test = ["pytest (>=6.2)", "virtualenv (>20)"] toml = ["setuptools (>=42)"] +[[package]] +name = "shap" +version = "0.41.0" +description = "A unified approach to explain the output of any machine learning model." +category = "main" +optional = true +python-versions = "*" + +[package.dependencies] +cloudpickle = "*" +numba = "*" +numpy = "*" +packaging = ">20.9" +pandas = "*" +scikit-learn = "*" +scipy = "*" +slicer = "0.0.7" +tqdm = ">4.25.0" + +[package.extras] +all = ["transformers", "ipython", "lime", "pyod", "pyspark", "sphinx-rtd-theme", "pytest-mpl", "nbsphinx", "pytest", "opencv-python", "numpydoc", "xgboost", "torch", "sentencepiece", "matplotlib", "pytest-cov", "catboost", "lightgbm", "sphinx"] +docs = ["matplotlib", "ipython", "numpydoc", "sphinx-rtd-theme", "sphinx", "nbsphinx"] +others = ["lime"] +plots = ["matplotlib", "ipython"] +test = ["pytest", "pytest-mpl", "pytest-cov", "xgboost", "lightgbm", "catboost", "pyspark", "pyod", "transformers", "torch", "sentencepiece", "opencv-python"] + [[package]] name = "six" version = "1.16.0" @@ -703,6 +966,14 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +[[package]] +name = "slicer" +version = "0.0.7" +description = "A small package for big slicing." +category = "main" +optional = true +python-versions = ">=3.6" + [[package]] name = "smmap" version = "5.0.0" @@ -711,6 +982,38 @@ category = "main" optional = true python-versions = ">=3.6" +[[package]] +name = "sqlalchemy" +version = "1.4.44" +description = "Database Abstraction Library" +category = "main" +optional = true +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" + +[package.dependencies] +greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"} + +[package.extras] +aiomysql = ["greenlet (!=0.4.17)", "aiomysql"] +aiosqlite = ["typing_extensions (!=3.10.0.1)", "greenlet (!=0.4.17)", "aiosqlite"] +asyncio = ["greenlet (!=0.4.17)"] +asyncmy = ["greenlet (!=0.4.17)", "asyncmy (>=0.2.3,!=0.2.4)"] +mariadb_connector = ["mariadb (>=1.0.1,!=1.1.2)"] +mssql = ["pyodbc"] +mssql_pymssql = ["pymssql"] +mssql_pyodbc = ["pyodbc"] +mypy = ["sqlalchemy2-stubs", "mypy (>=0.910)"] +mysql = ["mysqlclient (>=1.4.0,<2)", "mysqlclient (>=1.4.0)"] +mysql_connector = ["mysql-connector-python"] +oracle = ["cx_oracle (>=7,<8)", "cx_oracle (>=7)"] +postgresql = ["psycopg2 (>=2.7)"] +postgresql_asyncpg = ["greenlet (!=0.4.17)", "asyncpg"] +postgresql_pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"] +postgresql_psycopg2binary = ["psycopg2-binary"] +postgresql_psycopg2cffi = ["psycopg2cffi"] +pymysql = ["pymysql (<1)", "pymysql"] +sqlcipher = ["sqlcipher3-binary"] + [[package]] name = "sqlparse" version = "0.4.3" @@ -742,7 +1045,7 @@ python-versions = ">=3.6" name = "tomli" version = "2.0.1" description = "A lil' TOML parser" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -773,6 +1076,23 @@ category = "main" optional = false python-versions = ">=3.7" +[[package]] +name = "tqdm" +version = "4.64.1" +description = "Fast, Extensible Progress Meter" +category = "main" +optional = true +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[package.extras] +dev = ["py-make (>=0.1.0)", "twine", "wheel"] +notebook = ["ipywidgets (>=6)"] +slack = ["slack-sdk"] +telegram = ["requests"] + [[package]] name = "typing-extensions" version = "4.4.0" @@ -783,17 +1103,56 @@ python-versions = ">=3.7" [[package]] name = "urllib3" -version = "1.26.12" +version = "1.26.13" description = "HTTP library with thread-safe connection pooling, file post, and more." category = "main" optional = true -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*" [package.extras] -brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"] -secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"] +brotli = ["brotlicffi (>=0.8.0)", "brotli (>=1.0.9)", "brotlipy (>=0.6.0)"] +secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "urllib3-secure-extra", "ipaddress"] socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +[[package]] +name = "waitress" +version = "2.1.2" +description = "Waitress WSGI server" +category = "main" +optional = true +python-versions = ">=3.7.0" + +[package.extras] +docs = ["Sphinx (>=1.8.1)", "docutils", "pylons-sphinx-themes (>=1.0.9)"] +testing = ["pytest", "pytest-cover", "coverage (>=5.0)"] + +[[package]] +name = "websocket-client" +version = "1.4.2" +description = "WebSocket client for Python with low level API options" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.extras] +docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"] +optional = ["python-socks", "wsaccel"] +test = ["websockets"] + +[[package]] +name = "werkzeug" +version = "2.2.2" +description = "The comprehensive WSGI web application library." +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +MarkupSafe = ">=2.1.1" + +[package.extras] +watchdog = ["watchdog"] + [[package]] name = "wrapt" version = "1.14.1" @@ -804,29 +1163,31 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" [[package]] name = "zipp" -version = "3.10.0" +version = "3.11.0" description = "Backport of pathlib-compatible object wrapper for zip files" category = "main" optional = true python-versions = ">=3.7" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] -testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "jaraco.itertools", "func-timeout", 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"sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766"}, ] diff --git a/pyproject.toml b/pyproject.toml index fb2b192d..419f104e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,10 +29,12 @@ torch = "~1.12.0" torchinfo = "^1.6.0" scikit-learn = "^1.0" -mlflow-skinny = { version = "^1.27.0", optional = true } +mlflow-skinny = { version = "~2.0.1", optional = true } +mlflow = { version = "~2.0.1", optional = true } [tool.poetry.extras] -mlflow = ["mlflow-skinny"] +mlflow-skinny = ["mlflow-skinny"] +mlflow = ["mlflow"] [tool.poetry.dev-dependencies] matplotlib = "^3.4.2" From 78cf5b4e26ffd6dffa5e0dc364cbd51b220bd928 Mon Sep 17 00:00:00 2001 From: Kushal Batra <34571348+s0nicboOm@users.noreply.github.com> Date: Fri, 9 Dec 2022 00:50:44 +0530 Subject: [PATCH 03/15] fix: fix pipeline for 0.3 release (#106) Signed-off-by: s0nicboOm --- .../src/udf/inference.py | 20 ++++++++------ .../src/udf/train.py | 10 +++++-- .../numalogic-simple-pipeline/src/utils.py | 26 ++++++++++++++----- numalogic/models/autoencoder/pipeline.py | 4 +-- numalogic/models/threshold/_std.py | 6 +---- numalogic/registry/artifact.py | 6 +---- numalogic/registry/mlflow_registry.py | 18 +++++-------- pyproject.toml | 2 +- 8 files changed, 50 insertions(+), 42 deletions(-) diff --git a/examples/numalogic-simple-pipeline/src/udf/inference.py b/examples/numalogic-simple-pipeline/src/udf/inference.py index 4f270171..53e84233 100644 --- a/examples/numalogic-simple-pipeline/src/udf/inference.py +++ b/examples/numalogic-simple-pipeline/src/udf/inference.py @@ -6,7 +6,7 @@ from numalogic.models.autoencoder.variants import Conv1dAE from pynumaflow.function import Messages, Message, Datum -from src.utils import Payload, load_model +from src.utils import Payload, load_artifact LOGGER = logging.getLogger(__name__) WIN_SIZE = int(os.getenv("WIN_SIZE")) @@ -27,20 +27,24 @@ def inference(key: str, datum: Datum) -> Messages: payload = Payload.from_json(datum.value.decode("utf-8")) messages = Messages() - # - artifact_data = load_model(skeys=["ae"], dkeys=["model"]) + artifact_data = load_artifact(skeys=["ae"], dkeys=["model"], type="pytorch") + thresh_clf_data = load_artifact(skeys=["thresh_clf"], dkeys=["model"]) # Check if model exists for inference - if artifact_data: - # load model from registry + if artifact_data and thresh_clf_data: + LOGGER.info("%s - Model found!", payload.uuid) + + # Load model from registry pl = AutoencoderPipeline(model=Conv1dAE(in_channels=1, enc_channels=12), seq_len=WIN_SIZE) - pl.load(model=artifact_data.artifact, **artifact_data.metadata) + pl.load(model=artifact_data.artifact) - LOGGER.info("%s - Model found!", payload.uuid) + # Load the threshold model from registry + thresh_clf = thresh_clf_data.artifact # Infer using the loaded model infer_data = np.asarray(payload.ts_data).reshape(-1, 1) - payload.ts_data = pl.score(infer_data).tolist() + score_data = pl.score(infer_data) + payload.ts_data = thresh_clf.predict(score_data).tolist() LOGGER.info("%s - Inference complete", payload.uuid) diff --git a/examples/numalogic-simple-pipeline/src/udf/train.py b/examples/numalogic-simple-pipeline/src/udf/train.py index ae387646..66ca2954 100644 --- a/examples/numalogic-simple-pipeline/src/udf/train.py +++ b/examples/numalogic-simple-pipeline/src/udf/train.py @@ -5,10 +5,11 @@ import pandas as pd from numalogic.models.autoencoder import AutoencoderPipeline from numalogic.models.autoencoder.variants import Conv1dAE +from numalogic.models.threshold._std import StdDevThreshold from numalogic.preprocess.transformer import LogTransformer from pynumaflow.function import Datum, Messages, Message -from src.utils import Payload, save_model, TRAIN_DATA_PATH +from src.utils import Payload, save_artifact, TRAIN_DATA_PATH LOGGER = logging.getLogger(__name__) WIN_SIZE = int(os.getenv("WIN_SIZE")) @@ -50,13 +51,18 @@ def train(key: str, datum: Datum): clf = LogTransformer() train_data = clf.fit_transform(data) + # Define Threshold method + thresh_clf = StdDevThreshold(std_factor=1.2) + thresh_clf.fit(train_data.to_numpy().reshape(-1, 1)) + # Train step pl = AutoencoderPipeline(model=Conv1dAE(in_channels=1, enc_channels=12), seq_len=WIN_SIZE) pl.fit(train_data.to_numpy()) LOGGER.info("%s - Training complete", payload.uuid) # Save to registry - save_model(pl, skeys=["ae"], dkeys=["model"]) + save_artifact(pl.model, skeys=["ae"], dkeys=["model"]) + save_artifact(thresh_clf, skeys=["thresh_clf"], dkeys=["model"]) LOGGER.info("%s - Model Saving complete", payload.uuid) # Train is the last vertex in the graph diff --git a/examples/numalogic-simple-pipeline/src/utils.py b/examples/numalogic-simple-pipeline/src/utils.py index 9ebc4899..48c78881 100644 --- a/examples/numalogic-simple-pipeline/src/utils.py +++ b/examples/numalogic-simple-pipeline/src/utils.py @@ -1,10 +1,12 @@ import logging import os from dataclasses import dataclass -from typing import Sequence +from typing import Sequence, Union from dataclasses_json import dataclass_json from numalogic.models.autoencoder import AutoencoderPipeline +from numalogic.models.autoencoder.base import TorchAE +from numalogic.models.threshold._std import StdDevThreshold from numalogic.registry import MLflowRegistrar from numalogic.tools.types import ArtifactDict from numpy.typing import ArrayLike @@ -24,16 +26,26 @@ class Payload: uuid: str = None -def save_model(pl: AutoencoderPipeline, skeys: Sequence[str], dkeys: Sequence[str]) -> None: - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") - ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=pl.model, **pl.model_properties) +def save_artifact( + pl: Union[AutoencoderPipeline, StdDevThreshold], + skeys: Sequence[str], + dkeys: Sequence[str], +) -> None: + if isinstance(pl, TorchAE): + ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") + else: + ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="sklearn") + ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=pl) -def load_model(skeys: Sequence[str], dkeys: Sequence[str]) -> ArtifactDict: +def load_artifact(skeys: Sequence[str], dkeys: Sequence[str], type: str = None) -> ArtifactDict: try: - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI) + if type == "pytorch": + ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") + else: + ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="sklearn") artifact_dict = ml_registry.load(skeys=skeys, dkeys=dkeys) return artifact_dict except Exception as ex: - LOGGER.exception("Error while loading model from MLFlow database: %s", ex) + LOGGER.exception("Error while loading artifact from MLFlow database: %s", ex) return None diff --git a/numalogic/models/autoencoder/pipeline.py b/numalogic/models/autoencoder/pipeline.py index cf102dc6..4de4dafb 100644 --- a/numalogic/models/autoencoder/pipeline.py +++ b/numalogic/models/autoencoder/pipeline.py @@ -54,7 +54,7 @@ def __init__( lr: float = 0.001, batch_size: int = 256, num_epochs: int = 100, - resume_train: bool = False + resume_train: bool = False, ): if not (model and seq_len): raise ValueError("No model and seq len provided!") @@ -75,7 +75,7 @@ def model_properties(self): model_properties_dict = { "batch_size": self.batch_size, "num_epochs": self.num_epochs, - "epochs_elapsed": self._epochs_elapsed + "epochs_elapsed": self._epochs_elapsed, } if self.resume_train: model_properties_dict["optimizer_state_dict"] = self.optimizer.state_dict() diff --git a/numalogic/models/threshold/_std.py b/numalogic/models/threshold/_std.py index edc79b25..8bc3b0f3 100644 --- a/numalogic/models/threshold/_std.py +++ b/numalogic/models/threshold/_std.py @@ -4,11 +4,7 @@ class StdDevThreshold(BaseEstimator): - def __init__( - self, - std_factor: float = 3.0, - min_threshold: float = 0.1 - ): + def __init__(self, std_factor: float = 3.0, min_threshold: float = 0.1): self.std_factor = std_factor self.min_threshold = min_threshold diff --git a/numalogic/registry/artifact.py b/numalogic/registry/artifact.py index 5933778f..64d7d21c 100644 --- a/numalogic/registry/artifact.py +++ b/numalogic/registry/artifact.py @@ -38,11 +38,7 @@ def load( @abstractmethod def save( - self, - skeys: Sequence[str], - dkeys: Sequence[str], - artifact: Artifact, - **metadata + self, skeys: Sequence[str], dkeys: Sequence[str], artifact: Artifact, **metadata ) -> Any: r""" Saves the artifact into mlflow registry and updates version. diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py index 1ca60c7f..6c72233b 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/numalogic/registry/mlflow_registry.py @@ -18,6 +18,7 @@ class ModelStage(str, Enum): """ Defines different stages the model state can be in mlflow """ + STAGE = "Staging" ARCHIVE = "Archived" PRODUCTION = "Production" @@ -48,6 +49,7 @@ class MLflowRegistrar(ArtifactManager): >>> registry.save(skeys=["model"], dkeys=["AE"], artifact=VanillaAE(10)) >>> artifact_data = registry.load(skeys=["model"], dkeys=["AE"]) """ + _TRACKING_URI = None def __new__( @@ -119,9 +121,7 @@ def load( model_key, stages=[ModelStage.PRODUCTION] )[-1] elif version is not None: - model = self.handler.load_model( - model_uri=f"models:/{model_key}/{version}" - ) + model = self.handler.load_model(model_uri=f"models:/{model_key}/{version}") version_info = self.client.get_model_version(model_key, version) else: raise ValueError("One of 'latest' or 'version' needed in load method call") @@ -133,9 +133,7 @@ def load( return ArtifactData(artifact=model, metadata=metadata, extras=dict(version_info)) except Exception as ex: - _LOGGER.exception( - "Error when loading a model with key: %s: %r", model_key, ex - ) + _LOGGER.exception("Error when loading a model with key: %s: %r", model_key, ex) return None def save( @@ -159,18 +157,14 @@ def save( model_key = self.construct_key(skeys, dkeys) try: mlflow.start_run() - self.handler.log_model( - artifact, "model", registered_model_name=model_key - ) + self.handler.log_model(artifact, "model", registered_model_name=model_key) if metadata: mlflow.log_params(metadata) model_version = self.transition_stage(skeys=skeys, dkeys=dkeys) _LOGGER.info("Successfully inserted model %s to Mlflow", model_key) return model_version except Exception as ex: - _LOGGER.exception( - "Error when saving a model with key: %s: %r", model_key, ex - ) + _LOGGER.exception("Error when saving a model with key: %s: %r", model_key, ex) return None finally: mlflow.end_run() diff --git a/pyproject.toml b/pyproject.toml index 419f104e..f837b557 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "numalogic" -version = "0.2.6" +version = "0.3.0a0" description = "Collection of operational Machine Learning models and tools." authors = ["Numalogic Developers"] packages = [{ include = "numalogic" }] From 8384a28d7a7f54b6f55ba5ee67af8797c92b93d9 Mon Sep 17 00:00:00 2001 From: Kushal Batra <34571348+s0nicboOm@users.noreply.github.com> Date: Wed, 14 Dec 2022 04:38:01 +0530 Subject: [PATCH 04/15] chore: update docs (#108) * fix: documentation for release 0.3 Signed-off-by: s0nicboOm --- docs/quick-start.md | 43 +- docs/threshold.md | 36 ++ .../numalogic-simple-pipeline/poetry.lock | 395 ++++++++++++------ .../numalogic-simple-pipeline/pyproject.toml | 2 +- examples/quick-start.ipynb | 207 +++++---- 5 files changed, 443 insertions(+), 240 deletions(-) create mode 100644 docs/threshold.md diff --git a/docs/quick-start.md b/docs/quick-start.md index 181015dc..5f207d3f 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -18,22 +18,33 @@ In this example, the train data set has numbers ranging from 1-10. Whereas in th import numpy as np from numalogic.models.autoencoder import AutoencoderPipeline from numalogic.models.autoencoder.variants import Conv1dAE +from numalogic.models.threshold._std import StdDevThreshold from numalogic.postprocess import tanh_norm +from numalogic.preprocess.transformer import LogTransformer X_train = np.array([1, 3, 5, 2, 5, 1, 4, 5, 1, 4, 5, 8, 9, 1, 2, 4, 5, 1, 3]).reshape(-1, 1) X_test = np.array([-20, 3, 5, 40, 5, 10, 4, 5, 100]).reshape(-1, 1) -model = AutoencoderPipeline( - model=Conv1dAE(in_channels=1, enc_channels=4), seq_len=8, num_epochs=30 +# preprocess step +clf = LogTransformer() +train_data = clf.fit_transform(X_train) +test_data = clf.transform(X_test) + +# Define threshold estimator and call fit() +thresh_clf = StdDevThreshold(std_factor=1.2) +thresh_clf.fit(train_data) + +ae_pl = AutoencoderPipeline( + model=Conv1dAE(in_channels=1, enc_channels=4), seq_len=8, num_epochs=30 ) # fit method trains the model on train data set -model.fit(X_train) +ae_pl.fit(X_train) -# predict method returns the reconstruction error -recon = model.predict(X_test) +# score method returns the reconstruction error +anomaly_score = ae_pl.score(X_test) -# score method returns the anomaly score computed on test data set -anomaly_score = model.score(X_test) +# recalibrate score based on threshold estimator +anomaly_score = thresh_clf.predict(anomaly_score) # normalizing scores to range between 0-10 anomaly_score_norm = tanh_norm(anomaly_score) @@ -43,15 +54,15 @@ print("Anomaly Scores:", anomaly_score_norm) Below is the sample output, which has logs and anomaly scores printed. Notice the anomaly score for points -20, 40 and 100 in `X_test` is high. ```shell ...snip training logs... -Anomaly Scores: [[2.70173135] - [0.22298803] - [0.01045979] - [3.66973793] - [0.12931582] - [0.53661316] - [0.10056313] - [0.2634344 ] - [7.76317209]] +Anomaly Scores: [[6.4051428 ] + [5.56049277] + [6.17384938] + [9.3043446 ] + [0.22345986] + [0.48584632] + [3.18197182] + [6.29744181] + [9.99937961]] ``` Replace `X_train` and `X_test` with your own data, and see the anomaly scores generated. diff --git a/docs/threshold.md b/docs/threshold.md new file mode 100644 index 00000000..27c71e70 --- /dev/null +++ b/docs/threshold.md @@ -0,0 +1,36 @@ +# Threshold Estimators + +Threshold Estimators are used for identifying the threshold limit above which we regard the datapoint as anomaly. +It is a simple Estimator that extends BaseEstimator. + +Currently, the library supports `StdDevThreshold`. This takes in paramaters `min_thresh` and `std_factor`. This model +defines threshold as `mean + 3 * std_factor`. + + +Fitting the threshold model +```python +# preprocess step +clf = LogTransformer() +train_data = clf.fit_transform(X_train) +test_data = clf.transform(X_test) + +# Fitting the Threshold model +thresh_clf = StdDevThreshold(std_factor=1.2) +``` + +Train the model +```python +# Train the Autoencoder model and fit the model on train data +ae_pl = AutoencoderPipeline( + model=Conv1dAE(in_channels=1, enc_channels=4), seq_len=8, num_epochs=30 +) +ae_pl.fit(X_train) + +# predict method returns the reconstruction error +anomaly_score = ae_pl.predict(X_test) +``` +Predicting score using the threshold model +```python +# Predict final anomaly score using threshold estimator +anomaly_score = thresh_clf.predict(anomaly_score) +``` \ No newline at end of file diff --git a/examples/numalogic-simple-pipeline/poetry.lock b/examples/numalogic-simple-pipeline/poetry.lock index f00b00be..5c038bed 100644 --- a/examples/numalogic-simple-pipeline/poetry.lock +++ b/examples/numalogic-simple-pipeline/poetry.lock @@ -25,7 +25,7 @@ python-versions = "~=3.7" [[package]] name = "certifi" -version = "2022.9.24" +version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -40,7 +40,7 @@ optional = false python-versions = ">=3.6.0" [package.extras] -unicode_backport = ["unicodedata2"] +unicode-backport = ["unicodedata2"] [[package]] name = "click" @@ -83,9 +83,9 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "selenium"] docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] +test = ["Pillow", "flake8", "isort", "matplotlib", "pytest"] test-minimal = ["pytest"] -test-no-codebase = ["pytest", "matplotlib", "pillow"] +test-no-codebase = ["Pillow", "matplotlib", "pytest"] [[package]] name = "cycler" @@ -97,7 +97,7 @@ python-versions = ">=3.6" [[package]] name = "databricks-cli" -version = "0.17.3" +version = "0.17.4" description = "A command line interface for Databricks" category = "main" optional = false @@ -125,7 +125,7 @@ marshmallow-enum = ">=1.5.1,<2.0.0" typing-inspect = ">=0.4.0" [package.extras] -dev = ["pytest (>=6.2.3)", "ipython", "mypy (>=0.710)", "hypothesis", "portray", "flake8", "simplejson", "types-dataclasses"] +dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=6.2.3)", "simplejson", "types-dataclasses"] [[package]] name = "docker" @@ -181,9 +181,9 @@ optional = false python-versions = ">=3.7" [package.extras] -all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -192,7 +192,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=14.0.0)"] -woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "gitdb" @@ -225,31 +225,32 @@ optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" [package.extras] -docs = ["sphinx", "docutils (<0.18)"] -test = ["objgraph", "psutil", "faulthandler"] +docs = ["Sphinx", "docutils (<0.18)"] +test = ["faulthandler", "objgraph", "psutil"] [[package]] name = "grpcio" -version = "1.51.0" +version = "1.51.1" description = "HTTP/2-based RPC framework" category = "main" optional = false python-versions = ">=3.7" [package.extras] -protobuf = ["grpcio-tools (>=1.51.0)"] +protobuf = ["grpcio-tools (>=1.51.1)"] [[package]] name = "grpcio-tools" -version = "1.51.0" +version = "1.51.1" description = "Protobuf code generator for gRPC" category = "main" optional = false python-versions = ">=3.7" [package.dependencies] -grpcio = ">=1.51.0" +grpcio = ">=1.51.1" protobuf = ">=4.21.6,<5.0dev" +setuptools = "*" [[package]] name = "gunicorn" @@ -259,6 +260,9 @@ category = "main" optional = false python-versions = ">=3.5" +[package.dependencies] +setuptools = ">=3.0" + [package.extras] eventlet = ["eventlet (>=0.24.1)"] gevent = ["gevent (>=1.4.0)"] @@ -285,13 +289,13 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] +testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] [[package]] name = "importlib-resources" -version = "5.10.0" +version = "5.10.1" description = "Read resources from Python packages" category = "main" optional = false @@ -301,8 +305,8 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] +testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [[package]] name = "itsdangerous" @@ -362,7 +366,7 @@ python-versions = ">=3.7" MarkupSafe = ">=0.9.2" [package.extras] -babel = ["babel"] +babel = ["Babel"] lingua = ["lingua"] testing = ["pytest"] @@ -400,9 +404,9 @@ python-versions = ">=3.7" packaging = ">=17.0" [package.extras] -dev = ["pytest", "pytz", "simplejson", "mypy (==0.990)", "flake8 (==5.0.4)", "flake8-bugbear (==22.10.25)", "pre-commit (>=2.4,<3.0)", "tox"] -docs = ["sphinx (==5.3.0)", "sphinx-issues (==3.0.1)", "alabaster (==0.7.12)", "sphinx-version-warning (==1.1.2)", "autodocsumm (==0.2.9)"] -lint = ["mypy (==0.990)", "flake8 (==5.0.4)", "flake8-bugbear (==22.10.25)", "pre-commit (>=2.4,<3.0)"] +dev = ["flake8 (==5.0.4)", "flake8-bugbear (==22.10.25)", "mypy (==0.990)", "pre-commit (>=2.4,<3.0)", "pytest", "pytz", "simplejson", "tox"] +docs = ["alabaster (==0.7.12)", "autodocsumm (==0.2.9)", "sphinx (==5.3.0)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"] +lint = ["flake8 (==5.0.4)", "flake8-bugbear (==22.10.25)", "mypy (==0.990)", "pre-commit (>=2.4,<3.0)"] tests = ["pytest", "pytz", "simplejson"] [[package]] @@ -479,7 +483,7 @@ waitress = {version = "<3", markers = "platform_system == \"Windows\""} [package.extras] aliyun-oss = ["aliyunstoreplugin"] -extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] +extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"] sqlserver = ["mlflow-dbstore"] [[package]] @@ -506,7 +510,7 @@ sqlparse = ">=0.4.0,<1" [package.extras] aliyun-oss = ["aliyunstoreplugin"] -extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] +extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"] sqlserver = ["mlflow-dbstore"] [[package]] @@ -519,32 +523,25 @@ python-versions = "*" [[package]] name = "numalogic" -version = "0.2.6" +version = "0.3.0a0" description = "Collection of operational Machine Learning models and tools." category = "main" optional = false -python-versions = ">=3.8, <3.11" -develop = false +python-versions = ">=3.8,<3.11" [package.dependencies] -mlflow = {version = "^2.0.1", optional = true} -mlflow-skinny = {version = "^2.0.1", optional = true} -numpy = "^1.23.1" -pandas = "^1.4.3" -pytz = "^2022.1" -scikit-learn = "^1.0" -torch = "~1.12.0" -torchinfo = "^1.6.0" +mlflow = {version = ">=2.0.1,<2.1.0", optional = true, markers = "extra == \"mlflow\""} +mlflow-skinny = {version = ">=2.0.1,<2.1.0", optional = true, markers = "extra == \"mlflow-skinny\""} +numpy = ">=1.23.1,<2.0.0" +pandas = ">=1.4.3,<2.0.0" +pytz = ">=2022.1,<2023.0" +scikit-learn = ">=1.0,<2.0" +torch = ">=1.12.0,<1.13.0" +torchinfo = ">=1.6.0,<2.0.0" [package.extras] -mlflow-skinny = ["mlflow-skinny (>=2.0.1,<3.0.0)"] -mlflow = ["mlflow (>=2.0.1,<3.0.0)"] - -[package.source] -type = "git" -url = "https://github.com/s0nicboOm/numalogic" -reference = "test-mlflow" -resolved_reference = "c29d32461656eccae530e070730aa20ccb80abe7" +mlflow = ["mlflow (>=2.0.1,<2.1.0)"] +mlflow-skinny = ["mlflow-skinny (>=2.0.1,<2.1.0)"] [[package]] name = "numba" @@ -558,6 +555,7 @@ python-versions = ">=3.7" importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} llvmlite = ">=0.39.0dev0,<0.40" numpy = ">=1.18,<1.24" +setuptools = "*" [[package]] name = "numpy" @@ -624,7 +622,7 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa [[package]] name = "protobuf" -version = "4.21.9" +version = "4.21.11" description = "" category = "main" optional = false @@ -651,9 +649,9 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] [[package]] name = "pynumaflow" @@ -676,7 +674,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["railroad-diagrams", "jinja2"] +diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "python-dateutil" @@ -740,27 +738,27 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] +use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] [[package]] name = "scikit-learn" -version = "1.1.3" +version = "1.2.0" description = "A set of python modules for machine learning and data mining" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -joblib = ">=1.0.0" +joblib = ">=1.1.1" numpy = ">=1.17.3" scipy = ">=1.3.2" threadpoolctl = ">=2.0.0" [package.extras] -benchmark = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "memory-profiler (>=0.57.0)"] -docs = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.2.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] -examples = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)"] -tests = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=22.3.0)", "mypy (>=0.961)", "pyamg (>=4.0.0)", "numpydoc (>=1.2.0)"] +benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"] +docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] +examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] +tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=5.3.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"] [[package]] name = "scipy" @@ -774,9 +772,22 @@ python-versions = ">=3.8" numpy = ">=1.18.5,<1.26.0" [package.extras] -test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] -doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", 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urllib3 = [ {file = "urllib3-1.26.13-py2.py3-none-any.whl", hash = "sha256:47cc05d99aaa09c9e72ed5809b60e7ba354e64b59c9c173ac3018642d8bb41fc"}, {file = "urllib3-1.26.13.tar.gz", hash = "sha256:c083dd0dce68dbfbe1129d5271cb90f9447dea7d52097c6e0126120c521ddea8"}, diff --git a/examples/numalogic-simple-pipeline/pyproject.toml b/examples/numalogic-simple-pipeline/pyproject.toml index a1db8e59..dd6f636b 100644 --- a/examples/numalogic-simple-pipeline/pyproject.toml +++ b/examples/numalogic-simple-pipeline/pyproject.toml @@ -9,7 +9,7 @@ python = ">=3.8, <3.11" dataclasses-json = "^0.5.6" cachetools = "^5.0.0" pynumaflow = "~0.2.4" -numalogic = {git = "https://github.com/s0nicboOm/numalogic", extras = ["mlflow", "mlflow-skinny"], rev = "test-mlflow"} +numalogic = {version = "0.3.0a", extras = ["mlflow", "mlflow-skinny"]} [tool.poetry.dev-dependencies] diff --git a/examples/quick-start.ipynb b/examples/quick-start.ipynb index 47aa8692..a57762c0 100644 --- a/examples/quick-start.ipynb +++ b/examples/quick-start.ipynb @@ -18,24 +18,20 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "
" - ] + "text/plain": "
" }, - "execution_count": 162, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": [ - "
" - ] + "text/plain": "
" }, "metadata": {}, "output_type": "display_data" @@ -65,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -91,29 +87,21 @@ }, { "cell_type": "code", - "execution_count": 164, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] + "text/plain": "" }, - "execution_count": 164, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" @@ -132,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -141,25 +129,21 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] + "text/plain": "" }, - "execution_count": 166, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" @@ -173,25 +157,19 @@ }, { "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, "source": [ "## 2. Inject anomalies:\n", "\n", "Now, we inject anomalies into the test set using the `AnomalyGenerator`. Here we are adding `contextual` anomalies, you could also try `causal` or `collective` anomalies." - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 167, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, + "execution_count": 52, + "metadata": {}, "outputs": [], "source": [ "from numalogic.synthetic import AnomalyGenerator\n", @@ -205,27 +183,23 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 53, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "text/plain": [ - "" - ] + "text/plain": "" }, - "execution_count": 168, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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fIjU1VWVf5cqVUaJECbVTq3PlfsLO/8k9/wwqbZydndW++fTq1QtRUVHYtWtXgX2JiYnIzMzU+xpS6tWrF+7fv4+ffvqpwL5Xr14pZ9B169YNVlZWmDFjRoGWhbz3T9P9KIrQ0FDcv39fpcRBamqq2pjVsba2LvAz/u6775CVlVWoeEJCQmBnZ4dvv/1W5bxLly5FUlISwsLCCnVedcSMvVevXsjKysLMmTML7MvMzFT+3J49e1bgmrlJrra/JzIfbOEhi+Xq6orFixejf//+qFevHnr37g1PT0/ExsZi27ZtaNasGRYuXKjx+YGBgWjdujXq168PDw8PnDx5Ehs2bMDIkSOVxyxatAjNmzdHUFAQhg0bhkqVKiE+Ph5RUVG4d+8ezp49K1nc165dQ7t27dCrVy8EBgbCxsYGmzZtQnx8vMpgUXXnb9myJebMmYOMjAyUK1cOu3fvxu3bt/WOsX79+li8eDE+//xzBAQEwMvLC23btsW4cePw559/4vXXX8egQYNQv359pKSk4Pz589iwYQNiYmJQunRpg++J2Pr3749169ZhxIgROHDgAJo1a4asrCxcuXIF69atw65du9CgQQMEBARg0qRJmDlzJlq0aIEePXrA3t4eJ06cQNmyZREREQFA8/0oivfeew8LFy5Enz598PHHH6NMmTJYvXq1spChrlal119/HStXroSbmxsCAwMRFRWFvXv3olSpUoWKx9PTExMnTsT06dPRsWNHvPHGG7h69Sq+//57NGzYUNRiomLG3qpVK7z33nuIiIjAmTNn0KFDB9ja2uL69etYv349FixYgDfffBO//PILvv/+e3Tv3h2VK1fG8+fP8dNPP8HV1RWdO3cW7Xsj+TDhIYvWt29flC1bFl988QW++uorpKWloVy5cmjRogUGDx6s9bkfffQR/vzzT+zevRtpaWnw8/PD559/jnHjximPCQwMxMmTJzF9+nSsWLECT548gZeXF+rWravSXSRF3L6+vujTpw/27duHlStXwsbGBtWrV8e6devQs2dPredfs2YNPvzwQyxatAiCIKBDhw7YsWMHypYtq1d8U6ZMwZ07dzBnzhw8f/4crVq1Qtu2beHk5IRDhw5h9uzZWL9+PX799Ve4urqiatWqmD59uspsOzlZWVlh8+bNmDdvHn799Vds2rQJTk5OqFSpEj7++GOVsUYzZsxAxYoV8d1332HSpElwcnLCa6+9hv79+yuP0XQ/isLFxQX79+/Hhx9+iAULFsDFxQUDBgxA06ZN0bNnT50VnBcsWABra2usXr0aqampaNasGfbu3atxvJk+pk2bBk9PTyxcuBCjR4+Gh4cHhg8fjtmzZ4ta4Vjs2JcsWYL69evjhx9+wP/+9z/Y2NjA398f77zzDpo1awYgJzH6559/sHbtWsTHx8PNzQ2NGjXC6tWrRRlcT/LjWlpEZPFatGgBe3v7AhV9zdH8+fMxevRo3Lt3T2UGIBFpxzE8RGTxHj58aBJdaYZ69eqVyuPU1FT88MMPqFKlCpMdIgOxS4uILFZkZCQ2btyImzdvYvz48XKHY7AePXqgQoUKqFOnDpKSkrBq1SpcuXJF4xRrItKMCQ8RWayffvoJO3bswKhRo3SO2TJFoaGh+Pnnn7F69WpkZWUhMDAQa9euxdtvvy13aERmh2N4iIiIyOJxDA8RERFZvGKR8AiCgOTkZFHLthMREZG0xHz/LhZjeJKTk+Hu7o67d+/qXDCSiIiITENycjJ8fX2RmJhY5DpexSLhef78OQDpFwQkIiIi8T1//pwJjz5KlCgBAGzhISIiMiO5LTy57+NFUSwSntw1Z1xdXZnwEBERmRlda8fpo1gMWiYiIqLijQkPERERWTwmPERERGTxmPAQERGRxWPCQ0RERBaPCQ8RERFZPCY8REREZPGY8BAREZHFY8JDREREFo8JDxEREVk8JjxERERk8ZjwEBERkcVjwkNERGSJkh8AG4cD907KHYlJYMJDRERkiTZ/AJz7Hfi5ndyRmARJE55p06ZBoVCofFWvXl25PzU1FeHh4ShVqhRcXFzQs2dPxMfHq5wjNjYWYWFhcHJygpeXF8aNG4fMzEwpwyYiIjJ/T27IHYFJsZH6AjVr1sTevXv/u6DNf5ccPXo0tm3bhvXr18PNzQ0jR45Ejx49cPToUQBAVlYWwsLC4OPjg8jISDx8+BADBgyAra0tZs+eLXXoREREZkwhdwAmRfKEx8bGBj4+PgW2JyUlYenSpVizZg3atm0LAFi+fDlq1KiBY8eOoUmTJti9ezcuXbqEvXv3wtvbG3Xq1MHMmTMxfvx4TJs2DXZ2dlKHT0REZJ7Sn8sdgWGyMoFVPYCydYD2M0Q/veRjeK5fv46yZcuiUqVK6NevH2JjYwEA0dHRyMjIQEhIiPLY6tWro0KFCoiKigIAREVFISgoCN7e3spjQkNDkZycjIsXL2q8ZlpaGpKTk1W+iIjItBy79QQbou/JHYZO6ZnZGLT8Hyw+eFPuUPT3LAZ49UzuKAxzfTdw+xBwdIEkp5c04WncuDFWrFiBnTt3YvHixbh9+zZatGiB58+fIy4uDnZ2dnB3d1d5jre3N+Li4gAAcXFxKslO7v7cfZpERETAzc1N+eXr6yvuN0ZEREXW+8djGLv+LC7cT5I7FK22nnuAg1cf4cudV+QOpaC054AgFNx+dq3xYymqbGnH50qa8HTq1AlvvfUWXnvtNYSGhmL79u1ITEzEunXrpLwsJk6ciKSkJOXX3bt3Jb0eEREV3r1nr+QOQatXGVlyh6Be3AUgojzwx1DV7f/8BByMkCemolDkSUmmuQGZ6aKe3qjT0t3d3VG1alXcuHEDPj4+SE9PR2Jiosox8fHxyjE/Pj4+BWZt5T5WNy4ol729PVxdXVW+iIiILErUwpx/L2xQ3b59bMFjv2sAbP9U+piKQpEvJbm4SdTTGzXhefHiBW7evIkyZcqgfv36sLW1xb59+5T7r169itjYWAQHBwMAgoODcf78eSQkJCiP2bNnD1xdXREYGGjM0ImITIqgrhvDTC37+7as1z9wNQEt5uzH8VtP1O43q1t9foP67U+uA//8YNxYDJU/4clMFfX0kiY8Y8eOxaFDhxATE4PIyEh0794d1tbW6NOnD9zc3DBkyBCMGTMGBw4cQHR0NAYPHozg4GA0adIEANChQwcEBgaif//+OHv2LHbt2oXJkycjPDwc9vb2UoZORGSyou88RcNZ+/Dn2Qdyh6JV9J2n6LboKM7cTdR63D8xT40TkAaDl5/A3aev0OenY7LGIYo/hsgdQeEppJ1GL+m09Hv37qFPnz548uQJPD090bx5cxw7dgyenp4AgHnz5sHKygo9e/ZEWloaQkND8f333yufb21tja1bt+L9999HcHAwnJ2dMXDgQMyYIf50NSIiczH812g8SUnHR7+dxhu1y8odjkY9F+fMuH1rSSSuz+qssu/SA9ObPZutR0tOZlY2bKxNZZECC6qzk5UJrOmlum3bJ4DfbtEuIWnCs3at9lHiDg4OWLRoERYtWqTxGD8/P2zfvl3s0IiIzFamPu/MJiQjSzXe+ORUdP72SIHjTCGZ2HbuIUJremuMI2DSDnStUxYLetc1cmT53IsGzq757/GNvUBAiObjTd15NZOZsjOAK3+JdglTSVOJiKgQ+v50DJ9uOCt3GAYZ+kvBxSzHbziHmlN3IS5J3HEb2jxPzUDkjccq28LXnMKyo9rHFG05I3NX4qtnwM9tVbet6gk8uyNPPGJIeST5JZjwEBGZscibT7Du5D2TH8R85m4iklMzAADn1dTd+f3kXaRlZmPlsRijxfT2D8fQ9+fjBbbvv5KAX6Ni8MHqaLxMV18b5kqcDF1yD84A96OB5WHq9y94Tfc5Hl0TNSTRGOH3V/KlJYiISFxJrzIKbEvLzMam0/fxMj0LQ5pXlCEq7botylkj8cinbbQepzDiuJRLD9UnLcduPcWxWzkDqSt7uuCYmtlbHecfQcwXGhIPKWRlAD+2Kvp5FjUEBvwJVBLhXGaGLTxERCYm8WU63l8Vjb2X4nUf/K+MrGxM3HgeM7deQnyy8bqF1MnOFvAiTX3LSIs5B7Q+V+KJOgb7bv8NnIhRv0TD2buJ+OnwLWQZY0xVxkvxzpW/bo9JkP4eMuEhIjIxX+++hh0X4jD014JjXTTJ+6bbePY+rDspX4X5vj8fQ62puwr1XBPLd7TquugoZm2/jD+MsR6YkC3iuUyw+9MIMTHhISIyMYVpoXmaolqG/9MN58QKx2C53UHFxdV4I6xKLmpCIOQs0Lm4GfDSVH5WTHiIKB9TH5xKRVeYbp22Xx8SPxA5mFqflqkQ++9+zxQg/oJkK5MbLFvEFiwNmPAQmZEvdlxB49n78Oh5mtyhWJTYJy9xOjZnnIYgCBi3/ixm/HUJNx+9kCXBNObAXbHcfPQCMY9T4D9hW5HOY37fuZGI2qWV5/+ZJvJa8vSm5JfgLC0iM7LkUM6Lwg+HbmLy61xPrqgibz5G8qsMjFh1CgBwaFxrHLiSgPX/jslYdvQ2vuwZhLcbVjBqXClqpkKnpGXi9uMUPE9VPxhYTn9ff4x3lhac3l0Y5tjAY5ScWMyE54X+g+GN5uxvkl+CCQ8RFVt9f1J9k74W/wLT/rqksm38H+eNmvDcfpyCI9cfF9j+xsK/cfNRisHnEwQBComziD9OiTdo11itWw8SX4l2ri1n7mNKF4k/gIiZ8NzY89//040w/shEsEuLyAyZ46dgU3Ds1hNM+/MiXqVnqd1vpeG+Rt8x3sDONcdVq+VmZwtYdOBGoZKdvZfiUWfGHuy/Iu0n+r9vFEzQTF3vH8VbKPRJSjrSM6UegyJRM9LpVdKc1wQx4SGiYqP3j8ewIjIG3x+8gQtqqv1G3ixYYA4A7icar66NVb6s669zD/DVrquFOtfQX08i6VUG3l2h//T2whBzTJmxkvnYpyLWtQGw9dwD3EiQsLVEzBaeYopdWkRU7Hy3/wa+23+jwPalf6tfQ8mYA5et8r3j/3xE+7pOlsZcGy/HrMtZz+zC9FC42Evw1sqEp8jYwkNkhqQekyEWTqE3nHW+n626dacsWXqWeb+xP3kh0aynuPPSnNdUeEk/CYMJDxGJ6u/rj7H3Ujx2nH+IBp/vRZSGbiJjyyzCG6kx8zZN44hMWRk3B9HO9d3+G0h6WXCtMHMh2aDrgxHSnNdUlPCR/BJMeIhINFnZAt5ZehxDfz2J91efwpOUdPT5SbzBoUWxvgjl/wUjVIHNZS6td3m9Vb+8qOeLUrNYJ5nf74WpYcJDRKLRtIhi5E35Z/E8FHEaspSszbCJx9Za3LeSEauicTLGVJY8MIwZ5qumwcFN8ksw4SEyQ+b2mtr3p+NY+08sou88Q2qG+inhUrkSl4z3V0UXab0jdmlpJ8Wb/JtLosQ/6b/Mc2yZOcZsAHc/yS/BWVokmpjHKSjlYocSDrZyh2L2MrKycT3+BWqUKWE2XRwZWdnotuioxv0TNuYMumxRpTRWDmlsrLDQa0kUkotYnfixVANR1cg/Ld0cmMvvaC7mO/nPLRSLpim28JAorsU/R+u5B9E0Yr/coViED9ecRudvj2icJm2KGny+F5ceJus87sj1x0ZdC6yoyQ4AzN5+BV/uvCJCNLrln5ZuDp68SNd9kAmRMneQ7Mcn5bT0pR2Msnin3JjwmLAXaZn4I/oeEl+a/ovJoauPAADP00xvnZ+80jOzpZs2KqKdF+MAAF/vvobwNacKVso1wffEpFf6z6xpOGsvfomMwfbzDyWMSFyLD940QjVd8+zSWnbUfBJzQNourX9uSzT2SMqE594/Rlm8U25MeEzYxI3n8cn6s2g996DcoRgkOTUDtx8bXgbfGELnH0b9z/ci9slLvEjLxC+RMYhLMl4VXUO9ysjCtnMPJa+Ua6jnqRnKN43oO08xcNk/Bp9j6p8X8cHqUzh7N1Hk6KQT9u0Rya8hVQuPIQmpqXiYJM1Acw1j60UxZt1ZPE2R4EMqCw8WGRMeE3D7cQrWnbxbYIbLX2cfAAASX2ZonP0it6RXGTh4NQHP8rRCNZq1F23mHsT1IgwSLarsbAHDfz2JL3aodkPkJmJ7L8dj1NozmPrnRTSJ2IdsE72/mvxw6BaOyzR1N/LmYwRN242KE7dj7T+x6Lk4CoeuPSr0+W4+eiFidNK6niB9rFLN0ur3s2mUBzBEcMR+SRI1qcsMtP7qgPgn9W0o/jnzenxN2vPrJP1rMBMeE9Bm7kF8uuEc1p6I1XhMhglWHxUEAd2/P4pBy0/g+4P/NYemZuTEKueCgv/EPMXuS/FYckh9M62AnKQn16jfzxgnMBHJVd8m77pOuQORSTxStfBcuK97fJUpkqK1WOpBy2KMGyvA5zXxz5nX2r5AtnFnUGpVpnbOv75NRDslEx4TEh3zTO4Q9LblzH1UnLgdt7Ss4CznUIQ0HWMtzt1LVHn859kHJl/dNfaJ6mKHcjVKmeEQE1FJ3Roo5RieZ1J0tRQjNcq4yndxQ7q0KrUBhu0HPCoZdo3EO4YdLwXvIODjc0DfdUDINKDbEtFOzYRHJi/TM9Hnx2MFZuFkZws4fy+pQIuOXNMoBUHA4WuPCvSlf7z2jM7nZmTJ102ka1DiljMPCmyrPWM3Lj0w3U/BLdU0kz+QoZie2FOQzW2K8HGpBqX+y85GupflujP34M+zBX/3TdmzlHQ8TxX3w0h2IX/p6vi6633sGbHHphnS+tLzZ6BcfWDAn0CLsUD4Cf2eZwp/jJVaASX9cpaaaD4acCkt2qmZ8Mhk9bFYRN16gplbL6lsH7vhLLos/BtVJu1Q2V7YP9CiOnTtEQYs+wfBhZhuPmv7ZQki0o+6u6XP7KxVx+X5hHP7cQo2n75vcOvB+6tPSRSRZsW9hafPT8cw/a+Lko2rkzLhAYCPfjuNw0UYcyWGMe2rYlBTf72OHbziBIKm7Rb1fhf25bSyp7Pex44Ru5vckBYe53+TBHdfoN1ngGdVYGS0uPGYoWKV8Dx5kWYSFTavxCXj9pOCXUEbT9/HxlP31T7noQwziQRBwPCVBf9ITHUAtQo1ITb/UvdAQmP/emRlC1h38i7azD2IUb+fQYs5Bwwar3X2biI2FGGNqMIQe4iJGZadwfKjMfglMkaSc4u9TIM6Awoxq05MH7WrglLOdgY955WIFbp1/Zm72KvW5F3yTj2sHd4EFTyc9L7GfbFbX4s6S6t0ANDkA3FiMVPFKuFp9dVBjF1/TtYY7jxJQcf5R7DmuOYByuqEfHNIoog0+/vGY7V1R377x7DY5ZB/FkbC81RRXzDF0n/pcXy64b/fyfuJr/BrlGGtTGPXnzVqraYTIo81M4HPIIWy44L51BCyBGLmxfk/+Nar4K7yOPqzEJXHHWuVQZNKpQy6hq5xhAbjtPQiK1YJDwD8ccq4n4bzO3svSdbrGyL26Uu1281hUb9fIv9LGh4kvkKjWfv0et5v/8QatRhe5M2CU8uvxhk+jqjOjD3o9UOUSbRgFhdS3eri8iOUs2Uv/y1uHqA6TsTexlr5/7yDyJ3sZFyNScj3ga35aODd3TldVQ2GyBOTmSl2CY+c9l2Ox0e/nZY7jELbdzkefX48ZtCgzZ0yfQrOWxem6ReGjT/6QIZxMWL45/ZTs5x6bI5dWqS/c9M6YEt4MzQPKI121b2w6t911GwM7LoT8/ckf2PJ+60DlP/P7bZaPyIYQeXcsH5EU+W+ppVLoUfdcuIFYohXiaqPvWsBFRrndFW9/o1+59Anm85/HQvChMeIhvxiWtVyDTXkl5OIuvXEoPFEI1adwgsTX27C1CiK0HifYYbr4Zhri4ZUYRfmvC72NpjZtabosYjB1cEWtX3dsWpoYywd1BDNq+S0pvRtXAGlXfQfx1OUv4v88nZ5j+9YHY521jj+v3Z4v3VlrBmWk5A19PfAXx82R32/kspjrawU+ObtOqLFYZCohaqPK7cV/xo/tAS+9APO/Cb+uU0AEx4zkpqRhXl7ruHCfem7xTKyskWbyfHlDuMsumhuXqarTwR/P3m30OeUuksr4bn4g+ePyVQxuqikWlfL0L9vexsrnJvaAf2D/bG4Xz1UMmAmkZxcHWxxcnJ7vY+vMWUnYkQqQvg8T2HA4S1zatV4uzpgfMfqKF9S98Dkj9oGwNZagT6NKogSjy4ro2JUN/zvIeDkIf6F0v+tJP7XR+Kf2wQw4TEjiw7cwIJ91/H6d38b5Vq7LsbrPlAP+Yv8UY6/JKiHIvUEupFrxO+SXW/kWWZiOX8/SZIihD8evqX3sZ+0r4q/x7eF1b8DTToFlcH+T1qLHpOU/hrZHBM6VdfrWLHWFcw7UaAw7UZjOlTD5Rkd8Xm3WqLEo03C81R8tuWi6kY7NUnZG/+2APUWoXVGEIAM011jsLCKZcLjP2Eb/CdsM8rKx2LafyXBaNdaf1K8N6F0IxYgvPv0JXb/u9J4Udwyo/Wd8pK6e0iylaBNxPVZnRBSw1vv459LsYSAAT5sVwWeJewLbO8u1ziTQggq74ZhLQysCFxIey/F442FfyMqT6tiYccG2VhbSbbuWV4paXrOLq3XH/jsMVC9c9Evmp0BzC4LpJrPJBt9FMuEJ9eIVdE4czcRySJX8VRnyxn19XUMcdGEqwBrc/lhMu5qmPElpuTUDLSYc0Bt7SBDfbHjCm4/TkHIN4fwh0QtENZW4v/5aZpZR/qxtbbCqJAqeh+fJcMApO/71UMpZzv8NbK5xmPm6THOxJSSV2srBZpWNmzad2EM/fUkzuWbKSt25fC8itrFfCLmKVYaUqbC2lZXRPqfS8gCbh3U/3gzUKwTnv1XEtBt0VF0nHdY8mvpsxSDIeKTzau58Yud0o/j6TT/iGjnUiiA/208jxsJL/DJ+rOinVflGhKcc6xEsRYntcq54dC41pjyeqDOYwct/wf3nkmXZHYO8kHUxLb5tpVB9GftEVTerUjn/mzzhSI9P69WVT2LfI7Qmj4iRGJ860cEa9z32Zai3eO3lkRh2dHbug8kvRTrhCfXAxmqGBdV49n7cF7Cmj5if+hJM0LRPzErmwqC5kHFZFncHHM+Fc/uHqTc5lfKGe82r6jzuefuJeH9VdKVMZjVLQhl3Byxckgj+JVywu/D9V85WlfClm5ARW+N58jMRlZ2/jKfhVPZ00WEsxhfQ38PONpaq9236lhOkdbHL9Lw7b7rBdYk1JcTRHqP8m8hznmkYITWUiY8ZkzKIopiJzzmNvV496V4yaYdU0EjVkZj/xVxBskbopy7I85O7YCYL8LQt3HhZtxcjXsuclT/Kfnv8gstqnji0Lg2aGxAtV9dCVtRh5+kZmSh6uQdqPy/7Th157/q2+NCq2H7Ry3QokpprHtPc+tHfs0CSuk1CPiAEccy6uvY/9pp3R+++hS+2XMN7/x8vFDnb2sl0mSB6mHinEdKEnYxGi3h+eKLL6BQKDBq1CjlttTUVISHh6NUqVJwcXFBz549ER+v+qIXGxuLsLAwODk5wcvLC+PGjUNmJj95A9IWbBOz5gUgXc0SKYk1BVYTc7wnUtl5MQ7vrjB+nSp7WxFeAk24cGKnWpq7iWyKOIbs8sP/xhTmrbVVwcMJgWVdsXJIYzSqqP/UaYVCgXea+Ok8bvAKPVf+NqLcVkJNcou13nxUuNeU2lY3C/W8AhQKoEoHcc5lhoyS8Jw4cQI//PADXnvtNZXto0ePxl9//YX169fj0KFDePDgAXr06KHcn5WVhbCwMKSnpyMyMhK//PILVqxYgSlTpogeY94/XhK/O2f/lQSkZZreWlbaJEs8Ayfb3Jq9LJCuaeVl3Bx0niM9Mxt31CwGbAq8XTXHf/PRi5zK6SLWQbK1VqBzUBnRzmcJmszWb1kbbRRifjyyzlPssfNcnVe2JJInPC9evEC/fv3w008/oWTJ/ypWJiUlYenSpfjmm2/Qtm1b1K9fH8uXL0dkZCSOHTsGANi9ezcuXbqEVatWoU6dOujUqRNmzpyJRYsWIT1d3MUSC9vUaKkevxB/McrFB0X6lGIhpCoS+EDsVZqL4DM9Bv5KKX9CY2ej+pJX1t1R6/MPf9oGl2d01HmdVl8dxLV46bq2Ckvb71hmtoCoW0/w9o/HRLveV2/WNspUbXMSZ8AEkzXHY/FLZEyB7Sp3tMu3RQsodDbgUSkn2Wk4tGjnMjOSJzzh4eEICwtDSIjq6rPR0dHIyMhQ2V69enVUqFABUVFRAICoqCgEBQXB2/u/uhihoaFITk7GxYv5CjHlkZaWhuTkZJUvXZ6kGG+16eLKXCvqSkWqIoFDTWgJkyF6DPyV0p180/TfqF1W5fFXb9XW+nxbays42qkfkJrfoaviVCYXk5RtiOrOzXXRCi81Iwv/23QeU/+8iKf53o9UWniC3irahUr6AR+dBhoNK3Y/MEkTnrVr1+LUqVOIiIgosC8uLg52dnZwd3dX2e7t7Y24uDjlMXmTndz9ufs0iYiIgJubm/LL19e3iN+JaRJ7nI3UBCFnyYp+Px/D3F1X5Q5HdlL1aF1i96xSm3yVefMX5Cuno4XHEGK8d4jd6mep3aaXHiRjzyXjD3KXUkaeWXOp+Wa1qiQ8VkZcsX3TCCDBcpYGkizhuXv3Lj7++GOsXr0aDg66+8HFNHHiRCQlJSm/7t4t/NpEpuzyw2QsPnjTbCpGP36Rhj2X4nH0xhMsPHBD7nBkZ6lvRvnN6l4LlU1kfae8C0Gaop0Xil4lPC9z/BXLXdtKm87fHsGwX08aZV1BOYxcc0pzi7iVfi2OeqvTT/O+jBTgJwkWKZWJZAlPdHQ0EhISUK9ePdjY2MDGxgaHDh3Ct99+CxsbG3h7eyM9PR2JiYkqz4uPj4ePT87MAh8fnwKztnIf5x6jjr29PVxdXVW+5JS/eVIsUbee4MudV7D0b/MoTHXzUYrZJGfGIOVCn6ZUQ6hfYz/s03N9J6kXP5WSGBV7t4uc8AR4SVfbRt2PKkuEftqJeq6rBeQMvNaXlAUixZD3zp2KTUTvPGOrbgl5BoKLnfDU7K59f4ZpDsgvDMkSnnbt2uH8+fM4c+aM8qtBgwbo16+f8v+2trbYt++/EexXr15FbGwsgoNzajcEBwfj/PnzSEj4r+7Cnj174OrqisBAeQdDGuJ07DPdBxXBxQfm8yln1O9n5A6hUJ5JkLRK+da+7dxDCc8uHbFbOPLLm5MYUiNGHzO3Xipy8Uuxx/vqM8278Ar+BhsyQFcThUIBD2c73QcaYOOpe2j+5QFRz5nf/k9aYVHfepKcW5B0+IJ5DY0oCskSnhIlSqBWrVoqX87OzihVqhRq1aoFNzc3DBkyBGPGjMGBAwcQHR2NwYMHIzg4GE2a5FQT7dChAwIDA9G/f3+cPXsWu3btwuTJkxEeHg57+4IL5hVV0ivp19SSgpRrwVCOvHVGxCJlY8ajF2nSnVyHhX3romnlUlg9tLHK9tzupBZVSsNBQ/2bDSKuW5Z/1tS0LoGwt7FGWFAZtKhSGg399e/e2vGxfhVqv9hh+HiHyBuP8d7Kk4hPThX9rcfW2grvt64s8llz/H6i4FABsaq/63sf9PkbWnXsDsask37JlUqeLgh7rYzObtNpf6qfcGPSjZuPLWMIgqyVlufNm4fXX38dPXv2RMuWLeHj44ONGzcq91tbW2Pr1q2wtrZGcHAw3nnnHQwYMAAzZsyQJJ5z9xIlOa/Uv8hMd8yTIWN4RrYJUHkcWEZ7N61UA9q1FbLL5V/KGWuGNUGzgNIq238a0AAzu9bEwj710CFQ/XnETNQm51sralCznBlji/rVw8ohjQ36oFCjjKte33tGIbps+/58HLsuxmPixvOIeSJ+t8sHEiU8604WTE6N/aZ9Lf45vt59Fc+1LACd//dAarpWfl8RGWNaY4/0+TNYWB84/iOQat4TIoya8Bw8eBDz589XPnZwcMCiRYvw9OlTpKSkYOPGjQXG5vj5+WH79u14+fIlHj16hLlz58LGRppR6kmvMpR1O8QcSyD14FQpxsXo+16Qf2FDc7FndEvEfPFfmfWWOhY/3C9BOXtDhjt81E51BW9tCxYCwKIDNzBm3RnRx8Ts0KPLSdPvjoezHfoH+8PNSXNV2vwrWReF2KuBf9Orjqjny+9EzFOcuZso+nlLOOhaQRvIFGFdLQBwthfntblbvtl0mnx/8Ca+238Ds7ZdFuW6YtCnW1JdrR3ZSq876blcyY5xwJ8jpY1FYlxLK4+Ra06j0v+2o9cPUag3cw9+OCROoTwxBvJpI8U05Heb6Vc/pYzbf9N6v36rNpYPaih6LPowdH0dQ38kUzU0QxeFpmRkUFN/lcdTXg8sUDBP1xvLi7RMbDx1HyfvSDt+TB1ba8t8WXG0s8a1zzvhzfrlNR5TlN7l5xJX9tbmxyO39D528+n76Lrwb7X7etTTL1HRRZ+ZWnnJ8XteFPn/8mUdrF+2LtBmsn7HXtoibSwSs8xXpiL65/ZTPHuZgYhC9MeLqY6vO+r4uus8Lvap+M3g+rxp5bY6BFcqhbJuDni9dhlU9Skheiz6MHR9nfzrO9vIUB3W3Un9wMxpb9TE9VmdcGZKe5yf1kG5CGRhYsxfz6Mofjys/QPA4Gb+6FK7LKroMTNI0+rSAERd6kBsdjZWmKulWKGplhp4p4n2hVHn7NS/Ltao38/grIaWuNIu4oytNLRa840E/Wdr5RVSw6tQz9NGn9+Au3les+fsvIKKE7fjqpyVuluNk+/aRsSExwj8SxeuBsnm8Gb4dUgjkaPRT/6EQJ0x7asCANYMa4wj49vC3sYa5dwd8dOABlKHV2S570vDWlRE7fJueo3PEFtpF80zUWytreDuZKfSHVGYmSvfHxBvOY/Z27V/AJjapSa+61NXr7ExbzXQ3Eoi5lIHxrbrYjyiTbC1YUKnGqhbwV3uMIxu10XtXbDT3qhppEhUPUz6bzbb9/8uuTNw2T+yxGI6pP+wwITHBH3YNgB7RrcEALg62CKonJvRYzDkg6pCoVD5RNY+0FvL0aYh9/ubFBaILSOb6718gKgxGHj88sENUcfXHWvyzX7SJspIrSWGVizWZ1yJueq5OFLuEApwsbfBpg+aFfk8uqbda5p9ZygXkcYCvbcyWuv+/F3FYihsI98rEVtjzZt0re1MeIzAkD+AbnXK4pMO1VDF+7+uoS61da8+vDIqphCRaWaMPuVHz+WbOp2/BSukhgxJmoG3uGZZN2wOb4am+WY/6bLjvLQ1ef7XubrOQdT56dOCaKi7T18i7Nsj2HjqHqpO2iH6+fMKe808VwSvVU7z7L4MPQYu61qnza+UOBW1HWytsf0j/UoB5DoR8xTjN5xD4kv9a2Zp61qVWsLzVHy6Qfrp8qI78TNw4Q/N+++dBHZNAtIK180opWKV8NQsK2/F5fz8SjmhVb7ZQc2rFJwt9G6zijoHA3+25aJoqzVnZQtFHkDZXI835U/Wn5U8sbLP8wku76e5Us6qYw0cZHjhk+JNX533V5+S9PzDW1bWueq4MUzZcgEXHyRjzLqzSBdp1pEm9Spor7WyRKQJD2Lb+L7mVh59WhguG3GdtsCyrgZVin5rSRR+P3kXn/87Y0vXa0u9Cu4StTTq/ruOffoSjWbtUzu1X510GGH9rLCv9Ttu2yfAhnf/e5z8AEjPU43553ZA1ELgYME1NOVWrBKetcOb4HZEZ6NfV90bWyVPZxwa16ZAd5W6xjwbayu0qa57cN2TF+JUA+65OBJr1RQV2xyuf5P4gt51dB5z+Noj9PnpmOhJj4/rf2u35U1kTn/WHmuGNsYP/evDx63g+m7Rk0PQwIhrLZno+Fa1Dl8zvZXA80tJM16XgJOOLtDCFCA0Bm1dOHl/HyO2X8YHq6NlX+pjQe86cHUw7M3+zpMUrP0nVmfX4kYRuvjUkeKWbclqhjPZlYHmY8Q/ea6GQw07/kUCsLI78E0NYHZZIDUJyMrzQTlqIXBlm7gxFlGxSngUCoUsVYnV/QHkRiHmrA6xvjVNtUD0mTGWq5SLvV4vVMduPUWayHWENn7QVPn/HvXKoY6vOz5qGwBnexs0DSiN0JrqByiXcrFHZU/Nnyh1DYA0VO6PvnZ5N/w+vImo5xbbADUDKsu6OaCdHom4On4e4i8maqwWM0C86demJG+V6x8O38L283EG1UUaHVJV9JhqlnXDuWmheLuBr97PORHzDBM2nsep2ETR45FLGuzQLX0mEDJV7lD+M7cKcHP/f4+/qADMzFfTZ21f48akQ7FKeExJbuKV/yU6f3XavP54v6nGfYA0Q72aVPIAkDOQ2lB5Ew9jypv4OdhaY3N4M4zpUK3I59U1ALLQFAo0rlQKk8NqFLqOka5px1I4Mr4tfh5YuBl5jnbWWDVE/8HXpsbeRr6xH1KZufVSgYWOs/J8INNV4LSEgS0xhpjVvZZk5xabl2vB1mMyDUboGCR1jTh9GuW8QdUu767cFj05BKW01LHQtUbLSwlG+Tfw88Av7zZSvsBHTmiLv288xhu1y+p8boCXfjV5itLKlZaZhb/Oqg7KlWpZBQBIepmhtVKwIXK/69xoh+ooSZ/fiFaVseTQTUx5PdCgVaPFYmitlPzctdzHm49eaG1tU8ecughzydFlZG9jpbFV9czdZ3Cx/+/nYvXvp4ercc8ROv+wUeJTx8aMilnW9yuJSZ1rQKGAcjyR2bB1AjJMe1X5ojCf3yIzlr+p/Y/3gzH432q6oTW9saB3Hewd00prspNLW0vL/D3XihQnULCAl5VC9dNsWXdH9GrgK+og36JUol504CbGrled6VCUrj1d3SJzd+tfoE3ntf59sytsvOM7VkPkhLZ4t3lF2arSS6Xd14cMfo6p3QNN9Xi+3Xcd/ZceR3pmNi7cN/7aRLv/LXmhzrsrTqLXD1HKx7k5rT7JjiEDjC3dsJaVDP4AYxJGi19R3pQw4TGCvB/iFAqgvp8HrP59JVEoFOhap5zeLxa5xf7UOSfCgnTP8k/pNMKYp6KsvKFuSQkpI1557E6hq7rml7+Fx1AKhUI5O8rPw0nrsfm7Kgyx9p/YQj9XG12/WmHfHpHkumIpX1L7zLSeiyPxQE3dmm/2XMOR64+x48JDpGUav/aKIVPHrQz4+29RxbByCWSCnDzkjkBSTHgkcvTGY4xYGY2E5FSVT57d6xRtsKNCocChca3V7hMEYO+l+CKd/9hN1UJ1xhjinS3yWmN5B6b7ltSeCBRG/halwvrzzAMAwMUHRf+UP6iZv9b99WbuQUpa4UoNTNh4vlDP06WGjysqe2p+8734IBnT9FzD7HlqBl4YeS0qVz2mNP959gGyswXce1awm+DjtWfw5pIoNc8yHQnPU3Uf9C85JoQUJ/os2SIKB3fjXEcGTHh0uJFQuNo2/X4+jp0X4zBp8wWVfvrPRRh8p+0T2tBftRcG0+XrfN1idyVYpyu/LJHHMSgUwOqhjfFB68ropWUJg8J6npohynm2/VsQUIxZavY21vi0o/aB2VfijN99oo2VlQJrh2svWLgiMgZX47T/DWZkZSNo2m6jr0Wkz9izL3ZcQZ0Zu9H8ywP48+wDI0QlrndXFO31hMTRtU5ZLDPWwswfSlu3S07FMuH5oX99vafTjttwrkjX2nMpHnH/rpvi6+EIJzvzGie+8fR9ya8hegsPcma7fdqxusGDHYf92+/eo67mlribj1I07pOTrvdfkW+zKPRpFCjQzfqvpFcZiNh+GUdvPNZ5jp8lWN8tokcQbK11fwPJ/7Y8LT5omsUIddHn77OMmrpWJJ6IHkHw1dFtLRrnUrqPMVPFMuEJremDpYMaonZ53WtUpWUU/dN3bqXbrCzjvOPomj5qaorSwnNezbilojStV/EugcszOuLrXppXxAZQ6O6hXA+TtK9JJIWYx6aXqJXSY0FUTS0pM7dewg+Hb2HQ8hM6zxGkx9+6oepWKInLMzrqfXwRJ7WJqmc9/Vs+D1wtOE4urxGtKmPde4YtLVIYHWv6wNdD/orehhBrXJOUM08l9+yO3BEoFcuEJ9eS/vV1HiNmt/SDJP37w4vil8gYo1xHLIUde6Hpk2dRf2SOdtZQKBRY8o7m34+i/F5siL6HDyRe7kGdcRvO6Vz80dgUCgV2jdI8awgAJm48j1fpBQf3XhBhkH5R2VhbYWHfunodK8ZYLbHU83PX+9ghOtbPGtO+qlFaHxa/Uw+HxraR/DpiWjG4kSjnEbNArX5EfONb8Jp+xxnheyzWCU8ZN0fMeVP7D+Pig2TZy6sb6tbjws0iUvemUlSD/p1+r037eYWr73FHw/gisZLUjrXUV2QGCv+3ue7kXYxdfxanJagCq8/v6RkTrD5bzUd7vaY7T15inAkvsvj6a7prUuWKvvNUwkj0Z0jlYl2KWo9JXwqFQjm7tah0jXcTi1j3RtdSJqIbdQ6o8YZxr5lLwsHvxTrhAfRr2t16TtrVpk3Fd/uvi37OaW/UhH8p3Z/+CpNstZl7UO12MZt/x3ZQXwagsCnwp0UcE2YsiS/T8f3BG/CfYBpr4VjK32DPxfrPyurfxE+yOGysrbB8sDiDYE2pq04fH7SujA9aG145vrDKijC+yegz4NwrAG+vNO41jaDYJzz6ZODn7iVKH4iBprweqGVv4f44pFoJ+ccBDVC3grvWY8asOyPeBUV8begcVEbt9qVHbuN0rPrCcnLRp9Up8ZV+9Xg+XnsGc3ZqLrJ4cGxrPaPST+OK0tb/MKf35O0ftcDywQ0x+fUakl7HXstCooYw9ptxUVunxCyaSual2Cc85qpv4wroqGERzN8kKhRXWFW9S2CTjpWJd1wQb2FOMV9/K2lY3mDe3mvo/r321ZhN0aRNF5CZpXtQ+yEdq6P7lxZ38c9PO1Y3+DmGdCvamtHSBA62VmhTzUvy9bqCK5nnbJwvdQxD0MX442GK5sSkELlDsBjm8yogob9GNte6f+/lBL3eJIzJwdZa66DrhQZ0TwmCYBLjlObuEmfZBlP9NC/1PdZ38cYXRZxhJgUfPZr9E5JTlQPVI288RryeRfFGhVRBST1mg5kKY/0lKhQKfNdHvwHXmlTXMf7KFBn7pc7JvvClSP54PxieJXQvOUT6YcKDnCmr2lapvv04BcuPxhgvIBHM3a3fulpZ2QLeWHgUA5b9gwNXtX+ql9rCAzdEOY8xm9hvGzDVW+o6OL0b6bdiev4X/AeJr9B5wRGsO3FXgqj0U1KPBVkbzd6HSv/bjtnbL6Pvz8eR+FK/ApCjQjQvx2KKjPmGXNTWjk611Hf5mjJjf7Rb2LduodcZY/VqcTHh+Veb6l7orqXY3Nbz+g+aNKQce1G900S/NzlNbj56gfP3k3Dkuu7ibcYgRg0hY75ErDupPUm4Gvcc3+27jlfpWZjwh7QDlh1srRE9WXfzt4Cc1qZr8c+RnpmNWdsv49LDZHwqcXzaONnZYI+WRS3z+vHwLb3Pu36E9PVhAOD34U1Q2kWcViR9W+rEULOsq9GuZTKM3MRT3ccVe8e00iupz4/pjriY8OQxKUzzIMGzdxP1Pk/TiP0iRKOftnpWjNZk4ynpKykbYrSeg5e1rbBuyIKH+tBWPCz/lSJvPEa/n49h9fGcYluh8w/j6z3XMH/fNayPvidqXOqUcrHH+Wkd4KrlTfNVRhY2nb6PDvMOY/CKfwoUUZSiPIE+qniL3z3S0N84iyE2rlRK5zg1fbg62MDb1XhViwO8SmCDkZJCudXxdQcAvFHE9QyNiS084mLCk4dYv1qZRqzhX9Qp2EsOmVa5+216Tj8O11K4T+zXiJ8Hal6WQABUxnf1/fk4jt54gkmbLqhUU9ZWJE/bApqFUcLBFpET22ncP3DZP1j0b/fh0RtPCuz/86x8SXAlkQdDG5MYxff07ZYUU4MiJIVyvR8XphVsw4hgnPqsfaG7l4qqMMlLjTLmN0bKlDHhycPSsunIm4+RYWKDrcWy86J4s7p0sbXS/Gey+OBNtPrqILou/BsjVkar7Et+9V/LibbEtLSL+IMSXext8GXPILX7biS80Loe2Jp/5BvLo6kMgLlYUcTaNqYwecAc/P7vorPDWlTU+zk21lbwMKPB6+NCq0k+U0+n0NnyXl9kTHjycLbX/su1cP912Zr7C6PvT8dRZdIOWdZtKgpdSVqihsUkc4mdt+o63/3EVzh7L6lAEpZ3EPaxWwVbUnJJ9Ran73tn3uM2nrpnUPctqWpdrWhdzBVLy9P6UFhyfUQMLOuKmC/CMClMWz0y02JoGQCTqBcUHA74NpY7CtEw4clDVzY9d/c1zN+r3+wnY9GnHkpwxP4CnxxvJDyXLRGqoqNJue3XB7Xu77FYe/0bsRfaK2zL319nHyj/r62bU6zBrvnp27Oat7ttzDp5l3CwE6kYnrnq1UD/RT3FNLNrTVmuW5zM7q6+xTW/qt4uqOPrjj6NxFv+g3IU71eXQjirpepyfHKq0buQKpZ2Rjs9Bi4fzFNI7vGLNIR8cxjBRhxcDQBh/3ZXjGhVWetxd5++Qna2gOep6qcd39LSHQPIN66gsKZ2kebNRt8px09S9Ku+bAyDmvnLHYKsbGQqkNg/2L9Qz6vvV1LcQAqhX2Pjj3sqDDcnWyzVMh4QAJYObIBdo1pic3gzONkZb7aeVhbUzcqERyQXHySh8ex9qDJph9Gv/XWv2jqPuZdnoU1dCYNUvu1TF4fGtUbP+uV1LI0BBE7diaBpu3HrkepCqE/1eHM2s3xHslk55jgexNXBFiuHiLPCtLlpXc1T7hAMsnxQQzQN0DyD0VhmaWk5MbUWw3Y1vHHqs/Za91vaWFJTYlq/DWbgRoL6lci3nHmgdrsxuDvp7hLJ26WiT9KQS8w6HdZWCviVyumCG6hjFfXUjJyWst/z1Ln56fAt1Ju5R+d1+IKRw4iTBUXVooonfD0ci3ye5ibwZmyIMe3Nq0BimyKWxBDT8kENMaZ9VURPDkHF0s54p0kFnJnSHh+2Md4iofrSNHBaV8V/+ZjpC4kaTHjyGanjD+Txi3TM2XlFOQj1WUo6rsc/h52Jr9WTW7fmh0M3MWJVtI6j/yPFDCJAv0VbgZzxOLnLCczaflnP54jvEzN7MwKAehXk724orDVDmxT6uRVLO2NB7zr4/p16IkYkPbHHnhlqQifD1zMzFW2qe+GjdlVQysUeB8a2xufdguDuZGcWb9XhbSrjdkRnBJV3kzsU9WzVfPhwkDJW6f4OTPtdWgZjQ6vpPOb7gzfR+8djyM4WUHfmHrSfdxi3Hqtv+cmrQ6C3GCEWyufbLiPq5hNE7Lii93PcHG0xu4d+A+2ksuTQTdSevhtX4vRfyV2KBp4P21UR/6QSk+IFdP7bdbDpg6ainze/MnqsraWJk501utYpB1cHwyvbFmdDmus/xVvXB0NT0aFmzmuujxGLOeoj74ytT9pXM+1W6dfnA6WrAn7Ngb7rgPF3gGEHgPqD5Y7MYCYyKso8nYh5qvz/9vO668LUlfkT9yID16o6OTlE0lWm36pfXq/qw8/TMjH0l5N6n1eqF48mlTxw7NZT3QeakGldAjHtr0uina+bluVXxFSUwbum/N6RX+3ybjh7T3NRSmPS9bfuX8oJo9tXRZvqXmaTTFb3ccWRT9tI1lJdWN/3q4et5x6gS+2ysNKztVs2pSoDI0+obnN0B7rMB06vArL1W9POFLCFR43JWpaYyCvdwBlZQw0okiWFv2/ov17Wm/XLS5rsAICHAdOx7z2Tv5bQ4n71Ra3S2riiB1YNkbbGxaBm4v3OGftTcmHHjznKXL/EkCrAf7z/X2uZqSdqXiUczLLlzNfDCY52JlDTJo+SznboH+yv1/hL02YOnYb/YcKjxtAWlfQ6zpBJMDFfhEmaQNhai/tqOaWL9AW9rE39FT6fks52mCFivZLf3wtGcy3rdJma4S31+7sQi7YlPbSJ6PGayJEY5rdh+o8/kmsaemF8oaFyNxVjZjYT1Hz+2kzQqdhncoegJHZVTnP7FGcsYi9Mak60LdgqhTJuhZupJddaSblqlXMr8qK+cglvUxkl7NW3UFXyNK8q0ET5MeEpgvl7r8sdglkzr88GOfSdXaaLthXYxfauSN1acswiMae1j/KaJlExSamNC62OM1M7FNj+9Vu6a30RmTpJE57Fixfjtddeg6urK1xdXREcHIwdO/4rzJeamorw8HCUKlUKLi4u6NmzJ+Lj41XOERsbi7CwMDg5OcHLywvjxo1DZmZm/kuZtNNaCk2ROP7XuTreqF3W6N0uhdGrQXn8+q7xiutN0nNMmi5NDFwLSAy6ClSaKjcnzS2kTSurv4/l3Itee0gM+ZP6mC/C0LO+PEteEIlJ0oSnfPny+OKLLxAdHY2TJ0+ibdu26Nq1Ky5evAgAGD16NP766y+sX78ehw4dwoMHD9CjRw/l87OyshAWFob09HRERkbil19+wYoVKzBlyhQpwxZdSSN8ShWr5QHImXpsbko42OLbPnXxv87ivLlrki1Ct87n3YKMOg1VzN8NY+tSu6zcIYimuk8JHBjbGqXyzRg68mkb7BrV0iivE/r6vFstAMC8t9myQ1rU7CZ3BAaRNOHp0qULOnfujCpVqqBq1aqYNWsWXFxccOzYMSQlJWHp0qX45ptv0LZtW9SvXx/Lly9HZGQkjh07BgDYvXs3Ll26hFWrVqFOnTro1KkTZs6ciUWLFiE93XTW/zEFPw9oAA9nOwSWKXplZGNNPRaTsd7S61Rwh7dr0aa4mlq5e1Nmtsmamrx456iWqKhmsV9fDydU8ylhhKD0904TP8R8EYbuddmyQ1o0HCZ3BAYx2itvVlYW1q5di5SUFAQHByM6OhoZGRkICQlRHlO9enVUqFABUVFRAICoqCgEBQXB2/u/gn2hoaFITk5WthKpk5aWhuTkZJUvQ5XU0iRtihr4eyB6cohZfSIe0ryiydXH0MXexhqRE9qhjZmte/T6a2WK9HxzW/bAlJnjGmdEainM68Ob5NGeP38eLi4usLe3x4gRI7Bp0yYEBgYiLi4OdnZ2cHd3Vzne29sbcXE5Rfzi4uJUkp3c/bn7NImIiICbm5vyy9fX1+C4/zTZdU00UygUeq+QbQpKu9jjn/+1w5sijA8w5uQpaysF3BzNKyH+pledQj933tu18ZEZVpomIspL8oSnWrVqOHPmDI4fP473338fAwcOxKVL4lV+VWfixIlISkpSft29e1f3k/Lx9XASJZYIIy/N0F7G5SsKw8pKgU876l7Ow9SM62he6w4VpRuta215uzg1TZM2Za6ONmhSyUPtPvP5SEJkWSRPeOzs7BAQEID69esjIiICtWvXxoIFC+Dj44P09HQkJiaqHB8fHw8fHx8AgI+PT4FZW7mPc49Rx97eXjkzLPdLLt2NPB6mqnfRxgLMlWH6qVcJB3zTq2jXNfbCi6VMaICplN5u4Ct76ft6fua3CKpCodBcgJAZD1FBRuidMHoHXHZ2NtLS0lC/fn3Y2tpi3759yn1Xr15FbGwsgoODAQDBwcE4f/48EhISlMfs2bMHrq6uCAw0j+mq5jboso6vPCv2Sr2MhdjsChlvZc+Cg1ZN1aohjU2iuq5XCf3Gebk62OD7fqazQnremXghNcyzECGR0Uk4PkHSd5mJEyfi8OHDiImJwfnz5zFx4kQcPHgQ/fr1g5ubG4YMGYIxY8bgwIEDiI6OxuDBgxEcHIwmTXI+GXXo0AGBgYHo378/zp49i127dmHy5MkIDw+Hvb30g13faVKhyOcwt+UTArxMa7aIvmqWM24rnpWVlk/w2p4n4+/DuveCDTq+WUApk1jFeUKn6gip4Y0f+9dHLS0/51ndg9A5qGiDs8W2qG89tK3uhSXv1FduK+lsXuO/iCyFpJ3jCQkJGDBgAB4+fAg3Nze89tpr2LVrF9q3zynEN2/ePFhZWaFnz55IS0tDaGgovv/+e+Xzra2tsXXrVrz//vsIDg6Gs7MzBg4ciBkzZkgZttLMrrXQwM8Do34/U+hzmMD7hVkozH16r2Ul9GlUAQ8SX6FmWVYB1qVRRfVjSjQxhWQHAEq52CvX1Qqp4Y1K/9suc0T6C3utDMLyzZD7pH013Hv2SpTB+kTyMq/+WUkTnqVLl2rd7+DggEWLFmHRokUaj/Hz88P27fK8wCkUCtQqV7Q3UlN509DHD/3r6z7IhJRxc4B/aWf4q6ltYgylDVjtncQh93giMZR0tsOKwcartE1EOcxr4ARJKrSm5oHgVFApF3usGdqYrXgmgj8HItKGCY+E9B1sSYVT1NY3MTQNKG3QWmmssiyeEa0qY+a/SyAQEenCV1+JzO4ehL/Ht5U7DIvWwN+wMSlScXfSv2urKAUASZW1FdC/iZ/ysbnN9CMi4+IrhESc7a1l+zS/JbwZyrk7onvdcgjwcpElBkM1rVzaoOMLOy1cKvpWIjaVNZPEmIEot+YBOct7jGhVGU0rl0K76pz6TUSamda7hgkq7LgAOQcr1/Z1x9EJbTHv7Tpap8Xv+LiFEaPSzsPZDuemdcD1WZ30Ov6vD01r6Q9ta03lJr4NTKCAnl+pnAri77WsjN+HN8H2j0znd0BfJyaFYMOIYARXLgUgZ9r6mmFNYGNiSTCRxTOjpYwAiWdpWYLC/jzr+rqLGkdhaWtlKuvuaMRIdHN10K8+ydmpHcxqLatBTf0RWtNHlJXsi2rP6FZ4kZYJD2e7Asun2FlboaSzLeKT02SKTj+eJezhyfFxROYjKwOwlv81mx+JDDT3rdpoVVX3StlircVVVJY4qNOckh0gp9hgfb+ScLSzljsU2NlYFagfVM7dEVaKnETyr5HN4WBrxRoxRCSeCxvljgAAEx6d8vYIHR7XBm/WL49f3tVcQ6Oqtwu2hDczQmT6qePrjn2ftNK4f3JYDQBAaE3zWnTUnJj6dOmD41rj0oyOcLSzhperA85PC5VlTTUiMjd6doGkJUsbhp7YpaVD3i4tHzcHrccGlXMzubElAFDZU/PA5aEtKiG0po/JdW9psnxQQ7lD0GjVkMaYufUSrsY/V9lu4vkObK2tYGut+piIyNLwlc0AeT+pr3svGDO71lTZv/Tf8vdm499kztfDySQXOS3tojpOo2nlUmhjwjNxmlcpjU5BBYs3yrl+FhGRdMzrtY0Jjw6apj83quiB/sH+yselXezg5aq9BYgM07VOWa3dh6aoiprFV5nvEFGxptfsH+lnfDHh0cHXwxFv1i+PQU39dTT1811NbAoArap6YlxoNTjZWWNKl0C5Q9Kpc5APpnUJxKYPmiq3GVKYkIjIfOibpBiSzEj3XsoxPDooFAq9BnDWKif/lGNL4eZoi6RXGWhXI2cgdXibAIxoVdkku93yUygUGNSsIoCcGX37r8SjX2PzL/JHRGTumPAU0c5RLfDb8ViMbKtfpV3S7fC4Noh9+hJB5f9bK8sckp383qxfntO7iYhMpEAhE54iqu7jiuldLa/WjZzcnGwR5CT/wqBERCQG00h4OIaHiIiIpGMiLTxMeIqJoc1zxpX0qFtOuc3elj9+IiIqHtilVUxM7FwDXeuUQ2BZV7xZvzwUCgUcbOVf6oCIiCydabTwMOEpJqytFMpBwE0DSsscDRERmT19u6rYpUVERERkHEx4iIiIyOIx4SEiIiKLx4SHiIiILB4THiIiIpIQBy0TERERGWUmFxMeIiIiKgR9kxQD1kJUSLduIhMeIiIikhC7tIiIiIiMggkPERERWTwmPERERCQdLi1BREREZBxMeIiIiMjiMeEhIiIiw5lIV5W+mPAQERGRxWPCQ0RERBIyjZYgJjxEREQkIemqJxuCCQ8RERFZPCY8REREJCF2aREREREZBRMeIiIiKgQxW26kbwWSNOGJiIhAw4YNUaJECXh5eaFbt264evWqyjGpqakIDw9HqVKl4OLigp49eyI+Pl7lmNjYWISFhcHJyQleXl4YN24cMjMzpQydiIiIjE66Ac6SJjyHDh1CeHg4jh07hj179iAjIwMdOnRASkqK8pjRo0fjr7/+wvr163Ho0CE8ePAAPXr0UO7PyspCWFgY0tPTERkZiV9++QUrVqzAlClTpAydiIiItNIzOTGRAoU2Up58586dKo9XrFgBLy8vREdHo2XLlkhKSsLSpUuxZs0atG3bFgCwfPly1KhRA8eOHUOTJk2we/duXLp0CXv37oW3tzfq1KmDmTNnYvz48Zg2bRrs7OwKXDctLQ1paWnKx8nJyVJ+m0RERMWQaSQy+jLqGJ6kpCQAgIeHBwAgOjoaGRkZCAkJUR5TvXp1VKhQAVFRUQCAqKgoBAUFwdvbW3lMaGgokpOTcfHiRbXXiYiIgJubm/LL19dXqm+JiIiIzIDREp7s7GyMGjUKzZo1Q61atQAAcXFxsLOzg7u7u8qx3t7eiIuLUx6TN9nJ3Z+7T52JEyciKSlJ+XX37l2RvxsiIiIyJ5J2aeUVHh6OCxcu4O+//5b8Wvb29rC3t5f8OkRERGQejNLCM3LkSGzduhUHDhxA+fLlldt9fHyQnp6OxMRElePj4+Ph4+OjPCb/rK3cx7nHEBERkZGZyGBkfUma8AiCgJEjR2LTpk3Yv38/KlasqLK/fv36sLW1xb59+5Tbrl69itjYWAQHBwMAgoODcf78eSQkJCiP2bNnD1xdXREYGChl+ERERFQY5RvKHUEBknZphYeHY82aNdiyZQtKlCihHHPj5uYGR0dHuLm5YciQIRgzZgw8PDzg6uqKDz/8EMHBwWjSpAkAoEOHDggMDET//v0xZ84cxMXFYfLkyQgPD2e3FRERkVwU2qalm8aCoXlJmvAsXrwYANC6dWuV7cuXL8egQYMAAPPmzYOVlRV69uyJtLQ0hIaG4vvvv1cea21tja1bt+L9999HcHAwnJ2dMXDgQMyYMUPK0ImIiEgbvbu0TKPrS9KER9DjZjg4OGDRokVYtGiRxmP8/Pywfft2MUMjIiIiqWht/ZEH19IiIiIi6ZjI4GYmPERERCQytvAQERGRpWOXFhEREVk8a1vDjjdCtxcTHiIiIhJX568L9zwJW4aY8BAREVEhaGmVKRWg33FGxISHiIiIxMUxPERERFSsWBk4nkciTHiIiIhIXHlbeDwqqT8mKxPIzjJOPGDCQ0RERIXh20T7/vKNNO/LygTm1QS+q2e0woSSLi1BREREFsrWQft+beN4ku8BL3IWFEdmqngxacEWHiIiIpIQZ2kRERERGQUTHiIiIpKAli4tGRYUZcJDREREFo8JDxEREUlHXWuODIUJmfAQERGRcbFLi4iIiMyGR2XN+/RuxVHAGDO5mPAQERFR4ZStI8JJ8iY7XC2diIiIzBLH8BAREZE5azS8cM/jGB4iIiIyGxW0radlyBge6THhISIiIhkZp7WHCQ8RERFJR4buK3WY8BAREZF0Hl0BDn8FpKdoOMA4XVo2RrkKERERFS+5M7EORuT8+yoRCJ0lWzhMeIiIiEh6D04DByIA59JAQIjRL8+Eh4iIiArPxhHIfKX7uDtHc74A4KMzeXZw0DIRERGZumH7gdp9gSqhckeiFRMeIiIiKjzvQKD7YqCkf74dxq+mrA0THiIiIhKBIV1TrLRMRERExYmR6vQw4SEiIiLxaV0gNN8+IyQ9THiIiIjINEi4ijoTHiIiIjIyjuEhIiKiYoVjeIiIiMgiGX/KOhMeIiIiMjJ2aRERERGJTtKE5/Dhw+jSpQvKli0LhUKBzZs3q+wXBAFTpkxBmTJl4OjoiJCQEFy/fl3lmKdPn6Jfv35wdXWFu7s7hgwZghcvXkgZNhEREVkYSROelJQU1K5dG4sWLVK7f86cOfj222+xZMkSHD9+HM7OzggNDUVqaqrymH79+uHixYvYs2cPtm7disOHD2P48OFShk1ERERFpe8UcyMVHpR0tfROnTqhU6dOavcJgoD58+dj8uTJ6Nq1KwDg119/hbe3NzZv3ozevXvj8uXL2LlzJ06cOIEGDRoAAL777jt07twZc+fORdmyZaUMn4iIiKR2ZC5w4ifJLyPbGJ7bt28jLi4OISEhym1ubm5o3LgxoqKiAABRUVFwd3dXJjsAEBISAisrKxw/flzjudPS0pCcnKzyRURERCbo73lGuYxsCU9cXBwAwNvbW2W7t7e3cl9cXBy8vLxU9tvY2MDDw0N5jDoRERFwc3NTfvn6+oocPREREZkTi5ylNXHiRCQlJSm/7t69K3dIRERExYzxa+1oI1vC4+PjAwCIj49X2R4fH6/c5+Pjg4SEBJX9mZmZePr0qfIYdezt7eHq6qryRURERMWXbAlPxYoV4ePjg3379im3JScn4/jx4wgODgYABAcHIzExEdHR0cpj9u/fj+zsbDRu3NjoMRMREZEGNbvn/OteQd44NJB0ltaLFy9w48YN5ePbt2/jzJkz8PDwQIUKFTBq1Ch8/vnnqFKlCipWrIjPPvsMZcuWRbdu3QAANWrUQMeOHTFs2DAsWbIEGRkZGDlyJHr37s0ZWkRERKbErykQfgJwK5fz+PlDzcdqnIouXTeYpAnPyZMn0aZNG+XjMWPGAAAGDhyIFStW4NNPP0VKSgqGDx+OxMRENG/eHDt37oSDg4PyOatXr8bIkSPRrl07WFlZoWfPnvj222+lDJuIiIgKw7Pqf//PSNV8nAwUgmCkij8ySk5OhpubG5KSkjieh4iIyBjm1QKSNEwa+ug08G3dgttbTQDaTFQ+FPP92yJnaRERERHlxYSHiIiIxKepdQcAbh82Xhz/YsJDRERExvXXx0a/JBMeIiIisnhMeIiIiMjiMeEhIiIii8eEh4iIiCweEx4iIiKyeEx4iIiIyOIx4SEiIiKLx4SHiIiILB4THiIiIrJ4THiIiIjINCgUkp2aCQ8RERFZPCY8REREZPGY8BAREZHFY8JDREREFo8JDxEREVk8JjxERERk8ZjwEBERkcVjwkNEREQWjwkPERERWTwmPERERGTxmPAQERGRxWPCQ0RERBaPCQ8RERFZPCY8REREZPGY8BAREZGJUEh2ZiY8REREZPGY8BAREZHFY8JDREREFo8JDxEREVk8JjxERERk8ZjwEBERkcVjwkNEREQWjwkPERERWTwmPERERGTxmPAQERGRxWPCQ0RERBbPbBKeRYsWwd/fHw4ODmjcuDH++ecfuUMiIiIiM2EWCc/vv/+OMWPGYOrUqTh16hRq166N0NBQJCQkyB0aERERiUaQ7MxmkfB88803GDZsGAYPHozAwEAsWbIETk5OWLZsmdyhERERkViSH0h2apNPeNLT0xEdHY2QkBDlNisrK4SEhCAqKkrtc9LS0pCcnKzyRURERCZOoZDs1Caf8Dx+/BhZWVnw9vZW2e7t7Y24uDi1z4mIiICbm5vyy9fX1xihEhERUVFc2y3ZqU0+4SmMiRMnIikpSfl19+5duUMiIiIiXZ5L16VlI9mZRVK6dGlYW1sjPj5eZXt8fDx8fHzUPsfe3h729vbGCI+IiIjMgMm38NjZ2aF+/frYt2+fclt2djb27duH4OBgGSMjIiIic2HyLTwAMGbMGAwcOBANGjRAo0aNMH/+fKSkpGDw4MFyh0ZERERmwCwSnrfffhuPHj3ClClTEBcXhzp16mDnzp0FBjITERERqaMQBEG6Kj8mIjk5GW5ubkhKSoKrq6vc4RAREVm+aW6FfF6S8r9ivn+b/BgeIiIioqJiwkNEREQWjwkPERERWTwmPERERGTxmPAQERGRxWPCQ0RERBaPCQ8RERFZPCY8REREZPGY8BAREZHFY8JDREREFo8JDxEREVk8JjxERERk8ZjwEBERkcVjwkNEREQWjwkPERERWTwmPERERGQayjWQ7NRMeIiIiMg0NBgs2amZ8BAREZGJUEh2ZiY8REREZBoUTHiIiIiICo0JDxEREZkItvAQERGRpSvfULJTM+EhIiIi01A6QLJTM+EhIiIii8eEh4iIiCweEx4iIiKyeEx4iIiIyOIx4SEiIiKLx4SHiIiILB4THiIiIrJ4THiIiIjIePpvluWyTHiIiIjIOPyaAZXbaN4nISY8REREZPGY8BAREZHFY8JDRERE8qseJunpbSQ9OxEREZGSQv3mbkuAoLckvTITnn9lZ2cjPT1d7jDMkq2tLaytreUOg4iIzFX1zoC1tCkJEx4A6enpuH37NrKzs+UOxWy5u7vDx8cHCoWG7J2IiEgj6d87in3CIwgCHj58CGtra/j6+sLKisOaDCEIAl6+fImEhAQAQJkyZWSOiIiIqKBin/BkZmbi5cuXKFu2LJycnOQOxyw5OjoCABISEuDl5cXuLSIiMjmSNWfMmjULTZs2hZOTE9zd3dUeExsbi7CwMDg5OcHLywvjxo1DZmamyjEHDx5EvXr1YG9vj4CAAKxYsULUOLOysgAAdnZ2op63uMlNFjMyMmSOhIiITJamYQ9GGA4hWcKTnp6Ot956C++//77a/VlZWQgLC0N6ejoiIyPxyy+/YMWKFZgyZYrymNu3byMsLAxt2rTBmTNnMGrUKAwdOhS7du0SPV6OPSka3j8iIjJlknVpTZ8+HQA0tsjs3r0bly5dwt69e+Ht7Y06depg5syZGD9+PKZNmwY7OzssWbIEFStWxNdffw0AqFGjBv7++2/MmzcPoaGhGq+dlpaGtLQ05ePk5GTxvjEiIiIyO7KN0I2KikJQUBC8vb2V20JDQ5GcnIyLFy8qjwkJCVF5XmhoKKKiorSeOyIiAm5ubsovX19f8b8BC+Lv74/58+fLHQYRERVbZtylpUtcXJxKsgNA+TguLk7rMcnJyXj16pXGc0+cOBFJSUnKr7t374ocvfxat26NUaNGiXKuEydOYPjw4aKci4iIyBQZlPBMmDABCoVC69eVK1ekilVv9vb2cHV1VfkqbgRBKDAAXBNPT0/OUCMiInEN3glU7ah+X6evjBsLDEx4PvnkE1y+fFnrV6VKlfQ6l4+PD+Lj41W25T728fHReoyrq6tyKnRxNGjQIBw6dAgLFixQJporVqyAQqHAjh07UL9+fdjb2+Pvv//GzZs30bVrV3h7e8PFxQUNGzbE3r17Vc6Xv0tLoVDg559/Rvfu3eHk5IQqVargzz//NPJ3SUREZs0vGOj7u/p9jfP1Khhh4otBg5Y9PT3h6ekpyoWDg4Mxa9YsZe0WANizZw9cXV0RGBioPGb79u0qz9uzZw+Cg4NFiUEdQRDwKiNLsvNr42hrrddspwULFuDatWuoVasWZsyYAQDKcU8TJkzA3LlzUalSJZQsWRJ3795F586dMWvWLNjb2+PXX39Fly5dcPXqVVSoUEHjNaZPn445c+bgq6++wnfffYd+/frhzp078PDwEOebJSKi4iFkOrB3qtxRSDdLKzY2Fk+fPkVsbCyysrJw5swZAEBAQABcXFzQoUMHBAYGon///pgzZw7i4uIwefJkhIeHw97eHgAwYsQILFy4EJ9++ineffdd7N+/H+vWrcO2bdukChuvMrIQOEX8ae/6uDQjFE52un8kbm5usLOzg5OTk7I1LLcrccaMGWjfvr3yWA8PD9SuXVv5eObMmdi0aRP+/PNPjBw5UuM1Bg0ahD59+gAAZs+ejW+//Rb//PMPOnbU0DxJRESkTvNR/yU8MpYwkWzQ8pQpU1C3bl1MnToVL168QN26dVG3bl2cPHkSAGBtbY2tW7fC2toawcHBeOeddzBgwABliwUAVKxYEdu2bcOePXtQu3ZtfP311/j555+1Tkkv7ho0aKDy+MWLFxg7dixq1KgBd3d3uLi44PLly4iNjdV6ntdee035f2dnZ7i6uiqXjyAiIiqyqp3yPDCxLi1DrFixQmdVZD8/vwJdVvm1bt0ap0+fFjEy7RxtrXFphjwJlaNt0ZdkcHZ2Vnk8duxY7NmzB3PnzkVAQAAcHR3x5ptv6lwZ3tbWVuWxQqHg4qpERFQ4ASHAjb1A4zzFiEv6GzWEYr+WVn4KhUKvbiW52dnZKZfF0Obo0aMYNGgQunfvDiCnxScmJkbi6IiIiPLouw5IugeU9JMtBC4Nbqb8/f1x/PhxxMTE4PHjxxpbX6pUqYKNGzfizJkzOHv2LPr27cuWGiIiMi4r64LJTt7xPOa8lhZJa+zYsbC2tkZgYCA8PT01jsn55ptvULJkSTRt2hRdunRBaGgo6tWrZ+RoiYiI5GX6fTekVtWqVQsssTFo0KACx/n7+2P//v0q28LDw1Ue5+/iEgShwHkSExMLFScREZEpYAsPERERyYxdWkRERERFxoSHiIiIZGDcIoRMeIiIiEhenKVFREREFilvkqNmsozYmPAQERGRxWPCQ0RERMaXt1WHXVpERERkkYy8cjoTHiIiIrJ4THiIiIhIZuzSIg1at26NUaNGiXa+QYMGoVu3bqKdj4iISH+cpUVERERUZEx4zNCgQYNw6NAhLFiwAAqFAgqFAjExMbhw4QI6deoEFxcXeHt7o3///nj8+LHyeRs2bEBQUBAcHR1RqlQphISEICUlBdOmTcMvv/yCLVu2KM938OBB+b5BIiIqZqTv0uJq6fkJApDxUp5r2zrpNWp9wYIFuHbtGmrVqoUZM2bkPNXWFo0aNcLQoUMxb948vHr1CuPHj0evXr2wf/9+PHz4EH369MGcOXPQvXt3PH/+HEeOHIEgCBg7diwuX76M5ORkLF++HADg4eEh6bdKRERkTEx48st4CcwuK8+1//cAsHPWeZibmxvs7Ozg5OQEHx8fAMDnn3+OunXrYvbs2crjli1bBl9fX1y7dg0vXrxAZmYmevToAT8/PwBAUFCQ8lhHR0ekpaUpz0dERCQpI09LZ8JjIc6ePYsDBw7AxcWlwL6bN2+iQ4cOaNeuHYKCghAaGooOHTrgzTffRMmSJWWIloiIKA8jJD9MePKzdcppaZHr2oX04sULdOnSBV9++WWBfWXKlIG1tTX27NmDyMhI7N69G9999x0mTZqE48ePo2LFikWJmoiIyOQx4clPodCrW0ludnZ2yMrKUj6uV68e/vjjD/j7+8PGRv2PVaFQoFmzZmjWrBmmTJkCPz8/bNq0CWPGjClwPiIiIkk55ulhUEg/h4qztMyUv78/jh8/jpiYGDx+/Bjh4eF4+vQp+vTpgxMnTuDmzZvYtWsXBg8ejKysLBw/fhyzZ8/GyZMnERsbi40bN+LRo0eoUaOG8nznzp3D1atX8fjxY2RkZMj8HRIRkUWrFvbf/62sJb8cEx4zNXbsWFhbWyMwMBCenp5IT0/H0aNHkZWVhQ4dOiAoKAijRo2Cu7s7rKys4OrqisOHD6Nz586oWrUqJk+ejK+//hqdOnUCAAwbNgzVqlVDgwYN4OnpiaNHj8r8HRIRkUXzqg68dwT45JpRLqcQBEH68oYyS05OhpubG5KSkuDq6qqyLzU1Fbdv30bFihXh4OAgU4Tmj/eRiIjEpu3921Bs4SEiIiKLx4SHiIiILB4THiIiIrJ4THiIiIjI4jHh+VcxGLstKd4/IiIyZcU+4bG2zpn7n56eLnMk5u3ly5wFV21tbWWOhIiIqKBiX2nZxsYGTk5OePToEWxtbWFlVexzQIMIgoCXL18iISEB7u7uygSSiIjIlBT7hEehUKBMmTK4ffs27ty5I3c4Zsvd3Z0rrRMRkckq9gkPkLMuVZUqVditVUi2trZs2SEiIpPGhOdfVlZWrBBMRERkoThghYiIiCweEx4iIiKyeEx4iIiIyOIVizE8uUXxkpOTZY6EiIiI9JX7vi1GcdtikfA8efIEAODr6ytzJERERGSoJ0+ewM3NrUjnKBYJj4eHBwAgNja2yDdMTg0bNsSJEyfkDsPi8T5Lj/dYerzH0uM9ll5SUhIqVKigfB8vimKR8ORWT3Zzc4Orq6vM0RSetbW1WcdvLnifpcd7LD3eY+nxHhuPGKsgcNCyGQkPD5c7hGKB91l6vMfS4z2WHu+xeVEIxWCZ6+TkZLi5uSEpKYnZOBERkZkQ8/27WLTw2NvbY+rUqbC3t5c7FCIiItKTmO/fxaKFh4iIiIq3YtHCQ0RERMUbEx4iIiKyeEx4TNCiRYvg7+8PBwcHNG7cGP/880+BYwRBQKdOnaBQKLB582bjB2kBtN3n1q1bQ6FQqHyNGDFCxmjNk67f5aioKLRt2xbOzs5wdXVFy5Yt8erVK5miNU+a7nFMTEyB3+Hcr/Xr18sctXnR9nscFxeH/v37w8fHB87OzqhXrx7++OMPGaMljQQyKWvXrhXs7OyEZcuWCRcvXhSGDRsmuLu7C/Hx8SrHffPNN0KnTp0EAMKmTZvkCdaM6brPrVq1EoYNGyY8fPhQ+ZWUlCRz1OZF1z2OjIwUXF1dhYiICOHChQvClStXhN9//11ITU2VOXLzoe0eZ2Zmqvz+Pnz4UJg+fbrg4uIiPH/+XO7QzYau3+P27dsLDRs2FI4fPy7cvHlTmDlzpmBlZSWcOnVK5sgpPyY8JqZRo0ZCeHi48nFWVpZQtmxZISIiQrnt9OnTQrly5YSHDx8y4SkkXfe5VatWwscffyxTdJZB1z1u3LixMHnyZLnCswj6vF7kVadOHeHdd981VngWQdc9dnZ2Fn799VeV53h4eAg//fSTUeMk3dilZULS09MRHR2NkJAQ5TYrKyuEhIQgKioKAPDy5Uv07dsXixYtgo+Pj1yhmjV97jMArF69GqVLl0atWrUwceJEvHz5Uo5wzZKue5yQkIDjx4/Dy8sLTZs2hbe3N1q1aoW///5bxqjNi76/x7mio6Nx5swZDBkyxJhhmjV97nHTpk3x+++/4+nTp8jOzsbatWuRmpqK1q1byxS1+dLWdXjz5k10794dnp6ecHV1Ra9evRAfH2/Q+ZnwmJDHjx8jKysL3t7eKtu9vb0RFxcHABg9ejSaNm2Krl27yhGiRdDnPvft2xerVq3CgQMHMHHiRKxcuRLvvPOOHOGaJV33+NatWwCAadOmYdiwYdi5cyfq1auHdu3a4fr163KEbHb0+T3Oa+nSpahRowaaNm1qrBDNnj73eN26dcjIyECpUqVgb2+P9957D5s2bUJAQIAcIZut33//HWPGjMHUqVNx6tQp1K5dG6GhoUhISEBKSgo6dOgAhUKB/fv34+jRo0hPT0eXLl2QnZ2t9zWKxVpaluLPP//E/v37cfr0ablDsXjDhw9X/j8oKAhlypRBu3btcPPmTVSuXFnGyCxD7ovUe++9h8GDBwMA6tati3379mHZsmWIiIiQMzyL8+rVK6xZswafffaZ3KFYnM8++wyJiYnYu3cvSpcujc2bN6NXr144cuQIgoKC5A7PbHzzzTcYNmyY8vVgyZIl2LZtG5YtW4Z69eohJiYGp0+fVlZb/uWXX1CyZEns379fpQVOG7bwmJDSpUvD2tq6QDNdfHw8fHx8sH//fty8eRPu7u6wsbGBjU1OvtqzZ082nxpA131Wp3HjxgCAGzduSB6fJdB1j8uUKQMACAwMVNlfo0YNxMbGGi1Oc2bI7/GGDRvw8uVLDBgwwJghmj1d9/jmzZtYuHAhli1bhnbt2qF27dqYOnUqGjRogEWLFskUtfnR1XWYlpYGhUKhUm3ZwcEBVlZWBnWDM+ExIXZ2dqhfvz727dun3JadnY19+/YhODgYEyZMwLlz53DmzBnlFwDMmzcPy5cvlylq86PrPquTe69z36hJO1332N/fH2XLlsXVq1dVnnft2jX4+fkZO1yzZMjv8dKlS/HGG2/A09PT2GGaNV33OHdcX/6VvK2trQ3qainudHUdNmnSBM7Ozhg/fjxevnyJlJQUjB07FllZWXj48KH+F5J71DSpWrt2rWBvby+sWLFCuHTpkjB8+HDB3d1diIuLU3s8OEurULTd5xs3bggzZswQTp48Kdy+fVvYsmWLUKlSJaFly5Zyh21WdP0uz5s3T3B1dRXWr18vXL9+XZg8ebLg4OAg3LhxQ+bIzYc+rxfXr18XFAqFsGPHDhkjNV/a7nF6eroQEBAgtGjRQjh+/Lhw48YNYe7cuYJCoRC2bdsmd+hm4/79+wIAITIyUmX7uHHjhEaNGgmCIAi7du0SKlWqJCgUCsHa2lp45513hHr16gkjRozQ+zpMeEzQd999J1SoUEGws7MTGjVqJBw7dkzjsUx4Ck/TfY6NjRVatmwpeHh4CPb29kJAQIAwbtw41uEpBF2/yxEREUL58uUFJycnITg4WDhy5IhMkZovXfd44sSJgq+vr5CVlSVThOZP2z2+du2a0KNHD8HLy0twcnISXnvttQLT1Em7tLQ0wdrausB72YABA4Q33nhDZdujR4+EZ8+eCYIgCN7e3sKcOXP0vg4XDyUiIiJZNW7cGI0aNcJ3330HIKfrsEKFChg5ciQmTJhQ4PjcwcqXL19GtWrV9LoGZ2kRERGRrMaMGYOBAweiQYMGaNSoEebPn4+UlBTlrK3ly5ejRo0a8PT0RFRUFD7++GOMHj1a72QHYMJDREREMnv77bfx6NEjTJkyBXFxcahTpw527typHMh89epVTJw4EU+fPoW/vz8mTZqE0aNHG3QNdmkRERGRxeO0dCIiIrJ4THiIiIjI4jHhISIiIovHhEdGgwYNgkKhgEKhgK2tLby9vdG+fXssW7aMVTqJiIhExIRHZh07dsTDhw8RExODHTt2oE2bNvj444/x+uuvIzMzU+7wiIiILAITHpnZ29vDx8cH5cqVQ7169fC///0PW7ZswY4dO7BixQoAQGJiIoYOHQpPT0+4urqibdu2OHv2rMp5/vrrLzRs2BAODg4oXbo0unfvLsN3Q0REZJqY8Jigtm3bonbt2ti4cSMA4K233kJCQgJ27NiB6Oho1KtXD+3atcPTp08BANu2bUP37t3RuXNnnD59Gvv27UOjRo3k/BaIiIhMCuvwyGjQoEFITEzE5s2bC+zr3bs3zp07hx9//BFhYWFISEiAvb29cn9AQAA+/fRTDB8+HE2bNkWlSpWwatUqI0ZPRERkPlhp2UQJggCFQoGzZ8/ixYsXKFWqlMr+V69e4ebNmwCAM2fOYNiwYXKESUREZBaY8Jioy5cvo2LFinjx4gXKlCmDgwcPFjjG3d0dAODo6Gjc4IiIiMwMEx4TtH//fpw/fx6jR49G+fLlERcXBxsbG/j7+6s9/rXXXsO+ffuUi6wRERGRKiY8MktLS0NcXByysrIQHx+PnTt3IiIiAq+//joGDBgAKysrBAcHo1u3bpgzZw6qVq2KBw8eKAcqN2jQAFOnTkW7du1QuXJl9O7dG5mZmdi+fTvGjx8v97dHRERkEpjwyGznzp0oU6YMbGxsULJkSdSuXRvffvstBg4cCCurnEl027dvx6RJkzB48GA8evQIPj4+aNmypXIV2datW2P9+vWYOXMmvvjiC7i6uqJly5ZyfltEREQmhbO0iIiIyOKxDg8RERFZPCY8REREZPGY8BAREZHFY8JDREREFo8JDxEREVk8JjxGFBERgYYNG6JEiRLw8vJCt27dcPXqVZVjUlNTER4ejlKlSsHFxQU9e/ZEfHy8cv/Zs2fRp08f+Pr6wtHRETVq1MCCBQtUzrFx40a0b99eubp6cHAwdu3aZZTvkYiIyBQx4TGiQ4cOITw8HMeOHcOePXuQkZGBDh06ICUlRXnM6NGj8ddff2H9+vU4dOgQHjx4gB49eij3R0dHw8vLC6tWrcLFixcxadIkTJw4EQsXLlQec/jwYbRv3x7bt29HdHQ02rRpgy5duuD06dNG/X6JiIhMBevwyOjRo0fw8vLCoUOH0LJlSyQlJcHT0xNr1qzBm2++CQC4cuUKatSogaioKDRp0kTtecLDw3H58mXs379f47Vq1qyJt99+G1OmTJHkeyEiIjJlbOGRUVJSEgDAw8MDQE7rTUZGBkJCQpTHVK9eHRUqVEBUVJTW8+SeQ53s7Gw8f/5c6zFERESWjEtLyCQ7OxujRo1Cs2bNUKtWLQBAXFwc7OzslKug5/L29kZcXJza80RGRuL333/Htm3bNF5r7ty5ePHiBXr16iVa/EREROaECY9MwsPDceHCBfz999+FPseFCxfQtWtXTJ06FR06dFB7zJo1azB9+nRs2bIFXl5ehb4WERGROWOXlgxGjhyJrVu34sCBAyhfvrxyu4+PD9LT05GYmKhyfHx8PHx8fFS2Xbp0Ce3atcPw4cMxefJktddZu3Ythg4dinXr1ql0kxERERU3THiMSBAEjBw5Eps2bcL+/ftRsWJFlf3169eHra0t9u3bp9x29epVxMbGIjg4WLnt4sWLaNOmDQYOHIhZs2apvdZvv/2GwYMH47fffkNYWJg03xAREZGZ4CwtI/rggw+wZs0abNmyBdWqVVNud3Nzg6OjIwDg/fffx/bt27FixQq4urriww8/BJAzVgfI6cZq27YtQkND8dVXXynPYW1tDU9PTwA53VgDBw7EggULVKa0Ozo6ws3NTfLvk4iIyNQw4TEihUKhdvvy5csxaNAgADmFBz/55BP89ttvSEtLQ2hoKL7//ntll9a0adMwffr0Aufw8/NDTEwMAKB169Y4dOhQgWMGDhyIFStWiPK9EBERmRMmPERERGTxOIaHiIiILB4THiIiIrJ4THiIiIjI4jHhISIiIovHhIeIiIgsHhMeIiIisnhMeIiIiMjiMeEhIiIii8eEh4iIiCweEx4iIiKyeEx4iIiIyOL9HwNMmFldAl5RAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" @@ -250,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -261,11 +235,54 @@ "X_test = scaler.transform(outliers_test_df.to_numpy())" ] }, + { + "cell_type": "markdown", + "source": [ + "## 4. Threshold Estimator:" + ], + "metadata": { + "collapsed": false + }, + "execution_count": 1 + }, + { + "cell_type": "markdown", + "source": [ + "In here, we define the threshold estimator and fit it using the training data set." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 55, + "outputs": [ + { + "data": { + "text/plain": "StdDevThreshold(std_factor=3)", + "text/html": "
