




"""simple docstring"""

from typing import TYPE_CHECKING

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


a							      =     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''RoFormerForCausalLM''',
           '''RoFormerForMaskedLM''',
           '''RoFormerForMultipleChoice''',
           '''RoFormerForQuestionAnswering''',
           '''RoFormerForSequenceClassification''',
           '''RoFormerForTokenClassification''',
           '''RoFormerLayer''',
           '''RoFormerModel''',
           '''RoFormerPreTrainedModel''',
           '''load_tf_weights_in_roformer''',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFRoFormerForCausalLM''',
           '''TFRoFormerForMaskedLM''',
           '''TFRoFormerForMultipleChoice''',
           '''TFRoFormerForQuestionAnswering''',
           '''TFRoFormerForSequenceClassification''',
           '''TFRoFormerForTokenClassification''',
           '''TFRoFormerLayer''',
           '''TFRoFormerModel''',
           '''TFRoFormerPreTrainedModel''',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a							      =     [
           '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaxRoFormerForMaskedLM''',
           '''FlaxRoFormerForMultipleChoice''',
           '''FlaxRoFormerForQuestionAnswering''',
           '''FlaxRoFormerForSequenceClassification''',
           '''FlaxRoFormerForTokenClassification''',
           '''FlaxRoFormerModel''',
           '''FlaxRoFormerPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       a							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      return " ".join(
          ''.join(word[::-1]				) if len(_snake_case				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('''Hey wollef sroirraw'''))

