





import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_						       =	[
    """kernels/rwkv/wkv_cuda.cu""",
    """kernels/rwkv/wkv_op.cpp""",
    """kernels/deformable_detr/ms_deform_attn.h""",
    """kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh""",
    """models/graphormer/algos_graphormer.pyx""",
]



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Any:


						'''simple docstring'''


						for file in FILES_TO_FIND:
												if not (transformers_path / file).exists():
																		return False
						return True


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE_						       =	argparse.ArgumentParser()
						parser.add_argument("""--check_lib""", action="""store_true""", help="""Whether to check the build or the actual package.""")
						SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
						if args.check_lib:
												SCREAMING_SNAKE_CASE_						       =	importlib.import_module("""transformers""")
												SCREAMING_SNAKE_CASE_						       =	Path(transformers_module.__file__).parent
						else:
												SCREAMING_SNAKE_CASE_						       =	Path.cwd() / """build/lib/transformers"""
						if not test_custom_files_are_present(transformers_path):
												raise ValueError("""The built release does not contain the custom files. Fix this before going further!""")









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