StdDevThreshold(std_factor=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from numalogic.models.threshold._std import StdDevThreshold\n", + "\n", + "thresh_clf = StdDevThreshold(std_factor=3)\n", + "thresh_clf.fit(X_train.reshape(-1, 1))" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Training:" + "## 5. Training:" ] }, { @@ -277,21 +294,21 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2022-10-17 19:14:21,878 - INFO - Training sparse autoencoder model with beta: 0.001, and rho: 0.05\n", - "2022-10-17 19:14:21,881 - INFO - Using kl_div regularized loss\n", - "2022-10-17 19:14:22,805 - INFO - epoch : 5, penalty: 0.00251406105235219 loss_mean : 0.0480534\n", - "2022-10-17 19:14:23,684 - INFO - epoch : 10, penalty: 0.002498430432751775 loss_mean : 0.0316550\n", - "2022-10-17 19:14:24,563 - INFO - epoch : 15, penalty: 0.0022973380982875824 loss_mean : 0.0270600\n", - "2022-10-17 19:14:25,482 - INFO - epoch : 20, penalty: 0.002057327190414071 loss_mean : 0.0242054\n", - "2022-10-17 19:14:26,362 - INFO - epoch : 25, penalty: 0.0018031855579465628 loss_mean : 0.0217666\n", - "2022-10-17 19:14:27,218 - INFO - epoch : 30, penalty: 0.0015574340941384435 loss_mean : 0.0195179\n" + "2022-12-09 18:23:23,247 - INFO - Training sparse autoencoder model with beta: 0.001, and rho: 0.05\n", + "2022-12-09 18:23:23,248 - INFO - Using kl_div regularized loss\n", + "2022-12-09 18:23:24,959 - INFO - epoch : 5, penalty: 0.0014599192654713988 loss_mean : 0.0435277\n", + "2022-12-09 18:23:26,194 - INFO - epoch : 10, penalty: 0.0014999237610027194 loss_mean : 0.0194861\n", + "2022-12-09 18:23:27,491 - INFO - epoch : 15, penalty: 0.0013811790850013494 loss_mean : 0.0139388\n", + "2022-12-09 18:23:28,596 - INFO - epoch : 20, penalty: 0.001234247232787311 loss_mean : 0.0116860\n", + "2022-12-09 18:23:30,303 - INFO - epoch : 25, penalty: 0.0010954368626698852 loss_mean : 0.0103516\n", + "2022-12-09 18:23:31,807 - INFO - epoch : 30, penalty: 0.0009711519232951105 loss_mean : 0.0094082\n" ] } ], @@ -299,17 +316,17 @@ "from numalogic.models.autoencoder import SparseAEPipeline\n", "from numalogic.models.autoencoder.variants import Conv1dAE\n", "\n", - "model = SparseAEPipeline(\n", + "pipeline = SparseAEPipeline(\n", " model=Conv1dAE(in_channels=3, enc_channels=8), seq_len=36, num_epochs=30\n", ")\n", - "model.fit(X_train)" + "pipeline.fit(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Inference:\n", + "## 6. Inference:\n", "\n", "Now, we use the trained model from above to predict anomalies in the test data. The predict method return the reconstruction error produced by the model. \n", "\n", @@ -318,17 +335,28 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "array([[0.03953706, 0.02283082, 0.14682023],\n [0.11847361, 0.2631961 , 0.04124341],\n [0.01379496, 0.0057159 , 0.09038273],\n ...,\n [0.11696532, 0.20304612, 0.05622444],\n [0.19488005, 0.0442475 , 0.1566194 ],\n [0.21542044, 0.0984635 , 0.17402739]])" + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "test_recon = model.predict(X_test)\n", - "test_anomaly_score = model.score(X_test)" + "test_recon = pipeline.predict(X_test)\n", + "test_anomaly_score = pipeline.score(X_test)\n", + "test_anomaly_score = thresh_clf.predict(test_anomaly_score)\n", + "test_anomaly_score" ] }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -337,15 +365,13 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" @@ -364,24 +390,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Post-processing:\n", + "## 7. Post-processing:\n", "\n", "Post-processing step is again an optional step, where we normalize the anomalies between 0-10.\n" ] }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ - "from numalogic.scores import tanh_norm\n", + "from numalogic.postprocess import tanh_norm\n", + "\n", "test_anomaly_score_norm = tanh_norm(test_anomaly_score)" ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -390,15 +417,13 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" @@ -416,9 +441,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, "outputs": [], - "source": [] + "source": [], + "metadata": { + "collapsed": false + } } ], "metadata": { @@ -453,4 +480,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} From ed9461517f68192d275d81ef38727bcbe62e887f Mon Sep 17 00:00:00 2001 From: Kushal Batra <34571348+s0nicboOm@users.noreply.github.com> Date: Thu, 15 Dec 2022 03:48:30 +0530 Subject: [PATCH 05/15] fix: fix and clean mlflow test cases (#109) Signed-off-by: s0nicboOm --- numalogic/tests/registry/_mlflow_utils.py | 1006 +---------------- .../tests/registry/test_mlflow_registry.py | 66 +- 2 files changed, 70 insertions(+), 1002 deletions(-) diff --git a/numalogic/tests/registry/_mlflow_utils.py b/numalogic/tests/registry/_mlflow_utils.py index 3235b1c7..324a4857 100644 --- a/numalogic/tests/registry/_mlflow_utils.py +++ b/numalogic/tests/registry/_mlflow_utils.py @@ -103,9 +103,9 @@ def mock_log_model_pytorch(*_, **__): "env": "conda.yaml", }, }, - model_uri="runs:/f2dad48d86c748358b47bdaa24b2619c/model", - model_uuid="adisajdasjdoasd", - run_id="f2dad48d86c748358b47bdaa24b2619c", + model_uri="runs:/a7c0b376530b40d7b23e6ce2081c899c/model", + model_uuid="a7c0b376530b40d7b23e6ce2081c899c", + run_id="a7c0b376530b40d7b23e6ce2081c899c", saved_input_example_info=None, signature_dict=None, utc_time_created="2022-05-23 22:35:59.557372", @@ -127,9 +127,9 @@ def mock_log_model_sklearn(*_, **__): "env": "conda.yaml", }, }, - model_uri="runs:/f2dad48d86c748358b47bdaa24b2619c/model", - model_uuid="adisajdasjdoasd", - run_id="f2dad48d86c748358b47bdaa24b2619c", + model_uri="runs:/a7c0b376530b40d7b23e6ce2081c899c/model", + model_uuid="a7c0b376530b40d7b23e6ce2081c899c", + run_id="a7c0b376530b40d7b23e6ce2081c899c", saved_input_example_info=None, signature_dict=None, utc_time_created="2022-05-23 22:35:59.557372", @@ -144,15 +144,15 @@ def mock_transition_stage(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", - run_id="6e85c26e6e8b49fdb493807d5a527a2c", + name="test::error", + run_id="a7c0b376530b40d7b23e6ce2081c899c", run_link="", - source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", + source="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", status="READY", status_message="", tags={}, user_id="", - version="2", + version="5", ) @@ -163,10 +163,10 @@ def mock_get_model_version(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", - run_id="6e85c26e6e8b49fdb493807d5a527a2c", + name="test::error", + run_id="a7c0b376530b40d7b23e6ce2081c899c", run_link="", - source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", + source="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", status="READY", status_message="", tags={}, @@ -176,6 +176,24 @@ def mock_get_model_version(*_, **__): ] +def mock_get_model_version_obj(*_, **__): + return ModelVersion( + creation_timestamp=1653402941169, + current_stage="Production", + description="", + last_updated_timestamp=1653402941191, + name="test::error", + run_id="a7c0b376530b40d7b23e6ce2081c899c", + run_link="", + source="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", + status="READY", + status_message="", + tags={}, + user_id="", + version="5", + ) + + def mock_list_of_model_version(*_, **__): model_list = [ ModelVersion( @@ -183,7 +201,7 @@ def mock_list_of_model_version(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", + name="test::error", run_id="6e85c26e6e8b49fdb493807d5a527a2c", run_link="", source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", @@ -198,7 +216,7 @@ def mock_list_of_model_version(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", + name="test::error", run_id="6e85c26e6e8b49fdb493807d5a527a2c", run_link="", source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", @@ -213,7 +231,7 @@ def mock_list_of_model_version(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", + name="test::error", run_id="6e85c26e6e8b49fdb493807d5a527a2c", run_link="", source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", @@ -228,7 +246,7 @@ def mock_list_of_model_version(*_, **__): current_stage="Production", description="", last_updated_timestamp=1653402941191, - name="testtest:error", + name="test::error", run_id="6e85c26e6e8b49fdb493807d5a527a2c", run_link="", source="mlflow-artifacts:/0/6e85c26e6e8b49fdb493807d5a527a2c/artifacts/model", @@ -257,7 +275,7 @@ def return_scaler(): def return_empty_rundata(): return Run( run_info=RunInfo( - artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts", + artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", end_time=None, experiment_id="0", lifecycle_stage="active", @@ -274,7 +292,7 @@ def return_empty_rundata(): def return_sklearn_rundata(): return Run( run_info=RunInfo( - artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts", + artifact_uri="mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", end_time=None, experiment_id="0", lifecycle_stage="active", @@ -287,22 +305,7 @@ def return_sklearn_rundata(): run_data=RunData( metrics={}, tags={}, - params=[ - mlflow.entities.Param( - "secondary_artifacts", - "gASVRQIAAAAAAACMEHNrbGVhcm4ucGlwZWxpbmWUjAhQaXBlbGluZZSTlCmBlH2UKIwFc3RlcHOU\n" - "XZSMDnN0YW5kYXJkc2NhbGVylIwbc2tsZWFybi5wcmVwcm9jZXNzaW5nLl9kYXRhlIwOU3RhbmRh\n" - "cmRTY2FsZXKUk5QpgZR9lCiMCXdpdGhfbWVhbpSIjAh3aXRoX3N0ZJSIjARjb3B5lIiMDm5fZmVh\n" - 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run_uuid="a7c0b376530b40d7b23e6ce2081c899c", - start_time=1658788772612, - status="RUNNING", - user_id="lol", - ), - run_data=RunData( - metrics={}, - tags={}, - params=[ - mlflow.entities.Param( - "metadata", - "gASV+2AAAAAAAAB9lCiMEG1vZGVsX3N0YXRlX2RpY3SUjAtjb2xsZWN0aW9uc5SMC09yZGVyZWRE\n" - "aWN0lJOUKVKUKIwQZW5jb2Rlci4wLndlaWdodJSMDHRvcmNoLl91dGlsc5SMEl9yZWJ1aWxkX3Rl\n" - "bnNvcl92MpSTlCiMDXRvcmNoLnN0b3JhZ2WUjBBfbG9hZF9mcm9tX2J5dGVzlJOUQv0DAACAAooK\n" - "bPycRvkgaqhQGS6AAk3pAy6AAn1xAChYEAAAAHByb3RvY29sX3ZlcnNpb25xAU3pA1gNAAAAbGl0\n" - "dGxlX2VuZGlhbnECiFgKAAAAdHlwZV9zaXplc3EDfXEEKFgFAAAAc2hvcnRxBUsCWAMAAABpbnRx\n" - "BksEWAQAAABsb25ncQdLBHV1LoACKFgHAAAAc3RvcmFnZXEAY3RvcmNoCkZsb2F0U3RvcmFnZQpx\n" - "AVgPAAAAMTA1NTUzMTMzMzEwNzM2cQJYAwAAAGNwdXEDS8BOdHEEUS6AAl1xAFgPAAAAMTA1NTUz\n" - "MTMzMzEwNzM2cQFhLsAAAAAAAAAAs883PebKjj36NOy+zy7IvmXtM78Ve68+XkxDvFQluL6PW7O+\n" - "FKigvnXGAr41EGk+Un2CPmy2Kb2uRzE+885KP7kH/L4EcXg+YivkPhKaLD892AI/pzDgvsoFQz+J\n" - 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run_data=RunData(metrics={}, tags={}, params=[mlflow.entities.Param("lr", "0.001")]), ) diff --git a/numalogic/tests/registry/test_mlflow_registry.py b/numalogic/tests/registry/test_mlflow_registry.py index 1959d026..07892cae 100644 --- a/numalogic/tests/registry/test_mlflow_registry.py +++ b/numalogic/tests/registry/test_mlflow_registry.py @@ -20,8 +20,7 @@ mock_list_of_model_version, mock_list_of_model_version2, return_sklearn_rundata, - return_pytorch_rundata_dict_no_metadata, - mock_get_latest_model_version, + mock_get_model_version_obj, ) TRACKING_URI = "http://0.0.0.0:5009" @@ -32,6 +31,8 @@ class TestMLflow(unittest.TestCase): def setUpClass(cls) -> None: cls.model = create_model() cls.model_sklearn = model_sklearn() + cls.skeys = ["test"] + cls.dkeys = ["error"] @contextmanager def assertNotRaises(self, exc_type): @@ -51,13 +52,12 @@ def test_construct_key(self): @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version) def test_insert_model(self): - ml = MLflowRegistrar(TRACKING_URI) - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys status = ml.save( skeys=skeys, dkeys=dkeys, @@ -71,13 +71,13 @@ def test_insert_model(self): @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) def test_insert_model_sklearn(self): model = self.model_sklearn ml = MLflowRegistrar(TRACKING_URI, artifact_type="sklearn") - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys status = ml.save( skeys=skeys, dkeys=dkeys, @@ -91,14 +91,14 @@ def test_insert_model_sklearn(self): @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.log_params", {"lr": 0.01}) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) def test_select_model_when_pytorch_model_exist1(self): model = self.model ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch") - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) data = ml.load(skeys=skeys, dkeys=dkeys) self.assertIsNotNone(data.metadata) @@ -108,17 +108,14 @@ def test_select_model_when_pytorch_model_exist1(self): @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) - @patch( - "mlflow.tracking.MlflowClient.get_run", - Mock(return_value=return_pytorch_rundata_dict_no_metadata()), - ) + @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_when_pytorch_model_exist2(self): model = self.model ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch", models_to_retain=2) - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys ml.save( skeys=skeys, dkeys=dkeys, @@ -132,15 +129,15 @@ def test_select_model_when_pytorch_model_exist2(self): @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_sklearn_rundata()))) @patch("mlflow.active_run", Mock(return_value=return_sklearn_rundata())) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) @patch("mlflow.sklearn.load_model", Mock(return_value=RandomForestRegressor())) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_when_sklearn_model_exist(self): model = self.model_sklearn ml = MLflowRegistrar(TRACKING_URI, artifact_type="sklearn") - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys ml.save( skeys=skeys, dkeys=dkeys, @@ -151,19 +148,18 @@ def test_select_model_when_sklearn_model_exist(self): self.assertIsNone(data.metadata) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch()) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict_no_metadata())) - ) - @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict_no_metadata())) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_empty_rundata()))) + @patch("mlflow.active_run", Mock(return_value=return_empty_rundata())) + @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_model_version", mock_get_model_version) + @patch("mlflow.tracking.MlflowClient.get_model_version", mock_get_model_version_obj) @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10))) @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_with_version(self): model = self.model ml = MLflowRegistrar(TRACKING_URI) - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys ml.save( skeys=skeys, dkeys=dkeys, @@ -213,15 +209,15 @@ def test_no_implementation(self): @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @patch("mlflow.log_params", mock_log_state_dict) @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage) - @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_latest_model_version) + @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) @patch("mlflow.tracking.MlflowClient.delete_model_version", None) @patch("mlflow.pytorch.load_model", Mock(side_effect=RuntimeError)) def test_delete_model_when_model_exist(self): model = self.model ml = MLflowRegistrar(TRACKING_URI) - skeys = ["model_", "nnet"] - dkeys = ["error1"] + skeys = self.skeys + dkeys = self.dkeys ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) ml.delete(skeys=skeys, dkeys=dkeys, version="5") with self.assertLogs(level="ERROR") as log: @@ -239,10 +235,8 @@ def test_delete_model_when_no_model(self): self.assertTrue(log.output) @patch("mlflow.pytorch.log_model", Mock(side_effect=RuntimeError)) - @patch( - "mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict_no_metadata())) - ) - @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict_no_metadata())) + @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_empty_rundata()))) + @patch("mlflow.active_run", Mock(return_value=return_empty_rundata())) def test_insertion_failed(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] From 88d26ec000ee719f967dc90f4f78f57784b1db7e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Tue, 20 Dec 2022 13:05:48 -0800 Subject: [PATCH 06/15] feat!: convert AE variants to lightning modules (#110) Signed-off-by: Avik Basu --- numalogic/models/autoencoder/__init__.py | 5 +- numalogic/models/autoencoder/base.py | 68 +- numalogic/models/autoencoder/factory.py | 19 - numalogic/models/autoencoder/pipeline.py | 400 --- numalogic/models/autoencoder/trainer.py | 42 + .../models/autoencoder/variants/__init__.py | 22 +- numalogic/models/autoencoder/variants/conv.py | 83 +- numalogic/models/autoencoder/variants/lstm.py | 71 +- .../autoencoder/variants/transformer.py | 90 +- .../models/autoencoder/variants/vanilla.py | 89 +- numalogic/models/threshold/__init__.py | 3 + numalogic/postprocess.py | 14 + numalogic/preprocess/datasets.py | 45 - numalogic/preprocess/transformer.py | 12 +- numalogic/registry/artifact.py | 2 +- numalogic/registry/mlflow_registry.py | 2 +- .../tests/models/autoencoder/test_factory.py | 22 - .../tests/models/autoencoder/test_pipeline.py | 352 -- .../tests/models/autoencoder/test_trainer.py | 63 + .../models/autoencoder/variants/test_conv.py | 49 +- .../models/autoencoder/variants/test_lstm.py | 35 +- .../autoencoder/variants/test_transformers.py | 45 +- .../autoencoder/variants/test_vanilla.py | 62 +- numalogic/tests/preprocess/test_datasets.py | 44 - numalogic/tests/tools/test_data.py | 84 + numalogic/tools/__init__.py | 7 + numalogic/tools/data.py | 72 + numalogic/tools/exceptions.py | 4 + poetry.lock | 3049 +++++++++++++---- pyproject.toml | 22 +- 30 files changed, 3118 insertions(+), 1759 deletions(-) delete mode 100644 numalogic/models/autoencoder/factory.py delete mode 100644 numalogic/models/autoencoder/pipeline.py create mode 100644 numalogic/models/autoencoder/trainer.py delete mode 100644 numalogic/preprocess/datasets.py delete mode 100644 numalogic/tests/models/autoencoder/test_factory.py delete mode 100644 numalogic/tests/models/autoencoder/test_pipeline.py create mode 100644 numalogic/tests/models/autoencoder/test_trainer.py delete mode 100644 numalogic/tests/preprocess/test_datasets.py create mode 100644 numalogic/tests/tools/test_data.py create mode 100644 numalogic/tools/data.py diff --git a/numalogic/models/autoencoder/__init__.py b/numalogic/models/autoencoder/__init__.py index 88e2f573..b56c2b01 100644 --- a/numalogic/models/autoencoder/__init__.py +++ b/numalogic/models/autoencoder/__init__.py @@ -1,4 +1,3 @@ -from numalogic.models.autoencoder.factory import ModelPlFactory -from numalogic.models.autoencoder.pipeline import AutoencoderPipeline, SparseAEPipeline +from numalogic.models.autoencoder.trainer import AutoencoderTrainer -__all__ = ["AutoencoderPipeline", "SparseAEPipeline", "ModelPlFactory"] +__all__ = ["AutoencoderTrainer"] diff --git a/numalogic/models/autoencoder/base.py b/numalogic/models/autoencoder/base.py index c8a2a8a7..890cdc23 100644 --- a/numalogic/models/autoencoder/base.py +++ b/numalogic/models/autoencoder/base.py @@ -1,26 +1,60 @@ -from abc import ABCMeta, abstractmethod -from typing import Tuple +from abc import ABCMeta -from torch import nn, Tensor -from torch.utils.data import Dataset -from torchinfo import summary +import pytorch_lightning as pl +import torch.nn.functional as F +from torch import Tensor, optim -class TorchAE(nn.Module, metaclass=ABCMeta): +class BaseAE(pl.LightningModule, metaclass=ABCMeta): """ Abstract Base class for all Pytorch based autoencoder models for time-series data. """ - def __repr__(self) -> str: - return str(summary(self)) + def __init__(self, loss_fn: str = "huber", optim_algo: str = "adam", lr: float = 1e-3): + super().__init__() + self.lr = lr + self.optim_algo = optim_algo + self.criterion = self.init_criterion(loss_fn) - def summary(self, input_shape: Tuple[int, ...]) -> None: - print(summary(self, input_size=input_shape)) + @staticmethod + def init_criterion(loss_fn: str): + if loss_fn == "huber": + return F.huber_loss + if loss_fn == "l1": + return F.l1_loss + if loss_fn == "mse": + return F.mse_loss + raise NotImplementedError(f"Unsupported loss function provided: {loss_fn}") - @abstractmethod - def construct_dataset(self, x: Tensor, seq_len: int = None) -> Dataset: - """ - Returns a dataset instance to be used for training. - Needs to be overridden. - """ - pass + def init_optimizer(self, optim_algo: str): + if optim_algo == "adam": + return optim.Adam(self.parameters(), lr=self.lr) + if optim_algo == "adagrad": + return optim.Adagrad(self.parameters(), lr=self.lr) + if optim_algo == "rmsprop": + return optim.RMSprop(self.parameters(), lr=self.lr) + raise NotImplementedError(f"Unsupported optimizer value provided: {optim_algo}") + + def _get_reconstruction_loss(self, batch): + _, recon = self.forward(batch) + return self.criterion(batch, recon) + + def reconstruction(self, batch: Tensor) -> Tensor: + _, recon = self.forward(batch) + return recon + + def configure_optimizers(self): + optimizer = self.init_optimizer(self.optim_algo) + return {"optimizer": optimizer} + + def training_step(self, batch, batch_idx): + loss = self._get_reconstruction_loss(batch) + return loss + + def validation_step(self, batch, batch_idx): + loss = self._get_reconstruction_loss(batch) + return loss + + def test_step(self, batch, batch_idx): + loss = self._get_reconstruction_loss(batch) + return loss diff --git a/numalogic/models/autoencoder/factory.py b/numalogic/models/autoencoder/factory.py deleted file mode 100644 index 0834a799..00000000 --- a/numalogic/models/autoencoder/factory.py +++ /dev/null @@ -1,19 +0,0 @@ -from numalogic.models.autoencoder.pipeline import AutoencoderPipeline, SparseAEPipeline - - -class ModelPlFactory: - _pipelines = {"ae": AutoencoderPipeline, "ae_sparse": SparseAEPipeline} - - @classmethod - def get_pl_cls(cls, plname: str): - pl_cls = cls._pipelines.get(plname) - - if not pl_cls: - raise NotImplementedError(f"Unsupported pl name provided: {plname}") - - return pl_cls - - @classmethod - def get_pl_obj(cls, plname: str, *args, **kwargs): - pl_cls = cls.get_pl_cls(plname) - return pl_cls(*args, **kwargs) diff --git a/numalogic/models/autoencoder/pipeline.py b/numalogic/models/autoencoder/pipeline.py deleted file mode 100644 index 4de4dafb..00000000 --- a/numalogic/models/autoencoder/pipeline.py +++ /dev/null @@ -1,400 +0,0 @@ -import io -import logging -from copy import copy -from typing import Optional, BinaryIO, Union - -import numpy as np -import torch -from numpy.typing import NDArray -from sklearn.base import TransformerMixin, BaseEstimator -from torch import nn, optim, Tensor -from torch.utils.data import DataLoader - -from numalogic.tools.types import AutoencoderModel - -_LOGGER = logging.getLogger(__name__) - - -class AutoencoderPipeline(TransformerMixin, BaseEstimator): - r""" - Class to simplify training, inference, loading and saving of time-series autoencoders. - - Note: - This class only supports Pytorch models. - Args: - model: model instance - seq_len: sequence length - loss_fn: loss function used for training - supported values include {"huber", "l1", "mse"} - optimizer: optimizer to used for training. - supported values include {"adam", "adagrad", "rmsprop"} - lr: learning rate - batch_size: batch size for training - num_epochs: number of epochs for training - can be used when the threshold calculated is too low - resume_train: parameter to decide if resume training is needed. Also, - based on this parameter the optimizer state dict - is stored in registry. - - >>> # Example usage - >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> x = np.random.randn(100, 3) - >>> seq_len = 10 - >>> model = VanillaAE(signal_len=seq_len, n_features=3) - >>> ae_trainer = AutoencoderPipeline(model=model, seq_len=seq_len) - >>> ae_trainer.fit(x) - """ - - def __init__( - self, - model: AutoencoderModel = None, - seq_len: int = None, - loss_fn: str = "huber", - optimizer: str = "adam", - lr: float = 0.001, - batch_size: int = 256, - num_epochs: int = 100, - resume_train: bool = False, - ): - if not (model and seq_len): - raise ValueError("No model and seq len provided!") - if num_epochs < 1: - raise ValueError("num_epochs must be a positive interger") - - self._model = model - self.seq_len = seq_len - self.criterion = self.init_criterion(loss_fn) - self.optimizer = self.init_optimizer(optimizer, lr) - self.batch_size = batch_size - self.num_epochs = num_epochs - self.resume_train = resume_train - self._epochs_elapsed = 0 - - @property - def model_properties(self): - model_properties_dict = { - "batch_size": self.batch_size, - "num_epochs": self.num_epochs, - "epochs_elapsed": self._epochs_elapsed, - } - if self.resume_train: - model_properties_dict["optimizer_state_dict"] = self.optimizer.state_dict() - return model_properties_dict - - @property - def model(self) -> AutoencoderModel: - return self._model - - @staticmethod - def init_criterion(loss_fn: str): - if loss_fn == "huber": - return nn.HuberLoss(delta=0.5) - if loss_fn == "l1": - return nn.L1Loss() - if loss_fn == "mse": - return nn.MSELoss() - raise NotImplementedError(f"Unsupported loss function provided: {loss_fn}") - - def init_optimizer(self, optimizer: str, lr: float): - if optimizer == "adam": - return optim.Adam(self._model.parameters(), lr=lr) - if optimizer == "adagrad": - return optim.Adagrad(self._model.parameters(), lr=lr) - if optimizer == "rmsprop": - return optim.RMSprop(self._model.parameters(), lr=lr) - raise NotImplementedError(f"Unsupported optimizer value provided: {optimizer}") - - def fit(self, X: NDArray[float], y=None, log_freq: int = 5) -> "AutoencoderPipeline": - r""" - Fit function to train autoencoder model - - Args: - X: training dataset - y: labels - log_freq: frequency logging - - Returns: - AutoencoderPipeline instance - """ - _LOGGER.info("Training autoencoder model..") - - dataset = self._model.construct_dataset(X, self.seq_len) - loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False) - self._model.train() - losses = [] - for epoch in range(1, self.num_epochs + 1): - for x_batch in loader: - self.optimizer.zero_grad() - _, decoded = self._model(x_batch) - loss = self.criterion(decoded, x_batch) - loss.backward() - self.optimizer.step() - losses.append(loss.item()) - if epoch % log_freq == 0: - _LOGGER.info(f"epoch : {epoch}, loss_mean : {np.mean(losses):.7f}") - losses = [] - self._epochs_elapsed += 1 - return self - - def predict(self, X: NDArray[float], seq_len: int = None) -> NDArray[float]: - r""" - Return the reconstruction from the model. - - Args: - X: training dataset - seq_len: sequence length / window length - - Returns: - Numpy array - """ - if not seq_len: - seq_len = self.seq_len or len(X) - dataset = self._model.construct_dataset(X, seq_len) - self._model.eval() - with torch.no_grad(): - _, pred = self._model(dataset.data) - return dataset.recover_shape(pred) - - def score(self, X: NDArray[float]) -> NDArray: - r""" - Returns the reconstruction error. - - Args: - X: data - - Returns: - numpy array with anomaly scores - """ - x_recon = self.predict(X, seq_len=self.seq_len) - recon_err = np.abs(X - x_recon) - return recon_err - - def transform(self, X: NDArray[float]) -> NDArray: - return self.score(X) - - def save(self, path: Optional[str] = None) -> Optional[BinaryIO]: - r""" - Save function to save the model. - If path is provided then the model is saved in the given path. - - Args: - path: path to save the model (Optional parameter) - Returns: - Binary type object if path is None - """ - state_dict = copy(self.model_properties) - state_dict["model_state_dict"] = self._model.state_dict() - if path: - torch.save(state_dict, path) - else: - buf = io.BytesIO() - torch.save(state_dict, buf) - return buf - - def __load_metadata(self, **metadata) -> None: - if self.resume_train: - self.optimizer.load_state_dict(metadata["optimizer_state_dict"]) - self._epochs_elapsed = metadata["epochs_elapsed"] - self.num_epochs = metadata["num_epochs"] - self.batch_size = metadata["batch_size"] - - def load(self, path: Union[str, BinaryIO] = None, model=None, **metadata) -> None: - r""" - Load the model to pipeline. - - Args: - path: path to load the model - model: machine learning model - metadata: additional pipeline metadata - """ - if (path and model) or (not path and not model): - raise ValueError("One of path or model needs to be provided!") - if model: - self._model = model - if metadata: - self.__load_metadata(**metadata) - return - if path: - if isinstance(path, io.BytesIO): - path.seek(0) - checkpoint = torch.load(path) - self._model.load_state_dict(checkpoint["model_state_dict"]) - self.__load_metadata(**checkpoint) - elif isinstance(path, str): - checkpoint = torch.load(path) - self._model.load_state_dict(checkpoint["model_state_dict"]) - self.__load_metadata(**checkpoint) - return - - @classmethod - def with_model( - cls, - model_cls, - seq_len: int, - loss_fn="huber", - optimizer="adam", - lr=0.001, - batch_size=256, - num_epochs=100, - **model_kw, - ) -> "AutoencoderPipeline": - model = model_cls(**model_kw) - return AutoencoderPipeline( - model, - seq_len, - loss_fn=loss_fn, - optimizer=optimizer, - lr=lr, - batch_size=batch_size, - num_epochs=num_epochs, - ) - - -class SparseAEPipeline(AutoencoderPipeline): - r""" - Class to simplify training, inference, loading and saving of Sparse Autoencoder. - It inherits from AutoencoderPipeline class and serves as a wrapper around base network models. - Sparse Autoencoder is a type of autoencoder that applies sparsity constraint. - This helps in achieving information bottleneck even when the number of hidden units is huge. - It penalizes the loss function such that only some neurons are activated at a time. - This sparsity penalty helps in preventing overfitting. - More details about Sparse Autoencoder can be found at - - - Note: - This class only supports Pytorch models. - Args: - beta: regularization parameter (Defaults to 1e-3) - rho: sparsity parameter value (Defaults to 0.05) - method: regularization method - supported values include {"kl_div", "L1", "L2"} - (Defaults to "kl_div") - model: model instance - seq_len: sequence length - loss_fn: loss function used for training - supported values include {"huber", "l1", "mse"} - optimizer: optimizer to be used for training. - supported values include {"adam", "adagrad", "rmsprop"} - lr: learning rate - batch_size: batch size for training - num_epochs: number of epochs for training - std_tolerance: determines how many times the standard deviation to be used for threshold - reconerr_method: method used to calculate the distance - between the original and the reconstucted data - supported values include {"absolute", "squared"} - threshold_min: the minimum threshold to use; - can be used when the threshold calculated is too low - resume_train: parameter to decide if resume training is needed. Also, - based on this parameter the optimizer state dict - is stored in registry. - - >>> # Example usage - >>> from numalogic.models.autoencoder.variants import VanillaAE - >>> from numalogic.models.autoencoder import SparseAEPipeline - >>> x_train = np.random.randn(100, 3) - >>> model = VanillaAE(signal_len=12, n_features=3) - >>> sparse_ae_trainer = SparseAEPipeline(model=model, seq_len=36, num_epochs=30) - >>> sparse_ae_trainer.fit(x_train) - """ - - def __init__(self, beta=1e-3, rho=0.05, method="kl_div", *args, **kwargs): - super().__init__(*args, **kwargs) - self.beta = beta - self.rho = rho - self.reg_method = method - - def l1_loss(self) -> Tensor: - r""" - Loss function for computing sparse penalty based on L1 regularization. - L1 regularization adds the absolute magnitude value of coefficient as the penalty term. - - Returns: - Tensor - """ - l1_lambda = self.beta - l1_norm = sum(torch.linalg.norm(p, 1) for p in self._model.parameters()) - return torch.Tensor(l1_norm + l1_lambda) - - def l2_loss(self) -> Tensor: - r""" - Loss function for computing sparse penalty based on L2 regularization. - L2 regularization adds the squared magnitude of coefficient as the penalty term. - - Returns: - Tensor - """ - l2_lambda = self.beta - l2_norm = sum(torch.linalg.norm(p, 2) for p in self._model.parameters()) - return torch.Tensor(l2_norm + l2_lambda) - - def kl_divergence(self, activations: Tensor) -> Tensor: - r""" - Loss function for computing sparse penalty based on KL (Kullback-Leibler) Divergence. - KL Divergence measures the difference between two probability distributions. - - Args: - activations: encoded output from the model layer-wise - - Returns: - Tensor - """ - rho_hat = torch.mean(activations, dim=0) - rho = torch.full(rho_hat.size(), self.rho) - kl_loss = nn.KLDivLoss(reduction="sum") - _dim = 0 if rho_hat.dim() == 1 else 1 - return kl_loss(torch.log_softmax(rho_hat, dim=_dim), torch.softmax(rho, dim=_dim)) - - def calculate_regularized_loss(self, activation: Tensor) -> Tensor: - r""" - Loss Function to compute regularized loss penalty based on the chosen regularization method - - Args: - activation: encoded output from the model layer-wise - - Raises: - NotImplementedError: Unsupported regularization method value provided - - Returns: - Tensor - """ - if self.reg_method == "kl_div": - return self.kl_divergence(activation) * self.beta - if self.reg_method == "L1": - return self.l1_loss() - if self.reg_method == "L2": - return self.l2_loss() - raise NotImplementedError( - f"Unsupported regularization method value provided: {self.reg_method}" - ) - - def fit(self, X: NDArray[float], y=None, log_freq: int = 5) -> None: - r""" - Fit function to train sparse autoencoder model - - Args: - X: training dataset - y: labels (Defaults to None) - log_freq: frequency logging, i.e, number of epochs to be logged (Defaults to 5) - """ - _LOGGER.info( - "Training sparse autoencoder model with beta: %s, and rho: %s", self.beta, self.rho - ) - _LOGGER.info("Using %s regularized loss", self.reg_method) - dataset = self._model.construct_dataset(X, self.seq_len) - loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False) - self._model.train() - - loss, penalty = torch.Tensor([0.0]), torch.Tensor([0.0]) - for epoch in range(1, self.num_epochs + 1): - for x_batch in loader: - self.optimizer.zero_grad() - encoded, decoded = self._model(x_batch) - - loss = self.criterion(decoded, x_batch) - penalty = self.calculate_regularized_loss(encoded) - loss += penalty - loss.backward() - self.optimizer.step() - - if epoch % log_freq == 0: - _LOGGER.info(f"epoch : {epoch}, penalty: {penalty} loss_mean : {loss.item():.7f}") diff --git a/numalogic/models/autoencoder/trainer.py b/numalogic/models/autoencoder/trainer.py new file mode 100644 index 00000000..cba7fdf4 --- /dev/null +++ b/numalogic/models/autoencoder/trainer.py @@ -0,0 +1,42 @@ +import logging + +import pytorch_lightning as pl +import torch +from numalogic.tools.data import TimeseriesDataModule +from pytorch_lightning import Trainer +from torch import Tensor + +_LOGGER = logging.getLogger(__name__) + + +class AutoencoderTrainer(Trainer): + def __init__( + self, + max_epochs=100, + logger=False, + check_val_every_n_epoch=5, + log_every_n_steps=20, + enable_checkpointing=False, + enable_progress_bar=False, + enable_model_summary=False, + limit_val_batches=0, + **trainer_kw + ): + super().__init__( + logger=logger, + max_epochs=max_epochs, + log_every_n_steps=log_every_n_steps, + check_val_every_n_epoch=check_val_every_n_epoch, + enable_checkpointing=enable_checkpointing, + enable_progress_bar=enable_progress_bar, + enable_model_summary=enable_model_summary, + limit_val_batches=limit_val_batches, + **trainer_kw + ) + + def predict(self, model: pl.LightningModule = None, unbatch=True, **kwargs) -> Tensor: + recon_err = super().predict(model, **kwargs) + recon_err = torch.vstack(recon_err) + if unbatch: + return TimeseriesDataModule.unbatch_sequences(recon_err) + return recon_err diff --git a/numalogic/models/autoencoder/variants/__init__.py b/numalogic/models/autoencoder/variants/__init__.py index a080fbc5..0e7568b1 100644 --- a/numalogic/models/autoencoder/variants/__init__.py +++ b/numalogic/models/autoencoder/variants/__init__.py @@ -1,8 +1,18 @@ -from numalogic.models.autoencoder.variants.vanilla import VanillaAE -from numalogic.models.autoencoder.variants.conv import Conv1dAE -from numalogic.models.autoencoder.variants.lstm import LSTMAE -from numalogic.models.autoencoder.variants.transformer import TransformerAE -from numalogic.models.autoencoder.base import TorchAE +from numalogic.models.autoencoder.variants.vanilla import VanillaAE, SparseVanillaAE +from numalogic.models.autoencoder.variants.conv import Conv1dAE, SparseConv1dAE +from numalogic.models.autoencoder.variants.lstm import LSTMAE, SparseLSTMAE +from numalogic.models.autoencoder.variants.transformer import TransformerAE, SparseTransformerAE +from numalogic.models.autoencoder.base import BaseAE -__all__ = ["VanillaAE", "Conv1dAE", "LSTMAE", "TransformerAE", "TorchAE"] +__all__ = [ + "VanillaAE", + "SparseVanillaAE", + "Conv1dAE", + "SparseConv1dAE", + "LSTMAE", + "SparseLSTMAE", + "TransformerAE", + "SparseTransformerAE", + "BaseAE", +] diff --git a/numalogic/models/autoencoder/variants/conv.py b/numalogic/models/autoencoder/variants/conv.py index 38a743f9..c4ffc618 100644 --- a/numalogic/models/autoencoder/variants/conv.py +++ b/numalogic/models/autoencoder/variants/conv.py @@ -5,20 +5,17 @@ from torch import nn, Tensor from torch.nn.init import calculate_gain -from numalogic.models.autoencoder.base import TorchAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.base import BaseAE LOGGER = logging.getLogger(__name__) -DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -LOGGER.info("Current device: %s", DEVICE) -class Conv1dAE(TorchAE): +class Conv1dAE(BaseAE): r""" One dimensional Convolutional Autoencoder with multichannel support. Args: - + seq_len: length of input sequence in_channels: Number of channels in the input enc_channels: Number of channels produced by the convolution kernel_size: kernel size (default=7) @@ -29,14 +26,19 @@ class Conv1dAE(TorchAE): def __init__( self, + seq_len: int, in_channels: int, enc_channels: int, kernel_size=7, stride=2, padding=3, output_padding=1, + **kwargs ): - super(Conv1dAE, self).__init__() + super().__init__(**kwargs) + self.seq_len = seq_len + self.in_channels = in_channels + self.encoder = nn.Sequential( nn.Conv1d( in_channels, enc_channels, kernel_size=kernel_size, stride=stride, padding=padding @@ -72,22 +74,67 @@ def init_weights(m: nn.Module) -> None: if type(m) in (nn.ConvTranspose1d, nn.Conv1d): nn.init.xavier_normal_(m.weight, gain=calculate_gain("relu")) - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - encoded = self.encoder(x) + def forward(self, batch: Tensor) -> Tuple[Tensor, Tensor]: + batch = batch.view(-1, self.in_channels, self.seq_len) + encoded = self.encoder(batch) decoded = self.decoder(encoded) return encoded, decoded - def construct_dataset(self, x: Tensor, seq_len: int = None) -> SequenceDataset: + def _get_reconstruction_loss(self, batch): + _, recon = self.forward(batch) + x = batch.view(-1, self.in_channels, self.seq_len) + return self.criterion(x, recon) + + def predict_step(self, batch: Tensor, batch_idx: int, dataloader_idx: int = 0): + """Returns reconstruction for streaming input""" + recon = self.reconstruction(batch) + recon = recon.view(-1, self.seq_len, self.in_channels) + recon_err = self.criterion(batch, recon, reduction="none") + return recon_err + + +class SparseConv1dAE(Conv1dAE): + r""" + Sparse Autoencoder for a Conv1d network. + It inherits from VanillaAE class and serves as a wrapper around base network models. + Sparse Autoencoder is a type of autoencoder that applies sparsity constraint. + This helps in achieving information bottleneck even when the number of hidden units is huge. + It penalizes the loss function such that only some neurons are activated at a time. + This sparsity penalty helps in preventing overfitting. + More details about Sparse Autoencoder can be found at + + + Args: + beta: regularization parameter (Defaults to 1e-3) + rho: sparsity parameter value (Defaults to 0.05) + **kwargs: VanillaAE kwargs + """ + + def __init__(self, beta=1e-3, rho=0.05, *args, **kwargs): + super().__init__(*args, **kwargs) + self.beta = beta + self.rho = rho + + def kl_divergence(self, activations: Tensor) -> Tensor: r""" - Constructs dataset given tensor and seq_len + Loss function for computing sparse penalty based on KL (Kullback-Leibler) Divergence. + KL Divergence measures the difference between two probability distributions. - Args: - x: Tensor type - seq_len: sequence length / window length + Args: + activations: encoded output from the model layer-wise Returns: - SequenceDataset type + Tensor """ - __seq_len = seq_len or self.seq_len - dataset = SequenceDataset(x, __seq_len, permute=True) - return dataset + rho_hat = torch.mean(activations, dim=0) + rho = torch.full(rho_hat.size(), self.rho) + kl_loss = nn.KLDivLoss(reduction="sum") + _dim = 0 if rho_hat.dim() == 1 else 1 + return kl_loss(torch.log_softmax(rho_hat, dim=_dim), torch.softmax(rho, dim=_dim)) + + def _get_reconstruction_loss(self, batch): + latent, recon = self.forward(batch) + batch = batch.view(-1, self.in_channels, self.seq_len) + loss = self.criterion(batch, recon) + penalty = self.kl_divergence(latent) + return loss + penalty diff --git a/numalogic/models/autoencoder/variants/lstm.py b/numalogic/models/autoencoder/variants/lstm.py index 7d4b573c..1aa97783 100644 --- a/numalogic/models/autoencoder/variants/lstm.py +++ b/numalogic/models/autoencoder/variants/lstm.py @@ -6,12 +6,9 @@ from torch import Tensor from torch.nn.init import calculate_gain -from numalogic.models.autoencoder.base import TorchAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.base import BaseAE _LOGGER = logging.getLogger(__name__) -_DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -_LOGGER.info("Current device: %s", _DEVICE) class _Encoder(nn.Module): @@ -80,7 +77,7 @@ def forward(self, x: Tensor) -> Tensor: return out -class LSTMAE(TorchAE): +class LSTMAE(BaseAE): r""" Long Short-Term Memory (LSTM) based autoencoder. @@ -90,7 +87,7 @@ class LSTMAE(TorchAE): embedding_dim: embedding dimension for the network encoder_layers: number of encoder layers (default = 1) decoder_layers: number of decoder layers (default = 1) - + kwargs: BaseAE kwargs """ def __init__( @@ -100,8 +97,9 @@ def __init__( embedding_dim: int, encoder_layers: int = 1, decoder_layers: int = 1, + **kwargs ): - super().__init__() + super().__init__(**kwargs) self.seq_len = seq_len self.no_features = no_features @@ -113,7 +111,6 @@ def __init__( embedding_size=self.embedding_dim, num_layers=encoder_layers, ) - self.encoder = self.encoder.to(_DEVICE) self.encoder.apply(self.init_weights) self.decoder = _Decoder( @@ -123,7 +120,6 @@ def __init__( hidden_size=embedding_dim, num_layers=decoder_layers, ) - self.decoder = self.decoder.to(_DEVICE) self.decoder.apply(self.init_weights) @staticmethod @@ -138,22 +134,59 @@ def init_weights(m: nn.Module) -> None: nn.init.xavier_normal_(param, gain=calculate_gain("tanh")) def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - torch.manual_seed(0) encoded = self.encoder(x) decoded = self.decoder(encoded) return encoded, decoded - def construct_dataset(self, x: Tensor, seq_len: int = None) -> SequenceDataset: + def predict_step(self, batch: Tensor, batch_idx: int, dataloader_idx: int = 0): + """Returns reconstruction for streaming input""" + recon = self.reconstruction(batch) + recon_err = self.criterion(batch, recon, reduction="none") + recon_err = torch.squeeze(recon_err, 0) + return recon_err + + +class SparseLSTMAE(LSTMAE): + r""" + Sparse Autoencoder for an LSTM network. + It inherits from VanillaAE class and serves as a wrapper around base network models. + Sparse Autoencoder is a type of autoencoder that applies sparsity constraint. + This helps in achieving information bottleneck even when the number of hidden units is huge. + It penalizes the loss function such that only some neurons are activated at a time. + This sparsity penalty helps in preventing overfitting. + More details about Sparse Autoencoder can be found at + + + Args: + beta: regularization parameter (Defaults to 1e-3) + rho: sparsity parameter value (Defaults to 0.05) + **kwargs: VanillaAE kwargs + """ + + def __init__(self, beta=1e-3, rho=0.05, *args, **kwargs): + super().__init__(*args, **kwargs) + self.beta = beta + self.rho = rho + + def kl_divergence(self, activations: Tensor) -> Tensor: r""" - Constructs dataset given tensor and seq_len + Loss function for computing sparse penalty based on KL (Kullback-Leibler) Divergence. + KL Divergence measures the difference between two probability distributions. - Args: - x: Tensor type - seq_len: sequence length / window length + Args: + activations: encoded output from the model layer-wise Returns: - SequenceDataset type + Tensor """ - __seq_len = seq_len or self.seq_len - dataset = SequenceDataset(x, __seq_len, permute=False) - return dataset + rho_hat = torch.mean(activations, dim=0) + rho = torch.full(rho_hat.size(), self.rho) + kl_loss = nn.KLDivLoss(reduction="sum") + _dim = 0 if rho_hat.dim() == 1 else 1 + return kl_loss(torch.log_softmax(rho_hat, dim=_dim), torch.softmax(rho, dim=_dim)) + + def _get_reconstruction_loss(self, batch): + latent, recon = self.forward(batch) + loss = self.criterion(batch, recon) + penalty = self.kl_divergence(latent) + return loss + penalty diff --git a/numalogic/models/autoencoder/variants/transformer.py b/numalogic/models/autoencoder/variants/transformer.py index 7e6250f1..8fa8dd6b 100644 --- a/numalogic/models/autoencoder/variants/transformer.py +++ b/numalogic/models/autoencoder/variants/transformer.py @@ -2,11 +2,9 @@ import torch import torch.nn.functional +from numalogic.models.autoencoder.base import BaseAE from torch import nn, Tensor -from numalogic.models.autoencoder.base import TorchAE -from numalogic.preprocess.datasets import SequenceDataset - def _scaled_dot_product(query: Tensor, key: Tensor, value: Tensor) -> Tensor: r""" @@ -281,15 +279,15 @@ def forward(self, tgt: Tensor, memory: Tensor) -> Tensor: return torch.softmax(self.linear(tgt), dim=-1) -class TransformerAE(TorchAE): +class TransformerAE(BaseAE): r""" Transformer model without masking. Inspiration: `Attention Is All You Need `_. Args: + seq_len: sequence length / window length (default=1) num_encoder_layers: number of encoder layers in the Encoder (default = 3) num_decoder_layers: number of encoder layers in the Decoder (default = 3) - seq_length: sequence length / window length (default=1) num_heads: the number of heads in the multiheadattention models (default=6) dim_feedforward: the dimension of the feedforward network model (default=2048) dropout: the dropout value (default=0.1). @@ -300,26 +298,30 @@ class TransformerAE(TorchAE): def __init__( self, + seq_len: int = 1, + n_features: int = 1, num_encoder_layers: int = 3, num_decoder_layers: int = 3, - seq_length: int = 1, num_heads: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, activation: nn.Module = nn.ReLU(), + **kwargs ): - super().__init__() + super().__init__(**kwargs) + self.n_features = n_features + self.seq_len = seq_len self.activation = activation self.encoder = Encoder( num_layers=num_encoder_layers, - dim_model=seq_length, + dim_model=seq_len, num_heads=num_heads, dim_feedforward=dim_feedforward, dropout=dropout, ) self.decoder = Decoder( num_layers=num_decoder_layers, - dim_model=seq_length, + dim_model=seq_len, num_heads=num_heads, dim_feedforward=dim_feedforward, dropout=dropout, @@ -335,23 +337,67 @@ def init_weights(m: nn.Module) -> None: if type(m) in (nn.Linear,): nn.init.xavier_uniform_(m.weight, gain=2**0.5) - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - torch.manual_seed(0) - encoded = self.encoder(x) - decoded = self.decoder(x, encoded) + def forward(self, batch: Tensor) -> Tuple[Tensor, Tensor]: + batch = batch.view(-1, self.n_features, self.seq_len) + encoded = self.encoder(batch) + decoded = self.decoder(batch, encoded) return encoded, decoded - def construct_dataset(self, x: Tensor, seq_len: int = None) -> SequenceDataset: + def _get_reconstruction_loss(self, batch): + _, recon = self.forward(batch) + x = batch.view(-1, self.n_features, self.seq_len) + return self.criterion(x, recon) + + def predict_step(self, batch: Tensor, batch_idx: int, dataloader_idx: int = 0): + """Returns reconstruction for streaming input""" + recon = self.reconstruction(batch) + recon = recon.view(-1, self.seq_len, self.n_features) + recon_err = self.criterion(batch, recon, reduction="none") + return recon_err + + +class SparseTransformerAE(TransformerAE): + r""" + Sparse Autoencoder for a transformer network. + It inherits from VanillaAE class and serves as a wrapper around base network models. + Sparse Autoencoder is a type of autoencoder that applies sparsity constraint. + This helps in achieving information bottleneck even when the number of hidden units is huge. + It penalizes the loss function such that only some neurons are activated at a time. + This sparsity penalty helps in preventing overfitting. + More details about Sparse Autoencoder can be found at + + + Args: + beta: regularization parameter (Defaults to 1e-3) + rho: sparsity parameter value (Defaults to 0.05) + **kwargs: VanillaAE kwargs + """ + + def __init__(self, beta=1e-3, rho=0.05, *args, **kwargs): + super().__init__(*args, **kwargs) + self.beta = beta + self.rho = rho + + def kl_divergence(self, activations: Tensor) -> Tensor: r""" - Constructs dataset given tensor and seq_len + Loss function for computing sparse penalty based on KL (Kullback-Leibler) Divergence. + KL Divergence measures the difference between two probability distributions. - Args: - x: Tensor type - seq_len: sequence length / window length + Args: + activations: encoded output from the model layer-wise Returns: - SequenceDataset type + Tensor """ - __seq_len = seq_len or self.seq_len - dataset = SequenceDataset(x, __seq_len, permute=True) - return dataset + rho_hat = torch.mean(activations, dim=0) + rho = torch.full(rho_hat.size(), self.rho) + kl_loss = nn.KLDivLoss(reduction="sum") + _dim = 0 if rho_hat.dim() == 1 else 1 + return kl_loss(torch.log_softmax(rho_hat, dim=_dim), torch.softmax(rho, dim=_dim)) + + def _get_reconstruction_loss(self, batch): + latent, recon = self.forward(batch) + x = batch.view(-1, self.n_features, self.seq_len) + loss = self.criterion(x, recon) + penalty = self.kl_divergence(latent) + return loss + penalty diff --git a/numalogic/models/autoencoder/variants/vanilla.py b/numalogic/models/autoencoder/variants/vanilla.py index 2a6aad94..be7df5c3 100644 --- a/numalogic/models/autoencoder/variants/vanilla.py +++ b/numalogic/models/autoencoder/variants/vanilla.py @@ -1,9 +1,9 @@ from typing import Tuple, Sequence +import torch from torch import nn, Tensor -from numalogic.models.autoencoder.base import TorchAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.base import BaseAE from numalogic.tools.exceptions import LayerSizeMismatchError @@ -115,7 +115,7 @@ def _construct_layers(self, layersizes: Sequence[int]) -> nn.ModuleList: return layers -class VanillaAE(TorchAE): +class VanillaAE(BaseAE): r""" Vanilla Autoencoder model comprising Fully connected layers only. @@ -130,16 +130,20 @@ class VanillaAE(TorchAE): def __init__( self, - signal_len: int, + seq_len: int, n_features: int = 1, encoder_layersizes: Sequence[int] = (16, 8), decoder_layersizes: Sequence[int] = (8, 16), dropout_p: float = 0.25, + **kwargs, ): - super(VanillaAE, self).__init__() - self.seq_len = signal_len + super(VanillaAE, self).__init__(**kwargs) + self.seq_len = seq_len self.dropout_prob = dropout_p + self.n_features = n_features + + self.save_hyperparameters() if encoder_layersizes[-1] != decoder_layersizes[0]: raise LayerSizeMismatchError( @@ -148,13 +152,13 @@ def __init__( ) self.encoder = _Encoder( - seq_len=signal_len, + seq_len=seq_len, n_features=n_features, layersizes=encoder_layersizes, dropout_p=dropout_p, ) self.decoder = _Decoder( - seq_len=signal_len, + seq_len=seq_len, n_features=n_features, layersizes=decoder_layersizes, dropout_p=dropout_p, @@ -171,22 +175,69 @@ def init_weights(m: nn.Module) -> None: if type(m) == nn.Linear: nn.init.xavier_normal_(m.weight) - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - encoded = self.encoder(x) + def forward(self, batch: Tensor) -> Tuple[Tensor, Tensor]: + batch = batch.view(-1, self.n_features, self.seq_len) + encoded = self.encoder(batch) decoded = self.decoder(encoded) return encoded, decoded - def construct_dataset(self, x: Tensor, seq_len: int = None) -> SequenceDataset: + def _get_reconstruction_loss(self, batch): + _, recon = self.forward(batch) + x = batch.view(-1, self.n_features, self.seq_len) + return self.criterion(x, recon) + + def predict_step(self, batch: Tensor, batch_idx: int, dataloader_idx: int = 0): + """Returns reconstruction for streaming input""" + recon = self.reconstruction(batch) + # batch = batch.view(-1, self.n_features, self.seq_len) + recon = recon.view(-1, self.seq_len, self.n_features) + recon_err = self.criterion(batch, recon, reduction="none") + recon_err = torch.squeeze(recon_err, 0) + return recon_err + + +class SparseVanillaAE(VanillaAE): + r""" + Sparse Autoencoder for a fully connected network. + It inherits from VanillaAE class and serves as a wrapper around base network models. + Sparse Autoencoder is a type of autoencoder that applies sparsity constraint. + This helps in achieving information bottleneck even when the number of hidden units is huge. + It penalizes the loss function such that only some neurons are activated at a time. + This sparsity penalty helps in preventing overfitting. + More details about Sparse Autoencoder can be found at + + + Args: + beta: regularization parameter (Defaults to 1e-3) + rho: sparsity parameter value (Defaults to 0.05) + **kwargs: VanillaAE kwargs + """ + + def __init__(self, beta=1e-3, rho=0.05, *args, **kwargs): + super().__init__(*args, **kwargs) + self.beta = beta + self.rho = rho + + def kl_divergence(self, activations: Tensor) -> Tensor: r""" - Constructs dataset given tensor and seq_len + Loss function for computing sparse penalty based on KL (Kullback-Leibler) Divergence. + KL Divergence measures the difference between two probability distributions. - Args: - x: Tensor type - seq_len: sequence length / window length + Args: + activations: encoded output from the model layer-wise Returns: - SequenceDataset type + Tensor """ - __seq_len = seq_len or self.seq_len - dataset = SequenceDataset(x, __seq_len, permute=True) - return dataset + rho_hat = torch.mean(activations, dim=0) + rho = torch.full(rho_hat.size(), self.rho) + kl_loss = nn.KLDivLoss(reduction="sum") + _dim = 0 if rho_hat.dim() == 1 else 1 + return kl_loss(torch.log_softmax(rho_hat, dim=_dim), torch.softmax(rho, dim=_dim)) + + def _get_reconstruction_loss(self, batch): + latent, recon = self.forward(batch) + x = batch.view(-1, self.n_features, self.seq_len) + loss = self.criterion(x, recon) + penalty = self.kl_divergence(latent) + return loss + penalty diff --git a/numalogic/models/threshold/__init__.py b/numalogic/models/threshold/__init__.py index e69de29b..072397d4 100644 --- a/numalogic/models/threshold/__init__.py +++ b/numalogic/models/threshold/__init__.py @@ -0,0 +1,3 @@ +from numalogic.models.threshold._std import StdDevThreshold + +__all__ = ["StdDevThreshold"] diff --git a/numalogic/postprocess.py b/numalogic/postprocess.py index ca90f8c1..a9514719 100644 --- a/numalogic/postprocess.py +++ b/numalogic/postprocess.py @@ -1,6 +1,20 @@ import numpy as np from numpy.typing import ArrayLike +from numalogic.tools import DataIndependentTransformers + def tanh_norm(scores: ArrayLike, scale_factor=10, smooth_factor=10) -> ArrayLike: return scale_factor * np.tanh(scores / smooth_factor) + + +class TanhNorm(DataIndependentTransformers): + def __init__(self, scale_factor=10, smooth_factor=10): + self.scale_factor = scale_factor + self.smooth_factor = smooth_factor + + def fit_transform(self, X, y=None, **fit_params): + return self.transform(X) + + def transform(self, X): + return tanh_norm(X, scale_factor=self.scale_factor, smooth_factor=self.smooth_factor) diff --git a/numalogic/preprocess/datasets.py b/numalogic/preprocess/datasets.py deleted file mode 100644 index adb0a9fb..00000000 --- a/numalogic/preprocess/datasets.py +++ /dev/null @@ -1,45 +0,0 @@ -from typing import Union - -import numpy as np -import pandas as pd -import torch -from numpy.typing import NDArray -from torch import Tensor -from torch.utils.data import Dataset - - -class SequenceDataset(Dataset): - def __init__(self, data: Union[pd.DataFrame, NDArray], seq_len: int, permute=True): - self._seq_len = seq_len - self.permute = permute - data = data.to_numpy() if isinstance(data, pd.DataFrame) else data - self._seq_x = self.create_sequences(data) - - @property - def data(self) -> Tensor: - return self._seq_x - - def create_sequences(self, X_in: NDArray) -> Tensor: - output = [] - if len(X_in) < self._seq_len: - raise ValueError(f"Length of X_in: {len(X_in)} smaller than seq_len: {self._seq_len}") - for idx in range(len(X_in) - self._seq_len + 1): - output.append(X_in[idx : (idx + self._seq_len)]) - output = torch.tensor(np.stack(output), dtype=torch.float) - if self.permute: - return torch.permute(output, (0, 2, 1)) - return output - - def recover_shape(self, seq_x: NDArray) -> NDArray: - if self.permute: - output = seq_x[:, :, 0] - return np.vstack((output, seq_x[-1, :, 1:].T)) - - output = seq_x[:, 0, :] - return np.vstack((output, seq_x[-1, 1::])) - - def __len__(self): - return self._seq_x.shape[0] - - def __getitem__(self, idx: int): - return self._seq_x[idx] diff --git a/numalogic/preprocess/transformer.py b/numalogic/preprocess/transformer.py index 6e5cf5ba..83854685 100644 --- a/numalogic/preprocess/transformer.py +++ b/numalogic/preprocess/transformer.py @@ -2,17 +2,13 @@ import numpy as np from numpy.typing import ArrayLike -from sklearn.base import BaseEstimator, TransformerMixin, _OneToOneFeatureMixin - -LOGGER = logging.getLogger(__name__) +from numalogic.tools import DataIndependentTransformers -class _DataIndependentTransformers(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator): - def fit(self, _: ArrayLike): - return self +LOGGER = logging.getLogger(__name__) -class LogTransformer(_DataIndependentTransformers): +class LogTransformer(DataIndependentTransformers): def __init__(self, add_factor=2): self.add_factor = add_factor @@ -26,7 +22,7 @@ def inverse_transform(self, X) -> ArrayLike: return np.exp(X) - self.add_factor -class StaticPowerTransformer(_DataIndependentTransformers): +class StaticPowerTransformer(DataIndependentTransformers): def __init__(self, n: float, add_factor=0): self.add_factor = add_factor self.n = n diff --git a/numalogic/registry/artifact.py b/numalogic/registry/artifact.py index 64d7d21c..3be27f99 100644 --- a/numalogic/registry/artifact.py +++ b/numalogic/registry/artifact.py @@ -1,6 +1,6 @@ from abc import ABCMeta, abstractmethod from dataclasses import dataclass -from typing import Sequence, Any, Union, Dict +from typing import Sequence, Any, Dict from numalogic.tools.types import Artifact diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py index 6c72233b..462afb0e 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/numalogic/registry/mlflow_registry.py @@ -1,6 +1,6 @@ import logging from enum import Enum -from typing import Optional, Sequence, Union, Dict +from typing import Optional, Sequence import mlflow.pyfunc import mlflow.pytorch diff --git a/numalogic/tests/models/autoencoder/test_factory.py b/numalogic/tests/models/autoencoder/test_factory.py deleted file mode 100644 index 2098be09..00000000 --- a/numalogic/tests/models/autoencoder/test_factory.py +++ /dev/null @@ -1,22 +0,0 @@ -import unittest - -from numalogic.models.autoencoder import ModelPlFactory -from numalogic.models.autoencoder.variants import VanillaAE - - -class TestModelPlFactory(unittest.TestCase): - def test_get_pl_cls(self): - self.assertEqual("AutoencoderPipeline", ModelPlFactory.get_pl_cls("ae").__name__) - - def test_get_pl_obj(self): - model = VanillaAE(10, n_features=2) - ae_pl = ModelPlFactory.get_pl_obj("ae_sparse", beta=0.1, model=model, seq_len=10) - self.assertEqual("SparseAEPipeline", ae_pl.__class__.__name__) - - def test_get_pl_err(self): - with self.assertRaises(NotImplementedError): - ModelPlFactory.get_pl_obj("Whatever man!", seq_len=10) - - -if __name__ == "__main__": - unittest.main() diff --git a/numalogic/tests/models/autoencoder/test_pipeline.py b/numalogic/tests/models/autoencoder/test_pipeline.py deleted file mode 100644 index 7e3ed59f..00000000 --- a/numalogic/tests/models/autoencoder/test_pipeline.py +++ /dev/null @@ -1,352 +0,0 @@ -import os -import unittest - -import numpy as np -import pandas as pd -import torch -from sklearn.pipeline import make_pipeline, Pipeline -from sklearn.preprocessing import StandardScaler, RobustScaler - -from numalogic._constants import TESTS_DIR -from numalogic.models.autoencoder import AutoencoderPipeline, SparseAEPipeline -from numalogic.models.autoencoder.variants import Conv1dAE, LSTMAE, VanillaAE, TransformerAE - -ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") -DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") -torch.manual_seed(42) -SEQ_LEN = 12 - - -class TestAutoEncoderPipeline(unittest.TestCase): - model = None - X_train = None - X_val = None - - @classmethod - def setUpClass(cls) -> None: - df = pd.read_csv(DATA_FILE) - df = df[["success", "failure"]] - scaler = StandardScaler() - cls.X_train = scaler.fit_transform(df[:-240]) - cls.X_val = scaler.transform(df[-240:]) - - def test_fit_conv(self): - self.model = Conv1dAE(self.X_train.shape[1], 8) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - trainer.fit(self.X_train) - - def test_fit_lstm(self): - self.model = LSTMAE(seq_len=SEQ_LEN, no_features=2, embedding_dim=16) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - trainer.fit(self.X_train) - - def test_fit_vanilla(self): - self.model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - trainer.fit(self.X_train) - - # def test_threshold_min(self): - # self.model = Conv1dAE(self.X_train.shape[1], 8) - # trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - # trainer.fit(self.X_train) - # self.assertTrue(all(i >= 1 for i in trainer.thresholds)) - - def test_predict_01(self): - self.model = Conv1dAE(self.X_train.shape[1], 8) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5, loss_fn="l1") - pipeline = make_pipeline(StandardScaler(), trainer) - pipeline.fit(self.X_train) - pred = pipeline.predict(self.X_val, seq_len=12) - - self.assertEqual(self.X_val.shape, pred.shape) - - def test_predict_as_pl(self): - pipeline = make_pipeline( - RobustScaler(), - AutoencoderPipeline( - Conv1dAE(self.X_train.shape[1], 8), SEQ_LEN, num_epochs=5, loss_fn="mse" - ), - ) - pipeline.fit(self.X_train) - pred = pipeline.predict(self.X_val, seq_len=12) - self.assertEqual(self.X_val.shape, pred.shape) - - def test_predict_02(self): - stream_data = self.X_val[:12] - trainer = AutoencoderPipeline.with_model( - VanillaAE, SEQ_LEN, num_epochs=5, signal_len=SEQ_LEN, n_features=self.X_train.shape[1] - ) - trainer.fit(self.X_train) - pred = trainer.predict(stream_data) - - self.assertEqual(stream_data.shape, pred.shape) - - def test_fit_transform(self): - trainer = AutoencoderPipeline.with_model( - VanillaAE, SEQ_LEN, num_epochs=5, signal_len=SEQ_LEN, n_features=self.X_train.shape[1] - ) - pred = trainer.fit_transform(self.X_train) - self.assertEqual(self.X_train.shape, pred.shape) - - def test_score_01(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = AutoencoderPipeline( - model, SEQ_LEN, num_epochs=5, optimizer="adagrad", resume_train=True - ) - trainer.fit(self.X_train) - pred = trainer.predict(self.X_val) - - score = trainer.score(self.X_val) - self.assertEqual(score.shape, pred.shape) - - def test_resume_training_01(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=10, resume_train=True) - trainer.fit(self.X_train) - self.assertEqual(10, trainer.model_properties["epochs_elapsed"]) - - trainer.fit(self.X_train) - self.assertEqual(20, trainer.model_properties["epochs_elapsed"]) - - def test_score_02(self): - stream_data = self.X_val[:12] - self.model = Conv1dAE(self.X_train.shape[1], 8) - trainer = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5, optimizer="rmsprop") - trainer.fit(self.X_train) - pred = trainer.predict(stream_data) - - score = trainer.score(stream_data) - self.assertEqual(score.shape, pred.shape) - - def test_score_03(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5) - trainer.fit(self.X_train) - pred = trainer.predict(self.X_val) - - score = trainer.score(self.X_val) - self.assertEqual(score.shape, pred.shape) - - def test_score_04(self): - model = TransformerAE( - num_heads=8, - seq_length=SEQ_LEN, - dim_feedforward=64, - num_encoder_layers=3, - num_decoder_layers=1, - ) - print(self.X_train.shape) - trainer = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5) - trainer.fit(self.X_train) - pred = trainer.predict(self.X_val) - - score = trainer.score(self.X_val) - self.assertEqual(score.shape, pred.shape) - - def test_non_implemented_loss(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - with self.assertRaises(Exception): - AutoencoderPipeline(model, SEQ_LEN, num_epochs=5, loss_fn="lol") - - def test_non_implemented_optimizer(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - with self.assertRaises(Exception): - AutoencoderPipeline(model, SEQ_LEN, num_epochs=5, optimizer="lol") - - def test_save_load_path(self): - path = "checkpoint.pth" - - try: - os.remove(path) - except OSError: - pass - - self.model = Conv1dAE(self.X_train.shape[1], 8) - trainer_1 = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - trainer_1.fit(self.X_train) - trainer_1.save(path) - - trainer_2 = AutoencoderPipeline(self.model, SEQ_LEN, num_epochs=5) - trainer_2.load(path) - - # Check if both model's weights are equal - _mean_wts_1, _mean_wts_2 = [], [] - with torch.no_grad(): - for _w in trainer_1.model.parameters(): - _mean_wts_1.append(torch.mean(_w).item()) - for _w in trainer_2.model.parameters(): - _mean_wts_2.append(torch.mean(_w).item()) - - self.assertTrue(_mean_wts_1) - self.assertAlmostEqual(_mean_wts_1, _mean_wts_2, places=6) - - os.remove(path) - - def test_save_load_buf(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer_1 = AutoencoderPipeline(model, SEQ_LEN, num_epochs=5) - trainer_1.fit(self.X_train) - buf = trainer_1.save() - - trainer_2 = AutoencoderPipeline(model, SEQ_LEN, num_epochs=3) - trainer_2.load(buf) - - # Check if both model's weights are equal - _mean_wts_1, _mean_wts_2 = [], [] - with torch.no_grad(): - for _w in trainer_1.model.parameters(): - _mean_wts_1.append(torch.mean(_w).item()) - for _w in trainer_2.model.parameters(): - _mean_wts_2.append(torch.mean(_w).item()) - - self.assertTrue(_mean_wts_1) - self.assertAlmostEqual(_mean_wts_1, _mean_wts_2, places=6) - - def test_with_conv1d_model(self): - trainer = AutoencoderPipeline.with_model( - Conv1dAE, SEQ_LEN, in_channels=self.X_train.shape[1], enc_channels=8 - ) - self.assertIsInstance(trainer.model, Conv1dAE) - - def test_with_transformer_model(self): - trainer = AutoencoderPipeline.with_model( - TransformerAE, - SEQ_LEN, - num_heads=8, - seq_length=SEQ_LEN, - dim_feedforward=64, - num_encoder_layers=3, - num_decoder_layers=1, - ) - self.assertIsInstance(trainer.model, TransformerAE) - - def test_load_model_without_resume_train_01(self): - X = np.random.randn(10, 1) - model = VanillaAE(10) - model_pl1 = AutoencoderPipeline(model, 10) - model_pl1.fit(X) - model_pl2 = AutoencoderPipeline(model, 10) - model_pl2.load(model=model_pl1.model, **model_pl1.model_properties) - self.assertEqual(list(model_pl1.model_properties.keys()), ["batch_size", "num_epochs", "epochs_elapsed"]) - - def test_load_model_resume_train_01(self): - X = np.random.randn(10, 1) - model = VanillaAE(10) - model_pl1 = AutoencoderPipeline(model, 10, resume_train=True) - model_pl1.fit(X) - model_pl2 = AutoencoderPipeline(model, 10, resume_train=True) - model_pl2.load(model=model_pl1.model, **model_pl1.model_properties) - self.assertEqual( - list(model_pl1.model_properties.keys()), - ["batch_size", "num_epochs", "epochs_elapsed", "optimizer_state_dict"], - ) - - def test_load_model_with_resume_train_02(self): - X = np.random.randn(10, 1) - model = VanillaAE(10) - model_pl1 = AutoencoderPipeline(model, 10, resume_train=True) - model_pl1.fit(X) - self.assertEqual( - ["batch_size", "num_epochs", "epochs_elapsed", "optimizer_state_dict"], - list(model_pl1.model_properties.keys()), - ) - - def test_load_model_without_resume_train_02(self): - X = np.random.randn(10, 1) - model = VanillaAE(10) - model_pl1 = AutoencoderPipeline(model, 10, resume_train=False) - model_pl1.fit(X) - self.assertEqual(["batch_size", "num_epochs", "epochs_elapsed"], list(model_pl1.model_properties.keys())) - - def test_exception_in_load_model(self): - X = np.random.randn(10, 1) - model = VanillaAE(10) - model_pl1 = AutoencoderPipeline(model, 10) - model_pl1.fit(X) - model_pl2 = AutoencoderPipeline(model, 10) - with self.assertRaises(ValueError): - model_pl2.load( - path="checkpoint.pth", model=model_pl1.model, **model_pl1.model_properties - ) - - def test_exception_invalid_epoch(self): - model = VanillaAE(10) - with self.assertRaises(ValueError): - AutoencoderPipeline(model, 10, num_epochs=-10) - - -class TestSparseAEPipeline(unittest.TestCase): - X_train = None - X_val = None - - @classmethod - def setUpClass(cls) -> None: - df = pd.read_csv(DATA_FILE) - df = df[["success", "failure"]] - scaler = StandardScaler() - cls.X_train = scaler.fit_transform(df[:-240]) - cls.X_val = scaler.transform(df[-240:]) - - def test_fit_kl_divergence_01(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, beta=1e-2, rho=0.01) - trainer.fit(self.X_train) - - def test_fit_kl_divergence_02(self): - model = LSTMAE(seq_len=SEQ_LEN, no_features=self.X_train.shape[1], embedding_dim=16) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, beta=1e-2, rho=0.01) - trainer.fit(self.X_train) - - def test_fit_kl_divergence_03(self): - model = Conv1dAE(self.X_train.shape[1], 8) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, beta=1e-2, rho=0.01) - trainer.fit(self.X_train) - - def test_fit_kl_divergence_04(self): - model = TransformerAE( - num_heads=8, - seq_length=SEQ_LEN, - dim_feedforward=64, - num_encoder_layers=3, - num_decoder_layers=1, - ) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, beta=1e-2, rho=0.01) - trainer.fit(self.X_train) - pred = trainer.predict(self.X_val, seq_len=SEQ_LEN) - self.assertEqual(self.X_val.shape, pred.shape) - - def test_predict_as_pl(self): - pipeline = Pipeline( - [ - ("scaler", RobustScaler()), - ( - "sparse_ae", - SparseAEPipeline( - model=Conv1dAE(self.X_train.shape[1], 8), seq_len=SEQ_LEN, num_epochs=5 - ), - ), - ] - ) - pipeline.fit(self.X_train, sparse_ae__log_freq=1) - pred = pipeline.predict(self.X_val, seq_len=12) - self.assertEqual(self.X_val.shape, pred.shape) - - def test_fit_L1(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, method="L1") - trainer.fit(self.X_train) - - def test_fit_L2(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, method="L2") - trainer.fit(self.X_train) - - def test_non_implemented_loss(self): - model = VanillaAE(SEQ_LEN, n_features=self.X_train.shape[1]) - trainer = SparseAEPipeline(model=model, seq_len=SEQ_LEN, num_epochs=5, method="lol") - with self.assertRaises(NotImplementedError): - trainer.fit(self.X_train) - - -if __name__ == "__main__": - unittest.main() diff --git a/numalogic/tests/models/autoencoder/test_trainer.py b/numalogic/tests/models/autoencoder/test_trainer.py new file mode 100644 index 00000000..27dbd5c4 --- /dev/null +++ b/numalogic/tests/models/autoencoder/test_trainer.py @@ -0,0 +1,63 @@ +import os +import unittest + +import pandas as pd +import torch +from sklearn.preprocessing import StandardScaler +from torch.utils.data import DataLoader + +from numalogic._constants import TESTS_DIR +from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.models.autoencoder.variants import Conv1dAE +from numalogic.tools.data import TimeseriesDataModule, StreamingDataset + +ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") +DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") +EPOCHS = 5 +BATCH_SIZE = 256 +SEQ_LEN = 12 +LR = 0.001 +torch.manual_seed(42) + + +class TestAutoencoderTrainer(unittest.TestCase): + x_train = None + x_val = None + x_test = None + + @classmethod + def setUpClass(cls) -> None: + df = pd.read_csv(DATA_FILE) + df = df[["success", "failure"]] + + scaler = StandardScaler() + cls.x_train = scaler.fit_transform(df[:-480]) + cls.x_val = scaler.transform(df[-480:-240]) + cls.x_test = scaler.transform(df[-240:]) + print(cls.x_train.shape, cls.x_val.shape, cls.x_test.shape) + + def test_trainer_01(self): + model = Conv1dAE(seq_len=SEQ_LEN, in_channels=self.x_train.shape[1], enc_channels=4) + datamodule = TimeseriesDataModule( + SEQ_LEN, self.x_train, val_data=self.x_val, batch_size=BATCH_SIZE + ) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True, limit_val_batches=1) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.x_test, SEQ_LEN), batch_size=1) + y_test = trainer.predict(model, dataloaders=streamloader, unbatch=True) + self.assertTupleEqual(self.x_test.shape, y_test.size()) + + def test_trainer_02(self): + model = Conv1dAE(seq_len=SEQ_LEN, in_channels=self.x_train.shape[1], enc_channels=4) + datamodule = TimeseriesDataModule(SEQ_LEN, self.x_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.x_test, SEQ_LEN), batch_size=BATCH_SIZE) + y_test_batched = trainer.predict(model, dataloaders=streamloader, unbatch=False) + self.assertTupleEqual((229, SEQ_LEN, self.x_test.shape[1]), y_test_batched.size()) + + +if __name__ == "__main__": + unittest.main() diff --git a/numalogic/tests/models/autoencoder/variants/test_conv.py b/numalogic/tests/models/autoencoder/variants/test_conv.py index 63e0dd34..c7180908 100644 --- a/numalogic/tests/models/autoencoder/variants/test_conv.py +++ b/numalogic/tests/models/autoencoder/variants/test_conv.py @@ -4,24 +4,25 @@ import pandas as pd import