




import os
from collections.abc import Iterator

def    UpperCAmelCase_				( __UpperCAmelCase		:      str = "."				)   ->					Iterator[str]:
      for dir_path, dir_names, filenames in os.walk(__UpperCAmelCase				):
            SCREAMING_SNAKE_CASE_      		=     [d for d in dir_names if d != 'scripts' and d[0] not in '._']
            for filename in filenames:
                  if filename == "__init__.py":
                        continue
                  if os.path.splitext(__UpperCAmelCase				)[1] in (".py", ".ipynb"):
                        yield os.path.join(__UpperCAmelCase     ,     __UpperCAmelCase				).lstrip('./'				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					Optional[Any]:
      return f"{i * '  '}*" if i else "\n##"

def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      str				)   ->					str:
      SCREAMING_SNAKE_CASE_      		=     old_path.split(os.sep				)
      for i, new_part in enumerate(new_path.split(os.sep				)				):
            if (i + 1 > len(__UpperCAmelCase				) or old_parts[i] != new_part) and new_part:
                  print(f"{md_prefix(__UpperCAmelCase				)} {new_part.replace('_'     ,     ' '				).title()}"				)
      return new_path

def    UpperCAmelCase_				( __UpperCAmelCase		:      str = "."				)   ->					None:
      SCREAMING_SNAKE_CASE_      		=     ''
      for filepath in sorted(good_file_paths(__UpperCAmelCase				)				):
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     os.path.split(__UpperCAmelCase				)
            if filepath != old_path:
                  SCREAMING_SNAKE_CASE_      		=     print_path(__UpperCAmelCase     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     (filepath.count(os.sep				) + 1) if filepath else 0
            SCREAMING_SNAKE_CASE_      		=     f"{filepath}/{filename}".replace(' '     ,     '%20'				)
            SCREAMING_SNAKE_CASE_      		=     os.path.splitext(filename.replace('_'     ,     ' '				).title()				)[0]
            print(f"{md_prefix(__UpperCAmelCase				)} [{filename}]({url})"				)


if __name__ == "__main__":
   print_directory_md('.')





from argparse import ArgumentParser

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


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												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      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       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=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		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=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

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


