


from argparse import ArgumentParser

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


lowercase_	   =     logging.get_logger(__name__)  # pylint: disable=invalid-name
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if not path:
  return "pipe"

 for ext in PipelineDataFormat.SUPPORTED_FORMATS:
  if path.endswith(SCREAMING_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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						pipeline(
     task=args.task      ,	model=args.model if args.model else None      ,	config=args.config      ,	tokenizer=args.tokenizer      ,	device=args.device      ,	)
 __lowerCamelCase		:							Dict		 =						try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
 __lowerCamelCase		:							str		 =						PipelineDataFormat.from_str(
     format=SCREAMING_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(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	List[str]						,				a:	Pipeline						,				a:	PipelineDataFormat    ):
       __lowerCamelCase		:							str		 =						nlp
       __lowerCamelCase		:							int		 =						reader




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




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						self._nlp, []

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

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





import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	List[str]						,				*a:	List[Any]						,				**a:	Optional[Any]    ):
       warnings.warn(
           'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ChineseCLIPImageProcessor instead.'						,				a						,				)
       super().__init__(*a						,				**a    )

