
import warnings
from contextlib import contextmanager

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

class       a		(   __UpperCAmelCase					):
							lowercase_		:						int   							=  'Wav2Vec2FeatureExtractor'
							lowercase_		:						str   							=  'AutoTokenizer'







							def __init__(						self  :  Any      ,				snake_case__  :  str      ,				snake_case__  :  int ):
								"""simple docstring"""







								super().__init__(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							self.feature_extractor
								__lowerCAmelCase         =							False







							@classmethod
							def       UpperCAmelCase__					(						cls  :  int      ,				snake_case__  :  Optional[int]      ,				**snake_case__  :  int ):
								"""simple docstring"""







								try:
									return super().from_pretrained(snake_case__      ,				**snake_case__ )
								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: "      ,				snake_case__      ,				)

									__lowerCAmelCase         =							WavaVecaFeatureExtractor.from_pretrained(snake_case__      ,				**snake_case__ )
									__lowerCAmelCase         =							WavaVecaCTCTokenizer.from_pretrained(snake_case__      ,				**snake_case__ )

									return cls(feature_extractor=snake_case__      ,				tokenizer=snake_case__ )







							def __call__(						self  :  str      ,				*snake_case__  :  List[Any]      ,				**snake_case__  :  Any ):
								"""simple docstring"""







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

								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"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("sampling_rate"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("text"      ,				snake_case__ )
								if len(snake_case__ ) > 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(snake_case__      ,				*snake_case__      ,				sampling_rate=snake_case__      ,				**snake_case__ )
								if text is not None:
									__lowerCAmelCase         =							self.tokenizer(snake_case__      ,				**snake_case__ )

								if text is None:
									return inputs
								elif audio is None:
									return encodings
								else:
									__lowerCAmelCase         =							encodings["input_ids"]
									return inputs







							def       UpperCAmelCase__					(						self  :  Dict      ,				*snake_case__  :  str      ,				**snake_case__  :  Dict ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							kwargs.pop("input_features"      ,				snake_case__ )
								__lowerCAmelCase         =							kwargs.pop("labels"      ,				snake_case__ )
								if len(snake_case__ ) > 0:
									__lowerCAmelCase         =							args[0]
									__lowerCAmelCase         =							args[1:]

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

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									__lowerCAmelCase         =							labels["input_ids"]
									return input_features







							def       UpperCAmelCase__					(						self  :  str      ,				*snake_case__  :  Any      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				*snake_case__  :  List[Any]      ,				**snake_case__  :  int ):
								"""simple docstring"""







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







							@contextmanager
							def       UpperCAmelCase__					(						self  :  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








import fire

from utils import calculate_rouge, save_json






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]=None			,							**UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()]
	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()][: len(UpperCamelCase  )]
	__lowerCAmelCase         =							calculate_rouge(UpperCamelCase			,							UpperCamelCase			,							**UpperCamelCase  )
	if save_path is not None:
		save_json(UpperCamelCase			,							UpperCamelCase			,							indent=UpperCamelCase  )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)






