Configure isort to use the Black profile, recursively isort the `thin…

…c` module (#880)

* Use isort with the Black profile

* isort the thinc module

* Fix import cycles as a result of import sorting

* Add isort to requirements

pyproject.toml

@@ -9,3 +9,6 @@ requires = [
     "numpy>=1.15.0",
 ]
 build-backend = "setuptools.build_meta"
+
+[tool.isort]
+profile = "black"

requirements.txt

@@ -36,3 +36,4 @@ nbformat>=5.0.4,<5.2.0
 # Test to_disk/from_disk against pathlib.Path subclasses
 pathy>=0.3.5
 black>=22.0,<23.0
+isort>=5.0,<6.0

thinc/__init__.py

@@ -4,7 +4,6 @@
 from .about import __version__
 from .config import registry
 
-
 # fmt: off
 __all__ = [
     "registry",

thinc/api.py

@@ -1,52 +1,163 @@
-from .config import Config, registry, ConfigValidationError
-from .initializers import normal_init, uniform_init, glorot_uniform_init, zero_init
-from .initializers import configure_normal_init
-from .loss import CategoricalCrossentropy, L2Distance, CosineDistance
-from .loss import SequenceCategoricalCrossentropy
-from .model import Model, serialize_attr, deserialize_attr
-from .model import set_dropout_rate, change_attr_values, wrap_model_recursive
-from .shims import Shim, PyTorchGradScaler, PyTorchShim, TensorFlowShim, keras_model_fns
-from .shims import MXNetShim, TorchScriptShim, maybe_handshake_model
-from .optimizers import Adam, RAdam, SGD, Optimizer
-from .schedules import cyclic_triangular, warmup_linear, constant, constant_then
-from .schedules import decaying, slanted_triangular, compounding
-from .types import Ragged, Padded, ArgsKwargs, Unserializable
-from .util import fix_random_seed, is_cupy_array, set_active_gpu
-from .util import prefer_gpu, require_gpu, require_cpu
-from .util import DataValidationError, data_validation
-from .util import to_categorical, get_width, get_array_module, to_numpy
-from .util import torch2xp, xp2torch, tensorflow2xp, xp2tensorflow, mxnet2xp, xp2mxnet
-from .util import get_torch_default_device
+from .backends import (
+    CupyOps,
+    MPSOps,
+    NumpyOps,
+    Ops,
+    get_current_ops,
+    get_ops,
+    set_current_ops,
+    set_gpu_allocator,
+    use_ops,
+    use_pytorch_for_gpu_memory,
+    use_tensorflow_for_gpu_memory,
+)
 from .compat import has_cupy
-from .backends import get_ops, set_current_ops, get_current_ops, use_ops
-from .backends import Ops, CupyOps, MPSOps, NumpyOps, set_gpu_allocator
-from .backends import use_pytorch_for_gpu_memory, use_tensorflow_for_gpu_memory
-
-from .layers import Dropout, Embed, expand_window, HashEmbed, LayerNorm, Linear
-from .layers import Maxout, Mish, MultiSoftmax, Relu, softmax_activation, Softmax, LSTM
-from .layers import CauchySimilarity, ParametricAttention, Logistic
-from .layers import resizable, sigmoid_activation, Sigmoid, SparseLinear
-from .layers import SparseLinear_v2, ClippedLinear, ReluK, HardTanh, HardSigmoid
-from .layers import Dish, HardSwish, HardSwishMobilenet, Swish, Gelu
-from .layers import PyTorchWrapper, PyTorchRNNWrapper, PyTorchLSTM
-from .layers import TensorFlowWrapper, keras_subclass, MXNetWrapper
-from .layers import PyTorchWrapper_v2, Softmax_v2, PyTorchWrapper_v3
-from .layers import TorchScriptWrapper_v1, pytorch_to_torchscript_wrapper
-
-from .layers import add, bidirectional, chain, clone, concatenate, noop
-from .layers import residual, uniqued, siamese, list2ragged, ragged2list
-from .layers import map_list
-from .layers import with_array, with_array2d
-from .layers import with_padded, with_list, with_ragged, with_flatten
