





import numpy as np
from PIL import Image





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    np.array:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
			return 0.2989 * r + 0.5870 * g + 0.1140 * b





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    np.array:







			'''simple docstring'''

			return (gray > 127) & (gray <= 255)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    np.array:







			'''simple docstring'''

			UpperCAmelCase       		=    np.zeros_like(UpperCamelCase__			)
			UpperCAmelCase       		=    np.zeros(
			    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

			# Copy image to padded image
			UpperCAmelCase       		=    image

			# Iterate over image & apply kernel
			for x in range(image.shape[1]			):
						for y in range(image.shape[0]			):
									UpperCAmelCase       		=    (
									    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
									).sum()
									UpperCAmelCase       		=    int(summation == 5			)
			return output


# kernel to be applied
__A    :   Any          =       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
				# read original image
				__A    :   Tuple          =       np.array(Image.open(R"..\image_data\lena.jpg"))
				# Apply erosion operation to a binary image
				__A    :   str          =       erosion(grayabinary(rgbagray(image)), structuring_element)
				# Save the output image
				__A    :   Optional[int]          =       Image.fromarray(output).convert("RGB")
				pil_img.save("result_erosion.png")










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__A    :   Optional[int]          =       logging.get_logger(__name__)

class      A_	(a_      ):
	UpperCAmelCase__ =      '''speech-encoder-decoder'''
	UpperCAmelCase__ =      True
	def __init__(   self							, **_A		):






				'''simple docstring'''







				super().__init__(**_A		)
				if "encoder" not in kwargs or "decoder" not in kwargs:
							raise ValueError(
							    F"""A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"""
							    F""" `decoder` sub-configurations are passed, but only {kwargs}"""		)

				UpperCAmelCase       		=    kwargs.pop('''encoder'''		)
				UpperCAmelCase       		=    encoder_config.pop('''model_type'''		)
				UpperCAmelCase       		=    kwargs.pop('''decoder'''		)
				UpperCAmelCase       		=    decoder_config.pop('''model_type'''		)

				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    AutoConfig.for_model(_A							, **_A		)
				UpperCAmelCase       		=    True
	@classmethod
	def 					_lowercase    (   cls							, _A							, _A							, **_A		):






				'''simple docstring'''







				logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
				UpperCAmelCase       		=    True
				UpperCAmelCase       		=    True

				return cls(encoder=encoder_config.to_dict()							, decoder=decoder_config.to_dict()							, **_A		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    copy.deepcopy(self.__dict__		)
				UpperCAmelCase       		=    self.encoder.to_dict()
				UpperCAmelCase       		=    self.decoder.to_dict()
				UpperCAmelCase       		=    self.__class__.model_type
				return output



