


"""simple docstring"""


from typing import TYPE_CHECKING

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


__snake_case    					=					{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RobertaForCausalLM',
       'RobertaForMaskedLM',
       'RobertaForMultipleChoice',
       'RobertaForQuestionAnswering',
       'RobertaForSequenceClassification',
       'RobertaForTokenClassification',
       'RobertaModel',
       'RobertaPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRobertaForCausalLM',
       'TFRobertaForMaskedLM',
       'TFRobertaForMultipleChoice',
       'TFRobertaForQuestionAnswering',
       'TFRobertaForSequenceClassification',
       'TFRobertaForTokenClassification',
       'TFRobertaMainLayer',
       'TFRobertaModel',
       'TFRobertaPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'FlaxRobertaForCausalLM',
       'FlaxRobertaForMaskedLM',
       'FlaxRobertaForMultipleChoice',
       'FlaxRobertaForQuestionAnswering',
       'FlaxRobertaForSequenceClassification',
       'FlaxRobertaForTokenClassification',
       'FlaxRobertaModel',
       'FlaxRobertaPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



"""simple docstring"""


def    _lowerCamelCase   (     lowerCamelCase__	:							str  ):
 assert column_title.isupper()
 lowercase__       :       int				=						0
 lowercase__       :       Optional[int]				=						len(lowerCamelCase__  ) - 1
 lowercase__       :       int				=						0

 while index >= 0:
  lowercase__       :       List[str]				=						(ord(column_title[index]  ) - 64) * pow(26       ,		lowerCamelCase__  )
  answer += value
  power += 1
  index -= 1

 return answer


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

   testmod()