[NeMo-UX[ Move nemotron imports inline (NVIDIA#10255)

* Move nemotron transformers + tokenizer imports inline to reduce number of required deps

Signed-off-by: Marc Romeyn <mromeijn@nvidia.com>

* Apply isort and black reformatting

Signed-off-by: marcromeyn <marcromeyn@users.noreply.github.com>

---------

Signed-off-by: Marc Romeyn <mromeijn@nvidia.com>
Signed-off-by: marcromeyn <marcromeyn@users.noreply.github.com>
Co-authored-by: marcromeyn <marcromeyn@users.noreply.github.com>

nemo/collections/llm/gpt/model/nemotron.py

@@ -4,16 +4,17 @@
 
 import torch
 from torch import nn
-from transformers import NemotronConfig as HFNemotronConfig
-from transformers import NemotronForCausalLM
 
-from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
 from nemo.collections.llm.fn.activation import squared_relu
 from nemo.collections.llm.gpt.model.base import GPTConfig, GPTModel
 from nemo.collections.llm.utils import Config
 from nemo.lightning import OptimizerModule, io, teardown
 
 if TYPE_CHECKING:
+    from transformers import NemotronConfig as HFNemotronConfig
+    from transformers import NemotronForCausalLM
+
+    from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
     from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
 
 
@@ -123,6 +124,8 @@ def init(self) -> NemotronModel:
         return NemotronModel(self.config, tokenizer=self.tokenizer)
 
     def apply(self, output_path: Path) -> Path:
+        from transformers import NemotronForCausalLM
+
         source = NemotronForCausalLM.from_pretrained(str(self))
         target = self.init()
         trainer = self.nemo_setup(target)
@@ -155,10 +158,14 @@ def convert_state(self, source, target):
 
     @property
     def tokenizer(self) -> "AutoTokenizer":
+        from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
+
         return AutoTokenizer(str(self))
 
     @property
     def config(self) -> NemotronConfig:
+        from transformers import NemotronConfig as HFNemotronConfig
+
         source = HFNemotronConfig.from_pretrained(str(self))
 
         def make_vocab_size_divisible_by(vocab_size):
@@ -224,6 +231,8 @@ def tokenizer(self):
 
     @property
     def config(self) -> "HFNemotronConfig":
+        from transformers import NemotronConfig as HFNemotronConfig
+
         source: NemotronConfig = io.load_context(str(self)).model.config
 
         return HFNemotronConfig(