



import math
import sys

import cva
import numpy as np






def      __lowercase       (			a__							,       a__       )    ->			np.ndarray:
							# For applying gaussian function for each element in matrix.
							__SCREAMING_SNAKE_CASE									= math.sqrt(a__       )
							__SCREAMING_SNAKE_CASE									= 1 / (sigma * math.sqrt(2 * math.pi       ))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			np.ndarray:
							__SCREAMING_SNAKE_CASE									= kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]






def      __lowercase       (			a__							,       a__       )    ->			np.ndarray:
							# Creates a gaussian kernel of given dimension.
							__SCREAMING_SNAKE_CASE									= np.zeros((kernel_size, kernel_size)       )
							for i in range(0							,       a__       ):
														for j in range(0							,       a__       ):
																					__SCREAMING_SNAKE_CASE									= math.sqrt(
																					    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
							return vec_gaussian(a__							,       a__       )






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       )    ->			np.ndarray:
							__SCREAMING_SNAKE_CASE									= np.zeros(img.shape       )
							__SCREAMING_SNAKE_CASE									= get_gauss_kernel(a__							,       a__       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= img.shape
							for i in range(kernel_size // 2							,       size_x - kernel_size // 2       ):
														for j in range(kernel_size // 2							,       size_y - kernel_size // 2       ):
																					__SCREAMING_SNAKE_CASE									= get_slice(a__							,       a__							,       a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__SCREAMING_SNAKE_CASE									= vec_gaussian(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.multiply(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.multiply(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.sum(a__       ) / np.sum(a__       )
																					__SCREAMING_SNAKE_CASE									= val
							return imga






def      __lowercase       (			a__       )    ->			tuple:
							__SCREAMING_SNAKE_CASE									= args[1] if args[1:] else '../image_data/lena.jpg'
							__SCREAMING_SNAKE_CASE									= float(args[2]       ) if args[2:] else 1.0
							__SCREAMING_SNAKE_CASE									= float(args[3]       ) if args[3:] else 1.0
							if args[4:]:
														__SCREAMING_SNAKE_CASE									= int(args[4]       )
														__SCREAMING_SNAKE_CASE									= kernel_size + abs(kernel_size % 2 - 1       )
							else:
														__SCREAMING_SNAKE_CASE									= 5
							return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
			lowerCAmelCase__						,					lowerCAmelCase__						,					lowerCAmelCase__						,					lowerCAmelCase__		:					str							=parse_args(sys.argv)
			lowerCAmelCase__		:					List[str]							=cva.imread(filename, 0)
			cva.imshow('''input image''', img)

			lowerCAmelCase__		:					Union[str, Any]							=img / 255
			lowerCAmelCase__		:					Tuple							=out.astype('''float32''')
			lowerCAmelCase__		:					Any							=bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
			lowerCAmelCase__		:					int							=out * 255
			lowerCAmelCase__		:					Any							=np.uinta(out)
			cva.imshow('''output image''', out)
			cva.waitKey(0)
			cva.destroyAllWindows()











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