torch from sklearn.preprocessing import StandardScaler -from torch import nn +from torch import nn, Tensor from torch.utils.data import DataLoader from numalogic._constants import TESTS_DIR +from numalogic.tools.data import TimeseriesDataModule, StreamingDataset +from numalogic.models.autoencoder.trainer import AutoencoderTrainer from numalogic.models.autoencoder.variants import Conv1dAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.variants.conv import SparseConv1dAE ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") EPOCHS = 5 BATCH_SIZE = 256 -SEQ_LEN = 120 +SEQ_LEN = 12 LR = 0.001 torch.manual_seed(42) class TestConvAE(unittest.TestCase): - model = None X_train = None X_val = None @@ -33,19 +34,45 @@ def setUpClass(cls) -> None: cls.X_train = scaler.fit_transform(df[:-240]) cls.X_val = scaler.transform(df[-240:]) - def test_train(self): - self.model = Conv1dAE(self.X_train.shape[1], 8) - optimizer = torch.optim.Adam(self.model.parameters(), lr=LR) + def test_conv1d(self): + model = Conv1dAE(seq_len=SEQ_LEN, in_channels=self.X_train.shape[1], enc_channels=8) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader) + self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) + + def test_sparse_conv1d(self): + model = SparseConv1dAE( + seq_len=SEQ_LEN, in_channels=self.X_train.shape[1], enc_channels=8, loss_fn="mse" + ) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader, unbatch=False) + self.assertTupleEqual((229, SEQ_LEN, self.X_train.shape[1]), test_reconerr.size()) + + def test_native_train(self): + model = Conv1dAE(seq_len=SEQ_LEN, in_channels=self.X_train.shape[1], enc_channels=8) + optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) - dataset = SequenceDataset(self.X_train, SEQ_LEN) - train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False) + dataset = StreamingDataset(self.X_train, seq_len=SEQ_LEN) + train_loader = DataLoader(dataset, batch_size=BATCH_SIZE) - self.model.train() + model.train() + loss = Tensor([0.0]) for epoch in range(1, EPOCHS + 1): for _X_batch in train_loader: optimizer.zero_grad() - encoded, decoded = self.model(_X_batch) + encoded, decoded = model(_X_batch) + decoded = decoded.view(-1, SEQ_LEN, self.X_train.shape[1]) loss = criterion(decoded, _X_batch) loss.backward() diff --git a/numalogic/tests/models/autoencoder/variants/test_lstm.py b/numalogic/tests/models/autoencoder/variants/test_lstm.py index 01b922b8..5db859f8 100644 --- a/numalogic/tests/models/autoencoder/variants/test_lstm.py +++ b/numalogic/tests/models/autoencoder/variants/test_lstm.py @@ -8,13 +8,15 @@ from torch.utils.data import DataLoader from numalogic._constants import TESTS_DIR +from numalogic.tools.data import TimeseriesDataModule, StreamingDataset +from numalogic.models.autoencoder.trainer import AutoencoderTrainer from numalogic.models.autoencoder.variants import LSTMAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.variants.lstm import SparseLSTMAE ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") EPOCHS = 5 -BATCH_SIZE = 256 +BATCH_SIZE = 64 SEQ_LEN = 12 LR = 0.001 torch.manual_seed(42) @@ -32,14 +34,35 @@ def setUpClass(cls) -> None: cls.X_train = scaler.fit_transform(df[:-240]) cls.X_val = scaler.transform(df[-240:]) - def test_train(self): + def test_lstm_ae(self): + model = LSTMAE(seq_len=SEQ_LEN, no_features=2, embedding_dim=15) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader) + self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) + + def test_sparse_lstm_ae(self): + model = SparseLSTMAE(seq_len=SEQ_LEN, no_features=2, embedding_dim=15, loss_fn="mse") + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader, unbatch=False) + self.assertListEqual([229, SEQ_LEN, self.X_train.shape[1]], list(test_reconerr.size())) + + def test_native_train(self): model = LSTMAE(seq_len=SEQ_LEN, no_features=2, embedding_dim=15) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) - dataset = SequenceDataset(self.X_train, SEQ_LEN, permute=False) - train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False) - model.summary(dataset.data.size()) + dataset = StreamingDataset(self.X_train, seq_len=SEQ_LEN) + train_loader = DataLoader(dataset, batch_size=BATCH_SIZE) model.train() loss = torch.Tensor([0.0]) diff --git a/numalogic/tests/models/autoencoder/variants/test_transformers.py b/numalogic/tests/models/autoencoder/variants/test_transformers.py index 53339bfa..3b28938f 100644 --- a/numalogic/tests/models/autoencoder/variants/test_transformers.py +++ b/numalogic/tests/models/autoencoder/variants/test_transformers.py @@ -8,12 +8,14 @@ from torch.utils.data import DataLoader from numalogic._constants import TESTS_DIR +from numalogic.tools.data import StreamingDataset, TimeseriesDataModule +from numalogic.models.autoencoder.trainer import AutoencoderTrainer from numalogic.models.autoencoder.variants import TransformerAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.models.autoencoder.variants.transformer import SparseTransformerAE ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") -EPOCHS = 10 +EPOCHS = 5 BATCH_SIZE = 256 SEQ_LEN = 12 LR = 0.001 @@ -32,10 +34,40 @@ def setUpClass(cls) -> None: cls.X_train = scaler.fit_transform(df[:-240]) cls.X_val = scaler.transform(df[-240:]) + def test_transformer(self): + model = TransformerAE( + seq_len=SEQ_LEN, + n_features=2, + num_heads=8, + dim_feedforward=64, + num_encoder_layers=3, + num_decoder_layers=1, + ) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=EPOCHS, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader) + self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) + + def test_sparse_transformer(self): + model = SparseTransformerAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1") + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=EPOCHS, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader, unbatch=False) + self.assertListEqual([229, SEQ_LEN, self.X_train.shape[1]], list(test_reconerr.size())) + def test_train(self): model = TransformerAE( + n_features=2, num_heads=8, - seq_length=SEQ_LEN, + seq_len=SEQ_LEN, dim_feedforward=64, num_encoder_layers=3, num_decoder_layers=1, @@ -43,9 +75,8 @@ def test_train(self): optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) - dataset = SequenceDataset(self.X_train, SEQ_LEN, permute=True) - train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False) - model.summary(dataset.data.size()) + dataset = StreamingDataset(self.X_train, seq_len=SEQ_LEN) + train_loader = DataLoader(dataset, batch_size=BATCH_SIZE) model.train() loss = torch.Tensor([0.0]) @@ -53,9 +84,9 @@ def test_train(self): for _X_batch in train_loader: optimizer.zero_grad() encoded, decoded = model(_X_batch) + decoded = decoded.view(-1, SEQ_LEN, self.X_train.shape[1]) loss = criterion(decoded, _X_batch) - print(loss) loss.backward() optimizer.step() diff --git a/numalogic/tests/models/autoencoder/variants/test_vanilla.py b/numalogic/tests/models/autoencoder/variants/test_vanilla.py index 915c699a..fad029eb 100644 --- a/numalogic/tests/models/autoencoder/variants/test_vanilla.py +++ b/numalogic/tests/models/autoencoder/variants/test_vanilla.py @@ -8,20 +8,21 @@ from torch.utils.data import DataLoader from numalogic._constants import TESTS_DIR -from numalogic.models.autoencoder.variants.vanilla import VanillaAE -from numalogic.preprocess.datasets import SequenceDataset +from numalogic.tools.data import StreamingDataset, TimeseriesDataModule +from numalogic.models.autoencoder.trainer import AutoencoderTrainer +from numalogic.models.autoencoder.variants.vanilla import VanillaAE, SparseVanillaAE from numalogic.tools.exceptions import LayerSizeMismatchError ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") EPOCHS = 5 -BATCH_SIZE = 256 -SEQ_LEN = 20 +BATCH_SIZE = 64 +SEQ_LEN = 12 LR = 0.001 torch.manual_seed(42) -class TESTBVanillaAE(unittest.TestCase): +class TESTVanillaAE(unittest.TestCase): X_train = None X_val = None @@ -33,35 +34,37 @@ def setUpClass(cls) -> None: cls.X_train = scaler.fit_transform(df[:-240]) cls.X_val = scaler.transform(df[-240:]) - def test_train_01(self): - model = VanillaAE(SEQ_LEN, n_features=2) - optimizer = torch.optim.Adam(model.parameters(), lr=LR) - criterion = nn.HuberLoss(delta=0.5) - dataset = SequenceDataset(self.X_train, SEQ_LEN) - train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False) - - model.train() - loss = torch.Tensor([0.0]) - for epoch in range(1, EPOCHS + 1): - for _X_batch in train_loader: - optimizer.zero_grad() - encoded, decoded = model(_X_batch) - - loss = criterion(decoded, _X_batch) - loss.backward() - optimizer.step() - - if epoch % 5 == 0: - print(f"epoch : {epoch}, loss_mean : {loss.item():.7f}") - - def test_train_02(self): + def test_vanilla(self): + model = VanillaAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1]) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader) + self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) + + def test_sparse_vanilla(self): + model = SparseVanillaAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1") + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + streamloader = DataLoader(StreamingDataset(self.X_val, SEQ_LEN), batch_size=BATCH_SIZE) + stream_trainer = AutoencoderTrainer() + test_reconerr = stream_trainer.predict(model, dataloaders=streamloader, unbatch=False) + self.assertTupleEqual((229, SEQ_LEN, self.X_train.shape[1]), test_reconerr.size()) + + def test_native_train(self): model = VanillaAE( SEQ_LEN, n_features=2, encoder_layersizes=[24, 16, 6], decoder_layersizes=[6, 16, 24] ) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) - dataset = SequenceDataset(self.X_train, SEQ_LEN) - train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False) + + dataset = StreamingDataset(self.X_train, seq_len=SEQ_LEN) + train_loader = DataLoader(dataset, batch_size=BATCH_SIZE) model.train() loss = torch.Tensor([0.0]) @@ -69,6 +72,7 @@ def test_train_02(self): for _X_batch in train_loader: optimizer.zero_grad() encoded, decoded = model(_X_batch) + decoded = decoded.view(-1, SEQ_LEN, self.X_train.shape[1]) loss = criterion(decoded, _X_batch) loss.backward() diff --git a/numalogic/tests/preprocess/test_datasets.py b/numalogic/tests/preprocess/test_datasets.py deleted file mode 100644 index 97f19621..00000000 --- a/numalogic/tests/preprocess/test_datasets.py +++ /dev/null @@ -1,44 +0,0 @@ -import os -import unittest - -import numpy as np -import pandas as pd - -from numalogic._constants import TESTS_DIR -from numalogic.preprocess.datasets import SequenceDataset - -ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") -DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") - - -class TestSequenceDataset(unittest.TestCase): - df: pd.DataFrame = None - - @classmethod - def setUpClass(cls) -> None: - df = pd.read_csv(DATA_FILE) - cls.df = df[["success", "failure"]] - - def test_create_dataset_df(self): - dataset = SequenceDataset(self.df, 120) - self.assertTrue(len(dataset)) - self.assertEqual(2, dataset.data.shape[1]) - self.assertEqual(120, dataset.data.shape[2]) - - def test_create_dataset_ndarr(self): - dataset = SequenceDataset(self.df.to_numpy(), 120) - self.assertTrue(len(dataset)) - self.assertEqual(2, dataset.data.shape[1]) - self.assertEqual(120, dataset.data.shape[2]) - - def test_recover_shape(self): - dataset = SequenceDataset(self.df.to_numpy(), 120) - recovered = dataset.recover_shape(dataset.data) - self.assertEqual(recovered.shape, self.df.shape) - print(np.mean(recovered)) - print(self.df.mean()) - self.assertAlmostEqual(np.mean(recovered), np.mean(self.df.to_numpy()), places=5) - - -if __name__ == "__main__": - unittest.main() diff --git a/numalogic/tests/tools/test_data.py b/numalogic/tests/tools/test_data.py new file mode 100644 index 00000000..daaca3a3 --- /dev/null +++ b/numalogic/tests/tools/test_data.py @@ -0,0 +1,84 @@ +import os +import unittest + +import numpy as np +import torch +from numalogic._constants import TESTS_DIR +from numalogic.tools.data import StreamingDataset, TimeseriesDataModule +from numalogic.tools.exceptions import DataModuleError +from numpy.testing import assert_allclose +from torch.utils.data import DataLoader + +ROOT_DIR = os.path.join(TESTS_DIR, "resources", "data") +DATA_FILE = os.path.join(ROOT_DIR, "interactionstatus.csv") +SEQ_LEN = 12 + + +class TestStreamingDataset(unittest.TestCase): + data = None + m = None + n = None + + @classmethod + def setUpClass(cls) -> None: + cls.m = 30 + cls.n = 3 + cls.data = np.random.randn(cls.m, cls.n) + + def test_dataset(self): + dataset = StreamingDataset(self.data, seq_len=SEQ_LEN) + for seq in dataset: + self.assertTupleEqual((SEQ_LEN, self.n), seq.shape) + self.assertEqual(self.data.shape[0] - SEQ_LEN + 1, len(dataset)) + assert_allclose(np.ravel(dataset[0]), np.ravel(self.data[:12, :])) + + def test_dataset_err_01(self): + with self.assertRaises(ValueError): + StreamingDataset(self.data, seq_len=self.m + 1) + + def test_dataset_err_02(self): + dataset = StreamingDataset(self.data, seq_len=SEQ_LEN) + with self.assertRaises(IndexError): + _ = dataset[self.m - 5] + + +class TestTimeSeriesDataModule(unittest.TestCase): + train_data = None + val_data = None + m = None + n = None + + @classmethod + def setUpClass(cls) -> None: + cls.n = 3 + cls.train_data = np.random.randn(100, cls.n) + cls.val_data = np.random.randn(20, cls.n) + + def test_datamodule_01(self): + datamodule = TimeseriesDataModule(SEQ_LEN, self.train_data) + datamodule.setup(stage="fit") + self.assertIsInstance(datamodule.train_dataloader(), DataLoader) + + with self.assertRaises(DataModuleError): + datamodule.setup(stage="validate") + datamodule.val_dataloader() + + def test_datamodule_02(self): + datamodule = TimeseriesDataModule(SEQ_LEN, self.train_data, val_data=self.val_data) + datamodule.setup(stage="fit") + self.assertIsInstance(datamodule.train_dataloader(), DataLoader) + + datamodule.setup(stage="validate") + self.assertIsInstance(datamodule.val_dataloader(), DataLoader) + + def test_unbatch_sequences(self): + datamodule = TimeseriesDataModule(SEQ_LEN, self.train_data, batch_size=256) + datamodule.setup(stage="fit") + + for batch in datamodule.train_dataloader(): + unbatched = datamodule.unbatch_sequences(batch) + self.assertAlmostEqual(torch.mean(unbatched).item(), np.mean(self.train_data), places=5) + + +if __name__ == "__main__": + unittest.main() diff --git a/numalogic/tools/__init__.py b/numalogic/tools/__init__.py index e69de29b..d14a5f03 100644 --- a/numalogic/tools/__init__.py +++ b/numalogic/tools/__init__.py @@ -0,0 +1,7 @@ +from numpy.typing import ArrayLike +from sklearn.base import TransformerMixin, BaseEstimator + + +class DataIndependentTransformers(TransformerMixin, BaseEstimator): + def fit(self, _: ArrayLike): + return self diff --git a/numalogic/tools/data.py b/numalogic/tools/data.py new file mode 100644 index 00000000..fc4a4828 --- /dev/null +++ b/numalogic/tools/data.py @@ -0,0 +1,72 @@ +import numpy as np +import torch +from numalogic.tools.exceptions import DataModuleError +from numpy.typing import NDArray +from pytorch_lightning.utilities.types import TRAIN_DATALOADERS, EVAL_DATALOADERS +from torch import Tensor +from torch.utils.data import IterableDataset, DataLoader +import pytorch_lightning as pl + + +class StreamingDataset(IterableDataset): + def __init__(self, data: NDArray, seq_len: int): + if seq_len > len(data): + raise ValueError(f"Sequence length: {seq_len} is more than data size: {len(data)}") + self._seq_len = seq_len + self._data = data.astype(np.float32) + + def create_seq(self, x): + idx = 0 + while idx < len(self._data) - self._seq_len + 1: + yield x[idx : idx + self._seq_len] + idx += 1 + + def __iter__(self): + # TODO implement multi worker iter + return iter(self.create_seq(self._data)) + + def __len__(self): + return len(self._data) - self._seq_len + 1 + + def __getitem__(self, idx: int): + if idx >= len(self._data) - self._seq_len + 1: + raise IndexError(f"{idx} out of bound!") + return self._data[idx : idx + self._seq_len] + + +class TimeseriesDataModule(pl.LightningDataModule): + def __init__( + self, + seq_len: int, + train_data: NDArray, + val_data: NDArray = None, + batch_size: int = 64, + ): + super().__init__() + self.batch_size = batch_size + self.seq_len = seq_len + self.train_data = train_data + self.val_data = val_data + + self.train_dataset = None + self.val_dataset = None + + def setup(self, stage: str) -> None: + if stage == "fit": + self.train_dataset = StreamingDataset(self.train_data, self.seq_len) + if self.val_data is None: + return + self.val_dataset = StreamingDataset(self.val_data, self.seq_len) + + def train_dataloader(self) -> TRAIN_DATALOADERS: + return DataLoader(self.train_dataset, batch_size=self.batch_size) + + def val_dataloader(self) -> EVAL_DATALOADERS: + if self.val_data is None: + raise DataModuleError("Validation data is not provided!") + return DataLoader(self.val_dataset, batch_size=self.batch_size) + + @staticmethod + def unbatch_sequences(batched: Tensor) -> Tensor: + output = batched[:, 0, :] + return torch.vstack((output, batched[-1, 1::])) diff --git a/numalogic/tools/exceptions.py b/numalogic/tools/exceptions.py index 7f80c200..0f2d72ce 100644 --- a/numalogic/tools/exceptions.py +++ b/numalogic/tools/exceptions.py @@ -12,3 +12,7 @@ class InvalidRangeParameter(Exception): class LayerSizeMismatchError(Exception): pass + + +class DataModuleError(Exception): + pass diff --git a/poetry.lock b/poetry.lock index a3f9a22a..02db987a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,19 +1,100 @@ [[package]] -name = "alembic" -version = "1.8.1" -description = "A database migration tool for SQLAlchemy." +name = "aiohttp" +version = "3.8.3" +description = "Async http client/server framework (asyncio)" category = "main" -optional = true +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<3.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "cchardet"] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = "aiosignal: a list of registered asynchronous callbacks" +category = "main" +optional = false python-versions = ">=3.7" [package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -importlib-resources = {version = "*", markers = "python_version < \"3.9\""} -Mako = "*" -SQLAlchemy = ">=1.3.0" +frozenlist = ">=1.1.0" + +[[package]] +name = "anyio" +version = "3.6.2" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +category = "dev" +optional = false +python-versions = ">=3.6.2" + +[package.dependencies] +idna = ">=2.8" +sniffio = ">=1.1" + +[package.extras] +doc = ["packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (<0.15)", "uvloop (>=0.15)"] +trio = ["trio (>=0.16,<0.22)"] + +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "argon2-cffi" +version = "21.3.0" +description = "The secure Argon2 password hashing algorithm." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +argon2-cffi-bindings = "*" [package.extras] -tz = ["python-dateutil"] +dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"] +docs = ["furo", "sphinx", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] + +[[package]] +name = "argon2-cffi-bindings" +version = "21.2.0" +description = "Low-level CFFI bindings for Argon2" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +cffi = ">=1.0.1" + +[package.extras] +dev = ["cogapp", "pre-commit", "pytest", "wheel"] +tests = ["pytest"] + +[[package]] +name = "arrow" +version = "1.2.3" +description = "Better dates & times for Python" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +python-dateutil = ">=2.7.0" [[package]] name = "astroid" @@ -28,23 +109,68 @@ lazy-object-proxy = ">=1.4.0" typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""} wrapt = {version = ">=1.11,<2", markers = "python_version < \"3.11\""} +[[package]] +name = "asttokens" +version = "2.2.1" +description = "Annotate AST trees with source code positions" +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[package.extras] +test = ["astroid", "pytest"] + +[[package]] +name = "async-timeout" +version = "4.0.2" +description = "Timeout context manager for asyncio programs" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "attrs" version = "22.1.0" description = "Classes Without Boilerplate" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"] +docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"] +tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"] +tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"] + +[[package]] +name = "backcall" +version = "0.2.0" +description = "Specifications for callback functions passed in to an API" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "beautifulsoup4" +version = "4.11.1" +description = "Screen-scraping library" +category = "dev" +optional = false +python-versions = ">=3.6.0" + +[package.dependencies] +soupsieve = ">1.2" + +[package.extras] +html5lib = ["html5lib"] +lxml = ["lxml"] [[package]] name = "black" -version = "22.10.0" +version = "22.12.0" description = "The uncompromising code formatter." category = "dev" optional = false @@ -64,24 +190,51 @@ d = ["aiohttp (>=3.7.4)"] jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] uvloop = ["uvloop (>=0.15.2)"] +[[package]] +name = "bleach" +version = "5.0.1" +description = "An easy safelist-based HTML-sanitizing tool." +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +six = ">=1.9.0" +webencodings = "*" + +[package.extras] +css = ["tinycss2 (>=1.1.0,<1.2)"] +dev = ["Sphinx (==4.3.2)", "black (==22.3.0)", "build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "mypy (==0.961)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)"] + [[package]] name = "certifi" -version = "2022.9.24" +version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." category = "main" -optional = true +optional = false python-versions = ">=3.6" +[[package]] +name = "cffi" +version = "1.15.1" +description = "Foreign Function Interface for Python calling C code." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +pycparser = "*" + [[package]] name = "charset-normalizer" version = "2.1.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" -optional = true +optional = false python-versions = ">=3.6.0" [package.extras] -unicode_backport = ["unicodedata2"] +unicode-backport = ["unicodedata2"] [[package]] name = "click" @@ -110,11 +263,25 @@ category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +[[package]] +name = "comm" +version = "0.1.2" +description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +traitlets = ">=5.3" + +[package.extras] +test = ["pytest"] + [[package]] name = "contourpy" version = "1.0.6" description = "Python library for calculating contours of 2D quadrilateral grids" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" @@ -124,9 +291,9 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "selenium"] docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] +test = ["Pillow", "flake8", "isort", "matplotlib", "pytest"] test-minimal = ["pytest"] -test-no-codebase = ["pytest", "matplotlib", "pillow"] +test-no-codebase = ["Pillow", "matplotlib", "pytest"] [[package]] name = "coverage" @@ -146,13 +313,13 @@ toml = ["tomli"] name = "cycler" version = "0.11.0" description = "Composable style cycles" -category = "main" +category = "dev" optional = false python-versions = ">=3.6" [[package]] name = "databricks-cli" -version = "0.17.3" +version = "0.17.4" description = "A command line interface for Databricks" category = "main" optional = true @@ -167,40 +334,46 @@ six = ">=1.10.0" tabulate = ">=0.7.7" [[package]] -name = "dill" -version = "0.3.6" -description = "serialize all of python" +name = "debugpy" +version = "1.6.4" +description = "An implementation of the Debug Adapter Protocol for Python" category = "dev" optional = false python-versions = ">=3.7" -[package.extras] -graph = ["objgraph (>=1.7.2)"] +[[package]] +name = "decorator" +version = "5.1.1" +description = "Decorators for Humans" +category = "dev" +optional = false +python-versions = ">=3.5" [[package]] -name = "docker" -version = "6.0.1" -description = "A Python library for the Docker Engine API." -category = "main" -optional = true -python-versions = ">=3.7" +name = "defusedxml" +version = "0.7.1" +description = "XML bomb protection for Python stdlib modules" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" -[package.dependencies] -packaging = ">=14.0" -pywin32 = {version = ">=304", markers = "sys_platform == \"win32\""} -requests = ">=2.26.0" -urllib3 = ">=1.26.0" -websocket-client = ">=0.32.0" +[[package]] +name = "dill" +version = "0.3.6" +description = "serialize all of python" +category = "dev" +optional = false +python-versions = ">=3.7" [package.extras] -ssh = ["paramiko (>=2.4.3)"] +graph = ["objgraph (>=1.7.2)"] [[package]] name = "entrypoints" version = "0.4" description = "Discover and load entry points from installed packages." category = "main" -optional = true +optional = false python-versions = ">=3.6" [[package]] @@ -214,6 +387,28 @@ python-versions = ">=3.7" [package.extras] test = ["pytest (>=6)"] +[[package]] +name = "executing" +version = "1.2.0" +description = "Get the currently executing AST node of a frame, and other information" +category = "dev" +optional = false +python-versions = "*" + +[package.extras] +tests = ["asttokens", "littleutils", "pytest", "rich"] + +[[package]] +name = "fastjsonschema" +version = "2.16.2" +description = "Fastest Python implementation of JSON schema" +category = "dev" +optional = false +python-versions = "*" + +[package.extras] +devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] + [[package]] name = "flake8" version = "5.0.4" @@ -227,37 +422,18 @@ mccabe = ">=0.7.0,<0.8.0" pycodestyle = ">=2.9.0,<2.10.0" pyflakes = ">=2.5.0,<2.6.0" -[[package]] -name = "flask" -version = "2.2.2" -description = "A simple framework for building complex web applications." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -click = ">=8.0" -importlib-metadata = {version = ">=3.6.0", markers = "python_version < \"3.10\""} -itsdangerous = ">=2.0" -Jinja2 = ">=3.0" -Werkzeug = ">=2.2.2" - -[package.extras] -async = ["asgiref (>=3.2)"] -dotenv = ["python-dotenv"] - [[package]] name = "fonttools" version = "4.38.0" description = "Tools to manipulate font files" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" [package.extras] -all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -266,7 +442,15 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=14.0.0)"] -woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] + +[[package]] +name = "fqdn" +version = "1.5.1" +description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" [[package]] name = "freezegun" @@ -279,6 +463,49 @@ python-versions = ">=3.6" [package.dependencies] python-dateutil = ">=2.7" +[[package]] +name = "frozenlist" +version = "1.3.3" +description = "A list-like structure which implements collections.abc.MutableSequence" +category = "main" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "fsspec" +version = "2022.11.0" +description = "File-system specification" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""} +requests = {version = "*", optional = true, markers = "extra == \"http\""} + +[package.extras] +abfs = ["adlfs"] +adl = ["adlfs"] +arrow = ["pyarrow (>=1)"] +dask = ["dask", "distributed"] +dropbox = ["dropbox", "dropboxdrivefs", "requests"] +entrypoints = ["importlib-metadata"] +fuse = ["fusepy"] +gcs = ["gcsfs"] +git = ["pygit2"] +github = ["requests"] +gs = ["gcsfs"] +gui = ["panel"] +hdfs = ["pyarrow (>=1)"] +http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"] +libarchive = ["libarchive-c"] +oci = ["ocifs"] +s3 = ["s3fs"] +sftp = ["paramiko"] +smb = ["smbprotocol"] +ssh = ["paramiko"] +tqdm = ["tqdm"] + [[package]] name = "gitdb" version = "4.0.10" @@ -301,38 +528,12 @@ python-versions = ">=3.7" [package.dependencies] gitdb = ">=4.0.1,<5" -[[package]] -name = "greenlet" -version = "2.0.1" -description = "Lightweight in-process concurrent programming" -category = "main" -optional = true -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" - -[package.extras] -docs = ["sphinx", "docutils (<0.18)"] -test = ["objgraph", "psutil", "faulthandler"] - -[[package]] -name = "gunicorn" -version = "20.1.0" -description = "WSGI HTTP Server for UNIX" -category = "main" -optional = true -python-versions = ">=3.5" - -[package.extras] -eventlet = ["eventlet (>=0.24.1)"] -gevent = ["gevent (>=1.4.0)"] -setproctitle = ["setproctitle"] -tornado = ["tornado (>=0.2)"] - [[package]] name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" category = "main" -optional = true +optional = false python-versions = ">=3.5" [[package]] @@ -340,31 +541,31 @@ name = "importlib-metadata" version = "5.1.0" description = "Read metadata from Python packages" category = "main" -optional = true +optional = false python-versions = ">=3.7" [package.dependencies] zipp = ">=0.5" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] +testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] [[package]] name = "importlib-resources" -version = "5.10.0" +version = "5.10.1" description = "Read resources from Python packages" -category = "main" -optional = true +category = "dev" +optional = false python-versions = ">=3.7" [package.dependencies] zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] +testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [[package]] name = "iniconfig" @@ -374,34 +575,174 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "ipykernel" +version = "6.19.2" +description = "IPython Kernel for Jupyter" +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +appnope = {version = "*", markers = "platform_system == \"Darwin\""} +comm = ">=0.1.1" +debugpy = ">=1.0" +ipython = ">=7.23.1" +jupyter-client = ">=6.1.12" +matplotlib-inline = ">=0.1" +nest-asyncio = "*" +packaging = "*" +psutil = "*" +pyzmq = ">=17" +tornado = ">=6.1" +traitlets = ">=5.4.0" + +[package.extras] +cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt"] +lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"] +typing = ["mypy (>=0.990)"] + +[[package]] +name = "ipympl" +version = "0.9.2" +description = "Matplotlib Jupyter Extension" +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipython = "<9" +ipython-genutils = "*" +ipywidgets = ">=7.6.0,<9" +matplotlib = ">=3.4.0,<4" +numpy = "*" +pillow = "*" +traitlets = "<6" + +[package.extras] +docs = ["Sphinx (>=1.5)", "myst-nb", "sphinx-book-theme", "sphinx-copybutton", "sphinx-thebe", "sphinx-togglebutton"] + +[[package]] +name = "ipython" +version = "8.7.0" +description = "IPython: Productive Interactive Computing" +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +appnope = {version = "*", markers = "sys_platform == \"darwin\""} +backcall = "*" +colorama = {version = "*", markers = "sys_platform == \"win32\""} +decorator = "*" +jedi = ">=0.16" +matplotlib-inline = "*" +pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} +pickleshare = "*" +prompt-toolkit = ">=3.0.11,<3.1.0" +pygments = ">=2.4.0" +stack-data = "*" +traitlets = ">=5" + +[package.extras] +all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.20)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] +black = ["black"] +doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] +kernel = ["ipykernel"] +nbconvert = ["nbconvert"] +nbformat = ["nbformat"] +notebook = ["ipywidgets", "notebook"] +parallel = ["ipyparallel"] +qtconsole = ["qtconsole"] +test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] +test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.20)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"] + +[[package]] +name = "ipython-autotime" +version = "0.3.1" +description = "Time everything in IPython" +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipython = "*" + +[[package]] +name = "ipython-genutils" +version = "0.2.0" +description = "Vestigial utilities from IPython" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "ipywidgets" +version = "8.0.3" +description = "Jupyter interactive widgets" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +ipykernel = ">=4.5.1" +ipython = ">=6.1.0" +jupyterlab-widgets = ">=3.0,<4.0" +traitlets = ">=4.3.1" +widgetsnbextension = ">=4.0,<5.0" + +[package.extras] +test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] + +[[package]] +name = "isoduration" +version = "20.11.0" +description = "Operations with ISO 8601 durations" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +arrow = ">=0.15.0" + [[package]] name = "isort" -version = "5.10.1" +version = "5.11.1" description = "A Python utility / library to sort Python imports." category = "dev" optional = false -python-versions = ">=3.6.1,<4.0" +python-versions = ">=3.7.0" [package.extras] -pipfile_deprecated_finder = ["pipreqs", "requirementslib"] -requirements_deprecated_finder = ["pipreqs", "pip-api"] colors = ["colorama (>=0.4.3,<0.5.0)"] +pipfile-deprecated-finder = ["pipreqs", "requirementslib"] plugins = ["setuptools"] +requirements-deprecated-finder = ["pip-api", "pipreqs"] [[package]] -name = "itsdangerous" -version = "2.1.2" -description = "Safely pass data to untrusted environments and back." -category = "main" -optional = true -python-versions = ">=3.7" +name = "jedi" +version = "0.18.2" +description = "An autocompletion tool for Python that can be used for text editors." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +parso = ">=0.8.0,<0.9.0" + +[package.extras] +docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"] +qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] +testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] [[package]] name = "jinja2" version = "3.1.2" description = "A very fast and expressive template engine." -category = "main" -optional = true +category = "dev" +optional = false python-versions = ">=3.7" [package.dependencies] @@ -411,10 +752,200 @@ MarkupSafe = ">=2.0" i18n = ["Babel (>=2.7)"] [[package]] -name = "joblib" -version = "1.2.0" -description = "Lightweight pipelining with Python functions" -category = "main" +name = "joblib" +version = "1.2.0" +description = "Lightweight pipelining with Python functions" +category = "main" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "jsonpointer" +version = "2.3" +description = "Identify specific nodes in a JSON document (RFC 6901)" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + +[[package]] +name = "jsonschema" +version = "4.17.3" +description = "An implementation of JSON Schema validation for Python" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +attrs = ">=17.4.0" +fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""} +isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""} +pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""} +pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2" +rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""} +uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""} + +[package.extras] +format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] +format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] + +[[package]] +name = "jupyter" +version = "1.0.0" +description = "Jupyter metapackage. Install all the Jupyter components in one go." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipykernel = "*" +ipywidgets = "*" +jupyter-console = "*" +nbconvert = "*" +notebook = "*" +qtconsole = "*" + +[[package]] +name = "jupyter-client" +version = "7.4.8" +description = "Jupyter protocol implementation and client libraries" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +entrypoints = "*" +jupyter-core = ">=4.9.2" +nest-asyncio = ">=1.5.4" +python-dateutil = ">=2.8.2" +pyzmq = ">=23.0" +tornado = ">=6.2" +traitlets = "*" + +[package.extras] +doc = ["ipykernel", "myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] +test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "jupyter-console" +version = "6.4.4" +description = "Jupyter terminal console" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +ipykernel = "*" +ipython = "*" +jupyter-client = ">=7.0.0" +prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" +pygments = "*" + +[package.extras] +test = ["pexpect"] + +[[package]] +name = "jupyter-core" +version = "5.1.0" +description = "Jupyter core package. A base package on which Jupyter projects rely." +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +platformdirs = ">=2.5" +pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} +traitlets = ">=5.3" + +[package.extras] +docs = ["myst-parser", "sphinxcontrib-github-alt", "traitlets"] +test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "jupyter-events" +version = "0.5.0" +description = "Jupyter Event System library" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +jsonschema = {version = ">=4.3.0", extras = ["format-nongpl"]} +python-json-logger = "*" +pyyaml = "*" +traitlets = "*" + +[package.extras] +cli = ["click", "rich"] +test = ["click", "coverage", "pre-commit", "pytest (>=6.1.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "pytest-cov", "rich"] + +[[package]] +name = "jupyter-server" +version = "2.0.1" +description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +anyio = ">=3.1.0,<4" +argon2-cffi = "*" +jinja2 = "*" +jupyter-client = ">=7.4.4" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +jupyter-events = ">=0.4.0" +jupyter-server-terminals = "*" +nbconvert = ">=6.4.4" +nbformat = ">=5.3.0" +packaging = "*" +prometheus-client = "*" +pywinpty = {version = "*", markers = "os_name == \"nt\""} +pyzmq = ">=24" +send2trash = "*" +terminado = ">=0.8.3" +tornado = ">=6.2.0" +traitlets = ">=5.6.0" +websocket-client = "*" + +[package.extras] +docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] +lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +test = ["ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"] +typing = ["mypy (>=0.990)"] + +[[package]] +name = "jupyter-server-terminals" +version = "0.4.2" +description = "A Jupyter Server Extension Providing Terminals." +category = "dev" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""} +terminado = ">=0.8.3" + +[package.extras] +docs = ["jinja2", "jupyter-server", "mistune (<2.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] +test = ["coverage", "jupyter-server (>=2.0.0rc8)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"] + +[[package]] +name = "jupyterlab-pygments" +version = "0.2.2" +description = "Pygments theme using JupyterLab CSS variables" +category = "dev" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "jupyterlab-widgets" +version = "3.0.4" +description = "Jupyter interactive widgets for JupyterLab" +category = "dev" optional = false python-versions = ">=3.7" @@ -422,7 +953,7 @@ python-versions = ">=3.7" name = "kiwisolver" version = "1.4.4" description = "A fast implementation of the Cassowary constraint solver" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" @@ -435,56 +966,31 @@ optional = false python-versions = ">=3.7" [[package]] -name = "llvmlite" -version = "0.39.1" -description = "lightweight wrapper around basic LLVM functionality" -category = "main" -optional = true -python-versions = ">=3.7" - -[[package]] -name = "mako" -version = "1.2.4" -description = "A super-fast templating language that borrows the best ideas from the existing templating languages." -category = "main" -optional = true -python-versions = ">=3.7" - -[package.dependencies] -MarkupSafe = ">=0.9.2" - -[package.extras] -babel = ["babel"] -lingua = ["lingua"] -testing = ["pytest"] - -[[package]] -name = "markdown" -version = "3.4.1" -description = "Python implementation of Markdown." +name = "lightning-utilities" +version = "0.4.2" +description = "PyTorch Lightning Sample project." category = "main" -optional = true +optional = false python-versions = ">=3.7" -[package.dependencies] -importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} - [package.extras] -testing = ["coverage", "pyyaml"] +cli = ["fire"] +docs = ["sphinx (>=4.0,<5.0)"] +test = ["coverage (==6.5.0)"] [[package]] name = "markupsafe" version = "2.1.1" description = "Safely add untrusted strings to HTML/XML markup." -category = "main" -optional = true +category = "dev" +optional = false python-versions = ">=3.7" [[package]] name = "matplotlib" version = "3.6.2" description = "Python plotting package" -category = "main" +category = "dev" optional = false python-versions = ">=3.8" @@ -500,6 +1006,17 @@ pyparsing = ">=2.2.1" python-dateutil = ">=2.7" setuptools_scm = ">=7" +[[package]] +name = "matplotlib-inline" +version = "0.1.6" +description = "Inline Matplotlib backend for Jupyter" +category = "dev" +optional = false +python-versions = ">=3.5" + +[package.dependencies] +traitlets = "*" + [[package]] name = "mccabe" version = "0.7.0" @@ -509,50 +1026,12 @@ optional = false python-versions = ">=3.6" [[package]] -name = "mlflow" -version = "2.0.1" -description = "MLflow: A Platform for ML Development and Productionization" -category = "main" -optional = true -python-versions = ">=3.8" - -[package.dependencies] -alembic = "<2" -click = ">=7.0,<9" -cloudpickle = "<3" -databricks-cli = ">=0.8.7,<1" -docker = ">=4.0.0,<7" -entrypoints = "<1" -Flask = "<3" -gitpython = ">=2.1.0,<4" -gunicorn = {version = "<21", markers = "platform_system != \"Windows\""} -importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<6" -Jinja2 = [ - {version = ">=2.11,<4", markers = "platform_system != \"Windows\""}, - {version = ">=3.0,<4", markers = "platform_system == \"Windows\""}, -] -markdown = ">=3.3,<4" -matplotlib = "<4" -numpy = "<2" -packaging = "<22" -pandas = "<2" -protobuf = ">=3.12.0,<5" -pyarrow = ">=4.0.0,<11" -pytz = "<2023" -pyyaml = ">=5.1,<7" -querystring-parser = "<2" -requests = ">=2.17.3,<3" -scikit-learn = "<2" -scipy = "<2" -shap = ">=0.40,<1" -sqlalchemy = ">=1.4.0,<2" -sqlparse = ">=0.4.0,<1" -waitress = {version = "<3", markers = "platform_system == \"Windows\""} - -[package.extras] -aliyun-oss = ["aliyunstoreplugin"] -extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] -sqlserver = ["mlflow-dbstore"] +name = "mistune" +version = "2.0.4" +description = "A sane Markdown parser with useful plugins and renderers" +category = "dev" +optional = false +python-versions = "*" [[package]] name = "mlflow-skinny" @@ -578,9 +1057,17 @@ sqlparse = ">=0.4.0,<1" [package.extras] aliyun-oss = ["aliyunstoreplugin"] -extras = ["scikit-learn", "pyarrow", "requests-auth-aws-sigv4", "boto3", "google-cloud-storage (>=1.30.0)", "azureml-core (>=1.2.0)", "pysftp", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "virtualenv", "prometheus-flask-exporter"] +extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0.dev13)", "mlserver-mlflow (>=1.2.0.dev13)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "scikit-learn", "virtualenv"] sqlserver = ["mlflow-dbstore"] +[[package]] +name = "multidict" +version = "6.0.3" +description = "multidict implementation" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "mypy-extensions" version = "0.4.3" @@ -590,17 +1077,171 @@ optional = false python-versions = "*" [[package]] -name = "numba" -version = "0.56.4" -description = "compiling Python code using LLVM" -category = "main" -optional = true +name = "nb-black" +version = "1.0.7" +description = "A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically using Black." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipython = "*" + +[[package]] +name = "nbclassic" +version = "0.4.8" +description = "A web-based notebook environment for interactive computing" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +argon2-cffi = "*" +ipykernel = "*" +ipython-genutils = "*" +jinja2 = "*" +jupyter-client = ">=6.1.1" +jupyter-core = ">=4.6.1" +jupyter-server = ">=1.8" +nbconvert = ">=5" +nbformat = "*" +nest-asyncio = ">=1.5" +notebook-shim = ">=0.1.0" +prometheus-client = "*" +pyzmq = ">=17" +Send2Trash = ">=1.8.0" +terminado = ">=0.8.3" +tornado = ">=6.1" +traitlets = ">=4.2.1" + +[package.extras] +docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] +json-logging = ["json-logging"] +test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"] + +[[package]] +name = "nbclient" +version = "0.7.2" +description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." +category = "dev" +optional = false +python-versions = ">=3.7.0" + +[package.dependencies] +jupyter-client = ">=6.1.12" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" +nbformat = ">=5.1" +traitlets = ">=5.3" + +[package.extras] +dev = ["pre-commit"] +docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme"] +test = ["ipykernel", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"] + +[[package]] +name = "nbconvert" +version = "7.2.6" +description = "Converting Jupyter Notebooks" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +beautifulsoup4 = "*" +bleach = "*" +defusedxml = "*" +importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""} +jinja2 = ">=3.0" +jupyter-core = ">=4.7" +jupyterlab-pygments = "*" +markupsafe = ">=2.0" +mistune = ">=2.0.3,<3" +nbclient = ">=0.5.0" +nbformat = ">=5.1" +packaging = "*" +pandocfilters = ">=1.4.1" +pygments = ">=2.4.1" +tinycss2 = "*" +traitlets = ">=5.0" + +[package.extras] +all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"] +docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)"] +qtpdf = ["nbconvert[qtpng]"] +qtpng = ["pyqtwebengine (>=5.15)"] +serve = ["tornado (>=6.1)"] +test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-dependency"] +webpdf = ["pyppeteer (>=1,<1.1)"] + +[[package]] +name = "nbformat" +version = "5.7.0" +description = "The Jupyter Notebook format" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +fastjsonschema = "*" +jsonschema = ">=2.6" +jupyter-core = "*" +traitlets = ">=5.1" + +[package.extras] +test = ["check-manifest", "pep440", "pre-commit", "pytest", "testpath"] + +[[package]] +name = "nest-asyncio" +version = "1.5.6" +description = "Patch asyncio to allow nested event loops" +category = "dev" +optional = false +python-versions = ">=3.5" + +[[package]] +name = "notebook" +version = "6.5.2" +description = "A web-based notebook environment for interactive computing" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +argon2-cffi = "*" +ipykernel = "*" +ipython-genutils = "*" +jinja2 = "*" +jupyter-client = ">=5.3.4" +jupyter-core = ">=4.6.1" +nbclassic = ">=0.4.7" +nbconvert = ">=5" +nbformat = "*" +nest-asyncio = ">=1.5" +prometheus-client = "*" +pyzmq = ">=17" +Send2Trash = ">=1.8.0" +terminado = ">=0.8.3" +tornado = ">=6.1" +traitlets = ">=4.2.1" + +[package.extras] +docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"] +json-logging = ["json-logging"] +test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixsocket", "selenium (==4.1.5)", "testpath"] + +[[package]] +name = "notebook-shim" +version = "0.2.2" +description = "A shim layer for notebook traits and config" +category = "dev" +optional = false python-versions = ">=3.7" [package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -llvmlite = ">=0.39.0dev0,<0.40" -numpy = ">=1.18,<1.24" +jupyter-server = ">=1.8,<3" + +[package.extras] +test = ["pytest", "pytest-console-scripts", "pytest-tornasync"] [[package]] name = "numpy" @@ -653,19 +1294,58 @@ pytz = ">=2020.1" [package.extras] test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] +[[package]] +name = "pandocfilters" +version = "1.5.0" +description = "Utilities for writing pandoc filters in python" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + +[[package]] +name = "parso" +version = "0.8.3" +description = "A Python Parser" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.extras] +qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] +testing = ["docopt", "pytest (<6.0.0)"] + [[package]] name = "pathspec" -version = "0.10.2" +version = "0.10.3" description = "Utility library for gitignore style pattern matching of file paths." category = "dev" optional = false python-versions = ">=3.7" +[[package]] +name = "pexpect" +version = "4.8.0" +description = "Pexpect allows easy control of interactive console applications." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ptyprocess = ">=0.5" + +[[package]] +name = "pickleshare" +version = "0.7.5" +description = "Tiny 'shelve'-like database with concurrency support" +category = "dev" +optional = false +python-versions = "*" + [[package]] name = "pillow" version = "9.3.0" description = "Python Imaging Library (Fork)" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" @@ -673,17 +1353,25 @@ python-versions = ">=3.7" docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] +[[package]] +name = "pkgutil-resolve-name" +version = "1.3.10" +description = "Resolve a name to an object." +category = "dev" +optional = false +python-versions = ">=3.6" + [[package]] name = "platformdirs" -version = "2.5.4" +version = "2.6.0" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx-autodoc-typehints (>=1.19.4)", "sphinx (>=5.3)"] -test = ["appdirs (==1.4.4)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest (>=7.2)"] +docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.4)"] +test = ["appdirs (==1.4.4)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] [[package]] name = "pluggy" @@ -697,24 +1385,73 @@ python-versions = ">=3.6" dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] +[[package]] +name = "prometheus-client" +version = "0.15.0" +description = "Python client for the Prometheus monitoring system." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.extras] +twisted = ["twisted"] + +[[package]] +name = "prompt-toolkit" +version = "3.0.36" +description = "Library for building powerful interactive command lines in Python" +category = "dev" +optional = false +python-versions = ">=3.6.2" + +[package.dependencies] +wcwidth = "*" + [[package]] name = "protobuf" -version = "4.21.9" -description = "" +version = "3.20.1" +description = "Protocol Buffers" category = "main" -optional = true +optional = false python-versions = ">=3.7" [[package]] -name = "pyarrow" -version = "10.0.1" -description = "Python library for Apache Arrow" -category = "main" -optional = true -python-versions = ">=3.7" +name = "psutil" +version = "5.9.4" +description = "Cross-platform lib for process and system monitoring in Python." +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -[package.dependencies] -numpy = ">=1.16.6" +[package.extras] +test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] + +[[package]] +name = "ptyprocess" +version = "0.7.0" +description = "Run a subprocess in a pseudo terminal" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "pure-eval" +version = "0.2.2" +description = "Safely evaluate AST nodes without side effects" +category = "dev" +optional = false +python-versions = "*" + +[package.extras] +tests = ["pytest"] + +[[package]] +name = "py" +version = "1.11.0" +description = "library with cross-python path, ini-parsing, io, code, log facilities" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [[package]] name = "pycodestyle" @@ -724,6 +1461,14 @@ category = "dev" optional = false python-versions = ">=3.6" +[[package]] +name = "pycparser" +version = "2.21" +description = "C parser in Python" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + [[package]] name = "pyflakes" version = "2.5.0" @@ -732,6 +1477,17 @@ category = "dev" optional = false python-versions = ">=3.6" +[[package]] +name = "pygments" +version = "2.13.0" +description = "Pygments is a syntax highlighting package written in Python." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.extras] +plugins = ["importlib-metadata"] + [[package]] name = "pyjwt" version = "2.6.0" @@ -742,20 +1498,20 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] [[package]] name = "pylint" -version = "2.15.6" +version = "2.15.8" description = "python code static checker" category = "dev" optional = false python-versions = ">=3.7.2" [package.dependencies] -astroid = ">=2.12.12,<=2.14.0-dev0" +astroid = ">=2.12.13,<=2.14.0-dev0" colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""} dill = ">=0.2" isort = ">=4.2.5,<6" @@ -778,7 +1534,15 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["railroad-diagrams", "jinja2"] +diagrams = ["jinja2", "railroad-diagrams"] + +[[package]] +name = "pyrsistent" +version = "0.19.2" +description = "Persistent/Functional/Immutable data structures" +category = "dev" +optional = false +python-versions = ">=3.7" [[package]] name = "pytest" @@ -813,7 +1577,7 @@ coverage = {version = ">=5.2.1", extras = ["toml"]} pytest = ">=4.6" [package.extras] -testing = ["fields", "hunter", "process-tests", "six", "pytest-xdist", "virtualenv"] +testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"] [[package]] name = "python-dateutil" @@ -826,6 +1590,47 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" [package.dependencies] six = ">=1.5" +[[package]] +name = "python-json-logger" +version = "2.0.4" +description = "A python library adding a json log formatter" +category = "dev" +optional = false +python-versions = ">=3.5" + +[[package]] +name = "pytorch-lightning" +version = "1.8.4.post0" +description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +fsspec = {version = ">2021.06.0", extras = ["http"]} +lightning-utilities = ">=0.3.0,<0.4.0 || >0.4.0" +numpy = ">=1.17.2" +packaging = ">=17.0" +PyYAML = ">=5.4" +tensorboardX = ">=2.2" +torch = ">=1.9.0" +torchmetrics = ">=0.7.0" +tqdm = ">=4.57.0" +typing-extensions = ">=4.0.0" + +[package.extras] +all = ["cloudpickle (>=1.3)", "codecov (==2.1.12)", "colossalai (>=0.1.10)", "coverage (==6.5.0)", "deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "fastapi", "gym[classic-control] (>=0.17.0)", "hivemind (==1.1.2)", "horovod (>=0.21.2,!=0.24.0)", "hydra-core (>=1.0.5)", "ipython[all]", "jsonargparse[signatures] (>=4.15.2)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "onnxruntime", "pandas (>1.0)", "pre-commit (==2.20.0)", "protobuf (<=3.20.1)", "psutil", "pytest (==7.2.0)", "pytest-cov (==4.0.0)", "pytest-forked (==1.4.0)", "pytest-rerunfailures (==10.2)", "rich (>=10.14.0,!=10.15.0.a)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "torchvision (>=0.10.0)", "uvicorn"] +colossalai = ["colossalai (>=0.1.10)"] +deepspeed = ["deepspeed (>=0.6.0)"] +dev = ["cloudpickle (>=1.3)", "codecov (==2.1.12)", "coverage (==6.5.0)", "fastapi", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.15.2)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "onnxruntime", "pandas (>1.0)", "pre-commit (==2.20.0)", "protobuf (<=3.20.1)", "psutil", "pytest (==7.2.0)", "pytest-cov (==4.0.0)", "pytest-forked (==1.4.0)", "pytest-rerunfailures (==10.2)", "rich (>=10.14.0,!=10.15.0.a)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"] +examples = ["gym[classic-control] (>=0.17.0)", "ipython[all]", "torchvision (>=0.10.0)"] +extra = ["hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.15.2)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "rich (>=10.14.0,!=10.15.0.a)"] +fairscale = ["fairscale (>=0.4.5)"] +hivemind = ["hivemind (==1.1.2)"] +horovod = ["horovod (>=0.21.2,!=0.24.0)"] +strategies = ["colossalai (>=0.1.10)", "deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "hivemind (==1.1.2)", "horovod (>=0.21.2,!=0.24.0)"] +test = ["cloudpickle (>=1.3)", "codecov (==2.1.12)", "coverage (==6.5.0)", "fastapi", "onnxruntime", "pandas (>1.0)", "pre-commit (==2.20.0)", "protobuf (<=3.20.1)", "psutil", "pytest (==7.2.0)", "pytest-cov (==4.0.0)", "pytest-forked (==1.4.0)", "pytest-rerunfailures (==10.2)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"] + [[package]] name = "pytz" version = "2022.6" @@ -838,35 +1643,80 @@ python-versions = "*" name = "pywin32" version = "305" description = "Python for Window Extensions" -category = "main" -optional = true +category = "dev" +optional = false python-versions = "*" [[package]] -name = "pyyaml" -version = "6.0" -description = "YAML parser and emitter for Python" -category = "main" -optional = true -python-versions = ">=3.6" +name = "pywinpty" +version = "2.0.9" +description = "Pseudo terminal support for Windows from Python." +category = "dev" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "pyyaml" +version = "6.0" +description = "YAML parser and emitter for Python" +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "pyzmq" +version = "24.0.1" +description = "Python bindings for 0MQ" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +cffi = {version = "*", markers = "implementation_name == \"pypy\""} +py = {version = "*", markers = "implementation_name == \"pypy\""} + +[[package]] +name = "qtconsole" +version = "5.4.0" +description = "Jupyter Qt console" +category = "dev" +optional = false +python-versions = ">= 3.7" + +[package.dependencies] +ipykernel = ">=4.1" +ipython-genutils = "*" +jupyter-client = ">=4.1" +jupyter-core = "*" +pygments = "*" +pyzmq = ">=17.1" +qtpy = ">=2.0.1" +traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2" + +[package.extras] +doc = ["Sphinx (>=1.3)"] +test = ["flaky", "pytest", "pytest-qt"] [[package]] -name = "querystring-parser" -version = "1.2.4" -description = "QueryString parser for Python/Django that correctly handles nested dictionaries" -category = "main" -optional = true -python-versions = "*" +name = "qtpy" +version = "2.3.0" +description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)." +category = "dev" +optional = false +python-versions = ">=3.7" [package.dependencies] -six = "*" +packaging = "*" + +[package.extras] +test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] [[package]] name = "requests" version = "2.28.1" description = "Python HTTP for Humans." category = "main" -optional = true +optional = false python-versions = ">=3.7, <4" [package.dependencies] @@ -877,27 +1727,46 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] +use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] + +[[package]] +name = "rfc3339-validator" +version = "0.1.4" +description = "A pure python RFC3339 validator" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" + +[package.dependencies] +six = "*" + +[[package]] +name = "rfc3986-validator" +version = "0.1.1" +description = "Pure python rfc3986 validator" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [[package]] name = "scikit-learn" -version = "1.1.3" +version = "1.2.0" description = "A set of python modules for machine learning and data mining" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -joblib = ">=1.0.0" +joblib = ">=1.1.1" numpy = ">=1.17.3" scipy = ">=1.3.2" threadpoolctl = ">=2.0.0" [package.extras] -benchmark = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "memory-profiler (>=0.57.0)"] -docs = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)", "memory-profiler (>=0.57.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "numpydoc (>=1.2.0)", "Pillow (>=7.1.2)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] -examples = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "seaborn (>=0.9.0)"] -tests = ["matplotlib (>=3.1.2)", "scikit-image (>=0.16.2)", "pandas (>=1.0.5)", "pytest (>=5.0.1)", "pytest-cov (>=2.9.0)", "flake8 (>=3.8.2)", "black (>=22.3.0)", "mypy (>=0.961)", "pyamg (>=4.0.0)", "numpydoc (>=1.2.0)"] +benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"] +docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] +examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] +tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=5.3.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"] [[package]] name = "scipy" @@ -911,20 +1780,47 @@ python-versions = ">=3.8" numpy = ">=1.18.5,<1.26.0" [package.extras] -test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] -doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-panels (>=0.5.2)", "matplotlib (>2)", "numpydoc", "sphinx-tabs"] -dev = ["mypy", "typing-extensions", "pycodestyle", "flake8"] +dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] +doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] +test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] + +[[package]] +name = "send2trash" +version = "1.8.0" +description = "Send file to trash natively under Mac OS X, Windows and Linux." +category = "dev" +optional = false +python-versions = "*" + +[package.extras] +nativelib = ["pyobjc-framework-Cocoa", "pywin32"] +objc = ["pyobjc-framework-Cocoa"] +win32 = ["pywin32"] + +[[package]] +name = "setuptools" +version = "65.6.3" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] name = "setuptools-scm" version = "7.0.5" description = "the blessed package to manage your versions by scm tags" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" [package.dependencies] packaging = ">=20.0" +setuptools = "*" tomli = ">=1.0.0" typing-extensions = "*" @@ -932,32 +1828,6 @@ typing-extensions = "*" test = ["pytest (>=6.2)", "virtualenv (>20)"] toml = ["setuptools (>=42)"] -[[package]] -name = "shap" -version = "0.41.0" -description = "A unified approach to explain the output of any machine learning model." -category = "main" -optional = true -python-versions = "*" - -[package.dependencies] -cloudpickle = "*" -numba = "*" -numpy = "*" -packaging = ">20.9" -pandas = "*" -scikit-learn = "*" -scipy = "*" -slicer = "0.0.7" -tqdm = ">4.25.0" - -[package.extras] -all = ["transformers", "ipython", "lime", "pyod", "pyspark", "sphinx-rtd-theme", "pytest-mpl", "nbsphinx", "pytest", "opencv-python", "numpydoc", "xgboost", "torch", "sentencepiece", "matplotlib", "pytest-cov", "catboost", "lightgbm", "sphinx"] -docs = ["matplotlib", "ipython", "numpydoc", "sphinx-rtd-theme", "sphinx", "nbsphinx"] -others = ["lime"] -plots = ["matplotlib", "ipython"] -test = ["pytest", "pytest-mpl", "pytest-cov", "xgboost", "lightgbm", "catboost", "pyspark", "pyod", "transformers", "torch", "sentencepiece", "opencv-python"] - [[package]] name = "six" version = "1.16.0" @@ -966,14 +1836,6 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" -[[package]] -name = "slicer" -version = "0.0.7" -description = "A small package for big slicing." -category = "main" -optional = true -python-versions = ">=3.6" - [[package]] name = "smmap" version = "5.0.0" @@ -983,36 +1845,20 @@ optional = true python-versions = ">=3.6" [[package]] -name = "sqlalchemy" -version = "1.4.44" -description = "Database Abstraction Library" -category = "main" -optional = true -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" - -[package.dependencies] -greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"} +name = "sniffio" +version = "1.3.0" +description = "Sniff out which async library your code is running under" +category = "dev" +optional = false +python-versions = ">=3.7" -[package.extras] -aiomysql = ["greenlet (!=0.4.17)", "aiomysql"] -aiosqlite = ["typing_extensions (!=3.10.0.1)", "greenlet (!=0.4.17)", "aiosqlite"] -asyncio = ["greenlet (!=0.4.17)"] -asyncmy = ["greenlet (!=0.4.17)", "asyncmy (>=0.2.3,!=0.2.4)"] -mariadb_connector = ["mariadb (>=1.0.1,!=1.1.2)"] -mssql = ["pyodbc"] -mssql_pymssql = ["pymssql"] -mssql_pyodbc = ["pyodbc"] -mypy = ["sqlalchemy2-stubs", "mypy (>=0.910)"] -mysql = ["mysqlclient (>=1.4.0,<2)", "mysqlclient (>=1.4.0)"] -mysql_connector = ["mysql-connector-python"] -oracle = ["cx_oracle (>=7,<8)", "cx_oracle (>=7)"] -postgresql = ["psycopg2 (>=2.7)"] -postgresql_asyncpg = ["greenlet (!=0.4.17)", "asyncpg"] -postgresql_pg8000 = ["pg8000 (>=1.16.6,!=1.29.0)"] -postgresql_psycopg2binary = ["psycopg2-binary"] -postgresql_psycopg2cffi = ["psycopg2cffi"] -pymysql = ["pymysql (<1)", "pymysql"] -sqlcipher = 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{ version = "~2.0.1", optional = true } +pytorch-lightning = "^1.8.4.post0" [tool.poetry.extras] mlflow-skinny = ["mlflow-skinny"] -mlflow = ["mlflow"] -[tool.poetry.dev-dependencies] +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] matplotlib = "^3.4.2" black = "^22.6" pytest = "^7.1" @@ -45,6 +44,17 @@ freezegun = "^1.2.1" pylint = "^2.14.2" flake8 = "^5.0" +[tool.poetry.group.jupyter] +optional = true + +[tool.poetry.group.jupyter.dependencies] +jupyter = "^1.0.0" +ipykernel = "^6.15.1" +nb-black = "^1.0.7" +black = "^22.6.0" +ipympl = "^0.9.1" +ipython-autotime = "^0.3.1" + [tool.black] line-length = 100 include = '\.pyi?$' From 9dc10db9508cf8204fef171f184507fc40d255e5 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Wed, 21 Dec 2022 19:50:31 -0800 Subject: [PATCH 07/15] chore: cleanup requirements; bump version (#111) Signed-off-by: Avik Basu --- .coveragerc | 2 +- Makefile | 5 +- docs/ml-flow.md | 20 +- .../numalogic-simple-pipeline/src/utils.py | 10 +- numalogic/_constants.py | 2 +- numalogic/registry/__init__.py | 4 +- numalogic/registry/mlflow_registry.py | 6 +- poetry.lock | 3793 +++++++++-------- pyproject.toml | 10 +- {numalogic/tests => tests}/__init__.py | 0 {numalogic/tests => tests}/models/__init__.py | 0 .../models/autoencoder/__init__.py | 0 .../models/autoencoder/test_trainer.py | 0 .../models/autoencoder/variants/__init__.py | 0 .../models/autoencoder/variants/test_conv.py | 0 .../models/autoencoder/variants/test_lstm.py | 0 .../autoencoder/variants/test_transformers.py | 0 .../autoencoder/variants/test_vanilla.py | 0 .../models/forecast/__init__.py | 0 .../models/forecast/test_naive.py | 0 .../tests => tests}/preprocess/__init__.py | 0 .../preprocess/test_transformer.py | 0 .../tests => tests}/registry/__init__.py | 0 .../tests => tests}/registry/_mlflow_utils.py | 0 .../registry/test_mlflow_registry.py | 32 +- .../resources/data/interactionstatus.csv | 0 .../tests => tests}/synthetic/__init__.py | 0 .../synthetic/test_anomalies.py | 0 .../synthetic/test_sparsity.py | 0 .../synthetic/test_timeseries.py | 0 {numalogic/tests => tests}/test_scores.py | 0 {numalogic/tests => tests}/tools/__init__.py | 0 {numalogic/tests => tests}/tools/test_data.py | 0 33 files changed, 1968 insertions(+), 1916 deletions(-) rename {numalogic/tests => tests}/__init__.py (100%) rename {numalogic/tests => tests}/models/__init__.py (100%) rename {numalogic/tests => tests}/models/autoencoder/__init__.py (100%) rename {numalogic/tests => tests}/models/autoencoder/test_trainer.py (100%) rename {numalogic/tests => tests}/models/autoencoder/variants/__init__.py (100%) rename {numalogic/tests => tests}/models/autoencoder/variants/test_conv.py (100%) rename {numalogic/tests => tests}/models/autoencoder/variants/test_lstm.py (100%) rename {numalogic/tests => tests}/models/autoencoder/variants/test_transformers.py (100%) rename {numalogic/tests => tests}/models/autoencoder/variants/test_vanilla.py (100%) rename {numalogic/tests => tests}/models/forecast/__init__.py (100%) rename {numalogic/tests => tests}/models/forecast/test_naive.py (100%) rename {numalogic/tests => tests}/preprocess/__init__.py (100%) rename {numalogic/tests => tests}/preprocess/test_transformer.py (100%) rename {numalogic/tests => tests}/registry/__init__.py (100%) rename {numalogic/tests => tests}/registry/_mlflow_utils.py (100%) rename {numalogic/tests => tests}/registry/test_mlflow_registry.py (91%) rename {numalogic/tests => tests}/resources/data/interactionstatus.csv (100%) rename {numalogic/tests => tests}/synthetic/__init__.py (100%) rename {numalogic/tests => tests}/synthetic/test_anomalies.py (100%) rename {numalogic/tests => tests}/synthetic/test_sparsity.py (100%) rename {numalogic/tests => tests}/synthetic/test_timeseries.py (100%) rename {numalogic/tests => tests}/test_scores.py (100%) rename {numalogic/tests => tests}/tools/__init__.py (100%) rename {numalogic/tests => tests}/tools/test_data.py (100%) diff --git a/.coveragerc b/.coveragerc index 5f7a5725..291b730c 100644 --- a/.coveragerc +++ b/.coveragerc @@ -2,4 +2,4 @@ branch = True parallel = True source = numalogic -omit = numalogic/tests/* +omit = tests/* diff --git a/Makefile b/Makefile index b13e2449..d5a2c003 100644 --- a/Makefile +++ b/Makefile @@ -16,8 +16,7 @@ clean: @find . -type f -name "*.py[co]" -exec rm -rf {} + format: clean - poetry run black numalogic/* - poetry run black examples/* + poetry run black numalogic/ examples/ tests/ lint: format poetry run flake8 . @@ -28,7 +27,7 @@ setup: # test your application (tests in the tests/ directory) test: - poetry run pytest numalogic/tests/ + poetry run pytest tests/ publish: @rm -rf dist diff --git a/docs/ml-flow.md b/docs/ml-flow.md index d8d683a9..8b99ce71 100644 --- a/docs/ml-flow.md +++ b/docs/ml-flow.md @@ -19,34 +19,34 @@ Once the mlflow server has been started, you can navigate to http://127.0.0.1:50 ### Model saving -Numalogic provides `MLflowRegistrar`, to save and load models to/from MLflow. +Numalogic provides `MLflowRegistry`, to save and load models to/from MLflow. Here, `tracking_uri` is the uri where mlflow server is running. The `static_keys` and `dynamic_keys` are used to form a unique key for the model. -The `primary_artifact` would be the main model, and `secondary_artifacts` can be used to save any pre-processing models like scalers. +The `primary_artifact` would be the main model, and `secondary_artifacts` can be used to save any pre-processing models like scalers. ```python -from numalogic.registry import MLflowRegistrar +from numalogic.registry import MLflowRegistry # static and dynamic keys are used to look up a model static_keys = ["synthetic", "3ts"] dynamic_keys = ["minmaxscaler", "sparseconv1d"] -registry = MLflowRegistrar(tracking_uri="http://0.0.0.0:5000", artifact_type="pytorch") +registry = MLflowRegistry(tracking_uri="http://0.0.0.0:5000", artifact_type="pytorch") registry.save( - skeys=static_keys, - dkeys=dynamic_keys, - primary_artifact=model, - secondary_artifacts={"preproc": scaler} + skeys=static_keys, + dkeys=dynamic_keys, + primary_artifact=model, + secondary_artifacts={"preproc": scaler} ) ``` ### Model loading -Once, the models are save to MLflow, the `load` function of `MLflowRegistrar` can be used to load the model. +Once, the models are save to MLflow, the `load` function of `MLflowRegistry` can be used to load the model. ```python -registry = MLflowRegistrar(tracking_uri="http://0.0.0.0:8080") +registry = MLflowRegistry(tracking_uri="http://0.0.0.0:8080") artifact_dict = registry.load( skeys=static_keys, dkeys=dynamic_keys ) diff --git a/examples/numalogic-simple-pipeline/src/utils.py b/examples/numalogic-simple-pipeline/src/utils.py index 48c78881..b53de73d 100644 --- a/examples/numalogic-simple-pipeline/src/utils.py +++ b/examples/numalogic-simple-pipeline/src/utils.py @@ -7,7 +7,7 @@ from numalogic.models.autoencoder import AutoencoderPipeline from numalogic.models.autoencoder.base import TorchAE from numalogic.models.threshold._std import StdDevThreshold -from numalogic.registry import MLflowRegistrar +from numalogic.registry import MLflowRegistry from numalogic.tools.types import ArtifactDict from numpy.typing import ArrayLike @@ -32,18 +32,18 @@ def save_artifact( dkeys: Sequence[str], ) -> None: if isinstance(pl, TorchAE): - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") + ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="pytorch") else: - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="sklearn") + ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="sklearn") ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=pl) def load_artifact(skeys: Sequence[str], dkeys: Sequence[str], type: str = None) -> ArtifactDict: try: if type == "pytorch": - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="pytorch") + ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="pytorch") else: - ml_registry = MLflowRegistrar(tracking_uri=TRACKING_URI, artifact_type="sklearn") + ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="sklearn") artifact_dict = ml_registry.load(skeys=skeys, dkeys=dkeys) return artifact_dict except Exception as ex: diff --git a/numalogic/_constants.py b/numalogic/_constants.py index 67e44db9..dc341ba5 100644 --- a/numalogic/_constants.py +++ b/numalogic/_constants.py @@ -2,4 +2,4 @@ NUMALOGIC_DIR = os.path.dirname(__file__) BASE_DIR = os.path.split(NUMALOGIC_DIR)[0] -TESTS_DIR = os.path.join(NUMALOGIC_DIR, "tests") +TESTS_DIR = os.path.join(NUMALOGIC_DIR, "../tests") diff --git a/numalogic/registry/__init__.py b/numalogic/registry/__init__.py index d9d72261..fa178b58 100644 --- a/numalogic/registry/__init__.py +++ b/numalogic/registry/__init__.py @@ -2,8 +2,8 @@ from numalogic.registry.artifact import ArtifactData try: - from numalogic.registry.mlflow_registry import MLflowRegistrar + from numalogic.registry.mlflow_registry import MLflowRegistry except ImportError: __all__ = ["ArtifactManager", "ArtifactData"] else: - __all__ = ["ArtifactManager", "ArtifactData", "MLflowRegistrar"] + __all__ = ["ArtifactManager", "ArtifactData", "MLflowRegistry"] diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py index 462afb0e..fd25e320 100644 --- a/numalogic/registry/mlflow_registry.py +++ b/numalogic/registry/mlflow_registry.py @@ -24,7 +24,7 @@ class ModelStage(str, Enum): PRODUCTION = "Production" -class MLflowRegistrar(ArtifactManager): +class MLflowRegistry(ArtifactManager): """ Model saving and loading using MLFlow Registry. @@ -40,12 +40,12 @@ class MLflowRegistrar(ArtifactManager): Examples -------- >>> from numalogic.models.autoencoder.variants.vanilla import VanillaAE - >>> from numalogic.registry.mlflow_registry import MLflowRegistrar + >>> from numalogic.registry.mlflow_registry import MLflowRegistry >>> from sklearn.pipeline import make_pipeline >>> >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] >>> scaler = StandardScaler.fit(data) - >>> registry = 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"sha256:965f5259b566725405b05e7cf774052044b1ed30119b5d586b2703aafe8719ac"}, +] + +[package.extras] +test = ["pytest (>=3.0.0)"] + +[[package]] +name = "widgetsnbextension" +version = "4.0.4" +description = "Jupyter interactive widgets for Jupyter Notebook" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ {file = "widgetsnbextension-4.0.4-py3-none-any.whl", hash = "sha256:fa0e840719ec95dd2ec85c3a48913f1a0c29d323eacbcdb0b29bfed0cc6da678"}, {file = "widgetsnbextension-4.0.4.tar.gz", hash = "sha256:44c69f18237af0f610557d6c1c7ef76853f5856a0e604c0a517f2320566bb775"}, ] -wrapt = [ + +[[package]] +name = "wrapt" +version = "1.14.1" +description = "Module for decorators, wrappers and monkey patching." +category = "dev" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" +files = [ {file = "wrapt-1.14.