

import warnings
from contextlib import contextmanager

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


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """Wav2Vec2FeatureExtractor"""
			__A						  =  """AutoTokenizer"""


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				super().__init__(__UpperCamelCase ,				__UpperCamelCase							)
				snake_case_      		= self.feature_extractor
				snake_case_      		= False


			@classmethod
			def 			__lowerCAmelCase (    cls ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				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 ,				)

					snake_case_      		= WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase ,				**__UpperCamelCase							)
					snake_case_      		= WavaVecaCTCTokenizer.from_pretrained(__UpperCamelCase ,				**__UpperCamelCase							)

					return cls(feature_extractor=__UpperCamelCase ,				tokenizer=__UpperCamelCase							)


			def __call__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				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.'							)
					snake_case_      		= kwargs.pop('raw_speech'							)
				else:
					snake_case_      		= kwargs.pop('audio' ,				__UpperCamelCase							)
				snake_case_      		= kwargs.pop('sampling_rate' ,				__UpperCamelCase							)
				snake_case_      		= kwargs.pop('text' ,				__UpperCamelCase							)
				if len(__UpperCamelCase							) > 0:
					snake_case_      		= args[0]
					snake_case_      		= 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:
					snake_case_      		= self.feature_extractor(__UpperCamelCase ,				*__UpperCamelCase ,				sampling_rate=__UpperCamelCase ,				**__UpperCamelCase							)
				if text is not None:
					snake_case_      		= self.tokenizer(__UpperCamelCase ,				**__UpperCamelCase							)

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


			def 			__lowerCAmelCase (    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


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

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

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

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


			def 			__lowerCAmelCase (    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


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







			@contextmanager
			def 			__lowerCAmelCase (    self							):

				"""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.'							)
				snake_case_      		= True
				snake_case_      		= self.tokenizer
				yield
				snake_case_      		= self.feature_extractor
				snake_case_      		= False



import operator as op





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= lambda lowercase__			, lowercase__	: int(x / y	)  # noqa: E731 integer division operation
	snake_case_      		= {
	    '^': op.pow,
	    '*': op.mul,
	    '/': div,
	    '+': op.add,
	    '-': op.sub,
	}  # operators & their respective operation

	# print table header
	print('Symbol'.center(8	)			, 'Action'.center(12	)			, 'Stack'			, sep=' | '	)
	print('-' * (30 + len(lowercase__	))	)

	for x in post_fix:
		if x.isdigit():  # if x in digit
			stack.append(lowercase__	)  # append x to stack
			# output in tabular format
			print(x.rjust(8	)			, ('push(' + x + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)
		else:
			snake_case_      		= stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8	)			, ('pop(' + b + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)

			snake_case_      		= stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8	)			, ('pop(' + a + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)

			stack.append(
			    str(opr[x](int(lowercase__	)			, int(lowercase__	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
			# output in tabular format
			print(
			    x.rjust(8	)			, ('push(' + a + x + b + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '			, )

	return int(stack[0]	)


if __name__ == "__main__":
		A											=     input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
		print('\n\tResult = ', solve(Postfix))
