import os

lowerCamelCase_    :		Optional[int]   						=	{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       0

					__a		 =       0
					while index < len(__lowerCamelCase						) - 1:
										__a		 =       SYMBOLS[numerals[index]]
										__a		 =       SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       """"""

					__a		 =       num // 1000
					numerals += m_count * "M"
					num %= 1000

					__a		 =       num // 100
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 100

					__a		 =       num // 10
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 10

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals
def 							lowerCAmelCase(     __lowerCamelCase = "/p089_roman.txt"						):
					__a		 =       0

					with open(os.path.dirname(__lowerCamelCase						) + roman_numerals_filename						) as filea:
										__a		 =       filea.readlines()

					for line in lines:
										__a		 =       line.strip()
										__a		 =       parse_roman_numerals(__lowerCamelCase						)
										__a		 =       generate_roman_numerals(__lowerCamelCase						)
										savings += len(__lowerCamelCase						) - len(__lowerCamelCase						)

					return savings


if __name__ == "__main__":
	print(F'''{solution() = }''')





import warnings
from contextlib import contextmanager

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


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								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: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase				,						**UpperCAmelCase   )

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

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



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

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

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

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



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								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.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




