






"""simple docstring"""

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


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''graphormer'''
				UpperCAmelCase__										=					['''past_key_values''']







				def __init__(				self				:    Optional[int]    ,     __lowerCamelCase				:    int = 1    ,     __lowerCamelCase				:    int = 5_1_2 * 9    ,     __lowerCamelCase				:    int = 5_1_2 * 3    ,     __lowerCamelCase				:    int = 5_1_2    ,     __lowerCamelCase				:    int = 5_1_2    ,     __lowerCamelCase				:    int = 5_1_2    ,     __lowerCamelCase				:    int = 1_2_8    ,     __lowerCamelCase				:    int = 5    ,     __lowerCamelCase				:    int = 1_0_2_4    ,     __lowerCamelCase				:    str = "multi_hop"    ,     __lowerCamelCase				:    int = 5_1_2    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    int = 1_2    ,     __lowerCamelCase				:    int = 7_6_8    ,     __lowerCamelCase				:    int = 7_6_8    ,     __lowerCamelCase				:    int = 3_2    ,     __lowerCamelCase				:    float = 0.1    ,     __lowerCamelCase				:    float = 0.1    ,     __lowerCamelCase				:    float = 0.0    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    str = "gelu"    ,     __lowerCamelCase				:    float = None    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    int = 0    ,     __lowerCamelCase				:    bool = False    ,     __lowerCamelCase				:    float = 0.0    ,     __lowerCamelCase				:    int = 8    ,     __lowerCamelCase				:    int = None    ,     __lowerCamelCase				:    int = None    ,     __lowerCamelCase				:    bool = True    ,     __lowerCamelCase				:    bool = True    ,     __lowerCamelCase				:    List[Any]=0    ,     __lowerCamelCase				:    Optional[int]=1    ,     __lowerCamelCase				:    Tuple=2    ,     **__lowerCamelCase				:    List[str]    ,     )					->       Optional[Any]:
										__magic_name__      =  num_classes
										__magic_name__      =  num_atoms
										__magic_name__      =  num_in_degree
										__magic_name__      =  num_out_degree
										__magic_name__      =  num_edges
										__magic_name__      =  num_spatial
										__magic_name__      =  num_edge_dis
										__magic_name__      =  edge_type
										__magic_name__      =  multi_hop_max_dist
										__magic_name__      =  spatial_pos_max
										__magic_name__      =  max_nodes
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  embedding_dim
										__magic_name__      =  embedding_dim
										__magic_name__      =  ffn_embedding_dim
										__magic_name__      =  num_attention_heads
										__magic_name__      =  dropout
										__magic_name__      =  attention_dropout
										__magic_name__      =  layerdrop
										__magic_name__      =  encoder_normalize_before
										__magic_name__      =  pre_layernorm
										__magic_name__      =  apply_graphormer_init
										__magic_name__      =  activation_fn
										__magic_name__      =  embed_scale
										__magic_name__      =  freeze_embeddings
										__magic_name__      =  num_trans_layers_to_freeze
										__magic_name__      =  share_input_output_embed
										__magic_name__      =  traceable
										__magic_name__      =  q_noise
										__magic_name__      =  qn_block_size

										# These parameters are here for future extensions
										# atm, the model only supports self attention
										__magic_name__      =  kdim
										__magic_name__      =  vdim
										__magic_name__      =  self_attention
										__magic_name__      =  bias

										super().__init__(
										    pad_token_id=__lowerCamelCase    ,     bos_token_id=__lowerCamelCase    ,     eos_token_id=__lowerCamelCase    ,     **__lowerCamelCase    ,     )











"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

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

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			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()



