
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class 				_lowerCamelCase (      UpperCamelCase_ ):





					"""simple docstring"""







					SCREAMING_SNAKE_CASE_		   =  '''MCTCTFeatureExtractor'''
					SCREAMING_SNAKE_CASE_		   =  '''AutoTokenizer'''






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




											"""simple docstring"""







											super().__init__(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Optional[int]					=				self.feature_extractor
											UpperCamelCase__    :   str					=				False






					def __call__(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




											"""simple docstring"""







											if self._in_target_context_manager:
																	return self.current_processor(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

											if "raw_speech" in kwargs:
																	warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''  )
																	UpperCamelCase__    :   Any					=				kwargs.pop('''raw_speech'''  )
											else:
																	UpperCamelCase__    :   Optional[int]					=				kwargs.pop('''audio'''						,						__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Dict					=				kwargs.pop('''sampling_rate'''						,						__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Union[str, Any]					=				kwargs.pop('''text'''						,						__SCREAMING_SNAKE_CASE  )
											if len(__SCREAMING_SNAKE_CASE  ) > 0:
																	UpperCamelCase__    :   Any					=				args[0]
																	UpperCamelCase__    :   Optional[Any]					=				args[1:]

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

											if audio is not None:
																	UpperCamelCase__    :   List[Any]					=				self.feature_extractor(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						sampling_rate=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
											if text is not None:
																	UpperCamelCase__    :   Dict					=				self.tokenizer(__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	UpperCamelCase__    :   List[Any]					=				encodings['''input_ids''']
																	return inputs






					def    __SCREAMING_SNAKE_CASE			(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




											"""simple docstring"""







											return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






					def    __SCREAMING_SNAKE_CASE			(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Union[str, Any]:




											"""simple docstring"""







											if self._in_target_context_manager:
																	return self.current_processor.pad(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

											UpperCamelCase__    :   Tuple					=				kwargs.pop('''input_features'''						,						__SCREAMING_SNAKE_CASE  )
											UpperCamelCase__    :   Any					=				kwargs.pop('''labels'''						,						__SCREAMING_SNAKE_CASE  )
											if len(__SCREAMING_SNAKE_CASE  ) > 0:
																	UpperCamelCase__    :   Dict					=				args[0]
																	UpperCamelCase__    :   Dict					=				args[1:]

											if input_features is not None:
																	UpperCamelCase__    :   int					=				self.feature_extractor.pad(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
											if labels is not None:
																	UpperCamelCase__    :   Union[str, Any]					=				self.tokenizer.pad(__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )

											if labels is None:
																	return input_features
											elif input_features is None:
																	return labels
											else:
																	UpperCamelCase__    :   int					=				labels['''input_ids''']
																	return input_features






					def    __SCREAMING_SNAKE_CASE			(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   List[str]:




											"""simple docstring"""







											return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






					@contextmanager
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											warnings.warn(
											    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
											    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
											    '''your audio inputs, or in a separate call.'''  )
											UpperCamelCase__    :   Optional[int]					=				True
											UpperCamelCase__    :   List[Any]					=				self.tokenizer
											yield
											UpperCamelCase__    :   int					=				self.feature_extractor
											UpperCamelCase__    :   Dict					=				False




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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				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 (      UpperCamelCase_ ):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				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:
                 UpperCamelCase__    :   Union[str, Any]					=				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  )


