


'''simple docstring'''





from argparse import ArgumentParser

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


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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			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					: str ):






			"""simple docstring"""




			__A=	pipeline(
			    task=args.task,model=args.model if args.model else None,config=args.config,tokenizer=args.tokenizer,device=args.device,)
			__A=	try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
			__A=	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__					(					_a   ):





			'''simple docstring'''

			def __init__(  self							:			Dict			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	nlp
						__A=	reader
			@staticmethod
			def 					lowerCAmelCase     (  lowerCAmelCase_							:			int	)      ->		Optional[int]:
						__A=	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=__UpperCamelCase			,    help='Path to the file to use for inference'	)
						run_parser.add_argument('--output'			,    type=__UpperCamelCase			,    help='Path to the file that will be used post to write results.'	)
						run_parser.add_argument('--model'			,    type=__UpperCamelCase			,    help='Name or path to the model to instantiate.'	)
						run_parser.add_argument('--config'			,    type=__UpperCamelCase			,    help='Name or path to the model\'s config to instantiate.'	)
						run_parser.add_argument(
						    '--tokenizer'			,    type=__UpperCamelCase			,    help='Name of the tokenizer to use. (default: same as the model name)'	)
						run_parser.add_argument(
						    '--column'			,    type=__UpperCamelCase			,    help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'			,    )
						run_parser.add_argument(
						    '--format'			,    type=__UpperCamelCase			,    default='infer'			,    choices=PipelineDataFormat.SUPPORTED_FORMATS			,    help='Input format to read from'			,    )
						run_parser.add_argument(
						    '--device'			,    type=__UpperCamelCase			,    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=__UpperCamelCase	)

			def 					lowerCAmelCase     (  self							:			Any	)      ->		Tuple:
						__A,	__A=	self._nlp, []

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

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





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class