






'''simple docstring'''
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(  UpperCamelCase__				:						Tuple							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				List[str]									=SwinaSRConfig()

						if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=4
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				int									=4
												SCREAMING_SNAKE_CASE__     :				Any									=4_8
												SCREAMING_SNAKE_CASE__     :				List[Any]									='''pixelshuffle_aux'''
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=[6, 6, 6, 6]
												SCREAMING_SNAKE_CASE__     :				Tuple									=6_0
												SCREAMING_SNAKE_CASE__     :				List[Any]									=[6, 6, 6, 6]
												SCREAMING_SNAKE_CASE__     :				Tuple									='''pixelshuffledirect'''
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=4
												SCREAMING_SNAKE_CASE__     :				Any									='''nearest+conv'''
						elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=1
												SCREAMING_SNAKE_CASE__     :				int									=1
												SCREAMING_SNAKE_CASE__     :				Any									=1_2_6
												SCREAMING_SNAKE_CASE__     :				Dict									=7
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=2_5_5.0
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=''''''

						return config





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Dict							):




						'''simple docstring'''






						if "patch_embed.proj" in name and "layers" not in name:
												SCREAMING_SNAKE_CASE__     :				str									=name.replace('''patch_embed.proj''',	'''embeddings.patch_embeddings.projection'''							)
						if "patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE__     :				List[Any]									=name.replace('''patch_embed.norm''',	'''embeddings.patch_embeddings.layernorm'''							)
						if "layers" in name:
												SCREAMING_SNAKE_CASE__     :				List[str]									=name.replace('''layers''',	'''encoder.stages'''							)
						if "residual_group.blocks" in name:
												SCREAMING_SNAKE_CASE__     :				List[Any]									=name.replace('''residual_group.blocks''',	'''layers'''							)
						if "attn.proj" in name:
												SCREAMING_SNAKE_CASE__     :				List[Any]									=name.replace('''attn.proj''',	'''attention.output.dense'''							)
						if "attn" in name:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=name.replace('''attn''',	'''attention.self'''							)
						if "norm1" in name:
												SCREAMING_SNAKE_CASE__     :				Any									=name.replace('''norm1''',	'''layernorm_before'''							)
						if "norm2" in name:
												SCREAMING_SNAKE_CASE__     :				str									=name.replace('''norm2''',	'''layernorm_after'''							)
						if "mlp.fc1" in name:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=name.replace('''mlp.fc1''',	'''intermediate.dense'''							)
						if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=name.replace('''mlp.fc2''',	'''output.dense'''							)
						if "q_bias" in name:
												SCREAMING_SNAKE_CASE__     :				List[Any]									=name.replace('''q_bias''',	'''query.bias'''							)
						if "k_bias" in name:
												SCREAMING_SNAKE_CASE__     :				List[str]									=name.replace('''k_bias''',	'''key.bias'''							)
						if "v_bias" in name:
												SCREAMING_SNAKE_CASE__     :				List[str]									=name.replace('''v_bias''',	'''value.bias'''							)
						if "cpb_mlp" in name:
												SCREAMING_SNAKE_CASE__     :				int									=name.replace('''cpb_mlp''',	'''continuous_position_bias_mlp'''							)
						if "patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE__     :				Tuple									=name.replace('''patch_embed.proj''',	'''patch_embed.projection'''							)

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''layernorm.weight'''
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE__     :				Optional[int]									='''layernorm.bias'''

						if "conv_first" in name:
												SCREAMING_SNAKE_CASE__     :				str									=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:
																		SCREAMING_SNAKE_CASE__     :				Any									=name.replace('''conv_last''',	'''final_convolution'''							)
												if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																		if "conv_before_upsample.0" in name:
																								SCREAMING_SNAKE_CASE__     :				str									=name.replace('''conv_before_upsample.0''',	'''conv_before_upsample'''							)
																		if "upsample.0" in name:
																								SCREAMING_SNAKE_CASE__     :				str									=name.replace('''upsample.0''',	'''upsample.convolution_0'''							)
																		if "upsample.2" in name:
																								SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=name.replace('''upsample.2''',	'''upsample.convolution_1'''							)
																		SCREAMING_SNAKE_CASE__     :				Tuple									='''upsample.''' + name
												elif config.upsampler == "pixelshuffledirect":
																		SCREAMING_SNAKE_CASE__     :				int									=name.replace('''upsample.0.weight''',	'''upsample.conv.weight'''							)
																		SCREAMING_SNAKE_CASE__     :				str									=name.replace('''upsample.0.bias''',	'''upsample.conv.bias'''							)
												else:
																		pass
						else:
												SCREAMING_SNAKE_CASE__     :				int									='''swin2sr.''' + name

						return name





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Tuple							):




