
'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase			:				Any    		=			{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			['SqueezeBertTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Optional[Any]    		=			[
          'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SqueezeBertForMaskedLM',
          'SqueezeBertForMultipleChoice',
          'SqueezeBertForQuestionAnswering',
          'SqueezeBertForSequenceClassification',
          'SqueezeBertForTokenClassification',
          'SqueezeBertModel',
          'SqueezeBertModule',
          'SqueezeBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase			:				Union[str, Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










__UpperCamelCase					:       Optional[int]    					=      {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

