






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      :       int    =						{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       Optional[Any]    =						['''RobertaTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''RobertaForCausalLM''',
     '''RobertaForMaskedLM''',
     '''RobertaForMultipleChoice''',
     '''RobertaForQuestionAnswering''',
     '''RobertaForSequenceClassification''',
     '''RobertaForTokenClassification''',
     '''RobertaModel''',
     '''RobertaPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       int    =						[
     '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFRobertaForCausalLM''',
     '''TFRobertaForMaskedLM''',
     '''TFRobertaForMultipleChoice''',
     '''TFRobertaForQuestionAnswering''',
     '''TFRobertaForSequenceClassification''',
     '''TFRobertaForTokenClassification''',
     '''TFRobertaMainLayer''',
     '''TFRobertaModel''',
     '''TFRobertaPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''FlaxRobertaForCausalLM''',
     '''FlaxRobertaForMaskedLM''',
     '''FlaxRobertaForMultipleChoice''',
     '''FlaxRobertaForQuestionAnswering''',
     '''FlaxRobertaForSequenceClassification''',
     '''FlaxRobertaForTokenClassification''',
     '''FlaxRobertaModel''',
     '''FlaxRobertaPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
 from .tokenization_roberta import RobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roberta_fast import RobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaForCausalLM,
      RobertaForMaskedLM,
      RobertaForMultipleChoice,
      RobertaForQuestionAnswering,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
      RobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta import (
      TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaForCausalLM,
      TFRobertaForMaskedLM,
      TFRobertaForMultipleChoice,
      TFRobertaForQuestionAnswering,
      TFRobertaForSequenceClassification,
      TFRobertaForTokenClassification,
      TFRobertaMainLayer,
      TFRobertaModel,
      TFRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
      FlaxRobertaPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase      :       Tuple    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''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    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						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!''')






