



from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       )    ->			list[float]:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= coefficient_matrix.shape
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= constant_matrix.shape

							if rowsa != colsa:
														__SCREAMING_SNAKE_CASE									= f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(a__       )

							if colsa != 1:
														__SCREAMING_SNAKE_CASE									= f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(a__       )

							if rowsa != rowsa:
														__SCREAMING_SNAKE_CASE									= (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(a__       )

							if len(a__       ) != rowsa:
														__SCREAMING_SNAKE_CASE									= (
														    'Number of initial values must be equal to number of rows in coefficient '
														    f"""matrix but received {len(a__       )} and {rowsa}"""
														)
														raise ValueError(a__       )

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'       )

							__SCREAMING_SNAKE_CASE									= np.concatenate(
							    (coefficient_matrix, constant_matrix)							,       axis=1       )

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= table.shape

							strictly_diagonally_dominant(a__       )

							# Iterates the whole matrix for given number of times
							for _ in range(a__       ):
														__SCREAMING_SNAKE_CASE									= []
														for row in range(a__       ):
																					__SCREAMING_SNAKE_CASE									= 0
																					for col in range(a__       ):
																												if col == row:
																																			__SCREAMING_SNAKE_CASE									= table[row][col]
																												elif col == cols - 1:
																																			__SCREAMING_SNAKE_CASE									= table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					__SCREAMING_SNAKE_CASE									= (temp + val) / denom
																					new_val.append(a__       )
														__SCREAMING_SNAKE_CASE									= new_val

							return [float(a__       ) for i in new_val]






def      __lowercase       (			a__       )    ->			bool:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= table.shape

							__SCREAMING_SNAKE_CASE									= True

							for i in range(0							,       a__       ):
														__SCREAMING_SNAKE_CASE									= 0
														for j in range(0							,       cols - 1       ):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'       )

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
			import doctest

			doctest.testmod()










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






