






'''simple docstring'''



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__)











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