						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE__     :				List[Any]									=orig_state_dict.pop(UpperCamelCase__							)

												if "qkv" in key:
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=key.split('''.'''							)
																		SCREAMING_SNAKE_CASE__     :				List[Any]									=int(key_split[1]							)
																		SCREAMING_SNAKE_CASE__     :				Dict									=int(key_split[4]							)
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=config.embed_dim

																		if "weight" in key:
																								SCREAMING_SNAKE_CASE__     :				List[Any]									=val[:dim, :]
																								SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=val[dim : dim * 2, :]
																								SCREAMING_SNAKE_CASE__     :				Tuple									=val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE__     :				str									=val[:dim]
																								SCREAMING_SNAKE_CASE__     :				List[Any]									=val[dim : dim * 2]
																								SCREAMING_SNAKE_CASE__     :				Dict									=val[-dim:]
																		pass
												else:
																		SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=val

						return orig_state_dict





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[Any]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=get_config(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				Dict									=SwinaSRForImageSuperResolution(UpperCamelCase__							)
						model.eval()

						SCREAMING_SNAKE_CASE__     :				List[str]									=torch.hub.load_state_dict_from_url(UpperCamelCase__,	map_location='''cpu'''							)
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=convert_state_dict(UpperCamelCase__,	UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				List[Any]									=model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)

						if len(UpperCamelCase__							) > 0:
												raise ValueError('''Missing keys when converting: {}'''.format(UpperCamelCase__							)							)
						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
						SCREAMING_SNAKE_CASE__     :				Any									='''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Image.open(requests.get(UpperCamelCase__,	stream=UpperCamelCase__							).raw							).convert('''RGB'''							)
						SCREAMING_SNAKE_CASE__     :				List[Any]									=SwinaSRImageProcessor()
						# pixel_values = processor(image, return_tensors="pt").pixel_values

						SCREAMING_SNAKE_CASE__     :				List[Any]									=1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
						SCREAMING_SNAKE_CASE__     :				str									=Compose(
						    [
						        Resize((image_size, image_size)							),
						        ToTensor(),
						        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6],	std=[0.2_2_9, 0.2_2_4, 0.2_2_5]							),
						    ]							)
						SCREAMING_SNAKE_CASE__     :				int									=transforms(UpperCamelCase__							).unsqueeze(0							)

						if config.num_channels == 1:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pixel_values[:, 0, :, :].unsqueeze(1							)

						SCREAMING_SNAKE_CASE__     :				List[str]									=model(UpperCamelCase__							)

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				int									=torch.Size([1, 3, 5_1_2, 5_1_2]							)
												SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor(
												    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]							)
						elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Any									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
												SCREAMING_SNAKE_CASE__     :				Dict									=torch.tensor(
												    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]							)
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												# TODO values didn't match exactly here
												SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor(
												    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]							)
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.Size([1, 3, 5_1_2, 5_1_2]							)
												SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor(
												    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]							)
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
												SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.tensor(
												    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]							)

						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],	UpperCamelCase__,	atol=1e-3							)
						print('''Looks ok!'''							)

						SCREAMING_SNAKE_CASE__     :				List[str]									={
						    '''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'''
						    ),
						}
						SCREAMING_SNAKE_CASE__     :				int									=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(UpperCamelCase__							)
												print(f"Saving image processor to {pytorch_dump_folder_path}"							)
												processor.save_pretrained(UpperCamelCase__							)

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


if __name__ == "__main__":
		a_					= 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_					= parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=inspect.getfile(accelerate.test_utils      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
       SCREAMING_SNAKE_CASE__     :				Dict									=os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




 @require_tpu
 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Any									=F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
       SCREAMING_SNAKE_CASE__     :				List[str]									=[sys.executable] + distributed_args
       execute_subprocess_async(__lowercase					,      env=os.environ.copy()      )