"""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,
)


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

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase   :						Optional[int]      =    [
           "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:
       _UpperCamelCase   :						Optional[Any]      =    [
           "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:
       _UpperCamelCase   :						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

       _UpperCamelCase   :						int      =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()