


'''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,
)


a_    :							str         =					{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :							Optional[Any]         =					["""XLMRobertaTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :							Dict         =					["""XLMRobertaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :							Optional[Any]         =					[
         """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMRobertaForCausalLM""",
         """XLMRobertaForMaskedLM""",
         """XLMRobertaForMultipleChoice""",
         """XLMRobertaForQuestionAnswering""",
         """XLMRobertaForSequenceClassification""",
         """XLMRobertaForTokenClassification""",
         """XLMRobertaModel""",
         """XLMRobertaPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :							Tuple         =					[
         """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMRobertaForCausalLM""",
         """TFXLMRobertaForMaskedLM""",
         """TFXLMRobertaForMultipleChoice""",
         """TFXLMRobertaForQuestionAnswering""",
         """TFXLMRobertaForSequenceClassification""",
         """TFXLMRobertaForTokenClassification""",
         """TFXLMRobertaModel""",
         """TFXLMRobertaPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :							List[str]         =					[
         """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FlaxXLMRobertaForMaskedLM""",
         """FlaxXLMRobertaForCausalLM""",
         """FlaxXLMRobertaForMultipleChoice""",
         """FlaxXLMRobertaForQuestionAnswering""",
         """FlaxXLMRobertaForSequenceClassification""",
         """FlaxXLMRobertaForTokenClassification""",
         """FlaxXLMRobertaModel""",
         """FlaxXLMRobertaPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_xlm_roberta import (
         XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XLMRobertaConfig,
         XLMRobertaOnnxConfig,
     )

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta import XLMRobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_roberta import (
              XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMRobertaForCausalLM,
              XLMRobertaForMaskedLM,
              XLMRobertaForMultipleChoice,
              XLMRobertaForQuestionAnswering,
              XLMRobertaForSequenceClassification,
              XLMRobertaForTokenClassification,
              XLMRobertaModel,
              XLMRobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm_roberta import (
              TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMRobertaForCausalLM,
              TFXLMRobertaForMaskedLM,
              TFXLMRobertaForMultipleChoice,
              TFXLMRobertaForQuestionAnswering,
              TFXLMRobertaForSequenceClassification,
              TFXLMRobertaForTokenClassification,
              TFXLMRobertaModel,
              TFXLMRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xlm_roberta import (
              FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxXLMRobertaForCausalLM,
              FlaxXLMRobertaForMaskedLM,
              FlaxXLMRobertaForMultipleChoice,
              FlaxXLMRobertaForQuestionAnswering,
              FlaxXLMRobertaForSequenceClassification,
              FlaxXLMRobertaForTokenClassification,
              FlaxXLMRobertaModel,
              FlaxXLMRobertaPreTrainedModel,
          )

else:
     import sys

     a_    :							Tuple         =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""







def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    0
			for i in range(1      ,							10_01						):
						total += i**i
			return str(__a						)[-10:]


if __name__ == "__main__":
	print(solution())





