
"""simple docstring"""





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


UpperCamelCase_					:						List[Any]				= logging.get_logger(__name__)

UpperCamelCase_					:						Union[str, Any]				= {
    '''caidas/swin2sr-classicalsr-x2-64''': (
        '''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
    ),
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				"swin2sr"

			snake_case	      =				{
			    "hidden_size": "embed_dim",
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}




			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					Optional[int]=64       ,   _snake_case						:					Tuple=1       ,   _snake_case						:					str=3       ,   _snake_case						:					Optional[int]=180       ,   _snake_case						:					Dict=[6, 6, 6, 6, 6, 6]       ,   _snake_case						:					List[str]=[6, 6, 6, 6, 6, 6]       ,   _snake_case						:					List[str]=8       ,   _snake_case						:					Optional[Any]=2.0       ,   _snake_case						:					Optional[int]=True       ,   _snake_case						:					Optional[int]=0.0       ,   _snake_case						:					Dict=0.0       ,   _snake_case						:					Tuple=0.1       ,   _snake_case						:					Dict="gelu"       ,   _snake_case						:					Tuple=False       ,   _snake_case						:					Tuple=0.0_2       ,   _snake_case						:					List[Any]=1e-5       ,   _snake_case						:					List[Any]=2       ,   _snake_case						:					Tuple=1.0       ,   _snake_case						:					Any="1conv"       ,   _snake_case						:					Union[str, Any]="pixelshuffle"       ,   **_snake_case						:					int       ,   )      -> Optional[int]:







								"""simple docstring"""
								super().__init__(**_snake_case	)

								A_				 =						image_size
								A_				 =						patch_size
								A_				 =						num_channels
								A_				 =						embed_dim
								A_				 =						depths
								A_				 =						len(_snake_case	)
								A_				 =						num_heads
								A_				 =						window_size
								A_				 =						mlp_ratio
								A_				 =						qkv_bias
								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						drop_path_rate
								A_				 =						hidden_act
								A_				 =						use_absolute_embeddings
								A_				 =						layer_norm_eps
								A_				 =						initializer_range
								A_				 =						upscale
								A_				 =						img_range
								A_				 =						resi_connection
								A_				 =						upsampler






"""simple docstring"""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						len(__a				)
					A_				 =						[[0] * n for i in range(__a				)]
					for i in range(__a				):
										A_				 =						y_points[i]

					for i in range(2				,   __a				):
										for j in range(__a				,   __a				):
															A_				 =						(
															    (xa - x_points[j - i + 1]) * q[j][i - 1]
															    - (xa - x_points[j]) * q[j - 1][i - 1]
															) / (x_points[j] - x_points[j - i + 1])

					return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




