



from typing import TYPE_CHECKING

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


__snake_case						:Optional[int]						=					{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[Any]						=					[
     '''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:
 __snake_case						:List[Any]						=					[
     '''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

 __snake_case						:List[str]						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










# Lint as: python3
import itertools
import os
import re


__snake_case						:int						=					re.compile(r'''([A-Z]+)([A-Z][a-z])''')
__snake_case						:List[Any]						=					re.compile(r'''([a-z\d])([A-Z])''')

__snake_case						:Dict						=					re.compile(r'''(?<!_)_(?!_)''')
__snake_case						:Optional[int]						=					re.compile(r'''(_{2,})''')

__snake_case						:Optional[Any]						=					r'''^\w+(\.\w+)*$'''

__snake_case						:Union[str, Any]						=					r'''<>:/\|?*'''





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _uppercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    __a							=    _lowercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    return name.lower()





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _single_underscore_re.split(_UpperCAmelCase    )
    __a							=    [_multiple_underscores_re.split(_UpperCAmelCase    ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(_UpperCAmelCase    ) if n != ''''''    )





def 					__snake_case     (      _UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    return camelcase_to_snakecase(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    if not re.match(_split_re       ,			_UpperCAmelCase    ):
        raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'    )
    return f'{filename_prefix_for_name(_UpperCAmelCase    )}-{split}'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    if filetype_suffix:
        prefix += f'.{filetype_suffix}'
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return f'{filepath}*'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )

    if shard_lengths:
        __a							=    len(_UpperCAmelCase    )
        __a							=    [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(_UpperCAmelCase    )]
        if filetype_suffix:
            __a							=    [filename + f'.{filetype_suffix}' for filename in filenames]
        return filenames
    else:
        __a							=    prefix
        if filetype_suffix:
            filename += f'.{filetype_suffix}'
        return [filename]





