
"""simple docstring"""



import re
def    _UpperCAmelCase (					__lowerCamelCase  :			str						)	->   list:
  return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]'''					,   str_						)]
def    _UpperCAmelCase (					__lowerCamelCase  :			str						)	->   str:
  _snake_case   	=			split_input(str_						)
  return "".join(
      [''''''.join([char.capitalize() for char in sub_str]						) for sub_str in string_split]						)
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			bool					,   __lowerCamelCase  :			str						)	->   str:
  try:
    _snake_case   	=			split_input(__A						)
    if upper:
      _snake_case   	=			''.join(
          [
              separator.join([char.upper() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    else:
      _snake_case   	=			''.join(
          [
              separator.join([char.lower() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    return res_str
  except IndexError:
    return "not valid string"
def    _UpperCAmelCase (					__lowerCamelCase  :			str						)	->   str:
  return to_simple_case(__A						)
def    _UpperCAmelCase (					__lowerCamelCase  :			str						)	->   str:
  try:
    _snake_case   	=			to_simple_case(__A						)
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			bool						)	->   str:
  return to_complex_case(__A					,   __A					,   '''_'''						)
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			bool						)	->   str:
  return to_complex_case(__A					,   __A					,   '''-'''						)


if __name__ == "__main__":
 __import__('doctest').testmod()








"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[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:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    '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

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





