"""simple docstring"""







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE    	={
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __SCREAMING_SNAKE_CASE    	=['''RemBertTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __SCREAMING_SNAKE_CASE    	=['''RemBertTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __SCREAMING_SNAKE_CASE    	=[
           '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RemBertForCausalLM''',
           '''RemBertForMaskedLM''',
           '''RemBertForMultipleChoice''',
           '''RemBertForQuestionAnswering''',
           '''RemBertForSequenceClassification''',
           '''RemBertForTokenClassification''',
           '''RemBertLayer''',
           '''RemBertModel''',
           '''RemBertPreTrainedModel''',
           '''load_tf_weights_in_rembert''',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __SCREAMING_SNAKE_CASE    	=[
           '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRemBertForCausalLM''',
           '''TFRemBertForMaskedLM''',
           '''TFRemBertForMultipleChoice''',
           '''TFRemBertForQuestionAnswering''',
           '''TFRemBertForSequenceClassification''',
           '''TFRemBertForTokenClassification''',
           '''TFRemBertLayer''',
           '''TFRemBertModel''',
           '''TFRemBertPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert import RemBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_rembert_fast import RemBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_rembert import (
                  REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RemBertForCausalLM,
                  RemBertForMaskedLM,
                  RemBertForMultipleChoice,
                  RemBertForQuestionAnswering,
                  RemBertForSequenceClassification,
                  RemBertForTokenClassification,
                  RemBertLayer,
                  RemBertModel,
                  RemBertPreTrainedModel,
                  load_tf_weights_in_rembert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_rembert import (
                  TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRemBertForCausalLM,
                  TFRemBertForMaskedLM,
                  TFRemBertForMultipleChoice,
                  TFRemBertForQuestionAnswering,
                  TFRemBertForSequenceClassification,
                  TFRemBertForTokenClassification,
                  TFRemBertLayer,
                  TFRemBertModel,
                  TFRemBertPreTrainedModel,
              )


else:
       import sys

       __SCREAMING_SNAKE_CASE    	=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       int:


     '''simple docstring'''



     if not isinstance(__lowercase			,     __lowercase       ) or number < 0:
          raise ValueError("Input must be a non-negative integer"       )

     _UpperCAmelCase     							=   0
     while number:
          # This way we arrive at next set bit (next 1) instead of looping
          # through each bit and checking for 1s hence the
          # loop won't run 32 times it will only run the number of `1` times
          number &= number - 1
          count += 1
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()




