






import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								lowercase__    =			4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								lowercase__    =			4
								lowercase__    =			48
								lowercase__    =			'''pixelshuffle_aux'''
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								lowercase__    =			[6, 6, 6, 6]
								lowercase__    =			60
								lowercase__    =			[6, 6, 6, 6]
								lowercase__    =			'''pixelshuffledirect'''
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								lowercase__    =			4
								lowercase__    =			'''nearest+conv'''
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								lowercase__    =			1
								lowercase__    =			1
								lowercase__    =			126
								lowercase__    =			7
								lowercase__    =			2_55.0
								lowercase__    =			''''''

				return config






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				if "patch_embed.proj" in name and "layers" not in name:
								lowercase__    =			name.replace('''patch_embed.proj'''		,   '''embeddings.patch_embeddings.projection'''				)
				if "patch_embed.norm" in name:
								lowercase__    =			name.replace('''patch_embed.norm'''		,   '''embeddings.patch_embeddings.layernorm'''				)
				if "layers" in name:
								lowercase__    =			name.replace('''layers'''		,   '''encoder.stages'''				)
				if "residual_group.blocks" in name:
								lowercase__    =			name.replace('''residual_group.blocks'''		,   '''layers'''				)
				if "attn.proj" in name:
								lowercase__    =			name.replace('''attn.proj'''		,   '''attention.output.dense'''				)
				if "attn" in name:
								lowercase__    =			name.replace('''attn'''		,   '''attention.self'''				)
				if "norm1" in name:
								lowercase__    =			name.replace('''norm1'''		,   '''layernorm_before'''				)
				if "norm2" in name:
								lowercase__    =			name.replace('''norm2'''		,   '''layernorm_after'''				)
				if "mlp.fc1" in name:
								lowercase__    =			name.replace('''mlp.fc1'''		,   '''intermediate.dense'''				)
				if "mlp.fc2" in name:
								lowercase__    =			name.replace('''mlp.fc2'''		,   '''output.dense'''				)
				if "q_bias" in name:
								lowercase__    =			name.replace('''q_bias'''		,   '''query.bias'''				)
				if "k_bias" in name:
								lowercase__    =			name.replace('''k_bias'''		,   '''key.bias'''				)
				if "v_bias" in name:
								lowercase__    =			name.replace('''v_bias'''		,   '''value.bias'''				)
				if "cpb_mlp" in name:
								lowercase__    =			name.replace('''cpb_mlp'''		,   '''continuous_position_bias_mlp'''				)
				if "patch_embed.proj" in name:
								lowercase__    =			name.replace('''patch_embed.proj'''		,   '''patch_embed.projection'''				)

				if name == "norm.weight":
								lowercase__    =			'''layernorm.weight'''
				if name == "norm.bias":
								lowercase__    =			'''layernorm.bias'''

				if "conv_first" in name:
								lowercase__    =			name.replace('''conv_first'''		,   '''first_convolution'''				)

				if (
				    "upsample" in name
				    or "conv_before_upsample" in name
				    or "conv_bicubic" in name
				    or "conv_up" in name
				    or "conv_hr" in name
				    or "conv_last" in name
				    or "aux" in name
				):
								# heads
								if "conv_last" in name:
												lowercase__    =			name.replace('''conv_last'''		,   '''final_convolution'''				)
								if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
												if "conv_before_upsample.0" in name:
																lowercase__    =			name.replace('''conv_before_upsample.0'''		,   '''conv_before_upsample'''				)
												if "upsample.0" in name:
																lowercase__    =			name.replace('''upsample.0'''		,   '''upsample.convolution_0'''				)
												if "upsample.2" in name:
																lowercase__    =			name.replace('''upsample.2'''		,   '''upsample.convolution_1'''				)
												lowercase__    =			'''upsample.''' + name
								elif config.upsampler == "pixelshuffledirect":
												lowercase__    =			name.replace('''upsample.0.weight'''		,   '''upsample.conv.weight'''				)
												lowercase__    =			name.replace('''upsample.0.bias'''		,   '''upsample.conv.bias'''				)
								else:
												pass
				else:
								lowercase__    =			'''swin2sr.''' + name

				return name






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase__    =			orig_state_dict.pop(lowerCamelCase_				)

								if "qkv" in key:
												lowercase__    =			key.split('''.'''				)
												lowercase__    =			int(key_split[1]				)
												lowercase__    =			int(key_split[4]				)
												lowercase__    =			config.embed_dim

