




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

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           """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							      =     [
           """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							      =     [
           """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							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""






def 				A_       ( snake_case__       ,					snake_case__ = " "		)					->  list:
			_UpperCamelCase					:List[str]					     =	[]

			_UpperCamelCase					:int					     =	0
			for index, char in enumerate(snake_case__		):
						if char == separator:
									split_words.append(string[last_index:index]		)
									_UpperCamelCase					:Dict					     =	index + 1
						elif index + 1 == len(snake_case__		):
									split_words.append(string[last_index : index + 1]		)
			return split_words


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

					testmod()

