'''simple docstring'''





def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			str    	=       str(bin(a_		)		)
     binary_number += "0" * shift_amount
     return binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





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

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       str(bin(a_		)		)[2:]
     if shift_amount >= len(a_		):
          return "0b0"
     SCREAMING_SNAKE_CASE					:			Dict    	=       binary_number[: len(a_		) - shift_amount]
     return "0b" + shifted_binary_number


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	str:





     '''simple docstring'''





     if number >= 0:  # Get binary representation of positive number
          SCREAMING_SNAKE_CASE					:			Tuple    	=       '0' + str(bin(a_		)		).strip('-'		)[2:]
     else:  # Get binary (2's complement) representation of negative number
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(bin(a_		)[3:]		)  # Find 2's complement of number
          SCREAMING_SNAKE_CASE					:			Any    	=       bin(abs(a_		) - (1 << binary_number_length)		)[3:]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
              '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()




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase  :int  				=    {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :str  				=    ["""AlbertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    ["""AlbertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :List[str]  				=    [
     """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """AlbertForMaskedLM""",
     """AlbertForMultipleChoice""",
     """AlbertForPreTraining""",
     """AlbertForQuestionAnswering""",
     """AlbertForSequenceClassification""",
     """AlbertForTokenClassification""",
     """AlbertModel""",
     """AlbertPreTrainedModel""",
     """load_tf_weights_in_albert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Any  				=    [
     """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFAlbertForMaskedLM""",
     """TFAlbertForMultipleChoice""",
     """TFAlbertForPreTraining""",
     """TFAlbertForQuestionAnswering""",
     """TFAlbertForSequenceClassification""",
     """TFAlbertForTokenClassification""",
     """TFAlbertMainLayer""",
     """TFAlbertModel""",
     """TFAlbertPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    [
     """FlaxAlbertForMaskedLM""",
     """FlaxAlbertForMultipleChoice""",
     """FlaxAlbertForPreTraining""",
     """FlaxAlbertForQuestionAnswering""",
     """FlaxAlbertForSequenceClassification""",
     """FlaxAlbertForTokenClassification""",
     """FlaxAlbertModel""",
     """FlaxAlbertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

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



