






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE 							=						{
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						[
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE 							=						[
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE 							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import os
from collections.abc import Iterator

def      _lowerCamelCase	(						__A    :							str = "." )      ->					Iterator[str]:
    for dir_path, dir_names, filenames in os.walk(__A ):
        _UpperCAmelCase				:      List[Any]			  =				[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(__A )[1] in (".py", ".ipynb"):
                yield os.path.join(__A		,		__A ).lstrip('''./''' )

def      _lowerCamelCase	(						__A    :							Dict )      ->					List[Any]:
    return f'''{i * '  '}*''' if i else "\n##"

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					str:
    _UpperCAmelCase				:      int			  =				old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(__A ) or old_parts[i] != new_part) and new_part:
            print(f'''{md_prefix(__A )} {new_part.replace('_'		,		' ' ).title()}''' )
    return new_path

def      _lowerCamelCase	(						__A    :							str = "." )      ->					None:
    _UpperCAmelCase				:      List[str]			  =				''''''
    for filepath in sorted(good_file_paths(__A ) ):
        _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				os.path.split(__A )
        if filepath != old_path:
            _UpperCAmelCase				:      Optional[int]			  =				print_path(__A		,		__A )
        _UpperCAmelCase				:      Dict			  =				(filepath.count(os.sep ) + 1) if filepath else 0
        _UpperCAmelCase				:      Union[str, Any]			  =				f'''{filepath}/{filename}'''.replace(''' '''		,		'''%20''' )
        _UpperCAmelCase				:      List[Any]			  =				os.path.splitext(filename.replace('''_'''		,		''' ''' ).title() )[0]
        print(f'''{md_prefix(__A )} [{filename}]({url})''' )


if __name__ == "__main__":
  print_directory_md('.')