-from .layers import with_reshape, with_getitem, strings2arrays, list2array
-from .layers import list2ragged, ragged2list, list2padded, padded2list
-from .layers import remap_ids, remap_ids_v2, premap_ids
-from .layers import array_getitem, with_cpu, with_debug, with_nvtx_range
-from .layers import with_signpost_interval
-from .layers import tuplify, with_flatten_v2
-
-from .layers import reduce_first, reduce_last, reduce_max, reduce_mean, reduce_sum
-
+from .config import Config, ConfigValidationError, registry
+from .initializers import (
+    configure_normal_init,
+    glorot_uniform_init,
+    normal_init,
+    uniform_init,
+    zero_init,
+)
+from .layers import (
+    LSTM,
+    CauchySimilarity,
+    ClippedLinear,
+    Dish,
+    Dropout,
+    Embed,
+    Gelu,
+    HardSigmoid,
+    HardSwish,
+    HardSwishMobilenet,
+    HardTanh,
+    HashEmbed,
+    LayerNorm,
+    Linear,
+    Logistic,
+    Maxout,
+    Mish,
+    MultiSoftmax,
+    MXNetWrapper,
+    ParametricAttention,
+    PyTorchLSTM,
+    PyTorchRNNWrapper,
+    PyTorchWrapper,
+    PyTorchWrapper_v2,
+    PyTorchWrapper_v3,
+    Relu,
+    ReluK,
+    Sigmoid,
+    Softmax,
+    Softmax_v2,
+    SparseLinear,
+    SparseLinear_v2,
+    Swish,
+    TensorFlowWrapper,
+    TorchScriptWrapper_v1,
+    add,
+    array_getitem,
+    bidirectional,
+    chain,
+    clone,
+    concatenate,
+    expand_window,
+    keras_subclass,
+    list2array,
+    list2padded,
+    list2ragged,
+    map_list,
+    noop,
+    padded2list,
+    premap_ids,
+    pytorch_to_torchscript_wrapper,
+    ragged2list,
+    reduce_first,
+    reduce_last,
+    reduce_max,
+    reduce_mean,
+    reduce_sum,
+    remap_ids,
+    remap_ids_v2,
+    residual,
+    resizable,
+    siamese,
+    sigmoid_activation,
+    softmax_activation,
+    strings2arrays,
+    tuplify,
+    uniqued,
+    with_array,
+    with_array2d,
+    with_cpu,
+    with_debug,
+    with_flatten,
+    with_flatten_v2,
+    with_getitem,
+    with_list,
+    with_nvtx_range,
+    with_padded,
+    with_ragged,
+    with_reshape,
+    with_signpost_interval,
+)
+from .loss import (
+    CategoricalCrossentropy,
+    CosineDistance,
+    L2Distance,
+    SequenceCategoricalCrossentropy,
+)
+from .model import (
+    Model,
+    change_attr_values,
+    deserialize_attr,
+    serialize_attr,
+    set_dropout_rate,
+    wrap_model_recursive,
+)
+from .optimizers import SGD, Adam, Optimizer, RAdam
+from .schedules import (
+    compounding,
+    constant,
+    constant_then,
+    cyclic_triangular,
+    decaying,
+    slanted_triangular,
+    warmup_linear,
+)
+from .shims import (
+    MXNetShim,
+    PyTorchGradScaler,
+    PyTorchShim,
+    Shim,
+    TensorFlowShim,
+    TorchScriptShim,
+    keras_model_fns,
+    maybe_handshake_model,
+)
+from .types import ArgsKwargs, Padded, Ragged, Unserializable
+from .util import (
+    DataValidationError,
+    data_validation,
+    fix_random_seed,
+    get_array_module,
+    get_torch_default_device,
+    get_width,
+    is_cupy_array,
+    mxnet2xp,
+    prefer_gpu,
+    require_cpu,
+    require_gpu,
+    set_active_gpu,
+    tensorflow2xp,
+    to_categorical,
+    to_numpy,
+    torch2xp,
+    xp2mxnet,
+    xp2tensorflow,
+    xp2torch,
+)
 
 # fmt: off
 __all__ = [

thinc/backends/__init__.py

@@ -1,20 +1,23 @@
 import contextlib
-from typing import Type, Dict, Any, Callable, Optional, cast
-
-from contextvars import ContextVar
 import threading
+from contextvars import ContextVar
+from typing import Any, Callable, Dict, Optional, Type, cast
 
