



"""simple docstring"""

from typing import Dict

from .base import GenericTensor, Pipeline




class  _UpperCAmelCase (    lowerCAmelCase__):

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Tuple=None						,  lowercase_	: int=None						,  lowercase_	: List[str]=None						,  **lowercase_	: Optional[Any]       ):
     if tokenize_kwargs is None:
         snake_case_				:  str       =			{}

     if truncation is not None:
         if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''       )
         snake_case_				:  int       =			truncation

     snake_case_				:  Optional[int]       =			tokenize_kwargs

     snake_case_				:  int       =			{}
     if return_tensors is not None:
         snake_case_				:  Any       =			return_tensors

     return preprocess_params, {}, postprocess_params

 def 		_snake_case      (  self	: Tuple						,  lowercase_	: str						,  **lowercase_	: Any       ):
     snake_case_				:  List[str]       =			self.framework
     snake_case_				:  Optional[int]       =			self.tokenizer(lowercase_						,  return_tensors=lowercase_						,  **lowercase_       )
     return model_inputs

 def 		_snake_case      (  self	: Dict						,  lowercase_	: List[Any]       ):
     snake_case_				:  str       =			self.model(**lowercase_       )
     return model_outputs

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: int						,  lowercase_	: Optional[int]=False       ):
     # [0] is the first available tensor, logits or last_hidden_state.
     if return_tensors:
         return model_outputs[0]
     if self.framework == "pt":
         return model_outputs[0].tolist()
     elif self.framework == "tf":
         return model_outputs[0].numpy().tolist()






 def __call__(  self	: List[Any]						,  *lowercase_	: Tuple						,  **lowercase_	: Any       ):
     return super().__call__(*lowercase_						,  **lowercase_       )










"""simple docstring"""

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




def __lowercase    (							):
    snake_case_				:  List[str]       =			ArgumentParser(
        '''HuggingFace Datasets CLI tool'''	,    usage='''datasets-cli <command> [<args>]'''	,    allow_abbrev=_a					)
    snake_case_				:  List[Any]       =			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
    snake_case_,			snake_case_				:  Optional[Any]       =			parser.parse_known_args()
    if not hasattr(_a	,    '''func'''					):
        parser.print_help()
        exit(1					)
    snake_case_				:  Optional[int]       =			parse_unknown_args(_a					)

    # Run
    snake_case_				:  Optional[int]       =			args.func(_a	,    **_a					)
    service.run()


if __name__ == "__main__":
     main()





