




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''Wav2Vec2FeatureExtractor'''
			snake_case_  						=			'''AutoTokenizer'''


			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							super().__init__(lowerCamelCase__     ,		lowerCamelCase__				)
							__lowerCamelCase									=       self.feature_extractor
							__lowerCamelCase									=       False


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		**lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							try:
											return super().from_pretrained(lowerCamelCase__     ,		**lowerCamelCase__				)
							except OSError:
											warnings.warn(
											    f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '     ,		lowerCamelCase__     ,		)

											__lowerCamelCase									=       WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__     ,		**lowerCamelCase__				)
											__lowerCamelCase									=       WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase__     ,		**lowerCamelCase__				)

											return cls(feature_extractor=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)


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



							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor(*lowerCamelCase__     ,		**lowerCamelCase__				)

							if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'				)
											__lowerCamelCase									=       kwargs.pop('raw_speech'				)
							else:
											__lowerCamelCase									=       kwargs.pop('audio'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       kwargs.pop('sampling_rate'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       kwargs.pop('text'     ,		lowerCamelCase__				)
							if len(lowerCamelCase__				) > 0:
											__lowerCamelCase									=       args[0]
											__lowerCamelCase									=       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:
											__lowerCamelCase									=       self.feature_extractor(lowerCamelCase__     ,		*lowerCamelCase__     ,		sampling_rate=lowerCamelCase__     ,		**lowerCamelCase__				)
							if text is not None:
											__lowerCamelCase									=       self.tokenizer(lowerCamelCase__     ,		**lowerCamelCase__				)

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


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> str:



							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor.pad(*lowerCamelCase__     ,		**lowerCamelCase__				)

							__lowerCamelCase									=       kwargs.pop('input_features'     ,		lowerCamelCase__				)
							__lowerCamelCase									=       kwargs.pop('labels'     ,		lowerCamelCase__				)
							if len(lowerCamelCase__				) > 0:
											__lowerCamelCase									=       args[0]
											__lowerCamelCase									=       args[1:]

							if input_features is not None:
											__lowerCamelCase									=       self.feature_extractor.pad(lowerCamelCase__     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)
							if labels is not None:
											__lowerCamelCase									=       self.tokenizer.pad(lowerCamelCase__     ,		**lowerCamelCase__				)

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


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


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



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)






			@contextmanager
			def  lowercase_       ( self				)       -> Optional[int]:



							'''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.'				)
							__lowerCamelCase									=       True
							__lowerCamelCase									=       self.tokenizer
							yield
							__lowerCamelCase									=       self.feature_extractor
							__lowerCamelCase									=       False








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__				)


