


from argparse import ArgumentParser

from .env import EnvironmentCommand




def     UpperCamelCase    (      )					->							Optional[Any]:
     '''simple docstring'''





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

     # Register commands
     EnvironmentCommand.register_subcommand(_a		)

     # Let's go
     lowercase_       :Dict     =       parser.parse_args()

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

     # Run
     lowercase_       :int     =       args.func(_a		)
     service.run()


if __name__ == "__main__":
   main()





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :		str         =				{
    "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       :		Dict         =				[
       "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       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

