

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					:  Optional[int]             =      {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE					:  str             =      [
							    '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:
							_SCREAMING_SNAKE_CASE					:  List[Any]             =      [
							    '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:
							_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      [
							    '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

							_SCREAMING_SNAKE_CASE					:  Dict             =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''multiplicative_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   1
          for i in range(0				,		len(_A    )    ):
               total *= numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if not isinstance(_A				,		_A    ):
          raise ValueError('''additive_persistence() only accepts integral values'''    )
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values'''    )

     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   str(_A    )

     while len(_A    ) != 1:
          SCREAMING_SNAKE_CASE__   =   [int(_A    ) for i in num_string]

          SCREAMING_SNAKE_CASE__   =   0
          for i in range(0				,		len(_A    )    ):
               total += numbers[i]

          SCREAMING_SNAKE_CASE__   =   str(_A    )

          steps += 1
     return steps


if __name__ == "__main__":
       import doctest

       doctest.testmod()


