
"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCAmelCase__      :    Optional[int]    							= logging.get_logger(__name__)  # pylint: disable=invalid-name

def lowercase_  (   _snake_case						):
							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(_snake_case						):
																					return ext

							raise Exception(
							    f'''Unable to determine file format from file extension {path}. '''
							    f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''						)

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   pipeline(
							    task=args.task   ,model=args.model if args.model else None   ,config=args.config   ,tokenizer=args.tokenizer   ,device=args.device   ,)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   try_infer_format_from_ext(args.input						) if args.format == """infer""" else args.format
							SCREAMING_SNAKE_CASE__						:       Tuple           =   PipelineDataFormat.from_str(
							    format=_snake_case   ,output_path=args.output   ,input_path=args.input   ,column=args.column if args.column else nlp.default_input_names   ,overwrite=args.overwrite   ,)
							return RunCommand(_snake_case   ,_snake_case						)



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nlp
											SCREAMING_SNAKE_CASE__						:       Tuple           =   reader



				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   parser.add_parser("""run"""   ,       help="""Run a pipeline through the CLI"""    )
											run_parser.add_argument("""--task"""   ,       choices=get_supported_tasks()   ,       help="""Task to run"""    )
											run_parser.add_argument("""--input"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Path to the file to use for inference"""    )
											run_parser.add_argument("""--output"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Path to the file that will be used post to write results."""    )
											run_parser.add_argument("""--model"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name or path to the model to instantiate."""    )
											run_parser.add_argument("""--config"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name or path to the model's config to instantiate."""    )
											run_parser.add_argument(
											    """--tokenizer"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name of the tokenizer to use. (default: same as the model name)"""    )
											run_parser.add_argument(
											    """--column"""   ,       type=SCREAMING_SNAKE_CASE__   ,       help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""   ,       )
											run_parser.add_argument(
											    """--format"""   ,       type=SCREAMING_SNAKE_CASE__   ,       default="""infer"""   ,       choices=PipelineDataFormat.SUPPORTED_FORMATS   ,       help="""Input format to read from"""   ,       )
											run_parser.add_argument(
											    """--device"""   ,       type=SCREAMING_SNAKE_CASE__   ,       default=-1   ,       help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""   ,       )
											run_parser.add_argument("""--overwrite"""   ,       action="""store_true"""   ,       help="""Allow overwriting the output file."""    )
											run_parser.set_defaults(func=SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self._nlp, []

											for entry in self._reader:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   nlp(**SCREAMING_SNAKE_CASE__    ) if self._reader.is_multi_columns else nlp(SCREAMING_SNAKE_CASE__    )
																		if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									outputs.append(SCREAMING_SNAKE_CASE__    )
																		else:
																									outputs += output

        # Saving data
											if self._nlp.binary_output:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self._reader.save_binary(SCREAMING_SNAKE_CASE__    )
																		logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''    )
											else:
																		self._reader.save(SCREAMING_SNAKE_CASE__    )












"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





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

  # Register commands
  EnvironmentCommand.register_subcommand(__snake_case    )

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

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

  # Run
  lowercase							    =       args.func(__snake_case    )
  service.run()


if __name__ == "__main__":
    main()


