

from argparse import ArgumentParser

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


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str	)						->				Union[str, Any]:




 '''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_							:     List[str]	)						->				Dict:




 '''simple docstring'''




 _UpperCamelCase   :		Dict			=						pipeline(
     task=args.task							,       model=args.model if args.model else None							,       config=args.config							,       tokenizer=args.tokenizer							,       device=args.device							,       )
 _UpperCamelCase   :		List[str]			=						try_infer_format_from_ext(args.input	) if args.format == 'infer' else args.format
 _UpperCamelCase   :		Any			=						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 	lowercase    (				_lowercase							):


  """simple docstring"""




  def __init__(				self	,       __snake_case	,       __snake_case):
   _UpperCamelCase   :		List[Any]			=						nlp
   _UpperCamelCase   :		Dict			=						reader


  @staticmethod
  def 		A__					(				__snake_case):
   _UpperCamelCase   :		str			=						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=__snake_case	,       help='Path to the file to use for inference')
   run_parser.add_argument('--output'	,       type=__snake_case	,       help='Path to the file that will be used post to write results.')
   run_parser.add_argument('--model'	,       type=__snake_case	,       help='Name or path to the model to instantiate.')
   run_parser.add_argument('--config'	,       type=__snake_case	,       help='Name or path to the model\'s config to instantiate.')
   run_parser.add_argument(
       '--tokenizer'	,       type=__snake_case	,       help='Name of the tokenizer to use. (default: same as the model name)')
   run_parser.add_argument(
       '--column'	,       type=__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=__snake_case	,       default='infer'	,       choices=PipelineDataFormat.SUPPORTED_FORMATS	,       help='Input format to read from'	,       )
   run_parser.add_argument(
       '--device'	,       type=__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=__snake_case)






  def 		A__					(				self):
   _UpperCamelCase   :		Union[str, Any]			=						self._nlp, []

   for entry in self._reader:
    _UpperCamelCase   :		Optional[int]			=						nlp(**__snake_case) if self._reader.is_multi_columns else nlp(__snake_case)
    if isinstance(__snake_case	,       __snake_case):
     outputs.append(__snake_case)
    else:
     outputs += output

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







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




