

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE					:  int             =      {
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  Tuple             =      [
           '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TapasForMaskedLM''',
           '''TapasForQuestionAnswering''',
           '''TapasForSequenceClassification''',
           '''TapasModel''',
           '''TapasPreTrainedModel''',
           '''load_tf_weights_in_tapas''',
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  str             =      [
           '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFTapasForMaskedLM''',
           '''TFTapasForQuestionAnswering''',
           '''TFTapasForSequenceClassification''',
           '''TFTapasModel''',
           '''TFTapasPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE					:  List[str]             =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


