




import re



def       UpperCAmelCase__  (     lowerCamelCase   ):
  return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]",			str_   )]



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[int]      	=						split_input(str_   )
  return "".join(
      ["".join([char.capitalize() for char in sub_str]   ) for sub_str in string_split]   )



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  try:
    lowercase    :Optional[Any]      	=						split_input(lowerCamelCase   )
    if upper:
      lowercase    :Union[str, Any]      	=						"".join(
          [
              separator.join([char.upper() for char in sub_str]   )
              for sub_str in string_split
          ]   )
    else:
      lowercase    :Union[str, Any]      	=						"".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   ):
  return to_simple_case(lowerCamelCase   )



def       UpperCAmelCase__  (     lowerCamelCase   ):
  try:
    lowercase    :str      	=						to_simple_case(lowerCamelCase   )
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  return to_complex_case(lowerCamelCase,			lowerCamelCase,			"_"   )



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  return to_complex_case(lowerCamelCase,			lowerCamelCase,			"-"   )


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









from typing import TYPE_CHECKING

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


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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



