

'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''pegasus_x'''
				snake_case_           =   ['''past_key_values''']
				snake_case_           =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

				def __init__(					self,	lowercase_=96103,	lowercase_=16384,	lowercase_=16,	lowercase_=4096,	lowercase_=16,	lowercase_=16,	lowercase_=4096,	lowercase_=16,	lowercase_=0.0,	lowercase_=0.0,	lowercase_=True,	lowercase_=True,	lowercase_="gelu",	lowercase_=1024,	lowercase_=0.1,	lowercase_=0.0,	lowercase_=0.0,	lowercase_=0.02,	lowercase_=0,	lowercase_=True,	lowercase_=0,	lowercase_=1,	lowercase_=1,	lowercase_=32,	lowercase_=512,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					snake_case							 =				vocab_size
					snake_case							 =				max_position_embeddings
					snake_case							 =				d_model
					snake_case							 =				encoder_ffn_dim
					snake_case							 =				encoder_layers
					snake_case							 =				encoder_attention_heads
					snake_case							 =				decoder_ffn_dim
					snake_case							 =				decoder_layers
					snake_case							 =				decoder_attention_heads
					snake_case							 =				dropout
					snake_case							 =				attention_dropout
					snake_case							 =				activation_dropout
					snake_case							 =				activation_function
					snake_case							 =				init_std
					snake_case							 =				encoder_layerdrop
					snake_case							 =				decoder_layerdrop
					snake_case							 =				use_cache
					snake_case							 =				encoder_layers
					snake_case							 =				scale_embedding  # scale factor will be sqrt(d_model) if True

					snake_case							 =				num_global_tokens
					snake_case							 =				block_size
					snake_case							 =				stagger_local_blocks

					super().__init__(
					    pad_token_id=lowercase_,	eos_token_id=lowercase_,	is_encoder_decoder=lowercase_,	decoder_start_token_id=lowercase_,	forced_eos_token_id=lowercase_,	**lowercase_,	)

				@property
				def 				_lowerCamelCase						(					self   )						->       int:
					return self.encoder_attention_heads






				@property
				def 				_lowerCamelCase						(					self   )						->       int:
					return self.d_model








'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey

def     __magic_name__       ( A					,   A							)			->		Union[str, Any]:
	snake_case , snake_case							 =				img.shape[0], img.shape[1]

	def to_grayscale(A					,   A					,   A							):
		return 0.2_126 * red + 0.587 * green + 0.114 * blue

	def normalize(A							):
		return min(A					,   2_5_5							)

	for i in range(A							):
		for j in range(A							):
			snake_case							 =				int(to_grayscale(*img[i][j]							)							)
			snake_case							 =				[
			    normalize(A							),
			    normalize(greyscale + factor							),
			    normalize(greyscale + 2 * factor							),
			]

	return img


if __name__ == "__main__":
						# read original image
						lowerCAmelCase_						   =			{
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f"Original image with sepia (factor: {percentage})", img)

						waitKey(0)
						destroyAllWindows()