-from .ops import Ops
-from .cupy_ops import CupyOps
-from .numpy_ops import NumpyOps
-from .mps_ops import MPSOps
-from ._cupy_allocators import cupy_tensorflow_allocator, cupy_pytorch_allocator
-from ._param_server import ParamServer
-from ..util import assert_tensorflow_installed, assert_pytorch_installed
-from ..util import get_torch_default_device, is_cupy_array, require_cpu
 from .. import registry
 from ..compat import cupy, has_cupy
-
+from ..util import (
+    assert_pytorch_installed,
+    assert_tensorflow_installed,
+    get_torch_default_device,
+    is_cupy_array,
+    require_cpu,
+)
+from ._cupy_allocators import cupy_pytorch_allocator, cupy_tensorflow_allocator
+from ._param_server import ParamServer
+from .cupy_ops import CupyOps
+from .mps_ops import MPSOps
+from .numpy_ops import NumpyOps
+from .ops import Ops
 
 context_ops: ContextVar[Optional[Ops]] = ContextVar("context_ops", default=None)
 context_pools: ContextVar[dict] = ContextVar("context_pools", default={})

thinc/backends/_cupy_allocators.py

@@ -1,8 +1,8 @@
 from typing import cast
 
+from ..compat import cupy, tensorflow, torch
 from ..types import ArrayXd
 from ..util import get_torch_default_device, tensorflow2xp
-from ..compat import torch, cupy, tensorflow
 
 
 def cupy_tensorflow_allocator(size_in_bytes: int):

thinc/backends/_custom_kernels.py

@@ -1,12 +1,13 @@
-from typing import Callable, Optional, Tuple
-from functools import reduce
-import numpy
 import operator
 import re
-from pathlib import Path
 from collections import defaultdict
-from ..compat import cupy, has_cupy_gpu
+from functools import reduce
+from pathlib import Path
+from typing import Callable, Optional, Tuple
 
+import numpy
+
+from ..compat import cupy, has_cupy_gpu
 
 PWD = Path(__file__).parent
 KERNELS_SRC = (PWD / "_custom_kernels.cu").read_text(encoding="utf8")

thinc/backends/_param_server.py

@@ -1,9 +1,8 @@
-from typing import Dict, Tuple, Optional, Any
+from typing import Any, Dict, Optional, Tuple
 
 from ..types import FloatsXd
 from ..util import get_array_module
 
-
 KeyT = Tuple[int, str]
 
 

thinc/backends/cblas.pxd

@@ -1,6 +1,5 @@
 from libcpp.memory cimport shared_ptr
 
-
 ctypedef void (*sgemm_ptr)(bint transA, bint transB, int M, int N, int K,
                            float alpha, const float* A, int lda, const float *B,
                            int ldb, float beta, float* C, int ldc) nogil

thinc/backends/cupy_ops.py

@@ -1,13 +1,20 @@
 import numpy
+
 from .. import registry
-from .ops import Ops
-from .numpy_ops import NumpyOps
-from . import _custom_kernels
-from ..types import DeviceTypes
-from ..util import torch2xp, tensorflow2xp, mxnet2xp
-from ..util import is_cupy_array
-from ..util import is_torch_cuda_array, is_tensorflow_gpu_array, is_mxnet_gpu_array
 from ..compat import cupy, cupyx
+from ..types import DeviceTypes
+from ..util import (
+    is_cupy_array,
+    is_mxnet_gpu_array,
+    is_tensorflow_gpu_array,
+    is_torch_cuda_array,
+    mxnet2xp,
+    tensorflow2xp,
+    torch2xp,
+)
+from . import _custom_kernels
+from .numpy_ops import NumpyOps
+from .ops import Ops
 
 
 @registry.ops("CupyOps")

thinc/backends/linalg.pxd

@@ -2,10 +2,9 @@
 # cython: cdivision=True
 
 cimport cython
-from libc.stdint cimport int32_t
-from libc.string cimport memset, memcpy
 from cymem.cymem cimport Pool
-
+from libc.stdint cimport int32_t
+from libc.string cimport memcpy, memset
 
 ctypedef float weight_t
 

thinc/backends/mps_ops.py

@@ -1,8 +1,10 @@
 from typing import TYPE_CHECKING
+
 import numpy
 
 from .. import registry
-from . import NumpyOps, Ops
+from .numpy_ops import NumpyOps
+from .ops import Ops
 
 if TYPE_CHECKING:
     # Type checking does not work with dynamic base classes, since MyPy cannot