





"""simple docstring"""





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__					=   {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ElectraForCausalLM",
           "ElectraForMaskedLM",
           "ElectraForMultipleChoice",
           "ElectraForPreTraining",
           "ElectraForQuestionAnswering",
           "ElectraForSequenceClassification",
           "ElectraForTokenClassification",
           "ElectraModel",
           "ElectraPreTrainedModel",
           "load_tf_weights_in_electra",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFElectraForMaskedLM",
           "TFElectraForMultipleChoice",
           "TFElectraForPreTraining",
           "TFElectraForQuestionAnswering",
           "TFElectraForSequenceClassification",
           "TFElectraForTokenClassification",
           "TFElectraModel",
           "TFElectraPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FlaxElectraForCausalLM",
           "FlaxElectraForMaskedLM",
           "FlaxElectraForMultipleChoice",
           "FlaxElectraForPreTraining",
           "FlaxElectraForQuestionAnswering",
           "FlaxElectraForSequenceClassification",
           "FlaxElectraForTokenClassification",
           "FlaxElectraModel",
           "FlaxElectraPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













"""simple docstring"""





def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      while b:
            lowerCAmelCase						,		lowerCAmelCase  						=       b, a % b
      return a


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNAKE_CASE							,						a % b			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      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()






