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_  (		lowercase:				Union[str, Any]		)     ->					List[Any]:







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




def    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			ArgumentParser(
     '''HuggingFace Datasets CLI tool'''	,       usage='''datasets-cli <command> [<args>]'''	,       allow_abbrev=lowercase		)
 _UpperCamelCase:  int    						=			parser.add_subparsers(help='''datasets-cli command helpers'''		)
 set_verbosity_info()

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

 # Parse args
 _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			parser.parse_known_args()
 if not hasattr(lowercase	,       '''func'''		):
  parser.print_help()
  exit(1		)
 _UpperCamelCase:  List[str]    						=			parse_unknown_args(lowercase		)

 # Run
 _UpperCamelCase:  int    						=			args.func(lowercase	,       **lowercase		)
 service.run()


if __name__ == "__main__":
  main()
UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}