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3"}, {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef"}, {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:5a9a0d155deafd9448baff28c08e150d9b24ff010e899311ddd63c45c2445e28"}, @@ -3823,7 +3846,15 @@ wrapt = [ {file = "wrapt-1.14.1-cp39-cp39-win_amd64.whl", hash = "sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb"}, {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"}, ] -yarl = [ + +[[package]] +name = "yarl" +version = "1.8.2" +description = "Yet another URL library" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ {file = "yarl-1.8.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:bb81f753c815f6b8e2ddd2eef3c855cf7da193b82396ac013c661aaa6cc6b0a5"}, {file = "yarl-1.8.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"] +testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] + +[extras] +mlflow-skinny = ["mlflow-skinny"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8, <3.11" +content-hash = "85c275ef6529fe59d395393e8f66582808ad8039df5fba6acfa6fd298d8dd1e7" diff --git a/pyproject.toml b/pyproject.toml index c1464844..8d4983a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "numalogic" -version = "0.3.0a0" +version = "0.3.0a1" description = "Collection of operational Machine Learning models and tools." authors = ["Numalogic Developers"] packages = [{ include = "numalogic" }] @@ -22,12 +22,11 @@ classifiers = [ [tool.poetry.dependencies] python = ">=3.8, <3.11" -numpy = "^1.23.1" -pandas = "^1.4.3" -torch = "~1.12.0" +numpy = "^1.23" +pandas = "^1.4" scikit-learn = "^1.0" +pytorch-lightning = "^1.8" mlflow-skinny = { version = "~2.0.1", optional = true } -pytorch-lightning = "^1.8.4.post0" [tool.poetry.extras] mlflow-skinny = ["mlflow-skinny"] @@ -40,7 +39,6 @@ matplotlib = "^3.4.2" black = "^22.6" pytest = "^7.1" pytest-cov = "^4.0" -freezegun = "^1.2.1" pylint = "^2.14.2" flake8 = "^5.0" diff --git a/numalogic/tests/__init__.py b/tests/__init__.py similarity index 100% rename from numalogic/tests/__init__.py rename to tests/__init__.py diff --git a/numalogic/tests/models/__init__.py b/tests/models/__init__.py similarity index 100% rename from numalogic/tests/models/__init__.py rename to tests/models/__init__.py diff --git a/numalogic/tests/models/autoencoder/__init__.py b/tests/models/autoencoder/__init__.py similarity index 100% rename from numalogic/tests/models/autoencoder/__init__.py rename to tests/models/autoencoder/__init__.py diff --git a/numalogic/tests/models/autoencoder/test_trainer.py b/tests/models/autoencoder/test_trainer.py similarity index 100% rename from numalogic/tests/models/autoencoder/test_trainer.py rename to tests/models/autoencoder/test_trainer.py diff --git a/numalogic/tests/models/autoencoder/variants/__init__.py b/tests/models/autoencoder/variants/__init__.py similarity index 100% rename from numalogic/tests/models/autoencoder/variants/__init__.py rename to tests/models/autoencoder/variants/__init__.py diff --git a/numalogic/tests/models/autoencoder/variants/test_conv.py b/tests/models/autoencoder/variants/test_conv.py similarity index 100% rename from numalogic/tests/models/autoencoder/variants/test_conv.py rename to tests/models/autoencoder/variants/test_conv.py diff --git a/numalogic/tests/models/autoencoder/variants/test_lstm.py b/tests/models/autoencoder/variants/test_lstm.py similarity index 100% rename from numalogic/tests/models/autoencoder/variants/test_lstm.py rename to tests/models/autoencoder/variants/test_lstm.py diff --git a/numalogic/tests/models/autoencoder/variants/test_transformers.py b/tests/models/autoencoder/variants/test_transformers.py similarity index 100% rename from numalogic/tests/models/autoencoder/variants/test_transformers.py rename to tests/models/autoencoder/variants/test_transformers.py diff --git a/numalogic/tests/models/autoencoder/variants/test_vanilla.py b/tests/models/autoencoder/variants/test_vanilla.py similarity index 100% rename from numalogic/tests/models/autoencoder/variants/test_vanilla.py rename to tests/models/autoencoder/variants/test_vanilla.py diff --git a/numalogic/tests/models/forecast/__init__.py b/tests/models/forecast/__init__.py similarity index 100% rename from numalogic/tests/models/forecast/__init__.py rename to tests/models/forecast/__init__.py diff --git a/numalogic/tests/models/forecast/test_naive.py b/tests/models/forecast/test_naive.py similarity index 100% rename from numalogic/tests/models/forecast/test_naive.py rename to tests/models/forecast/test_naive.py diff --git a/numalogic/tests/preprocess/__init__.py b/tests/preprocess/__init__.py similarity index 100% rename from numalogic/tests/preprocess/__init__.py rename to tests/preprocess/__init__.py diff --git a/numalogic/tests/preprocess/test_transformer.py b/tests/preprocess/test_transformer.py similarity index 100% rename from numalogic/tests/preprocess/test_transformer.py rename to tests/preprocess/test_transformer.py diff --git a/numalogic/tests/registry/__init__.py b/tests/registry/__init__.py similarity index 100% rename from numalogic/tests/registry/__init__.py rename to tests/registry/__init__.py diff --git a/numalogic/tests/registry/_mlflow_utils.py b/tests/registry/_mlflow_utils.py similarity index 100% rename from numalogic/tests/registry/_mlflow_utils.py rename to tests/registry/_mlflow_utils.py diff --git a/numalogic/tests/registry/test_mlflow_registry.py b/tests/registry/test_mlflow_registry.py similarity index 91% rename from numalogic/tests/registry/test_mlflow_registry.py rename to tests/registry/test_mlflow_registry.py index 07892cae..11c6943e 100644 --- a/numalogic/tests/registry/test_mlflow_registry.py +++ b/tests/registry/test_mlflow_registry.py @@ -6,8 +6,8 @@ from sklearn.ensemble import RandomForestRegressor from numalogic.models.autoencoder.variants import VanillaAE -from numalogic.registry import MLflowRegistrar -from numalogic.tests.registry._mlflow_utils import ( +from numalogic.registry import MLflowRegistry +from tests.registry._mlflow_utils import ( model_sklearn, create_model, mock_log_model_pytorch, @@ -44,7 +44,7 @@ def assertNotRaises(self, exc_type): def test_construct_key(self): skeys = ["model_", "nnet"] dkeys = ["error1"] - key = MLflowRegistrar.construct_key(skeys, dkeys) + key = MLflowRegistry.construct_key(skeys, dkeys) self.assertEqual("model_:nnet::error1", key) @patch("mlflow.pytorch.log_model", mock_log_model_pytorch) @@ -55,7 +55,7 @@ def test_construct_key(self): @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version) @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version) def test_insert_model(self): - ml = MLflowRegistrar(TRACKING_URI) + ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys dkeys = self.dkeys status = ml.save( @@ -75,7 +75,7 @@ def test_insert_model(self): @patch("mlflow.tracking.MlflowClient.search_model_versions", mock_list_of_model_version2) def test_insert_model_sklearn(self): model = self.model_sklearn - ml = MLflowRegistrar(TRACKING_URI, artifact_type="sklearn") + ml = MLflowRegistry(TRACKING_URI, artifact_type="sklearn") skeys = self.skeys dkeys = self.dkeys status = ml.save( @@ -96,7 +96,7 @@ def test_insert_model_sklearn(self): @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict())) def test_select_model_when_pytorch_model_exist1(self): model = self.model - ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch") + ml = MLflowRegistry(TRACKING_URI, artifact_type="pytorch") skeys = self.skeys dkeys = self.dkeys ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) @@ -113,7 +113,7 @@ def test_select_model_when_pytorch_model_exist1(self): @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_when_pytorch_model_exist2(self): model = self.model - ml = MLflowRegistrar(TRACKING_URI, artifact_type="pytorch", models_to_retain=2) + ml = MLflowRegistry(TRACKING_URI, artifact_type="pytorch", models_to_retain=2) skeys = self.skeys dkeys = self.dkeys ml.save( @@ -135,7 +135,7 @@ def test_select_model_when_pytorch_model_exist2(self): @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_when_sklearn_model_exist(self): model = self.model_sklearn - ml = MLflowRegistrar(TRACKING_URI, artifact_type="sklearn") + ml = MLflowRegistry(TRACKING_URI, artifact_type="sklearn") skeys = self.skeys dkeys = self.dkeys ml.save( @@ -157,7 +157,7 @@ def test_select_model_when_sklearn_model_exist(self): @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_empty_rundata())) def test_select_model_with_version(self): model = self.model - ml = MLflowRegistrar(TRACKING_URI) + ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys dkeys = self.dkeys ml.save( @@ -173,7 +173,7 @@ def test_select_model_with_version(self): def test_select_model_when_no_model_01(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] - ml = MLflowRegistrar(TRACKING_URI, artifact_type="pyfunc") + ml = MLflowRegistry(TRACKING_URI, artifact_type="pyfunc") with self.assertLogs(level="ERROR") as log: ml.load(skeys=fake_skeys, dkeys=fake_dkeys) self.assertTrue(log.output) @@ -182,7 +182,7 @@ def test_select_model_when_no_model_01(self): def test_select_model_when_no_model_02(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] - ml = MLflowRegistrar(TRACKING_URI, artifact_type="tensorflow") + ml = MLflowRegistry(TRACKING_URI, artifact_type="tensorflow") with self.assertLogs(level="ERROR") as log: ml.load(skeys=fake_skeys, dkeys=fake_dkeys) self.assertTrue(log.output) @@ -195,14 +195,14 @@ def test_select_model_when_no_model_02(self): def test_transition_stage_fail(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] - ml = MLflowRegistrar(TRACKING_URI, artifact_type="tensorflow") + ml = MLflowRegistry(TRACKING_URI, artifact_type="tensorflow") with self.assertLogs(level="ERROR") as log: ml.transition_stage(fake_skeys, fake_dkeys) self.assertTrue(log.output) def test_no_implementation(self): with self.assertRaises(NotImplementedError): - MLflowRegistrar(TRACKING_URI, artifact_type="some_random") + MLflowRegistry(TRACKING_URI, artifact_type="some_random") @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict()))) @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict())) @@ -215,7 +215,7 @@ def test_no_implementation(self): @patch("mlflow.pytorch.load_model", Mock(side_effect=RuntimeError)) def test_delete_model_when_model_exist(self): model = self.model - ml = MLflowRegistrar(TRACKING_URI) + ml = MLflowRegistry(TRACKING_URI) skeys = self.skeys dkeys = self.dkeys ml.save(skeys=skeys, dkeys=dkeys, artifact=model, **{"lr": 0.01}) @@ -228,7 +228,7 @@ def test_delete_model_when_model_exist(self): def test_delete_model_when_no_model(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] - ml = MLflowRegistrar(TRACKING_URI) + ml = MLflowRegistry(TRACKING_URI) with self.assertLogs(level="ERROR") as log: ml.delete(skeys=fake_skeys, dkeys=fake_dkeys, version="1") print(log.output) @@ -241,7 +241,7 @@ def test_insertion_failed(self): fake_skeys = ["Fakemodel_"] fake_dkeys = ["error"] - ml = MLflowRegistrar(TRACKING_URI) + ml = MLflowRegistry(TRACKING_URI) with self.assertLogs(level="ERROR") as log: ml.save(skeys=fake_skeys, dkeys=fake_dkeys, artifact=self.model) self.assertTrue(log.output) diff --git a/numalogic/tests/resources/data/interactionstatus.csv b/tests/resources/data/interactionstatus.csv similarity index 100% rename from numalogic/tests/resources/data/interactionstatus.csv rename to tests/resources/data/interactionstatus.csv diff --git a/numalogic/tests/synthetic/__init__.py b/tests/synthetic/__init__.py similarity index 100% rename from numalogic/tests/synthetic/__init__.py rename to tests/synthetic/__init__.py diff --git a/numalogic/tests/synthetic/test_anomalies.py b/tests/synthetic/test_anomalies.py similarity index 100% rename from numalogic/tests/synthetic/test_anomalies.py rename to tests/synthetic/test_anomalies.py diff --git a/numalogic/tests/synthetic/test_sparsity.py b/tests/synthetic/test_sparsity.py similarity index 100% rename from numalogic/tests/synthetic/test_sparsity.py rename to tests/synthetic/test_sparsity.py diff --git a/numalogic/tests/synthetic/test_timeseries.py b/tests/synthetic/test_timeseries.py similarity index 100% rename from numalogic/tests/synthetic/test_timeseries.py rename to tests/synthetic/test_timeseries.py diff --git a/numalogic/tests/test_scores.py b/tests/test_scores.py similarity index 100% rename from numalogic/tests/test_scores.py rename to tests/test_scores.py diff --git a/numalogic/tests/tools/__init__.py b/tests/tools/__init__.py similarity index 100% rename from numalogic/tests/tools/__init__.py rename to tests/tools/__init__.py diff --git a/numalogic/tests/tools/test_data.py b/tests/tools/test_data.py similarity index 100% rename from numalogic/tests/tools/test_data.py rename to tests/tools/test_data.py From e8b53049f497d800112a05031e6faf558eae4c84 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Wed, 4 Jan 2023 11:42:51 -0800 Subject: [PATCH 08/15] Example 0.3 (#115) * fix: move torch dependency away from main * fix: simple pipeline example * use pip as a dependency manager instead of poetry * add threshold vtx Signed-off-by: Avik Basu --- README.md | 17 +- examples/numalogic-simple-pipeline/Dockerfile | 16 +- .../numalogic-simple-pipeline/numa-pl.yaml | 31 +- .../numalogic-simple-pipeline/poetry.lock | 2088 ----------------- .../numalogic-simple-pipeline/pyproject.toml | 18 - .../requirements.txt | 5 + .../src/udf/__init__.py | 3 +- .../src/udf/inference.py | 43 +- .../src/udf/postprocess.py | 5 +- .../src/udf/preprocess.py | 6 +- .../src/udf/threshold.py | 46 + .../src/udf/train.py | 27 +- .../src/udf_factory.py | 7 +- .../numalogic-simple-pipeline/src/utils.py | 19 +- poetry.lock | 175 +- pyproject.toml | 8 +- tests/models/autoencoder/test_trainer.py | 3 + 17 files changed, 252 insertions(+), 2265 deletions(-) delete mode 100644 examples/numalogic-simple-pipeline/poetry.lock delete mode 100644 examples/numalogic-simple-pipeline/pyproject.toml create mode 100644 examples/numalogic-simple-pipeline/requirements.txt create mode 100644 examples/numalogic-simple-pipeline/src/udf/threshold.py diff --git a/README.md b/README.md index 623e2da9..9b7fb029 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,21 @@ the result further or drop it after a trigger request. ## Installation +Numalogic requires Python 3.8 or higher. + +### Prerequisites +Numalogic needs [PyTorch]("https://pytorch.org/") and +[PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) to work. +But since these packages are platform dependendent, +they are not included in the numalogic package itself. Kindly install them first. + +Numalogic supports the following pytorch versions: +- 1.11.x +- 1.12.x +- 1.13.x + +Other versions do work, it is just that they are not tested. + numalogic can be installed using pip. ```shell pip install numalogic @@ -57,7 +72,7 @@ pip install numalogic[mlflow] ``` 3. To install dependencies: ``` - poetry install + poetry install --with dev,torch ``` If extra dependencies are needed: ``` diff --git a/examples/numalogic-simple-pipeline/Dockerfile b/examples/numalogic-simple-pipeline/Dockerfile index 5bf864f5..df0a894e 100644 --- a/examples/numalogic-simple-pipeline/Dockerfile +++ b/examples/numalogic-simple-pipeline/Dockerfile @@ -6,7 +6,7 @@ ENV PYTHONFAULTHANDLER=1 \ PIP_NO_CACHE_DIR=on \ PIP_DISABLE_PIP_VERSION_CHECK=on \ PIP_DEFAULT_TIMEOUT=100 \ - POETRY_VERSION=1.2.2 \ + POETRY_VERSION=1.3.1 \ POETRY_HOME="/opt/poetry" \ POETRY_VIRTUALENVS_IN_PROJECT=true \ POETRY_NO_INTERACTION=1 \ @@ -26,15 +26,14 @@ RUN apt-get update \ \ # install dumb-init && wget -O /dumb-init https://github.com/Yelp/dumb-init/releases/download/v1.2.5/dumb-init_1.2.5_x86_64 \ - && chmod +x /dumb-init \ - && curl -sSL https://install.python-poetry.org | python3 - + && chmod +x /dumb-init # \ + # && curl -sSL https://install.python-poetry.org | python3 - + && pip3 install --upgrade pip3 FROM builder AS mlflow WORKDIR $PYSETUP_PATH -COPY ./pyproject.toml ./poetry.lock ./ -RUN poetry install --only mlflow --no-cache --no-root && \ - rm -rf ~/.cache/pypoetry/ +RUN pip3 install "mlflow==2.0.1" ADD . /app WORKDIR /app @@ -50,9 +49,8 @@ EXPOSE 5000 FROM builder AS udf WORKDIR $PYSETUP_PATH -COPY ./pyproject.toml ./poetry.lock ./ -RUN poetry install --with mlflow-skinny --no-cache --no-root && \ - rm -rf ~/.cache/pypoetry/ +COPY ./requirements.txt ./ +RUN pip3 install -r requirements.txt ADD . /app WORKDIR /app diff --git a/examples/numalogic-simple-pipeline/numa-pl.yaml b/examples/numalogic-simple-pipeline/numa-pl.yaml index 6a242dac..cd480a30 100644 --- a/examples/numalogic-simple-pipeline/numa-pl.yaml +++ b/examples/numalogic-simple-pipeline/numa-pl.yaml @@ -18,7 +18,7 @@ spec: min: 1 udf: container: - image: docker.io/library/numalogic-simple-pipeline:v1 + image: docker.io/library/numalogic-simple-pipeline:v0.3.0a1 env: - name: WIN_SIZE value: "12" @@ -31,7 +31,7 @@ spec: min: 1 udf: container: - image: docker.io/library/numalogic-simple-pipeline:v1 + image: docker.io/library/numalogic-simple-pipeline:v0.3.0a1 env: - name: WIN_SIZE value: "12" @@ -39,12 +39,25 @@ spec: - python - starter.py - inference + - name: threshold + scale: + min: 1 + udf: + container: + image: docker.io/library/numalogic-simple-pipeline:v0.3.0a1 + env: + - name: WIN_SIZE + value: "12" + args: + - python + - starter.py + - threshold - name: postprocess scale: min: 1 udf: container: - image: docker.io/library/numalogic-simple-pipeline:v1 + image: docker.io/library/numalogic-simple-pipeline:v0.3.0a1 env: - name: WIN_SIZE value: "12" @@ -65,7 +78,7 @@ spec: env: - name: WIN_SIZE value: "12" - image: docker.io/library/numalogic-simple-pipeline:v1 + image: docker.io/library/numalogic-simple-pipeline:v0.3.0a1 args: - python - starter.py @@ -73,14 +86,16 @@ spec: edges: - from: in to: preprocess + - from: preprocess + to: inference - from: inference + to: threshold + - from: threshold to: train conditions: keyIn: - train - - from: preprocess - to: inference - - from: inference + - from: threshold to: postprocess conditions: keyIn: @@ -106,7 +121,7 @@ spec: app: mlflow spec: containers: - - image: docker.io/library/numalogic-simple-pipeline:v1 + - image: docker.io/library/numalogic-simple-pipeline:mlflow-v0.3.0a1 name: mlflow args: - server diff --git a/examples/numalogic-simple-pipeline/poetry.lock b/examples/numalogic-simple-pipeline/poetry.lock deleted file mode 100644 index 5c038bed..00000000 --- a/examples/numalogic-simple-pipeline/poetry.lock +++ /dev/null @@ -1,2088 +0,0 @@ -[[package]] -name = "alembic" -version = "1.8.1" -description = "A database migration tool for SQLAlchemy." -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -importlib-resources = {version = "*", markers = "python_version < \"3.9\""} -Mako = "*" -SQLAlchemy = ">=1.3.0" - -[package.extras] -tz = ["python-dateutil"] - -[[package]] -name = "cachetools" -version = "5.2.0" -description = "Extensible memoizing collections and decorators" -category = "main" -optional = false -python-versions = "~=3.7" - -[[package]] -name = "certifi" -version = "2022.12.7" -description = "Python package for providing Mozilla's CA Bundle." -category = "main" -optional = false -python-versions = ">=3.6" - -[[package]] -name = "charset-normalizer" -version = "2.1.1" -description = "The Real First Universal Charset Detector. 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-[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" diff --git a/examples/numalogic-simple-pipeline/requirements.txt b/examples/numalogic-simple-pipeline/requirements.txt new file mode 100644 index 00000000..edfaf763 --- /dev/null +++ b/examples/numalogic-simple-pipeline/requirements.txt @@ -0,0 +1,5 @@ +cachetools==5.2.0 +dataclasses-json==0.5.7 +numalogic[mlflow-skinny] @ git+https://github.com/numaproj/numalogic.git@example-0.3 +pytorch-lightning==1.8.6 +pynumaflow==0.2.6 \ No newline at end of file diff --git a/examples/numalogic-simple-pipeline/src/udf/__init__.py b/examples/numalogic-simple-pipeline/src/udf/__init__.py index 23c3ce1b..d4273f86 100644 --- a/examples/numalogic-simple-pipeline/src/udf/__init__.py +++ b/examples/numalogic-simple-pipeline/src/udf/__init__.py @@ -2,6 +2,7 @@ from src.udf.postprocess import postprocess from src.udf.preprocess import preprocess from src.udf.train import train +from src.udf.threshold import threshold -__all__ = ["preprocess", "inference", "postprocess", "train"] +__all__ = ["preprocess", "inference", "postprocess", "train", threshold] diff --git a/examples/numalogic-simple-pipeline/src/udf/inference.py b/examples/numalogic-simple-pipeline/src/udf/inference.py index 53e84233..3b24fbea 100644 --- a/examples/numalogic-simple-pipeline/src/udf/inference.py +++ b/examples/numalogic-simple-pipeline/src/udf/inference.py @@ -2,9 +2,10 @@ import os import numpy as np -from numalogic.models.autoencoder import AutoencoderPipeline -from numalogic.models.autoencoder.variants import Conv1dAE +from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.tools.data import StreamingDataset from pynumaflow.function import Messages, Message, Datum +from torch.utils.data import DataLoader from src.utils import Payload, load_artifact @@ -12,11 +13,10 @@ WIN_SIZE = int(os.getenv("WIN_SIZE")) -def inference(key: str, datum: Datum) -> Messages: +def inference(_: str, datum: Datum) -> Messages: r""" Here inference is done on the data, given, the ML model is present - in the registry. If a model does not exist, conditional forward the payload to - train vertex for ML model training. Otherwise, conditional forward the inferred data + in the registry. If a model does not exist, it moves on Otherwise, conditional forward the inferred data to postprocess vertex for generating anomaly score for the payload. For more information about the arguments, refer: @@ -27,35 +27,28 @@ def inference(key: str, datum: Datum) -> Messages: payload = Payload.from_json(datum.value.decode("utf-8")) messages = Messages() - artifact_data = load_artifact(skeys=["ae"], dkeys=["model"], type="pytorch") - thresh_clf_data = load_artifact(skeys=["thresh_clf"], dkeys=["model"]) + artifact_data = load_artifact(skeys=["ae"], dkeys=["model"], type_="pytorch") + stream_data = np.asarray(payload.ts_data).reshape(-1, 1) # Check if model exists for inference - if artifact_data and thresh_clf_data: + if artifact_data: LOGGER.info("%s - Model found!", payload.uuid) # Load model from registry - pl = AutoencoderPipeline(model=Conv1dAE(in_channels=1, enc_channels=12), seq_len=WIN_SIZE) - pl.load(model=artifact_data.artifact) + main_model = artifact_data.artifact + streamloader = DataLoader(StreamingDataset(stream_data, WIN_SIZE)) - # Load the threshold model from registry - thresh_clf = thresh_clf_data.artifact - - # Infer using the loaded model - infer_data = np.asarray(payload.ts_data).reshape(-1, 1) - score_data = pl.score(infer_data) - payload.ts_data = thresh_clf.predict(score_data).tolist() + trainer = AutoencoderTrainer() + recon_err = trainer.predict(main_model, dataloaders=streamloader) + payload.ts_data = recon_err.tolist() LOGGER.info("%s - Inference complete", payload.uuid) - # Convert Payload back to bytes and conditional forward to postprocess vertex - messages.append(Message.to_vtx(key="postprocess", value=payload.to_json().encode("utf-8"))) - - # If model not found, send it to trainer for training else: - LOGGER.exception("%s - Model not found. Training the model.", payload.uuid) - - # Convert Payload back to bytes and conditional forward to train vertex - messages.append(Message.to_vtx(key="train", value=payload.to_json().encode("utf-8"))) + # If model not found, set status as not found + LOGGER.warning("%s - Model not found", payload.uuid) + payload.is_artifact_valid = False + # Convert Payload back to bytes and conditional forward to threshold vertex + messages.append(Message.to_vtx(key="threshold", value=payload.to_json().encode("utf-8"))) return messages diff --git a/examples/numalogic-simple-pipeline/src/udf/postprocess.py b/examples/numalogic-simple-pipeline/src/udf/postprocess.py index df056ea7..ef18d744 100644 --- a/examples/numalogic-simple-pipeline/src/udf/postprocess.py +++ b/examples/numalogic-simple-pipeline/src/udf/postprocess.py @@ -1,7 +1,7 @@ import logging import numpy as np -from numalogic.postprocess import tanh_norm +from numalogic.postprocess import TanhNorm from pynumaflow.function import Messages, Message, Datum from src.utils import Payload @@ -24,7 +24,8 @@ def postprocess(key: str, datum: Datum) -> Messages: data = np.asarray(payload.ts_data) # Taking mean of the anomaly scores - payload.anomaly_score = tanh_norm(np.mean(data)) + normalizer = TanhNorm() + payload.anomaly_score = normalizer.fit_transform(np.mean(data)) LOGGER.info("%s - The anomaly score is: %s", payload.uuid, payload.anomaly_score) diff --git a/examples/numalogic-simple-pipeline/src/udf/preprocess.py b/examples/numalogic-simple-pipeline/src/udf/preprocess.py index 9cbe9f9e..083b61de 100644 --- a/examples/numalogic-simple-pipeline/src/udf/preprocess.py +++ b/examples/numalogic-simple-pipeline/src/udf/preprocess.py @@ -11,7 +11,7 @@ LOGGER = logging.getLogger(__name__) -def preprocess(key: str, datum: Datum) -> Messages: +def preprocess(_: str, datum: Datum) -> Messages: r""" The preprocess function here transforms the input data for ML inference and sends the payload to inference vertex. @@ -32,6 +32,4 @@ def preprocess(key: str, datum: Datum) -> Messages: LOGGER.info("%s - Preprocess complete for data: %s", payload.uuid, payload.ts_data) # Convert Payload back to bytes - messages = Messages() - messages.append(Message.to_all(payload.to_json().encode("utf-8"))) - return messages + return Messages(Message.to_all(payload.to_json().encode("utf-8"))) diff --git a/examples/numalogic-simple-pipeline/src/udf/threshold.py b/examples/numalogic-simple-pipeline/src/udf/threshold.py new file mode 100644 index 00000000..ad85e886 --- /dev/null +++ b/examples/numalogic-simple-pipeline/src/udf/threshold.py @@ -0,0 +1,46 @@ +import logging + +import numpy as np +from pynumaflow.function import Messages, Message, Datum + +from src.utils import Payload, load_artifact + +LOGGER = logging.getLogger(__name__) + + +def threshold(_: str, datum: Datum) -> Messages: + r""" + This UDF applies thresholding to the reconstruction error returned by the autoencoder. + + For more information about the arguments, refer: + https://github.com/numaproj/numaflow-python/blob/main/pynumaflow/function/_dtypes.py + """ + + # Load data and convert bytes to Payload + payload = Payload.from_json(datum.value.decode("utf-8")) + messages = Messages() + + # Load the threshold model from registry + thresh_clf_artifact = load_artifact(skeys=["thresh_clf"], dkeys=["model"]) + recon_err = np.asarray(payload.ts_data).reshape(-1, 1) + + # Check if model exists for inference + if (not thresh_clf_artifact) or (not payload.is_artifact_valid): + # If model not found, send it to trainer for training + LOGGER.warning("%s - Model not found. Training the model.", payload.uuid) + + # Convert Payload back to bytes and conditional forward to train vertex + payload.is_artifact_valid = False + messages.append(Message.to_vtx(key="train", value=payload.to_json().encode("utf-8"))) + return messages + + LOGGER.debug("%s - Threshold Model found!", payload.uuid) + + thresh_clf = thresh_clf_artifact.artifact + payload.ts_data = thresh_clf.predict(recon_err).tolist() + + LOGGER.info("%s - Thresholding complete", payload.uuid) + + # Convert Payload back to bytes and conditional forward to postprocess vertex + messages.append(Message.to_vtx(key="postprocess", value=payload.to_json().encode("utf-8"))) + return messages diff --git a/examples/numalogic-simple-pipeline/src/udf/train.py b/examples/numalogic-simple-pipeline/src/udf/train.py index 66ca2954..2c018d03 100644 --- a/examples/numalogic-simple-pipeline/src/udf/train.py +++ b/examples/numalogic-simple-pipeline/src/udf/train.py @@ -3,16 +3,18 @@ import cachetools import pandas as pd -from numalogic.models.autoencoder import AutoencoderPipeline +from numalogic.models.autoencoder import AutoencoderTrainer from numalogic.models.autoencoder.variants import Conv1dAE -from numalogic.models.threshold._std import StdDevThreshold +from numalogic.models.threshold import StdDevThreshold from numalogic.preprocess.transformer import LogTransformer +from numalogic.tools.data import TimeseriesDataModule from pynumaflow.function import Datum, Messages, Message from src.utils import Payload, save_artifact, TRAIN_DATA_PATH LOGGER = logging.getLogger(__name__) WIN_SIZE = int(os.getenv("WIN_SIZE")) +BATCH_SIZE = int(os.getenv("BATCH_SIZE", 64)) ttl_cache = cachetools.TTLCache(maxsize=128, ttl=120 * 60) @@ -49,19 +51,24 @@ def train(key: str, datum: Datum): # Preprocess training data clf = LogTransformer() - train_data = clf.fit_transform(data) - - # Define Threshold method - thresh_clf = StdDevThreshold(std_factor=1.2) - thresh_clf.fit(train_data.to_numpy().reshape(-1, 1)) + train_data = clf.fit_transform(data.to_numpy()) # Train step - pl = AutoencoderPipeline(model=Conv1dAE(in_channels=1, enc_channels=12), seq_len=WIN_SIZE) - pl.fit(train_data.to_numpy()) + datamodule = TimeseriesDataModule(WIN_SIZE, train_data, batch_size=BATCH_SIZE) + model = Conv1dAE(seq_len=WIN_SIZE, in_channels=train_data.shape[1], enc_channels=12) + trainer = AutoencoderTrainer(max_epochs=50, enable_progress_bar=True) + trainer.fit(model, datamodule=datamodule) + + train_reconerr = trainer.predict(model, dataloaders=datamodule.train_dataloader()) + LOGGER.info("%s - Training complete", payload.uuid) + # Define Threshold method + thresh_clf = StdDevThreshold(std_factor=1.2) + thresh_clf.fit(train_reconerr.numpy()) + # Save to registry - save_artifact(pl.model, skeys=["ae"], dkeys=["model"]) + save_artifact(model, skeys=["ae"], dkeys=["model"]) save_artifact(thresh_clf, skeys=["thresh_clf"], dkeys=["model"]) LOGGER.info("%s - Model Saving complete", payload.uuid) diff --git a/examples/numalogic-simple-pipeline/src/udf_factory.py b/examples/numalogic-simple-pipeline/src/udf_factory.py index ab9b8540..3f7554ce 100644 --- a/examples/numalogic-simple-pipeline/src/udf_factory.py +++ b/examples/numalogic-simple-pipeline/src/udf_factory.py @@ -1,4 +1,4 @@ -from src.udf import inference, postprocess, preprocess, train +from src.udf import inference, postprocess, preprocess, train, threshold class HandlerFactory: @@ -16,4 +16,7 @@ def get_handler(cls, step: str): if step == "train": return train - raise NotImplementedError(f"Invalid step provided: {step}") + if step == "threshold": + return threshold + + raise ValueError(f"Invalid step provided: {step}") diff --git a/examples/numalogic-simple-pipeline/src/utils.py b/examples/numalogic-simple-pipeline/src/utils.py index b53de73d..999bde54 100644 --- a/examples/numalogic-simple-pipeline/src/utils.py +++ b/examples/numalogic-simple-pipeline/src/utils.py @@ -1,12 +1,10 @@ import logging import os from dataclasses import dataclass -from typing import Sequence, Union +from typing import Sequence from dataclasses_json import dataclass_json -from numalogic.models.autoencoder import AutoencoderPipeline -from numalogic.models.autoencoder.base import TorchAE -from numalogic.models.threshold._std import StdDevThreshold +from numalogic.models.autoencoder.base import BaseAE from numalogic.registry import MLflowRegistry from numalogic.tools.types import ArtifactDict from numpy.typing import ArrayLike @@ -19,28 +17,29 @@ @dataclass_json -@dataclass +@dataclass(slots=True) class Payload: ts_data: ArrayLike = None anomaly_score: float = 0.0 uuid: str = None + is_artifact_valid: bool = True def save_artifact( - pl: Union[AutoencoderPipeline, StdDevThreshold], + artifact, skeys: Sequence[str], dkeys: Sequence[str], ) -> None: - if isinstance(pl, TorchAE): + if isinstance(artifact, BaseAE): ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="pytorch") else: ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="sklearn") - ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=pl) + ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=artifact) -def load_artifact(skeys: Sequence[str], dkeys: Sequence[str], type: str = None) -> ArtifactDict: +def load_artifact(skeys: Sequence[str], dkeys: Sequence[str], type_: str = None) -> ArtifactDict: try: - if type == "pytorch": + if type_ == "pytorch": ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="pytorch") else: ml_registry = MLflowRegistry(tracking_uri=TRACKING_URI, artifact_type="sklearn") diff --git a/poetry.lock b/poetry.lock index 498cb393..7fd034bb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4,7 +4,7 @@ name = "aiohttp" version = "3.8.3" description = "Async http client/server framework (asyncio)" -category = "main" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -113,7 +113,7 @@ speedups = ["Brotli", "aiodns", "cchardet"] name = "aiosignal" version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" -category = "main" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -269,7 +269,7 @@ test = ["astroid", "pytest"] name = "async-timeout" version = "4.0.2" description = "Timeout context manager for asyncio programs" -category = "main" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -279,21 +279,22 @@ files = [ [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" -category = "main" +category = "dev" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" files = [ - 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The encoder compresses the input into a lower dimensional code, the decoder then reconstructs the input only using this code. +It mainly consists of 2 components: an encoder and a decoder. The encoder compresses the input into a lower dimensional code, the decoder then reconstructs the input only using this code. -### Autoencoder Pipelines +## Datamodules +Pytorch-lightning datamodules abstracts and separates the data functionality from the model and training itself. +Numalogic provides `TimeseriesDataModule` to help set up and load dataloaders. -Numalogic provides two types of pipelines for Autoencoders. These pipelines serve as a wrapper around the base network models, making it easier to train, predict and generate scores. Also, this module follows the sklearn API. +```python +import numpy as np +from numalogic.tools.data import TimeseriesDataModule + +train_data = np.random.randn(100, 3) +datamodule = TimeseriesDataModule(12, train_data, batch_size=128) +``` -#### AutoencoderPipeline +## Autoencoder Trainer -Here we are using `VanillAE`, a Vanilla Autoencoder model. +Numalogic provides a subclass of Pytorch-Lightning Trainer module specifically for Autoencoders. +This trainer provides a mechanism to train, validate and infer on data, with all the parameters supported by Lightning Trainer. + +Here we are using `VanillaAE`, a Vanilla Autoencoder model. ```python -from numalogic.models.autoencoder.variants import Conv1dAE -from numalogic.models.autoencoder import SparseAEPipeline +from numalogic.models.autoencoder.variants import VanillaAE +from numalogic.models.autoencoder import AutoencoderTrainer -model = AutoencoderPipeline( - model=VanillaAE(signal_len=12, n_features=3), seq_len=seq_len -) -model.fit(X_train) +model = VanillaAE(seq_len=12, n_features=3) +trainer = AutoencoderTrainer(max_epochs=50, enable_progress_bar=True) +trainer.fit(model, datamodule=datamodule) ``` -#### SparseAEPipeline +## Autoencoder Variants -A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. +Numalogic supports 2 variants of Autoencoders currently. +More details can be found [here](https://www.deeplearningbook.org/contents/autoencoders.html). -So, by adding a sparsity regularization, we will be able to stop the neural network from copying the input and reduce overfitting. +### 1. Undercomplete autoencoders -```python -from numalogic.models.autoencoder.variants import Conv1dAE -from numalogic.models.autoencoder import SparseAEPipeline +This is the simplest version of autoencoders where it is made sure that the +latent dimension is smaller than the encoding and decoding dimesions. -model = SparseAEPipeline( - model=VanillaAE(signal_len=12, n_features=3), seq_len=36, num_epochs=30 -) -model.fit(X_train) -``` +Examples would be `VanillaAE`, `Conv1dAE`, `LSTMAE` and `TransformerAE` + +### 2. Sparse autoencoders +A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. +Specifically the loss function is constructed so that activations are penalized within a layer. +So, by adding a sparsity regularization, we will be able to stop the neural network from copying the input and reduce overfitting. + +Examples would be `SparseVanillaAE`, `SparseConv1dAE`, `SparseLSTMAE` and `SparseTransformerAE` -### Autoencoder Variants +## Network architectures -Numalogic supports the following variants of Autoencoders +Numalogic currently supports the following architectures. -#### VanillaAE +#### Fully Connected Vanilla Autoencoder model comprising only fully connected layers. @@ -52,17 +65,17 @@ from numalogic.models.autoencoder.variants import VanillaAE model = VanillaAE(seq_len=12, n_features=2) ``` -#### Conv1dAE +#### 1d Convolutional Conv1dAE is a one dimensional Convolutional Autoencoder with multichannel support. ```python -from numalogic.models.autoencoder.variants import Conv1dAE +from numalogic.models.autoencoder.variants import SparseConv1dAE -model=Conv1dAE(in_channels=3, enc_channels=8) +model = SparseConv1dAE(beta=1e-2, seq_len=12, in_channels=3, enc_channels=8) ``` -#### LSTMAE +#### LSTM An LSTM (Long Short-Term Memory) Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. @@ -73,7 +86,7 @@ model = LSTMAE(seq_len=12, no_features=2, embedding_dim=15) ``` -#### TransformerAE +#### Transformer The transformer-based Autoencoder model was inspired from [Attention is all you need](https://arxiv.org/abs/1706.03762) paper. diff --git a/docs/post-processing.md b/docs/post-processing.md index dc1a36af..766007b4 100644 --- a/docs/post-processing.md +++ b/docs/post-processing.md @@ -3,7 +3,20 @@ Post-processing step is again an optional step, where we normalize the anomalies between 0-10. This is mostly to make the scores more understandable. ```python +import numpy as np from numalogic.postprocess import tanh_norm -test_anomaly_score_norm = tanh_norm(test_anomaly_score) +raw_anomaly_score = np.random.randn(10, 2) +test_anomaly_score_norm = tanh_norm(raw_anomaly_score) +``` + +A scikit-learn compatible API is also available. +```python +import numpy as np +from numalogic.postprocess import TanhNorm + +raw_score = np.random.randn(10, 2) + +norm = TanhNorm(scale_factor=10, smooth_factor=10) +norm_score = norm.fit_transform(raw_score) ``` \ No newline at end of file diff --git a/examples/numalogic-simple-pipeline/src/udf/inference.py b/examples/numalogic-simple-pipeline/src/udf/inference.py index 3b24fbea..cadbc3ee 100644 --- a/examples/numalogic-simple-pipeline/src/udf/inference.py +++ b/examples/numalogic-simple-pipeline/src/udf/inference.py @@ -16,8 +16,8 @@ def inference(_: str, datum: Datum) -> Messages: r""" Here inference is done on the data, given, the ML model is present - in the registry. If a model does not exist, it moves on Otherwise, conditional forward the inferred data - to postprocess vertex for generating anomaly score for the payload. + in the registry. If a model does not exist, the payload is flagged for training. + It then passes to the threshold vertex. For more information about the arguments, refer: https://github.com/numaproj/numaflow-python/blob/main/pynumaflow/function/_dtypes.py diff --git a/examples/quick-start.ipynb b/examples/quick-start.ipynb index a57762c0..11bd1394 100644 --- a/examples/quick-start.ipynb +++ b/examples/quick-start.ipynb @@ -18,29 +18,52 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, + "execution_count": 1, "outputs": [ { - "data": { - "text/plain": "
" - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 1.51 ms (started: 2023-01-05 12:05:21 -08:00)\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%load_ext lab_black\n", + "%load_ext autotime\n", + "%autoreload 2" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "data": { - "text/plain": "
" - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 550 ms (started: 2023-01-05 12:05:22 -08:00)\n" + ] } ], "source": [ "import pandas as pd\n", + "import random\n", "from matplotlib import pyplot as plt\n", - "plt.figure(figsize=(20, 10), dpi=200)" + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "%matplotlib inline\n", + "plt.rcParams[\"figure.figsize\"] = [9, 2]\n", + "plt.rcParams[\"figure.dpi\"] = 200\n", + "plt.rcParams[\"agg.path.chunksize\"] = 100000\n", + "pd.set_option(\"plotting.backend\", \"matplotlib\")\n", + "random.seed(42)" ] }, { @@ -61,9 +84,17 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 472 ms (started: 2023-01-05 12:05:24 -08:00)\n" + ] + } + ], "source": [ "from numalogic.synthetic import SyntheticTSGenerator\n", "\n", @@ -73,7 +104,7 @@ " freq=\"T\",\n", " primary_period=720,\n", " secondary_period=6000,\n", - " seasonal_ts_prob=0.8,\n", + " seasonal_ts_prob=1.0,\n", " baseline_range=(200.0, 350.0),\n", " slope_range=(-0.001, 0.01),\n", " amplitude_range=(10, 75),\n", @@ -87,24 +118,31 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": "" }, - "execution_count": 49, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
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\n" 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\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 280 ms (started: 2023-01-05 12:05:24 -08:00)\n" + ] } ], "source": [ @@ -120,33 +158,48 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 1.23 ms (started: 2023-01-05 12:05:25 -08:00)\n" + ] + } + ], "source": [ "train_df, test_df = ts_generator.train_test_split(ts_df, test_size=1000)" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 51, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 188 ms (started: 2023-01-05 12:05:26 -08:00)\n" + ] } ], "source": [ @@ -168,9 +221,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 10.5 ms (started: 2023-01-05 12:05:28 -08:00)\n" + ] + } + ], "source": [ "from numalogic.synthetic import AnomalyGenerator\n", "\n", @@ -183,26 +244,33 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 53, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 157 ms (started: 2023-01-05 12:05:28 -08:00)\n" + ] } ], "source": [ @@ -224,9 +292,17 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 2.39 ms (started: 2023-01-05 12:05:30 -08:00)\n" + ] + } + ], "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "\n", @@ -237,18 +313,77 @@ }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Training:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "## 4. Threshold Estimator:" + "In the training step, we define the model and train it on the training data set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "2023-01-05 12:05:34,435 - INFO - epoch 0, loss: 1.13\n", + "2023-01-05 12:05:40,241 - INFO - epoch 5, loss: 0.119\n", + "2023-01-05 12:05:46,225 - INFO - epoch 10, loss: 0.0565\n", + "2023-01-05 12:05:51,849 - INFO - epoch 15, loss: 0.0362\n", + "2023-01-05 12:05:57,434 - INFO - epoch 20, loss: 0.0276\n", + "2023-01-05 12:06:02,940 - INFO - epoch 25, loss: 0.023\n", + "`Trainer.fit` stopped: `max_epochs=30` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 34.9 s (started: 2023-01-05 12:05:32 -08:00)\n" + ] + } ], + "source": [ + "from torch.utils.data import DataLoader\n", + "from numalogic.models.autoencoder import AutoencoderTrainer\n", + "from numalogic.models.autoencoder.variants import SparseConv1dAE, Conv1dAE, VanillaAE\n", + "from numalogic.tools.data import StreamingDataset\n", + "\n", + "seq_len = 36\n", + "\n", + "model = SparseConv1dAE(seq_len=seq_len, in_channels=3, enc_channels=8)\n", + "trainer = AutoencoderTrainer(max_epochs=30, enable_progress_bar=True)\n", + "trainer.fit(model, train_dataloaders=DataLoader(StreamingDataset(X_train, seq_len=seq_len), batch_size=128))" + ] + }, + { + "cell_type": "markdown", + "source": [], "metadata": { "collapsed": false - }, - "execution_count": 1 + } }, { "cell_type": "markdown", "source": [ - "In here, we define the threshold estimator and fit it using the training data set." + "## 5. Threshold calculation:\n", + "\n", + "After training the main model, we need to perform a threshold calculation. The autoencoder tries to encode the representation of the normal input data,\n", + "and tries to reconstuct the output. The difference between the actual input and the reconstructed output is what we call as the reconstruction error.\n", + "\n", + "Some amount of recconstruction error is normal, and we need to know what amount is normal, and what can be called as an outlier." ], "metadata": { "collapsed": false @@ -256,23 +391,28 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "outputs": [ { "data": { - "text/plain": "StdDevThreshold(std_factor=3)", - "text/html": "
StdDevThreshold(std_factor=3)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "text/plain": "torch.Size([7000, 3])" }, - "execution_count": 55, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 2.45 s (started: 2023-01-05 12:08:03 -08:00)\n" + ] } ], "source": [ - "from numalogic.models.threshold._std import StdDevThreshold\n", - "\n", - "thresh_clf = StdDevThreshold(std_factor=3)\n", - "thresh_clf.fit(X_train.reshape(-1, 1))" + "# Calculate training set reconstruction error\n", + "train_reconerr = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_train, seq_len=seq_len)))\n", + "train_reconerr.shape" ], "metadata": { "collapsed": false @@ -280,47 +420,44 @@ }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Training:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "In the training step, we define the model and train it on the training data set. `SparseAEPipeline` is a sparse autoencoder trainer that follows sklearn's API pattern. " - ] + "Now, we say that anything more than 3 times the standard deviation from the mean is anomalous." + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, + "execution_count": 12, "outputs": [ { - "name": "stderr", + "data": { + "text/plain": "StdDevThreshold()", + "text/html": "
StdDevThreshold()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "2022-12-09 18:23:23,247 - INFO - Training sparse autoencoder model with beta: 0.001, and rho: 0.05\n", - "2022-12-09 18:23:23,248 - INFO - Using kl_div regularized loss\n", - "2022-12-09 18:23:24,959 - INFO - epoch : 5, penalty: 0.0014599192654713988 loss_mean : 0.0435277\n", - "2022-12-09 18:23:26,194 - INFO - epoch : 10, penalty: 0.0014999237610027194 loss_mean : 0.0194861\n", - "2022-12-09 18:23:27,491 - INFO - epoch : 15, penalty: 0.0013811790850013494 loss_mean : 0.0139388\n", - "2022-12-09 18:23:28,596 - INFO - epoch : 20, penalty: 0.001234247232787311 loss_mean : 0.0116860\n", - "2022-12-09 18:23:30,303 - INFO - epoch : 25, penalty: 0.0010954368626698852 loss_mean : 0.0103516\n", - "2022-12-09 18:23:31,807 - INFO - epoch : 30, penalty: 0.0009711519232951105 loss_mean : 0.0094082\n" + "time: 3.59 ms (started: 2023-01-05 12:08:07 -08:00)\n" ] } ], "source": [ - "from numalogic.models.autoencoder import SparseAEPipeline\n", - "from numalogic.models.autoencoder.variants import Conv1dAE\n", + "# Calculate the threshold\n", + "from numalogic.models.threshold import StdDevThreshold\n", "\n", - "pipeline = SparseAEPipeline(\n", - " model=Conv1dAE(in_channels=3, enc_channels=8), seq_len=36, num_epochs=30\n", - ")\n", - "pipeline.fit(X_train)" - ] + "threshold_clf = StdDevThreshold()\n", + "threshold_clf.fit(train_reconerr.numpy())" + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "markdown", @@ -335,46 +472,107 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "text/plain": "array([[0.03953706, 0.02283082, 0.14682023],\n [0.11847361, 0.2631961 , 0.04124341],\n [0.01379496, 0.0057159 , 0.09038273],\n ...,\n [0.11696532, 0.20304612, 0.05622444],\n [0.19488005, 0.0442475 , 0.1566194 ],\n [0.21542044, 0.0984635 , 0.17402739]])" + "text/plain": "torch.Size([1000, 3])" }, - "execution_count": 57, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 327 ms (started: 2023-01-05 12:08:14 -08:00)\n" + ] } ], "source": [ - "test_recon = pipeline.predict(X_test)\n", - "test_anomaly_score = pipeline.score(X_test)\n", - "test_anomaly_score = thresh_clf.predict(test_anomaly_score)\n", - "test_anomaly_score" + "# Get the reconstruction error on the test set\n", + "test_recon_err = trainer.predict(model, dataloaders=DataLoader(StreamingDataset(X_test, seq_len=seq_len)))\n", + "test_recon_err.shape" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 14, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1000, 3])\n", + "time: 1.51 ms (started: 2023-01-05 12:08:16 -08:00)\n" + ] + } + ], + "source": [ + "# Get the anomaly score using the threshold estimator.\n", + "test_anomaly_score = threshold_clf.predict(test_recon_err)\n", + "print(test_recon_err.shape)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 114 ms (started: 2023-01-05 12:08:17 -08:00)\n" + ] + } + ], "source": [ - "anomalies_df = pd.DataFrame(data=test_anomaly_score, columns=outliers_test_df.columns, index=outliers_test_df.index)" + "# Convert the score array into a dataframe\n", + "anomalies_df = pd.DataFrame(data=test_anomaly_score, columns=outliers_test_df.columns, index=outliers_test_df.index)\n", + "anomalies_df.plot()" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 134 ms (started: 2023-01-05 12:08:18 -08:00)\n" + ] } ], "source": [ @@ -383,6 +581,7 @@ "ax2 = ax1.twinx()\n", "l2, = ax2.plot(anomalies_df[\"s1\"], color=\"orange\")\n", "plt.legend([l1, l2], [\"test data\", \"anomaly score\"])\n", + "plt.title(\"S1 data vs anomaly score\")\n", "plt.show()" ] }, @@ -392,41 +591,82 @@ "source": [ "## 7. Post-processing:\n", "\n", - "Post-processing step is again an optional step, where we normalize the anomalies between 0-10.\n" + "Post-processing step is an optional step, where we normalize the anomalies between 0-10. This can make the scores more human interpretable.\n" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 1.79 ms (started: 2023-01-05 12:08:21 -08:00)\n" + ] + } + ], "source": [ - "from numalogic.postprocess import tanh_norm\n", + "from numalogic.postprocess import TanhNorm\n", "\n", - "test_anomaly_score_norm = tanh_norm(test_anomaly_score)" + "postproc_clf = TanhNorm()\n", + "test_anomaly_score_norm = postproc_clf.fit_transform(test_anomaly_score)" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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Von2n/zYJIYQQQogroYn2APCLX/wCffr0AQDccMMNOPfcc/HMM89g3rx5mDFjBu666y4cc8wxWLx4MQDg9NNPx5lnnhlmlwghhBBCCCFk96Gj2f+6qkz7FJ32hBBCCCFhEmqA/GGHHYbnn38eEydOxLZt2zB58mRMnjxZXPa0007DU089FWZ3CCGEEEIIIaT3Equ2R9d07gIw0l97aUWmPeNxCCGEEEJCJfSqr5/97GexdOlS3H///Xj55ZexaNEiNDY2Ih6PY4899sAJJ5yAr33tazj33HMRiUTC7g4hhBBCCCEVy6zVO/Dqok0YPWoAzj16T/77meRjCAViO12c9ukUsPjf5rqfuACI5fxEZDwOIYQQQkhJCF20B4AhQ4bgmmuuwTXXXFOMzRFCCCGEENLrWLqpGV+9dwYyXXVFM4aBLx47qrSdIuVDJi2L7B1Nzus9931g4TPm5yX/AS56NKdNhWjPeBxCCCGEkFAJNdOeEEIIIYQQEgy/fmFJt2APAD958sPSdYaUHymF+71zl3qdjqYewR4AlkwGmjb0fFdl2jMehxBCCCEkVCjaE0IIIYQQUgHMWr2j1F0g5YwqssYpHkcS9Hd+3PM5w0x7QgghhJBSQNGeEEIIIYQQQiodVWRNh4NoLznpk20581XxOMy0J4QQQggJE4r2hBBCCCGEEFLppDrk6U5Oe8kxn2jt+awsREunPSGEEEJImFC0J4QQQgghhJBKRyWkO2XaS0J/7vJpivaEEEIIIaWAoj0hhBBCCCGEVDqqyBqneBwpUqd9Z89nxuMQQgghhJQEivaEEEIIIYQQUumohPTOJvU6UvHa9pyCx8WKx0m0OQ8uEEIIIYTsZlC0J4QQQgghhJBKwzCAhg+A7SvN75IAD7jE40iivYbTPkjR/qOXgTsOBW7dG3jj18G1SwghhBBSwVC0J4QQQgghhJBK4+lJwL2nAn/6JDDnIX/xOJL4Xux4nJev7SmW+/bvgF2bgmubEEIIIaRCoWhPCCGEEEJIJRApdQdI2bBtObDoOfOzkQEmX+4Qj+OUaS8Uom3TicdJ6vVTh8Y1+d+XTA6ubUIIIYTYaGpqwj//+U9MmjQJRx99NAYMGICqqioMGzYM48ePxx133IHGxsZSd3O3J17qDhBCCCGEEEII8cDqqfZpqXZ5Wc+FaBt7PitF+4Cc9pm0fVqUP1EJIYSQsHj55ZfxxS9+EZ2d9r/l27Ztw1tvvYW33noLt99+Ox577DGMHz++BL0kAJ32hBBCCCGEEFJZ1A60T2vZKi8ruemziIVoixiPkxQGGijaE0IIIaGxfft2dHZ2IhqN4qyzzsJdd92FN954A3PnzsV//vMfXHTRRQCATZs2YcKECfjggw9K2+HdGP6LiBBCCCGEEEIqCUm037VRXtZJYHcrRJsOOR5HGlCgaE8IIYSERlVVFS699FL87Gc/wz777JM379hjj8U555yDk046CT/+8Y/R1taGK6+8Em+88UaJert7Q6c9IYQQQgghhFQS8Rr7tJbN8rLpTsAw8qc1rgMe/TLw4pX25ZOtPWJ+UPE4yXZg6m+Bl68Ddn6cP91KrMpb24QQQgjR5qKLLsI999xjE+xzueyyy/DJT34SADB16lRs27atWN0jOVC0J4QQQgghhJBKwsjYp+3apF7eKr7/9xfAiv+ql+9y2xsZhaNeysJ34oUrgTd/A7z3V+C+z/Y49em0J4QQQgqioaEB1113HY477rjugrIjRozAUUcdhYkTJ+Khhx5Cc7NDfRsFn/nMZwAAmUwGq1evDrjXRAf+i4gQQgghhBBCKgmvon2qM9/BvuhZ5/bbdwL99oCRTiMizU97FO0/fKznc+tWYNkrwOHnyE77iLhFQgghhFiYNm0aJkyYYBPlt2zZgi1btmDhwoV44oknMHToUEyYMMFT27mFamOxWCD9Jd6gaE8IIYQQQgghFYVhn6TKtAd6RPbmjcDWpe7Nt+0wt6IS573E42QcBhikTH1VJA8hhJDeQSYDtO8odS+KS91gIBps2ElnZye++tWvorm5Gf369cMPfvADjB8/HsOHD0cikcDq1avx7rvv4rnnnvPV/tSpUwGYGfgHHXRQkF0nmlC0J4QQQgghhJBKQnLaOwkgqU5g44fAQxOATo1X5LPFaDNpRXsenPbJVvu06r5d7QhOe9U2CSGkUuhsAabdDrQ3AideBgw5sNQ9Ki/adwC/282OydUrgb5DA21y+vTpaGhoAAA89thjNif92LFjMXHiRNx1111oa2vz1PaLL76I+fPnAwDOOuss9O/fP5hOE09QtCeEEEIIIYSQSsJaWNaNdAJ4/QY9wR7IybRXFaL1INp37rJPy4r2SSHTnk57Qkil85//ByzqcjcvfRG4cgkQo/xGgmXTpp5YvHHjximXi8fjnkT3HTt24Ec/+hEAMxbnxhtv9N9JUhAsREsIIYQQQgghlYQf0X7lG/rLt2fjcRSFaAsV7ascnPaqbRJCSKWwKCeOpHUL8NGLxdv2qqnAP84B/vUtoLmheNslRWfkyJHdnx988MFA2kyn0/j617+ONWvWAACuv/56HHvssYG0TbxD0Z4QQgghhJAKgOU5STdSPI4TUna8EzvNH+tK17uX9iTRPnsx02lPCNkdKJZ43rkLeOJrwOq3gcX/BiZfUZztkpJw8skn44ADDgAAXHHFFRgzZgxuueUWTJ8+HYmEx4LxXfzwhz/EK6+8AgCYMGECfv7znwfWX+Idvp9DCCGEEEIIIZWEV9HeizMeALYt61rPYzzOyjeB1q3AYROA6j7mNEm0z74pwEx7QkhvQ3oTyuvbUX754DEg0dLzffmrwL2fAUYcCZx1M1BbJrnkdYPNjPfdibrBgTdZVVWFyZMn48tf/jKWLFmC2bNnY/bs2ebm6uowbtw4XHzxxbjooosQi8Vc2/vpT3+Ke++9FwBwyimn4F//+pfWeiQ8KNoTQgghhBBCSCXhVbTXzbLP0iXae8q0f/t24I2bzM97jAYufRuIRBSifVf/6bQnhPQ2xIivIon2Oz+2T2uYZ/7Xf09g/M+K0w83otHAi7LurhxxxBFYsGABJk+ejMmTJ+Ptt9/GihUr0N7ejldffRWvvvoq7rzzTrz00ksYPny4sp3bbrsNt956KwDguOOOwwsvvIC6urpi7QZRwHgcQgghhBBCCKkoPApATeu9Ld+yGWhvBDKKfHkpHicr2APApvnAyinmZyfRXnTaU7QnhFQwqudmMegqIi4y9bbi9YMUlVgshvPPPx/3338/li9fjoaGBjzwwAM4/vjjAQBz5szBpZdeqlz/L3/5C6677joAwOGHH45XX33VU+FaEh4U7QkhhBBCCCGkkvDqtG9c630b25Y7xON0iVLJDtktDwDr3zf/79lpz0K0hJAKRnLaFysep72xONshZc3IkSMxadIkzJgxA8cddxwA4IUXXkB7u32g/JFHHsH/+3//DwBwwAEH4PXXX8fQoXwLolygaE8IIYQQQgghlYRn0X6d921s+wiGKl8+3Qm8/wBwyyjgtn2BDx4XFuqqNuvZac9Me0JIBSO+LVQs0d7BaU92O6qqqnDqqacCAFKpFBobG/PmP/vss5g0aRIMw8CoUaMwZcoU7LnnniXoKVFB0Z4QQgghhBBCKgmvon2TH9F+mTqqpnMX8Or1pis+1QG8cp19mUhWtBfy9JlpTwjprUg1P4rltO9oLM52SFkwbdo0rFixQjk/kUhg6tSpAID6+noMGzase95rr72GiRMnIp1OY/jw4Xj99dex3377hd1l4hEWoiWEEEIIIYSQSsKrAOQnHmfrMqBW8Yr8ro3530WhqEu0T7TYZ2Xd9My0J4T0NkpZiJbxOLsVU6ZMwU033YRTTjkFZ599NkaPHo1hw4ahvb0dy5Ytwz333IO5c+cCAL7zne8gHjcl4JkzZ+KLX/wiEokEqqqqcNdddyGZTGLhwoXKbY0aNQoDBw4sxm6RHCjaE0IIIYQQUgFkjcuEeBbt/Tjtty4F9hrrfb0s2evVa6a9KHgRQkiFIA08enlmb18JJNuAEUd6/8PPeJzdjkwmg6lTp3Y76iXOO+883HLLLd3fX3nlFbS1tQEAkskkvv71r7tu58EHH8Qll1xScH+JNyjaE0IIIYQQUgEU6+16UgF4jcfxujwA7FyNyKCDvK/XjVOmfdfFzEx7QkhvoxCn/cx7uuLGDOC4i4Fz7/a47U5vy5OK5qqrrsLo0aPx+uuvY968eWhoaMCWLVsAAHvssQfGjBmDiy++GGeffXaJe0r8QtGeEEIIIYQQQiqK4ozgxDfO8b9yRKMQLTPtCSG9DSnTXue5ZhjAK9f2fJ/7MDDuGmDg3nrbTbTpLUd6DfX19bjgggtwwQUXeFrvhhtuwA033BBOp0igsBAtIYQQQgghFQDjcUg3fpzzPoi27yi8EadCtCmK9oSQCmTrR8B/fwHMvs/urM8ITnud2K+U4JJfP0u/T23b9JclhFQEdNoTQgghhBBSATAeh3QTlmi/z6eBtTMCakzHaS/F4zDTnuyGdLYAsSogXhN82y1bgfWzgRGfAAbtG3z7uxvtjcDfT+spst2yBRj/s575aWHgUXLfW5EGOGPV+v1q2aq/LCGkIqDTnhBCCCGEEEIqiTBF+6Dojsdpsc9zdNoz057sZvz3F8AtewG3HwKsfCPYtpvWA389EXhiIvCXTwPrC4i8IibLX+sR7AFg6m358/067aUBzmiVfr9aKdoT0tugaE8IIYQQQkgFwHgc0k1Yr13se2KAjTk57buEedFpz3gcshuxYzUw/Q/m545GU8APkul/BFrNwpRItgKv/sx5eeLOliX2aa3bez5Lrnq/Tnsv9Uso2hPS66BoTwghhBBCCLGRzhj4y1srcNHfZuDO/y5DMl2cHHWiQVhO+2GHArUDgmsv1QmkhZxmZtoTYvLBY/nfNy0Itv1Zf8v/vm5msO3vjkgRQ5s+7PnsOx5HGOCUcu5V6Ij2zNkjpKJgpj0hhBBCCCHExn8Xb8JvX/kIAPDe6h3YZ3AffPn4USXuFQEQnmgfrwPqBgEdTYW3lUnJ0TiAc6a9TowEIb2FIhWVJgEiRXhtnA8ceFrX/ADjcTyJ9hqFaDMps3YCIaQioNOeEEIIIYQQYuPHT3yQ9/2qpz6UFyTFJzTRvhqIBuTryqQVcQ/ocXtKghQz7cnuBEX7ykMS4Dd+6DxfR3wXRXthYFOFjtOeg6KEVBQU7QkhhBBCCCE2EimKSbsdsRpvhQ+dyCRlEQowhUrDkAUpxuOQ3YnQRXsWQwkcKepm0/yc+ZLTvkzicXT6QQgpGyjaE0IIIYQQ0otIpjP4w+vL8f2H38cL8xtK3R0SBmEJfbEgnfYpZ9E+nZT3g6I9qWSSHcCUm4CnJgEfT3dfPmzRPhoLt/3dEUn43r6y53nnOx5HeDNJqvuhQiceJ0infbIDeOPXwKNfAub9M7h2CSHdFDXTfu3atbj//vvx4osvYs2aNdi1axeGDRuG/fbbD+PHj8eFF16II488sphdIoQQQgghpFfx0PSPcdfrywAAry3ejH0H98VRowIsLkpKT1hCXzQKxAL6iZhOAgmHTHtV7ANFe1LJvHETMONP5ufFzwNXLgH6jVAvH7ZoHykTn+bGD4EX/xdItAKn/wI49POl7pF/ROHbALYtA/Y63r/TvkMS7QN22ksDCn6Z9Tfg7d+Zn1e8Dgw9GNh7THDtE0KK57S/++67ccQRR+DGG2/EnDlzsG3bNnR2dmL9+vV45513cNNNN+G+++4rVncIIYQQQgjplfzmpSV53296YXGJekJCI5sJHwZBZtqrCtpm0qZLU5xH0Z4EhGGYBUK3flS8bWYFewAw0sA7dzovH7poXyZO++d/BKyfDWxZDDx7qSxQVwoqAT7Rav5fdNr7jMeRinVLZDJAm47TPsB4nP/+Iv/7nIeCa5sQAqBITvtf//rX+PnPfw4AOOSQQ/C9730PJ5xwAgYMGIDt27dj3rx5eO655xCNlskoMCGEEEIIIWVGxGc28aptCrezD/7zYQMenbkGBwzti5+dfTj61waUf068EabQF2SmfXujPI9Oe1IMXroamP138/PpvwROubL4fdj5sfN8MSIqY771EgTl4LRPJ4FNC3q+dzaZbyEc983S9akQVMJ31hUvOu114nEKyLTvaNR7dgYVjyMNHG9bHkzbhJBuQhftp0yZ0i3YX3zxxbjvvvtQVZX/D8HTTz8dV111FRIJFsUghBBCCCFEwkCI7mpNfvz4PADArNU7UFcdwy/P+USJe7SbEqZoH1Q8TiYFtO+U5xkZOu1JuOza3CPYA8CUXwEn/ji461sbl8FW6V420ggsFKEcMu0lMXrzouL3Iyh8ifZ+C9FqZtrr5NkDwYn2uzbapw05KJi2CSHdhDrsmslk8IMf/AAAcPTRR+P++++3Cfa5VFdXh9kdQgghhBBCSEA8OP3jUndh9yVUp31QmfYuoj2d9iRMNi+0T0u2Fr8fbohO+3Rw7Uf8vaEVKAnhuO9YVfx+BIVStO8S2H0Xoi3Aaa+TZw8EF4+zZYl9Wv3wYNomhHQTqmj/2muvYfly8xWZa6+9FvF4sUe1CSGEEEII6R34jcchvZEwM+2DisdxEu0NtdM+TdGeBIA0+FSO15bSaR8QUqZ9UG5rXaSC1BUt2iuOX1YQl64zLae9VIhWM9NeV7QPalB069Lw2iaEdBOqaP/UU08BACKRCCZMmNA9fceOHVi+fDl27NgR5uYJIYQQQgghFPt7H5XgtM8k6bQnpSMmDD6lNV3LQeLmdA/daS9IPrqRK0EhOu1X6hdZLTfcnPbSfJ1rr9Kd9nx2ExI4oVrfZ86cCQDYb7/90K9fPzz22GO45ZZbsHBhz6tq2cK0l112GWpqajy1v379esf5GzcKOVuEEEIIIYQQUskw054QZ6Q3RootVusQttNeyrRPdgA1/YLbhhuSGG1kgC2Lgb2OL14/gkIp2ndN9x2PIzntg860D0i0l5z2xX6Dg5DdgNBE+0wmg6VLzRt56NChuPzyy/HHP/7RttyyZctw9dVX47nnnsOLL76IgQMHam9j7733Dqq7hBBCCCGkCKTSGby4wDRWnH3USMRjob742esxDAORcsgsJsXFCDMepxiZ9mk67Um4RCWHeUCCZZCITvsAB+XK1WkPAJsWVKZor7qOup32fuNxiuG0D+D5ahjA1o/s06XBCkJIQYT2K6mpqQmZrj82CxYswB//+EeMHDkSjz76KHbs2IG2tjZMnToVY8eOBQC8++67+Pa3vx1WdwghhBBCSBlwxZMf4PInev4jhZEJUbslZUyoon0xMu0zavGIoj0JAukWKYnT3i0eR+ho2Jn2RRfthUx7ANi2vLj9CAqVAN+daS/F47iI9qlOeRndCKFixuO0bpPfCijHmhGEVDihOe1bW3tGUzs6OtCnTx+8+eabOPTQQ7unjxs3Dm+88QY+/elP48MPP8Rzzz2H9957D5/61Ke0trFu3TrH+Rs3bsSYMWP87QAhhBBCygIjTHGKFJWdrQm8ML8nvvDFBRvxq5ZODK33FpFIejDvDzrtdzvc4nFi1d7FmYM+27VuUE77hLNorxLnKdqTIJCEb13XcjEJO9Near9cRPugz0fbDiBeA1T3DbZdK26Z9n7icSSXPeDBaa8ZjxOEG14S7AE+uwkJgdBE+9ra2rzv3/3ud/ME+yx1dXX4zW9+012o9sknn9QW7UeNGlV4RwkhhBBS1lCz7z1sb7X/+NxG0b4g9Jz2vIl6HW6ifXU90L7DPr2qL5AUoipqBwLjf2Z+Dioep6NR7RimaE/CRhK+S1GI1o2wM+0lkVZVTyIsOhWifSH3ejppRrT03xPoMxh44Urg/fuBmv7AaT8HxnzPvQhwIduWyArsfuJxVEK4dqa9rtM+ANFeFXfEeJyK4v3338fUqVMxe/ZsLF68GFu3bsWOHTtQXV2NPffcE2PHjsUll1yC8ePHl7qruzWhifb9+uUXNjnzzDOVy55++umIx+NIpVKYPXt2WF0ihBBCSAVCubH3wAGY4MnwoO6muJx3lWhfPwzYaRFcJr0CDDnInAcEF4/T4iAiUbQnYVMuTns34Thsp70kFpdLpr3fwYlEK/DA54BN84G6wcDnbzMFe8AUv1++Gtj2EfCF28MR7pVO+67rSxKvMymzVoFUawEIwGlfxHgc1flkIdqK4oorrsD06dNt0xOJBJYtW4Zly5bh4Ycfxle+8hU8/PDDNmM2KQ6hifY1NTUYNmwYtm41Hx5ORWNra2sxdOhQbNq0qXt5QgghhBBALUp+tGkXnp23HgcNq8eXjx/FYpwVSoTRLgVRzpp9Kp3BWx9tRb/aOD51wJBSd6d34ea0r6mXp/cdDuz8OH/aPmPzha2gnPZOIpJhqIVJ5iKTIJCur7BFez8PZKmfQTrtJSG1XER7v4MTHzxmCvaAOTj5/I/sy8y+Dzj2m8Cex/jbhhMqcTr7JodKGM8kgajizUKlaK+RaZ9Omm826RCEsC69rQVwwLXCqKmpwamnnooTTzwRhx9+OEaOHInBgwdj69at+PDDD3HPPfdg9erVeOqppxCNRvHEE0+Uusu7JaGJ9gDwiU98Am+99RYAIJ12fiBn58fjoXaJEEIIIRWGSrQ/50/vIJEyhaum9iS+e8oBxewWIWWBUcbvokx6aDamLTdzdq/47MG44rOHlLhHvQideByJ+uH2adYBz8Ay7R0EUiOjjlKg8EOCQHTahyxWu92XuusEORpbFk57hSDt915/49f531Ui+fYVIYn2imdbdzyOStRPmJn7Eh2qeByNgSZV7RBVHwqFTvtewauvvqrUX8866yxcdtllOO200zBz5kw8+eST+NnPfobRo0cXuZdE8W5OMIwbN67786pVq5TLNTc3Y9s28x/0e+21V5hdIoQQQkiFofrtmhXsAeDXLy4pUm8IKR3SyyR6mfbFZ+GGpm7BHgB+//ryEvamF+LXaT/kQPe2g3LaO5FJMx6HhEtGKsBagNN+6zKgYZ6zoO7HOR5mPI5hyPdTsTPtg3baJzXc54BZmDYMXONxFM+wlINgrnTaa5wrT6J9EJn2bfJ0ZtpXFG6G6bq6Olx++eXd36dNmxZ2l4hAqKL9l770pe7Pzz33nHK55557DkbXH79TTjklzC4RQgghpMIo5/gPQkpNuWbaf7i+sdRd6N24nffqvvL0E3+cL8offJZ9maAy7Z1wyrQ30nzwk8KRnPZ+C9HO+Avw5zHAvZ+Ro1iySNe0n0z7oOJxlAVTK7wQre55lOp6OLFjNTDnH8DGD12271aI1uG4t2xRzFMMRKQT8gBULl5E+yCE9YTifDLarGQ0NDTguuuuw3HHHYcBAwagqqoKI0aMwFFHHYWJEyfioYceQnOz4m0OB3JrlXZ0FPm5QQCELNqPHj0an//85wEAjz/+OKZMmWJbZtOmTbj++usBANXV1Zg0aVKYXSKEEEJIhVHO8R+ElBo9bbP4dQOouYaMazxOP3l636HAF/8GDDsM2H8c8Llb7MvEiiXaOwiTQRbiJLsnQWbav/pTdBd//uCfwM41im36EC0lgTeo61/pCC8Tp32Q2f0Sbdv1l92xCrjnFGDyj4F7xwOr3lIvqzqubpn2dx0B3H4w8PjX7OfY6Zy7na9yiceh074kTJs2DYcffjhuu+02zJs3D83NzUilUtiyZQsWLlyIJ554ApMmTcLbb7/tue3cHPvDDjssyG4TTUJ/9/H3v/89ZsyYgcbGRkyYMAFXXHEFvvCFL6Curg6zZs3CLbfcgvXr1wMAbrrpJsbjEEIIISSPco3/IKQcMMpUHS/PXvUi3M57VZ19WtZhf9SXzf9URGP++6WLk9MeMMWfSATYtADoNxLoNyL8PpHehZhp70G037XJfOukRhgAWz8bGLSv3jbdkITOwJz2CoFWN14mKJSZ9mGL9h6c9u/e3dNPIw28eBVw2fvysn7jcbJ89CKweipw4Gk905wGYlMdQHUf9Xwv+xmEGz6piMfxEL2TMTJo7GwsvC8VxMCagYhGgvVNd3Z24qtf/Sqam5vRr18//OAHP8D48eMxfPhwJBIJrF69Gu+++65j8kkumUwGW7duxaJFi3D33Xfj3//+NwBTsD/rLOHNPBI6oYv2hxxyCCZPnowvf/nL2Lx5M2699VbceuutectEIhH83//9H6655pqwu0MIIYSQCqNc4z9IMPBNisIo20Et4b41DAMRt6gIooeb014qdljTX6/tUsfjAKao+PD5wLqZQFVf4KuP5gtchLhRiNP+9V8B79wJxGqAM35ln6+q+yCK0C7PvFCd9i4xLlnadgAz/2Lu79gfqGti+EXpzA45TsVLPM6cf+R/3+5Qh8VvPE4uDR/kP9OcjoXbdVs2Tnv989nY2YhTnzy18L5UEFMvmorBtYMDbXP69OloaGgAADz22GOYMGFC3vyxY8di4sSJuOuuu9DWphhsAbDffvthzRr5DaIDDjgAzz77rGsGPgmHohz1k08+OW+kZvXq1UgkEhg5ciQ+85nP4LLLLsOxxx5bjK4QQgghpMKgZk+Imkoa1MoYQIyafUC4nHcp4qZWV7Qvwk9EN9F+8fOmYA8AyVZgyk0U7Yk3RKe9RixMy1ZTsAfMuJNXrrMvoxTtfYjQ0jpug3K6KB3hOU57wwAePtd8qwUA1s8Cvv6U0FbSHEyr6eee029FmWlfRk77eK35rHFj60cOTvOs015DtLe+0eQYj+PyZkQFivYkGDZt2tT9edy4ccrl4vE4+vfX/DdAzjo33HADfvzjH+dl25PiUrShkiFDhuCGG27ADTfcUKxNEkIIIaQXUK7xH8Q70pnk6S2Mcj1+UrcyhoFYCfL1eyVuol5McNrXDtBruyiZ9oazSDXzr/nfG+aG2x/S+5CKd+oIlqunui+jiriomEz7HOf25oU9gj0ALH8NSLTlx7FsWQI8/lVg58fA4ecAX37Q23NCJfJu/QhY+Sawz6eBqlq9trz80fPitI/XuIv2r11vxuio8OK0t14rTpFIQTrtgxDWVefTQzwOCYaRI0d2f37wwQdx+eWX+2rntddeQyKRQCaTwfbt2zF9+nT89a9/xY033oiPPvoIf/nLX1BfH/AbOESLUAvREkIIIYQUStnGfxDPSK7wchWdK4VyHdSSulVJbwWUPa6ifbV9mq5oX5RM+7R7PA4hheDXaS/dO1ZUTmpJbHdzpYeaaa/oZ+791bRBmG9xkk+7wxTsAWDJZGCVxsBGFsMAEgqnffN64JHzgftOB1KaDnCVYCzR5kHMjrsMGrTtcBbsAW+ivXV/HeNx3ArResm0ZyHa3sTJJ5+MAw44AABwxRVXYMyYMbjlllswffp0JBL65/qQQw7BkUceidGjR2P8+PG4/vrrsXDhQhx99NF45JFHcNJJJ6GlRXEfk1BhKBEhhBBCyppyFSWJd9LCCAyFXH0k6adcB7Wk+5anOkDcDqbkgu07TK/tssi0V2fvEqKFmGkfkGivcj6L17Rbpr2wTjGd9tIyiVag79Ce7wsscTmv3wAc/Fm9PiTb4BrntXmhuY1jv+7eXkej3nYBs7BsKgHENc6pVAckkwGiXT7XzYvc20h5iMexHnfpzRBruyqsTvvaAUBHk952/aB6I8FDkduBNQMx9SIPgz+9gIE1AwNvs6qqqruG6JIlSzB79mzMnj0bAFBXV4dx48bh4osvxkUXXYRYzNuA/KBBg/CPf/wDRxxxBObPn4+bb74ZN998c+D7QJyhaE8IIYSQsqZcRUniHUlnpJCrjxgvVKaFfFXxOCQgdJz2h00Alr7QM+2Uq/TaLko8jkYhWkIKQXTaawiWMQ2JRCX++xHbS+G0z81IlwRhNze7zjHSbSvLgn/pifZeomCyy/cb4b6c5LRPtPTUAlFFIuWSzbTXEa+t4rnTOXd7HlqPSd/hDqJ9iPE4Hpz20Ug08KKsuytHHHEEFixYgMmTJ2Py5Ml4++23sWLFCrS3t+PVV1/Fq6++ijvvvBMvvfQShg8f7qntww8/HAcffDCWL1+Op59+mqJ9CWA8DiGEEELKGjrtew+SaEshtzDKdVBL6le59rUicRXtq4Dx/wcMOwyo6gOMuwYYcYRe2+VQiNat8CIhbvh12uvU3VA5n1XCayoB7Nos96nUmfaSc93tTRedtxGydO7SW05XTG5v1N82ALRt11tOcuPnHRuNP2Dd8Tgag0PWa8jxeShcb+lUj+vBKtrXOwizfpz262YD790LbF9pfk+oCvEyHqdUxGIxnH/++bj//vuxfPlyNDQ04IEHHsDxxx8PAJgzZw4uvfRSX20PG2a+pbdmzZrA+kv0odOeEEIIIWUNdb7egyzk8gzrIsbjlKkSLg228VwHiI7TfsQRwI/e89520UR7B2FS2j/DcM8HJySLJKDrCJY6hTqVTnth3ca1wN9OAbYuBfY6HvjGM0DdoJw+SU57l/tbF9X+5jq3JdFelUGfxUuElq7TXldM9uy018x7l5577Y3AwH3Mz0mNAZ+C4nGcCtHmbLt1O/CvbwJrpgMHngZc9Kg9u98pCs1r7vyy14DHLgRgANX1wP+8o74+gihySwJh5MiRmDRpEr7xjW9g7NixmDt3Ll544QW0t7ejrq7OU1sbNph1L1iItjTQaU8IIYSQsoZCX+9BdtqXoCO9CJ3bo1x0zqB0KAK4Dmd6ccJaUYj2uwxvP/QdcXPaS2i5pAnpQsoI17mGtIqIehDtG+aagj0AbJgDzHvUsk6YTntVPE6Oc1tyrquc1Fk8xeNoFq/UFe29ZNoDZgFZHaTopNxtqXLcczHSpgM+6Hic3PM19x+mYA8AK98AZv3dzO7PxUm09+qGf+5SdP+9SbQAM/6sHoih077sqKqqwqmnngoASKVSaGxs9LT+7Nmzux32Rx11VNDdIxpQtCeEEEJIWUPNvvcgFyflCS6Ess2051sV4eKnEK0uinVbUGLRnjn35cOGOcDfTwfuORlY+WapeyMjZtq7FPQE9JzIykK0GiOTr12f/1102ocdj+PitA8yHkfXaa/7PPAaj6PjtO9oAjqb5elZ3AYysqQ7/V1Djk77nPP1piVT/PVf2pcPMh7HevwWPqO+Pry6+EnBTJs2DStWrFDOTyQSmDrVLPhbX1/fHXUza9YszJ0717HtDRs24Fvf+lb394svvjiAHhOvMB6HEEIIIWUNhb7eA3POg6fUx88wDEQEK780mMB7OUB04nH8Eo2Jk1uMOq24by0yGQB02lcs//4RsHWJ+fmZ7wL/+5E393UxEDPtNUR7HZe0F6e9G9I6mbQ5MJdJFTYAp5NpLzrtc9zx0qCCl3gc7Ux7TcHXazyOm9P+pauBWfcqttXY89ltICNLqlNPGPcUj5NzvnSE8b5DHbbrsH5nC7DkP6bof9Bn5WWq69UDIUZGb+CKBMaUKVNw00034ZRTTsHZZ5+N0aNHY9iwYWhvb8eyZctwzz33dIvz3/nOdxCPm8/pxYsXY9KkSTjxxBNxzjnn4JhjjukW9Dds2IA333wTDz74IJqazIGrz372s7jkkktKso+7O2X2l5UQQgghJJ+wdb57316JO15bhiF9q3H3147F8fsODneDRSKTMfDIzDVYtbUFX/nk3jhyrwGl7pKYv04htzBKffxUMeOy0z78/uw2hCray4JcK2r9t2nFyHi/IOi0Lw86mnoEewBo2wasmwnsd3Lp+iQhieGhO+19iPaSiPrRS8CLV5pC7udvA0Zf6L1dVduAJdO+yT4/11UuidWe4nF0nfaaor3XeBwnp/3G+WrB3rotXdE+2a6XBecpHidnkKh+D6Blk3rZ6nqgqq/DdhXHOZMG7ju9J8rp9F8Cp1xpX04xqNvTDt32xSaTyWDq1KndjnqJ8847D7fccott+rvvvot3333Xsf1LLrkEf/7znxGNMqilFFC0J4QQQkhZE6YmuWVXB255eSkMA2ho6sAtLy3F0z84MbwNFpG/Tl2J3736EQDgsVlrMf3a0zC8f4Cimw9YiDZ4Sn34MoaBqGC/lrrFKKQAcTuW8QrItPd6PdBpXx6IDnaHc9O5C3jrVqBpPTD2B8A+Y8PrWy5iPE4JMu3dkETODx/v+fzyNcDh5wBVOfdfe6N5D/VxMRn4dtrnCO3SYJmneBzdTPuwnPaW5Xd+bGbB73ks8O7dLttq7PmsG4+ju7+2eByHayfZAUz/AzD3EWfBHjCLHDsNqqiuiWWv9gj2ADDlV8DJP7Ev5yrasxhtMbnqqqswevRovP7665g3bx4aGhqwZcsWAMAee+yBMWPG4OKLL8bZZ5+dt95FF12EQYMG4Y033sDcuXPR0NCAzZs3I5lMYsCAATjooINw0kkn4Zvf/CZGjx5dil0jXVC0J4QQQkhZE6ao+9T76/N0o/fXePwxWEQa2xJ4bdFm7DOkD8YeMMR1+axgDwDJtIE/vrEcvz6/tEWk5Ez7EnSkF1FqIVxllqbTPmTCdNor4jg6UIOUEUU8EkD8ga9Me4r2ZYF03pyeQ69dD8x5yPz80cvA/y6VxeZMGlj6ovn5sAlAoa5OKaZDJ7ZEy2mvaMdrFn02AseJ9p3AjtXAiCPM77P+Drx8rfmK01k3A5+6VL2u70z7XNFeEKu9xOMELto3Os+v6pPf51yn/c41wF9PMvsUibo/R/047Tt9Ft51isdZN9McaNChbqBy4NXcjuI4rxXc1tI+R1zuSxajLSr19fW44IILcMEFF3har66uDueddx7OO++8kHpGgoLvNxBCCCGkrKETG2hPpPH5P0zDNc/Mx1fvnYnHZ6313MbiBqHIWpGh074wpCNVaiFcdf6YaR8yocbjyIJPAjEkUEC+di5+RPsU43HKAkkIdroes4I9YBbpfP8Beblnvw/865vmf885CNG6+HbaF5Jp71G0170HmhvM/yc7gNd/Ze5bJmV+VonEs/4OvHCFPC93AMyP097LgErQ8ThuTvsBe+d/z820f/+BnkEEnQib3OggXdHet9Pe4drRFewBoHag86CKF1FdOtYROu0JKSYU7QkhhBBS1lDmA56YvRYbm3p+ZP/02QWe2yiH45gWRNtSi85+WLW1Bdc+PR+/eXExmjuK5yqT3eulz7TXnV7qvvYuXI5lIcUrFaJ9EnEkgnpR28h4FzjptA+Wth3Ah08CG+Z4W08U7T3c29uW26e1bgcWPt3zfcG/3AuIuiHG+ATltC8w0z4btaIroO7qEu2bNwCJnMKuyVZg4wf25XesBl66St1edtAhncpvz9o/QBbtvRQb1Xaeaxy7RBuw9SPnZQaMyv/etr3n83t/0+tLljDjcazn3utbGiriNc5Oey+ifcsWYaLLvU6nPSGBwngcQgghhJQ1YcZ/SAU0y5F3lm8ruI1y0Esl0bbShNxUOoML/zYT21pM0Wbdjnbc883ji7Jt6ViV+vB5OX9edB7iQgnicZJGHB2oBqDpnM0lWpUvhtJpX1o6moC/ngjs2gggAnz5AeBIzXgFSZRTCY6ScC6J4rs22qe1bnXPbO/cBWxZCgw50L5sSTLtNYXX9h1AdR99d3nWaS8VjW0Wjt3s+5zbM9LmfnYo3sBzi8fxUmxU12mvE1206i3354BVtM+Nx6mq8/YcCTUex4PT3guxapdMew/nrnmDfVqnMMiTCwvREhIodNoTQgghpKwJ04kdrRTVPgDKQRqXM+3LoWf6vL5kS7dgDwCvLHIpChcg5eheV8bj9IIBmrKmJPE4VWg2+vhrs25Q/ndfor3C3Uy8M+/RHKHcACZfrr+uJK6qhEBJ4JPOu/S32O15sWszcM/JwP2fBf50ArBliWU7ggia7jQd3W/fDtx/FjDlJnvfda7LQp322bcIdNzlQI94KsWV7Fhpn9ay2b3NZLucZw+4x+N4EX4lJ7+EjtibrXnghDUep31nz4hxlcfnV67TXjseR3N/vRSi9UKs2sVprzE4kiU7WJSLNHCU1z5Fe0KChKI9IYQQQsqaMHU+SbKvNBFZmzLYL8lpXWnu6627ShfRIeXE65zWMIemvBWiLf012GtwO5YhxOMkEMdO9PPXZhCivSQeEn8sfDb/e6eHmieS6KcSsSWBTxLTpUEot+vjg38COz82P7dtA6b+1tKmwrm86DngjZvM4p7TbgfmP5k/vyCnveY1vWsjsPY9YMcqveWzbnpJtN++wj5NJ6+9c5e6qGtePI7ktPdw72pn2ru0mU4BH73k3s5Ai2hvZIDOruswXqPXlyy5gxq6++H3zYKg4nFi1c6Z9l6c8E3r7dPc4n+YaU9IoFC0J4QQQkhZE6bQJ5n7KjFjXYdy2K3eEI9Tyt5K16Yk5BcT1SBXORbN7VW4ivbBO+2TiKPJ6OuvTVt0iY9Me51oE6JHIYM6kqitOjfSYIC0rCT6u71ZMeVX+d8XWQYiVCPCz343//uLluz3gjLtNa/pxy4EHjjTfEtAh6zjWRLtFzwF/GkMsCmn1o1OPzqbgQ5FUddcYVbMtPcgzOrGxbixfnZ+1I0KazwO0PNmg1u0i5WOpp5nre6goXY8jkW0DyoeJ+7mtFdc39LfFMlp7wad9oQECkV7QgghhJQ14Trt7ap9qtKs3xWEJNpSyNVHHvTI/17sN0W8OO177VsspaAEmfYJxNFo1Ptrk0778sJJ1HOjUKe9JDyLQr7b+XZ5h0jXuWzdTiFO+6Dc0lac4nEAYNtHwGvX93zXiZLqaFY77XPd9ZJz3FM8jo8aGBIrp+gt12coELM46rMROblFaXVIJ3qeO9rxOIJoP/JoYNSY/GnWosiBZdrXuGTaK+JxpP3LLQ6tCzPtCQkUivaEEEIIKWuK7rTvpZp9OeilvSHTvpToRM7IbvzwUN2fOgMMpABcRftC4nHUhWgbUULR/r8/B+Y+XB4Ps0pHEu1189VF0V4hYkuFTiXhVBTtXd6sqKpznu9XBC1Gpr1XOhrNyBqVaA+YRVqz6IjTnc3q9twy7VdOAZ7+DrDxQ/ft6Ga8u7FCU7Sv7gv0GZI/rW2HeQz9DKpkj6WuaD/7/vzvVX2AS98GvmCJb7IWog00HseH0z6oQVHd5wghRAuK9oQQQggpa4qtz/RWp32pY1SA3uG0L5WDXBlDYxPti+209yLaV9jJLmdcRXuP2c25RGPi5GSpnfYA8J/LgKUv+OsD6UES9XRz7cV4HA9O+zbJaS+sn+ww19+lKKrqJtr7FUGLkWnvh10be2Je3Gjd6r5MR5P/QrSA6cL+51fcnfRenPaqY9+6HWiYlz/N+kzJUt3XHsfVvgNo3abfj1zatgFrZvTUT3DDKsZnB0Gtz+R0Iv8fFIHG4zgM2hpp2Z2iOyjhBp32hAQKRXtCCCGElDVhis0RwWpfjpq99EaAV8pBL+2tQm66CCMPqsNknV500V5xv0jHpDec6/IhxEK0TvE4vp32A/O/+8m0z/LKz/ytR3ooSLT34LSX2uxssouz0vpLJgN3HQnccQjwnx/b58cF0T63gKru9WUVU4uRae+H5g3OTvtctJz2u+Q3IYB8AddJzG3ZDHzwmMt2PGTaq7a16k3kPfOq+gD7nyovW9XHLui3bdcbyJB4/Qbgwc/5WxfoiaqRIsty74OgBnxiNe7Pf+kaTwQl2tNpT0iQULQnhBBCSFmjq4f6cTtLWng5Ou2D0DrLQS/traJ9Md4WUDva878X+3Cq+iWJ9m5929mawLYWjSxm4u60V7jltVBEK5iZ9j4L0dYOzP+eSfsXd5rW+luPOCO54iUkN7TVXezWptUxLong85/oEf3n/gPYtjx/flWt0G6Om1rXaV/bP/+7TryH0mkfpmjf4Czax7uOR7JDbwCms1nOXwfMgZnseXaLTcmN5bG1k1RfGxJJxXFdMz3/+36nADXCAGIkBsRr7E77th3514YXnPZPh6xYH5dE+5xj4/ZM97I9t+e/NPAWWDxOErGYuf10Os0IxN0cwzCQTpvPxWiU8rMfeNQIIYQQUtboirp+hFPJwZ7mD4zQkKNlit+PQpB+gBZj4EF1fdsz7csjHser0/7J2Wsx5ubXccJvXsfdU5YrlyNdBCXwSKgy7RHHTvTz116NRRg1DDoyC2HbCuDZS4HJlwNNG7yvLxV5VbmurRRaiBawO8Hd8usBYIGlKGZEECZzI1B0B+Ct16aO0z6TlAX6MK/p5g1mzIuKVKd5X+mK0x3Nzi74bKyNW2zKpvnubeiy8g35HwUtW/K/73mMHAFW3df8h12dFI/j02lfKKp4HCC/GG2x4nEAeeDNbzxO3DJ4lkmhutocoDAMA21tATn4SUXS2dnZ/e/m7HVBvEHRnhBCCCFlja4G6UeslJz2xYg6KQXlsFe9NTKlGLugiomyx+OE3xen7WeRBr+UAw8ZA9c+swDJtAHDAO747zI0tTEX15FQRfsopKdjEnE0+XHa9xlsd376zbQnphj98HmmE33OQ8Cz3/PehhSFEUY8jkq0t4rPKod1LtWWa08SGXMd/LpOe2u7Opn2gDxQEeY13d7oEo9jmMKvrjjd2ewsqneL9i4O7Ma16gESlZNfxb//B3juf4ANc4H7zwRuPwR4+3dmlE8utQPsYjFgRuMAdqf9+w8Ar/3cW1+CIhuP4+a0DzIex6kQLQDMe9T+x9uP076mP9BvZP60dBL9+/cMhO3YsYNu+92Y5uaevyt9+/p8U283h6I9IYQQQsoa3X/s+xLtBat9OYr2wWTal36/5HicEnQkYIrxdoY60748nfYZDwM0bUm7uDZvnWZ28+5K2OdZyEROGD4L0dYNsj/ECsm0L6TIbm9g20dA8/qe72umA7s2ua+382PTrb7zY1nwLiQeR+W0Vw0EzPgz8OL/Aivf7FpfQ7S3xqFIgnCuy1z3+rKKpbriqdRnv8Vvdehocs+0T3eaRVu12msGErvU87PXiI6Yu2OlPN2r0x4wB6P+Ph5Y956Zmf/Gr4G1M/OXqekni+DZARir0x6Qr/lB+wEjjvLeRy90O+0l0T5nACyoaydW5Z5p/9+fAx9Py5+W9HGuhhxo31Ymifr6+u5/X7e0tGD9+vVobW0ti3+HkuKQTqexfft2bN/e8zyqr/dZE2c3x2UIjhBCCCGktOhn2ntvOyqI4WUYad9rMu2lPlSa017qbXHicTQz7YXrN4AxHyWq+1MayFD9YJf6V2GXhZKOZBq/mrwIby/bhrEHDMFN538CfaoD+AkW9gGKVtkc1QlU+StEWzcIiFi8YoU47XPF212bTPF00H7+2qpEJKf7xg+Bfnuo19m8yHQuJ1qA6npZ8C4oHsej0/6jl8z/z74P+P5UteifS7VVtBdE2FYfmfapTuCN3wDv3w8MPlD/jYO3fweM/7/86zHsTHu3N2zSCQ9O+10u8Thd83RiU9a/Dww9WNiGR6e9Cmsufk0/RTyOwmkvcfJPgM/eALx9O7B5QcFdVBLv6mfR4nFq9GqarJgC7D+u57sfp/3gA4EtS/KnpVOIRqPYa6+9sGHDBhiGgZaWFrS0tCASiXTn3ZPeS26OfZZhw4YxHscnFO0JIYQQUtaE6bSXLOzlWIi2tyCdo97gvAozqaR7GyqnPcrTaZ8WjoluLr9Tu5XG5A8b8PisdQCAZ+aux7H7DMQ3xu5beMNhX3RCvEISMbTBh8s9cNG+K1d/7sPACz8x2xn7I+BzNzuvl04BL/4E+OBxYPjhwEWPVKbYLxVKbZgHHHKWep0pN/aIsKrIEm2nvZdMew0B/LXrgb2Od18u95mgKnDqx2m/YyXw9m+71td0qQPAzL+YWetfvj9nmyHG4zRqFGBOeRHt3eJxPDjtN34IHDNRaMNyrcVqvBWmVaFy2ld1Oe37DHFvo+/wnrbCpP+e5v9jcfM5mPvszovHCcppr5FpD9gHp/yI9kMOArZbatB01YTo169fnnAPmP/eS6UYi7a7MWDAAAwZonFPEhGK9oQQQggpa3Sd9kHFrJSjWBhIPE4ZpNpL56jS4nGky6MY8Ti6TvtiX7+qQRcxHkdxsqVxsjK8DX1x9dP5RRqv//fCYET7sO/nmCTax+HrvQ1RtE/rFfyUyIps/7msZ9rMPwOf/hEwYC/1eh9PM4V+wCyeOf2PwIQ7/fWhlEiiecMHzusse8W9Xe1Meykex6PTPpc104ERR7ovl3u9qMTmPKd9EUZTFz5tXkO1A8zvYTrtdUR7L077jibnzPnueBwNp73q2rH2uXYA0LpFXtYLNf3lTPus016Kx7HSd2jXOgHkbFvF+Fz65zyTYtX590oo8TjV7pn2QH6dAMPwF2U05CD7AEHO86Ffv3445JBD0NLSgubmZiQSCZsDm/ROYrEY+vTpg4EDB6K2VrhXiTYU7QkhhBBS1uiKzUGJlalKU5E1KQcRtLc6qouxD6otWLdd7KPpJR5HNbghF62t/OsiVErgtE9Aw70pIYn2kltcl3it/ED7eBpw9FfV671yXf739++vUNFecCo3zCu83Y7Gns/v3QvMewQYfgTw+duAbcvMOJi6wbLDWem01xDtDUMv0z6tIdrnOuXDFNBzWTuz5y2HMJ32KYsTum6QPeM+ncgfuHCis9lZtPcSjyOd/zd+0/MGQ5YgRXspIz4rwOvE42SXscYueWXwgcC5dwMPfUGeP2BUz+dYTf61Hlo8jkfRPtUJX/966DtUzLTPJRqNon///nnFaQkh+lC0J4QQQkhZo6vd+dGwJOdvORaiDYJy2CvJlV1ph7tUmfaq67uSCtGquibGJhXUq92A0EV7uRCtkoPPBJa/Js+rG2gT7Y10wn+thXRSFkfdrn3JIV6JSPvRsglo3gj0H+m/3WyUTcM84OWrzc+b5ptC6wePORctFYuyGprufUMv0z73nKuEZD+Z9oXy8TRTtE91AttXFGebgBnvIon27Tv01u9odsm09xCPY337Y8cqc5DHSvaNhEKp6deTFZ9LNh6n7zBn9zsA1A7qaqtA0X7fE4H9TgK+cDvw0lX2+bmifbwayL3U8+JxAhrwiVUB0aj7crmivc7AjIQ0QFDIgCwhxIbG3UwIIYQQUjp0RUg/ESVS271VtC8HpEPbKzLti7ALqvvAOrnYh1NVAkJ6Y8WbwF/510WohC7a24sFJp38Xl/7F/CDGcDIY+zz6gYDkfz2drX6FIkAtWi/uwz1qATujR8U1m5WYH/52vzps/7mLNgD+Y7h7mkdcpSPuL6OMJzrtFeIzaVw2n/8DtC0AfjLWGDpC8XZJmA6xa2Da6lOfQG2fYfzwEb2bQad9qzneeGzEO/H2oDc1m6FaGv7A0ec59xGdgChEKd9tAo45X/Nz9m4HSt58TiWPqfCiMfRrDuSO6DmJ88eMPffxWlPCCkMivaEEEIIKWt0tTs/DmNJRO6ton05iKBiPE6FHW/pOBbjmlHH41i/l4fTXo68kdvwsizpIuzzbBVi4CLaRyLAiCN6ii7mIsTjRAoRdjJJ2W3uOpDRSy4qVSHP7SsLazcbZbN5kfd1Jae9ThHa7vV1nPY68TglcNpv/BB482bTXV5M6gbb3ebpJJDUiBrSIZkV7TUEXev52/ihvFx1vV50ixvV9XJMU24+/QV/Bw6boG6jbmBPW3446XLgu/8FBu9vfu+jEO3z4nGs+e8hxeNIWOOEgnDax6rsA0dhRkQRshvCeBxCCCGElDW6IqQv0b5C4nGC0OfKYa96QyFaiWII5epCtNZ4nGC2pzvIo4y88eC0l+65MhhjKm+cBOoTfxzABuzhNQmdn46CQx91g2z9rUYBwk46oYjHKULh0XJAFfPTsrmwdrMiu1POuQpJdNfJs+9eP6BM+44m4OlvmznjTtEvXonG1WKkkQE+eDS4belSN0gQgTv13lrQIdFqvkqldW5yxeeM+faBRHV911s3Bdz/1f3M+BepEG1VjmgfqwI++W312w81Xa5/v/E4Z9yY/13ptM8ZyLQNsuTG4wTltFfUHqnpl/8mSu6gmm/RXih6y3gcQgKFTntCCCGElDXamfY+RL5Kjsfx7Jwvg92SM+3LoGMFUk7xOEG9uVDoGy6yEK+Kx9Fvl3RhFagH7Q8M3Bc49AumAzTo9tEj2l+R+GH+jFNycpwlF63gtK8qSLRPygLX7nLNqFzpLQUW+PQisluRhF2tPPvs+h4z7Z0GFhY+YxZA3fC+/vadiMSArz4GjDgymPaCom6gPQolnVA77fc9yVv7iTb9AYBc0X7zQnWufnXfwp32Nf3M/4uFaPtYllXE8VT16XHqV/crrD9ZRKd9JF9Et/a5mPE41mPRuavnmek3HidWBcQs55PxOIQECkV7QgghhJQ1hqbanDEMdKbSuOu/y3DFE/Mw+2P3YmyVEo8TESo2lmE3XemtjuqiCMyKTViF8KC6UugbLnIUkn4bFO1dsB6f8T8DrpgPTHxc7fj01L79ZCW7CtG+nBmDNcNPNyfu9UnghO/2LCSJr4JoH40UcH7TSVEYmv3xNry0YGNZRIGFiionvmVTYe22bQMW/dvfutJ59yTae3XaF1ATwSsTnzALzUoicSnpM9jep3TSfizPuBG45CXgW5P1884BMx5HV8zNPf+r31YvV1Mvv43jhaxoLxaitYr2CkE+tyBubqSOLod8zj6tz2D7NOvbALbzFUYhWsV1aj0WmWTPeVO9ueK6LSEep7cU/CakTKBoTwghhJCyRiX02ZYzgLv+uxx/mLIc//6gAV+9dya2NDsLAaLTvkIEH6fBBUm0Koe9kuNxyqFnhVGMgR7VJsLKtNfdJWVOvZd4nF5wDRQf6zETRvYKal7ttO9ENd4YfTvwy0bge1OA/iN7FuoUCpYKon1BKOJxnpu7AT/851zc9MISeb3ecp2pRPtdBcbjAMBT3/K3niS6e8lWDyrTPgyyTuJyEyPrBtlz3VOddqF95DHAfieZYrmXQrCJVv23L3KvyW3LHBaMBCfai4Vo6+VlreSK9qoMeBWxavltJmm/qiyivVSDIEtgmfYq0V4499nntdPgjNOzO1bNQrSEhAxFe0IIIYSUNdqO34yBe6b2FOJLZwz85S3nwnyVkmkv4XRcylUclx3VJehIwBRjH/Qz7e3LSW9q+N2eFZWrOe2hfoGX/HvShVVU93OSnTdgm5JbiNZARN6m5K6u6RewaJ8URfsozGPywPTVwW2rHFHG4wQg2vslI0QWecnJ1nLaa8bjBE3WSayKfCkVdYME53bCfiyr6no+t27Vbz/RBsz8i96yuddkR6N6uca1wcXjSGK7LR5HQ7SXnmNDD5HXO+cPwPenAvue6N5PwN1pn3vcgqrJoYzHEY5F9nntJNrXCW8QZIlWMdOekJChaE8IIYSQskZXupM0vtXbnN14ksO3UkR7J01TEjzLQQPtDZn2UneLEceh2oJ1enCFaPWW8yLEqwV+6T7U2/5ui020D/hnnXBOckV75X0rOe0jAbhrc8kkd29hSOX4bt+Rn5FdbKyDCTpCvGpdiZI57btE+7ZyE+2leJyEXYCVCrbqsGEO8P4DesvmXpNO7vxRJ3QVoi0AJ9E+Xpf/vboe4ltIuaI9AOzz6fzvE5+Qt338JcCIIxz6ZnGzH3GepX+WPuceK9tApM+BWFUh2oF726Nsup32DveTU6HeWBWd9oSEDEV7QgghhJQ1uoKoH/G3UjLtJZwiRcp1H6RuVVr+tCgwF2EfVAVm7Zn2wQzYFJppL8fjKNoQBPpUb1DtE234cexZXB9/BCOxPdi2rcc9cNFeHY8jbb6bToUDOvB4HLswFIPbNRPgfZpKAMteBRrm9UxLtALPfA/4/VHAS1cXVtjVibSDwN1aYDHaQrCK9F6KW+osmysMe3HxF0pW6NQtylosJKd9otVe0DTXaT/iKP32Wzbpu79zr8n2RnmZeC3wiS8G4LTvEsal7Hbr4GA0KjvMraL96b8A+g43P598JTDkQOCMm/KXGXmMe9/OuLHnc6zGHqNjFbjf/i3wx+OA3x5gv6atUT+6ZAcGjv1m/vSTrrAfC514HKdBn1g1M+0JCZkCn5j+ufbaa/Hb3/62+/ubb76Jz3zmM6XqDiGEEELKFF3B0Y9wKgmc5Sp4W3ESVUU3eBmk2veGeBwvBVaLgT0ex76Mn0NcsGjv4a0KaXqq0i4Mif/8P1xZ9QwA4Aux93By5x+RCcozFXY8jlgXI5rz2YPTHgh+UEFwccfR41Q1DAORwCODuhsHHjobWD/L/P6F24Ex3wPe+T2w4F/mtFn3AsteAb75b1MADBInV/quzcCAUcFuTxdr1n6oTvsixuPESiaZOFM3yO7cluKpckXXIy8ANi8Ivi+phHlfNG8Amhvy5x10BjDsUGD0RUC/EXpv3VT1AfY9CVjxX/s8J6e9NCBQ089+XKyi/b4nAlcuMZ+r2Uz4Md8Dlr4IrJtpvh1w4mXu/T7+EvO8bF4EHPkloP+e+fOl6JodihjH6r5AQvE8dSK7jc/+yhTQG9cAn/ofYMBe5rHIjXnqFu0dBsEcRXvJab8bvwVFSAiU5C/QBx98gDvvvLMUmyaEEEJIhaGr3flxbEsCfaUUxXQywJVrPE65Zu17oVT566ptWCfL5z6Yt1B0tp/FUyFaYdlSOO13dSTR2pnGHgMUIkX7TmDTQjPvuN8I9wYXPtP9cc/IDpwa/RBvZo4NprOhx+M4H3/l9aFyIxcioPcZCrRty58mOEOr0eMwTmcMxGMa2zQM7337+J0ewR4AXrqqR+DLpXGt6bj/5rPe2nfDyckq5doXK0rI5rT3Itp7yLRfNxuY96h+24WSdRIPOQjYvqJ42wWA2oHqjPg+g+2CqfR2R67T/sTLzMGVt26xL1dd738wJNUOPP5Vc6DKykmXA/uf0vNdJdrXDjDd7dGYKdgv/rezaC/Fx0jRMDpOe8A+OFNVB3zzOaBhLlA/Ahh6sNzvXCIR4BPnm/9JeCl6a83n1yIniqzvEOCCv+XPthYi1sm0dxLto3Eh055Oe0KCpOjxOJlMBt///veRSqUwfPjwYm+eEEIIIRWGvuPXT9v2acVy2s9ZsxOvLdqERMqfOOlciLY8RXs5074EHSkASUsuxrFVRstoiPZ+jnGhsVTSfaQU+MvAaT9lyWZ86uYpGHvLFPzsOcGJ2rQB+OtJwD8mAH/5lCneOyHs0x6RADOxSy7aK85PbjwEABxxvvl/v/3rPwq49G37dEFkqspx2mtfPn4EplVv2qd1NANbFtunb1nivX03nOJxWjbZp3lxvBeC1S3vJcJGJwc7kwRat5v3YDHJCsGn/by42wVM17ZErNp0o1ud2x0uTvtYFfCZ64BjvmFfrr4AbSadkAV7wC6QS274AXsDl80Djv8WcOw3gMH7m4MkYntdwnMfoUDqsMPs03RFe4nqPsB+J+sJ9jpIkT7Kbff13n68xnkQ0pq5n3XaJxzu1SqFaB+r7qpXYjmfzLQnJFCKLtr/8Y9/xOzZs3HYYYfhO9/5TrE3TwghhJAKQ1d7kUQkNwNlqeJx7pu2Cl/667v4/iNz8NV7Z/h7S8BJtC/TOPCgXOClRNqHYrydoTpO1v5IiwVV78HLcvLggV4uP1D8mKo/TFmOtoTp1H7svbVY1GBxrE7/gxn9AJiO+zcsecdWBDE4hQCLsdqejObDriOZxq8mL8I5d7+D37++zP8bCy6ivfKSOnoiMKRL4KrfAxj/f13d8/mzM14jC12CIFwVyRXtxQIa9ml+BCZrsUsAWPMuxL9W1siYIHAqNtsiZNrrRM8EgXVwIOjBgnQSWPee/3YP+iww/Ajg0LO9XY9ZUfKI84AJd5kDUf32dFwlMCRhGjDF/EjEu9M+iyTQ1+/hvX861A3M/y4Voh15tOkMz2Wv4+X2cuNxTru+Z/pJl8vHSxTtB9qnFQNPor0i037MpcAnvw2M/aHQvouT35Zpn3Xa+4jHye4L43EICZWixuOsXbsWP/+5OUJ9zz334M03BZcCIYQQQkgO2o5fHw5oMZajCGLhr1/scV/OXduId1dux0kHDfXUhlenfTnQK+JxPIjRwW5Xnm69P+TMfT+iffBOe1U3JF252E77+evzxa7HZ63Fr8/PKdo4yxIzoHKVZhGExYwRoF9K4bR/cvY6PDj9YwDAgg1NOGJkf5z5CR9inKtorzg/9cOBS6eibdMyPLE8gszSOL4xMI1a36J9rRx54eK01x708eO0lyIulr8WXPsSnS3AyimmI9lpIGBXOTntAy7cmkkBrVv9r3/Cd4FDP29+/sMxwM7Veutlr79IxBRLP/lt4J27gNdv8N8XXepUon3XdLdM+2iVHEfTT3gm1A/z3j8ddJz2Uh/772kOjuyyZOTnCs/jrgaO+KJ5bQwXXPbW5VV9KhZxD6J9lRCPUzsQ+EJXXcjZ99nnS8/KXHwVolUMBGTPoy0eh6I9IUFSVNH+Rz/6EVpaWvCtb30Lp556KkV7QgghhLhSqHjovI4wrQR5LbM/3uFZtHfa3XJ1tPeGQrRy7EvpnPb2THv3ZXTQvZ9U/RJFe8XJljPtS3thbGku0J0sCKvpIF9yth73LlH8l/9ZlDf52mfmhyLaO9631X3xzRfbMWfNTgAb8N7qHfj7WW75zBGITvV4tUK0tztDq3NFe+0K5n5Ee8F5ulzI3gaco2x0SXUC957alaeuOE5ZRKd9sUT7IjjtvWSCW8l1eHtpJypcf04530Hi5LQH7M5tq9NectkDCqe9Rp0Or0SiQLVFKJYEeknIB4BRxwNLrKK9JeJlqCJGR7U8UDrR3s0Jn4sUj9Mn520EqS2361ol2jvVMjjsHGDx8/bpSqc943EICZKixeP861//wgsvvIDBgwfj9ttvL9ZmCSGEEFLh6GovQQmTxXb4+sXJSSoKtyH2RZdSFXENErkOQvjbVR0lvUx792PckUzjydlr8e95G5DOGNr3kyqKyUs/xMihEmc8bdlVoNgqRJIEK9rrZdrvbPMroDhfAE7X1KqtLV2Cvcl/F29Gq1vtDskNC3Q57aV4HGenvcuYQw9uAtPWj4C/jYNx2/7ofOsOJNMZeZ2mtfL6QcTjLHgqpwCqy40pFaItWjxOJ7DwWeDxicCbN8v56oWQSRb25kI05x5xcyTnIi1byOCBlf95BzjuYnmeKtNeJdqvn53/XTW4IAn0YYj2Nf3zjzvgTbSXInJUzwplH8rIaV9opn3fHHOHdA36ddpb39DIMvJo4LCz5XnZfbEOarEQLSGBUhSnfWNjIy6//HIAwG233YahQ705yQghhBCy+6KfrR1MBEiliMhO/ZTcz+WwW0G5wEtJ6eJxdDPthXOv0f53/jEb01dsBwBMX7ENV555iFa/VI5m3XicpvYk1my3u6aTJR4826oj2jeuA+Y/AQzaHzjyS/lFNASHdbhO++CaFtvXmL1kYzOueupDLGqwC0AdSQOOZRWr62XhKF4jC3qC0z6OdPdn+bqURtxcBKYpNwIbP0QEQM1bN+Ibs/fE3Uc1QyGlCpvMmHERsQJ+di99UX9ZyTFbLKf9ulnA213RHR+9FHz76VRhLt5cp70Xx7N0/QXptO873BSn5z5sn6eKx+mjEO2tqIqISgK9VzFcB2uePaCIx1GIzZJo7/XYS0J2JcTjuDrthbY8Z9p3ifbWAbYx3wcOGA8cdLp6QCX7TGOmPSGhUhTR/pprrsGmTZtw0kknBVp8dv369Y7zN27cGNi2CCGEEFIatDPtfRSilR2+5agi23fE6bBIgpVRBl77oPLWS4ksRhcjHkcx3dYX+zJu/VuxpaVbsAeAp+asx4/Gu0QOdPdLNZjg3o+Zq7bj0kfmoKndLsSV+j7csktD6LznZKCj0fzctB44+YqeeUKx0FI47f237yba2+f/+sXFomAPAIZb/6pqTVHVSOdPj9V0FdysznetC0776oiPTHs3gWnpC3lfz2p6GvNW7YXT9Frv6kyiR+Bq3QbM/xcwcG/gsAnuf6QAbyObUjZ1sZz2WcE+LAp22vuMxxGd9gGK9rEquTgroOG0d3FWS0WTAVm0z6ThGr/kFUkc1820B4A9j7VMiJh1HbwgvXZTqkK0XgRtN9FeuobdBgVqLOcjK9ZbB0z3OwU47AvmZ9Xzp9tpb820p9OekCAJXbSfNm0a7rvvPsTjcdxzzz2I6PzDRJO99/b4wCaEEEJIxaEd0+ErHsc+rdRioYy3wYVydbRLfSjLw+2AXC+gNNs1t50/XY4gcm573Q67azmhmfmjLi7rnv3/6xcXi4I9ADOGpIQkdTL1s4I9ALz+y3zRXnDaR4MUw0IX7b1n2ucO/NiWd3sVIBo387etTvGsMBWtchXtc+NxxPtFOvwe42uqkMKKhm04zcuv6HQngD6meH7PKT2FNU//BXDK/3raviuSq75YTvuwSQui/aD9gSEHAited18/z2nvIR4n7Ez7mKJYLAAM3EeenhXt3QYfVE77mnr7tPrhplCsyjeP1wEpj8WFJXFcelap3Nw1/YBTrgKmdcUrH/N1oO8QeVkV0rOgVsi5LwbNDe7LZKny47R3E+2tTvusaL8rf3ru8YlEzPNjHXBgpj0hRSHUTPtEIoHvf//7MAwDP/nJT3DkkUeGuTlCCCGE9EJCLUQrKE9hi/ZBFS112l9pv8pBG6/kOKIspbhmAPXAgLU/8oCNi2va46BQ/vb8x+Ms3KDOvC6HwbOC+iC4m3PjWwrHGo9TXNHe65s7qYyGaC8JodlpVmFIiMdxFe0ll6tHV2gGUdTCY059dhvzHu0R7AEzekcLL057SbQvktM+bDJJuyA49GD9qJuo33gcQVAPMtM+Vq122u91PDD4QPv0rBjuJtKqnPaAGemVpaa/mV0uubuzqIriOqEdj+MwCnb6z4HvvQl8+zXgvD9574P0LPMyaBMkXp431ULxbtdM+4DicazLSQNX2XNmy7RnPA4hQRKq0/7mm2/G0qVLsc8+++CXv/xl4O2vW7fOcf7GjRsxZsyYwLdLCCGEkOKhK1f4iVkpRSFaf0KgXfByaqZcdXDZpV6mnVUgRQ+VMh7Heh1Ix9PPJZfScZo7tF1ojFAxC0KrrsFtLZ0Y0d+no1YQSmMI8O0BmxAVcKi9D6e9E2nDTbSPmU57K9m4B6s46eK0F5+zkgPUoys0hZh30T57Laye6m29LF6eL5ITutc47VN20TNWrS/A+nHaR6vkCKMgnfZRB6d9vAb4zE+BZ7+bPz3ruPabaQ8AZ99h5um3bQNO/LF5/1UJQjFgisGqeU6I8TjCvrqdj72O877tLHuPAWb+xf/6QXLsN4F5j+gt6yfT3jUeRxDtU532N8OsMTrSoEq3094yj057QgIlNNF+6dKluOWWWwAAd999N/r2dSw95ItRo0YF3iYhhBBCygt9p733tqUEjrAz1v2Jkd4EUDHTvgy0cTnnvPj9KATpmilpPI5tOf11u9sQZiczegKzSvCWBze0mgQApIoYj6Pq18amDv+ivRCPE48E6LQPOx5nz2OBdTOVs70OVCVdRfvCnfbVuU576fKRonCsrtDOXcDMv5ri8Ngf2BdHFLURr077RE/bvvBwrNMJ4Dd7Agd8Brjgb6ZI15uc9lbRPhr34LTPuUd0nfIqMTko0T4SM/ulun9jVcCRFwDv3AlsWdy1TrRHxC7EaV83CPj8rfnTqoXYHMC8jvy406V4HC+Z9kFw2ARg4L5A4xrz+2d+Gt623Bh1AnD8JcCch9yXlc5FnxynvZ94HOvAS7Ld7rIH7PFB4kALM+0JKQahifZ33XUXEokEDjjgALS1teGJJ56wLbNw4cLuz2+88QY2bdoEADjnnHNCEfkJIYQQUnnoCn1+HNvSOmE7fH3F+HgUYuV5pVfHZRd46fvlhVLtg3amvWLAxjCMvNpSz3+wAQ9O/xijBtXhlIOH2tbRd9rLy4kRTWXqtFe9/bKpqQPwW0JLKEQbqtM+aNH+rJuB+3rKrf5f8tv5m/d4epJuux6Ny65gpWgvOe17BkW6B40yGdNpXlUnxzZYhfynLunJR1/6om3xguJx/Ir2ng92K/DRi8Ds+4HhRwDPfd/fdsuNdNJ+vmLV7u7iLHlOe811pFgQwNnB7oVu4VMhWkfj5ryJjwNPf9sseH3K//Zk3fvNtFchRbIApojrR1jXLkQbYgBErAr4/lvAgqeAfnsAh58b3rbciEaBc/4AnH0X8MGjwH8uUy8rvdngVojWVbS3DOKk2u1FaAEzLikX0WmviMfxUmyXEOJKaE/Hzk5zRH/VqlWYOHGi6/I33XRT9+fVq1dTtCeEEEKIiaZgITl73ShFxrofMVLsp4MIJomj5aCNS32uPKd9iTLtFdOt14Za3O9JeWhobMflT3wAAPhgXSNmrLQXENV1uquuw0JjhIqZaa/q16Ymj0UXM+keYUty2geZaW/tsoNo35lKoybuUXAbdTxw0aNYNOVRPL1xGB5Ln5Y32+sgacrlclq4sRVGJo2jrDNi+vE48Ygl075lK/D4V4EN7wP7nmyK2VaSbcCSyaYYNuLI/IKmWxbZFk/7Eu27roVORYHPsHj9lwg8NqmUZFJCMcwqn5n2mqK9NfojS1BO+2w/VJn2WbF00H7A994Q1ndxvzs57SVUmfY1/dR9dELKtPdSiDYo+gwGPnVpuNvwQjQKjL4I2LIUmPlneRkpoqyvSzyOG1bR3sgArVvzp8Wq7YM9TvE41sEcl2g1Qog3Qn46EkIIIYQUhq52p+sMzkVaJfRMex/99O60t08rlTZuGAY+3t6GfrVxxeBDZan2hca++EUlklonq7PvDUS7BLw/v7kib972VrsImSxCIVon/NzPflENEGxs9pgFnmwHaroiDcRM+zDjcSLK/WjpSKGm3ofgdvg5eGHNwXhw/UrbLK/XfMpl+V0JIIM4YO1mVhy1ujndCtFmDGDOg6ZgDwBr3pE3/NiFPZ8/ZY/DsZJGFDURj/EPhTrtC3p6l9HzNV4nZ+7rklbE4+hG3eSKzn7WySWoQrTdbmVhO5GYnKeft37ATntVbn1Nfzleyg1rNjqgcNqXqDBsKYnXAJ+7GVj+KrB9hWVmRByYdHXauyHVDWnZnP/dmnsPyINDqgGnTJAF1wkhAb9H2cNDDz0EwzAc/8stTvvmm292T99vv/3C6hYhhBBCKgxdd27SRwZ2KURkSfSNuLghvUaylMoNLnHFkx9g/O1v4eTb3sArCzfZ5ldePI40rQhOe6UYb/2uiKvJmbxDEOmt6Drtlf1yKUTrdsxSmpn6QaB6S2dzk0fRPrfgpyDax0OOx+lMyWJJa6d/EcVpECh/OefzmUg7P+NSiKIDUmHFLmFKIx6n2hqP8+ZvHLdp472/ui7iy2mfvRYSgmj/t1OBl64BEvZBiG4q7BmpRIpKUdFvJDDy6PxpGUU8jnZRWRenvSQwG4p7pxhOe539KqbT3o8bXhp0KHamfbkjXYvRGDD8MPv03Jx7acDG7VkhivZb8r9bo3Gy/bFNyw44WSRF1T1DCPFFaKI9IYQQQkgQ6OoVkkPeLRigFJn2fsRzr67lcnG0L1jfhOc/aAAAdCQz2NVpzzqtMKO9PCBSlEx71XRrPI68nOHRcZssMNPerRiyW/vFdNqr7o2E14HAXPe34EoN22nfoQiO39XpvzCg7hsebs81N6d9GjF0QBAgu0V7azyOs9M+rEFKf/E4DoVoN34AzPqbS2HKCntIqpCiUiQOPRv436XA53+XPz2dMoX7XLzE47hl2tcPs0+T6iAAwTntsw5zq/AJ6InkgWfaq0T7/v5Ee2lQwUkA3h0Rj1Ec2GM0sPfYnmknXpY/CKJbyyEXaRDH6rS3FqHN9seKqhAtM+0JCZTd+OlICCGEkEogTKd9KRzpgWXaOxwXaVYxhGUrLyxocF2mGC71IClVPE4hhWjN5bxtT9fprhxMkOoX5Czsdr+WQyFaz8+CpLPTPtBCtPZQe3Qk5UGBlg7/IorqCFivO7fnSyqj47QXBEgPTvtc0T7Mx4pv0d4p6/nVnwKf/qE8r7eIYLUD9ZbLuoGtefIZIR4nVqUvXuYK46JoP8IeU2IdJMji1cGuIntdS057HSHbLdfcaz+V8Tj9gLZt3ts68DT7dIr2+UjnMBuNdPHzwOLnTSH9kM9Z1vMxcBSvgWlnyXlA7rK8ASk67R1Ee8bjEBIqu/HTkRBCCCG9CcmZ66bbSJpc2KK9H8e7mGnv0I4k3JYkHkdjk5UXjyM5yEsXj2PPtFfF43h12uuK9nK7kuifewkmXCqTFvN6VQnOnscBc/O6hUK0gYr2QjyOSrRvTRQg2mvELQHOhbEBIOEi2qcRQ4chiFcxD6J9TiHadDoc4SiONGojPkV7vwgDQBWJbjxOVrS35pyLmfY+nfaS0N9XctorRPtY3Gyv0CgQVTFPIBjR3rPTvl6eXtNPP3e+/yjz+Jz+Czkf3e8ARW9FFY8DmOfv6Iv013MjEjEHU3KLctuc9pp1CFT1GFiIlpBA2Y2fjoQQQgipBHTFRj8Z2JIgFbYj3Y+DWM60Vy9fTpn2bpRpt5TIUUXFEO01xVPN7Hs3dONpVIMY4oBYzrJu0TN+3pzxi+rR4fm85grJYqZ9mPE4UXU8TgFOe924JbfnpqJr3bhn2rvH41TnOO0j7TucN+iTONLenfbJDmDJZP8bTXmsrVCuSLEbEt1Oe4tIbKSFTPsq/agat0x7Kb7H6S2HeG2++OmHrPDpO9PezWnvVbR3cNrr5s7/eJ7z2w9OAvDuiN+MfylSSYeqWmfRnk57QsqKkmba33DDDd3FZz/zmc+UsiuEEEIIKVN0xUbdDO5cRAE29Ez7YGJ8nARFN8G0nKg0p72kJRdDX9bPtA/Gaa87uJRd7I2lm/H3t1dhQ2O7WujNFe3LyGmvzOX3HI/jLNrHIkGK9pa+RaLoUBSibRFqSfjdTBbroXHNtHe5R1SZ9o3Jrp+rVpev4ObMxuNcEH0bRzwjxHIEQBXSqIXHGgHPfhd48hv+N+rmtPdS4NWKJBaHQaxGX0BWOe0B+xsWsSp9x3Fepr0g9EuucKfXxYLItS/Uae+aaR9gIVrdQQS3uCJRpN6NRXtVPE5YWCOQbIVohfvAMdOehWgJCRMWoiWEEEJIWaNdiFZQTt0K0Uo6U9hZ2lL7boKq13gc2f1cgvx4txOAcLOnC6WlM4Wf/3shvn7fTLy0YCMA1VsPRXDaK8Qj61SlYJ5ze+h0V7qfJDKGgcfeW4tvP/Q+fvPSEnz+929je6ssMmY8OO3LIdPe83nNdUQLkSjxUONx1Jn2rYWI9qrrzjpY5HK+3OJxUop4nOsnL8fO1oSWYBhHGodG1uLO6nsQTzS5Lu+HKqRQE/Ff2NeRzhZ5uhAFlEchor0UCRMGVbX6IndWVJTc19ZjEfUg2kdd4nEkh7ETXl3sEiq3MqAZj+NyX3jtY5VCtK8doBePo4rXyYWZ9vmI8Th+j4fG3yzrQI410163EG1U8ZYInfaEBApFe0IIIYSUNfrxOAEVeA3dae9d9PUaj6OaV/SInArPtP/D68vwyMw1mL5iO374z7lYs71VfGOhGIMhOu51p754z7TXd9r/7LkF3d+bO1K4b9pq5bJZ3Jz2fuKu/KLM5ff69k5uZItYiDYgMcMwYLu5IlF0KjJoCipEq3LaWzblXojWeTtpQ47H2d4Zwf3vrNYSZquQwvmx6a7LFULfSIhRNa1b5OluTvuaAkT7+iKJ9vE6/ez5rNAsOu0tcTQ6zu4sEZd4HB3BORevefESWeFTijoJJNPeo9N+2CHydN14nBqK9p6RBl50o4j8YBvIsTy7vcbj2DLtKdoTEiQU7QkhhBBS1ugKon7icSSxLmyHryScu21TdNo7HBeVOF+OETnlLNr/3SI+3/XfZYpBl/D7oht7o44z8RqPoyeaS/fntOXbXPvgKtr7uJ/9Etj9ksx12kuZ9gENREj9cihEu6ugeBzVGx4enfYu5zOFmCjadxpVePOjLVqifU0khYMiDa7LFcIFsXfCa7xVvm/yChxL6GbFSxTVaa8prnc77XXiceL6gwG5wrh0PYnxOA6E7bQvRab9nscB+55kn17TT6/AaLXGMWQh2nyCjMfR+ZtljcexIjrtpeszK9pbzp2RKe9XKAmpMCjaE0IIIaSsKSQexw1JlyxFIVo3B7xYWNahn0q3dfHMy9pUUiHahsYO8XosyhsMmtnianHf2+b0nfb6bx7kTnYrNFvMeBzlMfN80IrktJcuBodM+8LicWR0CyBncYvHMQBZtM/m3GsWqtxpeHRLlxPWbOksTk77aJV3N3UufYf7X9cLXpz23Zn2wjlPWAoQRz0UonVz2nsW7cPOtNcQboN22kciwJfuA/oMzdlGDTDiE3r/GNNy2jPTPg8xHifMTHuXgRzJaS8NIGWnSQMMjMghJDAo2hNCCCGkrNHVzfyIfJLwnQ7Z4SsJgW6irxdh1Fxenl5sp73O1oqes18AGcMQz1Vx4nHchXBzOXl9r33UdbqLRY9VGfE508upEK1q/MDz/ZKbaS8IrfHA4nGkDkfQoYrHCaUQbf4M13gcl0NZhTQShl246xHt9VzaQyLNWsuVJVI8jmHkX1dWYtWFOb77DpWn6wrsulTVeci07xKaRae9RbSPVfvMtJcK0Xoc8AnEaa/IBQf0MuTdjqmfPvbfE/jmc8CexwID9wXO+xNQN0jTaU/R3jOFxOPELYMyR5znvo4vp70Uj9PVb6mvjMghJDB246cjIYQQQioB/Ux77zZyScgsR6e91CWn3VUds6Jn2mtQhl1SYkC+PopitNcU45Xivsft6d5PYl0IDbd/p6vTvnivhbgOMug+E5LOhWhjgcXjCO04FKLdVUCmvfL5ax0scrkJkmkDaSOCWEReriqSgiFUru5EFaoNaAuzIyI7tZYrS1q22qe55dnHqwt02ivicar7AO0u2/aCH9FezLS3xuN4KETr6rT3GDMUhNBcsNPeRdj3e22MHA18/638aTqiPTPtvSOdQ914nPP/DDzzPVMkH3Y4cOSX3Ndxuya8ZtpHBB8wnfaEBMZu/HQkhBBCSCWgKzb6ycD24hIOCjHqxofT3mlwQTUv7CK7VpwDMUzKOdPeSsYwEC1RPI5+pr37ctY8cgndeBxxQEnDnZ0so0x7ZSHa7I5kNEVvl3icUJ32kajSaR9OPI7Fae8q2meQQVQZEVQFWbRPGFk3p97P1ooW7Vsl0d6l8G2sprCYlnpFPE51PdAe4LGM1+qL61l3uCRmWvP9Y1X6Wfm5YrF0PXmNxwmCqEPEiM5fULc3IoKI8OlG45msk2kv5qPvxrKUGI+jeTyO/JIp1jetA/Yfp3cvWN35VkTR3qHmAp32hITKbvx0JIQQQkgloBvr8dZHdsHDbU0/Anqh+ClEK4nwTsdFNa88C9GWugf6GIZ8/oox8KCOvdFbzutx1q0RIQ0EqaN8cuJxyijTXlmINjs9ndRrKOXstI9GzH02DAORiM6QlgJlIVq5n+HE4wC7OpL4x7sfIxqNYNzBzgVNTdFevc/VSCud9gC0Bd9hkSat5coSKR7HVbSvlkW4qj7AOX8A5vwDWONQPFfptO/rvF2veHLad8V3RGMwhWuHZ0G0Sj/KJ1cYF+5PrWiXXHSc59b2Ey2WPnVd81HBrSw5mK243Rd+C5pKMNM+HMR4HA8p1iOOMP/TxdVpLwy8iOeMmfaEFIPd+OlICCGEkEpAVw/9aPMu2zQ33U+MxymJ0975x78XNzOgjs4pttO+t2XaG4aBtCAsFmcX3CNnzO/BvGWhK5p7yrTPmVxWmfZubydkNEX73OgO0Wmf6WoXiBWg2SvjcRSFaFsKiMdxenNj0oOz8f4a04396Iw1ju10JDOiKJ9F5bT3KtpXNGI8jotoH6+WC0tW1wOjLzRzyf/0SfX6KtHeLffaK/Fa74VoAVPQlAT23Pm6gwG5QqjUplenvVfRvs9gu2ifvb8k4VNLtHeRc1Q1C/wQVKa9mN+/G8tS0rMtyMEWK66FaCXRXipEm412Es6d13uDEKKEhWgJIYQQUtYU4mJ2EypLEY8jZ9o7r+MlN9xpXjHdy7pUUjyOAW/O8iDRiZxxWs5rF5O6TnvxLRD3Zd1Ee93tB4HqOdHjtNeNx3EW7bPRMAVfL4p4nE5FPE6bIutea1OKrq7d0dYt2ANAQ5OzuNyRTCPj8NOzCinshF3wS3SL9ruBqCfF4yR14nEkp33XtD5DnNdXzQ/Faa858JIr2rsVY9XNtLeKoP33tC9TXQ+c88f8aWffqW7Ts2gvHeuuG0yKGCnkbRwAOOnyYM9jYJn2dNrnITrtQzwebgNy0sCL10K0upFyhBBXduOnIyGEEEIqgUJ0ZjcBXnS9hyzA+nHai5n2DvummlWWhWgryJCVMQxRxCxG7JBO5Iz03W19FbqZ8tL2dArRuonyRXXau70Z4OT0zSU3bzutdtqnMwaqCjJSquJxZHE+XUB9AFX9g227vBUp7Uw5x+NURVKYmTkCm4xB2KMrl/69zGFII2b2wLfT3iVeJRR8btNXPE6VwmnfJdbWDlSvu8+n1VEZYYj2vpz2ccDpRZeoptPeKizuMdr8b9N88/uRXzK3ddRXgLUzgVVvmhnhoy9St+lZtHdwvft12kvscRRw7p+APY/xt34h6Djt/Rbd7a2ImfZhOu0d4nGq6+VoHqdMexaiJSRUKNoTQgghpKzRKZqpwk1M9SqGB4E8UOC8jiRsO+2abtHScqAc+6RClWlfjF1QbcM6WeUazytEq9HfpOZoipe3VXIF/s4yKkSrek6ksscgoHicrNO+4OtFFAsjStG+kDdslG9ueGzHFO3VImQ1Usggim8lrsXl8WfRjmr8NvnVngX8iPbRKtPd3LLJ+7qFUF0PJOxxba50NJnXTa4I7RqPo3LaZ3Pho8AxXwc++GfPvFEnmBEYn7tNfVx143EiMb2ik/FaD077nG27Ou2r/TntIxHgkheAOQ+Zx+/4S8zp1X2AL/5Vr59eRXspqib7MAhSpN17bDiCvZbT3mchWrfz3JspdjyOUyFa1aCL6LTPxuOwEC0hYULRnhBCCCFlTSECl1s8juiaDj0ex/7D14/T3k88Tjk67StIs0fGUJyLIhxX3YGY4ArR6q0gCd46UT7uhWiLGY/jMl23EG2uaC+48+Ndon3Bb2YoC9HKO1LI9pSDRR6bNONxnDPtAeAjYx/8MHmFfQE/cRF9BpsibLGp8SnaA6ZwXz+857tOIVrRaZ+z3+f8AdjrOKCzBTj+W0DdoJz2FW9M6Ajh0a5ommSr+7JVffSd9vGc/ZGiQ3KJxTX7KgiLtQPMCBm/BBmPE6TTfr+T/a3nBjPtw0GMxymR014Vb+QUj8NCtISECjPtCSGEEFLWFCKIurm4JTErbGHbj7s/qHiccnS1l2OfVBiGIR7bUo6F6Bai9Vrwt7BMe9WgUU77KbdrvniFk1Widvc9ppvPmyuwik77bCHacDLtVYVoC3mmqd508voGlGs8DlyOsR+nfZ8hQFXAMS866AiXKjqa87+rRPUssWqF0z5nv2NVwAnfBU6+Il+wz64vtqshou5xlHtRyyw1/fQLxnrJtNeNxwnDueynEK0KKZJEN9P+jBt7Pg89FDj8HG/90kXnuaWTaS/t1+4s2kvXeKiivcNApurZJQ4sOGTa02lPSGDsxk9HQgghhFQChchbrrEzJRDtJQezu2hvn+b0+1lHNC0XKku0l4Xk0mbaO3/vWd/b9pKaTnsvhXmNPKe9+4/6VMZAdbTAYowaKAvRZvvrx2kviK1Zp33BgxFKp71atDcMAxEfhS2Dddqr/WK3pL7m0AfDv2jvJnqHgY5wqaKzKf977jUlEa9xd9o7obomdETUz90CPP0dve3oivaxmnwR0G3wIFat19dCi7pKeBXtawcIbQTgtD/pcnMAZdcm4PBzwxN8tZz2GvE4Esy0zyfMeByngTZVvJGYaZ+NxxHuv0oqVkRImUPRnhBCCCFlTSGibjnG40jtu2VOexFGVdtwml5KyrBLSgwYokDv1cXuB9VvYOu2g6pnoBtPI4n76sKuOaK9S6a9Uzt5bWYMvLZ4MzKGgbM+sQdiPkR+ZX+7nfZ+4nEEp30k67T31D07otM+oozHyW4z5kO3VF3bfjLtDYXT/rX08Xgnc5RzAzrObyt9BptxM8WmIKe9pb+uTvsq50x7vzg63CPApy4F9hnrwWlfrxePY23PNdM+rifIh/GM3u8UYNMCS39qxHsfAFDTX5jolGnv4YY98DT9Zf1SP8J9Gb8DVm4xSL0Z0cUeokznVohWwnM8jubbaYQQVxiPQwghhJCyppDf2m6in1wUNmTRPqh4HMdMe/12wkRHzC6G4B0Uykz7IuyDagu6mfZeu6ibaS9l0+u4/XVEe52Bg2uemY//eXQOfvjPufjx4/Ncl5dQxuN0O+01BYisaJ/JiKJFt9M+lHgcdSFawH+NAFVPvQ4Adioy7Zcc+3N8P3klkm5eMj9O+7rBhYvXfnAS7d32wxaP4+K0j9XIDvZC91sloh49Ebh+C/D528zvTkUtc6npryfMWvvtmmmveV2EISKedHl+/v7nfwec92dg4L7y8k5FWoPMtA+LcVflfx+4j30ZnQEr6fm3O8fjSNdwmG8eON2zqmtUuj6z96YU7cR4HEICo8z+EhBCCCGE5FOIqOsmjpUiHkccKPARj+O0SiUVoi3DLgGQrzvDMETHezFih9QOet3ljJzP7tvTzbRPCuK7SgTPL0Tr3gm3gYNdHUk8PWd99/cXF2zElmaXwp1Sv1RvpmS3r+u0zwqsCqdtLLB4HDnTvtNhIMRvWoHqEap7fWQxM+3tPz3b4wOg5Sj2nWlfokK0Kuoccs0BoNNjpn1VreycLbQAr0o0jFUD8ZxzoZtTr/v2QdzqtHcRc92c+FnCEBH77QF8fyow7mrgS/cDY74HjP4KcMV84MTL7MtLgmj2BpOOdxiRPoUweH/ggr8Do8aYgzfjrrYvo+W0p2ifhxiPE6JM56cQrXQt5vbbKuqzEC0hgbEbPx0JIYQQUgkUom+5ivY+BPRC8Zdp7y2SRZlpX2RXu46uV66Z9tI5MQzV9CI47bUz7d2X0znmbpFNWSSnvU7+vp7T3rkPjW12MX1jUweG99eM7OjC3WmvG4/TNWCgEFrjCCgeRxK9HDLtgazT3rt7U3UuvYr2Hck00oJon9Ttk5/4jD5DgJbN3tcrFCeBus8QoGWTer7Vae+WaV8/wi50A4UX4FWJ4VaR3kkAzKWmn54w69lpr3ldhBXXMfww4LTr7dMlZ7LoYg4g076YjL7Q/A8APp5un0+nvXeKHo/joxCtdM5y+x2NAbl1aui0JyQwyvAvASGEEEJID0YBpWj9ONjL0mnvcR2Vnlaww9cjOuJwuTrtxex6lDAeRxl7oxePk9tHHUE+peu0F0V79z5IYr+tD0UqZudaA8KL094wlKJ9rEu0f23xJkx6cBZunLwYbQkfYqJYENI5Hse3014xXTc+KUtnKgPDsLs1kxlN0V7XUZ1L3UCgukDx2g8qt2o07u5E9uq0rx8hC+e6DngVKtHQ2q40YCBR08+MU1FFx2Sx7otrpr2uaF9kEVEa6BMz7buQIka8ZNqXgn0+DfQf1fP9oM/6j3XZrUX7Isfj+ClEK/3Nyb03bU57FqIlJCh246cjIYQQQiqBwpz2bvNLEI8jbNO1EG2FxuPoiK5hudS3NHegNZHG/kP9iXbSscoYhkK097UJT+jWKdCKx9HosFRgViKR0n/zILcPUqyOFa/CsF9cj5lupj0ApDpc43F+8fwiAMCbH21FVSyCn37hcP32AWU8jlMhWt8DIIpToPsmRpYORaZ9UsNDZhjwF49TVafvBA+SaoXwVdPfXYS2Zdq7xD31GykL54WKfiox3Lpv2oVo+5kRGxfcC7x8jXkNN20A2ndY2rOcL7cCxNqDOUUeHU622qfV9Af2PRlY807PtLE/UrdRjk77XKJR4JLJwLQ7zcGxU68toK3dWJYS43HCFO39OO2Fvx+5/bY+b+i0JyQwyvwvASGEEEJ2dwoRdYMu8BoEKjHYicDicYos2pcqHuff8zbg5NvexPjb38K1T8/31YaXeJxiHFfVGyfWqWpxv+ezjoCrK/JKjnnV8cgrRKtxcbgd16AuHVVXPDvtATPOJJUQZ2Wd9rn87e1V+m1nkZ4HkQjaHZz2fq/RoO5PM9NeEO0NzZ+jfuJxYjWFx8Q48bV/ydNV7v6afu5ius1p7yba7yEPTBQq+KpEVOubArqFaLNi4D5jgUvfBv7nHeCwL9iXC8tpX2wSbfZpNfXA6b8A+g4zvx89ERj1SXUb5ZZpLzH4AOC8P5mFifu41GvohvE4eUgDU6HG4/jItBdFe0s8Ti7MtCckMHbjpyMhhBBCKoFCNKNyjMeR3MNujmLpGDj1UzWr2Jn2Oo7uMA73/z23oFsUfvL9dfjeuANw0HDNQohdqJ329mWLkWmvI8Y79SV3us41rutylxzzquZz+6CXae+8jM7AQlsihVteWooP1zfi80eOxKXjDkA0mi+Gqa7TjGH2OaKbaQ+Yor3CaR9HQEKGIKAkXA6F33s/uIERQyxEqx2PoxvDkrdOTWhO+7aDz0WfoYfIM1WifW1/d5G5bQew+HmgbTtwxPl6or14bAoUfFWiodWJqxPDE6/NL16bpXagfZrNae9wvCLRcGNECiEpiPbxGmCfTwFXLDCfE24idyWI9n5gpn0+YjxOiN5ap4E2VTyONNDiWIg2pBoShOyG7MZPR0IIIYRUAoU4Pd3E1HKJx3HbR3kdh+XdMrqLhI5QGIbTvjWRL46+sXRzIKK9YcgCbzEOq07kjPS9Z3rPZ53rIKnptPdSkDTPaR9AIVqdiJaHZ6zBIzPXAADmr2/C6FEDcNJBQ/OWcbpO0xkDcS8CRKpDnWkfCSjnV4rYchkP8Bs1VEhNEXtbdhEyoVGI1oAB1A3yvsF4bWiivRGtUg8kqISvmgHu4uSyl83/AGDmPcCexzgv33e4LO4W6rRXieXWfdM5vqrIjbqB9mlWQdHpePmpc1AsEkI8Thbt2KZeKtpLhClSlzti9FeI5z4WN+8d6Q0yVbSXTiHavOXptCckKHbjpyMhhBBCKoFCBFE30bgUUSdpQQx1z7T3JvTr5JoXA51jWYwu+TmlsmhviNfUIzPX4Et/fRcbGtv9dE+LIAvRBum014m5kfqgVYjWpQ/iWyuWfbv15aV53381eZFtHceizoYhF5VUkWwD0nI8TphO+1TGWeTxe+8H+TiU4nESGc2fo35F+5AK0RqxKnWeu0qkru3vzVG87SNg3Sz1/NoBpoNdEtgLFUGV8TgWUU/nDQjVIEahTvtcsXPEUe79KCbJAP4WlHumvW+KXF+g3JGu8bD/YaQaNPISj5P7jLA57SnaExIUvfUvASGEEEJ6CYXF43hvO/R4HI8DBYZhiP10zrSXp3vQVwNB51gGPZAgHRc/51T1doOqv3PW7MTfpq70vB1dVNu1TtYZsNFxqOsXoi2l014Qr10u8o1N9rgRxwGwDLxl2idalZEmsaBEe0H0EuoB58/3+VwL8vZMCz89E7qZ9vFqtQvUaZ2QnPbpaLWD014hfNWP8B4D0rxBPS93IGP4J3o+R2JmtE4h6Mbj6Bxf1fEQRXtLkUwnN31uFvjnb+1Zd/AB7n0KG6kQrVd6azzOXseXugflheS0D9uprrpvVQOOErnXp81pX+R/bBLSi6FoTwghhJCyppC8cD8FXsMW7aV4FWfRXp7upE2q9nt3iMeRxEmdbH1bO4JonTEMOKXGPDxjjeft6KLjoAccRFaP8Ti6hWi9xOMYHp320lspuXgdAAOA6pj950+gTvum9cpCtHGhEK0vBEHEbYzFzz3QtTGf60ktCaK9rtMeAPp4dNvHa0MrROsYj6MSvgbt671wquKtDQD5oveZN5kifiTWVex0qHI1LZTxONZMex2nfX95eu0A+zTr2wu6Tvv9TgZ+9B5w8fPApdPc+xQ2B51ReBu91Wm/57HAAeN7vp92fen6Ug5Ion3YTvUgnPa50GlPSGgw054QQgghZU0hoq5rVrwk+oX8WrJXoVHVH6d987NOGGgVog3YkCWL7d7bkY6VgeIX8+3etkZWPeBcVDVLoIVoPWSl5x5THbHfrW2deBwrVV5F+4zhrahe0zpgyEHirMCc9jYBJYJkgfn/O1sTuPrpD/HBukZ87sg98MtzPoGqWDTQeBypKW2nPQDUDQYa1+ovH2Ih2kykSu2EVsXBDNwX2PpRcJ3IddofdDpw9Upz1C4WwE/8QJ32iuMhZdpb2/OSaT9wH/O/cuBT/wPMvg/obDa/f/Fe7230VtEeAL7+NLDidXPgZt9Pl7o3pUWMxwnZqT5gb2Dnx/bpXjLtc2GmPSGh0Yv/EhBCCCGkN1CIZuQmTqricQpx97vhNUdfHYvix51fXMFZJ5Ij6GMtFVDNFdqbO5L4zYuLcc3TH2Ll1hZlO1LfDcMo+sBHz7b1pgeVaa+bVe8pHidnUZ313PopxuO4jAJVxe1Cq+MAWMaj075xrbIQbWhO+0gUaZcBDrdj+Y8ZH+P1JVuwrSWBR2euxZQlW8xNBXi9SxK3jtO+uwt9BnvbYLwW6L+nt3U0STvFtqhy9Aft6z0exwlrzn805k+w/9yt+d8n/N4h097imo/XuLevzPgfaJ9m3a6j076M/Yf9RgD/Mw046xbgm/8Gjr7IRyO9NB4HMM/doZ+jYA+UJh5n+BHydJXT3u1f4lbRnk57QgKDoj0hhBBCyppCdGY3p7c6/9v/Nt2QxDMncVspwDvmcFeQ0z7gLknu61zh8eqnPsTfp63Gv95fj4v+NlOZgS71PWMUp3CuhKH40WwvRBtMpr1bNnwWL/E4uddsIJn2ktPeRbyWnfbq5U3R3iGixErjWrMYrUBwTnvLPkairoMVbqL9719fnvf92mfmm5vy3jslUWHQotNTPM4QbxuMVQOD9wf2GO1tPQ0yUUFoy6IS7QfuF65o75djvgYcerYpyB9xPnDUVzzE4xTgtJficawDZI6Z9g7noBwYtB/w6R8CB453XVSkNzvtSQ/SNR626D3iE/ZpsWr1INzAfZ3bs8XjeHg7jRDiCP8SEEIIIaSsKcTp6RZlopqvm+ftB0k8cyyE6WNgQTVvt8i0FxTY3P1+ddHm7s/bWjrxepej2NaOcKy8uMqDRj/TXiXu93zWKxCs1y9dR77ZB4+ivUvbXgfAgPxM+6a2JC57fB5ue2WpcvmM4TEep3Ed0LlLnBULzWkf0Sja6+0+a2o3xdMgHxmiaG/EhCUV1Plw2gPAcRd7Ww8A+jk79NMRBzFZlfPeZ7D3THsnghLtawcAEx8DfroOuPAfpjAvDS5Eq+yinjWDXkLl3pXicaxvqTi56Z0E/d5Aby1ES/IpRTyOJNo7DSged3F+fZBPftuyLgvREhIWFO0JIYQQUtYUouk6ae+GYSjbDlGzF8UzJ3FSJbD6EfqLLtqXwGkvZXtnBw8kQXtTU7vYjtT3zlTpXvnWHbwJKh5HF0/xODmb1RH73YRmyeXvJdP+wXdXY/KHDY7L+4rH6WgWZ8UjGQTiXRficdzeMPA7OBZkPE5U2PfOjAdh0nM8TpfAPPpCb+sBwFm/dnRyZxyz1qV5EVOEDdJpv/+44NqyIgnikviu43ZXFaKVXL1pi2jv6LTv5aL9nseVugekGEiDM2GL3sMOs09TvCEGwLz3v/tfU7w/9Toz9ikXFqIlJDQo2hNCCCGkrAmrEK1Ts2EWG5ViV5wz7d3b2dmawL9mr8Os1Tu61qkc0T7oTHtpACS7CamwqUrklfrupehq0Cgz7S3fdcR9r65rJ7zE42RyBk90RHv3THvpXuppV+pbVaxHILFGwij7kPEg2qfagZ2rlbODcdvb43Fcj1UJr90sEUG0T6TdRfvua9qD0z4Zqe4Rw2oH4JV+X9JeF4AZqXPu3Wjtt78429FpL4lw/Uaa/w9KtN/7U+GK9pLDXSpSmZQHPfNQxeNIDNg7/3tVH/WyvU20H399z+e+w4Djvlm6vpDSErZor8yud2DEJ4Bz7wbG/9T+hk3UIiuyEC0hgVHG1VsIIYQQQgpz2juJ747zQhS4RKHRcXDBWYRtS6TwuT+8jc3NpkPxd18erRbtixzIrue0D7ZPkrCe7YckFKv6WOxj5YY69saaaS+vn++0D04Q8KL/Z5dNZdRvueTiNiAg3Uu55789aRcOpEx7J9IZAwvXbceRXlbatFA5K4400tCIhEl1msJNlZAZLmTaSwWYc/E7YBfkbSDF43RoZNp3d92D0z4dqUKupPu3qm/ig2QVjoquwtmxWe4N1A4Ejv4qpsU+g9OeOgLVkfxrKe2UaS/RPwDR/sTLgIPOANp3mBn0YcanSP2UxPe9x7i3pSpEC5jO3bkPm5/jdcDRE/PnDzvUoY+9TLQfd5VZg6FpnXkcpHuf7B5UWrwMnfaEhAad9oQQQggpawoRdQ3DSfT2J+gXiiSe+XLad/XxnzPXdgv2AHD10/O13PnFQOfcBV6IVhAvu0V7IcpF5Tov9rFasWUXfvbcAtz132VoT9h/8BaeaR9OPI4Xsn3Qdee7u8edB2E6hONYHfco2hsG3l8l1z1QsmWRcpbktLeds8X/AX57AHDzXsD0P9gbsQk6Eddj5eeZZhgGdnUGV1BQ2nedQrTd97SHDPekRVRvTkZwT/pc/G/yB3oNdOWtpzIZcZAlHfEovmed9oW4wwcfABxwKvCJLwLxkIuw6sbjDNwHOOkK83PdYOBzt9qXURXmBcyYjZMuB466EJj0ElBridLZ8xj1ur3NaR+JAEd9GTj5J0C/PUrdG1JKiiF6H3RG/vdB++GBhQ/gU//8FCY8NwGLtqn/jtmwDvLRaU9IYFC0J4QQQkhZU6i+qMyEd9ANwyxE6yQqS6j7b07/75LN9nkhxuPMWLkdT72/rrtQpRM6MSzBF6KVsujNYy6JxapjEmSEjBsdyTQu+Mu7eOy9tfjDlOX45X/sTm1lPI6ht1zu7pRKtM+ea90cfLdIF7f6EG2SaO/RaZ/JGKhCcAJEXGjLthv//TmQaDGFj9d/BbTvzJ/vI9Pez9sVlzw4Gx+ua/S8nopoxN5HLad9tusenPapSL6onX0DoxNV6DQ0xN4uQTiVNpAURXuPgvG+J5r/L8RpP0iO6gkFqZ8qx/wZvwJ+thG4cjFwyFn2+VJ2fZaaeuCMG4Ev/R3YS8hwH7C3Ohapt4n2hGQphtP+zF8D6HlbZ+MJl+CuOXehLdWGNc1rcNecu/TbshaipdOekMCgaE8IIYSQMqcwgVHlMHUs5Bri7yXJZJwx1M5ut3gcSYBVtVVo6s+jM9dg4t9n4uqn5+PsP05DhxA/otOPXIJ+qUES5rMFZEWnvcL1XUyn/auLNqG5o8fR/K/319v7o3kdq5bLnVzMAYlcsveVtmjvI6c9dx0pHica9RYpkjYMUWj3S0xoK28gL5MGdn7c891IAyvfzF/BFo8TcR1otF7mmYyBJ2atxZ2vfYQNjXIu+dRlWx3b9IqUad+hkWnf47T3mGmfQ/aaMxDFYmNf55UjPT+Rk+kMUpJo7xbNcsL3ej7XDQaOn2R+LkS0H3yA/3W9ImXaO+VgV/cx41xigkAvTdMlEgH2PFaep1MEl5BKpBhO9eGHAZe8CHzyO8C5d+Op2nxp8L1N7+m3xXgcQkKDmfaEEEIIKWsK1RfVzmN1w2E67VWO17RhIAq7gOUWiyIJm2HF41z/7x4H+Pqd7Xh27gZ87VP7KJfXieQI3Gkv7GNWsOv0EI/jV9g2DAMRj1nTDY0d9u2nM4jnuMLdBm9U33umG+LnYpLdrnQeJNzc4dL8tIto7/Utg3TGQDwSXERMXIiIyetTqtM23+a6tDntI67Xq/VY3fHfj/DnN1cCAB6ZucZx3aAQ43E0RPu0L6d9vqieO5g3P7M/jo2uUK+cU/w0nTGQEn4yuzrtP3eLGR2zayNwwndNURuQRftB+5tu840fAksmA9s+ktssZmSKGI+jUVBWcr8X6ojf8xhg5RT79KCK+hJSboTpHMllv5PM/wB0zP6t/3ZYiJaQ0KDTnhBCCCFlTaECo594nDB/L6nENa8FUXuc9vbOFqsQ7fQV2xzn60SXB1+IVnLam9Mkh7dKQPZ7rKRCuG70q7WLTzvb8uOHVK1axXydGg6lctpnu6Cbae92LKX5eYVohXgcr/ueSmvE4xx0hinQaiA77XOzixLujQjxOG7Fs637nRXsAfu1FhZR4SpOapyP7mdcdb128VGb0z5PtD/QeeUc0T6ZMZASfjJ3i/YRxc/pWBVw0o9N8X5IzvYkoTlWBRxxLnD6z10y3IvoLJf6WeWQTd+9TB/7NM17Q8nIY+TpjMchvZUSFKKV/u2g+veEDTrtCQkNivaEEEIIKWsK1XT9xOOE6bT3mjevcsdnp0sRIapdK3aWuU6OduCFaB0y7ROCWCzlngNwFUFVJNMZ7OpI4hfPL8S3HpiFN5e6FzGtr7ELZNtb8x3XOrE3OstlMkbgkUS6dGfaB1SIVi7q3NO2JNp7zXZPpDPu8Th9BgNfvFervXhEcNrnXmtpQUB3ddpHNeJxSnTSc4gITnu3LH4gZ8AhEtF226vicQBgvuESM1NVl9O/DFKGPR4nlS1EqxLtVUhCc+5AhGpQIlZj7n+xkPpZVeu+Xk29WSg3y4GnAYMLzOJXDWQ4DWJ4KFpMSNlRguvXEAZVO9PCm18S1kx7Ou0JCYxQRfv3338fN954I84880yMGjUKNTU1qK+vxyGHHIJJkybhnXfeCXPzhBBCCOkFFOrEVoreTpn2IaqaKpFKNbjgFu8jC5feBgB846Ih6QiF2k4uTSTxMtGVaS85vNsTcvSJX6d9Km3gd69+hIdnrMHUZVvx7X/MxuZme/xN3raE47SjJd9xrRvz5CdOqVh4LkTr0tekcK7znPZCPI6OSJxLZzLj7rSPVQGjPgnXGwI+nfa2k2/NtI9qxOOUXrQXnfYaAzh5zy3NXPukQzzOSmNP55Wrexzl6YyBpBiPkxXt7YK+I1ZxC8jPj1e5x4ud3y457eMaoj0AXPB34Lw/AxN+D0x8ovC+DNgbqOlvn+4Uj3P+Pfnfz7278H4QEhZjvp///bTri96FjODu70g5/9ulG+u9SKc9IYERWhDcuHHjMG3aNNv0RCKB5cuXY/ny5XjooYdw8cUX4+9//zuqq1lIhhBCCCHB4zV2BghX2FT2RyEmqguQqtsrVjyOGzqHMbvMrNU78PLCjTh2n0E492gXUc0BKTLFKR5H6bT3eQ0k0hk8PKMnI9wwgL+8uQK/Ou9I5TqScLm9NV+8VYvx1u/O10up8uyBHu25QVH41IqqSHAW6Z7Jy7QPIB4nkU4jDpdM+1i1Kbb2GQK0OUdGSbnuaTfR3ip228SViK+3EoqNtO86b13knbN+ewBbl7iuk0SP+J1KZ/Luk4ybby3HaZ9MG2Ih2iS6frt6ddpLTvrcaSrRPl5Bon2sCjj2G8H1JRIBhh0KrJ9t2Y7DMTn4TOCsW4BlrwD7nggc/bXg+kNI0Iy7Bti1Cdi2HPjkJGD44UXvgmSg6EhrivbWwUs67QkJjNBE+4aGBgDAnnvuia985Ss45ZRTsM8++yCdTmPGjBm44447sGHDBjz88MNIJpN47LHHwuoKIYQQQiqYgp32itWdmg1T4FIJ58qse5d4H8ltrBRuix6P4769jGFgycZmXPi3GQCAB6d/DMMwcN4xe/napuSkTjiI9pIbG/B/DUgC/AYXkVoSLne0Wp32ijcxrN8rwGm/YkuL1vLuTnspIz0nHkdy2nvc/85kBvWuTvsu8bB+hC/RPu/tEDEex0W0j0Rd8//LQbT37bTP3f/hhwOr3nRdJ5ETjyMdm9uTX8FVVU/JK+fksqfSGaQF0T6VFbVPvQaY8queGQed4dwxVaZ992eFEF1sp700eKAr2ofBsMME0d4h0z4aBT79Q/M/Qsqd+mHARY+UtAtSPI6+094yeEmnPSGBEVo8zmGHHYYnn3wSa9euxe9//3t86UtfwgknnICxY8fiJz/5CT744AMccsghAIDHH38cb7/9dlhdIYQQQkgFU2i8vNp5rBaxsgLXfdNW4ahfvorxt7+F+esbC+uIpW0rO1oTWLqp2SYsuxUWldzGiVSRnPYuzenF4wA3Tl6cN+3Wl5f67pIUj5N12ktFZyU3NuBf5JQGDdzakgYTtrfkZ8mqWrBeH+pMe/X1Uiyyh0FftHe++SUnftolHsdrpn1HKo2qiEY8DmAKLy5I+fj5TnshQzhjEfItp3BraxILNzQ5breUgzVZIqJo7yHTHtB2oHY74SHfXw+mP4eX0mPQUTXQvrKlEK3Yp6yT//hLgKGHmp/rBrnHWoiZ9nH5c956ZeC018m0DwvpvDvF4xBCPJG0/p1BAU57ivaEBEZoov0LL7yACy+8ELGYnPM3dOhQ3HHHHd3fn3766bC6QgghhJAKRnL/eEEZR+MgYqUzBjY2teM3Ly3Brs4UVm9rxS0v+ReSc1Flap/1+7fxud9Pw5fveRetnT1xHG6xKJIQ35GSfzAVW7jTFb5nrNqe931jk+YPRQE5Hsc8Hp4K0RYQj2Nry6Upqc+2eBxFf7Y0d+bNc41TKmE8TvaYrtiqJ9p3JL0XV829xodumYmXq6/Dy9XX4tPRReZ8H5n2roVoc532LvjKtE9ZhHyL074zbeChdz923G4pY5GySG8Z6DjtDSPn+h9+hNa2EjmZ9tI92Yo6/DB5Be4/cYpdcDrsC90f05mMXEA3+8J6n8HApVOB770B/L/31UVTs4iZ9jrxODXO7QaNGI9TZ59WLIYdZp9G0Z6QwGhLttmmZZ32Ozt2YsHWBWhPKd4aZCFaQkIj1EK0bowfP77788qVK0vYE0IIIYSUK4XqzCrx1UnDyhgGHn9vbd4yVmHZL27i2fz1TXhhfoPr8lkRSxIhOxWRL0WPxymBUCi5r7NO22QR4nEkd7jbcZecwNZ4HFUTm5o78PX73kNbV0Fd90K0Bb66UgCGYSCTMbByS6vW8ptcBm+kwY7u/UsncebyG3B4dC0Oj67DnVV/RQQZ7/E4qYx7pn02k7x+uGt7cddMeyEex0W0Nwz3Ari5z4liPweyRH2K9kDOs2TYoVrLJ+As2udt/+weIxkG7QccPbH7ayptyLE+kRzBuKoO2Ot4oO9Q9465ZtqXczxOkQcOcpGc9i1bit8PQnopbSm7aN+easeS7Utw7r/Pxdde+houeuEiNHY02lem056Q0CipaN/Z2fMPUJUjnxBCCCG7N6p4GP315elO4nkqbSjF3ELREQ3/9OaK7s8q8dgpo1yKgTHb0umhjB+hrxTioBRn0V2IVnTay4Ks3wGHpBBN5DZQo1OI1umNkxmrtuOlBZvM5ZTxOOb/S5ltnjGAhqZ27XtrvUstACnqpnv/Nn6I/okeUW9kZAf2wE7P+9+ZSqPK1WmfFe19Ou1zBx8kp701Msci2mfgLtrnXoNSHYxiIMXj6L750H3eavppLZ/IiceRBuu6l0tlzMKPk14BvngvcOm0PHE6mTbkfsMhT90Jt0z7sonHEfZv6MHF7UMu/Ubap21fYZ9GCPGF5KLvSHXgTx/8CY2djQCA1U2r8Z+V/7GvbH1u0WlPSGCU9J2yqVOndn8+/HDvFbLXr1/vOH/jxo2e2ySEEEJIeVGoWVslvjrG4xhGaFEyOqJhVU5RL7dBB6m9TkWsSCERGX6EvlLkaDs57SVHuyoex2/fpePkmmkvifbWTHuX7vzi+YX48vGjHAovq9/MKBYZw9DOsweADTudRXuxEG12/3Z+bJsXi6S7nfi64n3Q8Thnxd7HnNQhQI7Qnu+014jHsYjIOqJ97vVcqmtAcqzr3mde70ddp333M2HfT5v/WTDjcYT+RHyK9jHh53eu4FUu8TixOHD014APHzO/73MisMdRxe1DLhHhLIwcXfx+ENJLEeNx0h14e31+7ck/f/BnXPyJi/MXtBWiLd0bfYT0Nkom2mcyGdx6663d3y+88ELPbey9995BdokQQgghZUihWcxqp7rzOmJedjqDeKywFxV1xMJ4rEegUO1/VoeS4k5UmfaFuKz9CH1+nfaxqLsIqUKMC3IQ7VWFaP32XXL1ul3DTvE4TW1JrN3R1p3LryJ7xNwy7UuZbZ4x9IvQAsC2lk50JNOorZLfyJWK6nZf4012c08NkmjvWkc3lqUzlUGVWzxOVrTv616I9vvxFxFDBjelvtk9Le8e9hGPk9F4eTpdBqK9lGmvS96zq34E0LLZcfnOXNHewWnvdh0kM4Yc6xPx+TNactLnifaqeByfgwSFcN6fgP1PAZLtwDFfK/72rZx1M/Dqz3q+H3exellCiCdUTnsrNTFhANEaj0OnPSGBUTLR/q677sKsWbMAABdccAGOP/74UnWFEEIIIWVMoWZtSaQ0DANvLlXn4aYzstO+M1Uk0T7HteTHOd2hymnXEGwNw8CLCzZizfY2nH/sXthrYJ1yO274jZgZ1Me/QKVyut/80hLc+/Yq27xUxkAilUF1PGqb7ocOQSB0a0tyAje2J7G4oRnfuP89W769RKTLiaraVEtnCoZhYM6ana5thYVhGPh4u16efZaGxnYcMKxenCed6+5jLTjta5DErmwtCF2nvU48Tk1X/zSc9gDwnfjL+E3q691ie94zweaqh919b3gXv3O3Uap4nGjE/8M87xid+Wvg2e85Lq/ttHcR7VPpjByPY/iMdpViZ7TicUqQJx+NlYdYn+WE75mDWhs/BI68ABh5dKl7REjFkjEyeGjRQ3hp1Us4fMjh2Nq21bZMR9ou2ldLA4vWQrQZl4FuQog2JRHtp06diuuuuw4AMHz4cPz1r3/11c66desc52/cuBFjxozx1TYhhBBCyoNCPaGSSP7L/yzCwzPWOK4juXg7Uxn0LVA70SkEWhVTRGfkkDEMpNJyYU1Vpr2Oe/zv01bh5peWAgD+NnUl3v3p6aiviRc1HicaiaCxLYEX5m/EngNrcdphemIooB5ckAT7LO2JtE209+u0b+u0/1h1a0py5xsGcNsrS7UEe6AnPUKVaf+7Vz/C7179SKutsMgYBlo7vTnwNjiI9qq3YQAAO1ba5lUj1X1NSjFKEp2pDOIRlz5XexPtAWAAWrAT/c2+uBaitQgnPpz2QcXjxKORksRe5Z3rI78EfPBPYNVbaEI96ox2VFvOUa5o75Rpr3pWZkkpMu3TGsV/RXw77YucaV+OxKuBk68odS8I6RXM3TwXd825CwDw0U753wa+nfYsREtIYBRdtF+0aBG++MUvIpVKoba2Fk899RSGDx/uq61Ro0YF3DtCCCGElBuFFqK1Ou07U2lHwR5wctoX/kNERyvMdfOr9j9tQFnQU5Vpr+Pyzwr2ANDckcLDMz7GDz9zkCj0ORVHBfwL37s6Ujj7j+9gQ1ch0p9+/jBceuqBWuvqirG5tCVTGGApLOlXmGwV4nbcjoPK7Tt1md35piLa7bQvXfyNGxnD+z3klGsvXZPd5227fZCmBsnu6yOpKVybmfYursFscdS6QVptAsDgyC7sNEzR3i3Tfs2WndjHMLrfprAWONAqRJvrtC+gInVVLIpUCQSZvGMUjQHf/DewYxU+f/cC3JK5A6fG5uct35FTiNbJTe92HaQyhnh0fd9nUqZ9rtNemWlP0Z4QEhx3zrnTdZm2lD3nXstpz3gcQgKjsPe7PbJ69WqceeaZ2LlzJ2KxGJ544gmMGzeumF0ghBBCSIVRqAZpNYirMsxzSaQzSAvOcpUY7gWpXSvxaG6mvbyMYRhK0V6Zae/jYM5YuR2A3hsCQWwPMAcjsoI9ANzy8lKHpfORipO6IRWj9SvKtSfsAq/bYEkhImqWrJ5bzvXfMobh+R7KvQ6sSNdkKp0xM7ibhUz7SKL7XOhn2uvE43SJ9tZifA4Mxq7uzykX0f6D1ZvxVu4AjsVpL8vK+eQ57QtwylvfSCkWtnMdiQBDDkRjpjZPoM+S57R3LETrfG5TmYyYae+7PojotC/TeBxCSK9lU6tzXRAAYmSOntO+jP8hQkiFUbR/dTU0NOCzn/0sGhoaEIlE8MADD+C8884r1uYJIYQQUqEUXIjWsr6OYNXamRLFX7coBR10tl8Vy820V8fjqAYgVJn2fpzvWZFVcjVHHMTCTMYoeMDFD36c9tJx9Bsh4mcAwKlQpi5uhWjLAcPwfg/5ctoLefaANR5HN9M+g2pdp70HBkeauz+nXQrRViOJnz6zoGeC5RzriPa52yhkkKhUor1KA0qlDbQLon2n0SN+J1Lqc+127yXThpjF73dA0jXTvpwK0RJCei2dSfe/G1va7LWfRNGeTntCQqMo/+ratm0bzjjjDKxaZb6mevfdd+Pii1ntnRBCCCHuFBqfbHVE6ghWuzpSopAbTDyORiHanEx7ldCeyTjE4yiEKD86dGfX8fLqtPctahWIbuxJLtJx9Nt/SbR3O+eJAjLGs2SjU8pYszed9l7jcRyc9tLAWjpjANvtefaAGY/T7bTXvJ47EinUwKWuQHVO5v7YH2q1OySS47TPPf+C074aKWxqzs0W9h6Pk/vYK0i0L7AQ96zaGtwxaCDeqqvztF7u86elM4V1O9qQyRhIpDPoMATRPqBCtMqaIkE67SM5508Zj0OnPSEkOJrb3f8OSKJ9JCL8vbEVoqVoT0hQhJ5p39TUhLPOOguLFy8GANx666340Y9+FPZmCSGEENJLCDrTXsfV3NqZRqtQUDQIp72WaB/Nddor2jEMUSAG1MKtTjSPlezxksRwp33xHR+hwMjN9HbAT4yPLLT7O9dtUjyOyzXsVChTl0pw2puifXDxONI5SqXVTvsaJJDKGDAMQ9tpn0olEROc1vkN5zjtT7kK2LUR2LIE7/Y7C/1WPI+jovb+DEau0z5XtO8U+52HrRCtN6d9IYVoc4tke2V+TTW+u8dwGJEIHgJw96atgL3OoUj2up63die++4/3sb01gZMOGgIAaIdd0O409ArRJh1c+IA5wCEWovV7y0qZ9tAQ7VmIlhASJBl3KXBruz0epzNl/xtli8eh056QwAjVad/W1oazzz4bc+fOBQD83//9H6699towN0kIIYSQXkbhmfbeRfuWzqQo5AaTaa8j2udm2svLG4aBDo18fi/blqJlsq5cOYpEfTyCFo91xV4/gqScQ++5GQDyAIBbn9zcvjpEKqUQrcd7qLHNHheTRRpISmUyQKJFXL46Yp7ndMbQdptnkhqqcq7Tvu8Q4CsPAT96D3NHfRMphUdqSE48Tn6mvRCPE7Fcnz4y7XMHjvwMbGWp8uG0r68xj8GdgwbCyBl4+9XQwdptZI/RvW+vwvZWcxBj+gqz3kanEI/Tbug57TtdroNU2kBUEu19x+O4OO2l+ByAoj0hJFAMDSlwR8cO27SOtPA30ea0d4mUI4RoE5rTPpFI4Itf/CKmT58OALj88svx61//OqzNEUIIIaSXEnSmvY5A+vdpq8XpQcTj6GTa5y6jzrRXx+OocNv1DkEY73baC0KfUxRN0E779kQatVUx1+X8xOME67S3t6WqMZAl0EK05avZw/ARj+M0yCZdY6mMAUhOQJjxONlldIuxRlLOon0yWosq0T1tnouUQhgZnBOPk++0t8fjZPvdjS+nfc82/NwjWfxk2vepjqGlM4U5dbV507fF3e/nLNmBr5cXbrLN64Bd6E7k/Mx1LkTrItorCtEGGo+TC+NxCCFFwDBiGn857CSEv1H2QrR02hMSFKGJ9hMnTsRrr70GADjttNPwne98BwsXLlQuX11djUMOOSSs7hBCCCGkQilUg7Rqr4UU/Sw0HkdX6Ml1wqpE+3RGHY+j3L7LAIhTjr9Xp33Qon1bMo1BlmkdyTSue2Y+Xl+yBSfsNwh3f+24AONx/PVTilVyE+2DLERbaJxUmGR8FKJNpDPKaCTpzZBU2hCFb8As6AqY16ZuwWLDRbRPxPoKkjG6t5NRiPaDkJNp7yLaV9tEe2shWnchPff+LSwex7to37cmDuySB1J0cXp2tRt2Qbsj12nvFI/j5rTPyO8xBFqINi8eh4VoCSFFwNAfNM1Fdtpb/i4Yhf+bhhBiEppo/+yzz3Z/fuONNzB69GjH5ffdd198/PHHYXWHEEIIIRVK4E77gkT7wtxDuu7eXCFJpUEbhuHDae+8fUlczh4vSQwvttPeyuQPG/DvDxoAAG9+tBV/emOFz3ic4Jz20jlxE6qDiccx/1/OTvt0xnumPWAenxrBlS3dT2kdp33a0HabGy7xOJ2xPuirWtcwkFIII7nxOHnXmhSPA+d4HC2nfc5zULcIr4Rfp32hOD07U7C3rxuP4+q0TxuISE5736K9y7FQOfFjdNoTQoLDyPh7LncKdVdszy067QkJjFAz7QkhhBBCCibgTPtCoiEKzbTXFbJz++gYjxO0095BcBad9g5imORE7Yt2fCqyBMOx062r9r4J+2qNyrhn6kpfArjotPcpyom1EFIZRwd8EE77bPPlnGkPuL91IKE6PtI1mcxkxGKuAFATycbjZLQjidzicToifZTz0oaBtEY8jpvTPtvvbvxk2udsoxCnfY0P0b5vTeE+Mae3lKRBi06jZ5tO99faHW244ol5ymepWYjWW38ckRzzEY1CtIzHIYQEir+i4rmFaLe2bcW3X/02PrXqEfxy6OCed8JYiJaQwAhNtDcMw9N/dNkTQgghRKJQEdK6fiLt/8eEk0v4vVXbMeHuaZhw9zS8t2o7OpJpm0ivKwTnOe0Vq6QzxXHatyXSSKUzotPeyf1qXXwQmvFi9c/wZM1NeL3mKhwdWaHX6e5+2GNn1u1os02bv77RU7sA0JaUCtEGJ9oDwJsfbcHvX1+GeWvtAxZBZNpnByvK2WkPeK/DADiI9sI1aTrtNeJxNN3mEcUAQJY2B9HezLSX3YyD0YzsiGTetSb03U+mfdQyKV+0L24h2r6+nfYZRGsaEIntcn7WCD9p2w29THsA+PcHDXhhfoM4L50x8Er6hLxpLUat7/gs90x7xuMQQsLHsL7BpUlHuqPbhPCPRf/A7E2z0WYk8Wy/erzTp85ciE57QgKDTntCCCGElDWFipBW8TWMeJx0xsAVT36AhRuasXBDMy66dyYO+/kr+NTNUzBnzY6e5TQdriktp73h2WnvJkSr2mvuSIlvKDi9tWAVRb8em4L9opsBAP0j7bgu/oRbd/NoE8TeYf3s7tPNzd6zs+V4HL+ivfxD+NsPvY/fv74cX7lnBhZuaMqbF4TTPvsWSDln2gO2OHYtVG9PSEKumWnvXohW942biCJqJ0sbapXzMhkDMSFaBQBqIinUo93sT25fdArRWl4/kpz21jihvEK0BTxUq2Le3Zl9HJ32qr6kUbfvveh7wB/R98DfYdGO95XXtjS1w+gRv3Xur3vfXiVOT2UM/Dl1PnYZphiVMSL43+QPCojHccm0ZzwOIRXFjo4daGiRB/3KmUjEv7CeyJh/p/6x+B950/86cID5gU57QgKDoj0hhBBCyhqjwHwcu9M++HictTvasLHJHqOxraUTd/53Wfd3XXdvvtNeIVQZ3l3LfuJxAKCpPek5Hse6qz+I/yfv+6djix37YqWjS1jf0NiO6Su2oS2Rwo5W2VHtldZO+37r1h+w4lYcOJUx8NC7H+dNKySyKUtnKg3DMMomHqcWnTgg0oAaFH6OvMTjpDIZpdO+Jq8QraZo7+K03wWHeJyMgb4RdbzOoK6InLwBIh+FaDOG/SddTVX+tFRATvtqobaAG45O+4g8yBXvtxjxPh+bi8QSeH7NA8o3nSSnfUcm12nvfq5Vz5L2RBobMQRfSNyMnycvwYWJn+PVzAn+a3a4ZdornfaK6YSQkvHKx6/gzKfPxFnPnIUb3r2h1N3xRtSf0x4AOhSxcS3ZV7zotCckMCjaE0IIIaSsKaBmIgDYYgwKc9rL6zoVqJ2+YntPX4KMxwnBaa/KG29qT6qjSFTbMtzdwF5oS6Qxa/UOnHHnVHz9vvfw+T9Mw8fbWwtqM0trp/3Hq9/M6jahLSvPf7Ah73sQTvuMYQqz5RCPMxLb8XL1dXij5iq8VP1TjMAO95Uc8ByPoxDaq7uy4ZOKuCdxHZvLPZ9WQ+2AzhhmHQcVQ2CK9vmZ9qpCtDnL2DLt7Viz5zN5on2RnfbVpoBuCIMLkags/lQNejfv++qWxZ7qIeTG4+gUP95niDz4kn1zZp0xAo+kz8T7xmEA/Ne8yMuvl1Bm2lO0J6TcuOv9u7oLsz6z/Bmsa15X4h55QDFgqkMinRDffKrP/p2haE9IYFC0J4QQQkhZU2jch1VcCSMeZ1eH3o8fP4VoVfufMbxn2rs5TlXtNbcn5XgcB+EznbEKiwWK9sk0/jBlWbeTfc32NnQUWBg4S4sgtPt22muck+H9eiJVDMPwVTxXojOVKQun/bfir2L/riikA6Mb8bX4G8pl49bwdQGV6CoWok0bolsdyHfa677dYI+myacpU6eclzEM1EPttB8cae7qT87+CX2PRgxUIee6sjrtNeJxcq/nQq63aj+Z9jVdfTGEcx1THJ+IvY/XPD1fXHSdMdw2Lff06tSMGNFPjjlSPRN9F6J1g/E4hFQEjR2NaGjNj8V5f/P7JeqNdwqJx+lId6Al2WKb3jf7t4zxOIQEBkV7QgghhJQ1hUojVtG7kKKfKvFwV4ezsJdF1+GaG1+hEvoNQ51Br8LNqdqekPevqT1pE+EB5/2xHuZCz2N7IpX31kKQSOfPr/its9rw/j0CnN/BAYnOZNpXZnzQ/E/8hbzvl8efVS5bW2WPC7G6ub1k2jsVoq3pKryXyhjaETFu8T7NGSenvYG+EQenfZdon++0V7wlkDt4YCtEK8TjWJ32ORdGYfE43n8+mk57QxTiVU77iLDsa4s3i8u+nTkK642h3d8np8fm3Qc6A7XS34VkOqMc3PEdjyOR675nIVpCKoKVTStt0+qq1IO4ZUcBTvvOVCe2tm+1Ta/P/juRTntCAsOlfD0hhBBCSGkp1DkcaCFahbPbzWlvGAYikYj2viTyCtHKy/hx2nc4xPgAzvE4Ek5uZbuoVZjTXjWgEAS7JKd9ADnzKobW9wi9QUTjZCkXp70XauJRtFh06tqqGJLpnnOizrSXhVZ1IVpTgE9nDO1irDUR5wG5nWl1Idp0xkBfB6f9UDR3L9ezkrw9J9FewpZp33U9N7Un8eH6JmkVLXw57atjQCSFSMR+zFWiPRQFfCUMRPGlzhvw7fjL2GX0wX3pLyBSlVN4V2OQQno2OtWn8B2P44YyHodOe0LKiZWNdtFelfVelhQg2j+65FGcMuoU2/RMdgCSTntCAoOiPSGEEELKmkINjTbRviCnvSI+xkW0T6YNVMcj2q7q3LxtlQibzhhobPNW5NMtTsapEK1UTHJbSycu/NsMXPu5w3D8voPy5ln7XajE1Zb0/wPTjRbh/IUpfudGaxTy5oeVShPtq+NRMeK7tiqWNxCmzrT36LTvEuBTGQNJzcESt3icnSl11njGAGochJFhkcbu/nTjEu0DQNNpn3+/pjMGlm/ehYl/fw/brKMkHqjy4bTvWxNHJCpvM+IhHsd8isiDf5sxGLekvt79vdrwKNoLz0anN5MCjccZtH/P56hCtGc8DiFlhSTatyaDqbNTDCIFiPbPLH8Gzyx/xja9PfsHvdBiVISQbhiPQwghhJCyplAN0ipihlGItlnhRM+SdXFqZ9rnbEeVab9+ZzvmrWvUai9Lp4szXyVSNbcnlQMOs1bvwI/+Ode2b41t+cek0Ex7r1FAXggy016HXFdvsE778ojH0cUa4aKa7kW0T2UMGMqIma54HA+FaN1E+x3pWnXdCZdrKCvapzVE++o8gUUn0z7/GKYNA3+YsrwgwR7w57TvUx0HoooBRoWYLzvt9Z8BucdepxCt9Hxxdtprd8XOcd/q+Vw3CBh9Yc/3aBSI2AdIGY9DSHlRyaJ9JmMU5LRX0Z6tUZMJz2RByO4GnfaEEEIIKWsKLURr1c0Kc9r7i8fpTGaAWg+ifc5yTt31emg6XMQrlTD+/9k76zA5qrSL/6paxz3uQhwChCRoEiBYcIdFFhYWFgmLw4fL4hZcAkGChoQQJJAQIe6uExt3l/bu+v7o6Z6urls9PZOZLLB9nidPpqtuVd2urrp167znPW+t3U1qvL6iuKTOwab8mqDa/qs1eTwwa6u6rwdbiLYDSXuby4vXp2AIKYrarp7VYQhV9UZ7PfaSSnnWOJUuUhXves9hhne8po3T7fuTkfYCchKtb7roHCmKov8befTscUKU9u1UiLZBseJw+4gTZKJ4FYUCJZMeUoVw24A9jsqKScceJ7LSXntvhZ9Dj0/hxy3Fwn23Bm3xtI8zy0g6NkO69jgipb3sBl90r6++1irthfY4+uP6QWW0nPkCpPSExjIYc7PW+kY2gjesPzF7nBhi+ENB5GkvKs76R4TD7UWS239OZY/Z48QQQ7sjprSPIYYYYoghhhj+0DhYDrJdPe117HFaKkQbULBHTdp7W7bHaQtcHl9E9W8ke5yWilcG1Opur49nft6pWS86qtQK3+rW+ve3FuFq+44k7Z1tUNo/ZPyc4w3b6S8X85zxA7Ko1rRxef9c9jgWHXuccDW36BxFzITQsccJ+MJ7fUrUNQusUmQLqgYljkYdctenKDzpvlp322alfcj301Pa426+JsNIe1FATFOItp2u5/AiwdHAajToKur17HFEhWglObqC3+AP1gYCvuHXz+AuSZr2onolkbJ7Dup8mqww7l4460XI6B/dNnoFamOIIYZDjlpnLRV2bTC20fXnUNrXOzvGe98uNT13YoVoY4ih3RAj7WOIIYYYYoghhg6DT/Hxze5veHrV02ws29jGfRys0l69/cF4iLe1EG1AoR+t5YqiNJPGB5tpoNcXEfSI8TqHu8XCnQFSv6TWIfT4F/put6BiVvWtA5X2cGhJ+1ClfbSK79MN64J/GySFa43zNG2cbh+Dvdl8ZX6Kz03/YZiUc9B97UhYTLIw/yIae5yIv49uIdpmpX172eM0EIfNKb42fT6Feb5RPOa+lgXeI1ngPVK1Xuhpr+fHj7t57IqCtLea1Mr/9rJ7aos9jsVkQNKxx2lVIdoWAiiaPTR95fB77IYT+/HKpUeolomswSLa43Tg+CAMccZI+xhi+MNAZI0D0Og5eNLe7rEzdetUXt/wujAw0FoUVNv4ZEUOq/dXBpfVu+wHvV8RgvY4MaV9DDG0G2L2ODHEEEMMMcQQQ4fhy11f8tya5wCYkT2DH8//kZ7JPVu1j4NViLav0l7H0z5qpX30x3Z7fRhkw0EX4hX1RWTlAfpBCb99TOS+BwhFvRiDkFjEhYPobB8aBb7z7YnwYrQdS9ofvKd9EjbNMqfbzbOel+gi+1/0p0hvcqrrRfSKd3YETET/O1mMBhoE7cMtWJyCQFvE4JteMddAIVqvrxX2OJGJYjuWCEp7AIlPvKfzifd0+kuFnGJoDl6mSDa1gr6Fvru8Pj8ZH9Z1UUAsnFxvzdhzeI8UthTUCte1pRCt1STre9rrFaKVtedUkl2tyrzyKQoGJM09ZjJI9EiLVy1rLWnfoRktiuC3itnj/KXhU3xsq9hGiiWF3sm9/9vdiaEFFDUWCZeHK+2rHdU4vU66JHSJet8PL3uYebn+oPy83HnMOX8OstS6cdfp8TJ/Rylen8LD322jvmn+9NaVRzHp8K40OA+utokeYoVoY4ih/RFT2scQQwwxxBBDDB2GAGEP/pfSdza/0+p9/DnscaJT2rdG5B8gJdubPBZ5Nwegp7S3u7wt2okECC69c6RH2keL8MK27Y0Gp3r/3g4k5driaR8OEQ1vrtpNF5qVeQPkIrpQFfwcrxOsaU8kEL2CT9ce52CV9jqe9uZQpX2U570lpb1DMet6n4dfQxVKiqZNJrVqFbyuPY6nuUB1FJ72FpP6HLbGXuqp84ZzwZHdxf1oQWkvmSqQrXmEjtxxEZX2OrY5ouWtsMeB5mskPMBjMcr+QEIIHB6fJqvJ7tYf1zsyqCfMrooVov3LQlEU7lh4B3/7+W+cN/s8vtvz3X+7SzG0gGqH1p4O1Er7uQfmMvHbiUz8diLPrn42qv16fJ4gYQ+QW5fLlvItreqboihc8v4C7pz7Bnf/PI16Z/PYe/9M/74a3R1jj+OQZX+OVExpH0MM7YYYaR9DDDHEEEMMMRwytMUi52C50/DtO6YQbWQyydlEmEVryQHNlg7treh06KjpQd+CxuH2tqhMDtjL6JGDoq1b8gsPRZWtddYYrUV44OWP5GkvC6xCRMs8gpR3s9T8vZKtHU/8JUpi0l7UX4tRRmoinIdKOXxu+g8zzI8z3LND1U50jvSuRxkfkg5hYAn1tI/y97XoFFAFyPdlUUmSriI7nHytJQGXog6cZEk1YUp7vUK0LtxeBVdZNix+Rn0cUfuwIr8V9dHfP93T4nj1spGcOVyrDo1UiNaYso6E/i+T0Pdt4np8EuyZtdX2OD5he71itnoIjJ3h47bJIGvsg7yC4sQR7XE6UmgvJO1jSvs/CyrsFdy1+C4unnMxM7Nntth+e+V2FhcsBsCreHltw2sd28EYDhp6pH2Dy1+IVlEUXl3/Ks4mq7Yvdn1Bfl1+i/uttFdqlpU0lrSqb8v3lrPX+BzWLj8S1/1rLJ1+bu5f0zwtktI+3hivuy4aOCQp5mn/J0Otzc3i3WVUNnRMBkYMB4cYaR9DDDHEEEMMMfyhES1pfZK8me/Mj/KJ6Tn6SsXB5eFq14NS2h+kp31riOCthbUoihJV0KJfZkLU+xXZQASgR7jnVNr4aPmBiPsNnAM94l8WUIvWVnja13QwaX9IC9GqPO1bvh5FGQkipb1I8B1P80tYclzHO2Mm6Sjt49C+DFqCxKnCK6Z3ON6wnWPkbG4tewJjiG2OuBCt+LxFsufxk/Z+wj7a4F240n6vrxvVSiKlSiqPeK5DQaZRx9Neew1JlJOqWpIltUJp7/VR9JG2sK0iqhdhbLvSPsHsv06MAlV9JKW9JXMhkuT/LsakXchxeUCTv75OgE5YiFZHfa9rsaMDPaW9iLQHbRZShxWibQmiQT9mj/OnwRsb32B+7nx2V+/m8ZWPk1uXG7H9/Nz5qs9Vjiqdlm3HgdoDfL7z81artv9MKGwo5JX1r/Dp9k9x+zo2M6/GWSNcbvP4beuqHFUUNxar1q0oWtHifsvt5ZplrbXGmbFjPgZL837MGcs0bRpd+kr7UV1Gtep44bBLEvg61s4whvbD/vIGTnh+IX+ftpbTXl3C/vKG/3aXYghDjLSPIYYYYoghhhgOGSSRF0YLiIa0jsPB26YpHCnvZZxhC88YPwyu09jjtEJp/+n1o1Wf9axf6uzRetpHT/Rc+9Ea7vhqU1RBi35Z0ZP2kQrRRiL0W0JLSnuzgKBvjT1OtB7kbcWh9LR3eX3B/UcTRBIR3pIgCOJzawnzeJpfzg+J0l6HtI8XkfZN9jjdqGSw3KxCTPbVMEhq/iy67/TsmiLZ2ciSghF/fYa22uN87R3Pkc73GeN8m8W+kQD69jiCQ5SHWeRkSrXNfvOKAjpkkxk3tsZ6+jh2adYJ7XGMbbNCkiSC1jEmWbtfkx5pL7mRzWqy0Zy6CvDvT09pj0Bpr2uZ02qlvf//8DHfbJSJE5H27uhJ+44cH2RJsG+5462tYmgfzNozS/X50+2fRmzvERCcbp2Mm7YgpzaHi+dczHNrnuOqn6+KijzuCBTUF/DQsoe4e/HdbK/c3q77dnqdXPXzVUzbNo0X173IPYvvEWestBP0SPuA0n5/7X7NOrunZeu4MluZZlm9q75VfSty7I643qf42Fa1Xnf9iMwRrTpeOOyyFLPH+RPhsTnbgzUPKhtdfLex8L/coxjCESPtY4ghhhhiiCGGQwapDQUxoyGtT5U3kCg1kz/HGnYQsGYIV0RGo2wGOKZPGklWtTJZRHh7fQqNEcgdaFZwtpbombO5iJ3FdS2265eVGPU+IxHzB0XaN5HeevsQkamtscfpaBxKpT00E9HRXI9pZtE51fZPCSuCB2q7muS4/549TrykJWf99jhwmKy1DQi9XsRKe/HvY26hEK4FN26voiL9+0rFPGScznWGuSqFf3g/AJxoz6He/S8ijcJ97bOoae6Ljsoe/DY9tdVa6wSIztM+WiSYjcHgqoig1ytEKxm1hWslk59sshoNuip5kT2OUH0Prfa0D4z9bqE9jvZ7hGdS2SKMh6KaFxX2Cm749QZGfz6ah5c9LCRj/yhQFIWnVz3NkZ8dyUVzLmpRDf6/BpfXxY/7f2RR3qKDJn9FRGwoRNdJvbt1RG0kfLrjU1w+//2noPDOJm1toR/2/cAJX53Ayd+czJKCJe127FA8uuJR5uybw7zcedz6263UuVqe20SLdSXrqLA313NZmL+QmXtatiYCP4m9u2o35Tatyl0Pup72bv8z+ECtNjOxxNayzY2oD609T05hTZfANaZw24LbmJHzhu72R3Y6Mupj3XX0XZpldkmOFaL9k6CywcnSPRWqZb9nR38fHCzK653c+fUmLn9/JQt2lh6y4/7ZECPtY4ghhhhiiCGGPzSieV+Ol7QvKQHFb7T2OEf0SKFPht/LM95s4L4zBmvUqiLSPlyhDZBkCSP7m8igaH20QzFvR8sT2f6tUNq3xR4nGkRW2itYBSrZ1ijtOxqH0tMemq+JaDI/siwiT3tB/1w2zaIEldL+v2ePI1baG5AkicGSlrQPJf8bnB5N8M2rQwqIMjrC13t9Cu6m/cXj4Hvzw9xo/JnHTJ9xr/FrdR/DAktOzJp92pzRFaIFKFdSVZ+zpNrmay0CaW/GQ0Od2DZDVOQ53B4nWoQWKzYatPvVs8eRzVoSS7aUAgqyLOkq7WVTPYbEsOwB3eK0rSxE23T+w+8xi1H2BxLC0Bqlvcge59Mdn7K6ZDV2j53v932vsT35I2FtyVq+3v01Hp+H7Ops3t387n+7S9g9dqbvmM70HdOjUiV3JG6afxMPLn2QyYsm88LaFwC/9/zakrX4FP0xW7iuBa2CiLQPKLbbAzOyZ6g+byrfpPpc76rnqVVPUeuspdxezhMrn8Dbzp7kVY4q1pasDX6udFRG5fcfLQobtOrgF9a+0KJK3af4uG3BbVz8w8WcPvN0FuQuiOp4ekp7h9eBx+cRKu0L61tWMJfZtQGeWqc2IBoJTp+oiLd//JWtBSwtXKq7baqpM0d3Pjqq49wy8hauG34dccY41fKY0v7Pg5kbCjTLDkVGZgD/+WkH320sZNX+Km76bD0VMU99IWKkfQwxxBBDDDHE0CEQpXe3yR7HVERcr/eI7ztFS+40waVoychUya94CudW9Ej7lHgzP00+kS9vHMuCu8dxTJ90jVrV61M0thp1giK0fcNI9AAZ1JKNjggtEf0GWaJnevSFw0SFaBVFYca6fErr2j5hbva01+5fT/1s+QOR9hqlfQem1kNz9kU09jgZVsE5lbTnVBHY4/xhlPYIlPZN99cggdI+lPz/am0+p722hJyK5kwCPbskcwsWKhbceELu4/MNy0kO6fNNxp807UPhVKJX2otu3XLClPZSTfO1FsESw4ILe20rlPZttMcJJe1FSnuzUTyGyyYBaW+sRzLWAPqWNwDxPT9GNjeTVeLitOj64uvB51NQFG2BWZNBRpYlTQAifGzUsz0C8fgwbds01edX1r/Sqv62Bf66J60fq6bvnK76/OP+H9urS0I0uBqERGYo7lh4B8+vfZ7n1z7PnYvvbJfjljaW8vya53l1/au66miAmdkzufSHS7nv9/tYXbyadaXrguum75zO25ve5vIfL+f6X6/nnt/v0d1Pg1tLtsstUC6NHm2GVDSWKPtr9vPdnu9UCvO2YHXxalWQpMxWFpUqvDXYVamdu32397t2s7ARkfZ2j51Vxasibrcob1GQxHb73Ly7JbrgVaRrqdHdyP4a7bW+qnhVi1kX7aG0d3kFdnpNpL0xaYdmXQCexv5c3PNBjHJ0wf00SxqAlrSPFaL90+Db9VrSXhbY4nUUZm8qCv7t8Sl8tSbvkB37z4QYaR9DDDHEEEMMMXQIAgW5DmofLg+mzrMwJhzAYC0mrtuXQuJGRFql4H95DldEunTIPoMECRYjx/bPoGuK/yVEpFYNV9uHK7QNskRqvFqNG9imqrH1JHVL3ttdU6xCf2Y9iPzB524r4d5vD65AXYPTTziKlPZ6PuPtpbRvQyxIg22FtUxdup+Fu0pRFEXXM729ECAIo/HqzzBryUORcl1ya+85VSHaQ+JpL77vRdkwgfurJaU9wN6yBj5Y2kyE6GVCtGiPI7nxen3B8z5MygFgm9nMpB5dGdu7B6b035vbR2GPY9chd0Vq7HClfWYrlPaOBjFRFE0h2mgRb24mbExCpb14rJFMNcLlhqZitP07azMUVO0S9jTvS1dp30rSXtGq7A1xB5i680W+3PUllrCfMrwQrc3lRbYUkdDvJRIHPYQ569fmfUeRiVPS2L7EZzjy6/O5+IeLGfnZSB5Z/khEBXg4RMVOWyre2VaCdUXRCk786kTOm30edy66U7if0sZSVhavDH5eXrj8oMloRVG4Yd4NTN85nY+2fcR9S+4TtttXs4/HVz7OzqqdzM2Zy+0Lb9e0eWdzs6XM/Nz55NWJySWRKlqSJH7Y9wOPrXiMhXkLg8sP1B7glt9u4af9P2m2WVywmPNnn8+kWZP4Pf93zfoNpRs4//vzeXTFo5z73bmtPlc2t421JWvJrctlT/Uezfrc2va1S9pVrSXtD9QeYGPZxoPab+CaL2ooEq7X+50CeGndS6rPu6p2tZhl4FN8ukp78J9bUYDK4XVw6oxT+XLXl7rbipT2rSbtfaI6IZHHTp8rHXvejXS2DIz6OGlWPdJejint/wTw+hSyS7VBxkadzMH2hug5UFyrXyD5fxkx0j6GGGKIIYYYYugQ2AQEYms97UvrbBjim1+6JIMTY4L2BVNECKY0Ke219jjRv0yI1KrhpH240j7RYiTOJFZwtoW0b4kb6pkWr18cUgCRPc7sdig8FVCqi/avS9q3srCkHg7vkXrQ+1h9oIqnf9rJ9R+v4/PVeVHVUjgYOINK+5avR5GnvUi5LgksJRJCFOvxlo4vZqmvtBfb4xgUD/0l7fWXJCD/P1/dPBbo1QJo2R7H41faN9nrBEj4l9JTyTOZaJRlrJ3mBj3aD8bTXnQN1Sjq+hMpNEblaW+W3LhsNcJ1okvV3EbSPsESao+j3YchTIUnGRrA0IhsFAcUAqR9n8zIAaNQdb2ep31b7HFCg2KSqZK43u/zS953PLP6GYxpy1XtQ+1wNufX8OOWYiydfkG2VCDJXiyZi0gY+DRxvaZibwr2BNAS4d0ReH3D62RXZ+NTfMzeO5tlhcui3tZqsGqWlTToBxmmbZvGUdOPYsI3E1hbspbs6mxu/u1m/vHrP9hUtkl3O4/Pw6PLH8Wj+J8Pv+X9plKxByBSIJfaDs7jeG/NXnLqcoKfVxWvotKuzVb5atdXqs/RWPPsqdHOQwDqnFqCdUHeAv5v2f8xa88s7lh0B2uK1+DwOPjn/H/qWpW8u/ld9tXuI68+j8dWPIYzTD397uZ3UZos0urd9Xy9+2vRbnQDLafMOIXrf72e82afx9ub39asP1Cn9mR3+9x8sv0THl72MGuK1wj3GQkipT3Aaxte47s937XaAkZRFJ5a+RRjvxjLdb9cx7aKbcJ2BQ1aJXEANrdNuL7cHtnTu95VjzcCKV1qK9W9dhUUXl73ctD7PhwVNm3wRXRNRYLTK9h3kLQXXw+K4h/3524r4eJ3VtBJOr7F4+gq7eWY0v7PgEYdscGhIu1F86bw+UUMfsRI+xhiiCGGGGKIoUPQHkr7nGpBKrGAuBERmKm6Snsx2SeyoREVcwwtILp4dxlrDqgVi0lWo8AL379NpQ5pbzyIiWr3tLhWqWpF9jjR+Oa3hG2Fddz37WbyKrW/u54NTnsp7Uf2SGm5USswe2Nhm+oP6OGoXqmaZa1R2qeZtC83oiK+ItI+tECznh95e0Lf015ciLY3RZgl7fdLkiKPH21W2uNqssdpKlKK/15dHxdCYkpgSt7kby+1TNrretoL+liP2soqSbKHKO0j2eO48drExJavw5T22n2EZrWY0peQMPAZEgf+B1OqWDErm/0kaUvPA8kQQkrqWem0Msjn8ymqIrTmtJVIUvNv4kn9XtX+mo/W8Mq83SzfW8GF76wAwJiYrWojGxswJuylLP59FVGvZ3shIonbCrfXHVQBu31ufsn5RbX+x33RW9yEk8DgV+6LUNJYwpQNU/D4PFTYK3hh7Qs8sPQBlhcuZ03JGu5afJfQDg9gScESDYG5rkRL2lc6tOdJRGC2BiKrkR2VO/D4PDy7+lkmzZrEEyufYHf17lbvu7ihWPXZ5rZR66yNinx+atVTzMieEXUmRqWjUlXY1Ol1qrISAD7Y8oFwW1FGBTTb+OiRz+GFiT/Z/gkvrXuJ7/d9zw3zbiC/Tnyt6GFn1U7h8o1lG3l0xaNcOOdCodBDD/Ny5/FN9jfYPXbWla7TJef1rmlAt+BupG1A388+gO2V2yOud3qdLC0QB2tEAYNaV+sCGi601kotZik1WUz+nl3Outxq9u85FonmeayoOK2+0r7J076DhQ8xHBxE9bhAaxUZgMfrY+rS/Tw8eyvbi1p3TYpQLXgfktsjbfYviI6vRhVDDDHEEEMMMWiQU5vDzwd+pl9KP07vc3qbvN7/6BCp1Rze1qU+5tUKFE+KVi0sUvEGPO3DiTO3R/wiIbJDEdrjuH34fAqXv7+K9bladWmy1YQ1SqX9xUf3YHi3ZI7omco/P1tPeX3rPeXT4k2tUtqL7HH6ZMSTIyDbW4tv1olfnMOJzwDai7Qf3DW5Ve0vP6YnfTITeG6uWP23LreajITIdh7RYsrlIzlvZHdGPjmPGlvzeQhkJERTiDZFQNqLrnlDC0r7tqqvW4NEPdJeZI9jkumniO0L9Mj/APSCHXrXWgBmPHh9SvC8J+LAJhp/Jf/68IBT6zzttX2sU8JIe2xB1T8e/fs/CTtehx5pr+2/tRWWWaEIVdqbBMHE4Eu17MCSNQ9Jinz9SiZ/n8OfB4rPpFLOG5O24HV0w1M/7ODtcSQ3sqWYKnsVZmNmcLEhPqfFTV9fuJv0bbuQEt3QcJhuO69czd7qvQzJGALo23Psr91PRlxGdP3WgQI8ueIpvt3zDd0SuvHqhFeFpLvI1zsUNreNA7UH6JPSR0gO6pGVPx/4WUXu7qpSj5vl9nK2V25nZKeRmm2/yf5GsyyUgA6gtUr7CnsF721+j0pHJZP6TuLkXidr5lGiQMCOyh2U2kr5YtcXAOTVt81DOZQk/nH/jzy2/DG8ipc+yX1a3DanLkeXZI+0zeD0wQDCzAav4sWn+JAlGZvbxivrX2FD2YY229yEk/ZTNkwJ/q2g8PXur7nnmHtQFKXF+avNbdPsLxxltjJm753NlUOujNhuRdEKpm2b1qJXfQAF9fpK+0X5i4TLCxsKOYZjVMvcXjePrXiMBXkLWgxAzsuZ12K/5ufO54y+Z2iOIQqytEZp7/UpeKUGbRi3KcivW1skrC6U4s6kYf9t3HOBi+GZw8muztZYGemS9oHnhuIDqeOz+2JoG/TIeT2l/ZQFe3hj4V7AP9df9eAppB/EPLnapn2eB+dCMagQI+1jiCGGGGKI4RCj2lHNpT9eGiQxKh2V/G3I3/7LvWonuO2w8k1orMDW/1jN6mjSzkNRXC9Q2gmIG6E9DjqFaHVIUpHdhkiZ7PT4WLGvUkjYQ+uU9kf2SuVvY3rrHisaJFtNmMLIWNlSjDlzEYrPjKv8NBRPM6ktUtpnJFrahbTXQ0d62h/RI4VES/RT2pcuOYKLj+7BnM1iki2A9lLaBwIqVqMBEJD2URSiTTZoz1+cgLSXvQLSPuTeOBRKez17HFF/LUYDWYpYiSyyxwnFwXjae7zNhWhTpAaqBedFaSIxwq9dB9qXVL2CpaIuhivtEyUHirdp+wj2OFlSDaU65I0iIO3bGqBRKe0F+wjwc4a4XCS55TT6gM1QONGleJKQzM0ElWyuIa77V3htvfDae+nsLAqlveQivs+7GKxFXL9wKk+Nfb35mN44QXs3hARirF1n4U5dTxzgdXSOeKj9tftbJO331uzlmC7HCNdFi21mM9/u8ZPfRY1FvL3pbYZmDNW0i6QALmks4W8//40yWxmd4zsLCfG8+jy2V2wnwZRAn5Q+weXRZAsUNRRpSPuSxhJWFK7QtBUp20We7OX2cnyKj3pXPSkWdTbVc2ue49ccf42B+bnzmdh7Is+e+CwWgyXYRhQIeHPTmy1+l2gQCHD4FB8vrX0Jl89/7+6r3RfV9tVO/UKmIoR6s+sR1utL11Nhr2BR3iLm5sxt1f7DEUqyi2ol/HTgJ/bX7mdl0UrGdB3DC+NeINmsDp5vKd/CjOwZZFdnB618IuHLXV8GSfu91XvZWbUTt8/NhJ4TSLOmUWGv4K7Fd+lay4hQ3FiM2+vGZNAGW/XU/4HgV05tDtsqtzG261gW5i3kh/0/RHXMDWUbWmyztHApdo9dRXjr1SUoaSzhtfWvkVefxwUDLuDYbscybds01pSs4YTuJ3D10KuRJf9YXW1zIRm05ye+18d47T2CNjjhEC33Obty44gzMRlkNpdv1qwP3JNCT3vwW+TIMdL+j4rwelwBNDrFIoQAYQ/+eeu05Qe4+7RBbT5+tU37PK+1Hxprnj8bYqR9DDHEEEMMMRxifLbjMxV5/dya5/46pP1P98Cm6ShA9Z7vIMWiWm13t460L23Uqo5ESiERIZgiNdnjaDzto7fHkSQJi1FW+dg7PV4W7NJXACZFUNqHp4OGqrmNgqKP0SDJagwjY73E9foQ2ej//rKxBnv+DSF90U7IO9rDUt/T/uBI+3izgTsnHtaqorEBMjMpAtFvNclRFZqMBgHSPtxqKXBNRaO0TzJqf584QaDK4NFmsoQq7VuTkdFW6JHtCTr2OAZFTMDokf8BuHUUWaYo7HG8Pl/wfk+lgSoRsaBIgKKpu+AUkPZ6L7lCexxFSxwb3A0s2FlKl7oKhun0u7NUTYVLa3sAYtK+7fY4IZ72AqV9YIlkjE79KRtt2D12TcDW50lGNmvHd0N8HhjEv300nvbGxF0YrH4C3eVzMXX768A1gJi0l03V+Fydmj44MaY0q0kN1si2YXtrmkkMPdJ+X010JG4kfJmcpPr8e8HvwuKU+fX5zNk3h1VFqxjbbSzn9j83uO6LXV8ESWw9BfunOz7l0x2fIiFx96i7uXbYtUCzlUokiBTr2yu2C8nanLocHB4HVmOzJZWIYJ+fM5/Pd36OzW3j6qFXc/eouwG/vciC3AXqtrnzSbem8/DYhwH/udCzP2kPBBTclfZKoaK/vREg0b0+r661yvW/Xt9uxytqKAr+RqLfpsJeEfTjX160nG92f8MNI5rnGR9u/ZDXN77equLIFfYKFEXhkeWP8P2+ZuuqVy2v8sr4V9hXs69VhD34Aw67qnYxImuEarnD49BV/xfUF7C2ZG3wfGbGZZJibl8LPrvHzuri1YzvOT64TFSEFsCjePhw24cALMxbyPkDzmfmnpmAP4DTPbE7p/Y+FYDCmlrdcdIQp591gE88H6pudNEp2Sq0vzLJ/kBIOGlvCyrtY772f2TozftdXh9Oj1dY0ysUm/JrDur4NQKlfa390NeG+TMg5mkfQwwxxBBDDIcYK4tWttzoz4pN02mUJP7ZJYt7wgh78JMot32xjhGP/8oNn6yjVqC0CEWFLTrSXkQIBjztw4kzfU978fJw8iu8EG04kiMo7cPtcdITms9RWwnV5DiTirQ3JOwLEvYAxsS9QHOfRUp7vYJU7YWOUNoP6pzE0vsmMH5QJ+LM0au5Ar9nolWftHe4fS3+ztEiEIyxhl0TgeCJO4rjJIiyS0T2OAL7qdB745DY4+gp7UX2OEaZRB2SX4/8Dyjk9QI1LRWiteDB7WsuUJoqNVIl8m43OIXXrcjT3q5jjyMqAhmutAcoKS/jH5+s44nZm7THa+I/OkvVmL1i8lQU/GrphVsPoUp7UVG4gBWGbI7ebzyvLo8aR41qmeJJEjcGDBadQpBRkPaGBDVJvqd2B5Kp6TkiCQKzpmbFs2yqbNHuJxS7KpuLkRY1tg9pL8r4ahD8DuFWFeC3LXlo2UP8sP8HHlr2EN/t+S64btq2aVH3IVAsc23JWqBl2x1QK8ED0LPb8Sk+VcADxCrjfbX7gkU/P97+Mftr9wOwu2p3sLBtKL7Z/Q3bK7Yza88szv7u7KgUz21FYUMhPsWn+7u3N/Lq8nB4HNy1+K42efC3FgpK8PcLnPdICLXsWZy/mNc2vNYqwh78waHVJatVhD34M0hunHcjL659sVX7C+DKn6/kvc3vBT+vLl7NVT9fpdu/woZCVfsKe0XUGRStwZ5qdTFjUQ2GcHgVb5CwD+DTHZ8G/86rERP/LUIRz4cqGlwoikK6cpxq+YjM5iCI0NMeYsVo/+DQs8cBfSFCKPaXN5JdKhYSRIPw9yGIkfZ6iJH2McQQQwwxxHCIoVf460+PJtL758R4VsUJbAia8OO2XOodHn7bWcpXayP7yda4arQLRUp7kT1OwNM+hDhTFEVfaa/nkR3mDe10+5AEytYAkuPESnunx6uZJIf6Qba1GG2S1YTJ2LytZBAQe3IzcesQeNrbopigHwwsOor6cNI+/LxFwrBuyWQkWlq9XYC0T4nTkq+hiEYBHw0CAZXwPjrd0SvtEwQ2JEkGF0+fP1y1zCiwxwkl0Q+N0l5M2guV9iYDSYiVk0k65H/gZdKrE2RruRCtG6/Pb4+TQS39pSKqDFqCW5J1SHuhp71OIVoBad+IFa+ivteTmwIUJql5P9WyzLVdOzGqTy9u6NKJBKmONMSkfZqg8KCoiHY0SGhJaR+wtG8FaT8je4aqnomiSHjtPVrdNymazBxF+71NyVv828vaa1AOJe0Fyv9IWFb0O19s/w63z60pTBpATl1Oq/ZpF2RCtTU1/tEVj+LwOITBo5YQCADYPfaIvuAB5NZrVcuR/OKv++U6rvzpSj7a9hE+xadbyDcUW8r9v+O2im26fX5mzTO8vqF1Cu+2wOl1UtxYrPu7tze2V27nn/P/ycL8hYfkeNB87ebU5kTdFvw2N3roktAl4n5eWvuScLlX8QYtiNqCNze9ySfbP2FG9gxumHdDxMDHxrKNrC5Z3eZjheKhMQ9x/fDr6ZfST7MuPKjV1qLVoQG8fJGlZBRQdEj7ykYnczYX8fjMSlzVowFINCZx+5G3B9vo2uP8Vd91/iLQK0QLWhW+KGuwsMbOaa8u4fE5kQsv60Fkj1MXI+2FiJH2McTwP4BGdyPFDcVtmrTHEEMMUOWo4oudX7RbqnU03p5/SjRZN/wnIz1is1Cl/LM6hUADqBN49IqIF2Eh2iYyUFEUGpwevlmbz2er9IuhiRSOoFXa6/lXB6DnaS9SlYTa47RVBa21xxEQbSF2EyJ7nPBggtkgkxRBid5aRGuPc0yfyNeOap8hwZTWFN0M/DYDshLpnaFVPbc3mu1xwpT2TcETvesuFCL7J4PPjVVWb2vyRb43LEaZo3qltni8g4Ge0l5YiNYok6hnj6ND/jc03X96hWjNLfieWyQXHq/CRY6ZrLHcQpzkolJA2iO7olba2/QK0Qp/WokG1CRHINARau3zZGY6G6x++5DVcVY+TU2iryQmCNMlAWnfpvFEwWqWgvNFoygDoel/WU8NL8DXu79Wffba+qK4MnVa68MQn6cKQIogCax1jAHS3qDd1q+0VwCfSnUfLZ5d9yivb3hdV41eYa8QFo0NQFEUihqKgsS4Q3AtHQyNcfNvNwsLv0aD4sZiftz/I8WNLRPTIqV9JNLe4XWwtWIrr65/ladXPR0VaR9Q42+v1CeJtpRvOSR2NQBnzDwjIkHdnnD73MLsio7EjsodrC5ezbNrnm2xbUF9AW6vm3pXPWtK1gjbpFvT+fC0DzHK+nOLjswieGndSzy58skO278IozqP4s6j7+T787/nzqPvVK0LLWYMkWtSREKiKRGAPaX1vPxbG7NL9Ej7Bhf3fusfP50lF9KQ/Qinp7zJsd2aa1bpFqKNKe3/0KiPoLQPfyeIRPB/sjKH0rrIz2URwu1CIaa010PM0z6GGP7i2Fy+mckLJ1PlqOKkHifx+oTXMcSKwsQQQ9Swe+xcPOdiyu1+guK+Y+7j6qFXH9Q+RUp7p9epKqD2p4TDX3DQK0VWjJ9uWM4kaS+rfUP43HtKxLZGT4FmtiIZtASIiBBMbfK093gVLn13JTuKI3sw6xUeTU8wU1DdTASVN+gTMOAn0UWq6soG9QTVIEsqtXdblfbJVpPKxkKStJNryWBHcTf3JRQer9YK5uc7TuCxOdtZvrd9yI9o7XGO6ZPO0j3RKcXiQkjwuFaQ9oHgiCxLfHnjWN5fsp94s4G3F7d/+juAqckeR2OzFFDae1oO4sXpKIzjw+xCRKR9uNL+oUlDuW7aGuoivIQdDPTIdnEhWpkERWyDkyyJlwcUYG0tRHuD4Weq86oZ65oRZKCF9jiyE4sgACAi7fW8YcPraQRQTzwpIfY/SZINFL91D8CC+Dh+S1AHlH5OjOdfNTqkPeqxzSBLGOXWkfaSsZa4Hp/y+v5CZpX14Kx+Z5GmnK5p5/W5MWcsxmBpow0D4KkbieJr2/Muod8r2HJvRnGLA3yiTCODtcjvwS8g/C2Zv2POWIriSUDxti2I9/nOz3H79MmG4oZiVWHXULy+8XWmbp2KhMSdR9/JhC6XatqIMkGixfrS9Zz3/Xlt3n76julRqdZrnDXUOmtVBWOjUeiDPxMjGpQ0lgD6Svv/BjrSgicSkkxJvDrhVe5fcn+7BSm6JXRT2f1M3TqVD7d+GNW2XsXL9b9ez6D0QXh8zeOhSTbxyvhXKLOVMb7neDrFdyLDmqFbVyEUEhIG2aDaXzQ4PPNwtlRsadU2HQGrwaq673sm9VStD1fat5W0TzYnoygKd8/YLCxCGxV0CtRWNDhV2amKN4GPlxXjcm/l78f14bDOSTF7nD8pItWyCl9X59B/vikKrNxXyflHdm/V8at1PO0VRQna8MXgR0xpH0MMf3F8uPVDqhz+dN8lBUtYXdw+6X4xxPC/gjl75wQJe4DX1r920PsU+hzrFBj8U8ERXWHCyZavOdm4midN0zhbXqXfsK6IvspW7XKBPU68wHojuckeZ2thbYuEPejb43ROtqo+l9Y6sLv1J7tJVpNGae8QKO3T4k3IIWR7W61LkqxGJEkiK6mJBBOpSeVmItUZZo/TKFB2JliMrSLCW0JrSPtw6MUyQgMj0Xraj5V30H/JZFj0DLhsdEuN4/Fzh3HfGYPbNbMgFKagPY7Y0z789xDBoogDRXFh173k0RLm4Z72R/dOY+E945lz2/EtHre1MOAlQRBAC+9HABZjBHscHU/7gAJMNzOmBV3yILmAseVqklBIispOLIKaCy4Bae/0+IRBBNUyQyPmjAWY0pdSGeZrH/iuAaX926naooe5JhO7zGJLp3ClvUGWhH70kWBOX4Ihzq8WL2go4P0t7zOv9F1Nu6k7X8PS6ZdW7VvVN8mIu254m0l72VSHKTUwl1UwxO/BmLQN8N9HklFsISSZqoVKewBJ8iGb6lssPKuHSIQ9+P3u7R47T696mst/vJwX175IQX0B+2r2MXXr1KZvovD2preptjdwv/tG1fb7DPr+/x2NaPzMA8ityyWnNoepW6eypGBJVAr91qC4sZhGd6MmcyDUXzsSbhl5Cyd2P7Fd+/TfwrQzpjGm6xhVIdPW4Nz+53LT4TcFPw9JH8KDYx7UtGtNZuim8k2arJrRXUczvud4Lh10KZ3i/QWfM+Oiy7I5vc/pKhuWUDw4+kFmnTuLM/qcEVyWZE7iyeOe5K5Rd0Xd59Yi2ZzMcd30/d1DcVj6Yaqsgh6JakuwksYSnlvzHO9veZ/dVbupdda2qU91rjrKG5xsKahFMraNtNezx9lZLH43+WJ1Hhe9vQKby6MqKA0hSvuYPc4fGpE87cPX1bcg8hAR8CKU1Tl4ZPY2/u+7rewo0r6XeX0KczYX8fK83ewsbtv98FdETGkfQwx/cSzKX6T6/P7W9zmu+3E6rWOIIYZwLMhboPp8MJ6aAYiU9g2uhqhfZP6waFLay4qCL4JK4opuXfBJEsOcTp4t+Qj4j7Cdd9fP1Io8laO2x/GTN0U1YuUvKBhT1mGMz8HTMASP72hhq64p6heSkjqHrh0G+El0r08hixoasWLDitPtE5D2ZtXngylEC/D34/rw4q+7Mcjalza1PY6a7BSpbeLNxlZZzrQEPcsSaxjBOrJnqqaNjqBa1b/wIq8idKaK6aZnMO7zwb454HHCxCeC65OtphZfTNqCgLJfQ9o3kfU1LRRjBjD4xER4plnd3zgByZxAqNLefz9lJlrITGz/zJ5UHc910ClEa5LJMDoQ1SO2Sm5MeHCHva4crNJehGqBKt0k2zQBALdiwIv4WrO5PCRZ1aR6s9JeIb7X+0FSeEq9gY9CEkqSpGbS3ilBtkU9NgTwa0I8Q1zaF9lEST0mGttA2hviczTLttQsAvk48DUrKZcW/dbivhSfAUkWj5EDkkayzheP4mujIhS/Ot5VfibmrF+wZP4OgKfhMOz51+sqTWVjvfDZcShQ1FDE7D2zmZszF/Dbu3y9+2tSLamqdg6vg701e/nJO4az5ZWcaNjGJl9fGg1/DvJrZdFKpm6dqqpf0J4obixmR+UOFZFslIzcduRt3DT/pghb+nHD8BsA+HH/jzy64tFWHTvBlMDE3hOZvXd2q7brCByWdhiD0gcBcEqvUzSFSUMxInMEm/KrkIz1uCrHoXiSQZF58uq7ARieOZwqRxUTek7AYrBglI2tVrZHwsk9T9Ysi2auazVYuenwm8iMy+TtTW+rLKbO7HMmVwy+AkmSeOr4p4Ik+vie40mzpqEoCg+PeZifD/yMSTZF5U8/sfdE5ufOj9gm3hjPCye9QN+UvkxeOJk9NXs4o88ZXD30aq746QpN+yHpQ1SfeyRp63h8vvNzAN7Z/A5Wg1WzPho0uBvYWeIX6LVdaS+mBZft1bdBq3d6mL2xiLhUHU/7mNL+D41I893wQrT1EZT2ALmVYpFFOP752Xo25ddEbHPHV5sAUBoOjc3ZnwEx0j6GGP7HEPO1jyGG1kH08un1eQ/KZsouUMP+JZT2Tr9qwqQoOCOQ9gFCf7vFwi/Jdv6l0869fznVOrYV4RARggmSExMe9KwWjcmbievmf9k1pa7HWZoCnKppF660L6lzRihD6yd/+y2/j7XWWdQq8dzivoMCzxgNaR9ahBaaCdXWItHin87dOmEARls5+/YuYm5YDyN52os8+hPMhkOjtJdcWHFys/EHusg1xFX31bQ5rHMi2aVaMri1SvsrjQsxSiEBi+WvqUj7wHlsbwSV9mH2OIHgSaXA1zMcsmDMADgs3YhRloLWTqL7wCx5g+R3a+smJGBntLyLblIl63yHsVvpFbF9wJJKBFFgrdZdilvgQR5AIjaqSVYta3R6sLu8PPPzTuE25jY4gIuU9iLSXmSNE4DN5RWQ9v7/ZWuBSsW9NsmLuwJM+N3UTXIdeBXMkoeaCM+W3xLi+Xe1lrT/3DsOZHuQXDfKEpKkYIjbj+JNwOfqLNyfbClCMtXhtfVBtmhV5l7FgzFpB57apoCm7KDe3XLWks/R3e8/L8CglGNYB9BGpX1I74KEPYAxMRvZXKarNJWMtUjywfnl2nJvBMlNfK+PW7XdEyuf0Cxzep1Ci5B9tdk00Imr3Q9icbtxyS4S5afb2uVDijc3vam7LsGUgN1jP6gCsSUNJWRXZ6uWDUwbyJguY8iMywx63uvBZPDfnxcMvID9tfv5ePvHUR335iNu5qy+Z2E1WFlbsla3fsGhwuguo4N/j+k6hnhjPDaPmDQ7pdcprFiqLf4amMOGK/VHZI5oN+98k2xiQs8JmuUtkfad4zvz0riXGJA2AIAnjnuCR5Y/goTE34b8jTuOuiNon2E1Wrlg4AWq7SVJ4rLBl3HZ4MtQFIWTZ5wc8do4vtvxvHjSi8zZN4e5B+aysnilan26NZ0RmSO4/cjbg8GSGefMwKt4McpGFEWhb0pfTQbI0Iyhqs9J5iRSLalCGxyPz0ODT//52RK2FfutqGSTdt/RQPGJ5z+ldZHtIPeU1XNUpo49Tkxp/4dGJKV9uJinJUHLvvLma3fV/koe+347bp+PhycN4eTB/vlHeb2zRcI+FHrCjP9FxEj7GGL4C+NgJsZ/NeRV2njn973EmYzcdvIADVkWQwx6cHi0jG+dq440a1qb9ylKga1365P25bZyvsn+hhRzChcfdrEmFbUj4PUpfLdqF5U1tZx3/Ei6pERxzCalvQkE9JwYb6elcp3LjVVg+9AoJ1EtILBEpH08Tlz4laipPh8n2B1IQAqNVKC1mgCwdvle9dmb8hNws6ZdF4E9TmIEK5VODTvokTsLgBTJxp3GmdzmPkZD2mckqschUdHHaBCqqD3XO583DDYgQdVGRdqH2bE0hClqrCYZo0FuV6V9JHucR42fcaVxoX/BR6dzz/gfeGlxMylyy/gB/PvrTdptQ/oXTdHNwyWBZ73PC03XWKTf9GAQqFVgCa9z0PQ7VOrUSDhTXs2zpqnIKEi7xISMRXEwrHsKm5tehES+8eAn32tICitYDAM7JbKnTEwUjJM386rpLdKbiHiXYuAy16NsVAYK2/fOiCelSl/lF25hZek8m8nLVmHqnMyT5W7ObtR+x0TJTrWiJu0bnF6e/mmHrid/S4VoRRB52htku8bT3oH+3EGUsRJQ2suCAqeVBgMmReGWLlnssKwn3l5KSmEnaiJ40eeaTNgkifgQAcYacwIvdColyfQE7tojcBRfjMFg4r5lk4nvswJFkXCWnIe7ZqxqX8aUtcGgpeK1IMni82lK2hok7WVTlW7fQuGNQNoPThkFlKN4D460FxWNNSTsQZLEc1/ZfPCqPZ8nGcWVhS3nJgwJ+7BktZx10Frk1O8FOgESTszIxtYXxwW/H/gfqeh9/5T+VDurNT7erUG9u571petVyw5LOwyDbOCigRfx3pb3ot5Xt8RuUbU7p9853Dry1uDn7877joL6Am5feHu7kfc+TzyyUTv+WQwWYRHjY7ocE/zbbDBzw4gbeH3j68J990ruhTCVSQejOo9qN9L+30f9m6z4LM3yjLgMzbJnT3wWGRmH18HE3hNJMjdbQk3qN4mJvSdi99hVNROigSRJHJF1hCZr9rkTn6PCXoFP8XHRYRdhkA1cMPACzh9wPo+ueDSYUXHVkKu4f/T9wv0aJWPw71N6nRK0ugogXGkPfl/7tnrXR8Ku8mKQFIyJ4mB2i9DxtI8G8Sa13Vu9Iaa0b29sr9zOjN0z6J7YnWuHXYvZ0HYeI7eyEavJENHTXmOP44w8rzpQ4Z/7ebw+7vx6E8W1/vneXd9sZvX/nYLFaKCyMdo3wxjCEfO0b2e4vC5s7ujSQ2KIoaMhUu7+kSbwhwpen8KVU1fx5Zp8Plp+gDu+ap/JaAx/Lby58U2OmX4M584+l91Vu4PLRar4akfbXqLB/5xojdLe4/Nw9dyreXfzuzy/9nmeXtXxijufT+GtD97jtF9P5qY1p7Pqtb9p1NlCNHnauyLq0LWoLcsVLm9sKMElsngQFKKNw8E/unbm/zplckuXTryW5n+xS4mg/JXCFb7WHGG78IBFca2dhgiqk27Zn6s+j5KzcXi8GkV1ePAwnFBtC7pueJl60TmT9e1xbGGT8wSz/0U0Wp/4aKBH2ltwNRP2AM46bkxdx9Vje3NEz1QenjSESYd3RZS4EUraS5IkzAxIDiHinSLCta6ZdIlOaa9wmryWp4wfcaq8vuXmhNjjhNc5cPtQFEUTzAG/TcpTpmmkSo26BVkBcDVydK/mAKLIHgea7VPClfa3nTwgmOFx6Sh16v6Dxi+ChD34FfsXGpbqduW8kd0jK+1DsgAkUyXmdH89C7ck8VaamIhJFhS1bXR6WLxbP2W/tfY4CmKlvWJwUJW+iTs7ZTI/3q8kbElpH46gUkzSzr3KDQaeykxnh8VPXhviCihKO0BtC+NAvqn5Ov2P+0puShuOz9Rkr5OyGWv3L5Hi9rKqeIX/0JKCOWOJZj+WzMXBv0XFvQMwJO4Jjh+i4IMIXofWBgKgU1wnusU3ZWsoZhSl7cXmrpmgPd+RVKayOboC15GguP0BJK+9L66KU/E0HCY+ltT2sXx52RysXWcEbS4kY8tZeOE2OwBHdz6a7omtKwzYkeiR1IM+yX0Oej9LCtTX8mFp/t/gn4f/k3E9xkW9n24JYtK+c7w6KyWUIAeIM8YxMG0g1wy9JupjRYLPlU7j/rux5Wv31zu5N6f0OkWz/OjOaiu/64dfz1unvMXE3hM1bcN91APQU7GGK+Mz4zL54fwfmDJhiopIbwn/GP4PrhkmPkfpVm3tmj7JfTir31lcOPBC4XHMBnOrCfsATu+jLqgdb4xnYu+JXDvsWq4bfh3J5ubAsCRJPH7s43xyxid8csYn3HfMfVEd47z+6mLPiaZEBqQO0LTT+z2iRahHfih2lhdhTNwdcSyPCEX/2dYSuiZ0VX0uMRhwSsCfUDyoKAq/5//O/Nz5EW2iNpRuYPLCyTy58smDeieMBtWOaq6dey0z98zk9Y2v8/oGcYAuGjw+ZzvjXlzMcc8tZOEu/WLyrVXaF9bYcbi9bMqvCRL24Ld/3F/uf5ZV1B+8vez/KmKkfTticf5ixn09juO+PI53Nr/z3+5ODGGwe+zk1Obg8v7vDBhtLWjzV8OGvGoKqptf/JfuqYiOgIzhD4M6Vx0LcheQWycmdw8We6r38N6W93B4HRyoPcCrG14F/JO30CK0AQSKO7cFdS6xtUCDS0x2bS7frFJzfb/ve/ZW7+W+3+/jjoV3sLOyjaqaCHhtwR7OLHw9SBaer/zG1nVa0kcDRy0OSRIT7ZE2K84WLq+ziSeU4b7EBrzkmiU2WZuVmx+lplBkNET02I4W4fY4dQ4P5TrqaACzR0uy+NxOqsJUJukJaqWpsQ32OCKiuU5kKRRSgDF0/Ju6dD9XTlX7vSY07bNdlfaS+NmbIGkzWSxV2Tx1/nC+v/V4bjixHyaDTIYgOyq8f1aT9nuPDCG0DQjG/eqc4J/RFKIdJ2/hffOrXG38jfdNr3CavLbFbSIVoq2ze4LWNqHoLxWRKUVR2Nlt5+jege+oqIjxUAR87cNJ+/NGdmf5/RNYc1YJL3hf5DrDXGR8GPEwWNYqYrtKYrXy3RMPw2qSSYtwv4Xa4xiTtqvWFZhMwnBDkiBgUe9wU17v35cRD6OkXaTSfM+Zwkh7l46CcF58HON6dWdU7554BFEhp9FNcdYWfkuI567OWewym3Dp+P4C/OennZTVq69nX9NvKxm032NmUiILEtQKxbK4OqG/fih2mM28k5rMa2kpTDMdgTuuRLXelLQDd5b6PUQ2V0Ho9W9ojFp5LkleDHF+1bxkjlJpb+uNz52qWT6221jk4PeTDsoip9y7WbNMthbptheR9ud1uw9nxfjoD6qo++t1iEnxUPuStsCUuh5L5zlIxlqs3b5psX14YUzwK8lHdR7V4rYiArUj0C+lH31S+kTdvntid/4x/B+a/oUrzw9L95P2ZoOZKROmcM+oe4Rk6RWD1Z7jPZN6atokm5OZftZ0/j7s7wxMG8jfh/2dSf0mCft33oDzhMtbC8WTCN4EvIIAULwxnltG3oJBah7Dju16rIa8NsgGTupxEo8d+5iK1LUarPRO7i08bp1dHEgfkTWCyUdOJt2azsiskXx42of0SenDyb1O5qhOR7X4fe475j6mnT6Nfx/9b902oqBWeLCkPXFyr5Ppm9JsvXf98OsjKpUNsoGjOh/FUZ2PCtrwtIQ+KX2ChX2NspE7j74zaMcUinBVemvxxVlfCIMzeQ27sXb/os37VZS20YKNTo8mGKdIEnlGE7RjbYRDhadWPcVtC2/jrsV3cddicUHjelc9N/92M4vyFzEjewbPrn62Q/s0a88s1bj3yY5P2rSf/CobH6/IAVq2nmlwtY60VxS/r/2yvdrnbGC+VhHhvSmGyIiR9lHA6XXy/JrnuXbutczaM0u33UvrXqLB3YBX8fLu5ncpatCfOMZwaFFQX8AF31/AObPP4bIfL+vwiOgfBaL0O5HVB0Cju5F3N7/LlA1TWvSE/LOhVGBoHXiAxPDHR62zlvNnn8+/F/+b878/n1XFq9r9GN/t/U71eXnhchRFoc5VR6Nba/dQ7Wz7GKIXTNNT2u+t3qtZdsGcC5ibM5eF+QuZvGgybm/r7SDqXfX8cuAX1pasVdW6KKl18P2ilQyU1Wnfxv1RFB901FLXAuEUDpOi4CnfI1xX5xITRH5f4mYCKh4nhSYtmfZlUhIpUtuLHQYgsgYSqaMDMPm0Y06Cu5pft6v9i7MSwz3tWz8tS7BoCUnRb2A11PGA8QueNE6ji8tPwG3Or+Hpn7RBn/gmhX1rPO0Hdkpk0uFdddfrKe07SzVR7T8rSfsbhHvEi8jvK0c3e7B3lgT3bVWzB200pP0lhsXBv2VJ4RnTh8FggF6sKqBkDw8qNDg9LNqtDUxZjDLdJX0luQpuG6P6+El7vXMMkNBkTSO6xjqVLafTwrtg5w88ZvqM8+TlZCIepzIl7fIjeqbyz3H9MEhSRKV9smQLEuqSpA2gVArU7okCpX1RrQOX10ccDuaZ7+Nby5Mst0xmmJQDaD3tixStf7ILeDIznSqDIeog4/upKVSjrzRdub+SC99egdvbrC4MCu0N2vMyMzlRs2yf1UOt4DyE4tGsDN5OS+XD1BTi+r6FJFDxixDq9W6IQG6LIBv9AaRwpb3XISbaFE8y9vxr8HnU3/GUXqdgCCHBlIMg7deXaYtLGhMEFlhNkMMCDimWFI7KmIDX1k/Y3tPYF8Xb7NU8OktbUNPnEKu1x3QdI1x+8xE3R61WNiZvJb73O8imyME7o2wUBgm6JnTlxB4ntngcPfI5VH2shyRTEgPTxHZZAGbZ/4xLtaRy3oDzolbavz/xfX656Bf+ffS/dRXxAQSU9uAnW68ddi3fnfcdv138W5DwTzYnc/FhF6u265vSlxGZI4Kf+6X0Y9oZ0+iS0IW7R93NrHNncfeou3XJ3QRTgobQ75fSjwRTgrC9HnyewPWgff7Em+I5LO0w/nPCfzgs7TCO7348D499WHdfKZYUbhhxQ/DzTUfchEES32M1OqQ9wI2H38jvl/3OZ2d9Rr/U5vtDRKzfMOIGJCRkSeaZE57h6qFXM6pL5GBRl3itx35HBo8sBgvTTp/GQ2Me4o2T31Cdo/bEbUfexqJLFzH/4vlcOuhSYZujOrcc+AjAXnAFiq/5eTCm6xiGZAzhlfGvkCEfrmpryVqgaw0WDSSpbQR7VaOLeFO85to4YDL+6exx7B47M7JnBD8vyl8ktMD6ef/PqozpQIHxjsKW8i2aZeEWyIqisDh/Md/v/V6YzQ3w89biqI8ZrrSva6EQLcCBigaW7dFySGVNnEuMe2k7YqR9FHh709tM3zmdDWUbeGzFY2wo3aBpU++qVylAfYqP33L9BEdxQzHTtk1jScGS/6kioNWO6j9M4OLLXV8GB929NXuDPnV/dYhIez2V7/1L7uetTW8xdetUbpx341/qWrULUtZFRH4Mf0zM3js7qHb3+Dx8uv1TTRuPz8MvB37h5/0/4/bpTyzcPjfLCpeRU5ujWi4i0utcdRQ3iic4BxP40yXtdTztW/K+LGksYUnBEp5e9TQ3zLtB49spgs1t4/pfr+feJfdy/a/XM237tOC61QcqOVYSTBDrtAXzwuFsrBZbs0SAF1AqxaS93RuBrJCbSfN4HFQIiK6ZSYmkyXpqUp2XG8HYl2gxkhRlkVKTQUIWBGBEZGeXFHXxrrYUok0I7Zfdf13WC0j7Aca93Gz8kWuM83nV8wy4Gnn3dzHBFVDvxwmU63qYeu0orjuuj+76SISyBgJ1VlaSlngIV66LlEATBmdx+TF+RaWQtK9uJu2jscc526AmCjOlOs6V/TYkAzuJybgAUW4Js8dZvLtc49WfYDZgMcr0kVq+3wBwNdI52Uq/rARdP3tozmgQFqJd/Lzq40Omz8mSavEBPyXE82VSIg1NRGuo+n9gp0ReuOhwPvvHaCxGAwZZimhHBdBZ8hOnZpP2txLdw0loFeo5Td6p5xuW008uafp+Th43fQyAJYx8KFK0/skHTKYWyfFwrLVahPsKRUG1nQU7S3lr0V4mf7mRkqa5hkhpL4JLlsgRBCDbA5IhMC4pGKyt8+KWTAHSXk18+3RscFDM+JzdsOX8C0/DANIsaVw99Gom9JygqsFxMKS9zXNwAdlEUyIGWcLn1BKIAO7q47Hl/YNEzzFcNeQqbhp2r6aNV4e0H9t1rHD5LUfcErWiWJJ8yOaaFtt1ju8cLNgZiqy4LE7tdSrHdzse8H/f64Zdp2l3ai9tMWu84gAAll9JREFU8XVApUzWQ+/k3rrq66ePf5ofLviB1ya8xnfnfUeXhC5RkfayJHN4VjMh2TVRPxicGZepS/Z2TujMjxf8yJsT3uHuoVMpLEtVvdtIksQHp33AQ2Me4qnjn+Lrs79WBQCiwR1H3qE6/j9G/IOXxr2E1yYu2B1uawNEtCWJN/pV2ZP6TWLmuTN599R3mzzq9XHryFv5+cKfmXvhXG4YcQO1OuS83vJICCeiJ/WbxB1H3cG8i+ex4ooVnNP/nKj246wbgFlqfl6O7zE+WBi3o5ARl8Hlgy9nfM+OPVZmXGbEQrun9jpVdc08duxjugEyT8NQbHn/pIvxKCb1m8TTx/vtMfOrbJRUtfNzoo1FugMClvDx4oDZ9KcrRCt659tfs1+zLKcuR7OsI+2xRdke5Ta1sOO1Da9x+8LbeXj5w1z3y3VCHifUtqYlNIbVumpJaQ+wuaCWjYJCswHORai0lzyYUldjzpyHbNa36/lfx/9MIdobfr2Bn677qdWDtNfn5aNtH6mWfbHri2CUtNxWzqMrHmVZ4TLNthWOCupcdX5ld5Mq85Gxj+hGXv9K+DXnVx5a9hBOr5NLDruER4999L/an093qEm+V9a/wnXDtRPXvxpE5KCIALS5bfxe8Hvw896aveyo3MGwzGEd2b1DBo2NheRiVdFaumYO/0P5fbYWDo+DzeWbMckmjux0ZNQpnH82vLXpLdXnpYVLURRF9X0fWPoAv+b8CvjHnyknT9Hsx6f4+Psvfw8qFp498VnO7ne27nEL6gsotYmJs44g7efnzmdtyVqSzEncM+qe4ARYL3AQin8v/nfw742lG/n+/O/pkaTvm/nhtg/ZVbUr+Pndze/SVT6Z5346QG6ljTdNWzXbJNZrJ67g9zH8ZEUOZoPMNdWVrVba+yQJZ52APPY4adSxVAF/MVrF5ye94yUnZQICrt4g40vcDzWCHYT72TdBWfMB0ph/apZ3TrFSr1OwMxRJVhNSjdZWJFOqJbykSNcwBX9blPaqYEKT1YuItDeFKH17SqWw43v2lonHv/gAad8KT3uryRCxwHerSHubNtDSKQrSXnhco4FnLxzB5aO60enjOs1vEKq0T7SoyZPDpX3cbpxNPXG86L4Mu04R0tuMs5ntOh6TUSLebNB4mzfb47T8+6Ynmqm1uekjlei22WwxsyA+nhFOJ6e6bEjAI2cP5enPC3S3CSjthXUTCtaoPmZKdWRJNbyYnsr0FD+ZMCM5kW8LS8iiFv9JlDj3iG5cekyzxYTN5SWVyERqdyopoBOd0pyE5xJUGA2aeomJkvY+DZD2J8nq4OIxcjYDpAKt0h4tgVJhbD1powDFLZD2ALd8voHwpI9QlXtLWGvtmELjppQNGJOmg+Rt0Sf9sLTDyK5uti2TmpT24cVfvY7umNCv7aC4M3h67BtcdHTz80g1VfF2fFF1PSSZk7C5vCieJHyeBOSQ38jnSsdTPwyQGMBI7h89hl0l2iCy4hYTxoPSBmmWndf/PCRJIsOaxV60GXRtRaf4TvRL0WYLpFhTMMgG3jn1HfLr88mMy6TaWa0K0ieZkhiSoS2WCX4SbnO51oIoFLcdeRt1rjq+3v21anmPxB6c3ud0rEarquBrNPY4fZL7qNTqXRLEQRWgRZI90ZTI54utzNuxH9jP34/rw+PnNr/bJJgSuHzw5S32SQ9dE7vy6Zmf8nv+7wxKH8ToLqNxuH3Ycm9BthST0K95Ljqq8yhem/AaJ3x1QtheQjJzXBkq26pIc9RICLX+qbG1H2k/KH0Q/z7q33y1+yt6J/Vm8pGTgci/UTi+WZvPfTO3IFuux5yxmNMG9+Xx4+9sdV8OFi6Pj1+2l6AoCpNGdMXYDjWFokG8KZ6Z585k7oG59E/pz3Hdj+Ob3d9QV6UeXxSfCRQTPntvBihjeO7E5oDPV2vzQjI0xJjQ5RIWlcyI2CYUUhsKuEMzad8nuY8qE/qAyfSnU9qXNGjnXbUu7TtbuModoNxeTm9TsxWV1+flh/0/UNpYyvkDzqdzQtvtn0TWzkWNRcF9Koqi4iu3V25nU/kmjux0pGqbncVRWC42QVOINgrS/uu1+ULbnbIm0l5kK2rOXIAlcxEAptR12A5MRvFqsxD/1/E/o7TPqcthV/Uu3XQRPYgmK7/m/OovTLFvIe98dZaQsAfIrc3llwO/qGwUXl31DL4SLRnyV8OUDVOC3lszsmewv1ZM9vw3cd/v97G2pGUv2vbAnuo9rChcofFhFKHSXsmW8i1tsrsIh4igb3Q3avadX68lmETpYH9WlNWFnHfZTnzv93hvzz2c8905moJWf1TUOGrYWr4Vl9eFT/HxwZYPmPDNBG6YdwPX/nItz615rsP7oCgK2yu3H/L7WTRu37HoDu5bch/vbHqH/Lr8IGEPsDB/oTDLZ1nhMlWK4SvrXgkqESrtWpIwvz5fX2kfwR7n2+xvOeGrE5j47URWFK3QrBdNAAEO1B5gY9lGlhQs4dHlzYHO1t6LLp+Lnw/8rLu+sKGQD7d+qFpm99h5cNEzlCW8Tnyv90mP26XZLsuRo1nm9Sn8a/p63l+ynzcX7WVXTkGrSXsAj007BtFYTnEEYi3o0S47icOuS8I5rWKbEdkgJq3sq94ULu+SHB25lGHxsshXy5S0FHaYm0ngUKW9ZKrCnP47uxuWBwtdubwu2iC0Vyvtq3PxADbBbxBO5Cs7vtfdZ2KT5U5rPO2tRgMZCfqqWUtrXggbtb+ZiLSP1r5HkiRGprmRREXRdDztLbj4yPwiEw3rudCwjJdM7zJCPqDdHugvF3OKvBFZkhjYSfuyEVAWW6Lob0aCheHdU+ito7TfazJxddfOTEtN5q7OWfxU7Z9TThjUiR9v1k+7HydvZqS0F/OSZ2Fvy9k4WVJ1kLAH2GM2s95qwSK5SWqyrJHDsmrK652kSZHJ4G6SP3XaatUGwCoEpIlIaV/UpBazClzwb05cGixE6wF2mU0ckLQqxkhjix58SFGR9iKr2EBR0Wiwy6IODmW42kcVas5YjmyuRjbVRbTUSTInBdXZAfhJfgXZrH72+ZxaIiLgjXzBkd156vzhXHiUOjjYWnscr71Hm/2WIyHRlIjT7QUkPA3NxLWiSNiLLoOmguqBccEgFEaIB22TwUS60qy2V3wmzuvzdwASDO1rA9ItsRtJ5iSGZwwPLrMarBzb9Vh/DyWJXsm9iDfF0z2xOxcMuADwK9pvP+p2JJ2aD72S+miWDUgdELT3OaXXKRzX7TiO7368SincPbE77098H6tR+8zsFN9JlYUwJF0bMMiKy1J9HtNFbDUELZP2K/dVMm9H81j6xeq8dq9p1Tu5N9cMu4YxXccgSVKwroXP2RV7wRV46geT5T2Dt055S1xINeRedJafGizOnG7sx/ie41s8vqIo7C2r1/WLbk/SHvzZBPMvns/U06eqAjLRILeykftm+ufhPmdXHEVXkNBwySGrqxCAovjnrpO/3MgdX23SZLx1NDLjMrl66NUc191fi0IkslG8zd734bYiu0vqIxKbnZjAiV3OaF2n2llpn2MyRq20r3XWtl/NQa8bGttm81vUqH13LGmMjsgvs5VRYa8ICrPe2PgGjyx/hDc3vckVP13Rag4yFCIBWeh7bpmg/tfqYnVWqM+nsKOoJdK+eSzS2ONEMV7o2YaW1gU87bXrTSnNQX/ZVIclY3GLx/lfxP+M0h7g8h8vxygZeXDMgxHV7o3uRuKN8UiSxMK8hcI2c3+ZzDlrPmdGX/0UtT01e/CGDVYNeNnw9aWM+tc6MPtVBLXOWpLNyVGpZOtcdcQZ4zDJajVYSWMJcw/MJd2azpl9z4xYXOVgsKV8CyuKVlBpr+S0PqdxTJdjNG0aXA0aEnhh3kL6jRB7RrYVTq+TT7d/Sk5dDhcMuEDon/fVmjy+3ZALcdrt5+bMZX7efD478zOGZw7XNmgl3F43Dyx9gN/yfuPwzMN5dcKrZMZl8s3ub3hq1VOAvzDV+xPf18342FS2iZt/u5lGdyMDUgfwxaQviDMKOh8l9BS9S/blcMphzR6UBfVadd7BFNr8oyE0smvp9AuGOD8J6va5+XDrh5zU46T/VteE2Fezj4eWPUReXR5dErvQL6UfSwuWYvPY6J3cm1uOuIXXN6orx3+560tuPPzGiCmZB4tHVzwatJb6x/B/cMdRd3S4ul9EpoPfZzCAUNVYAO9sfofrhl2n8uL8Pf93VZtyezk7q3YyNGOocFJ27xJtKnwA4feHoijsqdnDu5vfZX7ufMB//z2+4nFmnTuLBFNC8FxFUyB6U/kmlhcuZ8qGKeysan2h2ZnZM8mty2V7xXYy4jJIMCVQ0lhCTl2O7sTRm7gyOCl40pzAjwW1qklCuq8S7DUQlxpctr2oli0Fzd8nWbKR3wa1kttTAR4XGEOeXQ1lFBn1pymS7MDa/XNMyVspdicwxyj2ka2yiMmyzsZCYcnMqvoC4p31YFGrmMKL0eohOWEZkxP9pMPHKcnMKSimp8dDJk2TZdlBfK/3kc01PL5qLrXuEtIsaby07kUUp50HUmVy68fxpfcU/3Gp4grjQmyKha+9E6hF/aIWb1Yr7Rt0giZ1ssxukwmbLDHS6YIDS7BYrxe2TTAH7HGiJwstJhmLyHolsL41SvsG7QtKRqJIaa8+3qQRXfkpxLPzntNCCJ16nYyVUHucENL+BHmrygrmeMN2Nij63s03Gn/iGU5mQKckNheI7/FogiAZCWb+fephpLwvVtpPT0lCCRl336veRECLGScs5erHFcZFXGFcBEvx/7vkE8gaDFlaRTBAd6O26PcWi5ljHE4ypVrqFW0xvbJ6ByktKO27NRWy9co1mnUie5wsga1UAF0EdkcXG5ZS0nkItkqJv3XrzF6zGVnZSllDJx6sqiDd5w/clBha/wqkSFCstI1cag1pH44ERyaV5ijtktoIZ8UEzKmrQVJ4eMzDNIZZz8jGOiRDI5KsvsZ8LsH5aCLYX71spPBYssoep+Vx1VV5El5bP4zJW7B2mdNi+2iRaE5k4rAuPP7DDlxlfoJLNtbgrhmLz96smAzYZoUHqQLwNPZXeeknmhLxeH3k7T0ZS5YbyViPq+o4Fm3zcEwPsEhpmn0YSWD6pA9ZW7KWl9e/HKHXEpcedgnfZDcXpz25p99r//HjHuepVU9R76rn1pG3igli4InjnuDqoVdjNVrpmdSTkloHzvJTsGQ1B/OcpWeSZtISiRcNvIgLBl5ArbM2SNgmm5N5b+J7fLr9U9KsaS3ORadMmMI3u7/Bh4/LBl3GcV8ep1KuntpbbddzYo8T+dfhtzFrz2xK7er3FVEB3lB8ulI9jrm8PgqqbQzQsTJrD5SEWFB46o/AU38EiT1SggVID886XCUgcVc3ByU8dUdic3RDMtVy6mEntegQoCgKN09fz6/bS7EYZV6/4khOH6ZWvdfYxM+FtpL2B4PH52zXLNveIpHY/pi9qZAFu5qJzh+3FPPEuU7hPKMl5FXamLJgD9U2FzeP68/ovs1joqIoLNtbQY3NzWnDOmss8gLokdgCaR8WkCmotvsLGOugd8Jgkqytq62gV9ujJdQ5PLi9Pq09jsmE4vXqhDX9UBSFh5c/zJx9c8iMy2TKhCkqa6xWo3gLfHGpf7439Hy4eBq0QkgkEmqJ3g9Fy15b/xo7KnfgUTyc0usUlVVpub2chXkLOb7b8eyq3sVRnY4KcnWbyzfz+obXkSWZW0feyshOIwHYWr6VpYVLOabLMcLjhfY1rz5Ps/7dze+SEZfBef3Pw2wwk1tlo94pVsub8PCS6V1Okrew0DeS+93/1JD29VF42ushEMjUeNpLbk3NFlPGMrLKR5GvRJ+987+AQ0ba5+bm8vrrr/PTTz+Rn5+PxWKhf//+XHrppdx6663Exx9cJe1o4VE8PL3qafql9KN/an8a3A30SOwRJFOeWf0M3+z+hnRrOm+f+jYL88Wk/f+VL2VlZgu+lvUFWGUtef6LbGfUvkUogydx/9L7mXtgLl0SuvDhaR/SJaELK4tWkmhO1PjevbnxTT7c9iEJpgSeP/F5ju/uV8HY3Db+Of+fHKj1v3juqd7DPcfcE9xuacFSPt7+MZlxmdw/+v42R7OfX/M803dOD37+Nvtbpp81XWOhIho4NApzrwfm3A7Zv+DuexKc9xYmS8upMKWNpawvXU+FvYJVxatYWrgUgLkH5vLjBT9S7ahmdclqju92PI7GzjwwaxOSqYZErdUj4PfBvv7X61l06aJWFw4Kx88HfmZe7jzAT7i9v+V9rhx8Je9ufjfYZk3JGpYWLtVVTny49cNg0cu9NXv55cAvXDDwghaPXe+qxygbNQS/nhf2B8u3qUh7kdI+PNrs9DrxKb7gMcpt5eTXldBQ14kBnZLpltr24EJ7wOV1sbNqJ4PSBmnUPeVNEV45LhdzmjryvKFsg8ZqJYiaPMhfQ333IzGn9MBiaLv3amvw/Opn2V7pn9jWV9ezp7rZ7zu3Lpf7l96v2UZBYV3JOs7o27KyosHVQKJZfb95fB4URcFkEHtr5tfnq2pBfLjtQ1Itqfx9+N/1D9REjmgmTM4GKla9gd3VQM+xt0NSFxRFwaN4VAHJD7Z8oAlOiCAioWfvnc3svbN5/NjHueiwi6h2VLOjcoem3e8FvzM0Y6iuDY4eahw1wb/31+7n+TXPC1X1xY3FHPvlsfRI7MEDox9gXM9xUZH2ADf/dnOr+hSKosYi5uzzkxv7avUL8+mh0GRkv8nEYW71JM1btgtD72aV3JoD6uBFErY2Ke1rDEBNLmQ2j0vu+jKKIqhhjcmbMSX7VcYeUyN6isc8i5eAnUcouhvz2C1oXy0b6FG1H7oeoVoebmUDfsW8KXUNiicZd/VowMCBhOaCVB5JYnZiArfX1AaV9qaUjSqf4vc2v4ckSf6xX4LpnbwscH7IbkdPNikDeM/8CiNlf3bL4YZsbnPdrepDktUIlfugaj9UZAutcQBqDAYu7uH3Bj6zoZEXyisZoaxhG1p1dkIb7HEsRjliEM8SQihvN5t5I81PKN1WXctwVxip0KBV2qfEacemOMUBqz6HhhI48mr+NrYXC3aV4nD76J0Rz3XHh7xE6pH2jlqwVUF8uspqqIekVWqdLa/U/X5j5F308+7nvJFnMnNDM7HULeS6iRTUCCAj0cyILnEoOrUYZiapx+4cT4iy3d0KJdeMa/3/p4l9qztbtOOGU/L3P5NaDtAVOez3HtQlmdQ9YaEwyaBS250gb2OYnMPTAmWYqBDtMDlH5wso4mK9jhrS2MOsxAT2mv1zcJ+k8EuSlXxzFl8WlSIBJW1Q2jfIMoWIidCWcDCkfZ19ACRHfkaF27u0BorPiKv8VFzlpwMKZ11/FovzF6vaSMY6JFN4sFpG8Qi8mFtQxauuGx2Vt2p3nkQUbyKeBnGAqa1IMiXRPTWOf57Uj/eX7MdZfLGwXcA2S6y0B1f5RBVpf/MRN5NTaUPxJOMoviS4fNV+/z0tebXXkOxNY1jmMIZlDmPaxl+p8m3TtFG8Vuz5f+fEsZcwdvxYlhUu4+jORzOx90TAb10y/azpmu3Ar3B+eV42tXY3t4zvz5h+zc/a8non7uqxGJN2YLAW47X3wF07iji0XvKd4jthwIrsVf9uwzOH88K4F9T9VRQURRvsiDfFq+aOVw6+Mvh+mW5N11jCFNc6mbFgEAcqbiMp3snJx62h0LaXM/qeoVs7APyFExdna8eZvCo1aV9rd/Pc3J1klzZw6ageXHZMZM/4AGpsLp7/ZRf7yhv525henDfSn1FSKii2WFzr4MNlB1i1v5KhPS4k25CNw+vA09hPQ5b6XJ3B1ZktBfU8PHsru4rrsZhkJo3oxhWje6qesyv3VQYL3Ds9Pl6dn60i7feU1nP/TG2NIoBaHTJfD16fwrTlB9hRXMfFR/XguAGtEwk1Oj0sztaO2XlVNv33sDZCURTqnR6Srdp5g93l5bm52mzS/RWNrSbtV+yr4F/TNwQDIGsPVLHs/pNJifcf9+V52by5yG+FdVSvVGb+6zjh9xRZtSreZl6irM4ZPEeKovhJe6M+b9IvtTepAl7F68zCWXYWkqERg6UYc8ZyoMkOrK7tAsYDFY2UVamfBTZZpsxZSSRTmLUla4PvKhX2Cl7f8DpTT5/a5n6w4Inmud6O2bDldBh5paZZjc1FSpwpeD731ewj2ZJMcYN2nljaqH32ikj0LRXN95mottgHWz7g0eWP4vK56JrQlVnnzsJsMHPXorsos/vHqfWl63n2xGfpkdCdv/98FS58vLP5HeFXnbZtGpP6TqJrYldVXc0AvIqXJ1c+ycqilbwy/hW2FmrfPU1pyzClraaPy8e4qs2k+nxcZFjGZl9/ljsv9AvSyhqod3ioi8IeRw/NSnv12Bhe2D6AsxK/4b36yW0+3l8Rh4S0/+GHH7jqqquoq2uOpNhsNtatW8e6deuYOnUqP/30EwMG6DCr7QwFhet+bfYzH5oxlJsPv5k4Uxxf7voS8EfELvnhEr1dAPBDUmSSV0Fhj4AsmZ8Qz93Zc1mdmMjcA/6X+5LGEh756Rpkew3rDP6Xm1tG3sJ1w65jfu58dlbt5LMdnwF+leZTq55i7oVzkSSJuQfmBgl7gE92fMLigsWMyBzBDSNu4O7f7w6SWoUNhXx25metfjAWNxSrCHvwB0De3vw2b52i9pvWi/aV2yq5fPClDE4fDOs+hM1f8GVSIq81rsP69TgeH/8SE3pN0O3DV7u+4tk1zwp9xNw+N0+ufIIVRStRUHh1/avImEg8TMLn6hTxu9k9diZ8PZ7nT3qB8T3Hs7pkNUUNRYzrMY6MOG1gZlfVLhbnL+bwrMM5rttx2D124oxxfLhNbTfx5a4vg9dTKD7Z/gnrS9ezrWIb5/Y/N0jKK4rC4oLFqrYfbfuoRdJ+6tapvLXpLSwGC/cfcz9J5iT6pfajX0o/XXJwbZ5aqVIg+M2yy5qvqV8O/MKTK5/Eo3i48+g7cXqcvL7xddw+N57G/ijFN/Lp9ceq1AUtodZZy4/7f0RC4uz+Z+sW4YkGVY4qLvvxMkoaS0ixpPDN2d+oUjYDCgVzxu/C7SvsFWTFq9NxKdkGH53OO/Ey76emEGdK4OkTn+XkXn5Fk6IoVDurSbOksbJoJXn1eUzoOaHZsy5vFax8ExKyYNwDkNQZl9fFU6ueYl3JOib2nsidR9/pvxf3LoCf7gafF88Zz7C+jbZNa1a8wBk9J4DRP+kM3CtyE9Hi9Dq59bdbWV2ymiHpQ/jgtA9IsaSwpngNDyx9gDpXHTcfcTM3jLgBRVGoclQF74HtFVp1zMvrX2ZZ0TIuHHAhE3pNUAeN9i3EN/sWyrx26o+7ld7H/tuvKlAUvvz6PJ71laJIEn//+lfOv/AL7vj9Looairhs0GXce8y97KneExVh3xJe2/Aa1c5q3tj4hnDsWJK7kKsHX0WDu2Wv8lCsLF7J7QtuZ3nR8oiFbwMoaCjgtoW3MbbrWFUQ5o+MAyajhrSv27mQNI8Neh0LpjjW5qhJnGSpbaT9nZ2z+Gj/rxwTQtrXVxRFVNqbE0SUuxb1Bpl4Uwk2t5qAyDQVCUn7KoPsJ8HDSPsjeqaqG0pO4vu8jWz0XzuyuQJP3XDcYT78vyXEq0n7NHVwx+ZR23/UGgystVr5m/dnytLSeSDJQXdPJ4qNBgqNZaTVfkp18VWATFcqubHoHXhjaXD7OrM48BaKuYkJ3FxTy3DHRhCQ9qlGJ3hcrVLatzSvCCjt3cB9nTLIM/n7mWMy8UNBEapeO2vB7QBTM+Edal0TQPrK/8DmpkybbbM47l8r+O2uceRU2BiV5cVqCumTHmkPUL4Leh+nUtp3lbTZZn3lyMTpCPdWThx4Ncf2y2BlE0F3TUhxXqvJAJKbrolrcbrTqXIMIhBMOknezGh5FynKOVATjxTic2yT/MVJB7nEY8232d/y1a6v6IKBB40GuntaYf9QLbb8iTOXAeo5UGET0f2M6UNudU9Glgar1l8xuieeZWFjadfDoWhj8OOxhh3YJYn76Uk4Kg0GiM9Q1TQYIR3AgBcvgWtRIR4nRjwkC/zuASyOCrYnaucj2y0WCowGenq8lEQYWyKhyGABn9+XvVtKHIU10QRKFCSjfpG6UXYH6+L0Fecl7t70cS+hJOR+tPjA7k1BNtXidWbhKLgKS9fvMMbntOLb+OEnwwP79l+P4XMiydiAMUkd+FbcaSHbhSz3RlYxqzlcnYLgoS2a9qe4M3DXjQgGaw8WAfHC/501hFsnDGD6qlxe/FX7VAjYhRl0lPZeex/O6PxvGozrGJE5gisGX8H87dqgW3ZpAx6vD49LO9/1uFLYXVLPm4v2UlTUB2sXLWlvy/kXPldnPlx2gCmXjwuS9dHg/plbWLXfP6ZtL6pj4T3jgoRmWb0DxZuE7cBtSAZ7k8JXxudOJ8mURL3bHxiUJRmDqy9jn11Ajc3NpMO78uYV4npKi3aV8cj32/B4FR4+ewhnH65vo3LXqLvoEt+dvLoirh1+uUpU4vH6mPzlRg401bGot1lwFl/Ct1cLCrqGYe7WYhxu7fWVV6m+F19fsIcv1/jFS+tzqxnaNYURPVoOzr346+7gdmtzqhjaNZmBnZOC/s2hKK938tSPTffPDiv/ufgj3lm6kYLSNPTciveVN7KvvDkQt3xvJd1SrYwf1PxeO21FjmqbXSX1ONxerCYDtXY3V05dTXU72eO8v2Q/z//iJ7t/3FLMvH+fRJ/M6AVvu0rqEdTGpMbmprLRRWYbVO4ibC2o5e/T1lBjd3P12N6qGgYAczYXBknEUByoaOSYPtG/x1Y3urj18w2q81jv9LByfwVnDO8aDHIEsCGvhk35NRzZS5tp0z1JRNo3C1rtbi+NLi+JFiO1djcNTg+Skqrbt2Gd+pIWp/1tFG883iYrMA/gaRjCzaem89aPcRwMLXjaq0sAhU6DFOwh4+Sayu2cM/BM3e3Ca2GsLlnNp1s+YFR1GUOTesIRV0K0WXFeN7Z9C9hnNvNzYjzL4uLov/pJnhgyKZh1ZHPbuP3rpSzc7qFnWjyfXD+Kd3Y8xdwDc5ElWfieWBr2zuZTfK0WeoFaQFXcWMzMPTMZkj4kSNiDn896YOkDdDYm4mrh2VjnquPs785m+lnTyavT8jgBBOqlbStUB3Fkax7WLj8CkG+B10jl8Qr/M2Ky8TvmOc/j7cX7hM/E1qKs3oHH69PY50gmsTilJLkQIjst/s+hw0n7jRs3ctlll2G320lMTOTBBx9kwoQJ2O12vvrqKz744AOys7OZNGkS69atIymp49LV9LCjcgeTFx26aE6VwcC9Zb8Tt1+dXbDBVama+07bNo28ujx+3P+jZh+FDYUcqDtAv5R+TN/yvmZ9bl0uuXW5mm03l29mYf5CTul1imab7Opsfs//nRFZIzTKhYCiPRxLCpawq2qXn4hvgt7AMXPPDGbtmUmS60TOdK2iuHMWS+L9JJ9NcfH4ikc5ocdCjfVPXl0e3+39jqlbI0del4epXH24kQxgiNMvzBaA3evg/5Y9yDXDruXtTW8D0DupF59P+kKVXnqg9gDXzL1GpeyVkDm51wRV4CQS1pWuY13puuDfPZJ6UGYr0zy4ABWRqCgK3+/7nh2VO8iMy2RCTz9JGiAjPT4Pj65o9sF+fcLrukp7yWijosEZnCDll27StFletpjnVrzNTUddwdOrng5O2p9Z/YyqnTFhHzbLLj5faGb0KfHQ/SgwNZO3dpcXBaXZwsFWhbLgSe4tmc9Ks3+iuqRgCe+c+k5UwaSSxhK+2PkFRtnIVUOvIt2azgeb3wtGvWudtby/5X0eP+7x4DZl7s1YOu/AlKRVWgPsm3Y6WX1PhZMfBmsy2yu38+Svf2dH9+bJW73HxtOrnmZ8z/FU2Wv55/wb2FOTrdrP1M3v8uXEqWSZkvzpeY5adptM5BctZ8wR1/GFq4TZ+2cDfkuXgeX7OKe6ErLn4gW+SE5i5vL7cJnaNjSvtRXB053AmsovI8/jhbqtGCQDD499hHE9x/HxpndYXeLPNNhZtZP3t7zPPaPu4bEVj1Fu96tfpmyYQuf4zry6/lXK7eWc1OMknjnhGXZXix/aq4tXs7p4NaM6j+KV8a/wwtoX2F+zl95F29iQKlFqTIZ9n5GWN5vhnUZSVrOP3UpZsBLdxyYns+ZeQ11TKv70ndPpn9o/4gSkNahx1jBlg7YobQDbqnexbNo4oYVWSwgPskWD0CJN/22YZBOdjQkUuGqE6/eZzWBTE1Jpq56HVUBKT5Sbl7E5fzsD05ZRYRtOtXNAk9K+bVlL/9o1lVmDz6VXsl/lVldRSHEkYs0SvYVXpnUXeWGkfZJB7HlZZTD4SfswnDy4EyN6WNlRsxGfszOGuNwgYQ9gTl+BJGsJvP1mE+N7dufk6nKo9CCbalrs77yEePJS8qiNK6EWI4UhY4IvdQfGhm1QP5QvzU/Tp1b98qCntA/HKquVIU6tOuc2w3fcvnY2bEumf49TuNSQQrbPr/rXh74/dgABT/tdZnOQsAd/Vscmq4VjHGEv0Y1lkOq/FhRFoXuqmtQ04iFu2xfNC2pyYcdseoz8Gz0W3w2bv/ATwFd/5w/A1OsXdiVnGfQ+jqQQVV6kQrABbPQN4Ei5uaBkP89+JEnis3+MZsmeclLjzRwV8oKeaIbhvZ8mN87/XUeWDWdT5VVMkDcyzfwixQYD3xcu4bvVp3M2YAL2mYxc27UztQYDPd1ikuWJlU8AsBvY26Uz04tLyPT68EhG3nGfzfWGuSRITtZZLSyJi2OUw8FJdi2xFIoCwb2XbzJSZjBQYa3gK8ej7CisgY09wGCGlW/S1ZJMoamK6fFJ9HO5SVB8rEtJ5oQKkyrgICoaDX57nNq+x/KfsqWss1oYZ7PzUGU1v1vuxIyHWiWBdKmODKmeaiVyduY+neBVttlMT4+dUp0+tIQao48HTh3MUb3S+GVbCR8tj2LuJzuRJP1Ayk01teSZjJTpjHc+bwLdGtMoSW0qBqsoZOVPYqfteGRzJT5XJiCjuFufBeDzJOAs1Ra77BSnFrxIkg9zhroGUED57qo8PqjYBHCWnhXxmCryO4K3fgChNhCOwssY2SOd7XViEYaqf439kYx1GCziuiaJpub9psSZGBkemA20awrmRZqi9jSdxORT/hH8vFtQtLbB6eGtRfuw27XPSIcjkdNf859fyTQY0L77+X9nWLqngqOfns8NJ/TloUlDhf2Zs7mI5+fuIt5s4O7TBgUJe/CrHRftKgsqw8uCynCDyie7vM7NbUfexvNrn8en+Lhm6DW8s6A86JH+05Zizh7RlTNHqJ+tLo+Pe7/dElRVPjhrKycdliVUPQOs2V/LizOyaHCmUVdYzQsXN6vJ31m8j/W5ajXmL9tLuOOrjVw2qmdEtffKfWJCKK9K/Zz+cJn6Hv5sVQ4vXKwO2odDURQ+X50X8hm+WpvPI2cPpVRA2odj6uJKymqyiCZoFYpft5eqSPt95VrByd6yBoZ3T2Hu1mKtHUUIwkn7vEobHy0/gNVk4IYT+6pIdEVRgoQ9+H/jWRsKuOu06LNfRIWcQ/vcWtLe7fVhc3qDqnbwZwPc/uUGKpvIwY9X5HDeyG4qonz1fvHcMRAYihZzt5UIAyKbC2o5Y3hX9pc30BhWlH7pngohaS+yxwnPWCqvd5JoMVJQ7b9+FVemMMNK8ZkY3rmHUOgQntlk9QxiYs+xvOVbrm3bakgMc3hZF9983O/2zOScMXej4CeZzQazSuAlqj/2YpNg62+19Vy69Ut6Xzkbg8l/bWws28jbm94mzhjH5YMv59iuxwbHiqmrnuXt3j1whwzUOYB3/i28cfbnrCtZx7/m3YxDcZLUfQB5BdfzwNyv2eHzC2hFhD1ASV2eX1w3wM+dVdnKg7WoDgbvrJ/OBYO0z0qPz0OhzntZOFw+F29vejsoztPDW5veoj73RtUyc5o6c3RmUiKPVlQhA5lSHSV19nYh7AHcXoWcykZNkdpUc4HQ1DHX6sGIh4M/y38ddDhpf8cdd2C32zEajcybN49jjz02uO7kk09m4MCB3HfffWRnZ/Pyyy/z+OOPd3SX2ozjbHYcssQGa3S+tpHwu1mCnF8itrF77ELCPoB1JevYVbCCvYKiGZHw5tJHGX/5OAwhkct9Nfu46uergmT0bcOe4MajLqDGVc0zq59RFXkMxyU/XMKVg69kVJdRTOw9kdwI5LWCjzrz73xthnCWrMpZw9QtU/l+3/fUu+q546g76JLQhckLJ2tqA3QEGtyNQcIeILc+j0+2fsjkUXexvXI707ZNE54HBZ8wDSpaXP+r2FMY/NdAIB3u0x2f8tK6l4Lr3tz4BnEGi+5D5tV1L+H1iifIsrmUWTt/4/xhx9ApvhP5NQeEzhKf73mHJSU/6hbODCC+58f0r7JR82kVqfFZcPTfwZrK9+7RPDC/gjS5iGtHlvC3w8eQ+N0tbHdVsbJ7c/rm8qLl7Kjc4bdaUhTYvxh2fA8GExx/B2UmM6+uf5VSW6mqePD83Plc0/VEpu/+QtWfmXtm8uCYB7HIZlasehO561TMEV628hyljF3zHuQux335dO787TaK0U50y+3lfLJuBa+u/AolJVuzvtRRyWvfnM1/4gaCo5YfEuP5v6xMwM7ALa+xx6y2y5pasIBzCotRgMcz05mdFJmEaAk5ZhOTenTlqfIqHi1egL2JvHvk93v5ZcRdvBmWDfLpjk+Z1PcsChrUga3/W/Z/wb+XFCzhuq8nUmmOPO6tK13HxG8m4VT8wZ0dYZPFane9bvCvLsw794W1L7TLhCha3BvXfr6eFp8PZxuU5h2JVHMyXeI7M84tUVm+nQ3xCfgSsri3vJQdzhzeSksVbrdPEDzaZDGz3WLmxMZian96AKXrMkpkCVlZyfC8M7D63G1S2gM4FS9fb/2Ie49/HICy2nzV5PtgEB+Xq1FtmI21gNbCrtogQ+F6/1gUcvy9NdnUZjxFfFIlsk/G7DUT/mpuSt2ICJVGAzOynFhNs5CiKPb1XQtjgTF5K0c2GugTovz2ApssFl2yMhxr46z8mtBAgulp3NVjcVWczACpiLuM3yIrCtgqScv+hheadnerazI/+dQB/fHyRp41fYgRDyuWb+eD+l1Yuztxlk5C8aSq2gaU9hus2hfzVVarlrRvKIfUXuyq2sUDSx6g0lFJ994nU5g7GoCLetQiVYRts20WJHbxE/bgV2z/cAf8c3FQab/JYmZaSjJrrVZ8EkyuquHKA0tg3H1B72poJu19+AMhJkUhPkQm2KhY+NE7VkXaj3f8Bv/phrHbSE4++joYrM7YtBd9GiTsAQozt0KVi+sNc7FJEv/o2ol8kwkqVpCdnsr9VTU8lZFObRPBnG9q+bctNBm5o1MWnxaXUmbpzcv2S5ntPZ5LEqfzXpcqPJLENJKZUlrOyTZ9lXi+4P7faLUysWc3fJJEP5ebr/Y8B9nN56TEYODK7l38ga8AHAeY0q0LT1RUcUGDf6wv07GmKTYamOzJY0Oin9T8NjmJKoOBE+x2Tm+00clXE2ybJulnR/mAfTrnKtts4mSbvU2FaAEw1XPzuP4ALN0jIoN9hCtnJUPkTK6jHU5eLavgb93EHq6KN4HNZddxuDwVm9mGufoIVtv8tXhCs0kVT+tIe8VnwlFwFYqnmURKaLLESrema5SHkqSeb7pr/LWkXFUnYYjLR7YW4qk7Ak+DOgMjHCp7HEEwQ1GkYKFcnzMTVL73Ri7tfT/xaWeTXZ1NZlwmz655VrOPhn13o7gyiO/ztmZdAIGCqgEM6ybO+Aza4+go7QFemZ/NyYM7Mby7/zfYXSqWCb76WzaSwU5ieP1Upfl+U9x6RHTzNaso8MHSA5wxvAtH91argysanNz/7RbsTQVXb56+nnCsz61uJu0FqmOA0joHD55wJRN6TmBxdgmbDxhYl6u205yyYE+QtHd5fLy/ZB+zNxWpbBDqHR5W7avktGHa61tRFB6ZvY2GJv/kGesLOOvwrkwY1IkGp4f3l+wX9u37TUX8uKWYH247ga4pVtbkVDG8ewrdQ6w6N+XXCLfNq2pW2ov83pfu0Qb0qxtd/LiliO1FdRhkiYlDtaYfy/f6tyvROZ+haC1BHMDGvGrqHW52l9TT6PKyv1y7nz1l9QzvnsL8HaWCPTQjlLR3e33c+Om64HW7LqeKGTc3E6Ki6/m7TYVB0l5khbSjqI6V+ys5tl8GQ7sls6tYXzq7t6yBsf1aLvIdwIa8av7x8VqqbW4uPKo7L158BAZZYt72EnLCMil+zy7nyF5p2F1e4swGNupcFzlR/ia1djdzNhfxyGxtNgzAlgL//rcVad+dS3QCOqKCvpKsvo7K6hz0zUygoDrw/SS8tj7IyepMaJ8rnZ7pCbi8An4gjLRPiTNhMojHtRHdU4SWKpHQq7Yr6+Kbn4trfQ288estLHSWsLdmL+nWdB4d+yiju46m2lHN3hrtu3QAn6ck8TkFHPnt6bxw7lfkVu/l9oW3Y1f8Y8Wi/EWc2utUnjvpObZVbGPK3hnCyOriyi0sWfI0j+f9gEPxn1MlaS9JyWvY7p6L1MJUoNpgwPHTXVhvWY0NH2/+8q9WnRM9NLjr+H632Ia7NYhGQLa+dD2uqqOA/sFlhgTt2LrH3Cyw6B6/gYrkPHzOTrirj0WUESRL4NOJu6cYijHITqrcfTDgpWHFVO41bmCW90T2Kf5nT7o5H5E85oDJyHPmt3jNOoLCFr/d/wY6lLRfs2YNS5f6SZp//OMfKsI+gLvvvptp06axc+dOpkyZwkMPPYQpipeSQw2jovBMeSUZPh8/JMTzf506rthjtAgUN20t9nrquPK7cxjb51SuGfZ3MuIymLZtmko9/vrG1ygsHECu4Rk2VGptMcLxxa4v+GLXFzzktLDUaIM2Kpje3tw8uX561dMoUaj3OhIfbf+YUV3HctvC26KywGhvNLobuW7u1Wyt2I5LUZOYCmALrxUQggMCy5sALJ3m88aO+UzbZuIs+SqKcaPnBy3yuxfhg/R4PkiP52+19Qxf9wbrrFZ+TkzAOECiHnizDt5c9iVd02SKjdqJ+49bPmLo2IeRZl4P+0IeYmve56He/VklILpy6nJ4si5H2J9R00dxVkOjn0CMjyyjfiozHZsk0bduL7WfnkZxqj5B/cqOfxHJ0nZOnImLi1ZxBPBaCBkaTtiDX4H7bHoaX6S0X4ZRnsnEtd3ULxPVXjtjNv1H2P67H2/U++mD2KPYwdmyBUCAsD9Y6BVK/aNjUkMjj1ZUscNi5rqukVwcDx0WJY0lc8s3QPjLhZ9o9ES4N/aazbxlGYIjvpgqg0ytwcDvTe3fSvVxWMUCbE2WDj5Jomun2VCChrS3+nyc19DI18ktX+ezsmdwb+ZYfIedxkpHNhx8jByAmqQcKHeBYqZnehw9nHupN3oQkfZVBgNkz4XXR8I1cyCtN/n1+dw0/yaqXX7lnk/24ZBbVtOFw5S64eC+SGA/Cbu5wtD8YlRoNHBr5yx/dkSU+C3Bn+0n04Al6zck2cXplS5kHeXrqYnfMd/kwOdJwts4gAHeeqaYX2Nhopm1Vis/7PVb6JmSwWAtxJZzC4o3EUPiDgxxuRxwuBnuho0i0j7OynkNjZQbDBzpdCIDFdV7yTXBQ8seorChadoeP4vTRvVhkr2Oc/Oe03aycB1sCvN0Ltroz5yoySPfaOTGLp1whFyjL2SkcWLxenq6HUFlmoSPPlIJsxMTeDU9lSqDAVlROKehkSeblEjblL5s9Qn84N2NkLvc/8+SBIOa6oz4fCzf+rHqmnbIEv0Sl3GiextzEhJUpPxXyUkc6XCyPoJtih62WC2M7NuLIR4fJnkp+2pGMyU1C2NI4dbpyUkRSXuR0h789zr4n18zkxK5qq553H8jLUVN2DdBkSSeyEwn3etlnN2hq3IvNRopdakJs4UJ8SxMiOft1FQ+KS6ll8dDtSzza0I8aV4vJ9vshL81FBsNwaB1OLLNZmpkuc3BVcnoJzEa3Y2sqnuP+D67cNccjbv2KKzdZmBM3InX1g974d/AZwEUTCmR73sTcLjTxcyCYi7qofURV7zx1HiTWV74CABGWUKU3dI9qStiTbkaoxNuY8neAjyNAzQE8cuXjgTAIBvIsGYEs/DCIbt64nP6iSbFk4It95Yojty0bQixIgmUxo7ii7F0+hkUA47ScwmfpMSbjZzR94xgDZ95ufNYX9pMTPtcGSguv72P19YnqoxbgNR4M91TtZZHAaW9nqd9AGe/sYwTB2by2mUj2V2iPx8K9aoOLvOpr2JX5YmYM5qFDq7qY4T7+nhFbpC093h9/LS1mGnLc4KEvR7W5jSPBeUN4mdZSZ0Dl8fHy3PL+Ha9WCC2q6SeWpsbm9vDK/OymbFefK5XH6gKkvbl9U7eWLgHj0/hrOFd2R9Gln649AATBnVi1oYC3cKJ4FdVvzJ/NxvyaqhqdBFvNvDZP0bTNzORWRsKNORtAPkhpP0WQdFwh9vLrpI61uVUM6ZvOmkJZs55YxnFIQVmQ1X2AQRI8GiU9m3FrpJ6Tnn595DsCC2yS/1Bwpasu0JJ+7UHqlTE/LrcalbuqwxmMiwMKdoagM3pRVEUvliTx/Nzd5FgMfLixUdwwsBMNuZVc+l7K3F7FcwGma9uGsvO4shK+2jh9vr491ebgir3WRsKGdIlmRtO7Mu7giDP/B2lzNteyo7iOo7slaobMAksX59bzYKdpYzqk8bJg9VzeZ9P4cZP12nqOYVi+d5K6hxuthVqv++Oojq/TYjNRaek5me73SUYWyT1tR+weg0o7QG8tr6Ywkh7s5KF1WQQBxnDSPvkOBNmg/ZZKEnw+hVHcuHby4PnORoSf27t1aR0eonakH2+X7os+HeVo4p/L/53xH2EY6Orkonfiq3Afsv7jbtmX0yxMTJ3eMf+r/CEj9/dZ7f0+hvEyUke5OnHUNuOmizJaKNeyWm/HbYAOX0e1N8MSCC5kYzae26t1cogl5u1Vgv1Xb7F3PQ+IBlsuCqafwPZUsy4I0s4qffhbNrdk+WbdnK5YSFVJPO1dzwndv6U9em78QFH1WZySllXem2ZS1KczCWu3znT+RyVpGA2iecXPknihb6F1NW0T9b9XwEdStrPnj07+Pd1110nbCPLMtdccw0PPvggNTU1LFq0iNNOO60ju8UrpeVIQILPR77JxIvpqaoXOBEmNtrIaCqqePaI65iaP4v9UarZAujs8VDaRg/N9saOxgJ2bP+YX7Nn8eT4l/l+3/eq9bK5muq8v7FBx2bcqCjawQ/4j8WJyN+yLTgUhH2y10tdhACDF4Wbfrupw/sRCevLN3fYvhtkN0s8H+Bpx+vy85QkQJ+c07O7mJ4/j29zf+EIp4uxKcmsirMio3Bao01I2EeDnxOjt+l4OUObrthWXNOtCwZFwRuFSrg9Cfu24CupbUT7y6XlPJGZHvH+aU/cWVXNQJc7SPz2c7mZltr2OgitgUFRmFxdw6vp+tfIA5VVXFnXgAQMdYqLez1YUcUb6ak0dJASv5/dwD6rElRDnlUZT+aBbyJuMzC8AGgI9ptNvNutEdCe53qDrCETV8XFUWGQ2RRGyt5ZVcOV9Q0Md7r4KjmRHm4P3Txe4e/nkCRcX17G5KxeLD+4xBMVGs0OrF1n4Si+kLjMNVSb5rLNFy9sW930+zTW5PLMD1ey1mKm2CG20vmvweCiT/wmfjDF82lyMrss0ZP1ejBnLMHqlaHRn2WR6vWR5vORYzKy3WLm8UywSP4MQUmBqysaeTg+hcUJ2vMom6uI6/Ep7rojsHb5AYBHFZkexWYhab/FamFSTz8BOMTp4uq6Op7d9CL1Pi0xscP+Fs/k7EMSqcgctbD9O+3y9R9D7kq+Sk3UzPe8ksRZ3TIZPudCxve/CGv3hcQ7E8hz+jOgAuO4T5L4PimROEXh1EYbBzwD2enqHfGcsvJNSO7q71P5brbgBNTfv2vKEn6yx/NQllpl6JEk7u4cVmulldhp9GLt/BOWrF+RZDUBsDbOSp0skawjk9Ij7UPxaUpSkLTfZTYxJ0KGiFeSeDQrg5/yi3SV9pFQYTRwS5cs3iop519dsoIBjs4eDyfYHJxgtzPK4STfaIy4/wXxcWwQEOMABslA35S+7K3ZK1wPIJtq8Sk+7vn9HnY1LsMQB4a4fMwZS5HN/qCeMTEbS6efcZZcgKXzHMzp+gWMh1f2ZJk3iUTJwUL7CEBL8Cse9T02okcKG/NqNO0enHgsd/2utVoMR9+441lQo7bGenjSEIZ1S+HY/s3XYVZ8li5pf9ngS3i/9TXOAbVi3dMwGGOSuiikp/ZoPLVHI8paALCE/b5XD7laRdofn3Y9G5Ms/gKrNaMxpa5DMmiJ1AGpWsuv4d2TtaR9k6d9eEFVEZbuqeDaaWt0CWM/ZLyOrhiszXU2PLVHqlq4KsdhTNmIbGxA8ZlxVZ0k3NPcrcWUnz2UrCQLD323ja/XRSe02Vlcx1uL9nLjif10lfYltQ4emLmFWRsjax2PeHJei8f7cNkBThiQybH9M7j8/ZVBr/YvBOT3sr0VFNbY+TjMr12E33Y2k8k2l5ebp2/AbJAjEtahhU9FJGS1zc0Zr/kDJmaDTFaSRUXY66G41kFZnUPoad+eiETYg7/4bIPTQ7ZOtkcAq/ZX8fK83Zw+rIuQlP9sVW6QtF8kWF/Z6GLa8hyebPLqr3N4mPzVRpbdP4EpC/bg9vqfLS6vj5fn7WZXhEDWxytyOK5/BiuaLI3G9E3nx63FHChvxGiQuHpsby4Z5a+DMmtDgSpbAvz1BVxeH5sFKvrtRc3kuWjcDCCnspE1B6r429RVwb4/fs5QDu+ZSrzZQJdkK+tyqiMS9gEc/rj4ntiUX8Oo//xGjc3NhEFZvHPV0VhNBtYcqMLTMBBjYrN/uqtaLXYtrvFfVyrS3q4VDlgMfnGNSUDGK4r6uZ4cZxK265uZQN/MBH6afCJL95RzdO80Pl+d1yJpX6Z0YnDtQGrT2/hwaAOWNOS02EbEWbUG9YJz9GeDMT4XU+pq3DVjGRG/lBxBltsaq4UzGxq5PysDJUTAY8lagNfeE6+tP7Kpivje77Cu1sW6LV8zznIWbyV9yU8pBrK8Xp6sW8mLafUEgu17UirYk1LBVKU7HkniWLudV4pf42P32WTH1ej21y+86HinjT8LJEURlQRpH5x00kksXbqUhIQEampqMOq8AKxcuZLjjjsOgEcffZQnnniiXY5fUFBAz57+AX7QK4Mwpfsn+Fdmj+acpP0Md/gnxmutFm7s0kmXYOvq8fBJUSldvV6cJz2I4aR7ee3nJ/mkepawfZzPh1HR3uAPdL6CvpumcFPXyIVR/+g4wuHklppabupy6L/HWLudWtnAznYgKAD+XVXNlLRUlHasWB9DDIcSnTwebLLcYUSwCEZFYW2O37rk1J7dOpy4n1xVw421asVKjtHIOT216aS93G5qZTloJyFChsfLVXX1TElPjXjcwx1OXimrwKr4SPYpjOrdE1fYS3sPt5ufCoo1tMKl3bqoxqlbqmv4V00dhUYDXyclYZclknw+PkiNbGeQ5vVS3cL57edyM85m5/bqGl4zjmVOYhIT3YU80rijRRWJDziib68WWh0cuhedwC9OtYXV7MQEHgkjKQOQFSWo5m0r4nw+BrtcbGyDnd0Yu4N3S8q4t1NmUI3+v4TDHU62CIj19oBewL8tGGV3MNLp5NRGGxleHw2yTKMs8UNiAvtNJoY7XfT0uDlgMpFvNDLM5eK91JR2O76MhKPyRG5iAWc5KhjgclNpMAStdEwo/v8VMCkKLkliQq/uUQVzDxVeLKtgnM3O0jgrdQaZ420O3JJEnSxzRXexVUs4nimroNRo5P3UZF11+58B1w27ntP7nsbnOz7nh/0/RGzbPbF7c/ZHBLhrjtS1zAK/n3zjvrshJICYNOQBTbv6neqskpvG9eO937WK0p/u7cPlP14esU9GJZnbB0wPEm0B5Dw3SdP29gW3C1PvJ/aeyAsnvsgHS3P4dXsJI3umkpFg5uX5aqsD0T7BTypOfLXJH19ykdD/RWSTn9BzFJ+Pu2ascLsAvrhhjMrLPFDzaVXxKo7rdhzn9DsHgPEvLSa30oZkaES2FiKbqrF2nQ0oDEkfwvSzpmM2qN8nXl+wh1fCvsc3Nx3L6L7pVDY4Ofrp3yL2TQ/nHtGNOZub1eqGxB3EdfsKyeDCVT0GZ8kFABzVK5UNAWJRcmKIy8Pr6BlmEaTGoM5JXDG6J4//IK7bFAnH9Emj3uGJSKj+FTH3jhPpkmzl3m83q4j/g8XATonsaYVqvKNwxehefLnm4FSqkgQ/3HYCkgTnvLFM1wYjHBcd1YOZG6LLbmkNXrrkCDISzEz+cmPEDIw/E26d0J97ThvE/323jW+2Liau56dIBgceWx/suTcSLoR8+vzhvLN4X0hQykfSkP9TtUlzTWLJjf5nxpC3LsaY2OxL3njgVnyO5iLwE4d25olzh3Hcc2qblrMP78qbVx6lWvbY99v4ZKW2DlI4DOZi4vvr1xLraPRxufmmqIQLu3eloI014v7KOKkmjgxDeYs2nCJYPSY8BjeedpjGxvl8Lc4Z3VVudt/lv37z8/Pp0UNQ++F/BB16Je/cuROAAQMG6BL2AIMHN3sfBraJBgUFkR8IxcXFwuXveS/kvRq4xLCYp43TOMbh5N6qap7L8KcXWn0+bquuRQYOc7k4yuHEBNzr/icz5o2Aeb8ARxHfdxUGqzZd8L6qakbbnbyansryOCt2WaZrXVce2jmCF42juadyIy8JVL29XB762K0sSWn9g+hYu52VcW2opNgGXFzfwHF2B7dW1+h6IXcEni6v5LyGRl5PS2k30n6g3cSQeK/Gf/uPjtMbGvm1FSryGP66OKehESrH8o1lAN4uv2Aztq+NUx+Xm5ywrKIsr5dF3qMYKBVyUX1jhyreE3w+LqlvfgEqUDJxKib6eIo1D/zDHU4+Ly5FAapkmdfTU1kUH0e1wUAfl5t/lkOSoYZjHA7iFT+hNjMpkQNh3+9Em51zGxo5zm5XqVBHOp2sCVOXX1dbL9ABwtW1dUEbtXSvN/gdunu83FVdA/gJ849TkoW+7QZF4aL6Bu6vrOadtBTWWK2cYLfzUUqyRin8fWHzs+5ezyrurdE7m1ocCoptn6cfNUoCqVJzSnKaV189oUfYd7L0pkxQOFUEuyzzREUVl3Tr0mobjNVxVo7s4EBGtDjc4eTWmlpeT0thu6VjiPRwdBRhDwevdgrFujgr6+KsTNUJfK0Nu1dFGQEHAx8K5owlTMPENMTK7T86HsrM0AQiW4s/gl3kwcLs6ctdo+4EIN7U8nUSDWEP+jUuABzFF+CpH6Yi7MFvk9JS3YuxfTOEpH2X+JYDLT2SO3PZMT158dfdQQuVc47QBsBBfC7O7X8uTx3/FLIk86/x/fnXeL9Hrt3lZdqKHKqaCkDeeWq4aXszpNBxQDFjO3AHxqRt+FwZeG0DVW3PGNaFX7arXW/rHOr3JEmSOH/A+Zw/4HzV8ifOHcbfp61F8SbgbTwML3D+oAlcMjaBEVkjNIQ9iH3t45t8/s3Gtj0xB3dJ4vUrjuSCI7tz3cf+ukzehqE07P0/JNmF4mk+5nMXHc5pgYCGYtGcDxF2l9a3ibAHtU3OHxmDOidx40n9eHzO9qD//cHgzCniGksHi5YI+wSzQVOctCNwsIQ9+OsmnP3GMiTJ/3e06AjCHuCeGR2Xef7fwluL9vHWooAqvR+N++5FMtbjc2Yhci54WOOjL2uCw2nK6ODfzvJTkK1FyMZ6XDWj8DnUpGeKjtJ+qGAc7J0RHe/gdXWlkz2Bsri21W0AOLnByWarkUpB1pxBUbi+to5Pk5OE8/uHKqtw+eLILbsMQ/eZbe5DWyF6b44G0QpmLD4fGV4fRS0EJCRF4YKGRmaFkfNLUu1A21KZHe3IMfyZRR7/DXTY2XI4HFRU+NPJW4qKpKWlkZDgHwjy86NL6wPo2bNnxH+jR4/WbOMsPzn49wzveE5xvciD7n+wpOIGEvIuxlE6ictzB3FtXT1X19UzpomwL1AymeU9MWRPRux51+MoPQtHydk07LkfR/EF2HJv5JOKB/G6Mni1rIIVuQXcv7cP2YWTAZmXPZdwUoN2snFZXT0n5xzHmqI7ifO1rpL84Q4n5xf2xl3bXOneqCi8UlpOZ4//WANcLnq7D+5GO7++gf+UV3JuUyGxm2vq+LKwhIvr2q7OGNCQgI59rgrpXi9nNPqPO6Gx/fyu59tPR6lSXydJXh9nN7T9QaOH8+ob6HWQvwFAlj2BRflPcUlNy4WODjXiBTIMY8cl8wQhe9rJ+LqN6Ga3ckdBIsmuNvbDE4e94G+cXtl6UqmbS+Jj14UU1R9P6b6H8DrbngGTXjOAISHWLlfV1vF9YTHDnOprzdzQmxvd9zDB9TJJlUfqXtcGnwFn0cUi610NjnQ4eLNEq3a6qrae1KYxsVJJ4jznU5ziepkhjk/o29g86ZAVhfHlXXAqJiQgw+fjiYoqfs8rZFluPqNzxnN73cv8vXoqb7guQwKuratnTmExT5VXNn83n8LE0i6Mb3AHCXu7YuZX7yjqii/lqEaFPi43Rzkc3FtZrQooADgVE4c7PuCX6ut5p6iSeyqr+aiwnEJ3H8132+7rw9gG7cl5oLKKDTn5PFJZjRm4o7qWz4tL+VdNHVfWqse/w8OLd+pgpXcoa31aEuUJ99V06uByHR53Fqt8Q1XLklv5nAM4d8AkxvUYF1Xb01IG0TtjGI9VVB2SMSgajG9sTue+sraef1Vr04yHO53cXVnNAJeL8Y02ppSWc5zdwVdFpXxYHLmo3B8B1jb8rjH893CwhH006BzfGSlq51iQJZnJR05u1TYHiyxr9+DfiaZ29OXSgbt2JO6aMShe7bG8jVrLlnAc3Uds1ZZuTad/Sn/V53Ac32M0CRYj028Yw8ShnblyTC+ePm+4cH8i+5gHRj+ALGlfH+PMBn6efCJ3TzyMVy49gsmn6H+P5Dg12aB4E1l004NkP3wHP08+kQfOHMxDZw1h+j/G8OaVR2q2H9Aput9o/KBO/POkfqplpw8ayuiuo4kzioVOQ7pqyapkq5+ASbKahAVIW8K/T/UT7xMGdyIlLoTM8VlVhP1T5w1jYKdEBoZ9v3evOoqHzhrS6uP+lfDQpCFcfHQPNj92GleO+WME1duCT67XchORYDXJ3HiioHYKcN7Ibiy6Zzw/3n5Ce3RNFx0xhTpxYCZXjO7ZcsP/MSjeBHzOLoChqXZJy3CWn4m7bgReR1ccxRdyzpCRwXU+Ry8a995P/e7HcRZfTHh9kGSrCatJO56P7qN9dlx0dA8sUQYua6tP1Cx7rqyCbwuLeaukjO8Lihhl17eR+r3yeobUaIPQp9cYMOVcjbF8HP8WzKEvrG9grMPJK55LsNUdTZwjsvWtxefjt7xCVubkc1F9Q7u8L/y9oCuSR/t8Sfd6yfRoA3Y93W4eKAal6EJMIccXvVufWm1iWU4pvxYU8XGenVOqLZxbZ8fSNPc2KApXVbm4t6KO/xQYub3cxcTGSFZtMfxZ0GHy4vr6ZiI3MbHlyVVCQgKNjY00NHRMSpnPnYq7doDGE7BA6cSX3lP8Hxr9/97mOHoYvVxmXBxs947nXLxhEU/Fm4g7ZH/umjEAbAXOcD3PG0eX8dWmShZ6DycwSJaQweOOyRxj+4S18c0KD0PdYbzpPR8FmaG2dPITazTfQfLE4WoYyrGORlypm9lqMTPA7ea8ks7c6b4NV5kDUOhq2cu1tTVMtNk50e6gyGggxWXhooRJ0HlVxPM0wuFEQq20MyoKd5d6eKx6CsukMqbg4x3TaxwmFzLc5WJ2yUX0NmwmNyHyb9fF7aXE1HwOD3Mo7Mi/ly7m/WR2/4hcs8w5DY3+zIcQ5VaS18fT5ZVYmsax4S4XY+12VoVkFkiKpPLeigZ9XB5muidiq7VwOGaS4neT6YzDVXcEv3qO5PSuz/Jrqn+fI+0e4krHU5Cag8tSQ2lc5O+qODqT4lW4wbmbHWYzPT0exlfHUatM5Ij0hYBEnW0QDYn5GF0plFWdSnrKSjZk5aDI6gHdpCgMrslio9QLn8/KgYpTUEhALpvAHb6fWR5npdYgqwqdKooc9LUWId6ZQKOSgGRtW0po2v+3d+fxUVX3/8dfsy/ZFxIIO8SAiFBkKQiooOBWCmjVWlHccEN/VG3r1qqt+nWh1uIuiFC1FVSsgmgRFBQQiSwVRNlkX0JCtkkyk8nM3Pv7I2QgkAQIgaTwfj4eeTyG3HPP/UyYz8ydzz33nLBJwu4hbGs1Nzoq9qp8C/PyxrHHkoQzaQnJju1MKfuC00OVReBZMV5eTkzFFvaytaQfIVcBkfJWGBUpONzb6RYuJD7hW5bG1D0VSGYwzLay3hT7z8DqyiFc0nXfCU4Eb/sJ2I7gOZ1XVs5iS0dC3n2j5Qwb4bIs7HGVd/rYTOi3uxPzyi+gS8xCBtpX8M9k2yGjRG2GleKND7IuEsvjAP4yXClf4kz56pBjWkwT02IhJRyhT3k5u+12XKZJgj+NGfnjIBLDQn8iyQkvULDvpKiLL541sYFqo+7iIiYlNsu+OE2WlF5MUdUaAoabwPbReNu8EZ1b90DJub3ZWvBLkjo+SdhxwIe4rzPl/ky2Fv2cu+0vExe3Co9h8n3peQwIX8Ltua+xOaMEvw0wrPxQcAUAJlaeqLiZX+z4hILWiym1V77mepbasOcNYF7wfDCdYC/FlToPDCfluReDJUIPtrAjbQUlNivp4TBjcm38OzCadsmz2OKs/GhKjkTILGzNX8K/pJmliPcj55C/bxXgChysyLmD05hM2OHHKOjLE6UX8xrFXGj7lptsn9DBmoMFKAo3471I1QVbC69FfsF3ZkeaU0BH6y5uLfmYtEguPzid9Cs1uKH8fl6xlHKOdRVbzXQWGmdSvm8e6tO2t+FfzidoZql5Xsd3IoPwEcNs42zW+NqTYcnnBaM1e0kgnQIusy2in3UNJXh4Onw15zqdjCn+Pxa5I+TYbfQNlHNZSRlWoNj08mhoNC5LiBaWAr6KnMmKvBakxj9Jua3yvSk3fygl5vvEWWq+mGmYFm4N3c1coxcAd9vfY5y9cs7vN8NDmBK5iDPzyiFjaY37X9XpKi477TJ+/fGv67XOSGKkPyUVzfi3tT8X2b7dvyGYctj3qYP1aH4GF3YYxJc7vjw0zo4jmf7T/rnMLz/rTqxx7Rjy4gA67NrD/c1S6jXy5WBjbOlc2P0Grl0xgQC1X0D+ZWkZq1LasCVYmYe/yy9ktK+ENU4nNkw6V4QIUTl/5IFrA/ysPMj1vhKuP+hi+Oa2v+KjjbFcGfMR7x60oG9cxOCWZn0Y/N1M7m+Wwup9n99Wi5V7et6D2+bm8aWP1xjneWV+7iks4uqM5pQdwagXr2EQaxjkHnT3ZM/yCp7IyyMtHGG33c5tB8w5Xps4ewynlZex4gjORC2myfDSMj6sx+28tWnmqX2+7qakgzOJv276gfVOB39LTjzkb18lMRLhirIgk+Ibd0qnX/lKuKegiFVuF2WZ5zNw+GQmrZrEpNWTamx/8FQzD/38Ia7sdCUum4tXvnuFBFcCwzsO5+XvXq62n8WEy3wBnBaYk5hIQQ3rH1QxgqlYXbWsS2E4uKvXqOg/uzXrdhTPtn4igdb0aptEKGLw3b6FMC0WmDl2AHM2ePnHtv8XbdvKchH5ca7oPNaXnNmceLeD3w3N4q+f7Z/G5fERXbFYLDw58Eme/vZpQpEQ484ax6xNs/hw44cA2C12ru1yLQA92yYx6bpedcZ5cfuLefm7lwkbYSxY+L+B/0ecs/Z1eJonuLnr/MOPDE+LczPwtFQWbqj8P7mqV2taJVW+brtkxB8yyvOO8zry8oLK0ah9OyQfcdEe4L6LOpMc4+SbTfmcl9WMwZ3rHtiQkeihf2YKizdWvnd3bh5H6+T93zde/E0P3lu2g+JAiL/NXU9k38X9gaelcm3fttz73neUHHAnwJ9+0YWLuraotv+1k7OrHXNY9wz6tEtiVN+2lf+Hl53JuGn/Jb8syLjzs7ioawtM02RtTgkfrNxBosfBfRd15qUFG9leUPdAph5tEuucx7smVguHnQrF7bCS6HGS04Dzt5+REU9yjDP6uqjym5+34ZysyvU9bFYLQ7qk1zgXfm1iXXa6tUqIzpdeXy0S3FzZqzUTPt9w+MY1eGz4GfRsm0RmWmy1hVfbJHvZWxrEXxEhyevgyl6tCYYNOqbFMrRLOgkeB5+szqk2T3+sy87vhnaidXJl3nRuHvc/M8WR027ljvMy6dshmf6Zqbz4xUbW7SnBarFE8+lInJERz4OXnM7N/1hWbeHldileLjmzRfQ9ozbtU2Po0TrxsGs2HInJo3sx9esth7x26yPObWfu3eeS6HWwZpeP30z6hmC45nNlqwVu6tedSQsr3zPj3Xau7L3/YsjPWify3+1FYNZ83pCZFkuc20HPtkks31p5180ZGfGc1ebQYneCx8G/xvTljcWbaR7vpsgfqvWuir2+s2mW9gXl9soaQHrIoFNJLHudbWjVvgdJpRt4JKMnvy8rZG3Bj0QsYWzuyoEpoZIulPi78t/yJJonTIjWjq7I9fBO/n0EcPMMXbHnXsbZ7odZue8GgDahEIP3pnB58C6Wm50q49h9NbFtX8O0RrBE7CTvPp9Axqf4953v3lVYTPq+O3/H5oUpyjuXWY52EHHjaj6r2rojB7JEXARLzsSZsJwDR5+O3pXAo8E7KN1ahjPxG5IiFj4p/QDDXkFS2GSs/TL2ttn/XSg5bJKy6Rr+ZPwMAystdxqkpnyFLRTHtt3X0rvVU3y7ry7SqyzC7JxHmU8INxXkkAxlFpyEyLDm4PJuZ09Fe16paEHlaDkLZ1o2MX3Pn+kdX874lKQa7+zuVh4kaLGw7qBZLNpVhEiPRFjqqf/AyLRwmL022xFNedoaO0VGiJITMJjkf9Fxm9N++/bttGlTeSX82muv5c0336yzfZs2bdi+fTsdO3Zk48baF4A60JFMj1M12r7dnf8gJT0DX3mIin1vfBYLuO22am/0VVx2Cx+ds4vkHZ/zbl4b/lY0EMM8sheR1QK3nduRP1zUmc17y5iWvY0v1uayJb8Mw4Ss9Dh+2X4HnxaMZzsVZBWnkb37bkysOGwW7K48UjIm4rf7OKM8yACfi8/Kf8tuZzvS492kxLh4svse8pf+k/e2J/Bm+CJC+66/xLrs1W4bPMOdR8eKDWQbncghAW+bf2CL2YAZSsKVcz4tbTupCGZgGg6CFgtbg11pbcvDnr6KBNtKLg5t5WJLPP9MfJQ3f4rB67STmRbDyDMSaZ6zgBdWVLAskgmWEHFxKzBMG7eZCylM2IjX6uTL+CS2GH5iQm7yt9xJlxZvsCm2gJgIJORex6jzrqR1koeHP/iOQPEe8kjCgoEtfhVZCV9zcXAz/YutLIr0pNiM5X7HO1hNgxcjv+SDFj/nih6ZJFg6kZLgZ86G9ygJ+IhY81kRWI7dtBDe90aaaBg8ePp9rPrpff4Z2kS8YTIq4Sre33ouG3NLsVjgkq4t2Fsa5NstBRgm2Igw0DMXA1gZGEgJMZyWFsv2Qj9hzwpc6bOwYFK+5xeckRrLmc2+JVRRwm96juMnX2e+31HM8M2P0L1wLqYjhhmdnuUfu1sT67KzIbeUvaU1fcEMg8XA5tlKq7YraRffkh9+OIO8ksRDX2cY/Mn+FudZ/8si4wyesJ9PxLWXcGlnzEgM6Qk2Rqd9THb4c2ItNnpkDWdu8V7WbfdQuOscMFykp28hPvV7cv25+Au6k2T5GW06/5sNvu9IsGSS4GhGXsWPlIfLsYY60M1xGd7QDyzc3omScCytYheTlLCEcHkr1uZfgXnQha1bbLN40PEOhmnhr+EreZ2RxLnt5JftH83dIsFNaTC870uOgSMxG0f8j4T9behU5iSc8g07Y4qJDTu4dHcrPvaPYDcptEvxkh7vxl8RYVuBn+JACIujAGfyYkzDQUX+OVhdeTiTvsFi9TO4tIJMzyrcwJby63m3tD2u9E+wWMupyLuASKA99vhVWJ25hH3dMSoqv9i1T42hXYqXeM9e1uRtY3NhOZ4WH4ElSHnupYR9P4s+F5vVQs82SRimyfcl87AlfYEZ8RLePRwrqfR1bsBSDrFOO6Pjl+N3pjK+9EJW5+9/b2nlXsVpafPo07I1aS3uZcqPM9nGu1gsFgan3cCo+Bj+/v148qxWRpitmbB1HIGDb96xVGB17QHTgbf1W1gc+YRLOxHYcS2YdmyebXjS5hGO2KjIvTj6XAFSvXZGpO5kZU45y/ctspjodfDny5vz/veLWbQ6HjNc/fVos1q4YWAyjqSlLN5Qgm9PbxI8sfTtkExhWQWffp9DSdC/76Sx8kQp3m1lco/VUP4t8WmDmVrQn9mrd9PMWEHLtPcwrQbOvLP5PHAJTruN689uR0XY4B9LttQ66sdigYwED4FQBH8gwJWxq0iO5DK9rBe72T9/eufmcdx2bkcM0yTJ62TKnK85LbiWbs1shNqfx0Pz9lY7QbZaINHrpGfbJH7ZPYMhbW28NePfvLYhnrNta7gp/SecoSK+KGrO30Mjo+/HAMkxTqwWS7V8dzusnJGRwG3nduSC09Mo9vmY/tIfyQys5ntPL84fPISfflzF/21qz56QJ/rcqp631VHM6W03cGWPAbz2GbTyrWSM/ROaWQppYSkg3VIEQIEZyxOhUcwwzsFigXYpMZQGwySWbcGCyaWDz6NT8zj+MGMlLVL+yq64XE6zxHNRt1HkBPIY3GYwA1pWjuB6c82bPLv8WawWK2Gj8gXnME2uLfbRPr0XrzrKAAvnN+/L5SEr7sR2GB0G0TKuFXmlQRZvyKXXkrtonbeAIC7udT+K2b2Ar3KnH9GizYNbD+a5Qc9htVj514//4oWVL1AaqvzSe1GbITx17njmbpvL0t1LGZAxgPPb7rsYv20p/g/HYS9cx3txXuZ5vcQaBr8tLOK79NN4zFmOCUTM2m9XH1NUTAdXKinn/5l+p1XOlbxy91r+33/uoMhaWfStuigHcFoowsS+f8GZNZQ5mz6hZc6P9Nu5Fos/D5I7QofzID4DSnPZO+suxjVLZJXbRXtXCq9mjuK9Ta1ot3oCw2xLsGJC99/AL5/nq42FuLNfIK/wfV6Nd+NzuLgopi23dr+N5PaDYP6T7F02kRdS0/C16sFvzryJ3s17Y5gGzy57lhkbZtA+vj39W/bnpzUfck7ODwwvLaOMGBbHxvBUkiN6K3KM1UXfVgNwWp00x0bOlgWETYMO4XOZubUZLVq8xSZ3hKDdSbe0PozvdQ9Ju1dDKACFW/B/N42JthLejY8jbLEQOOj/96y0s/i/gf9Hmi2GP8+6ho8C24gxLfy9xRC8hVv5qiIfu9XKNSEH64s3kR4O0ypQysQ4Ny8k7Z8OJyUcqfH2aYDmOHB6kkgtzWcLFRTsWxvChoVfZo7gnp73cMOcG+pcdBSgg2ljUqmVP9l8fO2tPnoqxh5LMGwQ5uhGMnWLa8uE9d/xhtfKV14PrUMRrFYb2xLSGdJ6MHd1vRmfu7IwGW+PwfL187DjW4zThrKuXR/KwgF+lvYz1mz+nG2f3oMlUEDf+I4kDn+NB9ZNZd62eXRv1p3nznuOCSsmMGPDDJp5mvFQ34dYX7ieV/77yhFfhGtrcZGSksWqvd8TrmOf5jh4uPl5DNy1Dnw7oXlX+MXfISYV0zT5cMMH/LjqbXIK1rPUZSNidXDXWf+PEZkjeG75c2wr2caVna7konYXRfuMGBFs1sr/t8mrJ/P3FX8HKl8/v2xzM73Tz6JNagz+kJ85a94mtORFNoeKmRUbS/G+W/5Dvq4kVfwCR8tJFAbzsWChf8v+3N7tTn7M20qvjK50TNo/ctc0TSaumsjcde9j9e0i07Bwe98H2JLchs+3fU5BeQGt41rTrVk3+jTvw3PLn+PDjR8S64hl5Gkj6dasG5NXT2arbyshI0QzTzO6N+tOIBxg+Z6V2IKdOM16PX+69CyK/BXcP2M1pcEwD1zSmV90yyBimNw841W+zf+UOFtLpgz7Mx5HDG8s2ky8x8HNAzqQ4HWQVxLklreWsXJbEf0zU3j5mp7VR3HvU1RexLPLn2Vn6U6u6XzN/vfGI7S5eDOfb/ucrqld6dui7rnmj0Z5KMKcNTk4bVaGdEnHXsdCf6ZpsnDDXkqDYc4/Pe2QhWgbWmFZBZMXbSZkGIwZ2IHU2JqnLFi0YS+vffUTaXFuHv5FFxK8DnzlId79djsbc0v5RbcMBpx26PRRE7/6iUkLKwtfz17Znaz0Qy+EGIaJxXLQVEJAfmmQRK8Tm9VCSXmIKYu38OaSLewtrTyvtlkteBw2SoNherdL4o3re/Of73N4J3sbmWmxPHDx6STFOFm8cS9j/7WCIn/loBCLBdome/nDRZ1pk+zl4Y++j86tnxzj5K2b+vDhyp1MWriZWJed5676GWdkxPPsZ+upiBj89oLTaJvsxW6zMnnRZj5cuZMzWyUwIDOV1TuLCYUN3lteebEDKufSf+rybvy428f6nBJaJnm4/KxW2G1Wvlqfx8Mffc+WfD/nZjXj5WvOIsa1/7zKNE1ue3s5c9ZUFvmaxbn4/YWVRbrHP/4BX3mYlokeOjSLIRg2GDsok9RYJ6PfyI7+nQ5mt1oIH1QwvvysVpyTlcoeXzmxLgfnn55GWpyLV7/cxIJ1uXicNvp2SCHXF2TWql3EOG04bFa25vupiBg47VbuHJRJOGJweot4Lj6z8uLNwg153PrWcvwVEQaelsrEa3tR4K9gXY6Pn7VOIjnm0GmbdhYF+NfSrfyUW0aMy841fdtUK6x+sXYPd/1r5SFT77RN8TL2vEyGdc8gYppMy97Gk5+uxWax8NClp+O0W3n4o++jC6+2SHDz9s0/55GP1rBoY/UCdPN4N/8c83NmfbeLt5ZsxW6z0DUjgWv7taVNspdrJ2cfsgBwy0QPhmlGF/Lt3jqR8b/qdshrPhQxcNisFJRVcP+MVSzdXMB5nZpx30WdueudldGCMkBqrIvLzmrJ3Rdk4XHa+HrjXh6ZuYawYXL5WS257ux2OG1WrnptCd/tKMZltzKsewZX9GzF4o17KQ6EaJ3sZfjPWlIWDHP1pG+qLTQc57ZzSdcWBMMRrFYL15/djs7N49mSX8a/V+5kxdZCzmyZwJ6SID/u9nH5Wa2i04QFwxF2Fga4+c1lbMorI8nroFe7ZFZuK2RvaQUtEz38/dc/4+/z1kcvDB7IaoHHRnTlmp/vX+R++dYCXpr/E0X+Cvb4gtX+xv9vcCZ3D8li1qrdbN1bxsizWkYvgAJ8vXEvt769nJLyMEO7pHPTgPY8MnMNa3NKGHhaKq+M6kmsy05BWQWvLNhIxICbB7YnI/HwUy6bpsnb32zl2bnrsQAtEjxsyS/Dv+81eMHPglTEzGTNzjIKd19E31Zn8MLVPUg54P20Imxwxz+XM+/HXKzOXCw2P5FAa6qmBupoX8cZiV8QMtowt+BCwgeNN+7fPEjfuEl43AYjz7yZOf4+vPnN1ugFrDiXnY4Z5ewM/Mje/JaYoWSGJC4mKfYrykvT+LzoN9zby0nvjmmEEtrxxJxN0deawwZhSsBiYoZjcSR+iz1hBUZ5S4K5l4BpJzY2n4Q2M/BH9lKWN4BQ4Tm0SfaS2SyWH3f72FVcTkfLTgZZ/8sqMsmOZJGe8Qb+hA1YDQuh7dfi91e/E/lAXS0bGJw8HYvFySfF19HzZ7254PR0vttRxNZ8Pz/s9hHjtHHjgPYM61ZZ54wYZuVC3ou34LBZuTh1D1k7ZrDNt4qHPCXk7vuotWLlpvSzuXP3ViKpp/FRxz4syV2BWZpLu23LGL13N17D5AuvhxKblc+9sSzyVv7f2UyT7vvuws+z2WjpTqVPuyEsX/s+ay0RnJj8vMLgwdYXsyr3O6ZE8kixe7mlJMDCcCGrY5MIpHXivyVbKDSCuLDwdJ+HKN/9HY9vnUkEeLBZP777aQOP/rZyoNapPqf9cSva5+XlkZZWWQi66qqrmDZtWp3t09PTyc3NpWvXrqxevbpBYjhwIdrt27fTvEUGNqsFi8VCYVkFLocVr9NOWTCMw2YlFDHIL63AVx6iXWoMsQecIBiGSWlFmGJ/iOJAiASPg1ZJHsKGSUl5GJfdSozLTnkoQlkwXO0NqUrEMDFNs9qJaXkoyI7CEJ59q5LbDrq6VBKowDBM4jxOrDVceSosq2BbgR+7zcJpaXE47VaKAyG2F/hpFuciPd6NaZrsKi6nLFh5EhM0fSQ4EikPG9isFjbvLSPGaScxxsGuogCtk7z7T44MA+oYfZdfGmTz3jLsNiud0uOwWGCPr5w2rjIsniRCFthTtodkVyoRw0asy05eII8kdxIO6/4vF+GIUVlM8joxTZPykIFn3xySvvLK5xPrstM6xsASKmOPkUh6vOuQk9no36W8EKfNicfiYMuOxTRL7UJcbOUtreVFW7G6E3G6E6J/Q7vNQty+21+L/SFyfOV4nTZaJnqoiBjkFJeTGuci1mXHMEw255fRPMGBzWIhGLIQ77HXGgvFO8CbAo79H4CmaeIrD5PgcWAYJhHTxGGzkl8aZHdxOc0T3NEvCaGIEb2w0CLeg9tpZX1OKSHDoFWSh7Q4N/6KMDsLA5RVRPA4Kl9LCV5H1cHANGDfF99QxGBXUQDTrDyZs1gsGIZJflkF8R47LrsNwzRqvP25SlkwTEFZBbEuO0kxTnKKy9lbGiQ11kV6vIuwYbJr30lFG5cfIxIix0wiI6Hyam1eaZBif4hWSV48ThvhiMGW/DJSYlwkxTgxDJPdvnJiXXbiXHY255dgxUqi10l+WeVxEr3VT2qD4QjlIYMEj4NgOMLuonLapngpDYbZURggJdZJmjMEDi+mxcqWfD9pcS5iXHa25fsp9FcQ47LTPMFNrq+c8pBBnNtOqyRPtf/bwrIKEr0ODBN+2OWjfN+tbi67ldPS4qKv26q31j2+IGHDICPBU2MOQ+VJS9X7T9gwaJcSU61tfqDyxC7Fs6/wXF5c+ZPQmmDEYG9pBYGKCOWhCJF9ryfThLQ4F+nxTgKRMsIhN/llFVgt0CrJi3vfl7qt+ZX576+I4LBZ6NgsFqvVQnkowoY9pTjsFjqkxkbncq3623icNnYWBbBaKk/Ka3rPqxIxTDbvLSXO7aBZrIv1uSW0SPAcUmAIhiNsLwjQPMGNBVibU4JhmnRuHhfNz9yScnJ9Qew2C3arhTbJMVgtsL0wQIsEN27H/r9/1f9bYVkFRYEQZcEwVouFzLTYOuemLQ9F2FkUIMZpJ85tx+u01ZjfOwr9OO1W0uIqX9f+isrXWsQwiRgmSTFOMhLcWCwWCsoqyCsJkhbnItHrOKQ/0zTZXhCgdfL+11to33uPYZq0TPQQipgUBSrwOuzR/I4YJmtzfBhG5eil1BgHKRQTxsquoIeCQJjUWCfp8e7ovJVF/sovrVU5VOwPETIMUmKctb+PAaUVpRgYxDvj2VW6i6SIgcc0IfEobnEu2ARxLaLvh3vK9mAzIqSYVraYAZLdyXgdXvb695Iek45hGhQHi/e/9vcJGSGKg8U4bU7inUewpkIkBOU+8CZXFpZLdkNyB8rCfkKREAmuBHaU7KAkVILX7sXAwAwUkeovJsGTDM1OB3v19xzDNCgPBfD4dlNSvI1dgb3EOmNo2XoAFk/dCwxHlfuI+PdS6IknwZUQ/VwMRwwsFaXYgkWQeOhUAFXvL4f8f5lmZfWlBofsU7gVjDAktQOrjUjJbnL9eZQ4XLRNaIfLVnNOby/w43Xa6sx5ImEo+IlwTDNMdxx2i51tJdvID+TTzNOM1vHVXzP5gXxiHDG47Ycf0VMcLMZisVT+v5cXU2qECGCS6E6kpKKEBHsctvIiiG22/29SsIn84q3s9cTTIiUr+poxTIOcshzSvGlEzAhloTKSXEkES3PYVbAeW1wGbZIysQBm4RZyireQkNYVjzeVsBnGYXVgmiaFwUJ2l+0myZVE85jmRIwIoaCPUFkuIZudkDOWkBEiZIRw2Vy0imsFgcLKn4Q2YLHWea5VJ8OAkl2QsP/LTNgIY7fuP4f1h/zYrfboHN55/jy2l1ROR2mxWGgT14YYRww7S3eS4EogwZWAP+THYXVE5zWviFSwc9cyvI54ElI6k+PfQYIrAY/dQ07ZblrFta52zDpDNg3CRrjGOcXrkuvPJWJEaBFbyxoC4SCU5WF4U9gZ2EvEMHCYzUiJdRGhnN2lu2ke05xYZ8NOgVNSUUKMI6bO86ajVWuOH8AwTEKGgdNmrbOdnNyMfUWaUMSgzb7CeXkoEj0fqk2govJcJ95jJ9nrrPb91DRNCsoq8JWHaZXkiZ4/FJZV4Nz3nfdoBcMR9pZWkOBxVPuOXRPTNCkJhqNTE9W0fXtBALvNQot951lVx9hVVE7bZO8h59zBcIQdhQFiXXYcNithw4ierzWLc+GwWvkprxRfeZj0eFe14ufRCEUMdhYGSIpx1nghDSrPw/LLKmh/0Pn+sSjyVxAIRUiPc7O9sPJCcptk7yHvDb7yEBaInlcXllWwJb8Mh83K6S3isVkrvxduyS/DVx4mYpjYrRY6NY+r8zVlmiZ7fEGsVigsC5Ea6yQl1oVpmuSVBvEHI9HvnUcjHDHYVuDHarHQ/IDz/MMxDJOtBX7S4114nbW/3kIRg91F5dHz7wO/PxyLQEUEt6PyvbmqmN8qyYvTbsU0TX7KK6XIH8Jus5LgcVBSHiI93k16fN3nQfmlQQr9IVJinCTVcIHnYMWBEL5AqNr3WX9FuM6/ydE48O9VHoqQVxLE5dj/3ShimOSXBWkWW3PdxjRNdhQGSI114bRb2ZBbQihsYrdZSPQ6aJHgidYFdhcFMEyIcdnISPDU+vxDEQN/MEKc247VaonWwgzDjP4d8kqCmJjROKv221bgx2W3kpHgwWKBbQV+SoNhmsW5SItzs6PQzx5fELfDSmZaLC67DdM0KQ6EcDts1V6f/oow+aUVtE72kl8aZNPeMuLdDrzeIhJcyewpMvaNia/8rI9z20mPd1MWDLMht5R4t532qTFY9t2FcnCd8GiVh8vZWLSRsBGmRUwL0mNqme6twg/BEohNg0gFBEvBm0xuII9AOECqJ5UYXw74Cyq/V6V0rPrPBN+uyhpFSuYh35swzcrvX97U6LbtJduJc8SR6E4EIFLuw2r3YLE7WL1hNd2yKu9+VNH+OBXty8vL8eybvuTSSy/l448/rrN9bGwsZWVl9O3blyVLljRIDAcX7U/l/2gRERERERERERGRpkq13P2O20K0breblJTKkXGHm8amsLCQsn2LjFb9x4iIiIiIiIiIiIiInGqOW9EeoEuXyjmaNm7cSDh88KTL+61duzb6+PTTTz+eIYmIiIiIiIiIiIiINFnHtWg/YEDlAnZlZWUsX7681nZffvll9HH//v2PZ0giIiIiIiIiIiIiIk3WcS3ajxgxIvp4ypQpNbYxDIM333wTgMTERAYNGnQ8QxIRERERERERERERabKOa9G+T58+DBw4EIDJkyfXuMDss88+y48//gjAuHHjcDhqXmVdRERERERERERERORkZz/eB5gwYQL9+/cnEAgwdOhQHnzwQQYNGkQgEGDatGlMnDgRgKysLO69997jHY6IiIiIiIiIiIiISJN13Iv2PXr0YPr06YwaNQqfz8eDDz54SJusrCxmz55NXFzc8Q5HRERERERERERERKTJOq7T41QZNmwYq1at4u677yYrKwuv10tiYiK9evXi6aefZuXKlWRmZp6IUEREREREREREREREmiyLaZpmYwdxvGzZsoX27dsDkJ2dTYsWLRo5IhERERERERERERE52O7du+nTpw8Amzdvpl27do0bUCM67tPjNKa8vLzo46r/cBERERERERERERFpuvLy8k7pov0JmR6nsezZs6exQxARERERERERERGRo3Cq13VP6pH2nTt3jj7++uuvad26dSNGI03Z4MGDAfjiiy8aORIROd6U7yKnBuW6yKlBuS5y6lC+i5z8tm/fztlnnw1Ur+ueik7qor3b7Y4+bt26Na1atWrEaKQpczgcAHqNiJwClO8ipwblusipQbkucupQvoucWg6s656KTurpcURERERERERERERE/peoaC8iIiIiIiIiIiIi0kSoaC8iIiIiIiIiIiIi0kSc1HPaixypNWvWNHYIInKCKN9FTg3KdZFTg3Jd5NShfBeRU4lG2ouIiIiIiIiIiIiINBEq2ouIiIiIiIiIiIiINBEq2ouIiIiIiIiIiIiINBEW0zTNxg5CREREREREREREREQ00l5EREREREREREREpMlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V4a3datW7n33nvp3LkzMTExJCcn07t3b8aPH4/f7z/s/nv27OGPf/wjPXv2JDExEY/HQ/v27Rk9ejRLlixp8Hi///57br31Vjp27IjH46FZs2YMHDiQV199lXA4fMT9fPrpp4wcOZJWrVrhcrlo1aoVI0eO5NNPP23wmEUaU25uLh9//DEPP/wwF198MampqVgsFiwWC9dff/0R9eH3+/nggw+4/fbb6d27N0lJSTgcDlJSUujXrx+PPvooOTk5DR57Q+RpOBzm1VdfZeDAgTRr1gyPx0PHjh259dZbWbNmTYPHLNJYGiLXp06dGt3ncD9Tp05tsNiV6yJHriFyvcqWLVu47777oufxDoeD5ORkzj77bP7yl7+Qm5vboLEr10WOzrJly/jLX/7C0KFDo3kTGxtLVlYWN9xwA4sWLTpsH4Zh8MMPPzB16lTuuOMOevfujcvlir5vLFiw4LjErnwXkf95pkgjmjlzphkfH28CNf5kZWWZGzZsqHX/jz76qM79LRaLef/99zdYvBMnTjSdTmetx+vTp4+Zl5dXZx+RSMS86aabau0DMG+++WYzEok0WNwijamu1/ro0aMPu/93331nxsbG1tkPYMbHx5vTpk1rkJgbKk/z8vLM3r1719qHy+UyJ02a1CAxizS2Y8110zTNKVOmHDbXq36mTJlyzDEr10WOXkPkumma5ptvvml6PJ46+0tOTjY/++yzY45ZuS5y9AYOHHhEn8fXXXedGQwGa+1n6tSpde4/f/78Bo1b+S4iJwsV7aXRrFixInqiHhsbaz7xxBPm119/bX7++efmmDFjoh+GWVlZps/nO2T/r776ynQ4HNEPzN/97nfm/PnzzWXLlplvv/222bNnz2gfzzzzzDHHO3v2bNNqtZqAmZ6ebj7//PPm0qVLzU8//dS87LLLoscaMGCAGQ6Ha+3n/vvvj7bt0aOH+c4775jZ2dnmO++8Y/bo0SO67YEHHjjmmEWaggNPbtu0aWMOHTr0qL7cL1y4MNq+f//+5pNPPmnOnTvXXLFihTlnzhzz1ltvjeamzWYzP/nkk2OOuSHyNBwOmwMGDIi2veyyy8xPP/3UXLp0qfn888+baWlpJmBardYGiVmksR1rrptm9aL9nDlzzNWrV9f6U1hYeMwxK9dFjl5D5PqiRYuin91Wq9W84YYbzA8//NDMzs4233//fXPYsGHRPj0ej/nTTz8dU8zKdZGj17FjRxMwMzIyzHHjxpnvv/++mZ2dbS5ZssT829/+ZrZs2TKaD1dffXWt/Rz42e5wOMyzzjrLPPPMM49b0V75LiInCxXtpdFUXbm32+3m119/fcj2Z555Jvoh+cgjj1TbZhiG2bVr12iRbt68eYfsHwwGzSFDhpiA6Xa7zW3bttU71oqKCrNDhw4mVI7m3bhx4yFt7rjjjmi8tY3+W7dunWm3203A7NWrl+n3+6ttLysrM3v16hX9u9R1l4HI/4qHH37YnDVrlpmTk2Oapmlu3rz5qL7cL1682LzyyivNNWvW1Nrmww8/NC0WiwmYHTt2NA3DqHe8DZWnkydPjj7PO+6445DtGzZsiN4plJmZaYZCoXrHLNIUHGuum2b1L/abN28+fsGaynWR+mqIXL/00kuj+7z00ks1trnnnnuibcaOHVvveJXrIvVz6aWXmtOnT691QFpeXp6ZlZUVzYsvv/yyxnZVhe4lS5aYgUDANE3TfOSRR45L0V75LiInExXtpVEsXbo0+iF466231tgmEomYp59+ugmYiYmJZkVFRXTbt99+G93/mmuuqfU469evj7a799576x3v9OnTo/08+eSTNbYpKyszk5KSTMDs0qVLjW1uv/32aD9Lliypsc2SJUvqPEEQ+V9Xny/3R+Lyyy+P9rt8+fJ699NQeVr1/pWcnGyWlZXV2ObJJ5+M9vPuu+/WO2aRpqipF+2V6yINoz65XnXOnJKSUmuboqKiaL9nnXVWveNTroscP7NmzYq+5u+6664j3u94Fe2V7yLHbsuWLeY999xjdurUyfR6vWZSUpLZq1cv85lnnqk1Hw62ePFi85prrjHbtGljulwuMz093Rw6dKj5r3/9q8HjXb16tXnLLbeYHTp0MN1ut5mammoOGDDAfOWVV47qgtonn3xijhgxwmzZsqXpdDrNli1bmiNGjGjUu2lUtJdG8cADD0Q/4L755pta2x34QThnzpzo71955ZXo799+++06j1V1W1+7du3qHe/VV18dPd7u3btrbXfrrbdG261bt67aNsMwzIyMDBMwO3fuXOfxOnXqZAJmy5Ytj2nEsEhTdLyK9i+++OIxnzg3VJ6uW7cuGsttt91Wax+7d++OtqvrtmKR/0VNuWivXBdpOPXJ9ZiYmOhI2LqkpqaagNm1a9d6xaZcFzm+SktLo6/5Sy655Ij3Ox5Fe+W7yLE71nUnTbMyv6umwKvp59JLL43edXOsTvZ1J62INIKqVeZjYmLo2bNnre3OPffc6OPFixdHH+fn50cfp6en13msqu1btmxh27Zth2xfsGBBdOX666+/vs54O3XqRPPmzY86XoDNmzeza9euQ9rV1c/OnTvZsmVLnW1FpFIwGIw+ttlsNbZ59NFHo/k+derUQ7Y3VJ5WvWccrp/mzZuTlZUFHPqeISL1p1wXado6deoEVOZibXw+H3v37q3W/mDKdZHGdSTn3w1F+S5yfK1cuZKrrroKn89HbGwsTzzxBF9//TWff/45Y8aMAWD9+vVceumllJSU1NjHa6+9xp///GcMw6Bjx45MnjyZ7OxsPvzwQwYNGgTA7NmzufHGG4853k8++YTbbruNiooK0tPTef7551m6dCmffvopl112GQDZ2dmMHDmSSCRSaz8PPfQQkydPBqBHjx688847ZGdn884779CjRw8AXn/9df74xz8ec8xHS0V7aRQ//vgjAJmZmdjt9lrbde7c+ZB9AGJjY6OPi4uL6zzWgdt/+OGHo461tLSU7du3HxJPTWqL9+BjH0s/IlKzL7/8Mvr49NNPr1cfDZWn9eln+/btlJWVHXGsIie7G264gYyMDJxOJ6mpqfTt25c//vGP7Ny585j7Vq6LNK7bbrsNqByI8+qrr9bY5rHHHjuk/dFSroscXw1x/t1QlO8ix2bcuHEEAgHsdjufffYZDz74IP369WPw4MFMnDiRZ555Bqgs3D/77LOH7F9QUMB9990HQJs2bfjmm2+48cYb6d27N8OHD2fu3LkMGzYMgHfeeYcFCxbUO9ZQKMRdd92FYRjEx8ezePFi7rrrLvr06cNFF13EjBkzuOOOO4DKC3FvvfVWjf2sX7+ev/71rwD06tWLxYsX8+tf/5revXvz61//mkWLFtGrVy8Axo8fz8aNG+sdc32oaC8nXHl5eXTUTKtWrepsm5SURExMDEC0cA7VTwgOPFE4WG5uLmvXro3+u6aR9oezY8eO6OPDxdu6devo4wPjbch+RORQ3333HbNnzwbgzDPPrPeXhsbMd9M0q+0ncqpbsGABu3fvJhQKkZ+fz9KlS3niiSfIzMzktddeO6a+lesijevGG2/kuuuuA2Ds2LGMGTOGWbNmsWzZMj744ANGjhwZ/RL90EMPccEFF9TrOMp1kePHMAyeeuqp6L+vvPLKRoxG+S5yLLKzs1m4cCEAN910E/369Tukzb333hv9nj1hwgRCoVC17a+//np00OzTTz9Nampqte02m42XX345elfO+PHj6x3vv//9bzZt2gTAAw88QMeOHQ9pM378eJKSkuo81t///nfC4TAAL7zwAh6Pp9p2r9fLCy+8AEA4HOa5556rd8z1oaK9nHAH3kZz4Ij52lQV7UtLS6O/GzBgQDT5pkyZwoYNG2rc909/+lO122Bqu4WnoeKtivXgeBuyHxGpLhgMcvPNN0dz/Yknnqh3X8p3kcbXoUMHfve73zFjxgyys7PJzs5m2rRpXHHFFVgsFsrLy7ntttuYOHFivY+hXBdpXDabjX/84x+89957dO/enddff51f/vKX9O7dm8svvzx6G/3cuXN5/PHH630c5brI8fPcc8+RnZ0NwGWXXVbntLcngvJdpP4+/PDD6OMbbrihxjZWqzV6wb2oqIj58+fX2Ed8fHx0epqDtWrVKnoh/vPPP69Xje7geGub5trr9UYvJv7www+sX7++2nbTNPnoo4+Ayrtm+vbtW2M/ffv2jU7T99FHH2GaZr1irg8V7eWEKy8vjz52Op2Hbe9yuQAIBALR33m9Xh566CGg8sPx3HPP5a233iI/P5+KigpWrVrFqFGjmDhxYrVjHNhHlfPOOw+zclHmGufGO5p4q2Kt6VgN1Y+IVHfnnXeybNkyAEaPHh295a4mjz76aDTfa/pwV76LNK6RI0eyceNGxo8fz2WXXUbv3r3p3bs3V111Fe+++y4zZ87E4XAAcPfdd5OTk1NjP8p1kabvxx9/5M0332T16tU1bl+yZAmTJ0+uc0os5bpI4/jyyy+5//77AUhLS+OVV1457sdUvoscP8e67mRFRUX0Il6/fv3qzJ2qPoLBYPR7/IG07uR+KtrLCed2u6OPKyoqDtu+anGbg29Tueeee7j55psB2L17N9dddx2pqam4XC66d+/OP//5T9q3b88f/vCH6D5xcXHHNd4DF+I5ON6G6kdE9nvyySd5/fXXAejduzcvvfTSMfWnfBdpXAkJCVgsllq3/+IXv+Dhhx8GwO/3RxeNOlrKdZHGtXDhQvr168esWbNo2bIlb731Fjk5OVRUVLB9+3ZeeuklvF4v06ZNo0+fPqxZs6Zex1GuizS8NWvWMHLkSMLhMG63m/fee4+0tLTGDkv5LnIMjnXdyfXr10fvfD/eazieSutOqmgvJ9yBhfMjuYWsakGXg29Ns1gsTJo0iffee4+zzz672mr1CQkJjB07lhUrVlTbr2pKneMV74GLzxwcb0P1IyKVXnvtNR588EGg8kP0k08+qXaLan0o30WavltuuSVa2K9rXZu6KNdFGk8wGOTqq6+muLiY5s2b88033zBq1CjS09NxOBy0atWKO+64g6+++gq3282uXbsYPXp0vY6lXBdpWJs3b2bo0KEUFhZis9mYNm0a55xzTmOHBSjfReqrIdadPJFrOJ5K606qaC8nnNvtJiUlBeCwi7UUFhZGPwgPTJID/epXv2Lx4sX4fD5++ukntm7dSn5+Pi+++CKJiYnV5rs/44wzjjreli1bRh8fLt4Dk/fgeA98EziWfkSkcrX5qtXg27Zty9y5cw9Z6KY+GipP69OPxWI57MmCiFTehl91HlHXtBl1Ua6LNJ7//Oc/0dy96667ar2t/YwzzmDUqFEALF++nO++++6oj6VcF2k4u3bt4oILLmDXrl1YLBbeeOMNhg8f3thhRSnfReqnIdadPJFrQZxK61eoaC+NokuXLgBs3LgxulJzTdauXRt9XLVKdW28Xi8dOnSgTZs21UbdL1++HKi8WNCtW7ejjjUuLi76QX5gPEcbb9VzPtZ+RE51M2fO5LrrrsMwDFq0aMHnn3/eYCfJDZWn9emndevWx3yngMipoq4pdI6Ecl2k8Rx4W/lZZ51VZ9sD59U9XI7VRLku0jD27t3LkCFD2LRpEwAvvPBCdEHKpkL5LlI/DbHuZEOuBaF1J/dT0V4axYABA4DK28mqiuo1OfC29/79+x/1cTZu3Mh///tfoHJxu6rF645WVbzr1q2rddE7qDve9u3bk5GRcUi7mnz11VdA5Sj/du3a1SdkkZPS559/zpVXXkk4HCYlJYW5c+fSsWPHBuu/ofK06j3jcP3k5OREV7Gvz3ucyKkoLy8vegtvVb4eLeW6SOM5cK7cugbvAIRCoRr3O1LKdZFjV1xczIUXXhid//mpp55i7NixjRzVoZTvIvXTEOtOnsi1IE6l9StUtJdGMWLEiOjjKVOm1NjGMAzefPNNABITExk0aNBRH6dqsTrgmE4sDoy3pit9ULkg3rvvvgtUXp3Pysqqtt1isURvH1y7di3ffPNNjf1888030Sv2w4cPP+bRhCIni6+//prhw4cTDAZJSEhgzpw59Zryqi4NladZWVnRUTvvvvsufr+/xn4OfD8ZOXLksYYvckqYOHEipmkCcO6559arD+W6SONp37599PHChQvrbHtgwezA/Y6Ucl3k2Pj9fi699FJWrFgBwEMPPcR9993XyFHVTPkuUj8Nse7kiVwL4pRav8IUaSQDBw40AdNut5tff/31IdufeeYZEzAB85FHHjlku8/nM0tKSmrt/6mnnoruf91119Xabv78+dF2o0ePrrFNRUWF2aFDBxMw4+PjzY0bNx7S5o477oj2M2XKlBr7WbdunWmz2UzA7NWrl+n3+6tt9/v9Zq9evaJ/l/Xr19cat8j/qs2bNx825w62cuVKMzEx0QTMmJgYc9GiRfU69iOPPHLC8nTy5MnRY40dO/aQ7Rs3bjTj4+NNwMzMzDRDoVC9npNIU3W0ub5582ZzxYoVdbaZNWuW6XQ6TcD0eDzmjh07amynXBc5cY421wsLC02v12sCZlxcnLlq1aoa233yySem1Wo1AbNly5ZmJBI5pI1yXeT4CQaD5tChQ6Ov+3HjxjVY3wfm7vz58496H+W7SMNKSUkxAbN79+51tisoKIjmxhVXXBH9/erVq4/4veKDDz6Itn3ppZeOOlafzxfdf/jw4XW2XbFiRbTt73//+2rbZs2aFd323HPP1dnP3/72t2jb2bNnH3XM9XX09xiKNJAJEybQv39/AoEAQ4cO5cEHH2TQoEEEAgGmTZvGxIkTgcor3ffee+8h+69bt44hQ4bwq1/9igsuuIAOHToQiURYu3YtU6ZMid7y1rNnT55//vljitXhcPDCCy8wbNgwfD4f/fv3549//CN9+vShsLCQSZMmMWPGDKDydrprr722xn6ysrL4/e9/z1NPPcWyZcvo378/9913Hx07duSnn37i6aefZuXKlQD8/ve/57TTTjumuEWagkWLFrFx48bov6umtYDKKawOvnvl+uuvr/bvn376iQsvvJCioiIAHn/8cRISEvj+++9rPWZaWhppaWn1ireh8nT06NG88cYbLF68mJdeeomcnBzGjBlDUlIS2dnZPPbYY/h8PqxWK88//3y9bvsXaUqONde3bNnCoEGD6NevH8OGDaN79+7RPN60aRPvv/8+77//fnSU/V//+tdqi8UfLeW6SP0ca64nJiZy//338/DDD1NSUsLZZ5/NXXfdxZAhQ0hKSmLPnj189NFHTJo0CcMwgMrpOKzW+t0krlwXqZ+rr76azz77DIDBgwdz00031Xn+7XQ6D7nbvMrB7wtVU9hC5eLUW7Zsif47MzOz2hQ1R0P5LlI/Xbp0YeHChdF1J2t7Tde2HkRWVhY2my1ak6vLsa7hWLXu5Pbt20/+dSdP2OUBkRrMnDkzeoW6pp+srCxzw4YNNe777bff1rpf1c+wYcPMgoKCOmM4kpH2VSZOnBgd4VfTT58+fcy8vLw6+4hEIuaNN95YZ9w33XRTjaOJRP4XjR49+rC5euDPwaZMmXJU+1PL3TmmeWQjdEyz4fI0Ly/P7N27d619uFwuc9KkSUfz5xRpso411w/8PK7rx+v1mq+99lqdsSjXRY6fY8110zRNwzDM3/72t6bFYqlzX4fDYY4fP77WWJTrIsfP0Z5/t23btkH6qu07ufJd5Ph54IEHoq/tb775ptZ2Tz75ZLTdnDlzqm3r16+fCZWzUwSDwVr7uPDCC6M55PP56hXv1VdfHY1j9+7dtba79dZbo+3WrVtXbZthGGZGRoYJmJ07d67zeJ07dzah8s4/wzDqFXN9aE57aVTDhg1j1apV3H333WRlZeH1eklMTKRXr17Rq+CZmZk17tupUydefPFFRo4cSWZmJnFxcXi9Xjp06MC1117LnDlzmDlzJklJSQ0W75gxY1i+fDljxoyhQ4cOuN1uUlJSGDBgAK+88gqLFy8mNTW1zj6sViuTJ09m9uzZDB8+nIyMDJxOJxkZGQwfPpxPPvmE119/vd6jiUTk2DVUnqampvL111/z8ssvM2DAAFJSUnC73XTo0CH6fnLzzTefoGcl0rT17NmTt99+m7Fjx/Lzn/+cNm3a4PV6cTqdpKenM3jwYJ544gk2b97MLbfc0iDHVK6LNA6LxcJzzz3Ht99+y2233UbXrl2Ji4vDZrORkJBAz549ueeee/j+++/53e9+d8zHU66LnDqU7yJHryHWnazqw+fz8cEHH9TYx44dO5g3bx4A559/frV55esb78m87qTFNPfdYywiIiIiIiIiIiIip5RzzjmHhQsXYrfb+eqrr+jXr1+17ePHj+cPf/gDAI888giPPvpote0FBQV06NCB4uJi2rZty/Lly0lJSYluj0QijBw5klmzZgEwf/58zjvvvEPiWLBgQfSCwOjRo2ssyodCITp37symTZuIj49nxYoVdOzYsVqbsWPH8vLLLwOVFyIOnq4PYP369XTp0oVIJEKvXr346quv8Hg80e2BQIBzzjmHZcuWYbfb+eGHH07oNNYayisiIiIiIiIiIiJyipowYQIej4dwOMzQoUN58skn+eabb5g/fz633nprtGBf27qTycnJPP300wBs3bqVn//850yZMoVly5Yxc+ZMhgwZEi3YX3311TUW7I9U1bqTVqs1uu7kiy++SHZ2NnPmzOFXv/pVtGB/JOtOAtF1MKZPn86yZcuYPn06/fv3Z9myZUDjrDupkfYiIiIiIiIiIiIip7BZs2YxatQofD5fjduzsrKYPXt2rdNYQ+Uo/Mcee4zays2XXHIJM2bMwO1217j9SEbaV5k0aRJ33nknFRUVNW7v06cPs2fPrnMaa8MwGDNmDG+88UatbW666SYmTpx4wqex1kh7ERERERERERERkVPYsaw7WeXPf/4zixYt4je/+Q2tW7fG6XSSlpbGkCFD+Ne//sXs2bNrLdgfrZN93UmNtBcRERERERERERERaSI00l5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5EREREREREREREpIlQ0V5ERERERERERETkFLJ161buvfdeOnfuTExMDMnJyfTu3Zvx48fj9/tr3W/BggVYLJYj+nn00UcbJNapU6dG+5w6dWqD9NnU2Rs7ABERERERERERERE5MWbNmsWoUaPw+XzR3/n9fpYtW8ayZct4/fXXmT17NpmZmY0Y5alNRXsRERERERERERGRU8DKlSu56qqrCAQCxMbG8sADDzBo0CACgQDTpk1j0qRJrF+/nksvvZRly5YRFxdXa19vvPEGvXv3rnV7Wlra8XgKpwQV7UVEREREREREREROAePGjSMQCGC32/nss8/o169fdNvgwYM57bTT+MMf/sD69et59tln65zipn379nTt2vUERH3q0Zz2IiIiIiIiIiIiIie57OxsFi5cCMBNN91UrWBf5d577+X0008HYMKECYRCoRMao1RS0V5ERERERERERETkJPfhhx9GH99www01trFarVx33XUAFBUVMX/+/BMR2jHbtGkTzz77LMOGDaNdu3Z4PB48Hg9t27blqquu4j//+U+d+x+42O2WLVswDIOJEydy9tlnk5SURExMDN26deOJJ56oc6HehqLpcUREREREREREREROcosWLQIgJiaGnj171tru3HPPjT5evHgxQ4cOPe6xHYvNmzfTsWPHGrdt27aNbdu28e677zJq1CimTJmC3V53Sdzv9zN06FA+//zzar9fvXo1q1evZubMmXzxxRfExMQ02HM4mEbai4iIiIiIiIiIiJzkfvzxRwAyMzPrLFx37tz5kH1q8tBDD9G2bVtcLhdJSUn06NGDu+++m/Xr1zdc0EcgEongdDoZNmwYzz//PPPmzWPFihXMmzePl19+mTPOOAOAt99+m8cee+yw/Y0ZM4b58+czevRoZs+ezfLly/n3v/8dnU4oOzubxx9//Lg+J4tpmuZxPYKIiIiIiIiIiIiINJry8nI8Hg8Al156KR9//HGd7WNjYykrK6Nv374sWbIk+vsFCxYwaNCgOve1Wq386U9/4pFHHsFisRxz7FOnTo1O5zNlyhSuv/76atvLysrw+Xy0aNGixv1N0+TGG29k6tSpxMTEsHPnThISEmo9BsBbb73FqFGjqrUJBoP06tWL77//npSUFHJycg47ar++NNJeRERERERERERE5CRWUlISfRwbG3vY9lVTv5SWlh6yrUWLFowdO5Z33nmHpUuXRkei33TTTTgcDgzD4M9//jMPPfRQwz2Bw8RaW8EewGKx8Oyzz2Kz2SgrK2PevHl19nfZZZcdUrAHcLlc3HnnnQDk5+fzww8/HFvgddCc9iIiIiIiIiIiIiInsfLy8uhjp9N52PYulwuAQCBQ7fe9e/dm69atOByOar8/66yzGDFiBLfccgtDhw6luLiYp556iquuuoru3bs3wDM4cqFQiD179lBSUkIkEon+PiUlhdzcXL777jsuv/zyWve/5pprat124FoAmzZtolu3bg0T9EFUtBcRERERERERERE5ibnd7ujjioqKw7YPBoMA0Sl1qhxu8dU+ffrw4osvcu2112KaJi+++CKTJk2Kbi8rK2Pz5s217t+1a9fDxlaTUCjExIkTeeutt1i5cmWdz3Hv3r119nXgnP4HS05Ojj4+8O6FhqaivYiIiIiIiIiIiMhJLC4uLvq4pilvDlZWVgYc2VQ6B/v1r3/N2LFj8fl8fPnll9W2ffvtt3XOiV+f5VcLCgoYOnQoy5cvP6L2B989cDCv11vrNqt1/2zzB47ib2ia015ERERERERERETkJOZ2u0lJSQFgx44ddbYtLCyMFu1bt2591Mey2+1kZWUBsHPnzqPe/2iNGzcuWrAfMWIEM2fOZMuWLfj9fgzDwDRNTNOMPpf6XBg40TTSXkREREREREREROQk16VLFxYuXMjGjRsJh8PY7TWXhteuXRt9fPrpp9frWBaLpcbfn3feeQ1aNPf5fEyfPh2onIv+7bffrrVtYWFhgx33eNNIexEREREREREREZGT3IABA4DKqW/qmkrmwClt+vfvf9THCYfDrF+/HoCMjIyj3v9obNiwgVAoBMBVV11Va7u1a9ce0bRATYWK9iIiIiIiIiIiIiInuREjRkQfT5kypcY2hmHw5ptvApCYmFjn/PO1mT59OsXFxQCce+65Rx/oUQiHw9HHVVP61OTVV189rnE0NBXtRURERERERERERE5yffr0YeDAgQBMnjyZJUuWHNLm2Wef5ccffwQq54p3OBzRbYWFhSxYsKDOY2RnZ3PnnXcClVPk3H777Q0Ufc0yMzOjU/H84x//qHHqnVmzZvHiiy8e1zgamua0FxERERERERERETkFTJgwgf79+xMIBBg6dCgPPvgggwYNIhAIMG3aNCZOnAhAVlYW9957b7V9i4uLGTRoEN26dWPEiBH07NmTFi1aYLPZ2LZtGx9//DFvvfUWFRUVAPzud7+jZ8+ex/X5pKSkcMkllzB79mz+85//MHToUG6//Xbatm1Lbm4uM2bMYOrUqXTo0IGioiLy8vKOazwNRUV7ERERERERERERkVNAjx49mD59OqNGjcLn8/Hggw8e0iYrK4vZs2cTFxdXYx+rVq1i1apVtR7DZrPxpz/9iYcffrjB4q5S0wK3r7zyCgMGDGDbtm3MmzePefPmVdvepk0bPvzwQy655JIGj+d4UdFeRERERERERERE5BQxbNgwVq1axYQJE5g9ezY7duzA6XSSmZnJFVdcwZ133onX6z1kv4yMDN577z2WLFlCdnY2O3fuZO/evZSXl5OQkECnTp0477zzuPnmm2nXrl2DxVs1ch+oMa7WrVuzYsUKnn76aT766CO2bt2K2+2mXbt2jBgxgnHjxpGUlNRg8ZwIFrOmiX5ERERERERERERERBrZ008/zf333w/A3LlzueCCCxo5ouNPC9GKiIiIiIiIiIiISJO0Zs2a6OPOnTs3YiQnjkbai4iIiIiIiIiIiEiTk5+fT2ZmJkVFRWRlZbFu3brGDumE0Jz2IiIiIiIiIiIiItIk5OTkkJOTw9q1a3niiScoKioC4M4772zcwE4gjbQXERERERERERERkSbht7/9LRMmTKj2u+HDhzNjxgxsNlsjRXViaaS9iIiIiIiIiIiIiDQZVquVxMREunfvzrXXXst11113yhTsQSPtRURERERERERERESaDGtjByAiIiIiIiIiIiIiIpVUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSJUtBcRERERERERERERaSL+P8FuE6+uc3WiAAAAAElFTkSuQmCC\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 122 ms (started: 2023-01-05 12:08:22 -08:00)\n" + ] + } + ], "source": [ - "norm_anomalies_df = pd.DataFrame(data=test_anomaly_score_norm, columns=outliers_test_df.columns, index=outliers_test_df.index)" + "norm_anomalies_df = pd.DataFrame(data=test_anomaly_score_norm, columns=outliers_test_df.columns, index=outliers_test_df.index)\n", + "norm_anomalies_df.plot()" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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\n" 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xAwcPHkRmZiYuX76MNm3a4MiRI6hbt67FcRARERVpTg1PEBERPYZ2795tslL022+/laKjox1yvMLaQfDZZ5/Jx9BoNNIXX3xhsgLy8uXL8spRLy8voxWAImFhYdKUKVOkc+fOKR737t270quvvir31blzZ8W2tq4IHjNmjLR582YpPT1d+Hhubq60YsUKyc/PTwLydhJcu3ZNdf8iaWlpRrsnIiIizLbPzc2VypQpI7ffs2eP0eOhoaFGq1PDwsIU+4qIiJAmT54sHT16tECvwXDlsH61/NChQ6UHDx4YtUtLS5MGDx4st/X395dSU1OFfT58+NBoNXGtWrWkEydOmLT79ddfJV9fX7ldnz59FMdpuIPAw8ND8vLykhYvXizpdDqjdpmZmfJtwx0E3t7ekqenpzR//nyT3Q979+6VPxcApC+//FLy9/eXAgMDpXXr1pmMZfXq1UYrnfft26c47g8//FBas2aNlJSUJHxcp9NJf//9txQcHCz3d+DAAcX+7LGDQK3FixfL/bi7u0ubN282abNz5055NbyXl5f09ddfS2lpaSbtwsPDpXr16sn9jRkzRnjM+Ph4o+9Iw4YNpQsXLhi10Wq10jfffCNpNBqja5Q9dhDcuHFDGjdunHT06FHFXTJJSUnSBx98IB83JCREsa3h9czLy0vSaDRSzZo1hd/bNWvWSJ6ennL7VatWKY5zzJgxRu/NnDlzTMYQGRkpNWvWzGgV//Xr14X9ia4Do0aNkpKTk43a3b59W6pTp47R56tZs2aSRqORpk2bJmVlZRm1j4iIMNpZMn36dMXXtGzZMmnu3Llm/509c+aM1Lx5c7m/GTNmKLa19D3Q6XRS9erV5Ta7d+9W7EuSJOnBgwfyudFoNFJUVJTZ9ub069dPPu6AAQOkhw8fCttlZGRImzdvlt544w3h40lJSVKtWrXkvkqXLi39/vvvJtdFScrbcfLbb79Jw4cPF/ZluCPOz89P+uOPP0zaHD9+XKpRo4bcrnLlyiar//UMr1UeHh4SAOntt9+WUlJSjNplZ2fLn93Y2Fij7/9rr70m3blzx6TvuLg4qX///kbXiYLs5iAiIioKGCAgIiJygj59+phsZ9doNFLt2rWlV199Vfrhhx+ko0ePSjk5OQU+VmEECBITE6VixYrJx5g2bZpi23v37knly5c3GpPSZKQ1DCcg8k/66dk7ZUh+f/75p9z/pEmTCtzfa6+9Jvf34Ycfmm27detWuW2VKlVMJnEMJx2XLFlS4LGpkT/dzODBgxXbZmRkGKWI+PPPP4Xtpk6dKrcpWbKkdOvWLcU+//rrL6PjK022GwYIAEi//vqrxddmGCAAIC1dulSx7eeff27yXd+1a5di+5EjR1qc7LZGWFiY3J+5tGaFFSDYs2eP0WT1999/b9JGq9UaTU7+9ddfZvuMjY2VypYtKwF5Abrbt2+btJkyZYrcX9myZaW7d+8q9pf/PXPE9cKcN998Uz62Ugofw+sZkBdsjomJUexzwoQJcttnn31W2CYqKsooRdG8efMU+3v48KFUrVo1ua3S5HD+68Arr7yi2OfBgwdN/m389NNPFdv/+uuvcru6desqtlMrMTFRDjqYS/Oj5nvwxRdfyG1efvlls8f98ccf5bbPPPNMgV6DPo2Vt7e3yYS5NT7++GN5TMWLF5cuXbpkUz/5F0X8888/im2vX78uFS9e3GLQx/BaBUAaOXKkxXGMGDFCbv/OO++YbZubmyt16tTJ4r9HREREjwoGCIiIiJwgJSXFaIWa0o+fn580aNAgi6sPzSmMAMGCBQvk/itVqmSy0jM/w9XD9goQrF69Wu7vxx9/FLZxdIAgNzdXXvXftGnTAve3fft2s5P+hl5++WWzwYRRo0bJj4eGhhZ4bGoYTgx6eXlJsbGxZttPmjRJbv/++++bPK7T6YxWDIsmlvMzDBy99NJLwjaGAYKWLVuqem2GAQLD2gQiV69eNfq89+vXz2z7Xbt2yW2bNWumajyW1K1bV55EVlIYAYKoqCijPPxKE3uGO14snS+9r776Sn7Od999Z/RY/s/O/PnzzfaVnZ1t9Lko7ACBYT0R0XdBkkwDBPlfc34XLlwwCiaITJ48WW7TuHFjs9ccSTK+7np7ewtrQOS/DsTFxZnts0qVKkaBHHP/nqSnp8s7PTQajcmuBFsY7qA4e/assI2a78GdO3fk1e0+Pj6Kq+ElSTKqyfDbb78VaPz64FvJkiVt7iMzM9OoTsfChQtt7suwlsVzzz1nsf0333wjty9fvrzwM2h4rfLx8VHcJaF37949+XNSrlw5k/o8IkeOHJGPYW4HGhER0aOANQiIiIicwN/fH3/99Re2bNmCOXPmYNeuXdDpdCbt0tLSsHr1aqxevRrPPfccVq5ciZIlSzphxObt2bNHvj1o0CB4eXmZbf/SSy9h7NixyM7OVn2M9PR0hIWF4dy5c7h//z5SUlLknNIAEBMTI98+ffq0+sFb6ezZswgPD8eNGzeQnJyMrKwso8f1eabPnTsHnU4HNzc3m4/VuXNnlC9fHrGxsbh16xYOHDiAp59+2qRdWloaQkND5d9feeUVkzaVK1eWby9ZsgS9e/e2Oce1Ldq1a4dy5cqZbdOkSRP59o0bN0wev3jxIuLi4gAA7u7uqmpIjBw5Uq7lsHfvXovtX3rpJYtt8rOUe71GjRrw8/NDWlqaqvYNGjSQb1+/fl3VGCIjI3HixAlcvXoVSUlJyMrKkvP2A0BSUhIA4MGDB7h9+7bR56GwJCUloU+fPnIe/o4dOyrWRNiyZYt8e8iQIar679Spk3z74MGDeP/99+XfDT87Hh4eFvv09PTEkCFD8NVXX6k6trVycnJw9OhRnDlzBnFxcUhJSZFzxgNASkqKfFvt9eyFF14w+3idOnXg6+uLjIwMPHjwACkpKQgICDBqs3v3bvn2sGHDLObN79+/P4KCgvDw4UNkZWXhyJEjePbZZxXbt2/fHmXLljXbZ4MGDeT6Gn369DH774mvry9q1qyJixcvQpIk3LhxAw0bNjTb/7179xAWFoaLFy8iISEBaWlpRt+VEydOyLdPnz5tsT8l5cuXR+/evREaGorMzEz8/vvveOutt0zanTx5EmfOnAGQV5vi+eeft+l4epUrV8a1a9eQkJCA1atXY9CgQVb3ERYWhsTERAB59XqGDh1q83gM/z4YMWKExfbDhw/HRx99BJ1Oh9jYWFy+fBl16tRRbN+tWzeLfxft3LlT/nvj+eefN6rPo+Spp56Sr9sHDx602J6IiKgoY4CAiIjIiXr27ImePXvi/v372Lt3Lw4fPoyTJ08iPDxcLs6r9/fff6N9+/Y4cuSIyaSOs4WHh8u3W7dubbF9QEAAGjRoIBfONOfhw4eYOnUqfv75Z6NJM3Pi4+NVtbPGqlWr8OWXXyIyMlJV+5ycHCQlJRUooOPm5obBgwfj+++/BwD8+uuvwgBBaGioPPncpEkT1K9f36TNwIEDMW3aNOh0OmzevBkNGjTAiBEj0KNHD9SvX9/iRGBBqZlkK1WqlHxbX0TSkOHnrHbt2kbtlRgWuYyLi8OdO3dQoUIFxfbNmjWz2Gd+hhP6SkqUKCG/R6L3x1BQUJB8W3QeDG3evBn/+9//jM6NJfHx8YUeINBqtRg0aBAuXrwIAHjiiSewfv16eHp6CtsfOXJEvr1+/Xrs27fP4jH0QRAAuH37ttFjhuenTp06KFGihMX+1FzLrJWRkYEvv/wSP/30k+rrlJp2xYsXt/ieajQalCxZEhkZGQDyPluG/5ZIkmQUjFBTHNnT0xMtW7aUi2mfOnXKbIBAzXfF8Jpp6bsCqP++XLhwAZMnT8bWrVuNgsvmFPTfktGjR8vB22XLlgkDBIbFiV955RVVk9fmvPjii/j6668BAIMHD5aDBM888wzKlCmjqo+wsDD5dqtWreDr62vTWGJiYuQC9IC6z1RwcDBCQkJw6dIlAHmfKXMBAjXXbMPrydmzZzF27FiLzzGkDyT5+flZ9TwiIqKiggECIiIiFxAcHIwXXnhBXgGam5uLsLAwrFixAj///LO8svT8+fP4+OOP8eOPPzpzuCbu378v365SpYqq51SpUsVigODmzZt4+umn5dWkaqkNJKghSRJef/11rFixwurnpqSkFHjHxyuvvCIHCNatW4d58+aZrKj97bffjNqL1K1bF99++y0mTpwISZJw6dIlTJo0CZMmTULJkiXRpk0bdOjQAX379kVISEiBxixSvHhxi20MJ4tzcnJMHjf8nFWtWlXVccuWLQsfHx9kZmYCyJvwMxcgCA4OVtWvITWvzcPjvz+7LbU3bGu4qjy/adOmYfr06SpGaMye3w+13n//fWzbtg1AXrBk06ZNRhO7+d25c0e+vXr1aquPl5CQYPS7rdcoe0pISECnTp2s3uGk5v1S8xkEzH/HkpKSjO5T+x2rVq2afNvShLq9vyv524uuGwCwbds29O3b12THlyUF/a50794dVatWxc2bN3Hq1CmcPn0ajRs3lh/PyMjA77//Lv8+cuTIAh0PAD755BPs3bsXYWFhkCQJGzZswIYNGwAAtWrVQvv27dG5c2f06dNHcbHB3bt35ds1atSweSyG3ztfX1/V19dq1arJAQJLnyk1fRpeTw4ePGjTjoCEhAQGCIiI6JFl+553IiIichgPDw+0a9cOy5Ytw759++Dv7y8/tmTJEnkFqKsw3O1QrFgxVc9R8z/aQ4YMkYMDAQEBeO+99/Dvv//i2rVrSE1NhVarhZRXU8kojYEoXZOtlixZYhQcePbZZ7Fq1SqcO3cOCQkJcioX/Y/hpJo9xmG4IyAhIQGbN282evzevXvYsWMHgLy0O+ZSp3zwwQfYs2cPOnfubLRjQN/vpEmTULt2bXTp0gXnzp0r8NgN2WOHguHnzJqJGsO2lib8bFkpa+1rs8e52LFjh1FwoHXr1li8eDHCw8MRHx+PzMxMo89lhw4d5Lb2/H6osXjxYjmo6e7ujjVr1phdEQwY7wawRf7AiqOuUdZ4++235eCAl5cXRo4ciY0bNyIyMlJOMaR/vwxTS6l5v+z9/QLUv35rvl/O+K7cv38fgwYNkoMDVatWxVdffYWDBw/izp07SE9Ph06nk8/9p59+Kj+3oN8VNzc3vP766/LvhrsFgLzdMfrPeosWLfDkk08W6HhA3vuxb98+zJw50yh4AwBXrlzB8uXL8fLLL6NcuXKYNGmS8O8Jw/fR8O8Pa7nKNbug1xPAfLCWiIioqGOAgIiIyMW1adMGU6ZMkX/PzMzE8ePHnTgiU4YTCOnp6aqeo0+3ouTw4cM4fPiw3H9YWBi+//57dO/eHdWrV4efn59Rfn9HrYqeNWuWfHv69OnYunUrXnvtNTRo0AAlSpQwWc3viHEY7gow3C0A5K2u1k9cdO7c2WKe/w4dOmDnzp2IjY3F6tWr8c4776Bp06ZG53LXrl146qmncOjQITu+ioIz/JxZ+vwYMmzraum5bDVz5kz59ogRI3Do0CGMGjUKjRs3RqlSpeDt7W3U3hm7BoC8/OOG6TzmzJmDrl27Wnye4QThqVOnjIIdan7y17BwxDXKGjExMfjzzz8B5E0a//vvv1iyZAmee+451KpVC/7+/kY1QZzxfuWfCFb7+l39+7VkyRJ5grhRo0Y4e/YsPvzwQ7Rt2xbly5eHr6+vUSDC3ud+xIgR8nv722+/ybuZAOOAgT12D+h5eXlhwoQJuHbtGk6fPo0ff/wRL730EipWrCi3SU9Px8yZM/HMM8+YBAkM38f8gSNruMo12/B68v3331t9PZEkySTYQkRE9ChhgICIiKgIyJ/TOTY21kkjETPc4q82HVD+HOH57dq1S749dOhQ1KtXz2z7mzdvqjquNW7fvo0rV64AyEuL8tFHH5ltn5ycbJLaxB5efvlleQLrn3/+MVoN+euvv8q3X331VdV9li1bFi+++CJ++OEHnDx5EnFxcZgzZ46c1z8jIwNvvPGGnV6BfdjyObt3757RhFzp0qXtPq7CptVq5Zz8bm5u+OqrryyutLY2TZc9REVFYeDAgXLalzFjxqjO/W1YyFZfXLggHHGNssbu3bvlQrg9evTAM888Y7a9I65nlhQvXtwoBZHa82QYjHHF75fhvyWffPIJAgMDzba397mvWLEievbsCSBvt5Y+3c/Vq1fl77Gfnx8GDx5s1+MCeTswGjVqhHHjxuGPP/5AdHQ0Tp06heHDh8ttjh49ivnz5xs9z/D7p7ZQuojh9y4jI0N1TQd7f6bsfT0hIiJ61DBAQEREVATkL1qYf3WwszVp0kS+bVjcUElqaioiIiLMtjHMGaymwO3+/fsttrE2XYXhGOrUqaNYUFXv4MGD8iSgPVWuXFkuTpyVlYV169YByJuAPXbsGIC8Cab+/fvbfIzg4GC8++672Lhxo3zf+fPnce3atQKM3L4MP2eXLl3Cw4cPLT7HcBdEuXLlzNYfKCri4+ORnZ0NAChTpozFwqMXLlxwSOFuc5KSktCnTx/5PerSpYtVtVOeeuop+bY9drLk/+yoSTliWNi0oBxxPbM3jUZjlB9fv4PLnNzcXKMdbU2bNnXE0ArEmnOv1WodsnNq9OjR8m39roHly5fL/1688MILhbb7okmTJli+fLnRjoW///7bqE2rVq3k20eOHLE5rWHFihWNrk9qPlPx8fGIjIyUf7fHZ8re1xMiIqJHDQMERERERcCZM2eMfrd38cyCMlwNu3r1asVCkYZtLBWLNEx5YyklyJ07d4wmtpUYBlosjdHaMQDAwoULLbaxlWGaIf2uAcPdA/369bNLzvS2bdsaFY81LFbpbHXr1pVTKGm1WqPXr8QwhYelVdtFheHnUs3EnSM/lyJarRaDBg2Si4zWrl0ba9euNSoma0nv3r3l28uXLzfaBWKLOnXqyJ+d3Nxc/PHHH2bbq2ljDWuuJenp6fj555/tdmxrdOrUSb69atUqiwHP0NBQPHjwAEDe9bV169YOHZ8trDn3oaGhDllh3qNHD1SuXBlA3m6SqKgorFy5Un7cnumF1Hruuefk2/mv861atULJkiUB5KVcKsjn0fC6a/ialaxcuVKu/VChQgXUrl3b5mPrde/eXb7+HD582ORvKiIioscdAwRERESF7Pvvv8fOnTtVt09PT8eXX34p/162bFmjVZ6uYMiQIXLhz9u3b+Obb75RbPvgwQNMnTrVYp81atSQb+df3WhIq9Vi9OjR8opqc0qUKCFPFt2/f99ikKB69eryroOIiAizq+lXr16Nf/75x+IYbPXCCy/IAY79+/cjOjraqB6BpfRCaleQJyYmGuWctrQ6vTBpNBqjlbifffYZYmJiFNv//fffRkWd33zzTYeOr7CUKlUKxYsXB5C3Ul+fpkTk0KFDhR4gGD9+PLZt2wYACAoKwqZNm1CiRAmr+hgwYACeeOIJAHkp1d566y3Vu3NSU1NN8p27ublhxIgR8u/Tp0/H/fv3FfuYNWtWgVKr5Gd4PduyZQu0Wq1i2w8++MBpgblRo0bJ18hTp05h8eLFim0TExMxadIk+ffBgwfLn0tXovbfkvv37+O9995zyBjc3d3lz58kSXjllVfknQ1169ZF27Zt7XKcrKws1TUDDFNo5b/Oe3t746233pJ/nzx5Mi5fvmzTmAxT1W3YsEG+NojcvHkTX3zxhdFz7VGoumLFinKQXZIkvPbaa0hOTlb1XJ1OZ/ZaQURE9ChggICIiKiQHTt2DF27dkWLFi2wYMECsxNBR48eRYcOHXDu3Dn5vsmTJxutiHQFxYsXN5oomjp1Kr755huTSbArV66ga9euuHPnjklx3/x69eolTwzs3bsXEyZMMFktHRcXhwEDBmDz5s2qVs97e3ujVq1aAPJ2EISGhpptX7p0aTnVgk6nw8CBA00mSXQ6HebPn49XX30V7u7uJumg7KV48eLyqmqdTofx48cjKioKQF7qnC5duph9/osvvojevXtj3bp1iqtoY2JiMGTIEDnYEhISgpo1a9rxVRTc+PHj5UKbDx48QOfOnXH69GmTdn/++adRTu8+ffrIaZqKOjc3NzmnOQAMGzZMTjVlaM2aNejZsye0Wq1ddpeo8dNPP2HevHkAAA8PD6xbt07+zlnD3d0dCxculIu7rlixAr169cLFixcVn3P69GlMnjwZlStXFk7uv/fee3I+87i4OHTt2lXe5aCn0+nw3Xff4eOPP7Z4jbJGp06d5CBqVFQUhg4disTERKM2ycnJGD16NH766adCe7/yq1mzptGE7tixYzF//nx5RbdeVFQUunXrJp/nwMBAVYFfZ+jTp498+6uvvhLuPDp16hQ6dOiA27dvO+zcv/766/K/3UePHjW6315iY2NRuXJlTJgwASdOnFBst2PHDnz66afy7z169DBpM2nSJPn6n5SUhHbt2uHPP/8UBurS09Pxxx9/GAXh9J555hmj/gcOHIi1a9eatDt58iS6dOkify8qV66Md955R/nFWumLL75A+fLlAQBnz55Fy5YtsX37dsX20dHRmD17NmrXro3Vq1fbbRxERESuSP0+XyIiIrKrEydO4MSJE3j77bdRs2ZN1K9fH6VLl4aHhwfu37+P06dPm0xy9e/fH+PGjVPsc+rUqSYrJPOvJhTtPvjss8+M0g3Y4qOPPsKOHTtw6NAhSJKEDz/8ED/88AM6dOgAf39/REVF4cCBA9BqtXjqqadQs2ZN/P7774r91alTB6+++qqc2uC7777D77//jhYtWqBMmTK4ceMG9u/fj+zsbAQEBGDmzJmqVogPGDBA3pHx8ssvY+XKlXjiiSeM6gvMmjVLvj1jxgx069YNOp0O4eHhaNiwIdq2bYsaNWogNTUVBw4ckItGf/HFF1i8eLHDCoy+8sorcv2B9evXy/cPHjxYnkhVotPpsHnzZmzevBleXl6oX78+QkJCULx4caSkpODWrVs4cuSIPBHo7u6OH374wSGvoyBKliyJ33//HT169EB6ejouX76Mpk2b4qmnnkK9evWQnZ2NsLAwOXgCALVq1TJKNfQo+OSTTxAaGoqMjAzcuHEDrVq1QuvWrRESEoLs7GwcOXJEvn6MGjUKkZGRZnca2Ivh7qEaNWpg/fr1Rp9VJbVq1cK7775rdF+XLl2wcOFCjBkzBlqtFlu3bsW///6LevXq4cknn0RgYCDS09MRGxuLM2fOWFzlW7p0aSxbtgzPP/88tFotzpw5g/r166Ndu3YICQlBamoq9u/fL6/snjlzpsmYbFWyZElMmDABn332GQDgt99+w9atW/HUU0+hYsWKiI2Nxd69e5GWlgYPDw8sWLAAQ4cOtcuxrTVr1iycOHECx48fR25uLsaOHYuvv/4a7dq1g7+/P65evYr9+/fLAWAPDw8sW7YM1apVc8p4LRk6dCi+++47REZGIisrC6+++iq+/PJLNGrUCD4+PoiIiJAn0xs1aoTu3bvj22+/tfs4KleujGeffRZbtmyR7/Py8sJrr71m1+MkJibiu+++w3fffYegoCA0adIEFStWhI+PD+7du4ezZ88a7YYLCQkRfs4DAwPx119/oWvXrrh37x7i4+MxePBgjB8/Hm3atEFwcDAyMzNx9epVnDp1ChkZGWjUqJFwTCtWrEDbtm1x9epVpKam4sUXX0StWrXw1FNPwcvLCxcuXMDRo0fl4IOfnx/++OMPq3cemVOhQgVs3LgRPXv2RHx8PC5fvozu3bujYsWKaNmyJYKDg5GTk4P4+HhERETYdQcRERGRy5OIiIioUC1evFiqXr26BED1j6+vr/TZZ59JOTk5ZvseOnSoVf3qf1asWGGX15aUlCQ9++yzZo/Vpk0b6c6dO0ZjVTp+Wlqa1K1bN7P9VapUSTp48KC0Z88e+b4OHToojjExMVGqU6eO2T7zW7hwoeTh4aHY3s3NTZo6daqk0+mkqlWryvdfv37dLudVLzs7WwoKCjI5/smTJy0+t3fv3qo/D2XKlJFCQ0MLPN5PP/1U7vPTTz+12F7teyhJknTkyBGpRo0aFl9Lly5dpHv37pnty5b3rEOHDvJz9uzZY7G9tccw93nUCw0NlYoVK2b29Y8ePVrKzMxUNV4138kVK1bIbYYOHWr2dVrzY+793r17t1SrVi3VfdWvX1+KiYlR7G/t2rVS8eLFFZ/v7e0tLVq0SLp+/bp8X9WqVRX7Uys3N1d67bXXzI69RIkS0oYNG1Qd25bxqf0cpqSkSC+++KLFc12+fHlpy5YtZo9p7XVAzefQkJrP9uXLly1eL9q2bStFR0erGq+l74GS0NBQo2MOHDhQ9XPViI6Olry9vVV/Vzp27CjFxsaa7fPGjRvS008/raq/tm3bKvYTFxcnderUyWIfTzzxhHTs2DGzY7L2M5L/9XTu3Fn1OSpbtqz077//WnUMIiKiooY7CIiIiArZqFGjMGrUKERERGDfvn0ICwvDpUuXcPPmTSQlJUGSJAQEBKBcuXJ48skn8cwzz+CFF16QCwa6ssDAQGzduhV//fUXVq5ciePHj+Phw4coXbo06tati5dffhmvvPKK0Wp9c4oVK4atW7fi999/x6pVqxAeHo7k5GSULl0aNWrUwIABAzBs2DCULFkSe/fuVdVn8eLFcfz4cSxYsACbN2/GxYsXkZiYaLYewZtvvom2bdti9uzZ2LNnD+7cuQNfX19UrFgRnTp1wogRI9CkSRNVxy8IT09PDBo0yCinfN26ddG0aVOLz/37778RHh6OXbt24ejRo7h48SKio6ORlpYGb29vBAcH48knn0TPnj0xZMgQBAYGOvKlFFirVq1w8eJF/PrrrwgNDcXp06dx7949eHp6oly5cmjXrh0GDx6Mbt26OXuoDtO3b19ERETg+++/x/bt23Hr1i14eHigQoUKaNu2LYYNG/ZIpFV65plncPHiRYSGhmLz5s0ICwtDXFwckpOTUaxYMZQtWxZ16tRBmzZt0KNHD4s1WgYOHIg2bdpg7ty52LRpE27evAmNRoNKlSqhS5cuGDNmDOrWrYsbN27Y9XW4u7tj1apVeOGFF7B48WIcPXoUCQkJKFmyJKpUqYK+fftixIgRqFChgt2PbS1/f3+sXr0a48ePxy+//IK9e/fizp07yMjIQOnSpdGgQQP07t0bI0aMcFo6JGuEhIQgPDwc8+fPx19//YXLly8jOzsb5cqVQ8OGDTFkyBC8+OKLFndiFVTPnj3h7e2NrKwsAPYvTlyxYkU8ePAAu3fvxoEDB3Dy5ElERUXh/v378m67qlWrokWLFhg0aJDF1HQAULVqVezbtw+7du3C2rVr5V1zycnJ8PPzQ9WqVdGsWTP06tXL7E7EsmXLYteuXfj333+xevVqHDx4EHFxccjJyUGZMmXQpEkT9OvXz6q/EWxRtWpV7Ny5E0eOHMHatWuxf/9+3L59GwkJCfDw8ECpUqVQq1YtNG/eHN26dUPHjh2tKrBORERUFGkkSWW1LyIiIiIiIiKyyb59+9CxY0cAeRPV165dc7maQkRERPT44V8jRERERERERA5mWAtlxIgRDA4QERGRS+AOAiIiIiIiIiIHiouLQ/Xq1ZGZmQkPDw/cvHkTFSpUcPawiIiIiLiDgIiIiIiIiMhRtFot3n33XWRmZgIAXnzxRQYHiIiIyGVwBwERERERERGRHf3+++84duwYUlNTceDAAURGRgIAvL29ce7cOdSqVcvJIyQiIiLK4+HsARARERERERE9SrZv345Vq1aZ3P/9998zOEBEREQuhQECIiIiIiIiIgcJCAhAs2bN8MEHH6B3797OHg4RERGREaYYIiIiIiIiIiIiIiJ6DLFIMRERERERERERERHRY4gBAiIiIiIiIiIiIiKixxADBEREREREREREREREjyEGCIiIiIiIiIiIiIiIHkMMEBARERERERERERERPYYYICAiIiIiIiIiIiIiegwxQEBERERERERERERE9BjycPYAHieJiYkIDQ0FANSsWRM+Pj7OHRARERERERERERERmcjMzMTVq1cBAP369UOJEiWcOyAHYYCgEIWGhmL48OHOHgYRERERERERERERWWHYsGHOHoJDMMUQEREREREREREREdFjiDsIClHNmjXl2ytWrED9+vWdOBoiIiIiIiIiIiIiEjl//rycDcZwXvdRwwBBITKsOVC/fn20aNHCiaMhIiIiIiIiIiIiIkse5VqyTDFERERERERERERERPQYYoCAiIiIiIiIiIiIiOgxxAABEREREREREREREdFjiAECIiIiIiIiIiIiIqLHEAMERERERERERERERESPIQYIiIiIiIiIiIiIiIgeQwwQEBERERERERERERE9hhggICIiIiIiIiIiIiJ6DHk4ewBERERERET0eImPv4Wo48vhGxSCRk8NcfZwiIiIiB5bDBAQERERERFRoUlPTwK2NEYrjwQgAQh7eAWtenzq7GERkQ0yMzORmJiI9PR0aLVaZw+HiEjm7u4OLy8vBAYGwt/fH25uTKSjhAECIiIiIiIiKjTn9nyDpzwS5N9bJUwDwAABUVEiSRJiY2ORlJTk7KEQEQnl5uYiKysLKSkp0Gg0qFixIgICApw9LJfEAAEREREREREVGq+EQ6yGR1TEPXjwwCQ44OHBKSYich1arRaSJAHIC2rGxMQwSKCAV28iIiIiIiIqRBpnD4CICiA7Oxv379+Xfy9TpgxKlCgBd3d3J46KiMiYJElIT0/Hw4cPkZqaKgcJQkJCmG4oH54NIiIiIiIiIiJSJTU1Vb5dqlQplCpVisEBInI5Go0Gfn5+qFSpEvz9/QHkBQ0Mr2GUhwECIiIiIiIiKjTcP0BUtKWlpcm3AwMDnTgSIiLLNBoNgoKC5N+Tk5OdOBrXxAABERERERERFR5GCIiKtOzsbAB5k27e3t5OHg0RkWXFihWDRpP3B4j+Gkb/YYCAiIiIiIiIChEjBERFmU6nAwC4u7vLE25ERK5Mo9HIqdC0Wq2TR+N6GCAgIiIiIiIiIiIiInoMMUBAREREREREhUbiDgIiIiIil+HQAIFGo1H107FjR4t9bd26Ff3790elSpXg7e2NSpUqoX///ti6davq8eTm5uKnn35C+/btERwcDF9fX9SsWRNvvPEGzp8/X4BXSkRERERERERERERUtHg4ewCW6HQ6jB49GsuWLTO6PyYmBjExMQgNDcXIkSOxaNEiuLkpxzvi4+PRs2dPHD9+3Oj+a9euYfHixVi1ahXmzZuHkSNHOuR1EBERERERESsQEBEREbmSQgkQjBkzBm+99Zbi435+foqPffzxx3JwoEmTJpg0aRJq1qyJq1ev4ttvv0V4eDiWLl2K4OBgfPnll8I+tFot+vfvLwcHnn/+eYwaNQpBQUE4evQoPv/8c9y7dw9vvPEGKlasiB49ehTg1RIREREREZESydkDICIiIiJZoQQIypQpgwYNGlj9vMjISMyaNQsA0Lx5c+zfvx++vr4AgBYtWuC5555Dhw4dcOLECcycORMjRozAE088YdLPqlWrcPDgQQDAW2+9hfnz58uPtWzZEj169ECzZs2QnJyMd955BxcvXoSHh8tvriAiIiIiIiqCuIeAiIjsY+/evXjmmWcAAHv27FGVxpyIjLl0keI5c+YgNzcXADB37lw5OKBXrFgxzJ07F0BefYHZs2cL+9EHGYKCgjBz5kyTx5944gl89NFHAICoqChs2LDBbq+BiIiIiIiIiIiIiMgVuWyAQJIkbNy4EQBQp04dtGrVStiuVatWqF27NgBg48aNkCTjDauRkZG4ePEiAODFF19EsWLFhP0MGzZMvs0AARERERERkYNwAwERkWorV66ERqOBRqPBjRs3nD0cs/TjnDZtmrOHYlfDhg2DRqNBtWrVnD0UIodw2QDB9evXcefOHQBAhw4dzLbVPx4TE2NysdSnFrLUT7ly5RASEgIAOHTokC1DJiIiIiIiIosYISAiIiJyFYUSIFi7di3q1auHYsWKISAgALVq1cLQoUOxZ88exedcuHBBvl2nTh2z/Rs+rt8tUJB+bt++jbS0NLNtiYiIiIiIiIiIiIiKskKpxGs4SQ/k5fmPiorCzz//jH79+mHlypUoXry4UZvo6Gj5dqVKlcz2X7lyZfn27du3C9yPJEmIjo6WUxepZXgskbt371rVHxERERER0aNGstyEiIiIiAqJQ3cQFCtWDC+99BKWLFmCAwcOIDw8HNu3b8fHH3+MUqVKAQBCQ0PRt29f5OTkGD03JSVFvu3v72/2OH5+fvLt1NRUh/SjRuXKlc3+9OnTx+o+iYiI6NElSRJuPkhDfGqWs4dCRFRoNEwxRERk0d69e6HRaDB8+HD5vurVq8t5/vU/e/fuFT4/NDQUL7zwAqpUqQIfHx+UKFECzZs3x/Tp05GQkGD22JGRkRg3bhwaNGiAgIAAeHl5oUKFCmjcuDFGjBiB1atXIyvrv79fq1WrBo3mv2v79OnTTcZpWPvTGhkZGfjyyy/RqFEj+Pn5oVSpUmjbti2WLFkCnU5n8fk6nQ67d+/GhAkT0LZtW5QuXRqenp4oUaIEGjdujAkTJuDWrVvC506bNg0ajQarVq0CANy8edPkdRm+bgDIzs7Gpk2bMHbsWLRo0QIlS5aEp6cnSpUqhaeeegrTpk1DfHy8TeeCyFEcuoMgJiYGJUqUMLm/a9euGDduHHr06IHw8HDs27cPCxcuxDvvvCO3yczMlG97eXmZPY63t7d8OyMjw+gxe/VDREREZG8T153FupPR8PZww+xBjdGzYXlnD4mIiIiIirCEhAQMHDgQu3fvNro/KysLJ0+exMmTJ7FgwQJs3LgRrVq1Mnn+2rVr8corryA7O9vo/tjYWMTGxuLMmTNYsWIFzp07hwYNGjj0tcTFxaFTp05G6cTT09Nx+PBhHD58GOvXr8f7779vto/PPvsM06dPN7k/KSkJZ86cwZkzZ7Bw4UL8+uuv6N+/f4HHPHr0aDmgYOjhw4c4duwYjh07hnnz5mHjxo1o27ZtgY9HZA8ODRCIggN6ZcuWxbp161CnTh3k5ORg7ty5RgECHx8f+Xb+i1J+hlFLX19fo8fy92P4uzX9qJE/vVF+p0+f5i4CIiIiAgBcikvGupN56QmzcnWYuPYMAwRE9JjgDgIiIktatGiBc+fOYePGjfjkk08AANu2bUOFChWM2lWvXl2+nZWVhS5duuDUqVNwd3fHkCFD0LNnT1SvXh05OTnYv38/vv/+e9y7dw89e/ZEeHg4qlatKj//7t27GD58OLKzs1GmTBmMHTsWrVq1QunSpZGRkYGoqCjs27cPoaGhRmPYvn07srOz0bBhQwDAmDFj8NZbbxm1KVmypFWvPzc3F71795aDA926dcOYMWNQuXJl3Lp1CwsWLMC2bdvw8OFDi/2UL18e/fv3R+vWrVGjRg34+Pjg9u3bOHz4MBYsWIDU1FQMGTIEp06dQt26deXnvvXWWxg4cCA++eQTbNy4ERUqVMC2bdssHq9GjRro378/WrZsiSpVqsDDwwM3b97Ezp07sXz5cjx48AD9+/dHREQEypQpY9V5IXKEQqlBoKRGjRro2rUrtmzZgqioKNy5c0e+0AUEBMjtLKX7MSwonD+NUP5+zAUIzPWjhqUaB7GxsVb3SURERI+mfyPijH5Py9Y6aSRERM4nSZJJmgYiKrp0OgkJ6eYXez5KShbzgpubfa9hfn5+aNCgAU6cOCHfFxISgmrVqik+57PPPsOpU6dQokQJ7Ny5E82aNTN6vF27dnj55ZfRunVrxMbGYsqUKfjtt9/kxzdv3izPje3atctkh0CbNm3w2muvYd68eUb3h4SEGP1epkyZAu8uWLRoEU6ePAkgb1X+okWL5MeaNWuG/v374/XXX8fy5cvN9jNy5Eh8+umn8PT0NLq/adOm6Nu3L8aNG4dWrVohJiYGX375JX755Rej11GmTBl5AbSnp6fF1zV9+nTUqFHD5N+05s2bY8CAAXjrrbfQpk0b3L9/H3PnzsWMGTMsngsiR3NqgAAA6tWrhy1btgDIS0mkDxAYTrZbKv5ruHLfsGCxqJ/SpUtb7Eej0Vic7CciIiIiIiL70EmAO+MDRI+MhPRsNPt8p7OHUWhOftIFpfy9LTd0oNTUVMyfPx8AMGPGDJPggF7VqlXxv//9D2+99RbWrl2LxYsXyzU54+LyFrCULFnS7ES4LVk3rLVgwQIAeRlIZs+eLWzzww8/YNOmTbh//75iP+YCKkDevOHEiRMxfvx4/P333wUOWNesWdPs4w0bNsTIkSMxZ84chIaGMkBALsGhRYrVUPrS1atXT7596dIls30YPm64FcjWfipXrmxUsJiIiIiIiIjsRPD/gDpJcsJAiIgeHfv27UNSUhIAYODAgWbbPv300wCAnJwceZU+AJQvn5fuMiEhARs3bnTQSC2LjY3FhQsXAAAvvvgiihUrJmzn7++PF1980aq+k5OTcf36dZw/fx4RERGIiIiQ+9c/Zk8JCQm4evWq0fH0OxIuXLiAnJwcux6Piqbs7GwsXboU3bt3R/ny5eHt7Q1/f3/Url0bw4cPx+HDhx16fKfvINB/4QEY5VGrXr06KlSogDt37mDfvn1m+9i/fz8AoGLFiiaRwXbt2sm39+3bh5deeknYR1xcHCIjIwGARUKIiIiIiIgKEQMEREQFY5iKSD/Rr4Z+1wAAPPfccyhRogQSExPRv39/dOzYEX369MHTTz+Nxo0bw93d3a5jVnLu3Dn5dosWLcy2bdmypbxzQsnNmzcxa9YsbNq0CTdv3jTbNj4+HjVq1FA/WIFz585h9uzZ2Lp1q9H5zU+n0yEhIYF1CB5zN2/eRK9evXD+/Hmj+7OzsxEZGYnIyEisXLkS48aNww8//OCQlIxO3UFw/fp17NixA0DeFpyKFSvKj2k0GvTt2xdA3sr+sLAwYR9hYWHyyv++ffuanKSQkBB5V8GaNWuQnp4u7GflypXybXtULSciIiIiIiJ1GB8gIiqYe/fu2fQ8w3myUqVK4e+//0bFihUhSRL27NmD999/H82bN0dQUBCef/55/PPPP/YasiLDwsOWJs/Lli1r9vGtW7eiXr16mDdvnsXgAABkZGSoG6SCZcuWoWnTplixYoXZ4IC9jkdFW05OjlFw4Mknn8TKlStx5MgRbN++HVOnTpWz3MydOxfffPONQ8bhsB0EmzZtQo8ePeDhIT7E3bt3MWDAAGRn5xWtyV/dHADGjx+PxYsXQ6vVYty4cdi/f79RnrOMjAyMGzcOAODh4YHx48cLjzVhwgS8/vrrePjwISZNmmRSTOXq1av46quvAABPPPEEAwRERERERESFSKtjhIDoUVKymBdOftLF2cMoNCWLeTl7CNBqtfLtU6dOmRTlVZK/Bmf79u0RFRWF9evXY8uWLdi/fz+io6ORnJyMDRs2YMOGDejevTv++usvxdQ/9lSQ1dLx8fEYMmQI0tPT4e/vjwkTJqB79+6oWbMmihcvDi+vvPdt9+7d6Ny5MwBAKkDE+tKlS3jzzTeRm5uLMmXKYOLEiejUqROqVauGgIAA+T1Zvnw5Xn/99QIfj4q+jRs3ysGB1q1b48CBA0Y7dbp27YrnnnsOrVu3Rk5ODr755htMmDBBcb7dVg4LEIwbNw45OTkYMGAAWrdujWrVqsHX1xfx8fHYu3cvFi1ahPj4eAB5aYDefvttkz5CQkIwceJEfP311zhx4gTatm2LyZMno2bNmrh69Sq++eYbhIeHAwAmTpyIWrVqCccydOhQLF++HIcOHcL8+fMRFxeHUaNGoWTJkjh27BhmzJiB5ORkuLm54ccff7T7SSYiIiIiIiJlTDFE9Ghxc9M4vWjv46ZUqVLy7eDgYJOJf2v4+Pjg5ZdfxssvvwwgLwPI5s2bMXfuXERGRmLbtm34+OOPFYsHF1TJkiXl23fv3jXb1tzj69atQ2JiIgBgw4YN6NJFHLQy3LFQECtXrkRubi7c3d2xb98+1KlTx6HHo6LPsLbARx99JEzj1axZM/Tu3RsbNmxAYmIiLl68iIYNG9p1HA6dCb9z5w7mzp2LuXPnKrYZMGAAli5dCm9v8T8cX3zxBe7du4fly5cjPDxcWEPg9ddfx+eff654DHd3d4SGhqJnz544fvw41q9fj/Xr1xu18fb2xrx589CjRw+Vr46IiIjIdhrYP3ckEVFRIAmuf9xAQEQkpnYFfZMmTeTbhw4dwqBBg+w2hurVq2Ps2LF47bXXUL9+fURHR2PNmjUOCxAYTn4eP34cr776qmLb48ePKz6mX5kdFBSkGBwAjOs3iKh9D/THa9SokWJwQM3x6PGhz6wDwGzti5o1awqfYy8Oq0GwatUqTJ8+Hc8++yxCQkIQFBQEDw8PlChRAg0bNsQbb7yBw4cPY926dXL1buEA3dywbNkybN68GX379kWFChXg5eWFChUqoG/fvtiyZQuWLl0KNzfzL6V06dI4fPgwFixYgHbt2qFUqVLw8fFBjRo1MGrUKJw8eRIjR46081kgIiIiMi/Y4yEC3VKdPQwiokIjCpAyxQIRkZiPj498OysrS7Fdly5d5JQ/P/74o0Ouq4GBgXLRYH1WEEP6sZobpxoVKlSQ64muXbtWMU9/Wloa1qxZo9hPbm4uACAzMxM6nU7YJj09Hb/88ovZ8ah9XfrjpaWlKbaJjY3F33//bbYfenzUrl1bvn3t2jXFdlevXgWQF6xSyqBTEA4LEHTo0AFTp07F1q1bcfnyZTx48AA5OTlISEjA2bNn8dNPP6F169aq++vZsydCQ0MRExODrKwsxMTEIDQ01KoV/x4eHhgzZgwOHDiA+Ph4ZGRk4OrVq1i8eDHq169vy8skIiIistmMCgtwvN5rOFJ3GLoGhjl7OERETqPjFgIiIqHy5cvLt/WThCIlSpTA2LFjAeSlLXnvvfcUJ8WBvNQ8S5cuNbpv27ZtiI2NVXxOUlISjh07BiBvV4HSWM2NU60xY8YAAOLi4vDBBx8I27z33ntmizPrJ1LT09OFgQStVouRI0fizp07Zseif1337t1DSkqKxeNduXLFKHWMXnp6OoYMGcLCxCQbPHgwAgMDAQDffPONUS0RvfDwcGzevBkAMGTIELm9PTHZPhEREZETlMyNwqultwAA/Nwz8WXFeQBmOHdQRESFQBQK0JqZxCIiepw1adIEPj4+yMzMxP/+9z94enqiatWqciaNihUrwtfXFwDw2WefYd++fTh69Ch++OEH7N27F6NGjULjxo3h5+eHhIQEnD9/Hjt37sTWrVvRsGFDo2waf/zxB/r06YOuXbuiW7duaNCgAYKCgpCSkoKIiAjMmzcPMTExAIA333zTZKxt2rTB9evX8ffff2PRokVo27atvPo+MDAQZcqUUf26x4wZgxUrViA8PBwLFy7E9evX8eabb6Jy5cq4ffs2FixYgO3bt6N58+aKKXtefPFFTJkyBVlZWRg+fDhOnz6Nrl27onjx4jh//jzmzp2LkydPom3btjh06JDiWNq0aQMA0Ol0ePPNNzFu3DiULl1afvyJJ54AALz66quYO3cudDodevXqhYkTJ6Jdu3bw8fHByZMnMXv2bFy5csXi8cg13b17F9HR0WbbWFv7o3Tp0vjll18wePBgHDp0CC1atMD48eMREhKC1NRUHDp0CN999x2ys7PRtGlTfPfddwV5CYo0EvdyFprjx4+jZcuWAIBjx47J27KIiIjo8RO28R20SstXp2kI/ywjokdf+B890ET61+i+e71SUaa4n5NGRETWuHLlCnJzc+Hh4eGQVBdkavLkyfj222+Fj+3ZswcdO3aUf09JScGwYcPw119/Wez3mWeewe7du+Xfhw0bhlWrVll83ptvvon58+ebpPs+ffo0WrVqJUzFM3ToUKxcudJi34bu3LmDTp064fLly8LHu3Xrhg8++ADdu3cHYHouAGDFihUYOXKk4m6KQYMGYdSoUXKNAlEfOp0Obdu2RViYeMev4dTqZ599hk8//VTxNX3wwQdo0KABhg8fDiCvAHS1atUU25P92HLtMpzLVcPWafZLly7hu+++w7Jly0z6KFu2LD766COMGjVKTiNmbw5LMUREREREZjhojUbYtQf4NyIW2blcjUtErklYpJg7CIiIFH399ddYsmQJ2rdvj6CgILi7uyu2DQgIwPr163HgwAGMHDkStWvXRkBAADw8PBAUFIQWLVrg7bffxpYtW7Bjxw6j586ePRu//vorRowYgebNm6NixYrw8vKCr68vQkJCMHToUBw4cAALFy4U1gJt3Lgxjhw5gsGDB6NKlSrw9vYu0OuuUKECwsPD8fnnn6NBgwbw9fVFiRIl0KpVKyxYsABbt26Fl5eX2T6GDx+OAwcOoF+/fggODoanpyfKly+PZ599FqtXr8aff/5p9nwCefVRt2/fjk8++QSNGjWCv7+/YuHiqVOnYvPmzejWrRtKliwJLy8vVKpUCc8//zy2b9+OWbNm2Xw+6NGUnZ2Nn3/+GRs3bhQGGO7evYtff/0VO3fudNgYuIOgEHEHAREREemFhY5Dq/R5RvdJg3WK/7Ohxvw9UZi5LW+FVfOqJbH2zdYF6o+IyBHC/+yJJrqtRvfdeTYRFYKKO2lERGQN7iAgoqKooDsINm3ahMaNG5ttb22KobS0NPTo0QMHDhyAu7s7PvjgAwwfPhw1atRAZmYmjh49is8++wwHDx6ERqPBrFmz8P7771t1DDVYg4CIiIjIRUgSUJD5fH1wAABO3EzAsesP8VSNUnYYGRGRY4mK8hERERG5irJly1odALBk2rRpOHDgAABg2bJlGDp0qPyYl5cXunbtimeeeQbdunXDnj17MHHiRHTu3BmNGjWy6ziYYoiIiIjICUSBAJ2dN3aG3060a39ERPYhuAByYzsRERE9RiRJwvLlywFATuEl4uHhgRkzZgDIS8lobS0PNRggICIiInIROjvPjzG5EBEVFToddxAQERHR4+Pu3bt4+PAhAKBJkyZm2zZr1ky+fenSJbuPhQECIiIiIhchgStoiejRJyxSLDFAQERERI8PD4//Mv/n5uaabZuTkyN8nr0wQEBERETkFKbBAGbYIKLHlU7SOXsIRERERIUmKCgIgYGBAIAjR46YDRLs27dPvl29enW7j4UBAiIiIiIXwQCBa7mXnIkZ/1zArG2XkZKZY/kJRGQzSccAARERET0+3Nzc0KtXLwDAnTt38MUXXwjbJSQkYPLkyfLvvXv3tvtY7L8ngYiIiIhsUpAixRKjC3YlSRJeWhKGa/fTAADn7yRhxfCWTh4V0aOLOwiIiIjocTN16lRs3LgR6enpmDZtGk6ePImhQ4eiRo0ayMzMRFhYGObMmYNbt24BADp37oxu3brZfRwMEBARERG5CFGAYM2J2/jlyE1UK+2H6c/VR5Cfl/C5WntXOH7MnbqVKAcHAGDP5fvI0erg6c4NuEQFJUmmNQgkXsOIiIjoMVOnTh1s3LgRgwcPRnx8PDZt2oRNmzYJ23bq1Alr1651yDgYICAiIiJyEfmnx27Ep2HSurMAgHMxSSjh64kZ/RoIn6vlDgK7up+SaXIfTzGRfYi+StxBQERERI+jLl264NKlS1i2bBm2bt2K8+fPIzExER4eHihXrhxatGiBIUOG4LnnnoNGY7rIwh4YICAiIiJyEfnnx77fEWn0+y9hNxUDBEzfbV9awfmUhNOaj7msh0DWfSCgFqDh7gpSR/Rd0um0ThgJERERkfOVKlUKkyZNwqRJk5xyfP4VT0REROQi8qcYik/NUv1c7iCwL1G6J57ifO7tB/6uCfxTB9jdDdCxkDPZjnVUiIiIiJyDAQIiIiIiJxBtDs0/PWZNXQFRWwftQH0siAIEBSki/Ug68Q6Qk5h3++4uIHqjU4dDRYeoBoFO4g4CIiIiImdggICIiIjIReSfgLZmPlrHAp92JQq48BTnk3jG+PfI+c4ZBxU5wq8S86QREREROQUDBEREREQuQso3A21N2iCmGLIvUTCAKVCI7MV0B4GWAQIiIiIip2CAgIiIiMgpRDnujSfIrEkxxB0E9iVOMeSEgRA9gkRFivNf/4iIiIiocDBAQEREROQiTFMMcQeBs4gCLtxBQGQfoq8SAwREREREzuHh7AEQERERPZ5MU2xI+Yp0WpVi6BFa3v5vRCzWn4pBnXIBGNepFrw8Cn9Ni+jcP0Kn2EF4gkgdSXj9Y4CAiIiIyBkYICAiIiJyCtEEtPF91qTkflTSd1+OS8Gbv54CAOy4cBfeHm4Y26lWoY+DNQisl6uV+D8XpI4kmcRIdY/KRYyIiIioiGGKISIiIiIXIeWbIBPlwVfyqKQY+ubfS0a/z9oe6ZRxiFIMcQeBebFJGc4eAhUROtEOAgYIiIiIiJyCAQIiIiIiJzCdHgNQgCLFj0qKoevxac4eAgBxcIY7CMxLycp19hCoCOP3i4iIXFHHjh2h0WjQsWNHZw+FyGEYICAiIiJyETrJ9h0E4gntAg/psSUKuDwiMRgi5xOm8NKa3klEREREDscAAREREZGLkPLNQFszIf2oTGhrhFsrCp8w4MIivGa5yFtHRYDou6RliiEiIiIip2CAgIiIiMhF6PKtoLWqBoEwQFD0JrRdZZJZFFwpigEXIlckiWoQSAwQEBERETkDAwRERERETmF5Qt+augKiWMCjUpfAGUTBFVHhYjLgKts/yPWJLlgMEBARERE5BQMERERERC5Cg3w1CKwpUiyYcCuKAQI3F5lkFp37Irghg8hFsQg4ERERkatggICIiIjIReh0+YsUq3/uI5NiyDXiA9AKFjOzBoF5LvLWURGgEQYIWKSYiEhJREQEPv/8c3Tv3h2VKlWCt7c3/P39UatWLQwdOhRhYWFmnz9t2jRoNBpo/v8PrczMTMycORNNmzZFQEAAAgIC0LJlS8ybNw+5ubkWx3Pjxg289957qF+/PgICAlCsWDHUqlULb7zxBs6dO2f2ufpxTJs2DQCwZ88e9OvXDxUqVICvry/q1q2LGTNmIC0tzeh5W7ZsQc+ePeV29erVw1dffYXs7GzFY2VnZ2PTpk0YO3YsWrRogZIlS8LT0xOlSpXCU089hWnTpiE+Pt7i6xV5//33odFo4O7ujpiYGIvtmzVrBo1Gg3r16tl0vN27d2Pw4MGoXr06fH19UaxYMVStWhWtWrXChAkTsHv3brPPj4iIwLhx49CwYUP5PJQrVw5dunTBt99+i9jYWMXnHjx4EK+++iqqVasGHx8flChRAk2aNMEnn3yC+/fvKz5v79698vu9d+9e6HQ6LF++HM888wzKli0LNzc3DBs2zOR5p06dwptvvonatWvD398ffn5+qF27NsaMGYPIyEjV54zU83D2AIiIiIgeR6Kp5vwraEW7ApQIU+IUwQCBqxCfTycMhOhRJEgnxOsVEZHY3r178cwzz5jcn52djaioKERFReHnn3/Ghx9+iK+++spif3fv3sWzzz6L06dPG91//PhxHD9+HNu3b0doaCjc3MRrin/++WeMHj0aWVlZRvfrx7Js2TLMmDEDH330kcWxfP3115gyZYrR38CXLl3C1KlT8e+//2L79u0oVqwYxo8fjx9//NHouRcvXsSUKVOwf/9+/PPPP3B3dzfpf/To0Vi1apXJ/Q8fPsSxY8dw7NgxzJs3Dxs3bkTbtm0tjtfQyJEjMXv2bOh0Ovzyyy/48MMPFduePXsWp06dAgCMGDHCquMAwHvvvYc5c+aY3H/r1i3cunULR48excqVK4XBDq1Wi4kTJ2LOnDkm/69x9+5d3L17F7t27cKFCxewcuVKo8d1Oh3eeecdzJ8/3+j+rKwsnD59GqdPn8a8efOwdu1adO3a1exryMzMRPfu3bFz507FNjqdDhMmTBCONTIyEpGRkVi6dCnmz5+P0aNHmz0eWYcBAiIiIiInEKXTyH+fVSmGBG1Fq+BdncukGGLAxWqiVeFEakk67iAgIhLJzc2Fn58fevXqhU6dOqFOnToIDAzEvXv3cP78efz444+4efMmvv76a4SEhGD48OFm+3v++edx4cIFvPPOO+jTpw+CgoJw+fJlzJgxAxcvXsSmTZuwZMkSvPHGGybP3bx5M4YNGwZJkuDv748PPvgAXbp0gYeHBw4fPoyvvvoK8fHxmDJlCkqUKIExY8YojmPr1q04duwYWrdujXHjxiEkJATx8fH44YcfsHXrVrm/oKAg/Pjjj+jRowdGjhyJatWqITo6Gl999RXCwsLw77//YsmSJXjzzTeF565GjRro378/WrZsiSpVqsDDwwM3b97Ezp07sXz5cjx48AD9+/dHREQEypQpo/p9qVevHlq3bo0jR45g5cqVZgMEK1asAAB4eHjg1VdfVX0MAPjnn3/k4MCTTz6JMWPGoG7duihevDgSExNx/vx57Ny5E8eOHRM+f/To0Vi+fDkAoHz58hg7dizatGmD4sWL4/79+zh27BjWrVsnfO6HH34oBweqV6+OyZMno2nTpkhLS8Pff/+NefPmISkpCb1798axY8fQqFEjxdcxefJknD17Fs899xyGDRuGqlWr4u7du0hOTpbbjBs3DgsWLAAAPP300xg2bBhq1KiBYsWK4cyZM5gzZw7Onz+PN954A+XKlcNzzz1n1bkkZQwQEBEREbkIXb4JMmsmpEXBBE5o204UcGGOdCJ7MY1e8vtF9IiRdEDWA2ePovB4lwI0jsni3bhxY0RHR6NEiRImj3Xv3h1jx45F7969sWPHDkyfPh2vvfaacDW9nn6XQMeOHeX7mjZtiu7du6NevXq4e/cuFixYYBIgyMnJwejRo+XgwIEDB9C4cWP58VatWmHAgAFo3bo1YmNjMWHCBLzwwgsoXbq0cBzHjh3DgAEDsHr1aqPxdunSBe3atUNYWBh+/PFH5OTkYPz48Zg9e7bReLt06YJ69erh5s2bWLhwoTBAMH36dNSoUUNOraTXvHlzDBgwAG+99RbatGmD+/fvY+7cuZgxY4bieRMZOXIkjhw5gsuXL+Pw4cNo06aNSZucnBz8+uuvAIBevXqhbNmyVh1jzZo1AICqVavi0KFD8Pf3N3q8Y8eOePvtt/Hw4UOT5/79999ycKB169bYsmWLyeeoW7du+OSTT3D79m2j+8+dO4fvvvsOANCgQQMcOHDA6LkdO3ZEt27d0KtXL2RnZ2P06NE4evSo4us4e/YsPvnkE8VzvGPHDjk4sHTpUrz++utGj7do0QKvvPIKevXqhd27d+Odd95Bz5494eHBqW174FkkIiIicgYVOwisKTL8qBQpdhWiU8fTaZ6jNn9k5WoREZOECiV8Ub64r2MOQk4nCdIOEVERlvUA+Ev9auwi7/l7gE+wQ7pWmmDX8/LywsyZM9G4cWPcvHkTp0+fRrNmzRTbjxs3zig4oBcUFIThw4fj66+/xrlz55CUlITixYvLj2/YsAF37twBAHzyySdGwQG9qlWrYubMmXjllVeQnp6OFStWYOLEicJxFCtWDIsXLzYJZri7u2P06NEICwtDSkoKKleujG+//Vb4/KFDh+Kzzz7D2bNnTcYLADVr1lQ8DwDQsGFDjBw5EnPmzEFoaKjVAYJBgwZh/PjxSElJwYoVK4QBgk2bNsmpf2xJLxQXFwcgLyiSPzhgKCgoyOS+r7/+GkDeuVq3bp0wyKRXuXJlo98XLlwo10dbunSp8LnPPvssRowYgaVLl+LYsWM4fvw4WrRoIew/JCRErjshoh/rgAEDTIIDej4+Ppg3b54cGNqzZ4/F1EakDosUExERETmBcK453yS/NQtqxSmGit6Mdv4VXs6i1YlWODthII+5jGwt+s0/jAELj6Dzd/tw+KpthQTJtWiEOwgYICAiUiMrKwu3bt3ChQsXEBERgYiICKNFJmfOnDH7/JdfflnxMX1gQZIkXL9+3egxfe54jUZjdqL7hRdekCfqzeWb79q1q3BSG4BRqprnn38enp6eFtvlH69IQkICrl69ivPnz8vnTj/xfeHCBeTk5Fjsw5Cfnx9eeuklAHkr/dPT003a6NMLlStXDj179rSqfyAvLRAA7N+/H1evXlX9vAcPHsjFqwcNGoQKFSpYdVz9e1e/fn089dRTiu1GjRpl8hyRQYMGKe5sSU5Oxt69ewEAAwcONDuuunXrykGzI0eOmG1L6jFAQEREROQE4iLFximGHscixa4RHgBymbLJLHE6GPu/e+tPReNibF5u2vRsLaZuPG/3Y5ATCHdQMUBARKQkLS0NX331FRo1agQ/Pz9UrVoV9evXR8OGDdGwYUM0adJEbisqVGuoTp06io8ZTtinpKQYPRYREQEgLxd9cLDybgkvLy95PPrniISEhCg+ZrhaXW27/OPVO3fuHEaMGIHy5csjKCgITzzxBBo0aCCfO/2qdp1Oh4SEBMVjKRk5ciSAvEnu9evXGz0WFxeHf//9FwDw6quv2pQO57XXXgOQN+HfoEEDvPTSS1ixYgWioqLMPu/06dPy32vt27e36phZWVm4cuUKAJgNDgBAkyZN5ACOuff7ySefVHwsPDxc3q0wePBgaDQasz/6z7h+dwUVHAMERERERM4gTGFTgBRDgrm1ormDwNkjyCM6dwwQ/Ed0fhzx1v129JbR71H3Uh1wFCp8DBAQEal148YNNGzYEFOmTMHZs2eh1Zov6p6RkWH28WLFiik+5ub23zRh/uPoc9yrKeRbrlw