def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase = False  )->     Optional[Any]:






      '''simple docstring'''





      if not isinstance(_UpperCAmelCase						,    _UpperCAmelCase  ):
            UpperCAmelCase			:       str			     =f'''Expected string as input, found {type(_UpperCAmelCase  )}'''
            raise ValueError(_UpperCAmelCase  )
      if not isinstance(_UpperCAmelCase						,    _UpperCAmelCase  ):
            UpperCAmelCase			:       Optional[Any]			     =f'''Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase  )}'''
            raise ValueError(_UpperCAmelCase  )

      UpperCAmelCase			:       Tuple			     =input_str.split('''_'''  )

      UpperCAmelCase			:       str			     =0 if use_pascal else 1

      UpperCAmelCase			:       int			     =words[start_index:]

      UpperCAmelCase			:       List[str]			     =[word[0].upper() + word[1:] for word in words_to_capitalize]

      UpperCAmelCase			:       List[Any]			     ="" if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]  )


if __name__ == "__main__":
     from doctest import testmod

     testmod()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






