




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	List[Any]								=k_size // 2
				__magic_name__				, __magic_name__	:	Tuple								=mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				__magic_name__	:	Optional[Any]								=1 / (2 * pi * sigma) * exp(-(square(lowerCamelCase   ) + square(lowerCamelCase   )) / (2 * square(lowerCamelCase   ))   )
				return g




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__				, __magic_name__	:	Optional[int]								=image.shape[0], image.shape[1]
				# dst image height and width
				__magic_name__	:	Optional[int]								=height - k_size + 1
				__magic_name__	:	Optional[Any]								=width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				__magic_name__	:	Dict								=zeros((dst_height * dst_width, k_size * k_size)   )
				__magic_name__	:	str								=0
				for i, j in product(range(lowerCamelCase   )	,     range(lowerCamelCase   )   ):
								__magic_name__	:	List[Any]								=ravel(image[i : i + k_size, j : j + k_size]   )
								__magic_name__	:	int								=window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				__magic_name__	:	Union[str, Any]								=gen_gaussian_kernel(lowerCamelCase	,     lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=ravel(lowerCamelCase   )

				# reshape and get the dst image
				__magic_name__	:	List[str]								=dot(lowerCamelCase	,     lowerCamelCase   ).reshape(lowerCamelCase	,     lowerCamelCase   ).astype(lowerCamelCase   )

				return dst


if __name__ == "__main__":
							# read original image
							UpperCAmelCase_		:				Optional[int]									=     imread(R"../image_data/lena.jpg")
							# turn image in gray scale value
							UpperCAmelCase_		:				Dict									=     cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							UpperCAmelCase_		:				Dict									=     gaussian_filter(gray, 3, sigma=1)
							UpperCAmelCase_		:				str									=     gaussian_filter(gray, 5, sigma=0.8)

							# show result images
							imshow("gaussian filter with 3x3 mask", gaussianaxa)
							imshow("gaussian filter with 5x5 mask", gaussianaxa)
							waitKey()










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=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:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

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


						@contextmanager
						def     A__						(		self   :int ):
										'''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.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