5o+cUZBy2jhcAli1bhqZNm2LFihWqJpMtnTuRli1bomHDhgD+2y2g9/PPPyM3NxeAbemFAKBz586YN28efH19kZmZidWrV2PEiBGoVasWKlWqhDfffFO4a8QwUKTfhaCWYaDE0vvt6emJUqVKATD/fpcsWVLxsXv37lk1Pj3Rjg2yDWsQEBERETmBeAeBLt/v6vt7VCa0XSVAkKMVTWA6YSAuKlcnmf6PhAPeOxaufTRphAECvtdEjxTvUnl5+R8X3qUc1vWrr76K69evQ6PRYPjw4XjppZdQt25dBAcHw8vLCxqNBjqdTk7f4ujrqaukg7Tk0qVLePPNN5Gbm4syZcpg4sSJ6NSpE6pVq4aAgAB51fvy5cvlnPe2nruRI0fi3Xffxd69e3Hjxg1Uq1YNwH8Bg9atW5vduWHJ22+/jRdeeAG///47duzYgUOHDiEpKQkxMTFYtGgRFi9ejClTpuDzzz+3+RhK7PV+myucbRjcWbRokbCWg4i5oANZhwECIiIiIieQVKygFaUYkiRJ+Ie6KBhQJHcQuEiSIdYgMK8ofrbIlQiubTrzK2KJqIjRuDmsaO/j5NKlSzh48CAAmJ0ANrdy21706Yfu3r1rsa1+tb5SjYHCsHLlSuTm5sLd3R379u1TnKC3x7l75ZVXMGnSJGRlZWHlypWYNm0awsLCcOnSJQC27x4wVKZMGYwfPx7jx4+HTqfD6dOnsWHDBsybNw+JiYn44osv0KJFC/Tt2xeAcXHr2NhYq45lOPFu6f3Ozc3FgwcPANj+fut3IAB5O0YaNGhgUz9kO6YYIiIiInIG0WyzihRDSpPU4h0ENo3MqVxlURprEJgnOj+OeOt4ytW5Hp+GHj8cQK2Pt2DqxgjoXP3LrxD8JCIiY+fP/1d7Z9CgQYrtTpw44fCx6Cdtr1+/jvv37yu2y8nJQXh4uNFznEF/7ho1amR29b49zl1QUBCef/55AMCqVasgSRKWL18OIK+Qsbn3zhZubm5o2rQpZsyYgV27dsn3r1mzRr7dpEkTeVHR/v37rerf29sbtWrVAgAcPXrUbNvw8HC5uLOt73fjxo3lsR46dMimPqhgGCAgIiIichFqJsiUJqmFRYpdfZJQwEXiA8gVpBhigOA/3EHgWubvicLF2GTkaCX8fOQmTt2yvshiYdJAtEOHNQiIiPLT568H8goVK/npp58cPpYuXboAyPt7NX+ufUPr1q1DUlKS0XOcQX/uzJ232NhY/P3333Y5nr5Y8Y0bN7B582asXr0aADBw4EAEBATY5RgiTZs2lVf8G9YdCAoKklP1rFmzBnfu3LGqX/17d/78eRw7dkyx3dKlS02eY63g4GC0atUKAPD777+bDUCRYzBAQEREROQEwg0EKlJsKM3LiiZsRSmKXJ6LbCF4VHZkOEquIAWTI4hScZGpdSejjX6ftf2yk0ZSAAwQEBGZ0K/iBvJS5ogsXLgQGzdudPhY+vXrhwoVKgAAvvjiC5w7d86kze3btzFhwgQAealihg8f7vBxKdGfuytXruDw4cMmj6enp2PIkCE2FSYWeeaZZ1CzZk0AwKhRo5CcnAyg4OmFVq9ebXaMJ06ckIsKV69e3eixyZMnA8h7rS+88IIcuBGJjjb+W2LMmDFyEejRo0fLr8fQ9u3bsWzZMgB5xZpbtGih4hWJffLJJwCA5ORkDBw4EImJiYpts7KyMH/+fGRmZtp8PDLGAAERERGRi1CzQl2pjTBAUARntEXhAWekHhFPgBe98+koos9WUU0xlJyZgz+O3cKeS49OMU3Xjw1arsFCRER5aWL0aVsWLVqEQYMG4Z9//sHJkyexceNGvPDCC3jrrbfQtm1bh4/Fy8sLixcvhkajQXJyMtq2bYsZM2bg8OHDOHr0KGbPno3mzZvLK9VnzZpllAe/sL366qsAAJ1Oh169euHLL7/E/v37cezYMSxcuBCNGzfG3r177XbuNBqNHAzQ12B44okn8PTTTxeo38mTJ6NChQoYNmwYli9fjoMHDyI8PBw7d+7EtGnT0L17dwB5RYD1uxj0+vTpIxdgPnz4MOrVq4evvvoK+/fvx+nTp7Fz5058/fXXaNKkiTxBr9ewYUN88MEHAIAzZ86gadOmWLJkCU6cOIF9+/ZhwoQJ6N27N7RaLby8vLBo0aICvc6ePXvi3XffBZCXEqlu3bqYPn06du3ahdOnT+PQoUNYtWoVRo4cifLly2Ps2LFGO2yoYFikmIiIiMgJRJNhkiDthunzxPcLUwy5/iyhCdEGAp0EuBfyxgJxiqHCHYMrE50fR4QIHH3Kc7U6PDf3IG48SAcATH62DsZ0rOngozqep7trrwPTCN7Zoni9IiJyNI1Gg19++QWdOnVCQkIC1qxZY5RnHsibyF27dq28ut+RevXqhRUrVuCNN95ASkoKpk6diqlTpxq1cXd3x4wZMzBmzBiHj8ecFi1aYPr06fj000+RmJiIjz/+2KTNBx98gAYNGtgt7/2wYcMwdepUaLV5u4LttYMiMTERq1atwqpVq4SPe3t746effkLz5s1NHlu0aBF8fX0xf/583LlzB1OmTBH20ahRI5P7vv76a6SlpWHBggW4evUqRo8ebdKmePHiWLNmDRo3bmzdixKYPXs2goKCMGPGDMTFxWHatGmKbf38/ODu7l7gY1Ie1/7LkYiIiOhxUqAdBKL7it6Em2iKubDS2Rgf89Go6eAows+WQnzg9sN0nItOsun8OXr3yJaIODk4AADf/HvJoccrLJ6FHVGzmijHmuUUa0REj6PGjRvj9OnTePPNN1G1alV4enoiKCgILVu2xKxZs3Ds2DGUL1++0MYzdOhQXLp0Ce+++y7q1q0LPz8/+Pr6ombNmhg1ahTCw8Px0UcfFdp4zJk6dSo2b96Mbt26oWTJkvDy8kKlSpXw/PPPY/v27Zg1a5Zdj1ehQgV07twZQF6gZOjQoQXuc8+ePfjhhx8wYMAANGzYEMHBwfDw8EBgYCCaNGmCCRMm4MKFCxg2bJjw+e7u7pg7dy5OnDiB0aNHIyQkBH5+fvD09ES5cuXQrVs3fP/998Jz4ebmhvnz52P//v14+eWXUaVKFXh7eyMwMBCNGzfGlClTcOXKFXTr1q3ArxPIC4hNnToVkZGRmDRpEpo3b46goCC4u7sjICAA9erVw8svv4xVq1YhNjYWvr6+djkucQcBERERkVOIpj3VpNhQDBAI7hcFDVydRrCFwAnxAdYgsEAUQBGtCt8QHo2Ja88iVyehS92yWPJaM+F7rMTRp/zkjYcOPoJzuPwOAtG1jjsIiIgUValSBQsXLjTbxlxQfdq0aWZXY+t17NhRVXC+WrVqmDNnjsV2Imr7V9NOzXh79uyJnj17Kj4+bNgwxcl1ANi7d6/FcejpdDpcuHABANCtWzdUrFhR9XOVVK9eHe+88w7eeeedAvXTtGlTm9MAtW/fHu3bt7f6eWo/T/lVr14d33zzjdXPI9u59l+ORERERI8q0QJaVTsIFO4XTmgXvQk30dSxM4ot5wiiKyyY+x+1u1PeW31GDibsvHgXp28nWncgB59ya4IVRYmrBwhYg4CIiB5FO3bskIv96nP/ExUFrv6XIxEREdEjydYdBEpBhEe5BoEzUiWJjlkET6fD2Jr2abeVhYALcsqTMnKw7Xwcou6lFqAX1ye6Jrh6iiFhMXJnbBUiIiKyI/2q9/Lly6Nv375OHg2RekwxREREROQElgIEyoEAcX+iCe2iWYPAdOrQGa9DWIOAEQKZrQEUa1fs21qDIDE9Gz1+OIDYpEx4ubth6dDmeDokWDAem7p3KdmC3S4eRXEHgYoi7URERK4kJSUFd+/eRXJyMpYvX449e/YAACZOnAgPD065UtHBTysRERGRMwhzcP93n2iCGlCepH5UdhCIlha7yg6CIhhvcRjx59PyCXIrpAn5ZQevIzYpE0DeBPrnmy9ge0gHk3aigFRRk5ljei0piimGuEWHiIiKmvXr12P48OFG9zVp0gRjx4510oiIbOPqfzkSERERPZJEBV0N6wgoTYorFikWxBuK5g4CU84IdAhrEHACUybcQaDiedZOyEsASrgn47kSe9HAN0r18347esvo98i74jRDj8IOgqwcrcl9rp9iSPT94g4CIiIqmtzc3FC1alWMHTsWO3bsgKenp7OHRGQV7iAgIiIicgbhZNh/U6yiCWpAeZGtcAdBEZxvE03YKu2mcCTWIDAvN9e2D5e1Owj8kIo/a41Dea8H0EpuePvmZAC9LD4vUzBprpZOJ8GtsLY62EGW4L1w9R0EogApAwRERFTUDBs2DMOGDXP2MIgKzLX/ciQiIiJ6ZJmfIMvVWruDQFCDoAjOaItWmOtcJEBQJFM2OYhWFH3Kd3pEOy6sXbHf338Dyns9AAC4a3T4ocosVc8TTZqLiIaTU8Qia6JgiIeL7yAQ4veLiIiIyCkYICAiIiJyBsFkmOGEqtKqeaU5tEemSLGL1CAQTRIXwdPpMFpdrsl9+U+P6H2ztkhxB98DRr97u+Woep7az4xwx4pCcM5VCYMhLv4SNMLrn+27PoiIiIjIdgwQEBERETmBKAc3DCbIchVWMT/qRYpdJsWQYJKYNQj+I9pBkP/s6N+3+j5X0d7/FDyQ63I5/0UBC2d83gpCtIPA9YODTOFFRERElN+tW7fw6aefonnz5ggODoaPjw8qV66M9u3bY+rUqYiIiHDIcVmDgIiIiMgpRBP6/91WWsX8qO8gEHFGoEM0SfyInE67yNUKVnvne5+0OgmvlNqMzysuBAAcTW2AU9IGK4/k2JMuilfkKtT/cFWiHQSunl7MUoCUiIiI6HEzd+5cfPTRR0hLSzO6Pzo6GtHR0Th48CCSk5MxZ84cux+bAQIiIiIiZxAU5Lz1IBXf74hEvfKBqF0uQPg0xRoEwh0EBRuiM4g2Tjgj0CEKEBSFHQQ5Wh3Ss7UI9PGwOp2PNXQqdxDogwMA8JR/BO7mhAF4wmHjspqL7FgpCNEOAmfU7bCO4Pvl6nmRiIiIiBzk888/x//+9z8AQEhICEaNGoUWLVqgePHiePDgAcLDw7Fhwwa4uTkmGRADBEREREROYToZtub4TZzJ8AEAvPF0DeGzlOb9RBOCrjJJeDc5E/8LjcCdpAyMbFcD/ZpUVGwrCoA4JUAgWEXuIqdT0dX7qXh95XHceJCOznXKYP7LTeHj6e6QYwmLFJu0MT1hlbLCALxSsIOnRAHxR4Hg1oC/+HtSEEUvQFAUdxAIxscdBERFhn6CSqvVQpIkhwakiYjsQZIkaP9/B6y7u2P+PrbVrl275ODAa6+9hqVLl8LT09OoTefOnTFhwgRkZ2c7ZAysQUBERETkBKIJMsP/vV60/5rweYo7CEQr711kkvDrrZew/cJdRMQkY/zq04hLylRsKxqyMwIEomO6ek2HBXuu4saDdADArkv3sPvSPYcdS6sznczNf3qU6mhYQ3jKtzQCjryS99/EguVhdRPVIChyKYZENQicMBArCKcSXfz7RUT/8fLyApA34ZaVleXk0RARWZaeni7vBtZfw1yBTqfDmDFjAACNGjXCsmXLTIIDhhw1dgYIiIiIiJzCdAZPo7E8QaaU5kZYpNhFVkJvCI8x+v2nfVcV24qCGs4IdOSIUgwV+iiss/5UtNHvn/9zwWHH0opqEORvIziH1i4yFX4ntHlBEOSmAuGTrOswf/+C+3IU6n+4KtEOAlf57isTjc/5UY3jNx7i662XsPvSXWcPhcil+fn5ybeTk5OdOBIiIsskScLDhw/l3wMDA504GmPbt2/HlStXAACTJ0+Gh4dzkv0wxRARERGRE2gkyWR2Uph2Ix+leT9hkWIXXZGbnJmj+JirpBgSHbMo1CAw5ObmuJQP4hRDxudHXGjbyjFZOuWxW63rT0X3zizuLUkS/jh2G5fjktG3SUU0rVLS4nOEOwhc/LMqKlIs2WHHSUGci07CCz8dAQD8tA9Y+lpzdKlX1qljInJV/v7+uHs3L5D24MEDuLu7o0SJEi6XtoOIHm+SJCE9PR0PHz5EamoqAECj0cDf39/JI/vP2rVrAeSNq3fv3vL9Dx8+xIMHD1CqVCkEBQU5fBwMEBARERE5gaggp7oAgfoixc6c6LSVaMiF/TokSSqSKYbyc3dkgECwg8A0xZD5NFquQLTSPseJ+XlWHr6B6Zvydn78evQW9nzQEVVKFTP7nKK4g0B0rXN2keLJ688a/f79jkgGCIgUeHl5ITg4GPfv3wcA3Lt3D/fu3YO7uzvrERCRy9DXSdHTaDSoWLGiwwr92iIsLAwAUK1aNQQEBOD333/HV199hYiI/9Jo6osWjxs3Dt7e3g4ZBwMERERERE6gkXS27SBQmLt05SLF+WnMTBO7wutQKlLr5AXOVhPl17cX8XtifJ9ol4G1Q5JsCClYE1ASvdfOLFKsDw4Aea/jux2X8cNLTcw+pyjuIBCSrPuC3UnMwMcbziE6IQMj21fHoBZVCnT4C7HJZn8nImOlSpVCdnY2kpKS5PvUpJ8jInIGfXAgICDA5j7u3r2L6Ohos20qVaqkuj+dTodLly4BAEqXLo13330XP/74o0m7yMhITJw4ERs2bMDmzZtRokQJq8atBgMERERERE5hOoHnpqIGgXKR4qKTYsgc0esr7AlbpQnmonY2HbiBQFiAOP/5Eb1v1p9D65+Rnp1rcp+Xu3ilmPB740KRoDO3Ey22Ee0gcPXdQ8JgqJXXq5nbLmPP5bzVy5PXn0PbJ0qjUknzuy2cJSk9B9laHYIDHLPqj8gZNBoNKlSogKCgICQmJiI9PZ0BAiJyKe7u7vDy8kJgYCD8/f0LvHOgT58+FttYk5I0KSkJuv//u/PcuXM4fvw4ypcvj5kzZ6Jnz57w8fHB8ePHMXnyZISFheHw4cMYMWIE/vrrL5tfgxIGCIiIiIicwrYUQ0p/c4qCAa46R2huFbkwxVAhBzr0E9svBf2LTyssQabOC+/ffh866clCHUdBOTLFkE5nOgmf/+MrqkFQGHUc0rNNJ6i8PdUHCFypSLGa77BoB4Hrp8MSfTasC8zkL36+aN81zOjXoECjMmSvr8/Wc7F4b81pZOboMLJddXzSu559OiZyET4+PihXrpyzh0FEVOSkpaXJtzMzM1GsWDHs2bMHtWvXlu9/+umnsXv3brRu3RpnzpzBhg0bcPToUTz11FN2HYvrJF0iIiIieoyIinQWpAaBK6TmsQdXeB25Wh18NJmYWmEJfN2yUNIjBZ9WWOz0IqrWcmSKIdEq+/zvkrjQs4MGZEAUIPDxFBfOFKYYcqkAgeWxFMUdBG6C619B9+ikCXaOqCW6xpQJ8CnIcGQfbTgnv0dLD15HdEK6XfolIiKiwrVp0ybcvn3b7I81fHyM/9YYOXKkUXBAz9fXF1988YX8++rVq217AWZwBwERERGRMwgm/goUIHCBlff2IHp9hT3ZmauT0D7gNIq5Zcn3VfOOxVFdlplnuR6H7iBQUcjXPimGrCdKMeSuECwRBTpE6ZOcRc1XOCtHUIPAdV6CelbuIMhP6T1WIz7N9LtdJtA+6YAS03OMft918R6Gtqlml76JiIio8JQtW9aqGgOW5K+H0K1bN8W2nTt3hoeHB3Jzc3H8+HG7jUGPOwiIiIiInEC4g0BVDQLx/aJggKuuIjY3jecKAQLlGgQOTOrvAA7dQSCYzFWzg6Aw3krRDgJJITTh6jsI1MjKNX0vXD3FkLgGQQEDBAUIiMUlZZrcV6KYV0GGo8iRtUGIiIio6PD29kZwcLD8e+XKlRXb+vj4oHTp0gCA+/fv230sDBAQERERuQgPaFHZKw5+bsopKJRyuBd2ap79kffx8tIwvL/6NB6k2m9lvSvshMhRWH6tc6GV5Wq4ObQGgaAQZb73SbgS30k1CJRW1IuCGK60g0BdiiHR6y2CAQJh2iH1CvJ5FwUI7EF0vdY4MHBHRERERUv9+vXl25YKvesf9/Cwf0IgBgiIiIiInEC0g+DnGp/iQJ2R2F37TdTzuSZ8nuIOAsEDBZ1Yv3I3BUeuPkBuvtnVpPQcvL7qOA5FPcBf4TGYtulCgY5jyJV3EBR0hXNhc3fgPKSwvoCKNta+lba88xnZuXCDFv8rvwSn6g3Br9U/Rim3eGFb4Q6CQvq8SZJkMYinJkBQFHcQiIsUF2zMHgUJECSbBgjsEWAVFbx25M4eIiIiKlqefvpp+fa1a+L//wOA5ORkxMfn/T1bsWJFu4+DAQIiIiIiZzAzGVbW8yE+KPeL8DHlGgSiHQS2DQ0A1hy/je5z9mPwkjC8uuyY0eTdL2E3jCa+Np25Y1Xf5ubHXCFAoDRBLBW1AIFDdxCICsLm20EgWLZv9Ttpw1ufnq1FK/9zeD14I4I8ktEu4AyGFF8nbCuaBC5IiiGdTsLp24m4Hp9mtl1oeAzqTd2GhtO2YePpGOX+VAylMHcQ6HQSohPSkZple0FgQCnFUMHGXJCJ91jBDgJ7nENRH+78P3AiIiL6fwMGDJBvb9iwQbHdhg0b5P8fa9++vd3HwT9PiIiIiJxAtIPAUOdAcfEppQCBvXcQTFp/Vp6cPHLtAY5efyg/Fp+abXO/AKAxk8tfFNQo9ACB0gSxZH7br6txaA0C0XuS7y7RNunCWNielq3FFxXnG903tOR6YVtRMEgpxZQlkiRh9C8n0W/+IXT5fh/WHL8tbJej1WHapvPIyNEiLVuL6ZsuKB5TVZFiwQ4CR+yCyNHqMGzlcbT7Zg86ztyD07cTbepHkiSFFEPqv1+iwI69axCIrp/ZuTphEWwlOYILGlMMERERkd6TTz6JHj16AAD++OMP7Nq1y6RNXFwcPvnkEwCAl5cXhg8fbvdxOC1AMHnyZGg0Gvln7969Fp+zdetW9O/fH5UqVYK3tzcqVaqE/v37Y+vWraqPm5ubi59++gnt27dHcHAwfH19UbNmTbzxxhs4f/58AV4RERERkTVsm8BTmjAUzWnbM83I/sj/imEVNBWIOcKdEIWcLiVXpxNOYDrydReUeKWy4yYiJUENAtMUQ6IAgePPYUZ2Lnzd1NXFEAbWbJxcvxibgp0X78p9TFp/Vtju9sN0JKbnyL8/TMvGlbupwrZqzpdoB4Ej6o8cvBIvXwfiU7Px3fbLNvUjSQoF2a0YsmjivSAphmKTMkzuy38OD0XFo+WXO1Fv6jZ88+8lVf2Kgo1MMURERESG5syZgxIlSkCn06F379746KOPcODAAZw4cQILFixAixYtEB0dDQCYMWOGQ1IM2b+qgQqnT5/G999/r7q9TqfD6NGjsWzZMqP7Y2JiEBMTg9DQUIwcORKLFi2Cm5tyzCM+Ph49e/bE8ePGK/KuXbuGxYsXY9WqVZg3bx5Gjhxp3QsiIiIispJ4Ba1liimGRDnhpf9frWuHCSkvj//+xhKtrJ257RI2nYlFw4rF8WX/hihezFNxXOaIi8a6Rg0CnQunGBKtQHdkgECrIn+VVmu60row3sn0bK2ZPSrGhDsIbPy87b9y3+Q+0ffPU5BjJiNHvCrd1hoEjijsPW9PlNHvB66I6zpYIgEK74/6HQTCifcCfN6TM0zPf/5zOHPbZTmws3DvVQxuUQVVShWzME7T98aBX0siIiIqgkJCQrBp0yYMHDgQd+/exddff42vv/7aqI1Go8HHH3+MSZMmOWQMhb6DQD/Zn5ubizJlyqh6zscffywHB5o0aYI//vgDx44dwx9//IEmTZoAAJYuXSpvtxDRarXo37+/HBx4/vnnsXXrVhw9ehQ//vgjypQpg6ysLLzxxhtW7UggIiIiso2tAQLx/UqT2rashhZN6htOaoq6nL/nKm49TMfmc7FYcfj6f8e3cqJS1LcjVkObIyos+v8DKdRxWEMUIHDkSmWd4Fzkf6tFQYTC2ISRFyBQdyDRGEWTumr4eZuuvcoQre4XnIT0bPHkuJpXUVg7CAqyQt+QTiHFkDW7S4QBsQJ83oUFtfPdlz+l0uoTtyz2Kwo2cQcBERER5deuXTucP38en376KRo1aoTAwED4+PigevXqGD58OE6ePIkZM2Y47PiFHiD48ccfcfz4cdSpUwevv/66xfaRkZGYNWsWAKB58+Y4dOgQXnrpJbRo0QIvvfQSDh48iObNmwMAZs6ciaioKGE/q1atwsGDBwEAb731FtavX49nn30WLVu2xLhx43Do0CEEBgZCp9PhnXfeQW5uwQpvEREREZmjsXE1umINAivvNycz13TC0dtgB4Glibw5O6/8d3zBBJm5+TFR34Vdg0D5eK5bg0CcysRxxxPvIDAeg1YwiWttuihbdtqkZ+daESCw3+fNz8vd5D7DVEJ6ogCUUoBAzUS/sEixJOH2w3RcjkuxW1onw11EBaGTJLgJ3h+NVQEC++4gyBV8ni1dO7Ny1Oyise76R0RERI+vUqVKYdq0aTh9+jSSkpKQkZGBa9euYfny5fICeUcp1ADBrVu38L///Q8A8NNPP8HLy8vic+bMmSNP1s+dOxe+vr5GjxcrVgxz584FkFdfYPbs2cJ+9EGGoKAgzJw50+TxJ554Ah999BEAICoqymzlaCIiIqKCsjXFkNJkn9JEoi2L3jMEk5VGOwis6NPauUnRpFxhpxgSTRYCrl2DoLBTDKmpQaBT0cbicaxsDyhPtouIggGKO0gs8PE0DRAkZZgGCESfr7Qs8eIkW4sUR8Qko+Osveg+Zz8mK9RCsJa9dhBIEgBBDQLJQuF2Q/beQSC6xljaSCI67/mJaiU48ntJREREZItCDRC8/fbbSE1NxdChQ9GhQweL7SVJwsaNGwEAderUQatWrYTtWrVqhdq1awMANm7caPI/b5GRkbh48SIA4MUXX0SxYuJckcOGDZNvM0BAREREjmXjDgKFp9lzB4FogtUwQCBZMW1rdYohUaqPwi5SrJUgCbKkS5Lr7iAo7FQmWhU7YHLtUKRY9D5Ykp5lRS57Uc0LG1MMiYINoh0Eot0eyYJAAmA5QCJJkuJEtX48a05E4+aDNAs9WeYhqJ1gSyojSVIIkFqxq0p0DgXDK1B/ll5btooAgahfIiIiIldTaAGCNWvW4J9//kFQUJC8mt+S69ev486dOwBgMaCgfzwmJgY3btwwekyfWshSP+XKlUNISAgA4NChQ6rGSERERGQLW6durSlSDNiWLkWUssTT/b8RW9Ol9SmGRH2oP549aHXiHOnWTGAWthzBZKUjAwSSivoCOq0oQOCoEf0nI0cLjWiFusr0VbbuWBHtDBDtIBCtfk/OVNpBYCHNjYpJagA4eu2hqnbmGF4D9NIF1wpLlGoQWLOrKlu4Y8YNsUkZGLw4DM0/34mZ2y6pDkgJU00ZPFd0fRWNIT/Re13IG6KIiIiILCqUAEFiYiLeffddAMA333yD0qVLq3rehQsX5Nt16tQx29bwcf1ugYL0c/v2baSlFXylDREREZGYrTUIxPcrrdS3ZYWvqLCqYffWrOi3dsW46HWI8907jtIEcWGlGIpOSEfkXetyx4smpx2ZykTNeyIsUmzlcWxJxZWj1Qlz3IveVtEKb6UUU5aPa9pXUka2oH/1OwgsfX3V5MEH7PNZ8BQs0U9XSI1kjgRxgFSyIgAnmngHgAV7ruLItQeIT83C/D1XTQoLKxG9J4bXTtHjqnYQqCh+TERERORsHoVxkEmTJiEuLg5t27ZVVZhYLzo6Wr5dqVIls20rV64s3759+3aB+5EkCdHR0XLqImvHK3L37l3VfREREdGjzd41CJQWs9qSYkhUg8CwH2u6tHYHgyj4UPg7CJRqEDh+IGuO38ZHG85Bq5PwQrNKmPlCI1XPy84V5XR33ESk+FwYH09Yg6AQgiw5Wp3w+6WTJLjnm5oWfd5sTQsjep76HQRKAQLzYxEF80Q8BKv/reXhZhogSLOi3oOePXYQCFMCSRJ+CbtpdN8Xmy9i3Zg2FvsTfecNr3mioJGa3RvCfhkgICIiIhfj8ADBgQMHsHTpUnh4eOCnn36CxoqtzikpKfJtf39/s239/Pzk26mpqQ7pxxLDIAURERGRORobJ5uV5pYUixTbEiAQTDoaroS1pk9xgEL570HRy7AlyFEQikVqC6EGwSSDgrJrT0ZjTMeaqBFs/u9XQDyB6ciJSGEQJd/7pBUECApjbjRHK56A1uok5K8jLKxB4OAUQ6LJ7RTFFEPmj6k6QCCY3LeWKLijVFzZbD86wE2QAsqayKMovY9ofGrPj7hI8X/3ia4J6lIMOb+mChEREZElDk0xlJ2djdGjR0OSJLz33nto0KCBVc/PzMyUb3t5eZlt6+3tLd/OyMhwSD9EREREdiOaIFNBaXJJOcWQ9ccQ1SDQGkyGWTN/Kjq+ufUiwiLFhbziVmlivbBSDBk6FBWvql1h5zqXBJP/bhrjMehU1CmweBwbXoNS+hlxfQv7FSkWTQaLihSLjqm0g8DSLhDRd1XEHjsIsgTnxaYAARRqfFiRdk30HhVkp5HoPTFKMSToPDvX8rlX2ulARERE5EocuoPgyy+/xKVLl1ClShV8+umnVj/fx8dHvp2dbZq/01BWVpZ829fX12w/hr9b048l+VMb5Xf69Gn06dPHqj6JiIjo0aSxcZLI6iLFdtpBYDjPZc1EubWTYcKUL4UcIFA8noN3EIgmIdXuvhWuVC7sHQT5Jn21oiLF1qY9smFeWynFkOi7IFr1n2PjeROdE9UphjLEE+2WhqJ2hbyowLC1RDn3021KMQQIq1FYca2w98p80US+cYoh22oQ5Ah39lg5OCIiIiIHc1iA4NKlS/jqq68AAHPnzjVK3aNWQECAfNtSuh/DgsL50wjl78dcgMBcP5ZYqm8QGxtrVX9ERET06LK9BoH4frsWKc4WTWr9d59jaxCI7ivkAIHCBDMcXINAlNPcTXWAwHwOdXuTBBOf+c+ZuAaBtQeysj3+P8WQ4LQJ61uIJoZtrEEgLlIsSDGksINAuHvGwgnLVDlBr/ZzZI5oQjzVhh0EeTUITGms2EEgmngXBS7Vvmxxii6D44l2EKiY6ecOAiIiIioKHBYgmD17NrKzs1GjRg2kp6fjzz//NGkTEREh3969ezfi4uIAAH369IGfn5/RhLulAsCGq/fz1wLI30/p0qUt9qPRaCxO+BMRERHZztYaBEpFiiV4a7IwOvgvlHRPwfL45xCdUw5anYT07Fx8siECB6Pi0a5WaXzerwGKeSn/GSjcQWAwXGsmuKxq+/8TpB7IRU2faNzNCUKiNrDQi3oq7SBwdIohcYBA3XPFE5EFHZEynSBYkn9XjFbQxvpPvfUvIm8iW1C0WXBwURBFNPmsRoGKFGfkCMciScDyg9fxSquq8PIwzQ6rdgeBPT4K4h0ENqQYksQBUmu+XzmCsSidP0t0Okn4XTEcj+i9VbODQLQriAECIiIicjUOCxDoU/Vcu3YNgwcPtth+xowZ8u3r16/Dz88P9erVk++7dOmS2ecbPl63bl2jx/L307hxY4v9VK5c2aZdD0RERERq2LqDQDH7jQR8WXE+BgTtBgD0KbEfrS6uglaS8NepGPwVHgMA+OtUDFpWC8JLLasoHkNYg8Bg0tSqIsWCASvNeeskCb6aTPxRcwoaF4tEQm4Ahl+fBq2umurj2YNyDQJH7yAQ5fW3fQeBI1MMCXcQ5KurIapTYM3cqK0BGWtSDIlrEBR+keLkzFzFz91n/1zAhdhkzHqhkcljmTnqPpP2+CyIVsynZVmfYkiSJJN6FXms2EFgx4CY0k4b4xRDohoEKgIEFoofExEREbkChxYpLqjq1aujQoUKAIB9+/aZbbt//34AQMWKFVGtWjWjx9q1ayffNtdPXFwcIiMjAQBt27a1ZchEREREqtgeIFDeQaAPDgBAsGciehQ/BJ1OwiehEUZtP/zrnNljZAjSlhhOdFlVpNjKtv1K7kXjYnl/j5X0SMHHFZa5zA4CR9cgEE32qk2RIpowdeR5E+0O0ECyWNjVmgiBrePPCxCYUlvfwvbjqitSLE5nIykWKgaAdSfFu6nV7iCwx2fBbjsIIA4SWlOXRXQObV2Zr3RuDO8Xvbdqzr14nFYMjoiIiKgQOCxAsHLlSkiSZPbHsHDxnj175Pv1E/wajQZ9+/YFkLeyPywsTHissLAweeV/3759TYq5hYSEyLsK1qxZg/T0dMUx6/Xv39+m101ERESkhu01CNQXI67odc9uRYoNJ36tLVJc3+cq9tUeiSsN+2JiuVVm244OXm90Xwu/Cw7NpS8iLsAL2CdRizLRDgK17DlhqoYkCJa4QWd0TElnOnmsy3cOj994iDd+OYFpf59HSr4Jclvf9xytJPx+iVbRi+oNiHZjqCEKiIhqC4gmmwFxMMEStQECe0xKi2sQ2FKkWPz+WLODQDQWW3dJKAUEjYNdpm3SVbz2wi4eTkRERGQLl95BAADjx4+Hu7s7AGDcuHHIyMgwejwjIwPjxo0DAHh4eGD8+PHCfiZMmAAAePjwISZNmmTy+NWrV+Wiyk888QQDBERERORQ1hTkNKQ0tySadHKDBFvSqYsmHW3dQaDVSRhf7jdU9Y6Dp0aLt8usRWndNWFbnSRBVL7U1qKxtsrViVOgiFLm2FOWYAeB4m6GfPQTpmU8HqBZsQvw0WQ6NEAg+rxpIBlN6otWZkvSf+9vUkYOXl5yFNvO38XKwzfw2aYL+Y5h29iytTqTdEeA+HMrOr9qz7ma50kSkJJpHCgR7qwAkJCWbfUxs1QHCByTYsiWHQQ6hRoEomti5N0UzNt9BXsu3TO6X3SubU4xpHB9Mfwsi+pSpGXnWgyWigILhR3wJCIiIrLE5QMEISEhmDhxIgDgxIkTaNu2LVavXo0TJ05g9erVaNu2LU6cOAEAmDhxImrVqiXsZ+jQoXLaoPnz52PgwIHYtm0bjh07hnnz5qFNmzZITk6Gm5sbfvzxR3h4OKw8AxERERFsXY2umGJIcL8Gkk0Tg6IUQ4b9W1uDoGvgMaP7ns5ZJmyrkyAOEBT2DgKtBDfR++OEIsVq8pwDeROmzYpdwK7ab2L9E5Pwd6334a1LsfcQZaJ6DBqNZHSKdMIaBP81+OXIDaNJ57X50ujY8r5LkqSYYkhYg0Bl2iE1lHYe5K9DoNR/goUdBKLJaNF3VcRRKYZsrUGg5v5bD9LRZ+5BzNoeieErj2Pj6Rj5MdG5FgekLI9HtPsGMA5QifrWSeLvrKW+WaSYiIiIXE2RmAX/4osvcO/ePSxfvhzh4eF46aWXTNq8/vrr+PzzzxX7cHd3R2hoKHr27Injx49j/fr1WL/eeAu7t7c35s2bhx49etj9NRAREREZsneRYjV5tNUSBggMVsJa06doLkxSeO1K/RZ2So5cnQQ3wWpmRxcpFhWHVpvuJkerw/SKixDgnrfbNsTnFrro/gbQ3Z5DlOkEE59ukIzeQ53gfBneE5OYafYYtnx2tbq8IIXqFEPCIsW2phgSjzd/bQGlAEFiuvkdBDoJcM8X+VCfYqjg3yHRZLhNNQgkCHfo5N9BMHtnpNExx68+jb6NKwIQp+6xtai10vthtINA4TORlpULH0935b6ZYoiIiIiKAJffQQAAbm5uWLZsGTZv3oy+ffuiQoUK8PLyQoUKFdC3b19s2bIFS5cuhZub+ZdTunRpHD58GAsWLEC7du1QqlQp+Pj4oEaNGhg1ahROnjyJkSNHFtKrIiIioseZvWsQCFO+aCS71SAwnESzJke78PgKQ5IkCZBM137buqLbVlqdJExRY2taKLWEOwhUBwgkNPC9anRfL+91dhmXiCQIEOTfsSLeQWB42/z7astEqn7iWBggyHc8SZIUAgQ27iBQWIme/xhKAQhzRYoBIDYpAz8fuYHDUfHyfaLC1mrGYItsQY2M1CxbUgyJ9gmZ2nPZOK2Q4dsnugbZ+hIVAwQWahAAQLqFHRzigtRWDI6IiIioEDh1B8G0adMwbdo01e179uyJnj17FuiYHh4eGDNmDMaMGVOgfoiIiIgKwuYaBCpWu6ppb46wSLHRalrrUgzlp/TsvBRDgj4KOSVH3g4C0QplRwcIBDsIctW9dnHQxoE1CBRSDBnOhwo/ewbPszRpbcv7bi6gkv9wSsdXSjljidIkcv7XofT9sfS96vL9PjkgMOuFRhjYrJLqHQT2CBCIdxDYkmJIoQZBvs+Uubc/RzAWW68T5mqc6HQS3Nw0ip+JNAs7KIRFipliiIiIiFxMkdhBQERERER5lIsUm96ngXiFtCWiVDe5Rqtp1U+gilaJK82PaXXiIsWFnZJDq9MppBhybJFi0Wpwtbs1RO+JunXathEGCPLvIJBMJ08lo8ctHMOmHQT6cVlOJ6S0ctz2IsVKuezzH1fczlK9CcPPx4frz/7/fYWTYkiSJGHwJc3mHQSi8Zju8FCSI6w3YN/3Dfgv6KAUvLFUg0GYYogBAiIiInIxRaIGAREREdGjRjQBrYZykWLxhK1NKYYEq4INJzkdtYNAksQBgsJOMZSrk+AmSDHk+CLFttcgyLYxLY6tRCmG3PIHCLQ6k//bMNw5I/os336Yjn2R99GgYnGUDfS2elz68yV6//JPIJ+6laDQh40phpR2EJikGFLaQaD+mqD/TqgtUlzQr1CuThJ+/G3ZQaBT2kGQ75pobsjCFEOC06fmZZsLomp1EjzdzaUYMh8gEacYYoCAiIiIXAsDBEREREROYWsNAoX7Bfne3SAJJ80scXQNAqUixcq7Iwp3Qk2nkxRSQDk4xZBgB4HaGgSFvYNAlG7JDTqj91vUxnCSXjRR+uyc/UjL1sJNA3zWtwGaWTkufUomcQ2C/26vOxmNCWvPCPvQ2pxiSKEGgeoUQ9YfN1MQVBKOQXCuI2KS8O22y5AkCVN61kXd8oGKz1fa3WDLDgJAXOPD5Jpo5msvOte2phgyF4DUB7EUUwxZ2kEg6JvxASIiInI1TDFERERE5AS2FikWrbrWKeTMt7VIsShtiVHBTitmuITzalLeMWZtu4w3fjmB3Zfu5h1DEocOnFKDwAk7CESTvZbSzugJ3xMHDldcg8D4FImKFBsGWUTva9r/r0jXScAnoRFWj0sfUBGFRgw/w0rBAcD2IsVqit3mtbMtxZCI+h0ExmPQ6SSM+yMc+yPv48CVeLz560mzKXoUAwQWVtCLx6Jw/ctfg8BMH2pz+6sJkVnaQaB0PMBygEQUyGCKISIiInI1DBAQEREROYHtAQLT+7SSeEJbA9smo0STjlobdxCIji9JwLzdUZi3Jwrbzt/F66tO4EZ8GnQ6CZJkOqVX2Ck5tDpxjvQrcUmYtO4M5u2+ojr3uzVEOwhUpxgSTOA68qyJJpM10Bm9V6KdH4aFaG3NGW9OjhwgsD33e46KHQT3U7Jw4Mp9xKdmmRw7P7W1D2xJE6X2c5h/DBfjknE9Pk3+/eaDdNxNzsr/NIOxiV9bZo7O6u9nXg0CU/mDnGZrEIh2zNj4cTL3HdN/FJR2h1hKMSQMZHALAREREbkYphgiIiIicgJxChvLRJOcWsWUOBK0Vk46SpIkTDFka4BAvPpfwrw9UQbHBH7YdQXvdQkRpsUp7ABBrk6Cl2CC+U5iOo7ERAPIW+k++dk6dj1ulmCSX20+fNGqdEedNUmShFtD8gekREWKDXcQ2JjJxyxzq/+VAgRP+Z3D6OC/cDenFL6OGwat1sfsMaLupeLFRUfwMC0bpf29sPbNNqhe2k/x2PmPqzTZbFOKIUFQSST/dyg6IcOq45jb3ZCRo4W/t/r/rZQUdhDk3z9kbQ0CW2uVmN1BoC9SrNAmzcIODmENAu4gICIiIhfDAAERERGRE9iaHV60qlYnSXDXiCfwRJP9+n40GtNRZGt14l0KhimGrAg6iFbLihIJHb/xUHHizBk7CMQpm/47xwv3XnVAgECQYkjlpLE+935h0OrEOeTdoIPhfLXFGgQOmCjNNlOkWPQxCnRLxc/V/wdvt7xghrcmG18nfWT2GLN3ROJhWjYAID41G3N3X8H3LzZWnETO/xbao0ixntL3O7/8QYq7yZkmbcy9H6Lglfw8W3YQCHc86XApLhnLDlxHcIC32QLIosCZuA6HZeYCC/rXphTUsZxiiDsIiIiIyPUxxRARERGRE9i+g8D0PsUJbUiKOcoVU4Zki++3uUixKEAgeA0pmbmKK7wLO2d3rk4yCgbo5T/H9p7oE60Gz1GZl15NWhx7yVVIwaTRSMY7CASfE8PnOTLFkIj+s2j4mXyl1BY5OAAAA4J2K04G620+F2v0+1+nYgCYKVKc73OiFEhwbIDA+PfYJNMAgbnPs7kdBNZ+DyTJ9LuU148OAxcewdqT0Viw96rZPkTnSu1um/zMBTj0n2elNimZFgIELFJMRERERQADBEREREROYN8ixXmrt/Nzg6RYRFRpRXB6jri91mACWs1EnH7SUG3t3JTMHEiSJEwxZGvqEFtpdTrhBGb+c3w/VTlnuy1EOwjUThqL3hNHnTWlGg1uMC6KLUmmr8ewBoEjdoaYO1/6gIThpHqwZ4JJO1s/b0o7A0xqECiMMduGXSCZBgHA7oGH8VbwGlTxijVpl38Moh0E5l63uZ0s1u4EUUox5AYJqRZW5OuJUwzZFiRTs4NA6ZqXmJ5tvm/BOJliiIiIiFwNAwRERERETmDvIsXClB0aSTFNh6ggLiAuUJx3jP9uq5mI00+CCWsmiNJuSKapWOTHCrsGgVYSBlzyhy6iE9LtelxR0EZtiiFhehVBwWd7yNUpFcWWjHYFiNNLGQQIHPC2qtlBYFhYVnSOrEmhZXRshe+F4YTw5bgUbI2IEz/flhoE/x9UeiVoCxZV+xKTyv+MzbXeQZB7klG7/O+FMMVQIe0gyLsmiD8/aoneI1vOH2Ac/DR9zHyKocSMHLN9i3aLMMUQERERuRoGCIiIiIicwNYAgSgtS16KIXE6l3TFHQTiQIBSQEE/iSZJkrodBGZScyhNeqdl54qLFBfyilut0gR4vrRD1hZ6tSRLkC5GbW0B0eSoTpJwLjpJ0LpgsnK1iimGDIch3EEA8wGEgjK3Cl9/uPSs/8Yl3rFi40p0pSLF/3/gkzcfos/cg4rPt3aCO0erk7+Ln1daIN8f4J6B0cHrjdrm/w7FCVIMmQsQKF0vRH1bIkFcg0UU5FQiuobYmmJITWFrpbRQienmAwSiwEJhp0wjIiIisoQBAiIiIiInsL0GgWj1vVINAuBesjgNjlKKIaWc5vJKWpWTuvo5VtF4lVYjRydkiCdsHbHU3Iy8HPuWaxDYPUBQgB0EwhRDEtBn3kEs2BtV4LEZyszWKaa0MqpBIChSDIP77DVRahhoUJNiyDDtls6OKa0s1SD4fPNFs++nuVX6Iqlm8t+39Dtv9LvhOZIkSViDwNYdBNZ+P5WLFBdsB4F4F43lPgtSpDjJwg4C0Tht3OhARERE5DAMEBARERE5ga3JX0RzWTpJOeXL2pPRwn6UUgylKeQA/2+iTN0knrkUQ0qTpLcfpgsLGBf2ilulHQT577P/DgLRqmi1AQLldt/+e9nmMYkoBZE0kIwmmSWdaAeB/QMEhivYzaYYkvQphszvIJAk2+ojKK0y1x83/Fai+edbOXNsqUCuaAxA3qS2aKeQVichR6vDb0dvYumBa0b1AMymGLK6BoE4oCm6T4k9ihSnZuXiclyK2UCL/rUpBREsBQiEKYa4g4CIiIhcjIezB0BERET0OFK3WtY0GYcwp79OghuUU4CIKKUMUapBoJ8gU7uiXT/BKmquNEF2+6E4p78jitmak3c+RROY+VMM2bcGQWYBihSLAjeGk986nQQ3N/vUJMjI0cJNI9pBoDMK8EiiVD1GNQrsMhxodRI83fNumztfcoohg8+4TqFOQ45WB3c3d6vGYWkHgSXZVk5wJ2ean5w2HsN/t2MSxYEtrSRh0rqz2BAeAwD4+8wd/D223f+PzXKefpHE9GxM2XAOZ6OT0KtheUx6to5ikWJrUgzlaHXw0uRgTPBaVPK6h58f9EKOtqnq5996kI7BS8IUz4We/mUrfa4S07PNfrdE9Q0YICAiIiJXwwABERERkRPkz2cvbAPJZIWzcIW9znR1+/+3VuxbKcVQmkKAQGch1YZSe50Vq6JvJ6SLaxA4IUAgnMDMd19MoewgUDu5bP48Z2t18LFywju/kzcTcDc5E8W83BVyyBuvVBend/lvnLbm+s/PcHW3uUl2/Wcx3WiXjNLErvWfOaVV5qoDBGby/ItYEyAwfC/uJJqmFwLyvtv64AAAnI1OwrnoJDSsVFzxegGYTnjnaHW4l5KFUn5eWHLgGracyyvKvGj/NbSqUQp+3h7iHTpW7SCQMKncSowM3ggAeK7EXgy6tx6Aus/4kgPXLAYHAMs7p3QSkJqdi0AfT8VxKvVJRERE5CoYICAiIiJyAjVrud0gmewLEBV21Sql7DCzIldpwk+pqLF+8lNtfvb/UgypnwSOTsiAVNL5RYpzdTphgCD/qvmYxAxIkgSNxj4r80W7OsyldknLykVyZg7KBfpYnGzPytXBx9P2AMEvYTfxv9AI+ffexUVFsXX5ahCYL1Jsa1HZ/AwnXHPMTmTnPWYYBBPVINBAZ1PdC+VJZHV9WXs+rEoxZHCOUhQCC6LUUTGJ6WhYqbjZz6FhbCopIwevLT+GM7cTUTnIF7cfGk/CL9p/FeO7hAj7saYuS45Wh5HlNsq/e7vlol+xdTj9f+yddZQcVdrGn2od98zE3UhICAkhgaCB4O62yMKy7PItttgu7OLuLK5ZIDgJEJIgIe7ubhOfjGR8pq3q+6OnekreW3Wru2cie3/ncMhUV1dVV1dV3/vK8+BKrvd/Oq+Ya71miSH2sVXVh5gJAmFSLBAIBAKB4FBAJAgEAoFAIBAIDgA8wTCXJCOi6IO6VHw+wjDVtZIxYgX8KG1ydR9W7zMS6yBQzEFMVji9LhAhj5in0HxLaS02l9bh2K55yE6jg3W8RD0IqPOpJxCWUVEXRH6GP6H9abdnhNUZsGT7fvzpv4tQXhfE6UcUkd0H2m4Mpwa4RrTJAYAhEQNDAotIDkmaZYkek4ouQWAlMdTkidCgSYJRHSseKYJQHN0NrCAyb8W4Uw+Cahv9e90xaILSrCQfJS+mJr+sEwTN2/tu8U4s31EJAKbkAABsLauDwti/I5NiYhvt3LuINRND/WxWyZvK+hA65dGvUceZLGktgUAgEAgEgmQhEgQCgUAgEAgEBwCeYJgEwCuFMCh1PfaECrAz1JasPrUyKWbB8iCotzMpdthBQBnVsohKrJgDtmGbbczYUIqb/7sQoYiCDjmpmHDHCchJ83Hv17w/Pg8CgN2JYceczWX4aNZWtMtOxb1n9kF2qheNRAU3K2j8ym8bUF4XBABMXlsSXdiWvT9e7wheKL14lyTrElhUBwHAF8x3gjYwb50gIDoIFJdpPTcijmVgFEVhBpFbLEFg0UFgTHxoEzesTgeqg+DJCWtQFwhzexA8/tMa5noAkJnihUJeF9YdTypqxw6VsGD5SThD0Ui7SbHnmJW0WmVDkPkaKTEkOggEAoFAIBAcZIgEgUAgEAgEAsEBgCdB4JeCGNP9IQxM24SA7MVfiv8BReliWi9qqmtf8a6FqjgHLDoIOAJlWtSYoRMPAlYQkpJV0vKPsStjgbhdlQ0Yv3w3/nBcV+79GmF1EFABzHiC3PvrgrjhowWxYw6GZTx32UAy2cCSzJm5scx2P9qjTVa1vgqrg0AbLKYqxbWdLjzJFZ77JBCS8fj4Nfht7V6yaj12PIrZg4DaukeSHX+vVkkAfg8CZ/ustUgQGM+bNihNGecC9DNhR0UD7vl6OTrlpTL35STg7XW7mBI7PN91RFbgcUtkt0ai0j3prnr8p/PzGJm1CIvr+uK24oeavVcsvsMqi04O0qRYeBAIBAKBQCA4yDCXzAgEAoFAIBAIWhyeYNgFOdMxMG0TAMDvCuG5jq+T74oGtJ12EDgzKY5JDDk0KVYImRnWUQXDMhSiClhiVByrGM1GP1+wg+sYWYSZJsWUibDzwPsnc4t1lcVfLYoeL5kgSJJOf7ITBKwOC0VnUkz7FMSOiePc8VSV/7J6Lz6avdUyOQA0dzRor3GFmA65pYhjDwKrADJvAN3pd83yCyGPQXOqmRJDRAeBitW5ddJtUVEXIKWnAD6TYvVcUucqUemec7JnYWTWIgDAkPR1uDLvF1uTYiAqMcSCep/wIBAIBAKBQHCwIRIEAoFAIBAIBAcAnsDnJblTdH+38VayJYaoDgJLk2I6GNjAMimO2AfKtERiCQJ+iSFZoZMHsswfCAWA/PT45YUAtSODCoCblwXDzoN9uyvNwdZQRCYDrcGIPugeL63TQaDog+FUckjzOs8xUde1kScnrLVdR7vvel2CwIwHEcfSUdbSRi0jMcTq9gHMEkPa8+5EYogHJwHvstog2/CX45kY8wQgvh/qYzlJXrzQ6XXd3/e2/ay5c8oi+/Dw96tQWU/LDFFeFknK+QkEAoFAIBAkDZEgEAgEAoFAIDgg2EeJqIA0Fe9qDMkMiaHkdRDIHIEyLWpgTXZY1kuZxkqMimMAqCM8E3LjTBAoioKp6/dha1ldi0oMUVgFpJPRRRCMxBf8ZUF2rEhK7PpUFIXRQeDMg8AqyeUUpekcaCvvSQ8CKYKaRn4DYEmyTpzxXiK8/h4qn84r5l5XGyh3YlLsdNs86+6vDZCv8XQQlNdGA/FU4J1KVDj1kjBts2k3dvfgXz5bQi4nOwiExJBAIBAIBIKDDJEgEAgEAoFAIDgA8ATDJCJITVWT1wfDZMDWI7EDfmwPAkYHgSoxxFkxrwbBaKNaNlSCQFHYHQR7qxtNy/LSvI72qfLir+tx08cLAdDfD5VwSVqCwKJ6Oxn7iNdMGaCDrNS5cEHRabZTSRYoMj6ctRWjZ2+1rIBv3mbyOh9iEkMBmw4CKYIahr4/df+5JIkMWKscDKa0PB4ElEk2D9qAt8ThE7yvlpYr4pFdO/H5qbj1k0VoJJ5flDxZoueep4MAAOZuKUctkawkpZAOgutBIBAIBALBwc8DDzwASZJi/02bNq3F9iVMigUCgUAgEAhaGUWhNe6NuIngKBWnqg9GmJrwLFgSQ6ygbXPg11kHAVVF7hTFYp8lVeYEgccdXw3Mm1M3x/5N+Q1Qy3g9GeywCpbHmyDQJlsSkRii9s+SGFKDnxGZSvVEz+ETP63h3jdHvJkbWTZ3EFBH6ZZkVDM6CKjqe7ck2XQQJFfeKR60QXxWNXy8EkPaILwE+96oMiKpB/AlTQHg1zUl5HLquywur8eNHy9AdUMId53eGyf1bsO1DxX1vPF08TSGIsjw66fX1HefaFeDQCAQCASCw59ly5bh5ZdfbrX9iQ4CgUAgEAgEglZGUfgqo6muAKr6tD4YIbsN3FQFdxOsivL6AB0kDHOYdWqJeRAkQWIIYAcuqQ6CZOjtU5XQtMRQcoJ9N3y8gPlaMpIQiZwTav+U9I9LUppNXWWGLwZnEDi2zSR2EIDwIKAkhjxgdxBQyRKXy15iyBgU7urbhTc7P4M3Oz+DHv7ETLV50B4eK0AdbwdBWNdBYJ/SKa1lJAgsnleJMG19KZZsr8Rtny12LKPUbFJsf2zUtSFMigUCgUAgEDhFlmXceuutCIfDKCwsbJV9igSBQCAQCAQCQSujgE+KgwqOUrG9+mCYrL7tXZjK3DazgyBEB0bVQJkaLL4p/wcs6nctJvb6G/qkbDMfp6z+36nEEIHFNqgEgTZQN239Ptz26WK88Ms6ywCoUTqGOvfUOabMUuNhS2kd8zVjEoJXw1zXQZBAkoH6jKxqb/XYIhGFTKjwmHM7X59T9koxdxBQuKUIs4OASrTYSQzJimJ4n4IPuz6Bc3Nm49yc2XiryzPg/QzxIvN4EDBkx7QUePbjqryfMTRtFbltHkoZHQSJQicXm6kPRjBh5R5nnglKc8LLjhAhv0Z7JXDvXiAQCAQCwf8gr7/+OhYuXIi+ffvi5ptvbpV9CokhgUAgEAgEglZGVhRSrsYIVW1NexBEyIB2m3S2Fj/Tg4DRQRDRdBC09ZbhkQ7vAwAKPFV4oO1o/HHbo7r15ZjEkHl7VmE8SkfcJckIhCPwe9ym1yiJITUYu6OiHjc2eQpgdfR/953Zl9yvMWjHK9kUj/wPT3JItw9DUJoO9ltHHROTGOLzIACag/BhWSavcV4ZGbv96LcpQ4b52jCiyGYPAgorDwLq3NtLDCm6hFxbbzl6pOyM/d0nZTt6+ndgU6Cz5XElgs6kmHHN2lXXZ7rqMKnX39DGWwkAuHv7PRhXOVK3bZ5Le19NI5BtXm7XLZLhqodfCqI8kkO+znNl1QfDjjolWBJDbpdkSjRQRuDUdSEkhgQCgUAgELDYvn07/vWvfwEA3nnnHUydOrVV9is6CAQCgUAgEAhaGUXhC6RxSwwFwqScUIqHHYhiSgwxgoSRWKBMxo3543WvjcxaZF5fTRA41l+nNeH319EV3aTEUFMA9OXfNuiWaz0GjBgD/ZTcCSWrE2qFYJ8xKE1K/lDhUaXlPAhYgX7VcyIiK7TRs0MZGR6JIcLWmlxPDfZqtfbdhJG3W4qgxkkHgUuyTBRFZH0HAaUw085bxnx/MtA+N1jV8KyuIpVr8ifFkgMA8Ernl03b5kl+ldUEyOVWyaCRmQsw74gbsLj/dXik/bv2O2GwaNt+nP/GLO71Yx0Ehu/31SsHmdY1GrgrikKeayExJBAIBAKBgMXtt9+O2tpa3HDDDTj55JNbbb8iQSAQCAQCgUDQysiKwqW3zS8xFCGDaykWvaJUMDAiK0yjUjVQForI8El08FR3nE0HKifBpNgFGRV1QfK1vdXmYKMajN1SxpbtMWKs6mWZ8BqJR2KIR65EizEoTQap7ToIEpAYIt/LkP6JyGoHAW3Ebd8RoMCNSKz7gErKGDHeS5S5N9CcvKgLNHcHUOfN2oOAMCl2SZbfabSDoPmYqMRfgaeS+f5koA1KsyrY7ToITs+ifTK0l4fEkfqMROhzayUn9Wj7d5HhbgAA3FQwHt18u0zr2EkMAcCPy3dbynkZUU+V8fv1uiWkePVTaWOSiHVNiASBQCAQCAQCiq+//ho//fQT8vLy8OKLL7bqvoXEkEAgEAgEAsEBgKeDgFqHCi7VMSSG/G5nJsWs5AAQDSqqFbE8gbiYSTEhMWQFFTqzShBQEkPxyP4YpUBoiSHKpNj5vljdGyyM+6ASBFaG1Kz3xLt/wCLQL2s6CCgPAosEgV8K4D+dX8AZ2fOwuqE7/rTtYS5JIuM6rOSbLEcQjsiGYD19LpkSQ8R5lGBtYhtR9AkCKoGR7ASB8R7VSQwxunqs7n8AiBCGztHlmvMf74MN9DV1RMoWnJU9F539JbrlF+VOM63L81xyiproNJ4zj8sFr9uFRo1Um/E+Uc95lrsWdxeNQZa7Fu/suwwReUDSj1MgEAgEAsGhTWVlJe68804AwHPPPYeCgoJW3b9IEAgEAoFAIBC0MrKiMKuctVByLFTxaUMwTAZjPRYVuZQHgZ15a0RWEIrIXLrwsaChQ4khKsjnlmSU15k7BWRZQWmteXmsyps4WYu2VWBQpxx43PpgpzEASAWOk+VB4EQDPboPg9Y5GaQmrhXNv50mJcIRGW9M3YSl2yvRJT/N9DorcB9pOo/RDgJnnQ6jsubjjOx5AID+qVtwff5PXNeacT8sWSJFkVHfdO6zXLV4oN1oXJv/s2k9jxRBdQPdJUN93xFFITsLVGSDBwHVFZGf5ASB8bxpD4/ll2CXIAgrtM9DxOE9zvpOjcs7+/bg+573wO8yP5c8kvWzKllovVd0+3dL8HtcqNEsM3baqNfK8x1fw1nZcwEAIzMX4bqK71vseAUCgUAgELQcJSUl2Llzp+U6HTt2jGvb999/P/bu3YsRI0a0mjGxFpEgEAgEAoFAIGhlonFr55XRgFUHASHnYlG9T0kMsQyKVdRAKE+drhozlB13ENAJAqqDoD4UIeVS1AA6dYYve2cuhnbNxZe3Hge3q3lfxu2QHQREYDdoERhmkXAHAWmUm9wOgk/nFePVyRuZr7OCvM0eBDJ5vqwkg17q9Iru79sKx6I8nGV7rC5J0X3ZzHOhyNheXg8AuLftp2RyAADciKCK0UFAfXeRiMKsygfMHgRumO+JNi0tMaS5vlkSQ402EkMRhhF0RI5u86cVu7muM2aCwHBt3Nv2UzI5AABeIkHQEso9zdJqRomhaAeBFuNnV5MKanIAAHI9NRjumwFgZPIPViAQCAQCQYty/vnn266jxDEgmTlzJj744AN4PB688847kHhMnZKM8CAQCAQCgUAgaGWiHgQ8CQLKg8D8voZghDZ/VSK48fiu5LapQCfLoFglIisIR2Q+iaE4TYqpMTVLYohpJGtT1b9w235MWbdPt8xoNkxVv1PLWqODwGRSzOlBoP2enHoQvPjLesvXWdevmiCIJpKcehA4kyRircPqzpGVCGZujJoBX18wgbk9j4VJMauDgFWVD5g9CKgERr6nivn+eLCSGGIZa9t1EISIDgIPwpBlBfd9uxx3frmM69hY14DxexyRwd6eh0iytK7EkGRKEBiTCCHGsy9b2p/EIxQIBAKBQHAoEwwGceutt0JRFNx999048sgjD8hxiA4CgUAgEAgEglZGAY9ZKx2EVWN7iqJAUQCXS0JdMIx0antKBA+e3Rc5aV58MHMrajXmrFSQmldiyMtx7HKcHgRUwUxUYsicIKh1oBNvZPzy3RjVryj2d8ToQUDp5xPL4jEpDjhNEBhNiokgtZ3ptdMOgjqbZBHr+lVNqQNhmeHjkBw5Gqt1WOdCUWTM2lRquz23xDYpps5juOm+YBFR9B0E1Gcq8FbaHlci6E2K6WNtJGTHtESIBEGauxE1gTDGLjGbBrNgJX14vmsVj+TsHooXNdFplhhywevWP6xMJsWMpJGstH5VoEAgEAgEgsQZP348Bg0alNRtPv3001i3bh06d+6MRx55JKnbdoJIEAgEAoFAIBC0MorMVxlNBcwURcHLv67Hm9M2o21WCt645mg0BCPIogKvSgQpXjfuOr03+rfPxp8+WRR7idJM5+kgsNJa16IGJJ222VLV3y7IqKglOggCdBBXDdRZ7drYiWGs9iUlm+IwKQ5FZCwu3o+tZXXoXZSBIV3yWsSkmAq86zoIEjAppqC6KQBAbjqPgRAte8XTOeN0faM0DSsJEQiGsHCrffW2R5JRG4xWxrtc1kFgoKmzhlGVr75u10FQ6KmwPS42HAk7zfWeTA+CDFc9SmvMPiBWsGSmjNeLVUcA7UHQch0Exu/d45Lg8+jPB2VSTN0nYZEgEAgEAoHgkKSoqChujwGKdevW4ZlnngEA/Oc//0F6enrStu0UkSAQCAQCgUAgaGUUxC8xtLWsDpPXRuVxdlU24Lmf16EuGGZKDKnYVbsC9h0EYdlKaz3aF6GiSpoosjnoaFUpTH0OF8ODgFnlzSGnY0weGHXZqWOkAt6UB4EsK1iwrQLVDSG89OsGrC9ptjJ96fKj4jApjk9iyO49icAMcaoJgrBMfpfG85rhqsfZ2bOxN5RPbpOViNBivE9YHgQrdlYiGMm03Z4bEShKNAGVnerVvcZKEFglimRFb1JMJcEKPJVwI8LU+beCyzRcc31bJTOskAl12jRXI6oZckwsWNcO+Qxj4CU6CFrAgqDZpNhwzjxuCT7DM9WY+AtFZLLTIc7TLxAIBAKB4DDjlVdeQTAYRPfu3VFfX48vv/zStM6qVati/54yZQr27t0LIOqHkMyEgkgQCAQCgUAgELQyMmeEiAp0bi6t0/09b0sFCjJ8dMJBaQ6g+0x62c49CGSLDgIXZMia4GZzB4GzYDb1mhsyKur5JYZCMZNi9n6MHQTGqmqe4DYQPY+KoujMxP46Zgl+Xr2X3O/oOdtspVxM+wjr90sF+21Nih16EGT4PTpJKiMsGR+5KSHElhhqXuZGBD/2ugvd/bvZ+4nDg4DVQVBSVQ/APkGgBnVrGkOmBAGr+8OqK8RoUkydO5ekIN9TiX3hfNvjM8LyXNAdg+YUxZsgoILd6a4G7CeSd9awJIb4cTMlhhSclz0TvVJ24MfKk7A50MnhsemJKNEuKGMCkTIpNkkMyQppSF1RH8bLv23A/53aEz6PsAQUCAQCgeB/lUAg2oW5ZcsWXH311bbrP/HEE7F/b926VSQIBAKBQCAQCA5lqKA5Ba8md1ltEK4s2oNAxWsIRFFBZjvd+bAh0KnFLcmQNRIkaqyM8iCw0sungrtuRgdBbcDapNhKYsgY8DN2RtDyOOZj+3DWVnw8eyvaZPrx1rWDkZvmYyYHAGDd3mr4Pc6qxE0mxUSw367S3mkHAeUFod8fjZp4aQxFaJNizTk8N2eWZXIguj6PVwdfB0FjkK/S3R1LEJgTJKwEmZWvhFFiiJXAKPTujytBQCUHjfI82qQky4PAjhTJLCWU7m4g700r2CbFTjoIzN+NAgl/yJ+AJzq8AwC4tc1YnLTuA5SGcx0dnxZWUpQ0KSY7CCijeRde/30j3JKEO0/vFfexCQQCgUAgECQLUbIgEAgEAoFA0MrInAE6O+NZ3boMDwIVc7WrOei1t6rBch8RWYkGfikDX8P+VXNPiUiGWCU+SHNgyNhfHzQF9Z0YyRoxFlGHZQXtvfvwVuen8Vm3hzAsfZXpPaxqdlkBSqoDeP7n9Vixs8pyv6GIYlmZT7/HXmLIroPAie+Boii23SSsYK6aEAqEZcZ32bzsmLQ1tscSj1cHq6K+IcR33j1gJwhY15ZVV4ixg4D1XRV5yrmOzwiVHDKeE+29w+sjYsTvMidY0l2N2E9091jBklezS0ppIRMEihRLDgBAqiuAvxZ+7ejYjEQUWlbN63aZkq7G8xqOKKRXgrrWK5M3JHRsAoFAIBAIDm1Gjx4NRVEs/9MaF0+dOjW2vGvXrkk9FpEgEAgEAoFAIGhlFM5KWTo4ypLnsOkg4PAg2LyvzrRMi5ogoDAGPdVkA5UMUYPLRvkWgKX9L0NRgEpDINLOg8Cqg8BonhyRFTzb8T84J2cOTshcjva+MvNx2ATh52+tQIrXuYa8HcagNK8HgXaJE4mhQFg2JWN4eXXyBiwu3o9AOGIbuObpkInHg4D1PTWvZ71ftYOgusEcEGd5DWg9BoxEFP3rrORKuts6QcfCLjkE6CW14v1u/ZI5EZDmakBFnVMPgsQ7CGiTYjM8SSgrWMbsUQ8CQ1eW4dpgeRAoYgouEAgEAoHgIENIDAkEAoFAIBC0MgpnBwErWC4TRqZkUFSTIDAGs8KyAllW4HI1Jw42l9ZaHk9YVtAYkqEo5lJfj0Fr++mJ67BzfwMyCA1utYI41etGlSEIy5IYAoCKuiDyM/yx5axKfDWgZxUGjRgSBKGIjJMyl1q8g08jPdXXCgkCIkhtl7wIEgHsJdv346NZW9E+JxV3nd4Lab7o1KCOo8OBtT8JCp6btA5nHtnW1oOABx4zb2PlOSvQrO6bqj7XosrC1BASVqwOAqsODdnoQcA4Ph4vAQo6OWSQGNJc7/F6EFAdBBmuBscdBKwEgXE59ZxRSTfnFk2fORnIstl/AAA8Lhd8Hv3+jNdGTSBMehDIFp9LIBAIBAKB4EAgEgQCgUAgEAgErYyxep0FS6KFCiOSwUULiSEACMky/K5oQDsckVFcXm95PLKioDHMkBgiAsafzC3GxZ3qAYMEuBrQpILp5Gdu2nZ5XRBaxe6aRrpyOcII6mkxvsxTVc1VzZ6E2J/HJemCuPWGrg26g8C8TBtgNb6nqj6Ea96fF5PGqWkM4ZlLBkb3ZyMvBFgHeRdsq8ApfduQ1wRl/mwFn0kxnweBet3aJQjUDgLKBJvVicHqrAHMHgSs4+PpBKDgqbzXXt9hh4bVKnQHQSPXvePzuGLXIOsW4fVcAYChnTOBUv0y6t1OZIsoIopCni+vm/AgMKxX2xgmPQhaIpEhEAgEAoFAkAiiv1EgEAgEAoGglaGMeymogDQriEhWWsvNAU6jXjYQrbT/bU0JLnhjFka9MsNWhiYcURBgaK2zqp+Ly81dCWqQ2E8cE9lBADWIrQ/YWmn5hyKyZSLG+FqYQ5edJ1jNOj9OMEov1Rs+J9lBYHNsxvd8Nr9Yp5v/xYIdsX/zeCSw9qdeh4GQTIZBnYZGeYLGxmNhdxBEl9t2EKDZR8FIMjwI7CWQnMHjVaKN4ccrMZTiMicIMjhlkdpnp8T+zUwucXSLqPBKDPHD8BeRFYSoDgK3yzZBUNMYZngQiASBQCAQCASCgwuRIBAIBALBYcOW0lpc8c5cnPzCVHy9cIf9GwSCAwR3BwERtGJVsZPLLTwIAKCiNojbP1+CFTursLVM7z+Qm+Y1yRKpHQTksToIelp1EFDBQzUpUh/UB9tYHgSAvSmvMUjKU1XNE4itCyYeuMxO0ycIagMcHQQ2lcrG96zbW8Pcv/E8U7CCuer3Vx8MM757hx0EXBJDCiTISHM1wIUIO4nWdGw+yVozX30/dQ3F50Gg7yBgJVfi7yCwP0e6DoIkexDwUJilSRAwvlNH8lOy+Tukwu6KEn022nX/sJJGrA4Cj0uCz5DgNEkMNYZIDwI6dSYQCAQCgUBg5tFHH40ZE59yyiktth+RIBAIBALBYcNTE9ZiwbYKFJfX48GxK7CvpvFAH5JAQCLLfB0EVHDULvipQ5Mg8LvNwfgvFm5nVkT3aJMBl2GkqHoQUFCBMIAlkxTdRoqHT2JIDTobK9utEgSsQK6KbEjSyETA0XRsHAFMnup7O3LTfLq/jQF79Tvrn7oJ52TPQoarngy8a5Mtxu/ZShLHmJCgYBvNRpdX1ocYUlQak2IHFeNWZLlr8Vm3h7HmyMvxU6+70MG7j1xP3Z+PU2KIujdY94uVLJNJYijZHgQciYWWkhhKd/H9zmanNicc7a4dLoj7lToPA9I2Y3G/67BxwEW4tc13zM0ZPVRUIjJIk2Kv20WYFOvXq2V4EAgEAoFAIBAcbIgEgUAgEAgOG35f1xwUkhVgzLztB/BoBAIreDsI+KVkyEprbQeBx1y1WlYTYO67e5t0eAwZgogsI8AILLOCm+RnaDpWj1uC2yDab2VSXG8IXFsF44NhGVaNGsYi6nA4/qp5LTwGv3YYJYbqDMHnYETG+dnT8WPPe/BWl2cxodcdZKBWe7zBsIzFxftx6dtzcPk7czB/S7lpfTX4bZQ0omB7EES3UdkQsuwguPjoDrb74OXinKkYkbkCANAvdStuKviRcWyqB4F1MkgNFlNSTqzEk1WySlYUXYcBs9uGkWSzg6cro6VMitM5Owh8Hhdym5yFmbJRThJGVIKAsd08TzU8koz72/4Xue4qch2qg0BWJMiKgrDBVF6SALdLMnVlURJDXuI7dWrULRAIBAKBQNDSiASBQCAQCA5bkhGoEwhaAt4OAiqQ5ki/3Mak2MpzoEt+uil4H5HZlees4yI/g6oF73bBY9gHFYRXq3DNHQTsQO9Fb87G+hK2jI5R5onnO+HRw+epvrcjx86DICzjlc4vxRInXfx7cVX+L6btaAORgbCM28csweLi/Vi4bT+qiYB2Q1MiwpiQoLhqaEdyuWoKW1kftAyEXjioPYZ0zmW+7oRb2vyg+/uEzOXkerweBJYdBMwEAfta5DYpjruDwP661CUIOPw2zCikB0EapweBz+2KdcawOwj0n99Sq18xf4d2MkIeScb5OTPI16hrIgIXIrKCUFh/vN6mxCmPB4GbSBDEKyUlEAgEAoFA0FKIBIFAIBAIBAJBa8PrQUAEkljVwmTQTZMgMAbiAWtD3VSv25QgCMsyU9s/Hokhj0syHRfdcUB7ENRaVG3vs+iOAIgOggiPxJB9YM/qmHgxehBQHQQew7VxdtZs03a0AfryuiD2VlvLwaj+CXbJ1RuP74rCDB/5mnqO2BJD0dd9HheOaJdluZ9k4+KUGFLPLS0xRN+7Vh0EJpNiVrdN3B4EPBJDzf+Op4PAz+i6yODsIPC6JeSkqR0ENI6U+YkOAp7AOyvpQJkJy0o0QRCM6O8/1Vzd6EFgThDQHgQuITskEAgEAoHgIEMkCAQCgUBwWCATAQ/RxC84WOH2ILAw7DWtSy3XJAgkSSI0s9kBNZ/HZUoQyFYdBHFIDHk9LngMx0R+5qZtaKvzI7LCVenOwmhSLEfsA/s80iDJ6FzKSdUH343bpALX1HUhOQw4qwkYO6PlqLSKdaKqsiHE6B6JLvN7Wn8awt1BgHg6CKwTBAGOBEH8JsXOJIYisvP9UP4DAJDG6UHgdbuQlx69rh11QbEgOggSke6hpIBkuDB6zjZsKdUbuKuJAWMHgfF6YXkQ8HR8CAQCgUAgELQmIkEgEAiSRl0gjPdmbMZHs7Zamh8KBC0BVdXMWaQtELQ6Cq8HgYVhrxFSnsQQRDNqZrMMV4FoANctmTsIGsMyeQxOzJPVZV6qg8DC2FbbQZCoGbBRYijCIzHEEdirtQmu85Cd6tH9zTIp1kKdf6cB07qmBIxdksPrdjEfsOp3xZIYin33bhdaO43L7UEgWXgQMO4ZS4khRUFQ40HAe69QXT/k+4jr0rhEb1IcRwcB4T8AOPMgyEmju05UJCctBEQHAU9CjPX8JCWGlOhU+b5vV+iWxzoIbEyKaxrDZAdBvFJSAoFAIBAIBC2Fx34VgUAg4OOm0QuxYGsFAGDO5jJ8cMPQA3xEgv8ltAaQAsFBjxJ/gIgVXCSD14agt9fjAjRV91YJAqqDIBCWEZEVMsDFCnpRQTt1mcftgseQtKB0xNXKYm3gOtEEgbHpSE6SxFBSOgjSjB0EBomhsAy49e+hJFIcVWRDKzFk/TyNBvetOwhCEYWuFFdlfg5EB4HaueJKvgeBVTeLzNlBYFzudbsQ5khcUdszxtq1XXbxSQzRHQTcCQK3C+k+T9OxWRtcc0FJDCUQeCclhhi1dM0dBAaTYqKDIIeSGNLcFxFZMT1nBQKBQCAQCFobkSAQCARJYUdFfSw5AACT1+5DVUMI2QajRYHAirLaAH5dXYKehRk4tlueo/eydNEFgoMRJY4AnYoTKR+txBBglsSwSqz5PebgvRr8po03WdJDVlXkEjwuDokhSU0QNO8jUa1/o8QQj+xKa0kMGX87G0IRXSAxGDEnCCiJFKea9vVN59fYsWDE52F3EGiDv3YG1Q5V5xNG3be9B0H0PFC/K0adeR6iHQSaBAHLpFgy3q8SGuzzVlzSRBGdxFA8HQSMBIGbX2Ko2YOAde1EaestQ72cYr1B2ewxkoh0D3X/sBIWvpgHgf4mpDwIqOei9lkdishwu9ymdQQCgUBl0bYKPPz9KjSGIrhmWGe4JAkDOmRjWPf8A31oAoHgMEIkCAQCQVIoqzVP1GoaRYJAwE91YwhnvDIDFXXRIMSrVw7CRUd34H4/ZbbKK+MiELQ2cgIdBOwEgbVJMWCWxLBKrPkIiSG1SpoKSLKqd61Nis1JCNqYuUn6RhO4tpJ04cFYCS7L9oF9HokhKy16XowmxUA0aJ+ZEl0eDMuAtVoLgDgkhrg7CCRmF4w2+GuVHPIdQIkhn43EkHot1wfDpgrveJLRkYi+g4B1rxjvq2gAmuO6JM4jJTG0bm81/B43QnF4EKRYdhAosEv2eN0u5DZ1xrASBC7IeK7ja7gy7zfURlKR4bboToiYExM817v2KNN97tgzjZKdYm3P35QYMEm2mRIEYXjTrCWG4unmEAgE/zsoioIHvluBzU1eKE9PXBd77f3rj8GofkUH6tAEAsFhhvAgEAgEScFYAQrEV6Em+N/lo1lbY8kBAHh64lpH728kKqEPFQ+CxcX7cePHC/B/ny/B9vL6A304glYh+RJDZPWsqYPALBnEwud2m6Qv6tUOAtJ42LlJscctmfZh3UHQHCxNxKAYMFf78hhHc3UQJMGDIIdIrtdrpaE4q9gddxAEzYkYCiv/AO11SCVUYkH6AykxxNlBMGdzOQY/8RumbyiNvZacDgJ2gFyLz83pQcAhMRQIyzjr1Zk49cVpcf02sjoI3JLMlB/S4vNoTYrpA2jnK8eVeb8BgHVyAKA7CAhDYCsGdsyJ/ZvyCmAdZ3MHAdukOBiWEQjLDJNiTQeB6H4UCAQWVNQFY8kBI/d/u7yVj0YgEBzOiASBQCBICpSxnJB8ObTZWlaHsUt2oricHpQmmx+W7db9va/GPPm3guogOBSuwUA4ghs/XoBp60vx04o9uE8M9v8nkOMIMqqwgtSkHIaNxJCVjj/lQWApMcQIzlHVwjGzWLcLXkOCmTRAjnkQNO8j0cBacXk9Zm8qi/0tR3gqtXk8CBL3Q6HMXLXJESvvCC3OTYrDuv+ziJoU2yeErJJDxm6W1qDZpJjPgwAAqhpCeEaTsOY991oisl7Oi9UFZEz+eTmTKLRBdXJ///wWXReUPI9pHbdkKzHkCOL6cyox1K1NOvKbkhbUNcFKOKjXrvF5qk0eqc9WMvGg+fzxdHMIBIL/HfZWs2Xc9tcn1kkpEAgEWkSCQCAQJAWqoq4hwepOwYFj1a4qnPPaTNzz9XKc9epMrNtb3eL7TFS3m9JSD4QO/mtwxc4qnSTJ/K0VKCckuwSHF0oCwTt2pT5PB4EhQWAhh0MmCCwkhjzMzgZ28NLjIjoISFNjpWn/zccbTyW3kWs/mI+PZ28FwCcxxBOATNQbAQDSfG5Tt0c9p7m0FqeB2FgHgU2Sw0oeSHvU1DV5QDsIVP8DG4khjyEwvG5vTezf8Vx3sqKgUZPEZnUBGTtzjPcrC6uum2Thd7F/l1j+I1p8Hq3EUMvgNCHmdUk4unMuAGcdBH4vnSDYXFqH75fugqIoMQk02q9FIzEUOURaHQWHNI2hiJiXHaLsqeTzeRHwUVEXxNT1+7C70qZLTSD4H0QkCAQCQVIIEROchkMgOCugeenX9bHvryEUwXOT1tm8I3ESTRA0HqIdBFSwacn2ytY/kP8Bdu6vxz1fLcOdXy7FtrLW6YxhkoD+FW/1cdOOdJW2xopkq+e0z+2Ch9FBQFeG8ycumiWGXKZAOBnsjHUQhKE0nTtemR07Hhu/BkDyJIaScVxetwtpPr1VmLbbo6UkhtQEjJ1Jsddj4UGg2ae9SXHrEutecNBBoKLKJsbXQaCgUXOvsf064ksQUFJOvQrTHByhPVYdBKzPo8XrdiE33dqDIFFcHIkK7b7dLhcGd8mJHh9xTbA+l9pBQCW57vpqGX5bUxJL/JPm4QaTYoEAiGrN/7BsF+79Zjm+X7oradudtHIPhjzxG4589Be8O31z0rYraB32WHQQCJyxp6oBZ782Azd9vBCnvzwdy3dUHuhDEggOKkSCQCD4H6I2EMaY+cWYtHJPLMCSLMgOApEgSDqhiIyp6/Zhxc7KFt3P1PWlln+3BInqiVMdBI2HwDVIJdcWF+8/AEdy+PPnTxdj7NJd+GHZbvzxvwuT/hx0gpKASTHTg4AVpNN0EfBqmgPR4JeL0UFAVduyJIbIZIIaJKY6CIj11c8sK82Jv3gCtVYoPB0ESZZtIfchAW6XhHSfW7dcG7RvKYmh+qbOgVpbk2K+DgLSg0D1AXBLzG20FM0dBHYeBObzq57z+jh+q8IRWZewljg7CHjvV+re8yS5TJ/lQQDwdSv43C5kpXii97tDKSBenF7vHreE4d3zATAkhiQF1DWqJgaMyU2Vx8aviSUIqGSTzoNAdBAImpi6fh/u/HIZvl28E3d9tQy/ry1JeJuKouCpiWtRF4wgIit46dcNqBKyNIcUe0Sle9IYt3QXSqqj3XD1wQge/2nNAT4igeDgQiQIBIL/EWRZwaVvzcFD41bhL2OW4Plf1id1+1Q1Y6NoZU0qiqLg2vfn46bRC3HBG7MxukkW42BjzqYyvDp5A5Zsb90gN9UtQBkXH2xQSYwlIkGQdEprAli9u1kqa0tpHTbuqz1gx5NIcoLpQcAKumkTBA5kXfwecwdBPCbF1HGp63rcLngMVdLk+poAqFpJn+zAWoTDg6ClApta1POR7td3EGhlf/gTBPF1EDTwmBTH60HQdP1KlHlRCxOPB4GKes6tfDtY1Bue84l2ELTPTjG8j7ouk+1BYJUg4PAg8LggSRJyUr2OA/m8OE4QuCQM6piDvm0zyaRndJvm8+j3RJN3LB+NXZUNzR4ERPJG+/2HhQeBoIn7v12p+/uhcasS3mYwImPn/gbd37M03juCg5+9VaKDIFm8MWWT7m9RkCUQ6BEJAoHgIOKHZbtw2dtz8Pevl6Oijj0Ri4fZm8uwvqRZQ/ftacltMaWCFaKDILks2FqBBdsqYn8/Ov7gq3qYvqEU13wwH69O3ojL35mLlTurEtqeLPNP9qlAOyU7dLBBHffynZVJr47+X4fqcqpuOHBVdIl0ELClfOw7CJzIuvg9LrgMQVw16EUdA9ODgJSZaa4i55IY0mxbrXJPpjRHMCxDSZLEEIuehRlc66lBxzRDguBvXyzFiGen4JO527glhphJIwbqubWTZ7P2IGhebuVBcCBQA+m2HQREUDcQjlbgxtNBYMwH8nYBUfdrTpoXsx8cqVtGGoEryR2D+V2JSQyp3RD5Gb4WuwZ4EhXafXtcElwuCW9fNwTHd8sk1zdew+muelytPAKM743OxY/AA/paUj0IaG8DTQdBWHQQHO5U1AWxr8Y+yFtm8J+yMqflhfIdUA7gM1jgnN1VooMgWRRm+ltlPxFZwW9rSjB13b4D2q0sEDjFY7+KQCBoDbaX1+POL5cBABYV70eqz4UnLxqQtO0vKa40LYvIiknaIV5aS2JIlhV8uXAH1uypwgVHdcCx3fKSvo+DldmHQMXPP75bEft3RFbw4q/r8d8/Hmv7PlYwPBCWkWqQ2WBBBbQo2SH12D6evRVr9lTj0sEdMaJnAdc+ksHGkhp8PGcbCjP9uO3kHggwvBM2lNTgyA7ZrXZchzuUEWTYQQIq2SSUIGBVH7Mm/XEmCHweFzwMo1wnEkNUMLQ5QeCC2+XiXh/QdhAkL0HwxYLtmLhyF/7ay3q9eBMER3XKwdAuudjE0bWinnOjxBAQrUz+9w+ruffr9HjrgmFEZMX23rCSGNJWs5MG1Uk2z3VCzCDZItgN0IHmQFiOdVhkuWrxaucXMSJjOWbWDsLd2+9FjZzu4Dj4fESMniFANKht7L6g7j3WPuIlRWKbFPN8p+qzJz/dj/rqlnn22iV+jKjPnm4F6eh2bHtgHrGOJEMbw78672ccE/kBqAEKazbitKws/FJ9vOl9sQ4C0oOgeYMh0UFwWPP1oh14eNwqBCMy7hjZE/ec0adV908lNEW88sBR3RiCz+1CipdvbgOIDoJkUpDhx7byet2ycEQ2dbLGQ2Moglcnb8SS7fuxYGtzQd81wzrj6YuTF9MRCFoS0UEgEBwkfDxHLxfz2bztSd1+hJiAVMVZPRsMy1izuxqV9c1dDmSCoAUkhj6dV4x/jluJz+Ztx5XvzcWW0gMnEXIwcLCZ2+02DGKnb7D3LqisD2LH/nryNSrJ9Mvqvfj718vx+fztuqqMgIMOgg9nbcGTE9Zi7JJduPaD+a12HdUHw7j07Tn4fP52vDp5Ix4bv5opg2SsJEs2uyobcN83y3H7mCVYt7fa/g2HOFSyiEoatBYt4kHAIzHkMEFgDN7HZ1JsXi7FJIYkeDk8CLTbVrX4k2lC/siPq5kJDtZx8HJkhyx8/efh3AEBNZBqNCmOB6vjPapjNp686EjdsvpghKt7yeu2MCnWBD+pEgTdsiRXuduhXls+C8NdgA7qBsIR1Dbpyl+SOwUjsxbB7wrh9KyFuDB3mqPj4O0goO5XqrCDvveT+3yz7iDgkBhSEwQZPvK6SAYsmSDm+toEqEx/PmOi5eH2H+n+/lf798n3WXkQaJcdyN8hQcuiKAr+/cOqWMfX61M2Jb1D3A4yQdCqR3D4sqGkBou2VXB3Oz81YQ0GPvorjn1qMmZwzI+A6DW0RyQIkkYbooOgpCY5860vF2zHO9M365IDAPD5/O2xsbNAcLAjEgQCwUHC7hY2IIoQ5SLaAD8vNY0hXPjmbJzz+kyMfGk6Vu2KSshQLdItYRD7yI/NlZOKArwyeWPS93EocagPOF74ZR2OfuI3nPbSdPJ1Y4JgcXEF/vzpYny3ZCf+OW4lxi7ZFXuN9CBgXINPT1xnOI7kenKw+G7JLlQ3Nn9nXyzYwTzGeHSunfD3r5fhm8U7MWHlHlz3wQKED7JkU7Khro96G531liSRBAFLnoNZMSxrOwgcmBS7XTCurlZQUwFOlswIdbyxDgKXyxTwpNbXbrslOggAPqPVeKRRzh/YHn6PG35O/wc1YZLu568wZGHVQdC7KBOZKUafgzBfgsDDJzFEXZO6IHgrJwgkTokhKuAdCMuxa+/RDu/pXnuyw9uOjsPDSOYZr0Ef4TTscZmvI1LKKYFnDIW1BwGHxFDT9V+Q4XcsfcWLx2EHgc5jRaY/n91nK/TQGtZ7mmRBKLkq7T1wsBV6CJJHICybClUWbC1v1WOgirWEhGXijJ69FWe+OgOXvTMXf/5sse36xeV1eH9mtBiwujGMl37lm3fsrw/ZFkMICRt+jN5OQPJMoK2kf+MtyjxQ7Kioxw0fLcCZr8zAuKU7D/ThCFoRkSAQCAjKawNYu6e6VX9wVcMzLXYDuOrGEO78cilOeG4KHh+/xnKSUdtonjRVxvFj9d3inVi7J1ptXFEXxH+mRAP0pElxK+i/t/ZA+2CDFURWFAXzt5RjwdaKg3bguKeqAW9N22zZ6myc2Dz+01rd33//Znns39T1xnsNLtxWYb9SEliz2+zJ0BCkj5G6Z5NFZX0Q87Y0f+ay2gBW7krML+Jgh+oguOPLpfhm0Y4Dc48k0kHgQMonuq/ma4lXYsglRc1yjR0EJdUB5jGw9L9JmRloOggMx0Str/MgCCbfg0B7TImuY0T9fNwdBB7apFgLr3yM1Xo+j8vUpVAfjDCl2XTv5TUpJg2qNcvk1k3SxRJTdh4ExDUYCMuxqnA7Um2+a5+bkSDg6CAwyn4B9D0jIYIhaWtwSuYincl3vCSrg6CgBT0IvDweBJrTp0tOMjoI7GS66uRUcvmGvdHORNqDQCMxJBIEhy10YLd1zdmpQgg7E3qBPY+OXxObv/y2pgRrdlt34n69aIfu7+WcHm08BYS8nkQCkMVQu1q4SBM49HwZn564FtM3lGJ9SQ3+/vVylFQ3Yl91I5btqETkAMqzCloe4UEgEBiYubEUt36yGA2hCI7vkY9Pbx6WNJ1+K6jKwr7/moT7zuyLv5zSg3zPmHnb8cOy3QCAj2ZvxbDueTizf1ty3TKipTWeDoKXftug+/uX1SUADn+T4n3VjVhcvB/92mehS761zvDO/fV4dfJGuCUJd43qhXbZ9OTRKVQXCCtB8M9xq/DFgqhM1Y3Hd8WjF/RPyjE4xWNx74xfvttWB9VYXb98RyVzXSqoRckO0ftpncE11epdG6CDEi3ZQbB2T41p2eF0v1JQXg+NIRn3fbsCPo8LFw7q0KrHoyQwwGZJCXF5EHBWsavVvqx8gpMOAtKkuOkzRJMQ9ibFLrKDILmTFJ4qaKaMkwVq14bfy9lB0HTSKQ8CFV4pFavj9Xvcpn3UB8Nc0k1WHgTaACx9TWqWHTCJIZsOAkpiKCRzB2IyUjyWz1RWc4gx0E8l9KjfVSpx5a7bjO963g8AmFY9BDdue8zqkG2x8iDg6iCISQz5WyxBwNNBoE2WeHgSBDafjZUgWNNUTENKDOk6CESw5XCFGpdKrZsfQD3xHKprAQnY/yWoopI5m8vQr30W8z3Ufc7jBagW5VkRCMtkoaHADPU97K5MXMLJrqjzQHX8r91TDbdLQu+iTEfvm7Rqb+zfsgL8d8425KX78OSEtchM8WBYt3ycO7AtLj66Y7IPWXCAER0EAoGBd6dviU3q5mwux/jlu1tlv5Q5jqwAz/28jlmV8NzPepmURyyMC8sJPfPKeucdBKyscUuZFK/ZXY2fV+2xqJSn39cQjODeb5bj2Kcm4+6vlul+mDeX1uIfY1fi+Z/XcQVhd1TUY9QrM/CXMUtw1qszscwiSK0oCm76eCG+XbwTXy3agVs/sW875aUuQASXiWrG8tpALDkAAKPnbMPGkhquqtBkY1WtTH0eI1RAnQVtUswXzGkMRRCRFbw7fTP+/OkijF2ys0WqyqnPU1ZLJ+paNkFgfqZQAfTDCatr4QGNuXZroSRgIMqqYmdWi8fhQaCuR8mZAAyTYqauOiF/EpMYkgwV0Qqj6lzTQdB0byRbJoGnOyCewKb6HDQWApyWOR9fdX8Qr3R6EfnuythyNWhp1XHA28lgVf3s87iQ5jdKDEU4EwRsDwJdBwEpMXQAEwScEkPU9R2MyNydXZkW3R8AfwcBbVJM+BLYBLFPyVqMfilbLNexw6qDgMukuEkuKT+95ToIeBJn2nOlG3szJIb01zBRqBGhEwSWJsWaYzjc5f3+l6HGVa5WzhBQEkNOxtaJEpEV7KpsOKyqjuPxP6I+P884fxqHV0FLSPoerlBJ/mTIPLO89FRaWjaW4smf1uDs12bijFdm4BVDgadTymuDmLM5qtpQ0xjG5LUlJq8FweGBSBAIBAZmbSrT/f3w96taZb9W7Z6fzivm2oaVqWk5EYSMJ0EQdpAgaExwADp++W6c95+ZuO2zJTjv9ZmOBkA/LNuFbxfvxL6aAMYt3YWxS6L6eYFwBFe+OxdfLNiOt6ZtxoMcgcHXft8Y0w5sCEUsdSO3ltVh475mw9uVu6qwl2EuFZEVfLUwamjE081BVR9QA471Jebq8FGvzMA5r81MaBBkN4mlAupWeuc81RQ8SaZ9NY1YXFyB/cQ5DEZkrklJWFbw1cIdeGbSOvyyugT3fL0c81tg4ENN1Fj37b6aAD6atRWfzN3mKLkjywpmbSzDIgvZJCpBUHOI+1nYYfX8aK0OEi0HyqSY14PA3xScLmVcn05MiiVLiSGXroqXFTjUBk7V6sdkt9VzdRDEEdj0xBIEzQH/fHcl3uv6FIZlrMLFudPwz3bNxqdq94ZkEUjiOVa79fwel6mDoCEU4fqt9bpdzAy97qhtkj1a+avWQE2ieW1MikkPglCE2fFlJDPFjTOy5uKSnN9J7X6fi9ODgNOkmCfgPjAtsQCBlQcBpbNvRP0sBZkt10Fg1xkC6J8lPB0E2u8kw2UeQ7E6CGL7oBIE2g6CwyhwKtBzIApzjFDJgPpWGu/trwvigjdmYcSzU3DS81MxZ3OZ/ZsOIvZVN+K3NSWmeVw8CZZ4EgThiIyZHAmCw73AJ5lQ8QrVLyYRtpXVWb5e2xjGjA2lGPHsFBz71GRMWLEn4X1aUVUfwgeztsb+fu33jQklkhpCEczfopd1Pq5HQdzbExy8iASBQGBDbSAMuRUG71aDBF5tvLCsoLhc/wNVUt2IGz5aoAtaq1TWBzFlXQnOeGU6LnhjlqV8iwo1wHlz6ibsItrzEu0g+Oe4lVB3t628HpNW8f+YPjh2pe7vfzV1V0xes09Xsf0Txw/0t4v15jwzN7IHuFSgkRVQeGz8ajzw3Uo8O2kdLnlrju11Rl0jN368EGPmF+uC83sYrZKbS+vw5cId5GtGqACEsU25vDag60yhKmp8Fi2vdRwaqFRA3chpL03HpW/P1RkWa+GdoP1znP6aeXIC22wqXmoazddCaQ0dgP18/nY8/tMa/PuH1fjb50u593HHl0tx3Yfzcdk7c/G8octIZe1ec4KgJT0PDgbiqfhqURJIELCqx5lV5boEgbMOgvx0H/k6FXhmBQmpoLr6fo9b0lXxsgLw2qByTGIoyd8pSyJJC5XssENNyqRoJIZuLBivO4eX5k2J/VsNWg7okJ3QsaqwOkuoDgKAz0wvmsSwTwjRclGHZgeBEw+CP2e+jfe6PoWXO7+Cz7o/bHqd1Rxi9AqgEnrUMp6OEiVB7XOrc8aTsIp5EKT7W0xmxQMOiSHNserGOgp93WuTGTlu82+nbYKA9GvRJAgOtt8mQdKg5gSt3TFCFaBRskMtwdilu7C6qQt+V2UDrvtgPiaubNnAaLLYUlqLM16dgT99sginvzwdszUFhPHIxVAFDdScQMuyHZWo5vjNORgSUYcKVIKAimE4ZatdgiAQxkPfr8SuygbsqwngH2NX2BQuRXD/t8tx4vNT8O8fVjn+jjeVmuM++6rZhaRaqHjEku37TdJkx3XPd3RMgkMDkSAQCDiggmnJxmrSyVnwCQA4+YVpeHvaZrw2eSMGPPoLhj39O6Yzqg92VzXi7q+WY0NJLVbsrOKS2aASBC/8sl4naaOSaILAeE7u/mo5Y01+tpaZfzB5gtC8UOaBrMDkJ3ObO0O2lNUxvycVVhLpoXGrsGR7Zezv4gp2m+Prv2+03IcKpXFcr5EEGrd0J0Y8NwVDnpyMZyZGjYOriYGuz+Li5Qm08FQ72G0n3urwVbuSf99TyQCrzh+VX9eU2E4kgKgcljbp9da0zabJaDgiY0OJ+T44UPqYrYXd4Lq1298TkbBiVepzeRBwJghUOZz+DE1dqsKafVyUgaoqMaTvIGBtQxsQb2gpk+IW6iDwUh0EHrZBobr+KX3a4Pge9ASM51hV/lr4DVn9TXUQAEAF4VlEHiNHB4GVvBQAQG5tD4Lovu09CGjJOl6ZgHO8X8T+PTR9DY5M3aR7nSUxZOwCou5XKoHP21GSCFbyPTwJCrUzJr8FTYp5JIa06+jOL0cHQa7H3KEZVKzlpCgPAu19EZZFguBwhRp3sDrfWqoYjap2T+a8x4rNhiClrPDPQw403y/bHeu0rw2Ecd2H87GpqdiOOqd2Q7pqIvFuV5hjVDVg0dJdsJX1QczdXE7KFbckdYEw3puxGe/N2Iz6YLRgc9mOSmwvt5bzWbp9P677YD6ufHcuLnxjFoY//Tvu+XoZ6oNhhMLmL2pvMjoIyq0TBDv3N2BHRfN+qhvDloWZn84txteLdmJHRQM+mVuMH5clLnldH7K+3pbtqMSn84qxhYiV7NyvP0e9izLQJtOf8DEJDj6ESbFAYMDrlkwGNtM3lKJ/e3YlXzKwmnTyaLVrMXoTsBi/fLcueL1ubw0aQxFL3WMnJDIAbamBMmVOVFEfRAdfcoyESbNmzvOwbm8NTu1byHzd6hp5+bf1GHPLcAAwdZHEg9ftMiU26puqkBRFwYu/bIgNSN+dsQWj+hUhj6g0VvWTA+EIisvr0TE3FWm+6E8PT4IgGT4WxglasnXL7VhcXIHHf1oLRVGwm5CbYnkQGKmsDyEzxWu5zqpd5qBjXTCC7NTmIMjWsjryHBzuEkN2HQRltQEUZaW00tEkJjHEClJLHBJDPocmxVcc0wkv/mqWJnFkUmwhM+NxSzpNdZ4OAvWeNj7PM/2ehK7jFpMYagroaj0IZItqbjVo6XW7MOaWYdhR0YAXf12PHzWeSE46CO5r+yn6pBTjju3365b7Pa7Y81gLj+RdtIqdw4OAOKe64HCrSwzxdRBQQd1gmM+DwNgFAAC9/NuxqqFn7G+mxJDRg4A0KTYv4wm4K0piZfvUOYkdE0dgPma+7ffEdR/xYPe9AvrrU5ds4fAgyCU6CLw2n506Ju19IUyKD18o6ReWHMzSHftb5BioYHZrFYRQAeWS6sSrtVuDEsN4XVGAN6ZsxKtXHU12QNvNV6jOPLt50OZSvvkcqwCmrDaA8ct3o2NuGk4/otBStpDFzv31uPTtOSipDiAv3Yev/zwcPQudGd7Gy62fLsLsTVFZm3FLdyMzxYMFWyvgcUl46YqjcOGgDqb3hCIy/jpmCfYYvr+xS3ZhcOdcMkFX1RCCoihxnR+VbWXWSQuq8MKqMOmppuI7lU/nFePyYzpxHw8Ve6huYF9vU9fvw82jF4I3/HK8kBc6bBEdBAKBhlBEJgfqm/clHnBVFAWzN5Vh6FOT0efhSfh49lbd61bB391EZjsZBqpUwCyZJjqJaN1VckgcAJRdnDXUMe3nqJbkhRp4UOeUajG2azu2Ckys2NEcGC62qazggbq+1ElGbSBskr16bPwacqArKwrKagM469WoSdIZr8zAjqYOh/I6+0qUZFQ5GStrkpF04EWWFfzt86VYvqMSK3ayK4Z54JH9oDxC6g0TGZZk2eu/b8R5/5nJLWmWKJEmr4R1Nh1aEVnBr6v34ve1JQklDu00Wo2TiZYmkWc4K5DNDBrLzdcArweBmiAozErBj/83wvQ6FfhlHRclcaMGCL1ul67ziiWHow3Sqfe08Xmbl0HLIfFCdUWYjiMuiaGmDgJN8j2isIfg2u9IkiR0zk9DZoo+kG8VrKW4IGeGaZnf44bP4zJdExV19s8aqw4CbfCXutr0HgQHpoPAzoOAkoUJhCNcY6QUl3lMEVb0hRdeRoLAeH1RCT2qU5Hnukx01GjlM8Czf/1nOUhMijk8CLTPnhyig8A+2WSdTE12J5TAnmBYxgu/rMM178/DJ3O3xf173BiKWI7dG4nAbYBY/9EfV+PSt+eallPdvE6hxrytNQ6mgqIHndwjgxpCHnbelqi3FzU3sZNNpXz/7Aoatlt0hGuhOggaQxGc89pMPDZ+Df70ySJ8MLM57rCltJYpb2rk07nFKGmSpqmoC+KtaZu53pco+6obY8kBIOqdpprihmUFD4+jPSIXF+9njucf/n4V+byVFZjkc5xi10HgJDFGJXycziEpeWOreeQHM7dwJwcAMLtbBYc+IkEgEGhgDZgSCXQ3hiK4fcwS9H54Eq79YD5KawIIhGU8PXEt9tU0/1hYBX9LqhtNWeaWGtyxqkrqAmH8x2FbaCLHyDtwcTqopww3eeQUeKEqs3fub8DkNSW6VsJGYj27SaJVYEIbGLbqIEgzyElsLq3FtR/MwzmvzcTPq/YiFJHxw7Jd5EBJTRDsI76blbuqsIYwvg2EZHw2rzimzbhzf0NMjqqsxv68J+M6N96/idzPTtlXEyC7BuKBMmE2QlWjGO9pq46kVbuq8cHMLc4PziGyrODaD+bhug/n46xXZ2LMfLYR+11fLcOtny7Gzf9dhH8YvEWcYCcxlIwWY0ckEBhlehCwgnQJeBAAIKvMqWDhMZ3pqjKyg0A1i3VLBokhe/NWdXJufN5SXUxOiCf4z0OzxJC2g4D9PXiI78hv8HOJR1LGmHxRA7bG79f4rOneJh3d26TH/h7RM7+p05DlQaBNELCTQwBav4Og6bz5XM47CAJhmatDJVUy/0YGFX33l5chMWTuICDkhCiJIQcdJfFidc3Z7f/0rPlIWfMIUBH102F2OyUITweBNhHo4ZAY0j6Tct3OEwTUs1L7DA+LDoJW5/P5xXhz6mbM2VyOf/+wGnM3l9u/ycCHs7Zi4KO/4qjHfsXPDI80qjChLhDGr6v3Yu7mciiKgtKaAEbP2Ua+n7rXnWIsElGPoTUoJ+ZXrd3FGy+1xFhZPZfU+au36fanxvB2HWk7uBME5n3/sGyXbr6mVqTf8/UyjHxpOkY8OwU/rbCXrXl3hn5OwPJ7UxQFH8zcglEvT8dfPlvMJZ9qhV1yhPVbbJTCMcKaa1MSUACwt6oRD41biX+OW2kpbUQlgLRQCQIqWQjQ3eCFDuV8qKI91mcEoEvG8DCoc46j9QWHDkJiSHBYEZEV1AbCyPB74hpUsaqVEwlSTlq1BxMIQ6ZQRMGMDWW4bEhHANZVBKGIYpK/4DXKcwpru/d8vQy/rC5xtK3WSBDUBSJoDEUQisgx+RUqSKqaQ1Lb5Qm88kINfB/+vrnK4dyB7TCkcy6O7ZZnfq/NJNEqQaB+5qqGEPZbDFJy0/QBtIfHrcLcLdFBwZ1fLsWADtlYVEy3OavVMawqCMoAuTEUwauT9Ymlt6Ztxn1n9uHqINAOehOp8FJZtasKj//k3Hy4IRjBjI2laJ+digEds2PHs6GkFpkpHrTPoSWqeHwDeNlYUou1e6rRPicV5w5oZ2qFLa0JkD4WxkmO3cTw49nbsGt/A4Z2zcOlQzomHHSl+G1tSawSC4hOtK8d1sW03r6aRozXyKp8tWgHHj7vCFupJQq7qrVDqYMgEQ8CpxJDgN5cV4UKFuan08NKUoe+KejmMXoQsCSGtB0EMYkhQ4IgLdEOAh6Joeg6J/QswOzNZba6w0Bzxbf2PMpEB4EEGQpc0eRM1TogVA3kDwUkCX7DdxBPQNgrRRDU7FdNWKT73LrKMmPS3O9x4+Mbh+Kd6Zvh87hw+ylNUjlMD4Lm5bS81AE0KebtIEhAYojqIAgZdOp9LoXMrxjvbSqhRy1jJdaSiVV1vlXy4IKc6Xi98wvAWgDrngfOXZ2gXTIbrgQBs4OAHgtq77VcDyUxZH0tkR4EWokh4UHQ6jw6Xj8O/Oe4lZh236nc7y+vDeC5SesQjMgIRoAnflqLM/u3NY3LqA6CZyc1y8DeMbInBnbMYe4nGR0ElMQQtYxFOCLjv3OLsWlfDS4d3BHHdDXPX1hQBVhhWUE4IpOJcCfUB8O4/9sVWLq9Eucf1R73n9kHriScL5VaYvxeH4xAURRatsmmg6CK6iCwmCPUNIa4C9io8e3MjWb/gpU7q2IB/mBExhtTNuG8ge1jr9351VKU1gTwt5E9cetJPbj2rbJ6dzWenBBNQmzcV4uirBQ8ekF/R9vQsmO/fXKEuo7sin1Ykm7VjSG0h3kud9PohVjbVAC3eNt+TLzzRFOMSVEU25gHVVzHijst3Gaehzu5ZwE6ZsDTic5LVhzzMMGhQYslCKqrqzFx4kQsXLgQixYtwq5du1BaWoqGhgbk5OSgX79+OOecc3DzzTcjP9++RWXOnDl46623MHPmTJSUlCAnJwdHHXUUbrzxRlx99dXcx/XFF1/g448/xooVK1BZWYmioiKceOKJuP3223Hccccl8pEFB5iy2gBuHr0Qy3dWYVCnHHx4wzHIz3CWbWUmCDTLG0MRTFixB1mp3pie35zNZXh64lq4XS48dkF/DOqUE1v/49nbmPtTtRkD4YhtRcXuygZDgiB5D3kt1A9KYyjiODkAAA1B9meqrA8iEJaZmt+ltXzBuoZQBEOfnIyaQBh/Pqk7/nHOEWSGPNXrxsSVe8gBU0l1I5btqESHnNSEDHcWF+/HG1M3Wa4zYcUeTFhBVxoZg13BsIzZm8vQMScVPQszrBMETYEaO+MmrRmeLCux5AAQHWCykgNA833ASt5QZkusoGxVQ4hLd1fd5y+r9+Ler+MzqVaPoaohhGs/mO94gBSOyLj4rdlYtzdaOfjcpQNw5dDOuPurZfh+2W543RKeu3QgLhnc0fReyrg5XrSJjS2j6nDHab1if6/cWYVr3p9HJhqNCQEeiYxf15Tg1zUleHv6Znz/1xHonJ/GfZxzNpXhuyW70KdtBv44ohs5CXzPUJG0haGzurvS/BzYX2fvxUBhlyDY28oJAlb1NQ9MDwLWNuPpINAlCMy+NFQXA7OzwVJiSNJdI6xtaIN6atLPlCBohQ4CNRB7xdBOuP+sPigur0d+ug/XfDCf+R7KpJjyIPBJIQQUP051fQ1MeB6AAnS+AjjhK133Ae+xGvFIYV0le6yDwG/dQeD3uNA2m5rssySlNAkC5nXetE6rJwiaOgjsZGGI4+aXGDL/RholhjyMBIFR5orXpJh57xvWSgQrCS6r157s8GbzH0oYWP4P9C48MaFjYcGTKNF+t24eiSHNvZZDdBDYXUuUR4H2eUaZZgpal20OpTknry3RSdztqmxAbSBsGpvYSRu+PmUTXrhsIPP1umAE//p+FdbtrcaVQzvHisqcQM1tjcHMX1bvxZcLtqNHmwzcPao30jW/Ce/N3ILnf14PAPh60U5Mv+8UdMy1HxOGIzKzqjqYhATBv75fjZ+a5lPvTN+MYd3zcGofto+bU0h5WFlBMCKTyQCrDoJQhO4+s/o94ZUXAugOAup34pO523R/q/MaAHhywprYWPzpietw7sD26MAofqJ4Z7peemj0nG2JJQgq7Lt6qxvDpnHfVhsvAFa8hdLnL68NxJIDALC+pAY9/jkRfxjeBfee0QdvT9+MNXuqcf7AdpZ+AgCdINDOz+ZvKcfKXVUY2bcQi7ZVmNatDYRR3RjiDsxTxQzU/HdvVaNlFzeFJME0JhUcPrRYgmDBggXMwH1paSmmT5+O6dOn44UXXsBnn32GM888k7mtRx99FE888QRkTWCrpKQEv/76K3799VeMGTMG3377LVJS2OaCDQ0NuOyyyzBx4kTd8u3bt2PMmDH44osv8O9//xuPPPKIw08qOFj4auEOLG/SZ1u2oxLjlu7CLSd2d7QNVnZWHUgpioKr35+HpdsrAQB/Pqk77juzD+74YmnMaPTvXy/D5HtOjlWRWLVzqT/+PCbEe6oacbTm7+oW6iCgqot3cmTxKVhSLuOX78a93yxHICzjjyO64d/n9zOtw9tBADR3X7w7YwuuPrYzZKKqcX99CH8ds4R8/9MTo9U8aT43Rt90LAoyfFiyvRLHds1Dpzy+wdGHs7biiTgq07VodUzDERkXvjkba/dUwyUBT108wLJSNSIrCEVkWw1E7bVW4bBzQr02nOko0gMx3tbThqbukIfGrYzbfFS9Dj+bV+w4ORCKyJi2vlQ3iP7X96sxpEsuvl+2u2kdBS/9uoGRIGiZ+/Tl3zboEgRvT9/EPD9miSH+Y6qoC+LJCWvw3vXHcK1fXF6Haz+cH7tWIzLwl1PMVUhLtpsTUbwGYQ99vxLXDutMVutZEbCp7mn1DoIE/BRY1brM4Ji2gyAOiSEqQUBVE7OChJScSLNJscGDgCUxRHoQ6NdNNEHgpIMg1evGwI45GNgxRzeBpFBlYrRdAJTEkL8pQXCZ8lzzwu1fA5UPw+/RB2R4/BKMGL8zn6aDQIsxqMPsOmF5EEjaBIFNR4jcMs9IFuqx2crCUBJDIb4OgkHtra/DwWlrMSz8Nfma8d42ek8AdFUxz7WrJJggiLeDIMttGEPu/BE3HHctEL9iXEK4WB0Eir0HQTwSQ2QHgea+CIsOgkOOvVVENzJRvMCjt68t1KH4dF40eLeoeD+65qeZKvgVRcHva/ehpKYR5w5ohxxNJ11NYwizNpkLo7RzgW1ldfjzp4sBAFPXl8LjduHBs/vGXleTA0B0rvHfOdvw0LnmeZsRqzlGICQjkYa/DSU1+G7JTt2yhVsrkpogYM3NG4IRMhlg1UHAigUYu/bDERnVjWHkpnm55YUA2oPARYyP1zI8v2RZwfyt+qD0pJV7HMVRrObtsqygvC6IdL+blKyk4Ik9VDWETOM+u3kwy2eNKrxkzeE+nVeMSav2xuayM4jubSNUYkKNL/26ei9ubboHX/x1Pfl9AtFgPneCgJjrGYvWgmEZl749x7H3XJrXnZChs+DgpkVTP506dcL111+P1157DWPHjsXcuXMxe/ZsfPXVV7j88svhdrtRVlaGCy64AMuX05Wh7777Lh577DHIsowePXrgww8/xIIFC/D999/j1FOjrYATJkzAH//4R8tj+eMf/xhLDpx66qn4/vvvsWDBAnz44Yfo0aMHZFnGo48+ivfeey+5J0HQarzwy3rd32qbmxMaQvQPgRpgXLGzKpYcAKIB6dW7q2PJAQDYXFqn07nvnN+s22tE1WXnCdjtNjy8rRIPiUD9oDipYtDCard7/pd1sUHzR7O3koMgJwkCLd8t2Rm3ZFB9MILbPluMs16biXu/WY4zX52BlYQOIKDPwv+wbFfCyQFAf77Gr9gdCzrJCvDGFOvOBCAadLf7ruqC4Zi0yb5qZ+dYPb4Sh+8z4ve4dPeM3T7X763hXp9CHWhNXuu8C6a6IYTf1uzVLQtGZExaqV+2q7KBNNBtqfsU0A82JxqOR4s6adlRUY+r35uHl37b4Gg/v64piRmD2fGfKZt0McPnfl5nWqe8NkDGFamJdIQInMzcWIbbPluCaevtB+R229ey10HiKxkoSvxBIbaRr32CgLeDQGuom0IEiGmTYvqZb9VB4HEZPAg4/BViHQRJ9iDg0fVXq+O1ckF2lVQeF9FBQEgMMQONe6eY9hGPB4EX+u2rx2OcsJslhlifz76DAIyET+wbb+UOAvXe8cfpQWDXQTCsWx6eu7CXabn6fR2Xvhzf9HiA+X7jfUUFA6jKW57K+UQTBFbG2OxOEepAwujqoCst2WgTHXoPAnqcofMgICSG2qRbn1fag6B5mdFsXXDws4eQMaEC4jyeV5PX8I1NFcU81wWi84NbPlmEh8atwoVvzo511u2tasRZr84kpWMbNMHsD2bpOzqNleBGeM1SreRx4rnmI7ISK1B6zSBdCrA7UeOF1alfF4w4lm2q5EgQ7KlqwHn/mYXBT/yG456Zgid+4o9jUB5bVPx21S7z8ysUkUmvCCsoiUyjxx0QTQy8/vtGDHzsVwx9ajIGPPorPpq11bQeBU8HAVX0ta0svuuA6vi2is8k6rEANF8zT0xojiGwkgOAORZkBVU0Zjxfv60pcZwcAIBUziSP4NCkxb7dU089Fdu3b2e+fsUVV+D777/HxRdfjGAwiMceewxjx47VrVNRUYEHHogOojt37ox58+ahoKAg9vp5552Hiy++GOPHj8cXX3yBW2+9FaeccoppX1OmTMGXX34JADj//PMxbtw4uN3Rh9jQoUNxwQUXYMiQIdi+fTseeOABXH755cjNzU30FAgOMRRFYRrbqAO81bvNP6zUj1N1QxiFTV6NVoEDNUHA4ydgfIA79SAY1CkHp/RpY9KDN0Jtl+dHmkKtatcGpOoCYdP2pq7fh+uP66pbFm+CoD4YQUVd/EFZ7YC2IRTB6wxj5iFP/Ia7Tu+Fy4Z0wsPjVpHrOEWtVokaPekHUDw/4CXVAduBkaJEBw2v/rYRH83mG6QZj49qk3RCitfNvY3GUIQ0P3aCOnCOZ/JQ1RAiq2ep5FdVQwi5hgBlS3mFANGBYteCdFt5MtWD4JXfNthWqrH4cNYW0jfDyFKiM8DIHIYZYEMwYqpUt5Ipe+C7FVjw0OnM12saQ/hm0U74vS5cPqST7USdmvC3JIkkCFjVwswgnS5BwBck1HYQeNxRnwCtGTp1DGyJIcKDIGZS7IoF0FnrGretfpfGIEPCEkM8HQRNwcJUzbXqJzostPg8hAcBJTHkCgGU9JoSJjwI4ukg0D+PYh0EfmMHAWeCgNVBoDmPrO/zpSuObNrGgekgsDMpZnoQ2CQIhnTJhQc7TcvV7T3d8Q1HZr+8HQR8EkOJ4bE6bscJqwMnq+NyKDGk/U4oiaFsv4LnLx2In1fvxZR1+0yvG+87QH++hEnxocemfbWmZfuJICtPB4GTTtP5WytQUReM/dbJsqIr+igur8eibftxXI98jF26kzl3qA9FYl2ba4i5rQpV+EI9kygqLAp77KSXjGzaV4sr352L8rogTu3TBrOJcSRlPqvOQ6kuSCsURWE+6+sDYcfGz8bfVBVtEmLMvO2xbmWqYMXncTHH+1RQmbdbuCEUIa8Tq0r/+mBEJ0MFmKUKgWh3zMua6zMiK3h20jpcMriDrtOFgseDoLi8Dkd1zI5Vs1c1hBwnO1QoiSEeScFEWLGzErVEXISFk05nqtvR+Bnnb41vTkglgwSHDy3WQaAG4K246KKL0KdPHwDAzJkzTa9/8MEHqKqKZqmfe+45XXJA3cdbb70V29cLL7xA7ufFF18EAHg8Ht36KgUFBXjuuWg7d2VlJT744APbYxccXsiygv/7fCnu/HIZ+boaDPQQgZXrP1pgWqatYLf6gd5T1Yj6YJjrB8gYMHcSeDyqUw7G/uV43HV6b5OUgBHqeOPtIADMgVQqCKfq9tU0hvCXzxbj6Md/jcm3OCUYlslBerxMXmue7AFRHcqXf9uAP45eGLf0jZHqxhBW767CGa/MIJNRdpRUN6KYQ0d1/PLdjpMDQHSAPn9LOXbFKTmlUhsI43NOvcOGYASrGF0cvDSGIgiEI3GZM93++VJUEYNGKsFhHJQu2FqhM6MDgAEdsnFy7zaOj4OiuOm+LLaVlYoe/9ilu+Lel1ZiyQqeltN1jBbneiKAb2X6ZZVkUhQFN3y0AI//tAYPjVuFu79eZjtRL60JJGQcDERN6B79cTXu/HKprewMEkkQMCWGWAmC5mvYG4dJMWCWGaJlM/ilj9TP4EEjRpbdjk0DLsA3Pe5HkZfuVtF7EET/bQwIZKV6Sd1dXpx1EGgSBJwdBNqkC9VB4JNCdBeBEtZ1H/AeqxGjFrp63MZAQJ2hEpItMcQvyWTkzCOKmrbR2h0ETQmCODwIGjk8CNJ8biBs/o1UEzrd/LQHUWw9SUYbTwU+6fYvLDjiD+i843FT8F9NEDx18ZG697U0lJZ+8/4dfo8JPmsTgW1STI8RJM36uR7zb6EkB3HF0E7413m07Ar13egkhkQHwSGFoijYSCQISmsDWFxcoavy5ekgcMq4prHcuKU7cezTv5teV8ceWmkgI4rS/DuqTdCrqMdNVb4bA8MsrAK1VMW7Ffd/uzy2vanrS8lAubGie9zSnej3758x+Inf8O70zbHxXURW8P6MLbjzy6WYSiT0AGDpjkqwVCDrgxHag8Cqg4DhxaD9PaGSTlp6FWYwX6OuM9Y+jfy8ci82lJifa1bXLtWpn+IxxxemrTef32BExlXvzcPMjaVYvqMSszaWmRJR4YjMFQy/88tluPm/i2LXQ7zdAwDd8c0jKZgIE1fuxWkvTeNen3VOqLkLKTFk+IzlcXbniwTB4c0Bd5fIzIyWWTc2mi/477//HgCQlZWFSy65hHx/x44dcfrp0QrC33//HTU1+gdcTU0Nfv89+uN5+umno2NH2tznkksuQVZWFgBg3Lhxzj+I4JBm3tZyTFjJnrSpBk88lSCAvgrdLoO/rawetQH9A9vtknDLCd2Y2wScmRSf3LsNXE2ToC4WkkdAciWGAKDRMGCiTEfVQczXi3Zi0qq92M85qKEIhCNxSww5RVaQcHW7ltpAGM9MXEdOPHgoqW5EcYX94OgVhxIzKt8t2Ykr35uHJRqZrXiIyArmbeGTrGkIJZ4gCIRlywopK9buqcb45eZkFTUI1d6jszeV4cr35prup64F6bjgqPZxHYsR9b60m1TUBcIJByD2VDaSlWTxwOoKaCAnXPENzlfsrNJdpxNW7LFsdweik2Xt99UQjODfP6zCWa/OwAu/rOM6hw+OXYHRc7bhh2W7cdV786wnwQkEyCRmlT1jm7JzDwJj0DvFVMFOad6yJGfYAbK0vT+ia/0keCQZQ9PX4A/5P5Hb0HUQhGmJIZ/HhQzO4AWFEw8CbYKAGUBvQk3KaBNo1DfFThBEkmZSrNsfo4NAS667Cj1da4AINTFleRBoOggY0jep3qZz0eomxXweBFTAu6ohZGtEmOJ1AxFzIYSVfr8WCQr+3OY7nJS5FIXe/Sjc8RqOSdNLGKrFKhcN6oCLBrVHQYYfgztl2W7bkQyQgQ45qejZhu31xrp32J1nBzBBoDlWXeEPRwdBuouo9GySJmIFTajvXnuPGL1UBAc3ZbVBsuDk/m9X4NK35+KUF6dhyrqobBDvvNEJC7dWYOn2/bjn6+WkzAnvb6A6vqKS6qqUD9XNzashbzXmcnJeVhrGcyy0UqTBsIzHxq9BICyjPhjBM5PW4Zmmgp2PZ2/FUxPX4odlu3HT6IVYbyiAeXriWlzy1hzmfuqCYdKDoD4YRjAsk+NWVrBeW+xXE7Ce+3Zvw04QUOeTtyjq/u9W4P5vV5iW1wfDzHE/9Xko2ShK0giIFh394cMFuPDN2bjuw/kx/X2VPVWNtr+1KlPW7cPUpkSEnf+AFaTEUJxzECc4ke3dY+j02FXZgAvemIWeD03Cfd8s131fZAeB4TPujENeCABSRYLgsOaAJgjWr1+PZcuWAQD69u2rey0YDGLBgmhl9nHHHQefj92GdPLJJwMAAoEAFi1apHtt4cKFCAaDuvUofD4fhg8fHntPKNRyutGCg4Mflu3C8Kd/x8iXpuHGjxdarqtWWVRxBp61gyK7arPNpbWmboCebTLQr71+srevJoAPZ23Fvd8sx6yNZeQPGQvtpKV7G+cJAidGSUaMFcCUfp46EE2Gjn95bdCx+e7BQlUDbSbGy7ayetNAY2Rfs2FXInr+FB1z+Yyc46EukLjEUGMoYjLfShTjhALQ3/cv/7aBjP9mpXiQwdmebYd6X9ollOoCkYQNeIMRWeet4gTjID8YoQNlVPWVXfUdawIxj5BSskukAPrJ8JcLt+OTucVYt7cGb07dzOVhMWlVsxdEVUMIPzf9rSgKfl61F4/8sCqmN6wkEKxz3kHg3IPAGPTmqWB3g/69oxIX0cCxgrTFN+iWX53/K7kNN+FBYAys+dyJJQgkjqC7GvB24kHgpUxliX35XIwEgWxOEPAkM4wYA5WsDgKVfilbMLnPX3CPcj0waRAQMDxHGR0E2u+blcyKBYhb2aRY4kwQROV09Mdul2QEmibORIKAN6HjlmTc0uYH3bJHOryvX6fpekr3e/DqVUdj0cOn44pj6AIo/bYTS8ZYyVqxnknMrqxl9yd0LImgPQ+66mmWB4HmOvBKxNg7Ev3dYCUIqPOmvX9FB8Ghhd1YIhiW8f6MaJeu00p5HkpqGjFu6S5mjYE6j7PrplPHXI3EMe5tGjPuqzGPHXmvV6sOgr99sZTL2BUAPpm7jWu9strmLtDdlQ2mIPZ7M7Zgyfb9sUSBygu/NP9d1RDCezP0ngxG6gN0B0FZbRB9/jUJQ5+cjB+W6Tt2WR4E2jm3nTJA2yw/87VEOghY1AYiqGUEyI2FeJv21ZDf5+rdfAVek9eW6O4rHnkhLW9Ni/pmJKIgoMrvlFQ34m9fLMUV78xNmoRwsjDO5d6dvhkrdlYhIiv4ZvFO/KaZp9h5EMiygk1E5wgPooPg8KbVEwT19fXYuHEjXn75ZZx88skIh6MX71133aVbb8OGDYg0BRGMyQMj2tfXrtUbuqxZs4Zcz2o74XAYGzda67QLksu8LeW444uleG3yxlYZKNcFwvjH2JXYW92ILaV1threwYiMcETm/rHVdxBYDw4/nr3VpD+ZkeIxaSlv2leLJ35ag28X78R1H87H8h38VdX6BAG7AgFozjjvqKjHFe/MxTFPTuaWF6FQEwSKEjUqenDsStM6yTQHLa0NJFViqDVJ1GBrUbE5CP7mNYORlaSANIsxtwzDc5cOaJFtr95dZWnYxMOUdftMUj+JQg28tPf94mJaiz8zxcut32rH/C3lkGW61V1LXSDseLBNwfJo0UJNR41VLKznLZUgaLBo2QbYXiWUuTiPLqk2efbYeH3C0s74nqq0Uo3A35y6Cbd9thj/nVuMWz5ZhJkbSxPyIGBVAjODsQl6EABEB4EjiSGHx0vg1nkQyFAUxSQx5HW7TPeXBBmPt38bGwdciJ973Y5uPrbUFk/QXT1mrQeBXVcGZSpLBQ39Ugg+KgCphE0+B/FJDDE6CBiTvVvajEO+aspavR7Y8KbxwMj3STwJAvX6b+0Ogpi0lf3zwHg98LTjp3ppiSHKqJbep3m9NEPVOiUJou0SYuFJ1KfA4rsyPg/ciKCdtxRehZFYrkq8ICRetPcOjweB9vlFSlPFOgjo33YqGaXdZjhJ3XmC1oElk6hF9XtKdPxKsa86gIkWXe9q0DnVRndfHXNRXQh7LToIKDlIinKLopKtZXW4/qMF2LTPen7ZEIxg/Ao+ydlAWI7J4+1meEqtbAqmatFKydodDxD9/Cw5IUWJSvQ9PXGtbkzIKjDUqgHYydnkZ1gnCEIRGb+s3osTn5+CkS9NS3huXR8MM49J2+n/6bxijHplBu3J6ECiR+uRRqkNWFHTGMIzk9bi0fHx/66oHRyP/7QG45fvxoJtFUmTEE4Wxu/0k7l6ud5//9Cc0DCqUwD6BMGuygaTnCQvrjDzUwAA1G1JREFUqV5hUnw40yrf7ujRo3HTTTcxX3/wwQdxzTXX6Jbt3Nls8MWSBVLp1KlT7N87duxI2nb69aO1JFlo90VRUmJfffi/SEl1I67/aEEsaLS/PogHz+6LTftq0SU/DZkp3qTvc9amMkutQIrGsMysADCy34HE0JLtlWgwDCDT/R7kp7MHAgAcVZprB4ndC/g6CF6ZvAELtiVeda0G+L5dvFNnVKRlb4LVzVr2VQdQmMlugz+cWbFTHxQtyvIj1edGht/jaJDmlMLMFBzVKadFtp2MeTOvnFGi/HPcSrz82wYc0S6TuU5migeZ/uQ805bvrMINHy+w7Q6oDYaxM06jcS27KhswpEuu4/dVN4aQnRb9zBV1QWZQn0oG2E1Ed1U2oG22/n5XFAUL43x2WZmj2yVIWH4Jv6zeixd/1T/7vl28E1d1SiBBwOog4DIpTpIHQYImxVbLKYzV/YGwbEo2+TzmBMHgtHW4vmACAKBvajH+WvgN7tt5F7kPnqA7JTEkSRJuO7kH3pm+mXwPlZSh9Nx9UogOQCphUxIiHrkYcwdB9DNQ5oIAcEnuVP2Clf8GBvxLc1z2nhPsa/IAJQigQILMJfnjkSKIKM3fM1cHAUNiiLd6n7q3jUkW2uqFp/sl/nMtSbDs9tA+D7Jctfhvt0dwdPp67I0kR1IvmWivSd29qTASBLH1FaZHCBQZbpcLfo/LJPdBexBoJYZEB8GhhJPxRUtIDLGMh1XUeaddMUBdMAxFUVBWY36uNXcQmMdEdoUbKjzPyycnrMXom45lvj53S5mjJEtZTQAZfg/2MALMVBe7Fp65UtSk2PoclFQHUN0YihnxssaH2q4Bu33np7PVNL5cuANfLtzBfD0e6gIRZleD1nT58fGrk2Ipo5WMtBqLU2wprcO70607P+yYuHIvuv9jQlLmnS2F3T2lVRGgvrv6YDSR5HW7yE54XkQHweHNAU3/DBo0CO+99x6GDh1qek3rJZCRYV3xnJ7eHPCsrdVXUiZrOzxoEwwCfiavLdFN8kfP2YbP529HMCKjXXYKvrx1uKVu/uLi/Xh2krmy00oTmHdwY3wPbwfBB7O24sgO2bjgqPamNsQfbh+BO79cim0aM1mjmWWm34O8DPZAwCmpcUgMjV0Sv6GploZQBBFZwSM/rmauU5LEDoKy2gDK66wHFj3apGNzgtX6hwLqfcNrKBYP2alepPrc6GnTmfK/QlltADM3sq+/rFRv0iSGAGDmRvtEYbI6CHZxdBBEiFlCTWN0EvrK5I14e9omhBh6y6TEkM2z+tK35yDV68YVx3TEoxf0hyRJ2Lm/wZGmp5ZSop1ef4xh1AUiKMjwmQyZWVqlH8w0T1oWbduPKzvGPwthBtwZeu/aIKydXj5rPV1AHDK5LxdLYsipJBKBMSGhVsxp8bolU1HBg+1G6/6+PG8yM0HAE3RvlhjST5AeOKsPOuSk4F8/mH/rtEmZ3kUZ2FBSa/IDAFQPAmKcIYfhN3Rw8Gra647DsE+/TQeBPTbyQbC4JtVzrbRuhZ5Lki3NdrW4pYjuI1qZpquk+txAiPIg4JQY4upiIQJ/HB1JvMfAxOK70t7Ll+ROwdHpUYPUtm6+6t/WRHvv6DsI6OCLGuB3M5570feGALcfaT63KShM3avapIGQGDo4CEdksttLi6IoWMApWRmOyC1iUmxHbSA65rILODcEI6gLRsjnmlqpvI8YS/F6Q/F0bU5bby0zNIVhIszeZwBdC9Kxh9FBYKe5XsoxdqwLRmyL/4CoxI+aIGAlitRCB5/HZestWGDRQdAS1AfDzGPaXxddLssKc0zvFG3yhupqaQ0O5uQAEC2ijcgKahvDmL6Rfe8oisLs/qhuCCE/w4/Zm+OXNBYJgsObVpEYuuiii7By5UqsXLkSCxYswBdffIGLL74Yy5Ytw9VXX42ffjIb0mlNi638BwDA729+YDY06B/8ydqOoOXYQGQw1WqaPVWN+HzBduZ7wxEZt322GAu3meU8fG4XAuEI7vl6GQY88gtu/HhBrLKf1/hGS2MogqoGfumau75ahge+W2H6sclL9+FmgwGxkQy/x7JSwCnaB3k3zg6CZNEYiuCLBdstqy3214cw8NFfkrK/sKxg+Y5Ky3W++NNwbqPOQ5kueWkAWjZBcM6AdgCiEhrnDmzXYvs5XMhK8SRNYoiX+kCESx7Ijl2V9kkGlinWsz+vw+u/b7ScSFCTTp5Or4ZQBP+dW4zZm6Ltyb+s3mvzDjZ2/hwnPDcVQ5+ajP/7fKlJUoiVeKakw3ZVNqCiNv7EKNODgCnn0nxueTsIjJr32k40VhCT3UHg8HgJjJ+5IRQxSXNQHgRWuul2+6BQq7mN+s6SJOG0I4rI93g06/7j7COQ6nWTQUOfK8zwIAiZTYrj7iBQcFLGYozIWAZf03Hxmk6a4PAgYF+TTctbuYNAgkInYQh4ZYG0sCSGEukgMEJ2EHCcR1ccn0e/D74Ogkc7vJfYfloYnUmxzoOA1UGg+lZYXDcWMkM+Uo6t+b4IC5PigwIe6Zw1e6q5fbyqG8Mt0kFgR20gjOrGsO1cty4QRhmjUjtmUkwEanmL7Fjb5kVRFExdx+dToPL5/B34ZO42rN5Ny0BNWEFLM6njT8pzwUhD0L6DAND7DlglimoDUXNju2slP4mFgzzUBSNMiR3Vg8DON8EJ2kImpx0E/ysoCrCnqgHXfjgPd3yxlFynpjGEQFhmStdVN4ZRGwjj20XWyidWxD1mFBwStMq3m5OTg5ycnNjfQ4cOxVVXXYVPP/0UN9xwAy688EJ8+OGHuPHGG2PrpKQ0SwaoJsMsAoHmh0hqqt4sM1nb4cEob2Rk2bJlOP/88x1v93CnzKbCYKtFpfeq3dXMHxGvW8LElXtilfDT1pfi4znbcM+o3nEFwRtD/B0EKt8sNj9803xu5NnIB2WmeJDidSPN53YshUSh1Yqzk2ziqYpwwqdzizGDo8o5mRI4dvmfzBQvhnTJ1ekdHo4c2SEbAJDuT36m/6z+bXFM11xcf1zX2LLHLuiPrBQvvrBI6h1oMv0e04A3N82r09NsSbJSvAmZqMZDZUOQ9KdwirGD4Lc1JdhQUoPzBraLdatQk4UVOyu5Wn+pKjaeil2Vx39ajR//7wRbgzkr1N8TVoWc2t47YeUe3DSiK47pmhd7jfKbCcsKqcsKABtLaoA41aYkTYBrRM989CnKwvqSagwozACoBsg4PAiMiQStBwE7QeEsESA5CHIbt01da163i7tD51/n9cPsTWW6CkWeoLuVb0I6Y9KkTSac2rcQ0+8/Bb5FYwBDrIIpMRRp4DKJtsMjhfFSx1dwad6U6IIlu4GhbybwG8HhQcA0zm5a3somxS6WTAxBPKa+LJNifg8Cni4WYmFrdBBYfFdqwmtoV+cydK2NNgnjcfN7EJD3Zuy90d8G6l7yuogEgeYYhMRQ/OytakRVQwi9izJMXX1AtCBMAuCyMewFovOfLIs50s+r9uC2z5ZwH1tlfRCBVugg6FaQjq1lzXPlukCYy4utIRRhVmrHJIaIDu/6YASrd1ehuiGMY7vlxX7fqhpCuOerZZizuRwj+xaiuIKvc/XRH1fjgkHtMX19KVbvrkL3Nhm487Re2FXZYCunZOS7JTvx3RLngc/tFfXo2zaLKzA9ek4xKmw61QG9DI9V8N/OewCIJoVz01o5QRAI20oMVToonASAX+46CWe+OoN+bXUJLn9nDt65bohIEFjwwi/rsWoX2wdl475adMpNY77++9oS7KpsSMhfQXQQtAyLFi3CxIkTMWvWLKxZswalpaXwer1o3749RowYgZtvvhknnHBCix/HAU3//OEPf8BPP/2Er7/+Gv/3f/+HCy64AHl50cl2ZmazhrOd3E9dXfOPolFGKFnb4cHO42DPHrah0P8ydpWtlP6hilVbrsftwquT9WbTr/++EfeM6h3LfDuhIRTh9iCwIt3vsQ1gDOqcAyBaLVCfBO1w44PcKiC6oaQW/xxnNhKOl6k27aOtjdslIcXrwvOXDcQV78611W8/WDl3YDuc2LMAk1btxfQN5nN8bLc8XH5M9JnEClolwmMX9kdRll77vSDDj2cuGYDlOyqxZo+9iVtrc0S7LAztmmsydeqQm9pqCQI1+Wclc5WVEvWMaJPpT8ogeUOJc8k8Cu1E7YsF2/GPJsPxN6duwvT7TkVOmpcM6P+8iq+iX5sM3bSvBhV1IUcJyy2ldfh60Q7L3ww71MkypclrZMbGMl2CgEoqlNcGmFU8G/dVAR3iO05tcDjD78G/z2/yTJrms00Q8HZPuQzBFq1JLjsRQAdDJIYsB1t+xowxIE61vns9LltjRhWPSzJNuHkCwrHAXl1TUUh6s7xkKmPSZAxcFWamACnm78EnhZDqphIE9aYOAp5AspEiT0VzcgAANr4FDHo2/i4zDg8CdljuwHgQSFCQ6eYLXMUTUGd7EHBKDJEeBIa/yRYC++3Hk/Bo3iesJYaa9n/36b2BBXHvplXQdxDYSwypn80ysdT03lRivEVdR9rOJtFBEB8/rdiNe75ejmBYxtlHtsVb1w7W3Rsv/rIeb07bhKLMFLx13WAM7hxNXikMwXS78cYzk9Y5Or691Y2YzylHlAgDOmTrEgQ1jWGuee6dXy7DEe2yyNdUmUaqg2Djvlqc+/osAMDpRxTigxuiUtGfz9+O35sS7hMsTJSNjJ6zDaPnbGtesHYfJAnoXcj280o2xeXRBAHP+JFX/kZbHBKw8FGotpEWAqLjPOMYoKWJJggYEkNNcyYncyeXFJX4Pf2IQp0xtJaF2/bj3z+uPmASQ3Z0ykvFjiTEZRLhh2XWsn2bSmotk0lPTjDLcjuFNdYVxM9JJ52EmTNnmpYHg0Fs3LgRGzduxOjRo3H99dfj/ffft1XGSYQDrrFx4YUXAogG53/++efYcm2w3c78V1u5b/QBSNZ2BC3HTpsKA6sfCauqG1lWsIOxbaedAEA0eFWVYBDR45LgJ0wUtRRm+nFGv7YAYNtpwIvxQf7XU3parv/5/IO3AjxRMlM8kCQJnfLS8MWfhmNw5xy4XRIGNyVlDias5KD+Pqo3rjq2M47tlmd6rU9RJsbcMizWAtgSFetWAaUU7wH/aSEpyvKT1WEdcpx3jMWL2sHz1MUD0CWfrvCY/PeTMeaWYZh+3ynoYeMZ0pK0N5j/7trfEJtYj569Lba8PhjBBzO3MCuNVjFavY2oCYIvF2zHGa/MwBXvzsX3NgNhLWFZwdvTaJNYii75abhokN5AU50Ml3LI/xgna3VEt9ceC3+VeLxwVLQV+YuLK5tfYFURx+FBYIxBpmgq2Jn694xgbzIkhjyGZALVdeZzu7g/n8ctITdN/zzgMylWcFubb4EfugA/dAZWP928fycTeKJa+dRe2Xju4j7mdcP1Jg8CJ/4NKl39RNCmcV8C7eL2HQRsk+IDIzHkkmR08/F5LMUTUE9JVGKIOF8mk2LqjRznMZ6kkg4rk+Km67EvI+B4MOFmehDQY3z1s3ldHB0ERODE4yJMijXPM6OXioCPf/+wOuZhN2nVXp2szNayOrwxdRMUJRqof04T3GdJ71BdgCqyrKC43JmX0zXvz3e0frwc2UF/z9UF+RIEgNkDT6WkuhGyrNhq8k9euw9bSqNVCd8sTp5J7uQ1JdhcmpziFh6Ky+swevZWTOIsaOFBG2sIhNnXVlVDyDZJkOn3mLoIW5r6YITZ3RDrIHBQcFmQ4YfH7UKnPHZ1OxCVgdq4r/W+eyccCn57G0pquLpSEkF0ECSf3buj89327dvjzjvvxLfffosFCxZg7ty5ePnll9GhQ7Si7JNPPtGp7rQEBzyK06ZNm9i/i4ubqzp79+4Ntzt68a1bZ52x175+xBFH6F7r168fuZ7VdjweD3r16mVz5IJkUB8Mm0yMLh2s78QorQkwqz3qLQZzgbBMBjHrAmFHP2gq++uCCbcBp/ujweksiwTBDcd3jQUZkuVDYKyovP74LrjjtF44pU8bWz+EZEMWvrUi2mB514J0fPeX47HpqbPx3V+OP6gC22k+N/q2patnUryumKTLwI7ZptfvP6uPTh6kJTwI0iyqdFt7EMtLUWYKslLN56J9KyYI1P0P756P6fedSq5TmJmCET0LkObzoG/bAxds6VWkv/7qgpGYPNv6Er13zGfzipmVRkFODd6Gpgr8f4xbGbdRmJOOoLevHYLzBhoSBE3VY6UcHQTGz1VPVB/utTieeDTkqff2a6+5RuwSBLsmIGPVfTglc6HjfWqfj8zgNDNBwOog4D8HHpchQUB09PncLm6PBY9LQq7hN5bnO0l3NTQZHzcdz6qngHAcFWVENfaFAwvQo4AoDIjUm56r8ZgUk59PkZ1JDMma/TKuN610FDsJdIBMiqGgq58v8ciSBbKSxkpLVGKIy4MgPpPieBIefVK24W+FX2B4ynyuDoKDaRzFQnuOdc8LRoJAinkQWFyrEdWDgJAYIt6nvRdDB7sz5kHEql1V+HbxTuyrboxJ/qlM1FStf7VQH6zWVvKzvJCsOgio7khW9X1rkpXiMcmJ1AUiMRPZeAnLCraU1XLJkGxqCuZSfkvxsr2iHhsM48yW5NlJ6/Do+DVJ3aY+QcB+PlfWh2y1/DNTvM4KEJJA1KSYIbfZFEdhSWhSqM/aQzm4rM69D2a2ltWhJtCyXfGH8nd4sNK3b1989dVX2L59O1599VVceumlGDp0KIYPH467774by5YtQ+/evQEAX3zxBWbMoKW6ksEBH8Xt2tVcxaOV9fH5fDj22GMBAHPnzrX0D5g+fTqAqMnwMccco3tt6NChsRYMdT2KYDCIefPmxd7j9cYpDCxwBCUvdNvJ3XV/B8Iyzn9jFgY/8RuemrBGJytUx9CJBqLBG0paZVt5XVxyIsmQolGD01Y+ACN6FsT+nZekBIHxQe73uHHPqN4YfdOxuEGjId/S5KX78OJlR6FTXusFZI0Yq+klSYr998BZfQ/QUZkZ3DkX2an0ddKrMDNW9XZMlzwUZTUHlPq3z8KpfQp161slCOIxFk7zuS31XKkAgYdD/7WlcdJB0FIdJXYeIEaoBFCyYXVQtDVISAHRSTKVsK0LRhI2K6sPRhAMy2Dkgx0zrFseehWyq338XhfaZOqDsXuqGvHNoh1c7c3Gii+yg8BCPzeRZKk2sH5qnzaaVywC93t+A6afB8+m1zG622M4PmOZo32mcJgUt6v6Ed/1uBePtn8HKVLzbyYrEeAkSWKU6XjiJ3ObtNcjcbfhu10u5MTRQdDeZ/DUidQD9XGYvVHByEggFmjUL29Iikkx+T0oEaYMXaNMjEFCldo3M/bjwIOgtSWGJAXdeBMEjICwVSU+y4MgkQ4CI7RJMY8HAf+5vmhQe3T27cGPPe/G39uOwQsF/2RK8Gi3fbAWCGjRfn9uDomhWAcBh8QQ1Y1DnXfts8ZKLvVQpzEUwXM/r8NNHy9gGsTyMnX9Plz45mzc+81ynPriNNPr2muPMoVVk/qsogXqN7z5NfN3//ktw+wOucXpVpBukq3llRiy45fVJY7WT1ZRGxBN4szYYO9fl6wu25bI0ekkhiwSBFUNIVtvxIwUT1ISBKcfUcg976sLRJiFPxW1agcBfzxFlSntXUQXvx0K2HU/HAxUNYRavIOAktITJMZPP/2EK664IlYgb6SgoAAvvfRS7O9vv/22xY7lgCcIvvnmm9i/BwwYoHvtoosuAgBUV1dj7Nix5Pt37tyJyZMnAwBOO+00necAEPUgOO200wAAkydPZsoMjR07FtXV0Ta7iy++2PkHEcSFUQKoIMNPPnxX7apGRV0Q78/ciid+as7wW/2gBiMymdneWlYXl5dAiYVUBC9qlZ6VxJBW8iBZgy0r+QBeQ8dkUJDhw6VDOmLm/SNx3fDOrbZfLVbn/qYR3fDNbcdhSJcDb7J3TNdc5rFqB1epPjfe/cMxGNWvCBcf3QEf3jDUFLynWt5VjDrjPNh1JFABgs5JGFSN6Jmf0PsLs1KQRSRdqERcS1WJWH0XFAM75piWsRJH8fL730/G8O5mqaqCTPN5aQzKqG6gn7v7ahJ7RjYEIwlvQ8vtp/a0vN/9HnOCAADu+3YFJq2yD2QYEyKUB4GVxFAiHQSDO2WhY24qrjimI64+VvMsteogWHCrbtGj7d9ztE+9STE7kDIkfR1uLPgJNxWMjy1jGfs6khgyyHRQSRyv28XtseB1Ex4E8X4n8WR7KLkWOUAHKcOEB0EcEkM+iRj7yAFmNVitTCQPA+WaP+jv76kObyLLHa0qZZ6ZA2ZSLKOrjy9BkOuhK1hZ178EOSrFFTFLkfD6GfB01dAmxcmVGLr91J54qNNX8Lv4xsvq9eg+CIoB7NCeY7f23lXoz6o2HvIlCMz3kpvoHtF1EBzGCYJ3pm/G29M2Y+r6Utz++RJsTKAq/IWf18fkgahgvlaGjXokq0FzVje4VQcBJT+U5ndjQIeWL+KwoltBuqnwqaw2YPLaAoDXrhqEtY+fhauP5ZNR5vWPqg2EEY7ISUlKaOHp2r++FYvcnKI18LWSGKpsCDID8SqZKZ6Enq1tMv1469rBeOvaIZYd4FoaQhFmh0BdMIKGYMQyQVCQoR9fqfP+M/q1NRVnHCp0yuUrcDy1TxvcNKIrbj2pu/3KNljJDVM4TRB2d7h9wFpFQNBynHpqs/LA5s38krpOabEEwejRo9HYaD3Rf+WVVzBx4kQAQLdu3XDiiSfqXr/llluQnR394X3wwQdRXl6uez0SieCvf/0rIpHoQ/e+++4j93PvvfcCAMLhMG6//fbY+iplZWV44IEHAAA5OTm45ZZbeD6iwAHBsIyq+hD2VDVgS2ltbDBsbEfsmJuKFK/bMqjz37nFmLwmWtVgZyhFtYRuLa2LS2IoGR0EamA11etm/tDnaAIWydKOt2r5diQtkCAFGc3BuJzU+JMfP9w+An3irECwO6dDu+bhz0n4QU+UY7vmIcNPD6CM0kODOuXg/euPwStXDkLbbHPVt1VAf0QP50F3uyA3db11ZCQInAx4n71kIP7vVGv/DCuKslLIDoI0nxvHa86Dz+3C9cd1iXs/VhilITJtrscBRAdBIByB102ft4Eds/HfPx7r6JhSvG5yAEp5oDSG2UH8BVv3O9qvkfpgJGmm4ZIUNeqmEkIqfo8b+Rn0c2j2pnJyuRajxA0VQLDqhmAFzXkY2Scfsx4YiecvO0pX2c9MEMhhoG6bblHvFGdeM1oPAp7g9APt/hv7N8uM2JHEEIehscclcVfZuV2SaZIaj64/gGjlv1OoYGQkEE0SmJbXQ5IkXfKDCjrakeqitt3I/I1QFOI5E9BUdTKuN48k48kObwGwSoQdIA8CKOjO2UHw3qVF5HJWJb5HikQT9ITkFO/3RQXxzR4ELS8x1KsoE2fkLuFeP5GEZ2ujyj25JDQXVCgKU2LonCOjXZnJ7CBw6ToIDl+JoVcnb9T9/fJvG8j1FEXB5/O34/bPl+CrhdvJTsU1DL18FW0SlfrtVZPKrISMVdGZcb7pcUWfx/eM6m2ScW1NuhIJAiAq0aPlD8O74MJBHZDqc+OZSwZyFUKt3FXFdQy1gTDKaoMtUoWvpR0xvzmxV8FBMWej0PoWWpkU76iox3+mbLLcltPuYyPXDeuCcwa0g8/jcmQwW2LhQdHvkZ/xymT6fk71uvHaVUfrlp07ICrpmepz48tbh+OKYzpSbzVBFfLw8vFNQzHxjhOx+OHTk3KftstO5Zq3HtM1D4+c3x//OLtvwsWeTgP4NY0hLC7mn48d0T4LXRmeeCyExNCBIRBovh9ZnQbJoMUSBI8++ig6dOiAW2+9FZ988glmz56N5cuXY9asWXj77bdxwgkn4J577gEQlRN67733TB80Ly8Pzz33HICoP8GwYcPw8ccfY9GiRfjxxx8xatQojB8frVC7+uqrccopp5DHMnLkSFx11VUAEHvfjz/+iEWLFuHjjz/G8OHDsX17dKL83HPPITf3wFcPH07UBcLo/fAkHPX4rzjumSkY+dJ0/PuH1fh60Q48NVEvEaB2D9j9GHy3ZGfTtp1PLLeU1cVlUmylJc2LOoiTJIkc0Llden8CKsHhlFSvm9arbcJpO/ifTuwWd4W9LkGQQPXAUZ1y0NNCOsSKDI5B1km92+gMWo1Gli2NxyXh6M7sDoI+DG8CFimMQVFeui8uiaF4Ogi6MBIEhcS9Tl2uPrcLHXJSce+ZffD8pQP5DtRAUZafrL73e9147IL+GNo1Fz3apOPFK45CjzivL6c8a/gsN43oqvubek40hmQUZponSgCQm+aLK+lDBTUyiZbmxlCEOWGYt8U+qG5FfSh5CYLmZLNFgsDrgt/jxjFxPs94OgisSCRBwA4GOvMGsMKo5c8jMcSCFTx0cg7cNgkCn8cFSZJMho0sPK4kdhDIcVy3VDDSooPASDzJjFSJThCwZJnIgKi2g8AiA3ZBTlQflZUcgiI3XcetGxwtTAc6+fdxresP0Z1ErARZLAlASgzF30HQ2V+C73veg34pWwCwGlY4EgRN69x2cg9cOrgjOuSk4g/D2clwSeKfJsbjiXGgUM+xR/uMs3hGntw7H13y0+gOHJWmxB5VdOMiOwg0JsVy6yZXWCa9WhRFwdglO/GXzxbjw1lbEQhH8MaUjfjzp4t0Wv9OYZni/rxqL/45biUmrNiDB75biQkr9+D5n9fh1k8WYeo6vvtV+7GoymfVs4CVILD6Da83dCyk+aJzq1P7FmLhw6djzoMjkxKofuicI8jlrC7cbgXpXD5jRr8dp1XJVtQ0hrGnKg4fHgcUZPjJbrSOuWm4e1TvA97JQVHJKTH0xYId2Fpm7d+QaMGgVoqWNSe8cFB707ISi65eqwKYdL8bI3oWYMwtw3DbyT0w5pZhOE4zN+nbNgvPX3YUpvz95JjsMOszUnKnvPQoyEC/9lnIz/DjlhMT91zMSPEgh6OLWz3HkiRhcIKqBN0dymhVN4YxZzP/fKxnmwz8/Yw+jvyDnCSZBMlDK5dv9N1NJi2qLVJRUYH3338f77//PnOdjh074qOPPsLpp59Ovv7nP/8Zu3fvxhNPPIHNmzfjj3/8o2mdc845Bx999JHlsXz00Ueorq7GxIkTMXXqVEydOlX3usvlwr/+9S/ceuutjC0I4oX6IfpiwXZ8scBcvditKYNZmOm3NDtSvQvsOggoxi3dZb8SwZ5q/eBnUKccbNpXa6sbqEWr85uZ4jENYHNSvbpgfrxBcC08Wd68dJ/J7ItF76JMZKd6HWWnVbQJgkRlUo5ol4kJNpOUQZ1ysGxHpW4ZzyArxevGpDtPwsRVe5Cf7sNJvdvguGd+j8u7AgCO656PuQ6CpwM6ZiPVx+6kYZkXs6ioMweF/nlOX1xxTKe4qlJYetUqfmKQwUpqtMn0m4LCHXJSTf4k7XNSYpV+8Q5MirJSSF1alyShV1Emvrnt+NgyRVHg87i4DHbvPK0XAmEZ70y3bvfrRxjajepXhLP6t8XPq/eib9tM/HGEeQDbt20m1u1tbsu/+OgOKC6vi+l5aslI8cDjdsHvcVlOSIxQXSEZfg9SDOfASgbIeK85pSEYttTsd0KPNtFnp53EEAA8fckA3PLfRaaKOztMHgQcCWuvW4oZJEocFfFMWMEsp8sZ+DwunNlfXz3NKzFEwZISsqzINWAXYFWr6wd3ziWf/UY8LpcpQdC6HQTEZ48wEgRNkjVayYV4khnHdUkBjAofkUZmEQEZ9NV2EHAcA7uyXG717gEAOL97A7CXb7/ewG4A/UzLWYbDMRkhQmKI955hfa+D0jbg8Q5v47LNLzBMijkkhpqOoV/7LFxwVHMw6NN5ejmSWKeKxP9b65LkhHxVWhP1WeLh8B8AgDSPhEl3nogtq6sBs/WJ7v3U+MQNwqRY86wJhVsnSTZ3cznu+HIpqhpCuPeM3rj1pB7sdbeU456vlwMAJq3aiy8XbMfGJjPaX1aX4PvbR2BQpxzHxxBhRBWN46f/+3xp7N+/rinBzPtPNb7FhHZ8R81pypt001njulqL33CjB4F2LpHh9yDD70F2EoqJLjq6A6Zt2GfqYhzaNQ9pPrduLAhExzo8UrF5hmNLZoKgNhC2LaIryPChrDZ+CaIebdKxZrc5uaQWsXx563Dc+ukisvvzqqGdEJEVfLPYLDPdJtOP/XVBhBNsf5Akc8Bcq1ZgJTHEQ1aCcsCFNgmC7gXpeOGyo/DDMn13XUmcRTvqc3BEzwKdt6Jpv20yMOXvp2B/fRBtMvy479sV+NbwPbEq//sUZWK9jWSZNmHLSow4IcPvQW66D+U2MRPtMY/okY/f1jjz89DSvY2zWFBtIOwoNtW9TTrOP6o9TuxVgEBYxvsztuCDWVst32MlXf2/QklJCVO6XqVjR74uGR5kWcazzz4b+/uKK65I2raNtFgHwS+//IKXXnoJl1xyCQYOHIiioiJ4PB5kZmaiR48euPTSS/Hxxx9j/fr1GDVqlOW2HnvsMcyaNQvXXHMNOnXqBJ/Ph8LCQowaNQqff/45JkyYgJQU6+xiamoqJkyYgDFjxmDUqFEoLCyEz+dDp06dcM0112DWrFl49NFHk3gGBCpuF79p4IVHdwAAtGFUx6qs3FWFU1+chs+JJENLsaNCH7jKSfOSweaPbjwGVx5D6ztqqzyowKyxqv6sI9taBrgAfdCdgieY6kSuJzPFE/ePrLYzJCctsZa7y4/pxJRZAYCzj2xLasvbnU+V7DQvrj62M87o3xYpXrepmpaXe8/ojc//NMzRROqO03oBoI81N83ruN3yuB76wZnHJeHWk3rEvoN/nWcOgFhhJ0tFnasjiOA4QHcQdCQ0HjtolsXbJpqf7iMlhkLEZFGSJLSxubcA4MmLjsTdo3rjwbP7Wk64vG4p9r1q8XlceOcPQ7DhybMx6c4TSQ+WJy46Uvf35cd0JKWkgGbJIp6KMi1pxPrpfvO9fuV782JBg2SzcNt+PDNpXVK2ZZcgkKTmIFjvokzMuP9Ux7Jl8XQQaO9FJ/r7JpheA4l1EHQvSEebTD+evniA6TfKn0AHAcuolpS8YWCn4e51S0AkAClQhi//NMy208jtNksMxd1BEElSB0HDLmD7d+blRAeB0yQNALTPIK45i+4HL1Ux3bgHWHo/8PMxQPkC230yu0SUA5MgQM167lWjCQIzTPNi9TshOghYSQXzNtjX4DHpawEotK+DA5NiO2PPNPU33kGCwI0IvK4Dbm3HhdrpoTcotioAkZHm8+DIthYyDE3m4lQBBdVBoH3WhFupg+DJCWtQWhNAMCzjuZ/XY+d+dlL8mYn632I1OaDy0q/895EW1kddvtNazuYNGwkWAAhoEgSUBrca1GNp21fVB/Gf3zfiinfn4o0pG3Xm0caCNGrMlIh0qkp2qpf00clK9WDMLcN0cphDuuSif/ss26IdAMgzjGe7JtFnq7YxbNv9SXVA5KR5dZ/Hih6FGTjSoksg3e/B30aax9hA1FPshcuPwpOGsTQA3HJCN3xx63Ac1519HHYxjLevHYzNT52DT2/Wy3vymhTzwDt3ZaHtOqbmUGl+N3wes4eTlXG3FU787bxuFwozUyBJEm472Zy0ZH12Ho8KbfKMNxZluT2/h0tVINXXvK+Lj44/SDyiZz4prZVMuhdE50s5aT4UZaVwFXAKiSHg/PPPR6dOnSz/SyavvPIKFiyIjrkvueQSDBkyJKnb19Ji6Z8+ffqgT58+MRmhRDn++ONx/PHH269owzXXXINrrrkmCUckcEKaz2374/jOdUNiQR2ewJxdO15Lk5PqJSuluxVkoE1mJfmeDE1gldIeNwbN03wejPvr8Rg9ZxuKMlPw5cIdpqrhoiw/adbYvA37h/gDZ/fF7WOWoKS60baKIsPvhd8bXxWI1rAo3g6CY7tGzVSLslLwr/P64ZmJ65Du9+CVK4/Cgq0V+GRuMXoXZeCR8/vjsfGrieOP77EXb4KgV1EmJEnC61cdjed+WYeGYARTiHbpR87vh4IMP/q1z9IEN83nqE/bTEvJKIqjO+XgmC65WFS8H5IEPHOJ3hD+uuGdUVoTwJLt++GWJNtuB7vgM1Wd1SEnFdmpXlPXDJXs6JCTBqBCt0x7b8TbQeBxu8iBJivYPqx7HsYu0Xccndy7DfZUNWBrWR0uHaw3iGVdW/npPnz3l+PR1SKBYKWbPrRrHr66dThmbizD8T3ycXyPAmY1inoMhZl+UwWdxyUx72+6g8B9yLaRqvcQlRACohMF43008ohC22okLaYOAo6JVM82GVizuwpltcEEJYYM+4oEgUW3A2Vz+dYnGN49D1/eehzzdZ3EUJI6CNJc/IF1uw6CPinbgZ/6AHXFSGl3Nq446XvcT8TaoyjwulymBFg8QXcATdJAEWdmxZQ5b/GX9LpkRXocAQdiO+zkhgK/izjGtS8CQd4OQoXdKTPnD7TfQkvTyF/J526kO05Zcjp3jWzqACMlhviuLVbyQcUnhengC0eCQO3msKsejgUcHUgMuSXZsmjjYEK9d3TjOqsEgfr8tOgyYHUQuBAhn/V6k+KW7yBQFAWrNRXYEVnBmPnb8cBZfcn17fTnZ24ss3xd3acR2UqXxAKeDsVGzdiT6iBQu2lZ5/u/GmPfBVsr4Ha5MGtTKTbvqzPpjlNjpmQYr/o8LnK+keZzIz/Dj09vHoYZG0pR3RjCmf3bQpIkuKVo0NdKltZYANG1wBywb5edEpfM46Z9taYuJCNd8tOxZHulblma142nLh6Af/+wCqU1AZzatxBvT6M7cY/ulIOzj2yrm5/842z9tUsVhQHRDmQgOm80kpPmxdCuefji1uEAgA9mbsFn84rRvU0G7hnVGx63hFBYwflvzGJ+tlP6FMLlkkwJosr6EBRFgSRJZPeyExKVGNJ2EKQSsQv1mZ/mdyNYn3jCkiq84qFnYQbO7F+EX1Y3/04f1yMfvxPzZrtz6nVLOsnbZHQQpHhdXAWO2iRMdpoXR3XMtk2CAsClgzvimK65WFy8H5kp0aTXrv2JdVaf0qcNpq0vJV/r0SYd/dvrC/isvNtUDtW54aHK9OnT8eCDDwIACgsL8fbbb7fo/kR/iKBVSPN5bOVZTunTJvbvRAxpWoucNB85SUvzuXWBcC36DgK6OtxIz8JMPHlRNKCbl+HDQ+NWxV4b1a8I5RbJAYCv2npQpxzMfnAkFEXBic9PNcm7aMlIicqOxEOBroOAbyB9bLc8rNldjdpAGD6PCw9oBoTXH9cV1w3rEpOeObFXG9wzqncs8EdVC8Q7yOI1vjS9r2mg3zk/DW9eMxgAMOCRX1BjqEZqm5WCswfo/QCoY+3blk9fW4vLJeHzPw3HouIKFGb60bNQP1Hwe9x4sOm8Tt9Qap8gsKlWogZt2aletM9JNctqEQOtDkQHgfZOs+uaAYDje+TrNBhVXXKP24XzBrbDTyui8lRHtMsyDY5UHr2gP6rqQ7GBqSQBt57UHcf3yEcoopiuCda1dUb/IsvkAA/DuudjmKbCiVVRoiaVTuxVYGpFL8pKwRHtMjF5bfNAW9VtTSW+03S/R2dMeyihVsiy2rIpn4xuDivqahrDsckfANRztPRmp3rRr302ZmwoZWuz82AMBu75Gdj8gcX6HPIjNsZrqboEgVMPgpZPEFybMxaoawpS7JkE7P4JAP2scEMmP2/cRqtL7weqzgVSinBsxj1YUMvRlUWZFLOQQ6YAZlzdDmGisIKRIGBWvHMnBwC/FGJ3ypTP495OUnHQ7SHV7yCXs67FG4c3VQoSHR+8Gv12nTIprgCdh+K4x9VtG+UB+qVsgQQFqxt7NL0eTweBDG8SKjRbA/XecXNKDMWetxwJAuP4iPV96iSGOCphE4UKHm90kBCn2FfTSPohbSmtxYKtFaS0JOV/QCUSjJQTUplGtGNPymtOlRjiPd/P/czuaKQKZXi0yXmg7iP1nnW7or4HRjJSPMwEgd/jMnUNUR0E/dtnx5UgsJszFGX5ScncVJ8b3QrS8enNwwBEJSw/nLWVLDIa1i0fHXNT8c9z+uLnVXtxVKccXH9cV906rARBu+zonILSsjcWkdxyYnfccqLeS8KqIPH8o9rHgqXGeW1YVvDlwh3o0SYDjRYmxTyw5GB9HheGdM5FRV0Q/Ttk4fulu0xm0S4JyE+3lhhSr+d0nycun0YjwQSSno9e0B+rd1dj5/4G9G2bicuHdMKTE8zabnaFp1Y+WvEiSRJXB4FxXy9cfhTO+88spryZxyXhr6f2xN2n94IkSbris0T9KE87oohMEFx/XBfcdnKPWAxFhafTgqdr6XBn/PjxGDRoUIvvZ/Xq1bj44osRDoeRkpKCb775BoWF5t+AZCK+XUGrYJdpzE/36R6mh0KCIDvVS07S0nxuXSBci12CwC4rfd6A9vhg5lZsLatDht+D/zu1J56eaP7R1OIkyytJUtNgySJB4PfYBpJYaDtDeAbSFw5qj4fP7YdAOIKF2yowoIPZnNj4w6atCqYGAzxanRQ8lXEuCaaBGZVYyEr1mhIEVDcKdY3Eqxvq87hwvEFqiIInoWTXQUAN2lK8UZNho0EdFVQ/kgjYa6V3uhWkW1bDA8AFR7VHdWMIq3ZVw+d24c7Tesdee+mKozC4cy4aQhFcN7wLsyMjK8WLD244BtM3lGL+1gqcoNHS9HnM72FdW6wq9kRon2NOomiPYWTfIrw/U68hKUnAvWf2wYwNZQhGZLgkxBJD1BlI93mQkqQqkSM7ZKG6IexY5z9eVJNp1qSKGgBnpTp7NkRkBfXBSOx+MGoUU2SnetC/fRZmbChFQuasxmDggj9Zr09Vqxuw08HWeRA4NSlmBMmSmSA4P2OSfsHiuwDQVTYeKQwP8UyPqyofAKqaEveNe3Ff0Se4vPZZ6/UBru9Eh6EqPS6/BIsOAqOerxN/CBY+qwTBoUDDbrgQgQz9c5CVPJEQiQpREx0ELiRHo595z3B0EJBdDEsfwMTezwMA3tl3CZ7d+0eNfIqTDoKIJiAjobXNp53gajoPOg8Cq4Sdgw4CY9eum3Gt6CSGWqGDoLrBfD8Xlyf2ezx3czkuHNRBt2z93hpc+OYsZkCUGrZVN9o/a+w0v4HmBEEgHCE1uGMSQwnKvQC0BncyPAgAwM/oILDC6tHSt12W3pAb0XF81/w0bNNcA385pQcmr41fK13LuQPaYef+eiiIGi+vJvwDjOcw1efGKb3b4FdDh2xRlh+d8lIhSVF5VJZ3BmteqRbUaI16VXi8B6huESDaGa01laa+/3+MXWm7fR5Y84vOeWmx7gcA+FPvYgQX3YeQ4sEju/+M1Q090SbTr5u3U3EB9fqyk5DlJRHZtHbZqZh67ynYtb8BnfLSmDGHAR2yMX0DXRkPmD0hkiExBJgNvymMc+neRZl4+9rB+GRusemYj+qYjXf/cAyzm71tVgraZ6dgd1Pyzud24baTu+N1Dtk1AEz5rMcvNEtuAXQ8wogTQ+PDlaKioqR6DFBs3boVZ5xxBvbv3w+3240vv/wSJ510UovuExAJAkErYRd0bJejfyh2YAS/EuGPI7ph9Jyt5OA0HnLSvIwOAg9TIinDxoPALiudnebFT387AWv3VKNLflQr2k7nz6k0jp3OYVaKx/SjS0G1u2orv+1a2HoXZeC1q46O/d0x10L7lQE1GKCknXjgOY85aWazZ1aCwCgVRVVqU8HzTnnJvze08CUIrNehOggkSUKHHKp6x4PrhnfGZ/OifiIdc1Nx2hFFOGdAW0xcubfpvcDVQ5urKXweF7oVpJs0cbVkp3ox9i8jsGT7fnTISdUlGPweN/54gtkMmEKSJJzSpxCn9LHP1rOurUS1QylYz0j1GI7pmmt6raYxjL5tszD+bydg5sZSDO6Si8Gdo+tRbf+qSXGiDOiQja//fBzu/25FqyQIirL8yG8awLOC/tQAOJ5ETk1jOJYgaOCQGMpK9cYSyIkFTg2TL6oyXAsVGDbgJZJeWhKRGGLJKTlJEDg+X3IA95/VB9hofskjRWLBwT8M7xKTR4jbg0DD0PRV9isBzjoIAFNVenwdBMR10CTzc+fpvXD750tiv+9eSl7IIT4pxPSfOCRQwjili4IpBvUM5vWvhJmySR5JRqon8QFomqsRdLyEI0FgXCdUDax9PvbnbYVj8ca+q5DuawooODEphkxqpx+MqFX9uiShpcSQbL8OI0HASrRpO8iCEVnXjdYS1DSaj724oh4RWaG7qYiCFyNzNpXj9COK8Nm8YszfWoHOeWnYXFprWS0dIQKHdga3gDnYR6Hul1UBXWHjQeAEahyc6U9SB0EcCQLKc0GFKroBgLtH9cZ936xAMCLjokHtMaRLLtNMWBug5OG8ge10XdGbSs3jdSpQfd5R7U0Jgt5FfNKqxiSIihp4zSfm5j04DGCp43z8wv74g6HAKN75JQ+secTgzjnNf8hh9Nr4J3jSogHolzq+grM2vqnrHgAYHQSqxFCSKsPjlRhS8bpdtp3Xd5zW0zJBYDRET0YHAQDk8kgMEdfMaUcU4bQjivDq5A14dXLzwPSNawYzkwNAtGvoxSuOwqM/roaiRH0D0/0ergRBYabf1nPICNVhbaQlf6sEUXbv3o3TTz8du3fvhiRJ+Oijj3DhhRe2yr5FgkDQKthVsavtfyqDu+QwBynxcmSHLBzXIx+zN7FbIU/oWYArh0ZNRYrL6/DirxuY60YTBOblbpfE7CDQ/gBQ1QA8unbpfg+OadLhBwDFJmiyySKISsGquFXJSPFwhWna5aRgS6k+aJWvkV5K8botNTPjNaLVktwOAo6MOhFMJc3GiGOgApb5GX4UZvqxryYQe99wCyOtZKA1VmJh10HAktqhpIMyUjx49Pz+6NM2C/vrgrhmWGe4XRL+ec4RqA9GsGt/A/50Ynd0ztcniHq3zbRMEGSkeODzuFr8fGlhDeDt7ql4oM4l0Hx9e90unH5Eka4a7Lrh0SRLn7aZptZ/6p6mTIqdcM2wzuiYm4qbju+GVJ+7VYJHbpeEv5/RJzZ4ZZ17KiHHo7tp5KcVu3HzCd0gSRKXB0F2qhfdmyakSfUgsKwfBBCyl5K45YTulq9rz5njDgLG+mkufm1Vx/I/kQCuPbYLyneZf1ejCYLo9fi3kT2xc389Vu6qjq8q34Y/DO9Cv+C4g8CQIIjnWC0khs4Z0A7f/3UELnprNhQlGtxPFJ/rEO8gAHD/SRnYPNGlq7ZmygXJYToJg2hSoXte4maDqa5GenLOIyMmRfD61c2FF6jbblqnnbcUt596avQPhx4EVFfOwYj6LPFoTZUtJYb4OwiM4yNWMsn4PAvLSot6OBg9c4BoJf3m0lr0LjJLAbldEmSbzobZm8twxiszTAUvVtQFIqZkyJ6qxDS2VdTiFMp/AEBMkjXR4CVAB1I75KbGreOvhSosomQgtVj5WPRvT5v7XjioA47rno/qxnAsiDioU66pi6BDTio65qZyJwh8bheO7ZanW0YFHamkx2mEfNKofkVc+2Whzt/cLklXkDSkSy6OaGe+9o1k+D3okJMau87TfG5cMrij6Tmsys/YSSrHg1rA8sj5/fDY+DUAoh1Qd5ymMWYunQ1PqDlg3je1GJmuOqT79QVD1PxafW4ly3y2NXxVBnfOxfOXDsT4FbsxoEM23jL4VxilyxKter+lqbCMy6TYYu50+6k94ZYkbNhXiyuP6aQrYGNxfI8C/Hr3ybG/t3H6cB7XI99xMJ8yFBe0LmVlZRg1ahS2bNkCAPjPf/6D66+/vtX2LxIEglbB7gfHqKft97hx3fAuugxrovQuysQ9o/pgSfF8ZlA6N92H849qDwD4ePZWch2VnFTagwCIZmwlSV/x0rdtJkZqBj60B4F9gsCIXVVNLUfrrhY7mY1Ur5vLZKx7QYYpQWAMsqf52AmCZGT6yQ6COIO1PMFNP3HMrA4C03uJwbPbJeGJi47EP8auRERW8PiF/ZNW3cGC57zbJQj+dFJ3jF3abO54yeBoC3rUfFhPpt8Lj9tlCqJ1zE3D6JuOZe6jT1EmJmBP3MfYEjAlhhxK1/BQkO6Hz+MytcprkzN/P6M3ZmwsRTAsIyvFg0sGs1shVS8CLT6PiytRZ2xTB6Jm4k9frDfD5mlbjZcTehbg0Qv6I93v1iWcWUkbyqwung6CJyesRVhWcNvJPbg8CLJSveiSl4YMvyfBBIExwGEzAWDoxquJ+JN7t8FJvduQ66hovz/eQHrbrBTsrW5k+i2kO+kgcGogLAeRneZFdkE6YKgL8CASq5otzErBx03PmvJf3wbsvTdtObFXPmZuLEfX/DTcNKIr4/icdhDog2hxGSrbmBQf1SkHfxjeBZ/MLU6axFBC1/lBQN+saky/72Ic98zvscCfZQcBIS8ERJMKj13QG5if2PGkSgG9NE5s3/b3ZMccH448sq32TaZ1Tu7TBsf3aEqsu/h/u9yarpyDHfX7U+QwsPpZoGQqkNbe4h38HgTGgiiWB4ExwVfTGGZqqCcDlozPH0cvxK93n2QaW7pdkm2Qz8qvjEUwIqMxJOvOE08HAQ+NYRk799fjAoahbHkSOwgyiA4Ct0vCS1cchcebArj764MoqXZuxE4VJLFkbnjoaxEEL8xKQaGmweCu03th5sZSnVTo0K65XBJPQHQsdsuJ3UzV+tR8jIoNpPs9+OOIbvioaQ6emeLBWbpnVmI8fsGRGN49H3WBMC4c1IEreCpJEp64qD/u/WYFAqEIHrmgP7MQqmdhBhZu4/fp4UW9X248vivSfR6s21uDi45ur++uJ37fJUkxzetoD4LoMqNPXLyEEpAY4kWSJFwxtBOuaCrsNCYIjB1Q8cQV7hnVGxtKatAlPw1/GxlNxvAUc1rty+t24W/axE4c5DG8Lo2o8kJ3nd5LF1P76ym0TBcA9G+fxW2qLEg+VVVVOPPMM7FmTfR35Nlnn8Xtt9/eqscgEgSCVsE+QWCuhr12WBe8NXVzUgZyLin6o53idWP6fafg59V7saW0DqPnbNOt11ajT2gXGMtO8zIHFpkpXozsUxgzN+2an4bPbhmmG/TFIzFEYTf1volTSkXFLkgmSRJXhcFfT9VrWQ4zVJOo22LhxDuBhdtlHpDGa1JsJ78R3Z95HSpBQLWhsiobzuzfFmf2b9vi7ecqXBJDNt9N37aZuO/MPvhk7jZ0yU/H3adH9f/bExJDaXHqXfYusm4Ljvd7ToQMRnt5strOtbhcEtpnp5gC89qA+BHtsjD9vlOwpLgSg7vkkM9Z7bon9irAzI3R6Oj/ndoTgH3FzUm92+D64V1wyyeLdMvPJCZ0ydL/pEj1uUkTPFZCkJJoijeR898523DbyT24OwhcLgm9izLgqklmB4ENjbSu8PT7TkVVQwjtslMcPV94DVf/fX4//HXMkqSYFDvuIGBIvQBRDwKqWjc/LTnVc59cPxCVQQ/S/R62wb1TiSFjB0GyJIYMpr3qby/vd2yFXwomZsZ9MNAQTURrC0KYBs4yO0EwoF06unbMTDhBkOEJ4PJjOplf4EgQDOqQAWiDj0Sxx8Pn9kfMLMGpSfEhIjGk3jvHYBKw/GX7N8gOOgiMgXbODoKy2kDLJggYgb+d+xvwym8b8NC5emP1aHdFywT5qhpCujF+ohX3Ko2hCF6dvJGZ2KhpDCMQjiTFFJpVrHN8jwL8fFdUI/rTecX41/ecknMaqPmG3ZyI5ctVkOFDf4bEEMWRHbLx/GUDcfdXy2IB1uuGd7H1uwOi48Z7z+xDvkZ2RXjpc/jA2X2Qn+FDcXkdrjq2M2mEHS8ul4TzBlolA2lG9i3Ckn+Nsp2L3XFaL9w+ZgmXr4aRzBQPahrDOKFnAWZt0lcqqM8VNShOQowLvQibCtCoeZ56Pfdpm0n6RQDRuavRP4/FI+f1s1/JAek+N9cYW4upg4AoxLP7TKcfUaTv0gDbDFtLMmIYVmT6PSjI8KOs1joBOahJgurKoZ3w7eKd2Lm/AR1yUnUmyEYkScJntwzDmPnb4fe44Pe48c9xzV4alJeHIDnU19fj3HPPxZIlSwAADz30EB544IFWPw6RIBC0CqxBgAoVOGyT6cfjF/bHg2NXQpIAr8tlmywwVu2rdCtIj2VzC7NScP1xXQFEzbTmbimPvfc6TRWz0fzWSE4qLTGk8ua1g/HZvGKEZQXXDutsClRRMjM8WWkTxOdVq0LbZ6fg8iHODFR49NJP61tk+2M9uHMurh3WGWPmb0deug9/P8M8aLSaSyZDYogySYpXD55n4ktdD1TnAVVJbaf511p6fzyDGrvqfEmScPupPXF7U5BZhQrK8ujKUlAt8VoOSIKAZSKW3zLtmkVZ1gkCIJp8PXcgn2/FRzcOxe9r9yErxYPjm8yYWVUwlwzugJN6tcHZA9rC63KhZ2FGTM4sxevCuRrtWRUeXUsg2t7q1KuAlbSinrMAQ+4qzmtmT1UjymsDqOcwKVYTsB1y0yAllCAwPNvsng/lC8zLJBfS/R7ubhtt8pg3OH1GvyIc3yMfEuO3u0UTBBYBU48k08Z3ThMvDCS5AbnpNvJmTiWGDMH9+OSQiGvOkCBQAxHJkRgKQ2qhIGOr0XR+tNcLM3misCWGuuX7o68nyI3DCukABc+1a9o/cT3o7htnEkPMZNhBhlq9/3T71znfwd9BYEyqe0B/58YOgrLagO24JhFqLAKWVMWzMbiWTKoaQjrJ1WR1EARCESzZbl29PXbJrqQYx/KYuV4+pCNe+W0DU/LIiBqIpE2KrX+nHzr3iJj0jEq3gnQ8cn4/7rGXyoWDOqBzXhrmbC7H8T3ycXTnXK6At5WOOm8HQXRdt2n+wMu5A9thworm7mJVFSBZ2M3FTuzVBrMeHIm3pm7GO9M3k+uw/D3m/uM0BMMyctO8OOvVmVhfEpWGHNAh2/LcxiDGPF5X2DSvo6RkU5ueWyP7FmLskl2m1wGgZ1EGlm6vtD2Mvm0zcdHRHWzXc8Lzlx2F2z9fEvv7qYtpg10t5g4C8+fOSfdaJghotYfEJIaSgSRJuP+sPnho3ErLTq8ueVHpsHbZqfjlrpOwrbwOXfLTbec7mSle3HZytMsgGJbx4awt2FxaB0kCHjy7b/I+iCBGMBjExRdfjNmzZwMA7rzzTjz55JMH5FhEgkDQKtjpmrMqW686tjPOOrItagNhTN9QiofGWVdi5KfTvgV929HVE/84py/u+GIp9tUE8LeRvdAlv9nIZV+19YA1J82HoV3zmJn2FK8bt5zI1nSmfjxy0+PpIDD/MMy8fyTWl9Sge5t0x7IZPOun+tx445rBePHX9cjwe7B0R6VJ6gQAnrp4AO4/s29Uf5wYHLqtOgiS8ONKJZTi7iCwSRC0z06BRMh8UJ+bGqy3pPyKE6gKCyPGCjleCjL8MckRIHpuBnSktVHt0N6rFAdCYojqDMlN86InhwlaPOQQg1RWFwMPXrfL1MrNShBcNqQjju9REPv73T8MwUPjVqKyPoQ7T+tFTmZ4Ogh8bhcePLsv/jpmie26Wlj6vOk+D5k4ppJVHrcLGX4PajkrpLSs2FnFpbmqfmftslNQmVQPAhsoaRkH1cFAdPI/rFse5m+t4A5Oe9wujLllGBp+yyOle5wkCHgqpEmI3xmPFNbrj8f2kZwEAcL1gN+QIKhcCWwbA2T1Bbpe51xiKFKP20/tgTenRoMOyTBUBgDI+u9gSJeoXnGyJIYOdQ8CNfDLlSCQwwAY36sScf6dE5zSnZVw5rgejNc3dTzaZY46CCLN4yRWtc5BgrupA8THa8TN40EQib6WmeJFXrovFhT2u+nvxSg9VFYbxKJtFXjipzWIKAr+ec4Rut/YRKE8CFSMBreKoqCeIf+ZDCrrg/h0XjGWbt+Ps49sh91J8yCQTdKmRpKRHAD4zFxTvG78cPsIfL5gO96eZg4We90SUr1uVDeG0SEnNeYTRRWo2XXuXjm0E1bvrsa8LeUY2bcQD5/bL6GE3dGdc3F052btelYHipa2hHSjCq8HQaLcemJ3/LamBMGwjBSvC38bGV+iIRGyUrz480ndMXbJzpiHnBa/h5bXzfB7gKbi7I9uGorXJm+AS5Jw5+mccjRUgkAK4wyDhwM1rld/384d0A71l0Vw/7crTOt0L4gWArGSjWNuGYbCTD8656c5TkrZcUb/Ivz1lB6Ytr4Ux3bLw6UWkqksrMyZWVAJgkQlhpLFFcd0wml9CxGKKHhr2iZ8MrdY93pRll+XHEr3e5h+JFb4PC789LcTMX1DKTrmpuJIQpJWkDhXX301fv31VwDAyJEjcfPNN2PVKnbc0+fzoXfv3i1yLCJBIGgV7AZSRg8CLTlpPuSk+dAmw76lKTeNkSBgVOUM7JiDafedSr42omeBpUlxVooHfzmlB8bML44Fhv7hIKtKVQ8ky4Mg1efGoE45jrcF8FfYn9q3EKc2eSr8+dNF+GU1LWGRbZFpt6rESEnCwDFMBOziHZBaeRBIEvDIBf3xym/m64XsICAG7a0xmODBrnMGiF8WyOWS8O/z++H+b1egIRTBA2f1jUv3HYgOZu84rRde/532KUlErzVeqHvn2G55XOc0HrIJL4t4TbhZsK5L46C6R5sMfHnrcZbbskuCDeiQjWcuGWCZxGuT6UddIIx6Q/cS6752uSRk+D2mCQ3L5DkrxZwg8LgknNS7DboVpGP2pjKs22s2/L1p9ELmMaucfkRh7Hy2zUpBVSLSK05NisltON//hzcOxX/nbEPnxhKAU2ZXkiSkeenjO61XGlDJtx1rU15nn8UjRWhDVTlJQTGjzEzDXuCXYc3LG0ucV5OH63HNsC6YtHIvtpTVoTAjSfe6oYPg+B75UU+SisQTBP7DwINADQprH+OWHgSsILIcTkoHAatDgevaNXatUDJc2uN3aFLckia7ycRx940aeItYdRBEz6XbJeHBs/vi4XGrEJZl3DKiE1BqXt0ovbWjoh6P/bg6pvV+26eLMfOBkbHfeVlW8NTEtfh+6S7075CNly4/Cm0y+aUerDoIisvrcfV783DLid1w2hFFqA9GWjS/88GsrfhtTXTOwKpWjofGcAQpXhcaQ86+3yM7ZOG6YV3gcklYvqMSY+abzbuN8BYbdcpLwwNn9cX09aVYs0dfUDakSy5ev/pobCmtQ//2WbFO8wgxQbTr7k3zefDi5UdxHVM8WCWYVCw7CIjxX0vIsBzVKQcT7zgBS7dX4thuebbFRC1FbroPX//5OJzy4jTTa36vy5QgMMqmdshJxfOXOf0+zdd99zwvzuYo/FHn45Ik4YpjOuGzecVYYdCgL8j04baTe+CFX9aTez+ue36LzXe8bhfuP6sv7j+LHWdxuyTdvaPq76tQ16BdzIO6z3OIuRd1LK2B6vVBfQ61eyAZpPrcSfUCEZgZO3Zs7N9TpkzBwIEDLdfv0qULtm3b1iLHIhIEglbBrhqcMow0cmKvNshO9Voa6OQydOFYHQRWHNUxB4M752AJ0U6XmeKBx+1CUVYKxv11BL5bshO9CjNxFUsXkIDSu6YCfnYkewxPGejacd+ZfXQJgj+O4PM9oAo4VdKSEDCndEbjleqhJr53nNYLRVl+9G2bhSFdcklTbaqCh+4qODg6CHhIZNhzzoB2GNm3EJLELznD4p5RvXF8j3xc9d4802ueA6CFTA0kh3WzkRlJACq5koz7RgvLg4Cnvd4IlSzLTfPi278cj/pABP3bZ8HlkrCvhq4qP6FnAT67ZRg+mLkFT07Q6+FaJYSoyTbVQQBEn3+7DXIHFx3dITbxHvDIL8z9WPHPc/riD8O7xv5un5OCja1pUkxuIxzdjoMgYIbfE23937UemO5kX3TwslOWwp0g8Egy2mT6UUpV5EkWgTuiqs4jRXSa8nbH6Rhjx8buSfqkwbIHAZdDHddIPTrkpGLinSeitCaAdsWzAXtZaI7t6q93SZLw9Z+Pw9z5pcCOxDbtdx0GHgRNgV8uDwKrBIESTkoHAcKsCmmeDgJjgoA41jg7CFyS1oPg4E4UsIyDmfB0EKx5Fuj/EODNwBXHdIp5R+UE1gGTzKsbJdPembZZJ3VR3RjGFwu2x2Qepm8sxYezosatMzaU4v2ZW/DPc46wPOxgWMY70zdjfUkNltlIg8zdUo7Fxfvx/e0j8P7MLZbrJoqaHEg29cGI4+QAAOSn+3FVkyZ3A6fOudNiIypgmOH3ojAzxaSxT3Ujxtu5myz+NrIXMzCsYpUgoMZ/LdFBAAA9CzPRs7Dl5Lp46VqQjtOPKMTktft0y/0eF/5ySg9dV8nD5yZBs5/4fXnn2gGm+RAVkzGOh9pnp5oSBHlpPvz55B44/YgifL9sl+74rxveucWSA7y8cNlA3PP18tjfDxiKNmlzZvZ9deGg9uRc0uN24aJB7fH9st0AognG3DRfzMPN6FnQGlB+a11aSN5WcPgjEgSCVsFqEHBW/7ZcbZCpPjdG3zQUb07dhKwUL8YuNVedUBIfANAnDl1Pl0vCl7ceh9d/34g3pm7SvaaV9jiyQ3Zc7VbHds3TBTuO7ZoXVxV5snVC49Ho71mYiecvHYgxC7ajR5t03Mn540gGaJrwJiFgXsDRdcILJTHUrSANFx/d3OZIehBwJgOsOhQONuLpdNGSzG6J4d3z0b99FlPqqzXxE5/rWMKcO1mo5lNakj1AZyV3edrrjVDnx+dxoYdBgolVmdcpL435OktiCICp2wBgT2SppIt2We+2mVhczFk6r+HWk3oY9p+aWGV10gLZAcBDJEtqNgM7vwdyBgLtRsW/f0VpkvihgzYuZrDTjIQIfvy/Efhl1V50KUjHHZ8vjQXUUlzOqrY9iNC2DcmUGNKyj8imWJgoW20zxeuO3gvbkyQxFGkEGvcBngzAE73HUn1ujOydm3CCICoxdIh7EBASQ8xuFvkAJgh4JLiM1zdVEa87fv7fk0PJpNjFSvCwUM+tVYIAAGZeCoyMJpFjBT+Myn1jFwOlgz169jbcfEI3eN0uPGyQWH1vhn2C4N3pm/Ey0dnKIhiRcc7rM7nXP9goJww7j+ueH/OaY6G9brsW8FXdOpWxpIoqTunThlw3TBQ3tbTpqR2XDemIT+ZuQ0l1AGk+NzmuyrOYG1DFJgdL53RLQo1X/R43bj2xO4rL67ByVxUuGtQBJ/ZKgpxYxHz9pxISZ7TEkP7v9kQRjVqE2adtJu4Z1RtlNQFMWLkHR3XMwT2jaHPq1uTiozsgLCtYur0SZ/QrMikpUBK6rDjEkxcdiSstij5fuPwoDOmah8ZgBFce2wlpXjemrNuHzBQvjuvRcoVhLMgOApEgOKRoSd8fp4gEgaBVYCUILjiqPZ69dAD3do7unIsPbhgKAGSCgCVhwWXuQ+DzuHDhoPamBEEyKjk8bhc+vnEoXvt9I1K9btx/1oH/cQXoLLSK1TFeMbQTrnDQQQEA957RB3/7Yin5WjKek9cM64zXft8YqyC+ZHD8pklUwsLn1l/X1EDDQwRsjdVCQOuZEPMwpEuuLgiqml4D0fZd3glUa0F1ihwIehZmwO9xIdDkx1GU5ccRcXQv8XJm/7bokJOKXZXRyuSrmyrgkgmvxBAPVGKMCiilet2khHXnpgQBNTF3WonGCmRRnV3agffVx3aOK0FgpF12SoLa7A5NipmbCQAwTAT/v73zDo+iWv/4d0t6T0iDhFBD74TeQayIqIBe+1VEvTbkWq/3h71h96pYwXJVuBYQQQQpSu9deu8kQBLSs9n5/fFmtp7Znd2d3c0m7+d5eNjMnjlzduadmXPeWnYS+LUrYKKi04jJAdKGAG0fBpK60ja1qVKkGkBnVFZe1qg3EECqQWZCFG6vjVB7c3xXTPhyAwAgUqegbK8uEhYDNirl1/dXBEGkWBHkU59ajfX4T/QvLBEY+AOQMYy2u1OGqiBcV40IY915t3lFrdIlNS7Ckl7MZZFif6cYUrpn1MiDpymGJPUGDaOuRjjfqYsY9RIVuRTXEHXGEkHg5nycXghUXwTCbJySFIqRqzGcnS6uwKoD5zA4N9XynveENzwwDtQHRKlbr++R5dZAEG7zjOqisi6WpwaCLlmJWHPwvOXvG/Ky8TeFOZtJ8EOCHWWcHh+JBQ8NwtbjhWidHof+ryxxauPKQUUUMezKSay+IEr7GWHUIykmHB/c1EPbg5kF0bcCI7BBr0O4UW9XO9Cx3knjROd1qq0BKMygx9SxXTDVj2mtPEVOjzSup1gXIcoEIIowTo2LwM19clweK8ygxy0ObUZ2CF4KHpHupmmQ0msxoU9ouHowIY+SkunRS9t45YmqhGgCkhIT7lOhpmRB2iKtjHwdmyTgk1t74t0buyEryTtLr9b2RqWUIoNyU3FjnrYKyJEd0tEzJ0n4nVmDk9woNgIf39IDfVokY0y3Jm69rVyhppaAaG4sUvxf1SXTLiXKoFwNlEca8uilbSxyP75nNpY9OhSTRuTiwWGt8OUdvYI8OmdEtSaCQWyEEc+N7oC4CCNS4yLwynWd/ZqHMsygx0/39cODw1tjyqj2eG50B82PofQ88MabTRg5I9im0+mEBojsZFJkizzxtApVF0YQ2KRdG9UlE7f2zRG+F5R47XrnPJKNYiMATWsQeEmNYEG5732rcQAASo8Ah74AFg20ei+rPb6sUFMyEHgQQeDYx7C2afjXFe3Qr2UK7hnQWLxPdZFQyWnQmcUp/fwVQVDpWkGlrk+Hc6VVvQSZ6kJgx7M2/fvu7f7m9e0QFxHiS41ahfnkkVYHCYOSB7rZJPTiBKBhBIFCDQJvIgjcpRjyYLx6mDWJ/AwEBtTgJk8M6mojCADBfSqWB4ogc/8OOHpe4XoDWH3gHMwirXgDoEWqOuVXN0GkpSO28/vE6HC0VNG3p3Wu7hvSCqO6NEbL1Bg8emkbvDSmk6JCXcv0qFqSFBOOIW3SFFM0ukI01wsVg6IvxEY4zzPc1ePyGtF8TsHI++zVHSzrk5v7NLVE6MqIrnFSjHc14+oKontIpGsIxWeqKNq7GUcQMF7CEQRMQFAyAvii1EmKDsOFMvsXn0iJ60kRLxEiJYIWymutaJcZj81ucot6guj3rnpimDDc0FcijAbMnNgXB/JLMPKtP+2+0+oFPbxdOoa3S/e5H5HngdOEV+UEPjrciI9v7Yl3Fu9DYlQY/m+UBrknNaRPixSsfHwYKk01SKz1GHloROBzKqqlRWosDhZ4oGj0I+PzmmJ8XlOYzVJA8nGmxUfikUty/da/yLgbbtB7ZXQVGXCVUmtFGPVwzBZgiSAQvE+0MjSLarDE23iARRgNeG50Rzw3uiMmzdyCnwSRbLb0b5WCUZ2dldcGvc631Cta1CAAxMqro9+L25pKgKP/A1rcrl45LVWDIhSUDAQl4u3CvuyPadDrMGFQC0wY1AIo3A7MF+xTJY4guLFnhoLjglZpe8qB43OALU8CYfFA+Snf+3Q8V1oZM2w5a/Me1iiCQHs3hgBTex66ZCXg0Uvb4Nt1R9ElM1bc1mUNgpq6V4NAZMywHb8H4zXozOjQ2H8Rc5qjxqBiaauiBoGMo5JOyaADSstUA9froGIXdddu/GQNBrRqhK/u7FUnFMiB5I2xXVBpMgtrUMlEhxtUFal1jCjskZOEA/mu55TRHkYQJESH4b0bu6lqW1ecXrRE5CAS7Jz1gUCU+sXX+muKiAwECs+sG3s1xeDcVFRU16BFqvP7TFTT0Zs6iXUdUQSBKIKnriNKT6NlkWKmYREarh5MyKNkCPBFqdOvlXO+PpFVXk0BZFeICtTUJQPB/UNb2XmuTxrhm7IwMyEKPWy8+rtmJ/rFOCBj0OuQK6gRUdfez6KUJGoiCJTo36oRZk3si49v7el19Ig/iQo3WIwDdR1HBflkPyrM1VJfFj4iJWq0FwWKAfHzWSlsXlSM3tsUQ086FCqb4sIgFy9YzCkVbn/1us7C6AAAmPOP/tj6fyPx9Z29FaMtfJIQR+Wwt8ohkYLQ4OJ5X36y9vgqU6W4iyCo9sBAACgbJkwK6TeqC4VjHds9U9xeK6V7RT6w6iageBdwbi1QdtT3Pqsv2v/tDwOBLRoYCFBT4Zkiti5Se4/odDr8Y2grrHh8GO4dqOB9HogUQ4o1CFTIg6oixd4ZCML1Ztzat5l8INX7eY3Bx3nTRdcFV+3xIILAUUnnmBrMBsc6BCKKy6uF70OZFfsLsGJ/gfMw6tokWiWNVaaEbRQbgeZu0l3GRhhh0Ovc1iNzjHzpoRDZbIunEQSeEIwc5r7iro6aSCluaABGLVENAqXIXJ8RGQhEdWZqaZwYJTQOAOIInVRBetxQR/ScVBN1VNfo1TzZzpGxRWoMEqLrn0GHCQxsIGACgkhBotP59pL858g2djqRB4e1Ek5Q0nyMIBBRl+bdjROjMHNiX1zfIwtPXN4W/xja0v1Obvjstp64d0hL3DO4JabfnqfBKD2nLhlhAAUDgYPytP5Pdesm7RvH450bumJwbiomDq71KGY0QRS26m0NlgjBPaQUiSDy4JG9l0QGAlcGmfF52RjQqhF0OioKOFYhPykgNgYoFW4PN+oxrme2cDHRqUkCEqLDXHp1ahtB4CWiCAKjC8VbeK3iRLMUQ54aCBTSt9QoGAiqisRKTiXFp1Zpe47/5Fn6JDUEIoLAFi0MBOZKaBaVESxE50FJ0R9qRYrdpRjyoAZBh8xYUoZJUmCMQkYfvSPneZCSz6MIAodnkYsIAjXvgOKKahxzkWYIAP634bjTNldGBYCisOsifVs6O3+JSI2LEBYdtUXO/35bX9f5xB3Xjl2z3RsItEyP68gVnTItDhGAa6eGYHFH/2Z2f796vet6gqK5XrALLwcCpSLFfsGDFEPuSIuLxDVdrdGv1/fIqpcRBGZJckrPOrkOFFz2lMTocPzfVe0RHW5AWlwEnru6Y7CHxIQwnGKICQgi704qROm9SrV5oxh8dltPzFx/DG3S43D/sNZ4Z7FzQS5fIwhEdGyirohVoMhrloy8Zsma9ZcYHY7HL2vrvqGGJEaHodAmZdTlHYNX7EeEaHLruKhoCAW36iqjuzbB6K7eF6FmxIgiCLxd1IkiCJSKBYuQ3xeiGgSi8FqZxOhwfH1Xb0sbV+8dkTFAVJfAltv7NcPmo1ssf9/aN0dVBEmiLwoaJ+WwtxEEggWlqwgC2Ws3GDUIABqvyIChZCCoLhIrc5UUvFop3asKtenHFqcIAg280V2hSQRBZehHEAgL+SrJj5saBF4qa+xQ8khXc549LVLsgUEj0lj7DPZWLnVGz/Y1xgCV+d4dy1M8qUHgFEGgXFxYTaH6ovJql3UIAODYBefvL5S5HmvTlBhcKCt0e3xHYsINKK3yj3HSoNeha9NE/LDJ2eBhS0JUmGJtO1viapWzdw8mp5Fdp4sxf/tpp3aO8/vWabFolRaL/WeVDdi+1LZzR7hRj7kPDMBvO06jcWIUBrRWZzQJJH/v3xzL9xVg/9kSDM5NxeUdFaLyajHodWiXGY9dp4oBkBwNaVO36q/5A6UixX7BgxRDanhzXFdc1bkx9HpgSG6aDwOru9SYJdzUOwdVJjO2nyjClZ0y0UllofK6xi19m+GWvs0gSVKDSznHaAsbCJiAIPJC1eIFOaxtOoa1teaXDzc4Hyct3vcIgn+OzMXrC63GhweHtfK5T8ae50d3xCOztqC6RsLA1o3Qq7l2Bg8tEL1rHRWebCBg6huiKC9vQ+uFNQi8eA+IIhhEUQWOqJkwG/XO43HnNTWqc2Os2FeAudtOolt2kl0xU1eM6ZoJHFHV1BmtlK4iZabexe+VFYpqFXkWhajCeJUiApQQLYABFxEEhWIl575pQOoAIDzRfrtmXvl+iIAzBSjFkNkE6I3aRRCEvIFAowgCs0nZsOAJdTTFEKQa4MI2YP096vexRR8O1HhoIAgUnkQQmNWnGIoOB8rdPAK3HisSKrVtOXbe+flX6MZAkJMcja3HCl0fXEB2cjR2n77ovqEXpMZGIDXWfWrL9Np1XYRRD52OglZEyPOCCKMBDwynOloPfbcZc7actGvnWGNMr9fhvRu74bUFu2EyS8hrlow3Fzk7oPmThKgwjMtTjnYMNtnJ0fjt4UEoqzIhNsKoan714piOePKH7SitMuHJy9v5NQqjrhAnjCDwk4HA8dkDuEwx5A69XocR7X2v4VeXGZSbCoNeh7sG1p/IczYOML5S/5/MTJ1AnGJI+weYyENVixRDEwa1QHWNhN2ni3Ft9yy0FuTMZ3xjVJfG6JqdiPOlVejUJKHOveBECxDHCIJ0lblTGSZUEBp3VXjuiRAtikQKeYBqn2yxUV70bWHNyRsTYUTnrARsO14EgJQF3Zu6TwngLe4iCPR6HaaO7YJXrusMgwe1J7KTIn0wEGgUQSBS0Lvy6pcV9FpFEHiKaAEMuI4gECllT84Dfu0GXLUbMNjMEbRSuisZMnwhUDUIzJVkIPBBsWChphL1pUix/TYXEQT1KsWQBwp7s4nqbhTtUL+PLfpwl8p0J4wKhaL9gScRBI71UFykGEqLNeKcGwPBiULlCASZgpJKSJKEvWdKcKKwDH1apOB8qWtZa5bieQ2Hqzpn4lSRH55ttaTFRyBJRe2rtNpc6DqdDhFGPSqqxbIvSu8iihIURTK2y4zH9Dt6AQDMZgnzt5+yGEbYSYww6HWIczM/sqV70yT8NmmQH0dU9xBHEAS/SHFD5bXrO+Ox77cBoCje2/s1C+6AGKYOwgYCJiAEyktANMlL0yDFUITRgEl1oPBpfSc7ORrZyZ4vWgKBKIWJo0Hq4RGtMXer1TOJJx5MqCMK4zd6WYDZk0XRA8Na4c4vNgCg6J2nr2pn9/07N3TDS/N3odJkxqMj23ikmHdFqsCgLFrgifB0DDqfFKeOChEv+xJFEDgqou3a1y5A1ebq19pA4E0EgZKSs/QwcPhroOWdZAE+/DVQuFWLUQJV57Xpx5ZARRDUVJB3tibpcOpPkWI7FFNUuTIQ1GhzThUNeF6kGBL9Nm8jCCpOARVn1bd3xBAOeHJ66kEEQVZCJHad0yZV2AfLDuD1hXsgSUDbjDjc4iLvvlGvQ2aii1RyAm7u0xSTRuTi4ZlbfBypMmlxEUiKUWEgsIkMjwwzKBsIVKYMdBfJqNfr8ON9/fDL1lNIjgnH8Hb1M90Koz0iI5Xf/N80rEFQXxnXMxsZ8ZE4kF+CyzpmIFGFQZJhGhpsIGACgqgGgT+oqHZeMPujSDHT8BAVpnZUeLZMjcXU6zvj67VH0aJRDB6qDWlmmFBFFJXlrTJe1JeSYnt4u3R88fde2HD4PIa1TUOHxvY5QZs3isEnt/b0ahyu6NcyBWlxETh7kRRnA1s30sz44IwPBgInT2AvF4Eij/zqYuX2lggClUots5sUQ56iZCBw9NqVqVYoUixzaiEZCPJXAKtv9X18Mv4wEFQHqEixxQjERYoBeJFiSMElXKsUQ4o1CNSkGFITQeClgaDqgvq2IvQeKmpc1UrRGksEgYrz4fiMchFBkJ0UDkAbA8HU3/ZYPu8+fRELdiinJUqPj0SyCkW8zNgeWXjhGipCK4oq1IrUuEihh78jcgQBgNpCxeLrIkrvIvJ4d4wGFhEdbqzTKX+YuonISFVV46d3omh+pEUkYD1jUG4qBuXW//oXDOMtbCBgAoLf8u05UFTuPEkUeYQyjKeYRREEArke2zMbY3vyIoKpH4iUAd6m/xItwl3UFsbg3FQMDvAk3mjQ45sJvfGfJfsRHWHEI/6MHPPFs1qNok8NIg9ikwsDgaUGQYhEEFQXujZmyN9tmuTTsJzQIpWMI4GMIAC0USzUixoEnhYprssphtREENimGPJgvL7+No8NBAFM6agUQdDmIWDPO/bbHJ9FLiIIspMiAHiQVskDlu8rUPyubUac0HHr31e1xxWdMtD35SV22y9WWOVGTb0fEVd2ysS87adctkmLi0BGfCRapsbgQL5yqrt0uwgC5fWlaKzxUepSDDGMFsRFOBukqms0SLsnSfQ81xkAXa38coohhmE0gN+ITEAIVD75ixXOCxS/5fpjGhRqahAwTH1DlGLI4OXjXBRBUBezk7dKi8PbN3TDS2M6oVGsPw3MvkQQOCgDvV0EOio/JUllBIFK5bQUbAPBRddjlRW95zf6Nq5AUH3R/kWkhTe6CC0jCGrqg4HATZ5+u+1uahBolWJINCFRc54dZcZVBIFkDuy1c1UcXUQgDQRQqEGQ0htIG2y/zYMIgiaJ/ni/SIjQVUHnInKnbWacMNXO9d2zkJngHJlRabI+Q0U15ZKi3V87VymPZNLiI6DT6fDODd3Qp0UyejVLRozgeOk2qWNFcxQZkfe2KIKADQSMvxAZsKpNPj5XTeXA8jHAd+HAr92B0tpiVqKIUE4xxDCMh/AbkQka/jAZOHput04LYBEzpl4jqkGg91vqEYapG4gWzt6m3BFHENRFE0GA0CqCwFzjvTe5ozLLXOnaEzjoKYYUDAGuDASu8Ienv7+QHNLX+K1IsdYphkL8HhdFBHhTg0CrFEOQFHJNa5xiyF/3Rmp/8XadpwaCQKYYUogg0Ic7Gyocr43LGgTOURO+TCvjDSX4tsVT2NPpWnzf8jGkGAqF7dpmxKNTkwQ0salD0CMnCQm1iv6BrRvZtb+9f3PL52iBQl5kVLAlKykKfVqkYGgb1xGBcuqgjk0S8N3dfTHrnr64snOmoJ3VsBLhwkAgSjEULzAauKtBwDDeInKQNJm9nA+dWgjMbgrMigaOz6FthVuBPe/RZ04xxDCMBvAbkalXdM1KxIja4lGxEUb8+6r2QR4RU18IcRUHw2iGtxFhRpGBwNfBhDQaRRD44iHmmGLEVfQAEHpFih3T8jhiOXchYuy1rUMQCjUIlGpDhBKiFEPBLFIMiJXO3qQYEqZPqh1j5TnXfem8jM7t/hYwZIHzdr2HqWsCmmKo9tw6KtuEBgIHmXcVQSAwEBj0OkUjgbt0rdckLkXf2O0AgB4xuzEueZGwXduMOOj1Osy4Iw9Xdc7Etd2b4J0bulq+f3hEa0tUwMDWjdC/ZYrlu2iB0j0zQflahBv1ePrKdgCA18d2QWMXbUU140SRDnYRBC7OifoIghB5/jP1gipvUgxJErD+PqDsmPN3u9+g/znFEMMwGsA1CJh6hV6vwye39sThc2VIiArzqAgXw7giw8WihmEaEgYNU8Y15AAC7SIIfFgAOioI3RkILDUIPIwg8LuBQGF70U7X/YVSBAFQa/Co9e4NBQOBkuEmlBB62XtRpFirGgQApRmKSHHY6I2BQCGC4NB/gdU3u+5LH+HSO14RQzQQlyz4wsP3Sl2oQaAPd45k8CCCID7S2chSXSMhMToMhWX2stI0ORpGvQ4HC5Rz8z/X5CO7vx/P/AIf5o91ate8UQwAoHV6HP7zt+5O3/fIScYfjw1FYWk1spKi7KJlRbULRPPjKztn4oFhrZAUHW5R6KfERuDbu/vgmZ93YumefKd90uKdDQQGgfLetracqxRDMeHOao4EQQ2CQKXBZRgAqgpxO1FdCJQccN2GDQT1lzPLqFaWZCYje8awYI+IqcdwBAFT79DpdGjeKIaNA4ymXNstC7E2nlNjujUJ4mgYJnjoNZw5iIp/Nxw0iiDwJYTcKYLAjce9pzUI/rgSODFPOwOBKMcu4L0iWq2ho65ge338biDQQJmtZLgJJUSFfJXkZvsUoFTg4QlomGII4kLFWqYY2vxP930ZvMyfb4xRqDfgoZJW78/6MA5ICjUIDCpSDLmIolGqEyDymu/UJMGlMtwTRNF8ojE0TYl2SqUpMhA0TnROMdQzJwltM+LtvP0BICclBtPv6IV+LR0NXBDW/OmWnei0zfY8VLnI5642gqCy2k/PUoYBcGMv+/TH9w9t5Xkn7iK6AAUDQYg5QTDOmMqBleOBC1uAwm3AqpvUR/EyjBdwBAHDMIwKosINmDmxDz5dfgiNYsPx0IjcYA+JYYLC8LbpwR5C/SAYEQQ5NwBHvrPZ12FBqTbFkCfK6RXXA0aN6gF5mmLIHfLiWacLjXAWUyANBBxBAMCzCAIAOLdGvF2rIsWAgoFATQSBmeRc9pgWGT+qC4GK0+778lZBb4wRG1g89eKuCxEEujBA7ybFkKsoC8E93Cg2HAlRzkrs9o3jcbrYd4PbHf2b+bR/tMArPyPe+Vp0zkpw2U+FQCkvqnk0vF06miRG4UQhndfbHIodi6IOZOIinM+jyPhS4WvRWIZxwUPDc3GysAIHC0pwU+8ctM2I87yTygL3bUQOFBxBEPoc+RaoOGv9u+I0UH4SiMlW3sdXzCbgxM/0fmt8uefvZyak4QgCJmCMdygg/H+juD4AE1p0aJyAt8Z3xb+ubG8XTcAw9ZlXr+tk+dy8UQxGdWkcxNHUJ3xRSNsoNNQuADs946zU87YGgSee9zUV7he30U3V9yXC21z35lCrQWBrIPBT9ENDMhCoUXKbq5yNR96ee5FC3qt+vKxBALg3LqrxVAW8V9AbYxQKEntqIAhkkWI5gsDBwKMmxZCLGgSQzJjisBZ67LK2wvllh8bxiPIxgqBLdiLuGdzSpz5EEQS56XF2BY9zUqLRJSvRZT+pgnoDIsIMevz0j36YfEkuXhzT0am2nKvjiCIIIsOcVR8cQcD4k4yESHzx915Y/tgw3DO4pXcprdQYCDjFUP3k6CznbSInAS35cwyw/DqKAt74sH+PxdQ5WMPFBIwJg5pj5YECHL9Qjn4tUzCyfUawh8QwDMO4YXxeU7RKi8OJwnIMb5uGcDeFEj0hFJy2/UYgIgh6fUS5SxPaA+0eAzY+YP+9tzUItE4dE9cKKDvqvp1wAVwD5C/37rihFn5vCkCRYrOWBgIvctQHEkOEcs0ACxKw6RGg87NAWHztJm8NBBrdN96mGAJqx167/BP9djWKKMD7FEOGSIX7zkOlmXwtAoFSBIGaFENuIgjG52Xjr5PFWHXgHAblpuLqLo3x6fKDTk07NPY+xdDjl7XFPYNbAPA9375o7/goI6bd3AMv/7oLep0Oj13Wxm0ao3sGt8RvO89Y/r65j7KROC0uEg8Mby38blxeNnacKMKPm084fScytOh0OiREhaGo3CqDI9pxVCRTx/HWQKBV1BoTHCrygdO/O2+3nQtqzcX9wMlfrH/vfRfo/rpCakCmPsIGAiZgtEqLw++PDEZxeTUaxUY45bVkGIZh6iY9cpLQIyfJ534cM7lc0r4BL8zdKiZdIKmMIMgYCbS62/q3UzoMhzGYVEYQqPUyVktcK+DMEvftHBfAkgT8cRVQXeTdcS3KuxCZj1QXA1UXgPAk/+Wg5QgCZ/a8DZQdAwZ+T397a1jS6nwIvQdVGhzNJkDWM4vql6i9t71JMWSIBnR6saLBU8V1Sm8gqjGlWvA75tr0TA6GIb3IQOBwjV1GENQgOtyIqWO72G0+dt5ZTlLjIhAl8N7/W++mGNCqEb7feFx4iC5ZCbh3iG9RA7bUCKz6cZFhyEmJwTcT+qjup2t2Ih65JBcz1x9DbnosHhgmNgC4IzbCiDfHd8Ur13XG0NeXWVIRNUuJRqNYcS26F67piMn/24oqkxnje2ajWW3RZoaps7gzEEiS2EDgS40qJvjkLxcb//1pICja6bytqgiIbOS/YzJ1CjYQMAElMsygWZEthmEYJrR4a1xXTJq1BZIENEmMwtieWcEeUvBQ66krQm0Egc7Bi9PR69dxQak2xZDWBoKY5uraOebYvbAZOLXA++PKKXt0Ot8yPgWKtXcCa+8C2jwYIjUI6niRYk+84I/9YP3sbbFhv0YQaJRiqEqtgUCsfHWJsVYRKzIQeBpRpQ8D+n8LbJoMnN/g+Vg8QaoRG4XcpRiSJKrpoIj4N1/eMcPOI95Y61AVJUiPkxYXgSs6ZWL/2RLgjNPXyEjQtlZDhNF5DedNyk2dTocHh7fGgwqRAZ4SbtTjo1t64I2Fe6DT6fDE5W0VoyVGdWmMPi1SUFFdg+zkaE2OzzB+xd180VwN4SSm8ixwcAYQ0wxIH6L9uBj/UnlevN2fKYZEc8vqQjYQNCDYQMAwDMMwTEC4plsT5KRE4+j5MgxpkyYseNhgqPDFQKAygsDRQODo9esuxZA+zF4xJiu/1CoR1RCZrr6IsaOCtUChIKxaKvOB85t96yPgSMCed8gb2x80qAgCD5WnZhOgN/qQYkij8yGsQeBJiqFaRFFMaqMjvKlBYKyVWZ3gue/pOdUZgLRBwGXrgXmdgKIdno9HLZJZfD+IIghO/QocnwvE5wLLrnTdr0IU0I29m9oZCN4c3xUAhDUI5JoAEQqp/7R2yhrYuhFiI4woqaTr1alJgqZpB32hY5METL+jl6q2amsgMEydwJ1ThtK75fTv1hQ1Pd8Hcu/TdlyMf1GKFFDaXrSL3rMxOeLv1SByZPA2SpcJSerGG51hGIZhmAZBt6ZJGN21CRKiGng+y8p87/dVG0HgOM1zVGbJCsKDM4A5zYGdL9l/H5Embi9arDbq613akch0yuWtBseFixZ5yBd0r5u1CNx5aGuR37/9k0Dm5fbb5Gvs7pw4Gp9E+FJnIxB4mkdf9toLtoHAlwgC2+gHX4xA3tzrcgSByLPb06gMnY3iW40s+oJUo95AAAB/Xg0sHgqUHHDfr4C8Zsn49NaeuLFXU7w5rguu6pQJAIgUpBiSjexKBgJfCxs7EhlmwNvjuyI3PRZdshPx8rWdNO2fYRgB7iII1ChwN/xDm7EwgUOOcnVEZCBYNxGY1x74uSWw7yPvj1l1QbCt0Pv+mJCjAbvuMQzDMAzDBImApBhyUA45pRiqpCJoayeIlZ6RaUC5TfFHpRRDI/4E0gaSx+zJ+e7Hb0tYvPp0JZ4UAK2LpPQGzq113y4mBxi5Btg+Bdj/sf/G0+I2YIdD7nK1EQRhiUCVQvh7qOCpkttUAoQn1O8UQ2rxpkixwUWud48jCPTiz/7AZQRBlPN2ACg/paJj5Ws2on06RjjU6BEp+2MiaiMIFAwB/kjrKhobwzB+xN18UaTUZUIfpUiBaoftpUetc0WpBtj4ANB6onfHFKU14giCBgVHEDAMwzAMwwQanwwENoolV0Xo3KUYqqkADn+trJyLTHNuX1MFmBy8miJSavv3IiokLB7QqdzPUcHqzzysWhOZAbS4Q13b+PZAVAZgjPPvmIRFVtUaCBL8M6ZA4nEEQe2i3OsIAn8aCLxIMeRYpNwTvEox5MpA4GFNDbsIAj/XNvM0gsCTfj1AZCCICiNfv0hBfQIAiDbW8SgehmHco5WBoK5H9TH2OM61LdsdDASnF9v/ba72fr7BEQQNHjYQMAzDMAzDaEFNFbDrTWDrv4Cyk67b+eKRo1WRYnOlsyeSLRGpDu0rxF7j4bUGArWKflvC4r1PMeRq7HWJhA7AyJVAeJK69vJ18kX5qAZ9mA8GAg3SOwUbpfOrZOiSF+VeRxBolWJIFDmjUvEzJweY2wY4+2cQUgy5qJvhS4ohfy9nJbPYECu6fzzq10MDgSDFkCWCwCA+B7HGOpg+jWEYz3BnIFDrcCIrekuPcdRBKKA0x3U0EOgFSWG8vb6iOT5HEDQo2EDAMAzDMAyjBevvBTZPplz+C/sqK718LvLrbZFiR2WwGw9iJwNBtbh2QkRybf9eGgjUphgyO6YYCpEIgp7vA7EtlNOROCIrX9UaFLxF58JA4CoyBaBUO460f1KbcQUKJSW3kjxW15EIApHce+IZenEvsO4ecZFitXiTYshvEQQBqEEgCRTt+jD197SIpZcD5adVNxelC5JrEEQaxDIZa9Sg2DhTPzi/GTi7ApCkYI+E8QRzjft0fivGquursoBy1c9pCvzUBDj2k+/jY/yH2ggCkUp3Tg6w7RnPo0ZEssYRBA0KNhAwDMMwDMNowcHPrZ/LjgIn54nb+ZJeCKAFI1Dr2eoqD7+7CIIKccFQGccUQ4BzZERYvNUwEOgUQ6ESQSAr4Y0eKhOTumg/FlsMghQpZpURBEZBBEHW1Z4dP7qpZ+21RknJrWQ4sEQQeOmV7dcixR4q2It3+Zaiy5cixSI8rkEQwBRDMDs/e/RhZJjwJYLAXAnsfFl1c1Eh4ujaqIIovVgmYwxsIGBATgsLugO/DwRW3xLs0TCeUPyXdqmBzi6z5qqvKac6R0zdRakGgeO7WzS3MFcDO54Fjv/s2TFFkQccQdCgYAMBwzAMwzCMr5gFCrri3eK2FQIvfM8ORmmMvgsH1rjIa+/oWeuY076q0LU3ochAYFu0GLCmFwIAnSDM2R1GH1IMOXpSN+oLxLb0fAxe4cKw4oisRFTrbSwrS9OHAS3v9OxYnuAqgsCbFEOeelP3+cyz9lrjGFFj2a4gjz7XIPCngSDAuaW9UYxrWqQ4wBEEjgoSWf59TQO2913VTXUCY25MbQRBlEEcnRJj8CFKhKkfSLXzBZnD/wVKDgVvPIx6Lu4H5nfWrr/tz9j/XbgdOLdBu+g2RluqVUYQKEUaAMDGhzw7JkcQNHjYQMAwDMMwDOMrpmLnbUpevb5GEFQXk0egO69hR8VZVIb93xVnXEcgOKYYAoByhwiCCBsDgdcphlQq2dxFEGSMcJ3nXEtSeqlvKyvO1SrQZQ91nR7o/Slwowno6AdPP32YIO2UbCBw4yWvhYEgYwQw4Hug6TjP9tMKxQgCNwYCr2sQaFWk2IcaBFqhdQ2COp1iyOysIAlLpP99STHk6TAExtzo2hoEkTqxQS+KDQShR8VZ4NRC+l8LRErGc+u06ZvxL/s/0ra/8lPO237LAxb0oHklU7dQiiBwnPu6unZlR9Udq2g3ULAWqBTVICgEyk4oGyyYegUbCBiGYRiGYXylShCCK4oqAHw3EKjFMfVGpIOBQKpx7UkoTDHkEEGghYEguYdzdIMIRw9sR09qY0zgFHZRjdW3tUQQqDSEOCrndXogTMX58RSlIsWSpLwwlRHVINCHqa8nIdP0OjKCBAMlJbeS4UCWN28jCBxraHiLKIJA6VnjL7SuQeBxkWKbJay/UwxJNc4GgvBE+t/fhcRthyEI9pJTDEXqxYaAKD2nGAopivcB8zoASy8F5rUHivf63md1oWCjn6LSGNdIEnBhK1CqVmn7l3/HY3ucw/8NzLEY9aiNIPDVuLP3fXreLOwDVAjq4hyfA8zOAn5uDpxd7tuxmDoPGwgYhmEYhmF8RZSjUylNi6jQr19wmOZFpsFJMXDRhQLC4xRDXhoIjFHA8MXu27pLMWSMDZyBwBPFoKcRBKKCqFobCHQGcQ71mgpabLpTgosiCKADEtp7MRYvUlNpgSEC6P+d/bZen7iOIJAk4PwG745n0ijFkLA4dyhEEPgpxZC/l7OS2VnJKkcQqI1+0oC0eOdzHmmk8xABBQOBjiMIQoayk8CWx60OBJXngF2v+96vKKe4pxE7jDasGAv82hX4uSVw6Gv37S/u8/uQLGx9OnDHYtShWIPAQwOB7EBQegzY8SJw6Cv7tITb/g1ARfHyynPAjufdt2NCGjYQMAzDMM6UHATyVwXeK5FhAk3FWbH3v6eIDARCzz0EMILAYZqnNzor/V0tQMOTnb1z/RFBAAApeUDTsa7bOnpgO4ZZBzKCwBAJtPun/bbwZOW2gOcphmxRE2HhCfK1EhUpViOfYYIIAskEJOd5P5ZAY4gEsq8FOjwFJHUH2kwCmt+ibCCoLgHWTfT+eP6MIAiJGgQuUgx5HEEQ4BoEju8IOYLG08LjImrUefn3aZ6CrCTr8S5pnw69ngy+EQqGAKXIAp8p3qveC5pxz44XyEP3+E/22w984nvfovzhnE4m8FzYChz7gT5LJvcFgs3VtBZjGi5KtQWcDARuUv+UHaM2C3sD254GVt9qrUdRUyk2IipxepH6tkxIwgYChmEYxp4js4Bf2gKL+gNLhgde8cAwgUAyk7Lvx3RgTlPgxC/q9qsoAE4tAsrP2G8XGRmUJt3BMhAAQFSm/d+uUskYY509hf1Rg0AmPMl127oWQdDucSCmOf0d1YQUzUptAd8MBFpHEMhKcFEEgSoDgSCCoKaSDD0ej8UINL7C8/2c+hHIn618OhLbkvbp8iJw+Uagx5sUVaDkHV96CDjgQzokrd6lohoEgfYIrksRBL6mGHIXweLvCAKRcVmAXq/DrIl9cXu/ZvjH0JZ4a3xX63AgNj5F6vxQfHTDA8AvbYCfWwD7PtS+//pI5TnnOYNMVREZCNR48HqD0ECggVME4xlHZ9n/XXJQnDfMXA0U7qTiwRzp0fDIXwUsuQRYerlyLShHJwF3Br+Sg8D+T+xrUBz6iv73Zj3CeoF6TZBiehmGYZg6y/b/s05Kzv4BnFkGZAwL6pAYRnP+egXY/zF9ri6m8OomV7nep+QQ8FtvShEUngRcssKaUkW04BYV+wKAikClGBLkGXasQ6CEMQbQG0iBbFvI2DE/aXiQDAQnfwOK99h/H9AIgiggshFw1S6gaCcpm4t3O7fT6W289etgBIGoSHHlOff7ywpSWySTdwYCAOg/E9j9Bh0/rjWw9k7P+2j7T+Cvl+23RWUp/57MkeLtBoUIgqKd8JsSzxMq8ymaISzWui3gEQRaGwiCVKS475dATDPg90HKbVzWIPDiPDhSXQRECgrCC2icGIVnBkmAMRyIsC7jwxUiCMIVUg95TdlJylcN0HnZ/gzQ6h5AxzntFTk4g5wRzFVAx/8DOj9r//35jYDZj6mgRI4KIqMBE3iqC+3nPaZy4I8rgTNLAz8WvoeDj6kcWH6duA6AXTsHxx6TOwPBAWfnhtLDZKDyphh61QXXzhdMSMMRBAzDMIw9jkq3Q18GZxwM4y+K/gK2/sth2073SrZ9H1jrB1RdAPa8Z/1OZCBQjCDwYkLuDaIFn2MEgRKy4t5dKhEtIwhESmdbpBpKRbL3A2DZZc7fG2O0Sfmhhuim9L8hAkjuTilHItOd29nKlNrzk9jJeZvmNQhko4WDglNtBEFCO+s5ACh1VUJHIKGDd+MJiwU6TQG6vgxENHL+vtU9rvfPuISKXTsS3UTcPi4XiG0h/k7JO74upXv4uQVwbr3171CPIPAUu2ebD8vZlD4qxmV2fr7LKbbUGifSBikbZz1R1q6bCMzvBMxtbVXUAzCaxfUtwrWOIDizBHZGsoqznqWnaGhIErDlSWs9op0vOSvk/GkcAMSpDpXSHzL+QxQt4CgLe98LjnGAqRuc/t29cQCw1kOScZdiqOQAcHGP83bTRe9qolUoREMx9QI2EDAMwzBWagQLFQ4lZOobh7913iaZ3HvSOBYM3D/N+lkU4lulEEHgmKYnkGhtILDtz9sixTLuIggAUmBvmiT+zpMUQ52eAzr+W11bEfFtnLeJijrbouSh19m26JsO6Pgv5zZaRxDIRgDH61uZD6y5zf3+hiigz2dAbCtKs9T7c4o40YcBSd18G5sogqLpdcrtMy8H+sygVEWORCkYCBpf7mIAClECotz/waIyH9j5IilnzyzzPEWPr+jDPPfcd1WDwKex+JBiyBDp/pnoKoJADWmDgRF/AFGNxd+rTfdSeswa9SaZyXu/VklkkBQiCCQ/pBhyRI1Cq67i7/lt1Xn78yOZ6H61xV1EoUix7NEYRBEEAU4xJElAyeEARk/WQURzRNs5p6mUilQrEZer/Zhs8VXOGN8RKfFFSGb7iFp3KYbO/il+1pWf8e6eZANBvYYNBAzDMIwVoScBTxqZeoKplJT8O18Qf19+wnmbJFEKAHcLarURBDWV6lK4+ItIlQYCY63i3p2nsK3Sy5sIAlvvXbUGArNCUU9PUgxFpgER6tJ6CIkXLNaNXihAdXqg/eNAl5eAZjcDw34Xe7ZrHUEgK5TVFJsV1RswRAIZI4Cr9wGjDwJNrrR+1/YR38YmMhAoKZd7fw4MnQ9ENxbnoleKIMgURKBYjq+uaGzQOT4HmNsGWDw08MfWGTzP/a9VBIHT/eHDclY2EGRcotzGVQ0CNRhrU0EpRXmIIgiKdgN7/gOc32TddmaJfZvKAquhT1SXAsq1CbxG9J6zzWsdKlRfBBaPAGbFAKtuBcx+isApFxhPHI157gwsNeJrqxphDQLBNn9hrgFW3Qz83ByY0ww4Njtwx7aMQSGXeyARRY7aGggOznC9f2JnTYfjhGONJybwXNyvvq1tmiF3BoKC1eLtFWe8iyAQPdeYegPXIGAYhmGsiLwC6sLEmmG0YPl1wKnflL8vO2GfpsRcAyweDOSvdK/8FhYpPk8GBlvP8WArU6JU1iDwJoLAGwOBrReyGq9cV7lWPYkgMET6pnSPzvF+X1viWtN56/Ck63ZaGwhk1BRZDU9xXoC6Kuza7Ca6DrumAufWej4mSVSDIdZ5G2AvPyIPOaUIgpReyscXRdKJyLkBKD0KFKxS194feLO41wK9sdZA4MH8QAsDQVg80OMd+22+1CCQU2wN+gnY+x96Xkc1to+iEUYQJKg/hnzvmhTSQDgq3Yt2Ab92s6aeGb4USB8ilu+yY1S/oEacYsgoibd7jWiOGOx3mjdseQI4s5g+H/6KiqQ3u0H744iU/44OAu68cSsLfLt3gl2DYN8HwJFv6HNNGbDtaSD7msAc++J+4I+r6P/W9wI93g1ern1RhKrttT/7h+v9EzsBx7533UYf5v2araaM3n1a1FVhvKNwm/q2phIAqfTOUnq3uKPiNKcYYpzgCAKGYbyjpgo4+j8qFClJtEDY+z5weon7fUOVk78Cvw8FVt5IisT6iGgCG6h86QzjTyryXRsHAOcIguM/knEAUF50ydtFnpVSjXMxMcf0Qq4Urf5AyxRDxlh7xbU3BgJb1EQQlBx2MR4PIggMkfYFlj3Fl7QmtmQoFMp1xNf0LEqRIGoiCLKusb+2kRmuDRY6HaUEGrkaSBviySgJ0b0W09R5Gx3M+lFUOyE6S9BXDhCR7OL4Kg0EkZnAiGXAiOVUfLQhoTN6/uzyJsLGlraTgWvznYvJexrJYItsIDPGUCRPhyeclf8X9wGlR+y3eRNBoOTl6aisPfSFvQzufIn+F83Fyo7R/wpe5vq971AaCa0QKjlDzJu0ppKU1rYcnemfY6kxqKgxELii7AStS34fDBz/2fl7YQRBgFIMlZ0ENj5ov61oZ2C81SUJWDGe6qpJNWQAlOdznlBTQVEP5zb4Nh7hvWOzrfSo6/0T2rs/hi9RkUBwo1sbOpLZCwMB6F3hrVGoglMMMc6wgYAJLOc3AQc+q7/K1YaCJAFLLwVWjKNCkevvpaJpG+4Hlgyna1zfKD8DLL8eOLsMOPIdsP6+YI/IP4gmsBxKyNQH1ITuOr6bDn7hfh/5/lBacDvWIXA0EIgUmI60ech9G0e6vCzertpAUKsAdpViyLEvb2oQ2KLKQOCiUKynBoIILw0EvtQDaP0PmzFEAR2eUrefr16PjRVS6qgxEMQ2A7q+SopYQxTQ/U11Xts6HTB0AdDvvx4NVZjiJyxBXLzYdhyiNCuifdzVSFCbYigyjQwnaQOARn3V7VNfkMyeK+YNPkYQGGMAQ7jzdl8iCIRGTVF/DqkWbaOdurzk+hjy80LJQOD47vjrVfu/Ty+ieXeZoHZNqWwgcBEpsGSE57n2C9ZQypNKh/dXXYsgqKkCNj4MzO8CbP23ujzqpxY4b3N8r0hmUrYf/QEwq6zvUV0MHP4GKLCJmhLNn8uOU07wC9tovO6UbYXbqW7S4W/EBvIN99O65OyfwIqx9tejpkLsme5pBEHVBSB/teepmA4pzKEuHvCsH284/hNwYZP9tgIPDQTFe4F5HYHlY4Df8oB9NnWn8lcCc1oA/0sA9rznvi/RdbY1+snGPiXi27o/hq8GAqW6WfWFirPA8rF0TXe/rW3fxft802+VHPKszpHc1l16IVeUcwRBXeXIkSOYPHky2rZti5iYGCQnJyMvLw9Tp05FWZmPaefcwAYCJnCc+AVY0BNYexcwr4O6CaUkAYU7abLFxXOUKdwObH0aODLLf3k07Y63lRTlMvs/svc6+Os1/xy3ppJ+44HPxCGz/uTw1/YeWid+Vj7X1Rfdy+uRWcAv7YGF/en61RXchcAyTKjiSrEs4xhBUHZc/T6KBgKHZ5WTgUAhBYpMyzvJs1Ut+nDy3O7whPj7qMbUxh1yBIGr+9/RQOBrBIEar9zSQ+Lt+nA6vlGlgUDvg4EgrrV3+wFAt6lA+yeApuNJea425ZOvdJ1qr0htO5n+V2MgiGgEtJ0EXF8IXFcANLtR/XENEUCzv3k0VGG0hE4HRAuiCGx/U/YY++9S+oiV2EldXR9fbYoh26LU7gpU1zeqi32rQeD47Ejp7X5/xcgOL5ezhkix4U3N7wqziTJo/wTQ7xv79HR2bWsjCJQMT47vDpHBo3iPuLh9+XEyfu96XXmsRTuAC1uVv3fk0FfAwr7AmjvI+cdWASV0IhGs507/DiwbBay/X3m+fm4DsHESGSK8XbtsehjY8w553u58ATiswhh5+FvBRoc5+7q7gT9HAyuuJ2cod5jKyEix6iZgYR+rc4EouuLwV+Tt/2sXSsPmbo695g5g1d+o73ntSVFvOW45cHy29W9zlTWKoCIfmNdJnH7EkxoEBWuAH9OBRf1ISe6Jt/KFzeLtF/eq70MJSQJ2vwOsuMG5roEkAdufdd5HzTxQpvIc8PtAoMTGmLFrqvXz+vtoPlJdDGx6xLVy2FwtVr7L95O52rVeRB8OxDZ3P2aRQdwT5ncio8f+T33rR2skicb057V0zb19XmybQmmainYCmyYBBevsv6+poufD8TmeGVXX3wf8kks1Nrx1kvQkegAgg+CiQcAcFXKhBNcgqJPMnTsXnTt3xptvvok9e/agrKwMFy5cwIYNG/DYY4+hW7du2L/fg3oVHsIGAiZw7HkXlglYdRFw6Ev3++x8CZjfkSZba+9Sd5wT84Gll9PDuiGEypWdpInmzheBleOBAx/TYmH/x2TN9gRJAirchLICwBk3eRIv7nX2OlJz7PxV5CGjZA3f9Aj9xrV3AX9eExhjiIwoj3KZQzioJAFrJwDfJwFzW1OROREVZym/bfEuyl28doL24/UWUQh71XmguoQWInWZ6hLg4Je1Hl9cN6HOcn4zcGQmXS9fKD/jPiTblhIVHmsHZ5AxGyDPu+K/3O8jLwqVihg7PgsdlTyiHOmdXwB6/gfoMwPI+wgIT1avjEvpBTTqo/y9IRJoOtZ9P7KBoHiXchvbAsWABimGEt23UVrgy8pHd0WVZXyJIHAV6h/uInUNQAaMri8DA74D0gZ5d3xviG8NDPieivO2eRjo/BxtV2sgAEjR6WuaGDU0vd7eSJA+nP6PyRY0tlHwRjchb259GEXmdJsqVgBrGUEg443nZos7KNLHXdRLUxUKykBTXeRcbNUdtrLWx8azWKcHerztfn8lr2dvUwwpPSvURCTYPqt0OjKa5U0Tt3UXcWT7u5RSr5xdJjYQ/PUq8Es71/0DwILuwI4XlZVeZ5YCW/9F8/s9NjUeyk+Sd7qMSJntqAQvP0OKvJO/APveB9b/w3mfsuNUXHvP26QAd0z5Y8vZP4E/RgPr7rFPh3FmGbDvQ/u2259x7Z0umcURBLYppKoKgYPTrX8f/8k5xZQjx34ESg9b/979Jv3vTpG27WnPnIRqyoG971r/Fq1NLtau/fZNA0oUlEimUvvICMlMylKRMWfzY9Y59YXN9vLgjqKd4u0XPVyfitjzNhmIjs4kD3/bFEBlR8UKV3fX0ZbdbzsbxEoO0jmqumDfv2QCTswV91N+hhS5IuT+y07AyUhlS3RTddGRWhiqSw8B6+9xnc4x0JycB6ybQPfipofVGQJF7Hd4Rts+dyQJ+ONKMsb9eQ1F5lRfpHTKhduVHf+K91mfQ5IJ2PKk5xFbgH1BejVseRzIX+5bEfOKM2Kjr5r9GL+wefNmjB8/HsXFxYiNjcWLL76IVatWYfHixZgwgfRFe/fuxZVXXomLF72sPeEGNhAwgUEyU4isLSLLvi3lp4DtU6x/H/zcfYqIksM0STi1gB7Wi4eJJzuSmcIGA5ED0d/sn2b/G9ffRxEa6yaScaVwh7p+qi5QUbQfU4GF/ehvJSWro5etiPwV6o4rs/d9YFF/8pD5fbDzsauL7V/sZ//0/BjeIEm0cDr6P+fvih0MACfnAQc+pVyXJQfIoCHi+Bx72Tu31llO931EBVUPfO7b+D1FKVft/+KAH9OA/Z+o6+fMH8Dq28jIp9Yb0xfM1ZTuas1t5PElWpAGAlO51fskkAasUOHQf0lRsfIGYH5ncUoQR85tAJZeASy7Eriwhbbt/QCY3RiYkwNsUZmiRY2BAKCFQcFaMmSoMTTJShu1EQSOaSJEBoKweCD3H0CL2yjXvSFCXXg5QPnV3aFGKSkbCFwRqXEEgV5FTnMlj0S1aSBkDFFepgrSAR3/rfx1b4dnZJuHvTiGn8geAwz9FejxllXRb4gGYlu63s9bQ4q3GKOBgd+TsSvzUiCvdvHtLoIAoGLPN1QBo49S6h+RYdutgUDlOyvCNoLAQwNBUjegz+cU6eMqPcyl64ABfsqP7gsxTT1PbWBrrMm8BBi2iGo3DP/DtVFTRtFA4OVy1rE+jOr+dOLnY0J72BmsZOQIAiVsFcvFe8XKpfX3KhfDVmuo2fY0sOsN5+1nltJ6aedLwOIhwPmN9t/vfMX62V0EQfVFYM9b9l7rR761fw+WHqM5mu353/6MeMwV+fROPvEzRSuvuYO2SxKwabJz+5IDwNxWwPbnxf2VHBK/q6uLrfJ1YYvzNTi3znEP+q0Fa0lBuNchxUzhNnr2uKvP4I0zi20Ko7N/On8vr9G2u6mLIp8HczVFjCzsTZECZ5fbtCkmJaQtB9x4l8upT8zVJM8iPDEQlB6jNe2aOyiSBiBPb8c1lq3S2FGGZdRGEJjKKApGxIWtYqOOUsTp1ieBc2vE38lOWe7SC8XkqDRcJvuWck1GqqH7tq7gmJnANpJDLSInHtv7umin/TXf9yFF7KwYR+uVo7Ps9y0/Q88TR4NjZb66lKaOnPexxoU3VJzmGgR1jIceegjl5eUwGo1YuHAhnnrqKfTt2xfDhg3Dxx9/jNdeo3th7969eOMNwftcA9hAwAQG0YPS3Qvs0Ff0grLl7HJxW5njs+09vwq3UYEiW6tv9UVg0QDglzbAT03UK9BPLqBJstr2ajg+l/Lg/dZHeTKjhLmalG07BJNg+RyYq9Sn+9n+PKUOAoCC1cD3yfTvoCDSQ805EE1aRRz6igr/bnzAuu3CFlKw2lKwxnnCfvwndcfwxpIvc+ATWjiJOLeOrmFJbcoLx4XXqV/tDQGmcppsFwk8cs/bKL2OzCTvjWM/AmvvJO+dM394rgDzFHON64LEphIqNuYuyqTkILDsCooS2vove0OfZKbfcvJXZcNB9UXgwHSSb9vzV1NFcrriBkrRJEm0WL14gAwXtsXHDnxiXUj4yrHZwLp7gWNu5E0yA39ebfU+WX+Puv4liTzhvTFYmk0UeeNptJAn1FTRs2/dRODceu/7qS62L1ZXesi9F5CpjML8T/0KnJxPuUOri4HNj1rv679esd6DrlC7MJRqyPtPaUHnSPkJGqfSAs9tDQIHL3xArCh3lxZFpt2j7tvEZLtP+yIrz10puJ3GHoBppdJ9LSuk1D7vldKLKJHQgVIgdX0ViM9VbtdkFJBTm4InqRul5vE3RjcKSFfodEDfr0i+lIxQvqYtEB7Xjdd348uBS9dSGqb42pROokLFSnNJ+domdbE3hsW3cV/3Q3WKIRujgJpIDFvUGvz8/d73hqhMoPEVnu2TMUK8rfOzZMhRg1KqGm8VYo5rDJnYFq73C4sXH9MYDcS1Emx3Y4g8+wetA04vcR2xpQU7HaIIairsIwZEyIp8U7k4XY1sIDj4JTA727mGAgCcmEf/H/gMmNOUFP62VJ23Rn1LEq2JCtbQus72up+cR+/7C5uc88vbsuM5mi/OzgZ+62tdh7qaw8je5aJ0TEV/keJbMtO/dfdQtPDCPpRaRGRAKNrpH0Va6SHg9yHA7Kb282uZ4j3q0vLKBoLD31jHb64GNvzDur8oYryqkL4/9DWlkDqztLa/YkqbOisWWDyc1uBKxiu1KYbMNTSv3v8xRXj+PoTkwTatksyhL4Btz1CU+f6Pxf2VHqY+zSaad4vO075ptP5VmgNe2Ez1IxwRzU0ks300iiMVHhgI1BCeIJ4DJXZWt78t8lpVMnsf8Xt6MbDlCXq2+YKjkapoh+epp0URJbYGujPLnL+3NfpsszG47fsI+CkT+LmFcxFugHRMq25R//6WJG0NBOnDlVPe2VJ2XDndWGSGctrPyrO+6VYYIevWrcPy5STrd955J/r2da5tNXnyZLRrR1GD77zzDqqrtc+YoMJVi2E0QDRxMpXRZMs2J6mMJFHEgCMFK4GWdygfR6SUPr2IvG5S+9PfO54nBThAE9JNjwDDFtLfFfn0vU4PdHgaiKxdFO//lELbAGDbv4Dchyh0Xa8irLlwB01MM4bZexxVnAVW3Wj1tFjQE8h9AGjUj7z8DLWhz+YaOn9R6bRoMZXSxGTPOxRW6Y7DX1HYfasJrhc9e95y3mYqAdbdBWQMpz7MJpp0nfrV/XHzl9Nv3/8xLcbbPOxcXO7sn+RlLgqr3PMOkHUNKdMu7he/uM8sIS8OY4z4t0kSeSXtep3G0P9bILm7+7HLmKspX6ESsseTPhwYPNcqV7ZsfBjo9hoZPNbeVbu4EEwYzm8kGQGA3Q7XYtdU+tf8VqCvm6KppUfpvKf0VB9qWnkO+HMMRTK4S61QUwGcXuhauXjgc/uQx73/oZQpeiOw6Z9WWWtyNTBotr2STjKTcv1M7WTyxBxgQK3XxranrV4jR2dSuilX7H0f6Pmu+LsLW8lrM6W39filR8jDJLGTddux2RSVBFAEy6A5ZMAo2kn3aUqetc+C1fbeJwc+BVrcTs+e6osgr0MHRV75GWD5tfSMim4KDPpJvYyaa2hfOay55/tAbm3xbMlM16H8JOWwl/PcF+8hRblUQ/dYZCbQ/XW6Tw99CSR1B/p8Zv9c3viAdbF16CtgxB+k0KipAFrfB0S4Sakis+sNZ2X5qQX0bFLi2I/2CvWS/aR8sAuplch4kCuIGqk8T5E++nDPDEZHZzl7CylxfiPlHlZCNkyc/JUWS2cW23/vmKYHAHSC6VliFwACg0pcLilTC7cDzW8jhagaGvV1nStVNlK0uIMW3iLlnGMEgSSaqOqgGDrf8z9qRuohKheN8jtWLVeqdA7QhwH9vwH6fa2NJ587rjlB75Q5KhUIIlL7ApfXGqk3PuI8HwjXIIKg+1uU81emvxde8dGiFENuzrExGuj9OYXjh8VSyi53hqGm48jI7A5f0jnEt7F+7vY6sPmf4nZKXu6BJrEzzUEq86nQtihaSB8unkOkDQF6qCji2f5xsXJZRtFA4GWKISXiWgEt71L2lLatP+BIYidn72g1Brxza4Gll3inyPOE6iJS6GYMp8Kqmx91HzFTme86NUt1EbDnP/aOPo6svoXmgqKUODL5K4Cs0XQvyCl6RByZaR91IUIyWT3oy44Df1wFJPUAjnyjvE/pEXp/Fm5x/m77M2RcMcZRVJMa7+oLm91HEHiLqPCwzMW96lLpFKwBYpo7O2oUbifjS3IPsRf9xX3Ajhes53f/R8AlK+mcyFEuZ5aQglSJor9oTVm8i+4PQySt8yrzybCeNpDanZhjjRwF6HxufVocSVh1AdjhJjuBuZp+05rbqa+0QcDgeda5edkJYMMDrqNyNj0iTiV47Htg5U00t+j8PM27RQ5hjmOuqdLOQKD0bBr8C0UyFO+m5/FuFZ7Hxbsoen7TZHKEaXEn0PFfdP+VnaB3QMkBWnNGptE6u+IMkH090OZ+4NQiYOmlACRauw1ZQJFjtuSvJj1NdRGQfR2Q2s95HEopi7/VA11eBAwxtOaV9TxKiAx/pYfJIcAQIb7vbbm4l6JZwhNr5zJu5pqHvybHEqV6YJLZOkcsOwpUunG+a3YLpW1zV4MxsRMw/HeKgHB0PtXp7RX7orR1Mj3epvaiGizmajLUxrmJPmU8Yvbs2ZbPd9wh1nfq9XrceuutePLJJ1FYWIilS5di5MiRmo6DDQRMYBB6bEiUTqbXJ86h2WcWi5U5th7CNRX0cIptSQ+wk/OVPcp3vEBh9Re2OIelnV5ESjpjFPBbb2sBxJO/Ulj7wc/tJ0+SmRbPOh2F5hesJo/B3PvJYGAqq31otqb91k0gZVxsC1KsyZ5rh//rXK1+73v0LzqblKJSDXntFu2sNVr8i17Wjqlt3PHXK6QM7PIiENeGvAGTe9Di5+j/XOcUNlcDs7OAnBto8q6maCdARo35nax/n/0TGPwzRTTseJ7GUHUOii/Y/BXAd27SVRRup7A/6IDsa4HUgTSprCqkBWN1MXkRAfRi/2MUcMU253QJpxbR9UjsSEYaWXF0fI66yb25qnYSJGD/R/TPHefWUBRBWJzyAurQl0ByHnlBRGUCrSaClKO/AgntaLL959X0uyMzyPCVWHsNjn5P44hpBrR7jDwyayrouqy/17PCXad+czYQmKtpsbH/I+fFhqmU7t2IFHvF04mf6b5tcmVtHzW0TTYOACSfp5eQIt5VjloRB2dQ+gLZ0Cez+XFgV21kTdNxVFxw3/s0CZZMJEv9Z9I9t+1f9vv+Odr6+a+XgVZ3A93fpufH0e+dx7BxEilv/3oVgAR0eob2OT6HlPA7nrfmaC07Sml0hi+mBdjB6eRl2uFfNLE9/BXd+7EtKc92/gr7nKcb/kFGlJYTaHEoGxD3vQ9cuoEWlGtud/aaPPyV9fPFffS7+9cuomUjn0xNOfBbL+vf+z8GLlluv3iRzGQQCYu3KuKO/kCLa0eO/Qj81BiADug0hc6NLSLPq50vOW87tZAMBKXHSJ7PrXU/2dYKpTB0mb9eoUm6Y5o9GaGBwIPCqjFN1eXvdsRtUVDZA7szcMUOKgDpmO/ZMYJAlC4h937n9AsALSKb3+p+nIZIkim1eeHVejXJCs6YHHtlSq9PrA4BljF4kXc/EMYBwHoNOr9AypHIdPXvaRGJnZy3iRw5PKXlXRSlmL8SaHwVzZs8RZRiSA3NbqB/aunwBCkSHedotujDfDsvthEEzW6iZ50oX7c7b/aAoQPaCVK62JLQwVlxl9IbGLFU3SHaPETvhIv7KPWaYzpLpfPtjzSGvT+hVGKr/ma/9gBcG20SOtFvsCVMZSozyWyvDPUXW56g37fxIag2qM5pBnRwkc7PlXFAxpVxACDnkIyR5ITiiq1Puj+WI8V73DsJWCIItoi/lwvNqk29cuwH7/J7+4qpVJwW1ZFVN9XWbBBEoO7/FMjrBpz+zfm7mnL79EWSieYHjjn4XUXDVBa4XuP1eI8cXkTpiD1dCziy7DLr57N/UgrVhI609lbjAAeICw4DVgNUwWrg8q3qUuEW7aS5qytE0XMilAwEMdnktACQM6QaA8H5DfbK4QOfKBvObWtdnP2D5v/7p8HyfJHM5IEvGwjMJppnHZxh3W/3W0CvadZ1QE0VOQO5em5slddoOqDvl7R+KztO6yS9gY5bU0lrtEKBgUAykY6qeJe64sJnFtP83FVaQLvxPUnr5YLV5GB1YQs50pjKaA2e2JGeecd+cN9X1mhy5lh/n+t2sjNqtMCo1PxW+3MuwhBNOp/s6+i39v6c1i+Oz70Dn1I9LUYzVqyg50VMTAx69FCOABk8eLDl88qVKzU3EOgkydP4HMZb1q9fj169SLGybt065OXludmjHrGwn9i7WsYYQ14z+nDynCw5oOwdkn09vTDO/kmT9LBEelC784CJaUYKOH+FRMXlkqIhf4XrF0f6UPqdpwSTLsb/hCWSEeHiPvIEqimnUEWZyAyaYFQVuvY0ChXSh1P4oKMXQUJ7UjZ7ez+kDydDSlQmLboKVnnfV0JH8p5RyuHuK60mUjFCnaE26kQwSRQRnaVe0aZmUas1YYnKoaG+0viK2tyzKhY3YYlAk6toUlp6mJ71VRdoe1JXWkiJQntFJLQnzyyphhaQnuSpbXwlpR/wBJ2BFlRKiz0ZfZj/Cl9ftRtYMdaa01YfDlx7xrlgb8VZyg/sSNOx1igbTzDXAN+58BPp9bF9dEfBWkqnYMsV2+wVyge/ICOULd3fpqJytowtUq5x8PsQe+/Inv8hj1c1C6i/SXTPzhZ5mjsw+ggtuk/MB5ZfQ9c3rjVw2SYy6O18wdp20GxanAWbXa+Tx69M3odAa0Eqs58a2+cFB+jcqKG6hKIR5Hui8VXAEIXii4Gm7AQ5LNiSNw1oPVH7YxXvJQO2bdSDI47n9BsPUlZdvsU+2qemgpREu6ZalTCZlwND59PnOc3F8+KYHFK2lJ8gY5q/amsldHCOovmtj73ypvdndJ+enG/dNnge0MSDdESmUooajWlGBnRbRdbwZUD6YOd9lM6NGtzdFyd/s1coAkDqADKMizj6A9VBsmXkGqBRb8/kQybvQ1LUuVJeRaRY0/MwvpFdG5Xpr/c904BwET3pKcOXkP7A3TOk37eUncCWyHTgWhtnt5oqYKaHEZSeEpYgXtOlDSZ9z+lFys6OCR1Jie5LmtjINCC5Z+16pJDW/WpTH9dF9OHAuDIAZuCHRq7r/zQZRQ6ZhTupFqUtwxbT+0zp+RbbCrhaYd214QGKApOJSAGuOe55esUQxt+63NTUVBQUFKBLly7YsmWLYrsLFy4gOZmimMaOHYtZs7xYA7qAIwgY/2OuVi4qKGMqdW1AsOWYg5euWgWZt4sHtVzcqy6n4hmVnlSMf6gudPZysaXitO/eKXUJx3QmMkV/+adfbyja4b6NL6iJ4BDhiRduoI0DgP+MA4C9gscd1YUUSivafnaZZ8f1RS49NQ40u5m8mvVhwLq7xd67Mm0eJiWcKBWbL0RlkqdT9zeBlX+jSX+3qc7GAYAWPFGZzorfWEHOazW4S5HnmCM9oQPlcpcN4KLitqJFh20qFTV0fwNYMpIU1AntKZ9/ch5FCKoxQkZnAW0mub9WctqPJldQhETJQVL8hcUCbR8mQ+K5dUDT8d55u/uDlndSmqqzy4DMy8jzXIQvjhBhscCQ+WQkiUimaKW6QmSG8zZfoiVcEZ9L//JXiI1TbiNwXKEjY5QthkiK7Ov1EaVXrCmzN0p1eZE8fmVyH6SUCo2voGtWcojOz49pvqclim/rrLwRRgf9A1i9DoBEaUGa30pGjwtbKHVB46ucU0q4wxhjNZy0e5TGUbiNZD1VoVZBUhfnOX7vT0nBdHw2AB0pi86ts/d0VUPjSyl12yGb9I6u6kckdHDeJkcrdX4e2OaiwLkjhkggawyQM961gWDgbOD3gXabikwxSDC6iICpTzQdT4bknS+4r6fgDsfoj1Ag9357xR1TR9DQB1cUaSoiXBBBkOiQdtIQTnO3kgO+j0sJJYevs3+4To8FaLMmrDhrv44JZeMAAAyZVztnNwA93nV2xLElu9ZAHd/GPhIvtiUZaJrdIk7jrQ+jd5QSre+zf85UnqN6gC1URALXQ86cOYPjx13PP7Oy3NS7sqGiogIFBQWq9ktKSkJMTAxKS0tx7Jib6CMvYAMB43+KdvrPo4lhAkVYIr2gF7nJccgElw5PA/s/ZG86xjXZ1wL9bFIryZ6xJ38jj1XbdECGaPLSjs6mdHgX95Hy+sQ813lqbZXpIuJaA32/plz/GSOA61SkIsgeC+y1qasR28I37+mWd4oVTwkdnZVxYbGUsmtrbZqJTv/nnJ4uob1zX+nDyINNLtYY38Z10c7kHhRVUXqE+jNGAxG96Nhy3RcRbR+xfu7xJhUHnqMQlp8+3L52hqwMlolIIQ+sukZ4EqVLdEeHp+1TfogKxLqiUW9gUB1UlImMWp7UFfKG3p9RaqMTc+2Vy1nXOLdNH2rvBBKXC3R+jp4ntvdZ0+uVUzvqdGKP+5wbAegoWi/7WjqWLbHN6f9urwPrBVElMil9qNCsK4No3jRg8VDYKbiyrnZu1/wWMgyUHiUZ0xvp/h21rzbVYbpnhcAdiUoHhvzivl3WtZS2zzL+D+jZBlANKJkLW4Bfu1n/dpUyx5Yeb9Pz6OwyithwVbg9rrW9UsYYazUo5N5PaftOLXBWoImiAHp9QucAcDZSAKTQafc4FXnO+8CSeuJYVTou2fM+/pa5Af/X6BV1vzFUMcaSUTmyEdU5OTrL2YheF+n/HaVUcZd2KaIRkHEJpV1Zeqlzqr3LNtA9V3lOfeqjYGCIBC5dR4ZnxzSkqtDQG98bIhppk7ZSNvjJef3VEJ3t7IyhRGQa1ciwzVLQVfAM6PYa1QD01picNpiec6WH1Dt4MurQhwMDvqe0rBWnyVBuO4drdhO970RptbNGWx1H9EZg4A+UUk5noGuuN1B0cPowmpfodLU1PwaRnDmmYLYloR3tZ5sGeN/7DdZAMGqUe8chTxL1XLx40fI5NtZ93SLZQFBSon2dqgAlSGUaNGGJQPsnPV+gyuQ+KC6G5hIdhSK3f1ycjy/nRvXWeFua3UIFnfxFQkfKo2+LyGPOliZX02LM1flN6Oh9PmTZOzU6i85ZxgjruYtvS+HPbSZRMdZBs4Ehv2qTrxigIpqdnqststoUgI4UYv2+BeLbaXMMNSR1B4b+RsWTXC0MAUqP0uoe73JWM+pI6UWFU2UiM0jp0f0tUsb0/kz7ooUiXBUebH6buIiZiMg04JJVtXVM/Bz2K5PgEHYqKoprS3xbUqb4EkoaFk9KjEtWed+HNzhOeJO6kce+iMaXUu2O0UeAnL9R6qiBP9BzRx8GdHgS6PM5PeuuOUbe/mlDnD20ur8FXLKCPGqjmwIRqdSu71dAnxnAsEXAlbuARr2cx+CKzs8BbSfTIrP725SmRG3xOhGdniOlM0Cy1/IuoNsbNHaRYq/DE3RurjlK71dHGvUlZYVMm4fIW63f15RuKjkP6POle6VhZCoVnbNVonb4F0V9iIjKdK5fEZMNDHDIw5x5GeUVr4vKby1pcav1OkSkAl1dFH4NNbrY5LyNziYPen8SnkAGp6v30byy1d30/BDl4+/+tv1cq9trpAzq/SlwbT7Q6Vl6ZvQR1FZxh04HNLsR6Pmes3HAluzrrIVuo5qQMsAQRX/H5AADZtK8Td6mjwAGzaG5ns5AdZjSB1NtHpmwBHsDnC2JHcmgYQi3bjNGA1EZvhkHPKHZTbTOSOpGNZZa3i1ul9SVzn3qQPKGVGsgCE8Ehv1OBcGv/AtIFEQJyOgNlEc7Mo3OW8//WJ9j4YnAgO+AsYU0ToDkpce7NI9vM4kUkeFJJDPNbZ53XV6y1uDQGSl109iLQJdaj8/W92JXh7mYfGwSrt73JiqkSAwa+TAVBu3yEr1vLt9M50iJ2JZUJ0mmzUPA0IXi9ZI+jN4XjnOtxlfazye6v20dd8YIMnr0eM9+baZU7Ds6m+Z2ken0zrv2DN17tmvC7m9QMViA5K3lBOd+RP3rw2oLnHoxV49tRe/87m+RIs8yb1Mh71GNSe6ajgPaPGjzhY5yfbd5iIyPw5dQ+qvr8qkmVPoQmj9F1hqM9OH0LJSf8y1uVz5mSi96B2Ta1ErTGQXzTZ2yIjpjJMmmjCEKaPF35VSBtkSmkbwmdqK5wIg/gR7vkJGk6Xhru/h24kL0ESm0Bhu1j971AN03LW4nubadl+rD6Z8hmtai2dfWzgNcXJuIVOqr47+dI7sAcpYYc5LG2+Zh6l8JV2uPpG7AwO/pnTB4jvL6PqoxMOhnmncntKc5o772nur5vkPbJtbPCR3pGF1epHtOZ6B5WrLgns++lu6nsUX0XHNcE9gip4CW6fEeMGIZ0P+/wMhVypGMaohuCgxfSs87WbY9IcPDCLVAEtfa/h6LbUHXpvPzQOt7rY4yEY1oHpN9Pem8Rq4GskYBl64BRh+mSD1b9EaS6St2UG1L2QCdNbrW6chGBhvV1gAa/rvVmUJvAJrfBPSdQe/DNg/Su9GVcUAm9376X2eguUbXVwDOVq8JFRVWZ+rwcBfPmFoiIki2ystV1sPwAK5BEEAadA0CGclMudDPb6Qw3NLDFB4elQlUFdEL31xFFtOaCpqQZI2iIsKnF5L1vvIc/YtIoRdaZT7ll5M9DQtW0yRIrmZvKiOPq4v76WGcNoAWSWUnKZWMuZKU/oYommQk96RUQRc20yS9yVX0IDSVU/h32XEq3lR+gib7zW6hthc2U7uUXqQ4LFhNx07sRA/e/OX0EC0/QQVzwhNo3EldaKJZcYYWfYZIKrhZdYEmasndyQt1zzu1efNjgYzhlK86LIEm6GGxdL6OzKTzEdMcyBhGxXdjcugFUbSLrM0JHanvygI6Zwntgf2fkDdTbEsKmz/9O73YcsaJDQuSRGmhDJHWSYstleeAE79QiHnqQHopHvuRzqsxljwPLu6lcxHflo559HvKaZ/Ymby8Mi+xVzTJ8iOPx2yicxCTQ+er4gxd2/ObaMJUtJOOEZlOk+3EjsChrymXqzGGtleeo/3C4khmyk9TnzWlJCtxrckokNjFutA11wAnf6GidfpwmmRFpNDvrb4ItLiN5NlcDUBH5/X8RjpXSV2pGNLZP+i3NOpD4yw5SBPF5B6UJqDsWG3+xB4UFhnbEohrRdel6gJZ+hM6UkHfsuMko+Yq6ltnIJk5s5SOb4ggOYxrSQsscxXJavlJyjUd35Y8QCrP0cTy3Foae1JXym1euI3SK8S2ACLSqN+a0toFo4720xvpfkvpRec+fzmNO7kn7V9UW6hMpycPhMZX0OKkYA15YF/cT/daXGsan94INOpH2wpW0f415ZR/OioTaPl3Oj/lp+g6hQkU9efWkwdNdRHJqrm6tjivBERl0XWVJPIINJVSv4360D15ZhkVtKouoTE0vpyU/Ye/ovMf15oWRjVldO+Yq6nf8lMk35kjKSVJdSHJkzGO5K/iLHDsJ0qZkNCOzl1NGSlk5AVu5Tm6jy/uo8LMF7ZQv2FxZCiLbUUhnmXH6Bwao0nxfHEP7dv6HjpfZcfp3giLpyLWVefJozOqMS1A5cLNhTvIgyixC31/6Eu6J8KT6PkWnkSyltiJ7oHiPVSYKropKVgKVgInF1AfkokWZSl5ZBg+t5bOV0wOebemD7MWazy9hFICpfYnuSw9TM+IshPkMRkWT4XUaspI4dCoLxX4KlhFv9sQSc+R6Ca1z5oTQHgKHSexC3mHH/mOxp59LbU5s5iOl32dfwrImqsphYUx1j63eF2n8hw9k5K6alMQ1VQKHP6G3k9Nx2qvJCw/BVzYRjKS1JnSUsW1sho6HDn2E72Pm4yiPLQNhZoqeo5EN1Y+N6GIJNG7r/QYKVoiU4M9InsK1tH4GvXzLPe+ltRUACWHybEjLJbmGEW76dksn6+iXfSMzhhB7QCaV9nO6c4spXdEk1FAbLNA/4rQRpLcP/tKj9L7zKnYu0k8ty4/QymvkrrQM0/A8n35WLGvAL1bJGNYW4HCTZJozpy/nNY/jfrRXLbsKCmtw5Nr1ykV1mPUVAD7PqI2kWk0X00fQu/2C9uosGtlAc0hc26k+fGp30i20gfT/tUlNFeWz4mpjOa/Sd1o3qrT1e63qPb9PojmaSKK9wEn5tC+GcOdz93B6TTPissFssfQbzo6i+ZTTcfSOPSRdN4Lt9OcSzLT2kCqqZ3TmWtrKBXS+0NnBBpfRvMJRyry6b0U05Tee2f/pPlQVGOasxTvpfun5QT76ypJVEy4YBUp91wZ/iz7mOmdHRZP83vbvk4vonVCeCKNGzqgyZW0DpPP+4lfaF6eNYbmS3s/oOtgjKFz06gvzfHOb6LfIJnpPOf+g+aR+StofpfSk8ZQvI/SeJlKaQ6p05FjRPFu4MJWmqO2ulvZCATQWqDiLBnwTSU0x60soPMZ24K2y0a2yvN0vhI7WZ0jLmylqITY5uTYIUqzc/p3+u0pven6m0qp3+oiMhDISlVJojVRZAb9npJDtIa2nTMW76X5pCGG5K9gDXBuDZ3TlF7AvmnAhY107sJTaB4bkULGBdv3VU0lpfmpKqQ+yk/SOW1xu3JRYkmi31q0g+61hPbkTV51nq6f7bveXK3ewbKmkuZKYbFkqDn2Q+093ZWOU7CGfnP6cCqU67jv/o+tz4D85bQe1ofReWxyFb1rCrfT+daH1a7D+lsjzwB6JpxaQO8unYHOe00lrbnNNfTMiW9H6XGis2rryxlp3bDvQ1onJnah59n59bSejGpM56HsOAAzPROaXk/Po5Pz6V5J7EzyJD8LEtrR+Au30/nIvJTks3BH7X1U+08eX8Eq0iFlj6m9PwvIcBGeQOMu2kG/u1Ff5+tReZ6umS/zZLOJfqshyv9GebOJ6iQ1v8U6b2hA2Opy586di65du7ps70mKofz8fKSl0XNy/Pjx+O6771y2T09Px9mzZ9GxY0ds375d9XHUwAaCAMIGAoZhGIZhGIZhGIZhGIZhmLqPP3W5FRUViIqiyM4rr7wSv/ziOr1ibGwsSktL0adPH6xerW2aL04xxDAMwzAMwzAMwzAMwzAMwzABIjIyEikplObJXfHjCxcuoLS0FACQnS1Iy+YjbCBgGIZhGIZhGIZhGIZhGIZhmADSvn17AMD+/fthMpkU2+3evdvyuV077WtysoGAYRiGYRiGYRiGYRiGYRiGYQLIgAEDAAClpaXYuHGjYrs//vjD8rl///6aj4MNBAzDMAzDMAzDMAzDMAzDMAwTQK655hrL5+nTpwvbmM1mfPnllwCAxMREDB2qori9h7CBgGEYhmEYhmEYhmEYhmEYhmECSK9evTBw4EAAwGeffSYsPvzGG29g165dAICHHnoIYWFhmo/DqHmPDMMwDMMwDMMwDMMwDMMwDMO45J133kH//v1RXl6OkSNH4qmnnsLQoUNRXl6O7777Dh9//DEAIDc3F5MnT/bLGNhAwDAMwzAMwzAMwzAMwzAMwzABplu3bpg5cyZuvvlmFBcX46mnnnJqk5ubi3nz5iEuLs4vY+AUQwzDMAzDMAzDMAzDMAzDMAwTBEaNGoVt27Zh0qRJyM3NRXR0NBITE9GzZ0+8+uqr2Lx5M1q1auW343MEAcMwDMMwDMMwDMMwDMMwDMMEiZycHLz55pt48803A35sNhAEkIqKCsvnnTt3BnEkDMMwDMMwDMMwDMMwDMMwjBK2+ltbvW59gw0EAeTAgQOWz3fccUcQR8IwDMMwDMMwDMMwDMMwDMOo4cCBAxg4cGCwh+EXuAYBwzAMwzAMwzAMwzAMwzAMwzRAOIIggFxzzTWWzy1btkRkZGTwBsN4xZkzZzBq1CgAwNy5c5Genh7kETH1CZYvxp+wfDH+hmWM8ScsX4w/Yfli/A3LGONPWL4Yf9LQ5auiosKSEcZWr1vf0EmSJAV7EAwTKhw/fhzZ2dkAgGPHjiErKyvII2LqEyxfjD9h+WL8DcsY409Yvhh/wvLF+BuWMcafsHwx/oTlq2HAKYYYhmEYhmEYhmEYhmEYhmEYpgHCBgKGYRiGYRiGYRiGYRiGYRiGaYCwgYBhGIZhGIZhGIZhGIZhGIZhGiBsIGAYhmEYhmEYhmEYhmEYhmGYBggbCBiGYRiGYRiGYRiGYRiGYRimAcIGAoZhGIZhGIZhGIZhGIZhGIZpgLCBgGEYhmEYhmEYhmEYhmEYhmEaIDpJkqRgD4JhGIZhGIZhGIZhGIZhGIZhmMDCEQQMwzAMwzAMwzAMwzAMwzAM0wBhAwHDMAzDMAzDMAzDMAzDMAzDNEDYQMAwDMMwDMMwDMMwDMMwDMMwDRA2EDAMwzAMwzAMwzAMwzAMwzBMA4QNBAzDMAzDMAzDMAzDMAzDMAzTAGEDAcMwDMMwDMMwDMMwDMMwDMM0QNhAwDAMwzAMwzAMwzAMwzAMwzANEDYQMAzDMAzDMAzDMAzDMAzDMEwDhA0EDMMwDMMwDMMwDMMwDMMwDNMAYQMBoylHjhzB5MmT0bZtW8TExCA5ORl5eXmYOnUqysrK3O6/f/9+fPvtt5g0aRL69++P6Oho6HQ66HQ6zJgxwy9jNplMmDZtGgYOHIjU1FRERUWhZcuWmDhxInbu3Ol1vx9++KFl7FqP/9dff8WYMWOQlZWFiIgIZGVlYcyYMfj1119V97F69Wr8/e9/R5s2bRAbG4uIiAhkZmbi0ksvxSeffIKqqirNxqsVLF9WtJYvs9mMv/76CzNmzMB9992HvLw8REREWPpftmyZR/2VlZXhtddeQ15eHpKTkxETE4O2bdti8uTJOHLkiE9j9ScsY1bqqowdPnwY7733Hq677jq0bt0a0dHRiIyMRFZWFq655hp89913MJlMPo3VX7B8Wamr8qXEr7/+ajfeZ555xqf+/AXLmJVQkLHS0lK8//77GD58OJo0aYKIiAikp6eje/fueOCBB7Bw4UKfxqw1LF9W6rp8LViwADfccANatGhheU9mZ2dj9OjRmDlzJsxms0/j9QcsX1a0lq+ysjL8+OOPuPfee5GXl4ekpCSEhYUhJSUFffv2xTPPPIPTp0971B/P81nGbNFKxnieH1ry1axZMzs5UvrXrFkzzcashS7MX/dVyCMxjEb8/PPPUnx8vARA+C83N1fat2+f4v7Lli1T3BeANH36dM3HnJ+fL+Xl5SkeMyIiQvrkk0887vfEiRNO50KL8dfU1Eh33nmny/N01113STU1NYp9mM1m6YEHHnDZBwCpQ4cO0pEjR3wes1awfFnxh3zNmDHD5flZunSp6r727dsntW7dWrGv+Ph4ae7cuT6N1x+wjFmpqzL29NNPSzqdzu3zKy8vr049vySJ5cuWuipfSpSUlEg5OTl2/U2ZMsWn8foDljEroSBjS5YscZIrx39dunTxacxawvJlpS7LV0VFhXTddde5fU8OHDhQunDhgk9j1hKWLytay9fWrVul2NhYtzIRHx8vfffdd27743k+y5gjWskYz/NDT77czWPkfzk5OT6PVwtdmFa/u77CBgJGEzZt2iRFRUVJAKTY2FjpxRdflFatWiUtXrxYmjBhgt2Dsbi4WNjH0qVLLe30er3UoUMHqVevXn57KJpMJmnAgAGW/q+99lrp119/ldauXSu9++67UlpammUs8+fP96jvMWPGSAAsfWg1/ieeeMLSX7du3aRvv/1WWrdunfTtt99K3bp1s3z35JNPKvbx0ksvWdrFxcVJU6ZMkRYuXCitWrVKmj59utSxY0fL9x07dpSqq6t9HrevsHzZ4w/5mj59uqWvsLAwqXv37lKnTp0s29QuTIuLi6Xc3FzLfhMmTJAWL14srVq1SnrxxRctk8fo6Ghp8+bNPo1ZS1jG7KmrMiZPCmNiYqSbb75Zmj59urRixQppw4YN0ldffWU32WvdurV08eJFn8asFSxf9tRV+VJi0qRJTuOtawYCljF76rqMLVq0SIqMjJQASImJidITTzwhLViwQNq0aZO0YsUK6ZNPPpFGjx4t9enTx6cxawXLlz11Wb7uvvtuyz5paWnS66+/Li1ZskRavny59MEHH9gpcy699FKfxqwVLF/2aC1fy5cvt/TTv39/6eWXX5YWLVokbdq0Sfrtt9+kiRMnSnq9XgIgGQwGl+PleT7LmAitZIzn+aEnX/I7ZfTo0dL27dsV/+3Zs8fnMWuhC/PnfVUfYAMBowkDBw6UAEhGo1FatWqV0/evvfaa20X13r17palTp0rLli2zPOxtJ8taPxQ/++wzS9/33Xef0/f79u2zWIFbtWqlWlE+e/ZsCYCUmpoqvfHGG5qNf8+ePZLRaJQASD179pTKysrsvi8tLZV69uxpuQ4iC3VVVZWUmJgoAZDCw8OFE7fq6mqpd+/elnH/73//82ncWsDyZcVf8iW/FFevXi2Vl5dLkiRJU6ZM8Xhh+u9//9uyz2uvveb0/cqVKy1yPHjwYJ/GrCUsY1bqsow99thj0quvvqo4uTaZTNK4ceMsfT777LM+jVkrWL6s1GX5ErFhwwbJYDBYvIncXadgwTJmpa7L2NmzZ6WUlBQJgNS1a1fp9OnTim0rKyt9GrNWsHxZqcvydfr0aYsSLikpSTp27JhTm6KiIqlZs2aWftevX+/TuLWA5cuKP+Rr5cqV0rhx46SdO3e6PK7sud2yZUvJbDYL2/E8n2VMhFYyxvP80JMv2UBw2223aTo+R7TQhUmS/+6r+gIbCBifWbt2reUmmzhxorBNTU2N1K5dOwkgT6mqqipVffvzoSiPJzk5WSotLRW2efnlly3HnzVrlts+i4uLpaysLAmA9MUXX2g6/nvvvdfS1+rVq4VtVq9e7fKBt3XrVsv31157reKx5syZY2n3yCOP+DRuX2H5suJP+RLh6cK0qqpKSkhIkABI7dq1UwzvmzhxoqXfdevWaTxqz2EZs1LXZUwNBQUFUnh4uARA6tSpkyZ9+gLLl5VQky+TyWTxSHr22WftvLvqkoGAZcxKKMiY7CEZHR0tHT58WPMxaQ3Ll5W6Ll9q5+/vvPOOpd27776r4Yg9h+XLSqDlyxHb1FQbN250+p7n+c6wjHmGOxlTA8/ztUEr+QqUgUALXZgk+ee+qk9wkWLGZ2bPnm35fMcddwjb6PV63HrrrQCAwsJCLF26NBBDU2Tv3r3YtWsXAGDcuHGIjo4Wtrv99tstn3/66Se3/T755JM4fvw4hgwZYvm9WiBJEubMmQMAaNu2Lfr06SNs16dPH7Rp0wYAMGfOHEiSZPe9beHhFi1aKB6vZcuWwn2CAcuXFX/Jl1YsXboURUVFAIDbbrsNer34FePp7/Y3LGNW6rqMqSElJQWdO3cGABw4cCDIo2H5siXU5Outt97C5s2bkZubi8cffzzYw1GEZcxKXZexCxcu4JtvvgEA3HzzzcjJyQnyiNzD8mWlrssXz/MDQ32Vr6FDh1o+i+ZPPM8PHA1VxtTA83zf8Zd8+QutdGGh9ruDARsIGJ9ZsWIFACAmJgY9evRQbDd48GDL55UrV/p9XK6QxwzYj8uRjIwM5ObmAnA/5jVr1uDDDz9EeHg4PvzwQ20GWsuhQ4dw8uRJAK7Ha/v9iRMncPjwYbvvWrduDZ1OBwA4ePCgYh+2L1v5IRssWL4If8qXVqj93T179rS8kIN9rQCWMZlQkDG1VFZWAgAMBkOQR8LyJRNq8nX48GFMmTIFAPDhhx8iIiIiyCNShmWMCAUZ++WXX1BeXg4AuPrqqy3by8rKsH//fpw+fdppQRtsWL6IUJAv2zk7z/P9R32VL3nuBIjnTzzPDxwNVcY87Yfn+d7hD/nyJ1rpwkLtdwcDNhAwPiNb4Vq1agWj0ajYrm3btk77BIu//vrL8tl2XCLk748dO4bS0lJhm+rqatx9990wm8149NFH3fbpKd6MF3A+zwkJCbjxxhsB0CJ127ZtTvubTCa8/PLLTu2DBcuX/+VLK9T+bqPRiFatWgEI/rWyHQPLWN2XMTWcPXvWcn3atWsX5NGwfAGhKV/33nsvysrKcNNNN2HYsGHBHo5LWMZCR8bWrFlj+dypUyesX78eI0eORFxcHFq3bo3MzEykp6fj/vvvx5kzZ4I4UissX6EjX506dUK/fv0AADNmzLAoVGy5ePEi3n77bQAUZTBy5MhADtEJlq+6I19//PGH5bNo/sTz/MDRUGVMDTzP9x2t5QsA/vzzT3Tt2hVxcXGIjo5G8+bNMX78eMyePdtnxwetdGH++N31DTYQMD5RUVGBgoICAEBWVpbLtklJSYiJiQFAN1owOX78uOWzu3FnZ2cDoNAm2/1smTp1KrZv344WLVrg6aef1m6gtXgzXkB8nt988010794dVVVVGDhwIJ577jn8/vvvWLNmDb744gv07NkTa9asQXR0NL788kukpKRo90M8hOWL8Ld8aYU8/piYGCQmJrpsK//u/Px8O2+SQMMyRoSKjKlh6tSpMJlMACh8NJiwfBGhJl/ffPMNFixYgMTERLz55pvBHo5LWMaIUJEx28Xp0qVL0a9fPyxatAhms9myPT8/H++//z66du2KrVu3BmOYFli+iFCRLwCYPn06mjdvjvPnz6N79+548803sWzZMqxYsQLTpk1Dly5dcOjQITRq1Aj//e9/ER4eHrSxsnwRdUG+tm7dinnz5gEgQ5NI8crz/MDRUGVMDTzP9x2t5QsgL/+tW7eipKQE5eXlOHz4MGbNmoUxY8Zg4MCBOHHiREDHCzifZ3/87voGGwgYn7h48aLlc2xsrNv28kOxpKTEb2NSgyfjlscMiMe9f/9+PP/88wCA999/H5GRkRqN0oqW401PT8fy5cvx9ttvIyoqClOmTMEll1yCvn374vbbb8e2bdtw1113YePGjXbh78GA5Ssw8qUV8u/25FoBwb1eLGOhJWPuWLt2rcUzMisrC/fee29Qx8PyFXrydf78eUyaNAkA8PLLLyMtLS3II3INy1hoydj58+ctn++55x7odDq88MILOHr0KCorK7Fz505L7tvTp0/jmmuuQXFxcZBGy/IFhJZ8AUBubi7Wr1+P559/HqWlpZg8eTKGDh2KgQMH4t5778Xx48fxz3/+E5s3b1bM4xwoWL7qhnxVVlbirrvuQk1NDQDgxRdfFLbjeX7gaKgy5g6e52uDlvIVHh6Oq6++Gv/5z3+wbNkybN68GUuXLsVLL71kUbKvXLkSl1xyiaWGSbDGq+Xvrq+wgYDxiYqKCstnNR4ocg5fOf9qsPBk3LZ5h0XjnjhxIioqKjB27Fhcdtll2g3SBi3HCwBLlizB119/LQxfl4vAzJgxI+iFy1i+AiNfWiH/bk+uFRDc68UyFloy5oozZ87g+uuvh8lkgk6nwxdffKFYfCpQsHyFnnz985//xNmzZ9G7d2/cfffdwR6OW1jGQkvGbMPUKyoq8Nlnn+Ff//oXsrOzER4ejvbt22P69OkW2Tt8+HBQ892zfIWWfMnMnTsX//3vf4VKjerqasyaNQvffPNN0OtdsHzVDfm6//77sWHDBgBUfHjUqFHCdjzPDxwNVcZcwfN87dBSvtatW4c5c+bgH//4BwYPHoyuXbtiyJAhePLJJ7Fz505LGrtdu3bh2WefDep4tdap1UeUk2QxjApsLdBqlMlyiGFUVJTmYyktLcWhQ4cUv+/YsaPls+O4XVnSbcMiHcc9Y8YMLFmyBPHx8RZrtj/w5Dy7Gi8AvPPOO5g0aRIkScKgQYPw73//G7169UJkZCQOHDiAzz//HG+99RZeffVVrFixAgsWLFBlEfcHLF+BkS+tkH+nJ9cK8M/1UgvLWGjJmBIXL17ElVdeaQkBfeWVV+pE3niWr9CSr2XLlmH69OkwGAyYNm0a9Pq670fDMhZaMmb7Ozt37oxbbrlF2O6ll17CF198gcrKSsycOROPP/54oIZoB8tXaMkXAEyePNmSGu2aa67Bo48+ii5dusBgMGDXrl147733MH36dDz++ONYu3YtZs2aFbRCnyxfwZevl19+GZ9++ikAIC8vD++//75iW57n+wbLmHsZU4Ln+e4JhnwBcJluLC4uDrNmzUKLFi1w/vx5fPzxx3jllVc8Tm2nlS5My99dX2EDAeMTcXFxls9qQm9kzyl/KJzXr1+PoUOHKn5v6yXjOG5XDwdbby/bcefn5+Of//wnAOD5559H48aNvRq3Gjw5z0rjBYBt27bhkUcegSRJGDFiBBYsWGC3KGjXrh2mTp2Kdu3a4c4778TKlSsxZcoUvPHGGxr9Es9g+QqMfGmF/Ls9uVaAf66XWljGQkvGRFRUVGD06NHYuHEjAPIAf+yxx4I8KoLlK3Tkq7KyEhMnTgQAPPjgg+jatWtwB6QSlrHQkTHA/ne7Kg6bkpKCnj17YuXKldi6dSuqqqqCkiue5Su05GvevHkW48Dtt9+O6dOn233frVs3fP7558jKysLzzz+PH3/8ER988AEeeOCBYAyX5SvI8vXRRx/hqaeeAkAFOefPn2+XVsMRnuf7BsuYexkTwfN8dQRavtSSkJCAG264AR988AFKS0uxYcMG9OvXz6M+tNKFBfJ3hypsIGB8IjIyEikpKTh37pzb4h0XLlyw3Gi2xUOCgW1RkuPHj6NRo0aKbeXiJjqdzm6/Tz/9FOfOnUNiYiJSUlLw3XffOe27du1au8/yQ2jYsGEe5TV2HK8rbIuxOJ7n6dOnWwrhPfvss4oeQ3//+9/xyiuvYN++fZgxYwZef/116HQ61ePVCpavwMiXVmRlZWHt2rUoLS1FYWGhS48C+XenpqbahfAFGpax0JIxR0wmE8aNG4elS5cCAO666y5MnTo1yKOywvIVOvL1448/Yu/evQgLC0P79u2F47UtMLtjxw5Lm969e6N58+YBG6stLGOhI2MAnfc1a9ZYPrtrCwBmsxnnz59HRkaG38fnCMtXaMmX7KUr17ZQ4qmnnsJbb72FkpISfP7550EzELB8BU++vv32W9x3330AgJycHCxatMjl7wB4nh9IGqqMOcLzfP+ghXx5Qvv27S2fvSlWrJUuLNC/OxRhAwHjM+3bt8fy5cuxf/9+mEwmGI1isdq9e7fls7dV610xZMgQ1bk0bR9Su3fvdukpKI87OzvbzuIthx0VFhbi5ptvdnvMadOmYdq0aQCApUuXevTSdRyvK1yd5127dlk+d+/e3WU/3bt3x759+3D+/HmcPXsW6enpqserJSxf/pcvrWjfvj1++OEHAPS7lIrfmUwmHDhwAIB/rpWnsIyFjozZYjabccstt2Du3LkAgPHjx+Ojjz4K6phEsHyFhnzJ462ursaECRPctv/hhx8sz7vp06cHzUAAsIyFiowBQIcOHfC///0PACzFGpWw/V7pmgYClq/QkS95np+WloYmTZootouMjESHDh2wdu1at+sKf8PyFXj5+vnnn3HrrbfCbDYjMzMTixcvVqUA43m+b7CMeaZk5Xm+ZwRavjzBV0dTrXRhgf7doUjdT67K1HkGDBgAgMJw5NAvEX/88Yflc//+/f0+LlfIYwbsx+XI6dOnsXfvXgDBHXPz5s0tYYGuxgsAf/75JwCgSZMmaNasmd13ti8sk8nksp/q6mrhfoGG5St0UPu7N2zYYPGgqAu/m2UsNJk4caLFG2rUqFH4+uuv62TOeJYvxt+wjIUOgwYNsnw+ePCgy7aygi0yMhLJycl+HZcrWL5CB3m+7m6OD1jn+cGc4wMsX4Fm8eLFGDduHEwmE1JSUrBo0SK0bNlS1b48zw8cDVXGbOF5vv8ItHzZRuB6k+pKK11YKN9XAUNiGB9Zu3atBEACIE2cOFHYpqamRmrXrp0EQEpMTJSqqqpU9T19+nRL39OnT9dw1JJlPMnJyVJpaamwzcsvv2w5/qxZszw+hpbjv/feey19rV69Wthm9erVljb33Xef0/f333+/5fv58+crHquqqkrKzMyUAEgJCQmS2Wz2aey+wPKljD/HL0mSNGXKFEv/S5cuddu+srJSSkhIkABI7dq1U5SbiRMnWvpdt26dxqP2HJYxZeqajMlMmjTJst/w4cOliooKzcemFSxfytRV+VJi6dKllv6mTJnic39awTKmTF2TMZPJJKWmpkoApJycHMlkMgnbHTx4UNLr9ZZnXDBh+VKmrsnXVVddZWn/119/KbY7d+6cFB4eLgGQOnXqpOGIPYflSxmtx79y5UopJibGsr7bsGGDR/vzPN8ZljF7fJUxGZ7nE6EuX5IkSYWFhVJKSooEQIqOjvb6WmqhC5OkwP3uUKXumeCYkKNXr14YOHAgAOCzzz7D6tWrndq88cYblrDXhx56CGFhYQEdowi5aM/58+eFhW4OHDiAl19+GQDQqlUrjBkzJqDjc+Thhx+21Ax44IEHUF5ebvd9eXm5JYeo0WjEww8/7NTHqFGjLJ+feOIJFBcXC481ZcoUnDp1CgBwxRVXBKX+gAzLV+gQHh6OBx98EACFub/++utObVavXo3PPvsMADB48GDk5eUFdIwiWMZCi2eeeQZvvfUWAKBfv36YM2dOUPPbuoPli/E3LGOhg8FgsPzuI0eO4Pnnn3dqYzKZcN9991lqRt1zzz0BHaMjLF+hg+08/+GHH0ZVVZVTG7PZjAcffNDy3VVXXRWw8Ylg+QoMW7ZswZVXXonS0lLExMRg3rx56NGjh0d98Dw/sDREGQN4nh8otJCvBQsWOOmkbCkpKcG4ceNw7tw5AMCdd97p9bXUQhcGhN59FWi4BgGjCe+88w769++P8vJyjBw5Ek899RSGDh2K8vJyfPfdd/j4448BALm5uZg8ebJiP99//71dZfIVK1YIPwNARkYGLrvsMq/HfNttt+Hzzz/HypUr8f777+P06dOYMGECkpKSsG7dOjz//PMoLi6GXq/Hu+++G/QQ3NzcXDz66KN45ZVXsGHDBvTv3x+PP/44WrZsiQMHDuDVV1/F5s2bAQCPPvooWrdu7dTHyJEjMWzYMCxZsgTbtm1D165d8dBDD6FXr16IjIzE/v378fnnn2PBggUAgJiYGEyZMiWgv1MEy1dgmDFjht3fW7ZssXxesGABDh8+bPm7VatWdmF6Mo8++ihmzpyJvXv34rHHHsP+/ftxww03ICoqCkuXLsVLL70Ek8mEqKgovP322/75IV7AMhYYfJWx9957D88++ywACh197bXXcOjQIZfHbNOmTdAn4ixfgUGLZ1iowjIWGLSQsQcffBAzZ87Epk2b8Oyzz2LPnj247bbbkJaWhgMHDuCtt96yKBiuuOIKXHfddf74KR7B8hUYfJWv22+/HW+//TZ27dqFhQsXomfPnnjggQfQpUsXGAwG/PXXX/jwww8t8pWeno5HHnnEXz9HNSxf/uXAgQO49NJLUVhYCAB44YUXkJCQgB07dijuk5aWJsxBz/N8ljERWskYz/NDS75eeeUV3HTTTbj22msxYMAAtGzZErGxsSgqKsKqVaswbdo0HD16FABdp2eeecbr8WqhC9Pqd9drgh3CwNQffv75Zyk+Pt4SjuP4Lzc3V9q3b5/LPnJychT3d/w3ePBgn8ecn58v5eXlKR4jIiJC+uSTT7zuX+uwsJqaGunvf/+7y/Ny5513SjU1NYp9nD9/Xho6dKjb85uamiotWrTI5zFrBcuXM1rLl9pzA0C67bbbFPvZt2+f1Lp1a8V94+Pjpblz5/o8Xq1hGXOmrsnY4MGDPeoDgHTo0CGfx60FLF/O1DX5ckddTTEkwzLmTF2VsZMnT0o9evRwuf8VV1whFRcX+zxmrWD5cqYuytfhw4elLl26uN2/efPm0ubNm30es1awfDmjlXzZ9qP2n6t3HM/zWcZc9eOLjPE8P7TkS+31Gjx4sHT8+HGfx6uFLkyL312f4RRDjGaMGjUK27Ztw6RJk5Cbm4vo6GgkJiaiZ8+eFoteq1atgj1MOxo1aoRVq1bhgw8+wIABA5CSkoLIyEi0aNECEyZMwMaNG3HXXXcFe5gW9Ho9PvvsM8ybNw+jR49G48aNER4ejsaNG2P06NGYP38+Pv30U5cFfJKSkrB48WLMnj0b48aNQ/PmzREVFYWwsDCkpqZiyJAhePXVV7F7926MGDEigL/ONSxfoUOrVq2wefNmvPrqq+jZsycSExMRHR2NNm3aYNKkSdi2bVvQQ9pFsIwx/oTli/E3LGOhQ2ZmJtasWYNp06Zh8ODBSE1NRVhYGDIyMnD11Vfjxx9/xLx58xAXFxfsoVpg+QoNcnJysH79enz55Ze4+uqrkZWVhYiICISHhyMjIwMjR47EBx98gO3bt6Nr167BHq4Flq/Qgef5gaOhylgo0hDl6/XXX8crr7yC0aNHo23btmjUqBGMRiPi4+PRtm1b3HbbbViwYAGWLl2KJk2a+DxeLXRhWvzu+oxOkiQp2INgGIZhGIZhGIZhGIZhGIZhGCawcAQBwzAMwzAMwzAMwzAMwzAMwzRA2EDAMAzDMAzDMAzDMAzDMAzDMA0QNhAwDMMwDMMwDMMwDMMwDMMwTAOEDQQMwzAMwzAMwzAMwzAMwzAM0wBhAwHDMAzDMAzDMAzDMAzDMAzDNEDYQMAwDMMwDMMwDMMwDMMwDMMwDRA2EDAMwzAMwzAMwzAMwzAMwzBMA4QNBAzDMAzDMAzDMAzDMAzDMAzTAGEDAcMwDMMwDMMwDMMwDMMwDMM0QNhAwDAMwzAMwzAMwzAMwzAMwzANEDYQMAzDMAzDMAzDMAzDMAzDMEwDhA0EDMMwDMMwDMMwDMMwDMMwDNMAYQMBwzAMwzAMwzAMwzAMwzAMwzRA2EDAMAzDMAzDMAzDMAzDMAzDMA0QNhAwDMMwDMMwDMMwDMMwDMMwTAOEDQQMwzAMwzAMwzAMwzAMwzAM0wBhAwHDMAzDMAzDMAzDMAzDMAzDNEDYQMAwDMMwDMMwDMMwDMMwDMMwDRA2EDAMwzAMwzAMwzAMwzAMwzBMA4QNBAzDMAzDMAzDMAzDMAzDMAzTAGEDAcMwDMMwDMMwDMMwDMMwDMM0QNhAwDAMwzAMwzAMwzAMwzAMwzANEDYQMAzDMAzDMAzDMAzDMAzDMEwDhA0EDMMwDMMwDMMwDMMwDMMwDNMAYQMBwzAMwzAMwzAMwzAMwzAMwzRA/h/shMTI7zytAwAAAABJRU5ErkJggg==\n" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 137 ms (started: 2023-01-05 12:08:23 -08:00)\n" + ] } ], "source": [ @@ -435,17 +675,9 @@ "ax2 = ax1.twinx()\n", "l2, = ax2.plot(norm_anomalies_df[\"s1\"], color=\"orange\")\n", "plt.legend([l1, l2], [\"test data\", \"anomaly score\"])\n", + "plt.title(\"S1 data vs normalized anomaly score\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } } ], "metadata": { diff --git a/numalogic/models/autoencoder/trainer.py b/numalogic/models/autoencoder/trainer.py index cba7fdf4..161b7dd6 100644 --- a/numalogic/models/autoencoder/trainer.py +++ b/numalogic/models/autoencoder/trainer.py @@ -2,6 +2,8 @@ import pytorch_lightning as pl import torch + +from numalogic.tools.callbacks import ProgressDetails from numalogic.tools.data import TimeseriesDataModule from pytorch_lightning import Trainer from torch import Tensor @@ -20,8 +22,12 @@ def __init__( enable_progress_bar=False, enable_model_summary=False, limit_val_batches=0, + callbacks=None, **trainer_kw ): + if (not callbacks) and enable_progress_bar: + callbacks = ProgressDetails() + super().__init__( logger=logger, max_epochs=max_epochs, @@ -31,6 +37,7 @@ def __init__( enable_progress_bar=enable_progress_bar, enable_model_summary=enable_model_summary, limit_val_batches=limit_val_batches, + callbacks=callbacks, **trainer_kw ) diff --git a/numalogic/tools/callbacks.py b/numalogic/tools/callbacks.py new file mode 100644 index 00000000..d2e8d9b5 --- /dev/null +++ b/numalogic/tools/callbacks.py @@ -0,0 +1,34 @@ +import logging + +import pytorch_lightning as pl +from pytorch_lightning.callbacks import ProgressBarBase + + +_LOGGER = logging.getLogger(__name__) + + +class ProgressDetails(ProgressBarBase): + r""" + A lightweight training progress detail producer. + + Args: + log_freq: Interval of epochs to log + """ + + def __init__(self, log_freq: int = 5): + super().__init__() + self._log_freq = log_freq + self._enable = True + + def enable(self) -> None: + self._enable = True + + def disable(self): + self._enable = False + + def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None: + super().on_train_epoch_end(trainer, pl_module) + metrics = self.get_metrics(trainer, pl_module) + curr_epoch = trainer.current_epoch + if curr_epoch % self._log_freq == 0: + _LOGGER.info("epoch %s, loss: %s", curr_epoch, metrics["loss"]) diff --git a/poetry.lock b/poetry.lock index 7fd034bb..00bda9f3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -635,63 +635,63 @@ test-no-codebase = ["Pillow", "matplotlib", "pytest"] [[package]] name = "coverage" -version = "7.0.0" +version = "7.0.3" description = "Code coverage measurement for Python" category = "dev" optional = false python-versions = ">=3.7" files = [ - {file = 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Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 14:29:46 -0800 Subject: [PATCH 10/15] chore: attempt to fix workflow Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ef40f857..47999bf0 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -30,7 +30,7 @@ jobs: - name: Install dependencies run: | poetry env use ${{ matrix.python-version }} - poetry install --all-extras + poetry install --all-extras --with dev,torch - name: Test with pytest run: make test From c9ac29fc7895b3341858d7334e6ccc8348235392 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 14:36:27 -0800 Subject: [PATCH 11/15] fix: github workflows Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 2 +- .github/workflows/lint.yml | 2 +- .github/workflows/pypi.yml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index de41c1d1..8a8a11d8 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -30,7 +30,7 @@ jobs: - name: Install dependencies run: | poetry env use ${{ matrix.python-version }} - poetry install --all-extras + poetry install --all-extras --with dev,torch - name: Run Coverage run: | diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 7a6fca17..1ed6977e 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -30,7 +30,7 @@ jobs: - name: Install dependencies run: | poetry env use ${{ matrix.python-version }} - poetry install + poetry install --with dev - name: Black format check run: poetry run black --check . diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 0de0907b..20a0c310 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -30,7 +30,7 @@ jobs: - name: Install dependencies run: | poetry env use ${{ matrix.python-version }} - poetry install --all-extras + poetry install - name: Build dist run: poetry build From b60be1ad8159a0f887d35be8ab10f0ddfb4f551a Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 14:42:40 -0800 Subject: [PATCH 12/15] fix: codecov workflow Signed-off-by: Avik Basu --- .github/workflows/coverage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 8a8a11d8..6a81e74a 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -34,7 +34,7 @@ jobs: - name: Run Coverage run: | - poetry run pytest --cov-report=xml --cov=numalogic --cov-config .coveragerc numalogic/tests/ -sq + poetry run pytest --cov-report=xml --cov=numalogic --cov-config .coveragerc tests/ -sq - name: Upload Coverage uses: codecov/codecov-action@v3 From 7e55f8ad55ba698e96310d88f6dfe9074f956676 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 15:10:12 -0800 Subject: [PATCH 13/15] fix: code coverage loss Signed-off-by: Avik Basu --- numalogic/models/autoencoder/base.py | 4 --- .../autoencoder/variants/test_vanilla.py | 26 +++++++++++++++--- tests/test_postprocess.py | 27 +++++++++++++++++++ tests/test_scores.py | 18 ------------- 4 files changed, 50 insertions(+), 25 deletions(-) create mode 100644 tests/test_postprocess.py delete mode 100644 tests/test_scores.py diff --git a/numalogic/models/autoencoder/base.py b/numalogic/models/autoencoder/base.py index 890cdc23..ca9a1992 100644 --- a/numalogic/models/autoencoder/base.py +++ b/numalogic/models/autoencoder/base.py @@ -54,7 +54,3 @@ def training_step(self, batch, batch_idx): def validation_step(self, batch, batch_idx): loss = self._get_reconstruction_loss(batch) return loss - - def test_step(self, batch, batch_idx): - loss = self._get_reconstruction_loss(batch) - return loss diff --git a/tests/models/autoencoder/variants/test_vanilla.py b/tests/models/autoencoder/variants/test_vanilla.py index fad029eb..13b3f6f4 100644 --- a/tests/models/autoencoder/variants/test_vanilla.py +++ b/tests/models/autoencoder/variants/test_vanilla.py @@ -46,7 +46,7 @@ def test_vanilla(self): self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) def test_sparse_vanilla(self): - model = SparseVanillaAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1") + model = SparseVanillaAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1", optim_algo="adagrad") datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) trainer.fit(model, datamodule=datamodule) @@ -58,7 +58,8 @@ def test_sparse_vanilla(self): def test_native_train(self): model = VanillaAE( - SEQ_LEN, n_features=2, encoder_layersizes=[24, 16, 6], decoder_layersizes=[6, 16, 24] + SEQ_LEN, n_features=2, encoder_layersizes=[24, 16, 6], decoder_layersizes=[6, 16, 24], + optim_algo="rmsprop" ) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) @@ -81,7 +82,7 @@ def test_native_train(self): if epoch % 5 == 0: print(f"epoch : {epoch}, loss_mean : {loss.item():.7f}") - def test_train_err(self): + def test_train_err_01(self): with self.assertRaises(LayerSizeMismatchError): VanillaAE( SEQ_LEN, @@ -90,6 +91,25 @@ def test_train_err(self): decoder_layersizes=[6, 16, 24], ) + def test_train_err_02(self): + model = VanillaAE( + SEQ_LEN, + n_features=2, + optim_algo="random" + ) + datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) + trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) + with self.assertRaises(NotImplementedError): + trainer.fit(model, datamodule=datamodule) + + def test_train_err_03(self): + with self.assertRaises(NotImplementedError): + VanillaAE( + SEQ_LEN, + n_features=2, + loss_fn="random" + ) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_postprocess.py b/tests/test_postprocess.py new file mode 100644 index 00000000..9016608b --- /dev/null +++ b/tests/test_postprocess.py @@ -0,0 +1,27 @@ +import unittest + +import numpy as np + +from numalogic.postprocess import tanh_norm, TanhNorm + + +class TestPostprocess(unittest.TestCase): + def test_tanh_norm_func(self): + arr = np.arange(10) + scores = tanh_norm(arr) + print(scores) + + self.assertAlmostEqual(sum(scores), 39.52, places=2) + + def test_tanh_norm_clf(self): + arr = np.arange(10).reshape(5, 2) + clf = TanhNorm() + scores = clf.fit_transform(arr) + + self.assertTupleEqual(arr.shape, scores.shape) + self.assertAlmostEqual(np.sum(scores), 39.52, places=2) + + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_scores.py b/tests/test_scores.py deleted file mode 100644 index 070409e5..00000000 --- a/tests/test_scores.py +++ /dev/null @@ -1,18 +0,0 @@ -import unittest - -import numpy as np - -from numalogic.postprocess import tanh_norm - - -class TestScores(unittest.TestCase): - def test_tanh_norm(self): - arr = np.arange(10) - scores = tanh_norm(arr) - print(scores) - - self.assertAlmostEqual(sum(scores), 39.52, places=2) - - -if __name__ == "__main__": - unittest.main() From b98928b179c9e37ff1f62698f67a95d391fc3b2e Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 15:13:16 -0800 Subject: [PATCH 14/15] fix: black format Signed-off-by: Avik Basu --- .../autoencoder/variants/test_vanilla.py | 23 ++++++++----------- tests/test_postprocess.py | 1 - 2 files changed, 10 insertions(+), 14 deletions(-) diff --git a/tests/models/autoencoder/variants/test_vanilla.py b/tests/models/autoencoder/variants/test_vanilla.py index 13b3f6f4..b2a9082c 100644 --- a/tests/models/autoencoder/variants/test_vanilla.py +++ b/tests/models/autoencoder/variants/test_vanilla.py @@ -46,7 +46,9 @@ def test_vanilla(self): self.assertTupleEqual(self.X_val.shape, test_reconerr.shape) def test_sparse_vanilla(self): - model = SparseVanillaAE(seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1", optim_algo="adagrad") + model = SparseVanillaAE( + seq_len=SEQ_LEN, n_features=self.X_train.shape[1], loss_fn="l1", optim_algo="adagrad" + ) datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) trainer.fit(model, datamodule=datamodule) @@ -58,8 +60,11 @@ def test_sparse_vanilla(self): def test_native_train(self): model = VanillaAE( - SEQ_LEN, n_features=2, encoder_layersizes=[24, 16, 6], decoder_layersizes=[6, 16, 24], - optim_algo="rmsprop" + SEQ_LEN, + n_features=2, + encoder_layersizes=[24, 16, 6], + decoder_layersizes=[6, 16, 24], + optim_algo="rmsprop", ) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = nn.HuberLoss(delta=0.5) @@ -92,11 +97,7 @@ def test_train_err_01(self): ) def test_train_err_02(self): - model = VanillaAE( - SEQ_LEN, - n_features=2, - optim_algo="random" - ) + model = VanillaAE(SEQ_LEN, n_features=2, optim_algo="random") datamodule = TimeseriesDataModule(SEQ_LEN, self.X_train, batch_size=BATCH_SIZE) trainer = AutoencoderTrainer(max_epochs=5, enable_progress_bar=True) with self.assertRaises(NotImplementedError): @@ -104,11 +105,7 @@ def test_train_err_02(self): def test_train_err_03(self): with self.assertRaises(NotImplementedError): - VanillaAE( - SEQ_LEN, - n_features=2, - loss_fn="random" - ) + VanillaAE(SEQ_LEN, n_features=2, loss_fn="random") if __name__ == "__main__": diff --git a/tests/test_postprocess.py b/tests/test_postprocess.py index 9016608b..807cab0a 100644 --- a/tests/test_postprocess.py +++ b/tests/test_postprocess.py @@ -22,6 +22,5 @@ def test_tanh_norm_clf(self): self.assertAlmostEqual(np.sum(scores), 39.52, places=2) - if __name__ == "__main__": unittest.main() From 286695d807172e803f9da14f6db8381f0c1477e5 Mon Sep 17 00:00:00 2001 From: Avik Basu Date: Thu, 5 Jan 2023 15:43:34 -0800 Subject: [PATCH 15/15] chore: add tests for thresholding Signed-off-by: Avik Basu --- numalogic/models/threshold/_std.py | 22 +++++++++++++++++----- tests/models/test_threshold.py | 27 +++++++++++++++++++++++++++ 2 files changed, 44 insertions(+), 5 deletions(-) create mode 100644 tests/models/test_threshold.py diff --git a/numalogic/models/threshold/_std.py b/numalogic/models/threshold/_std.py index 8bc3b0f3..d70e5997 100644 --- a/numalogic/models/threshold/_std.py +++ b/numalogic/models/threshold/_std.py @@ -4,6 +4,15 @@ class StdDevThreshold(BaseEstimator): + r""" + Threshold estimator that calculates based on the mean and the std deviation. + + Threshold = Mean + (std_factor * Std) + + Generates anomaly score as the ratio + between the input data and threshold generated. + """ + def __init__(self, std_factor: float = 3.0, min_threshold: float = 0.1): self.std_factor = std_factor self.min_threshold = min_threshold @@ -24,14 +33,17 @@ def std(self): def threshold(self): return self._threshold - def fit(self, X, y=None): - self._std = np.std(X, axis=0) - self._mean = np.mean(X, axis=0) + def fit(self, x_train: NDArray[float], y=None) -> "StdDevThreshold": + self._std = np.std(x_train, axis=0) + self._mean = np.mean(x_train, axis=0) self._threshold = self._mean + (self.std_factor * self._std) self._threshold[self._threshold < self.min_threshold] = self.min_threshold return self - def predict(self, X: NDArray[float]) -> NDArray[float]: - anomaly_scores = X / self.threshold + def predict(self, x_test: NDArray[float]) -> NDArray[float]: + anomaly_scores = x_test / self.threshold return anomaly_scores + + def score(self, x_test: NDArray[float]) -> NDArray[float]: + return self.predict(x_test) diff --git a/tests/models/test_threshold.py b/tests/models/test_threshold.py new file mode 100644 index 00000000..8a87cc11 --- /dev/null +++ b/tests/models/test_threshold.py @@ -0,0 +1,27 @@ +import unittest + +import numpy as np + +from numalogic.models.threshold import StdDevThreshold + + +class TestStdDevThreshold(unittest.TestCase): + def setUp(self) -> None: + self.x_train = np.arange(100).reshape(50, 2) + self.x_test = np.arange(100, 160, 6).reshape(5, 2) + + def test_estimator_predict(self): + clf = StdDevThreshold() + clf.fit(self.x_train) + score = clf.predict(self.x_test) + self.assertAlmostEqual(0.93317, np.mean(score), places=2) + + def test_estimator_score(self): + clf = StdDevThreshold() + clf.fit(self.x_train) + score = clf.score(self.x_test) + self.assertAlmostEqual(0.93317, np.mean(score), places=2) + + +if __name__ == "__main__": + unittest.main()