												if "weight" in key:
																lowercase__    =			val[:dim, :]
																lowercase__    =			val[dim : dim * 2, :]
																lowercase__    =			val[-dim:, :]
												else:
																lowercase__    =			val[:dim]
																lowercase__    =			val[dim : dim * 2]
																lowercase__    =			val[-dim:]
												pass
								else:
												lowercase__    =			val

				return orig_state_dict






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			get_config(lowerCamelCase_				)
				lowercase__    =			SwinaSRForImageSuperResolution(lowerCamelCase_				)
				model.eval()

				lowercase__    =			torch.hub.load_state_dict_from_url(lowerCamelCase_		,   map_location='''cpu'''				)
				lowercase__    =			convert_state_dict(lowerCamelCase_		,   lowerCamelCase_				)
				lowercase__ ,						lowercase__    =			model.load_state_dict(lowerCamelCase_		,   strict=lowerCamelCase_				)

				if len(lowerCamelCase_				) > 0:
								raise ValueError('''Missing keys when converting: {}'''.format(lowerCamelCase_				)				)
				for key in unexpected_keys:
								if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
												raise ValueError(F"""Unexpected key {key} in state_dict"""				)

    # verify values
				lowercase__    =			'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
				lowercase__    =			Image.open(requests.get(lowerCamelCase_		,   stream=lowerCamelCase_				).raw				).convert('''RGB'''				)
				lowercase__    =			SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				lowercase__    =			126 if '''Jpeg''' in checkpoint_url else 256
				lowercase__    =			Compose(
				    [
				        Resize((image_size, image_size)				),
				        ToTensor(),
				        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]		,   std=[0.2_29, 0.2_24, 0.2_25]				),
				    ]				)
				lowercase__    =			transforms(lowerCamelCase_				).unsqueeze(0				)

				if config.num_channels == 1:
								lowercase__    =			pixel_values[:, 0, :, :].unsqueeze(1				)

				lowercase__    =			model(lowerCamelCase_				)

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								lowercase__    =			torch.Size([1, 3, 512, 512]				)
								lowercase__    =			torch.tensor(
								    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]				)
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								lowercase__    =			torch.Size([1, 3, 1024, 1024]				)
								lowercase__    =			torch.tensor(
								    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]				)
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								lowercase__    =			torch.Size([1, 3, 1024, 1024]				)
								lowercase__    =			torch.tensor(
								    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]				)
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								lowercase__    =			torch.Size([1, 3, 512, 512]				)
								lowercase__    =			torch.tensor(
								    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]				)
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								lowercase__    =			torch.Size([1, 3, 1024, 1024]				)
								lowercase__    =			torch.tensor(
								    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]				)

				assert (
				    outputs.reconstruction.shape == expected_shape
				), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
				assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,   lowerCamelCase_		,   atol=1e-3				)
				print('''Looks ok!'''				)

				lowercase__    =			{
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
				        '''swin2SR-classical-sr-x2-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
				        '''swin2SR-classical-sr-x4-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
				        '''swin2SR-compressed-sr-x4-48'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
				        '''swin2SR-lightweight-x2-64'''
				    ),
				    '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
				        '''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
				    ),
				}
				lowercase__    =			url_to_name[checkpoint_url]

				if pytorch_dump_folder_path is not None:
								print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
								model.save_pretrained(lowerCamelCase_				)
								print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
								processor.save_pretrained(lowerCamelCase_				)

				if push_to_hub:
								model.push_to_hub(F"""caidas/{model_name}"""				)
								processor.push_to_hub(F"""caidas/{model_name}"""				)


if __name__ == "__main__":
							A__	:			Union[str, Any]			   = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
							    type=str,
							    help='URL of the original Swin2SR checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

							A__	:			Any			   = parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












from math import asin, atan, cos, radians, sin, sqrt, tan

A__	:			Optional[int]			   = 637_8137.0
A__	:			List[str]			   = 635_6752.31_4245
A__	:			Union[str, Any]			   = 6_37_81_37






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			(AXIS_A - AXIS_B) / AXIS_A
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			atan((1 - flattening) * tan(radians(lowerCamelCase_				)				)				)
				lowercase__    =			radians(lowerCamelCase_				)
				lowercase__    =			radians(lowerCamelCase_				)
				# Equation
				lowercase__    =			sin((phi_a - phi_a) / 2				)
				lowercase__    =			sin((lambda_a - lambda_a) / 2				)
				# Square both values
				sin_sq_phi *= sin_sq_phi
				sin_sq_lambda *= sin_sq_lambda
				lowercase__    =			sqrt(sin_sq_phi + (cos(lowerCamelCase_				) * cos(lowerCamelCase_				) * sin_sq_lambda)				)
				return 2 * RADIUS * asin(lowerCamelCase_				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




