



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
			# Create universe of discourse in Python using linspace ()
			lowerCAmelCase__		:					Optional[int]							=np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

			# Create two fuzzy sets by defining any membership function
			# (trapmf(), gbellmf(), gaussmf(), etc).
			lowerCAmelCase__		:					Optional[int]							=[0, 25, 50]
			lowerCAmelCase__		:					str							=[25, 50, 75]
			lowerCAmelCase__		:					List[Any]							=fuzz.membership.trimf(X, abca)
			lowerCAmelCase__		:					int							=fuzz.membership.trimf(X, abca)

			# Compute the different operations using inbuilt functions.
			lowerCAmelCase__		:					str							=np.ones(75)
			lowerCAmelCase__		:					List[Any]							=np.zeros((75,))
			# 1. Union = max(µA(x), µB(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_or(X, young, X, middle_aged)[1]
			# 2. Intersection = min(µA(x), µB(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_and(X, young, X, middle_aged)[1]
			# 3. Complement (A) = (1- min(µA(x))
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_not(young)
			# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
			lowerCAmelCase__		:					str							=fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
			# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
			lowerCAmelCase__		:					Any							=young + middle_aged - (young * middle_aged)
			# 6. Algebraic Product = (µA(x) * µB(x))
			lowerCAmelCase__		:					Any							=young * middle_aged
			# 7. Bounded Sum = min[1,(µA(x), µB(x))]
			lowerCAmelCase__		:					Union[str, Any]							=fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
			# 8. Bounded difference = min[0,(µA(x), µB(x))]
			lowerCAmelCase__		:					Any							=fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

			# max-min composition
			# max-product composition

			# Plot each set A, set B and each operation result using plot() and subplot().
			from matplotlib import pyplot as plt

			plt.figure()

			plt.subplot(4, 3, 1)
			plt.plot(X, young)
			plt.title('''Young''')
			plt.grid(True)

			plt.subplot(4, 3, 2)
			plt.plot(X, middle_aged)
			plt.title('''Middle aged''')
			plt.grid(True)

			plt.subplot(4, 3, 3)
			plt.plot(X, union)
			plt.title('''union''')
			plt.grid(True)

			plt.subplot(4, 3, 4)
			plt.plot(X, intersection)
			plt.title('''intersection''')
			plt.grid(True)

			plt.subplot(4, 3, 5)
			plt.plot(X, complement_a)
			plt.title('''complement_a''')
			plt.grid(True)

			plt.subplot(4, 3, 6)
			plt.plot(X, difference)
			plt.title('''difference a/b''')
			plt.grid(True)

			plt.subplot(4, 3, 7)
			plt.plot(X, alg_sum)
			plt.title('''alg_sum''')
			plt.grid(True)

			plt.subplot(4, 3, 8)
			plt.plot(X, alg_product)
			plt.title('''alg_product''')
			plt.grid(True)

			plt.subplot(4, 3, 9)
			plt.plot(X, bdd_sum)
			plt.title('''bdd_sum''')
			plt.grid(True)

			plt.subplot(4, 3, 10)
			plt.plot(X, bdd_difference)
			plt.title('''bdd_difference''')
			plt.grid(True)

			plt.subplots_adjust(hspace=0.5)
			plt.show()










import warnings
from contextlib import contextmanager

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

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Union[str, Any]      =  '''Wav2Vec2FeatureExtractor'''
						UpperCamelCase__      :			Union[str, Any]      =  '''AutoTokenizer'''






						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													super().__init__(_A				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.feature_extractor
													__SCREAMING_SNAKE_CASE									= False






						@classmethod
						def 	_A     (   cls				,							_A				,							**_A		):







													'''simple docstring'''

													try:
																				return super().from_pretrained(_A				,							**_A		)
													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: '				,							_A				,							)

																				__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(_A				,							**_A		)
																				__SCREAMING_SNAKE_CASE									= WavaVecaCTCTokenizer.from_pretrained(_A				,							**_A		)

																				return cls(feature_extractor=_A				,							tokenizer=_A		)






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







													'''simple docstring'''

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

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

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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

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

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

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						@contextmanager
						def 	_A     (   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.'		)
													__SCREAMING_SNAKE_CASE									= True
													__SCREAMING_SNAKE_CASE									= self.tokenizer
													yield
													__SCREAMING_SNAKE_CASE									= self.feature_extractor
													__SCREAMING_SNAKE_CASE									= False






