



from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info

def   _lowerCAmelCase   (						A__	):
      return {key.lstrip('-'	): value for key, value in zip(unknown_args[::2]						,  unknown_args[1::2]	)}

def   _lowerCAmelCase   (						):
      lowercase__     =  ArgumentParser(
          'HuggingFace Datasets CLI tool'						,  usage='datasets-cli <command> [<args>]'						,  allow_abbrev=A__	)
      lowercase__     =  parser.add_subparsers(help='datasets-cli command helpers'	)
      set_verbosity_info()

      # Register commands
      ConvertCommand.register_subcommand(A__	)
      EnvironmentCommand.register_subcommand(A__	)
      TestCommand.register_subcommand(A__	)
      RunBeamCommand.register_subcommand(A__	)
      DummyDataCommand.register_subcommand(A__	)

      # Parse args
      lowercase__,	lowercase__     =  parser.parse_known_args()
      if not hasattr(A__						,  'func'	):
            parser.print_help()
            exit(1	)
      lowercase__     =  parse_unknown_args(A__	)

      # Run
      lowercase__     =  args.func(A__						,  **A__	)
      service.run()


if __name__ == "__main__":
       main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
