



'''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			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						int	=     SwinaSRConfig()

			if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						A_	:						List[Any]	=     4
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						A_	:						List[str]	=     4
						A_	:						Any	=     48
						A_	:						Tuple	=     """pixelshuffle_aux"""
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						A_	:						Tuple	=     [6, 6, 6, 6]
						A_	:						int	=     60
						A_	:						Optional[Any]	=     [6, 6, 6, 6]
						A_	:						Optional[int]	=     """pixelshuffledirect"""
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						A_	:						int	=     4
						A_	:						str	=     """nearest+conv"""
			elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
						A_	:						str	=     1
						A_	:						Union[str, Any]	=     1
						A_	:						str	=     1_26
						A_	:						Optional[int]	=     7
						A_	:						List[Any]	=     255.0
						A_	:						Dict	=     """"""

			return config



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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

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

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

			return name



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						A_	:						Any	=     orig_state_dict.pop(lowerCamelCase__    )

						if "qkv" in key:
									A_	:						str	=     key.split("""."""    )
									A_	:						Dict	=     int(key_split[1]    )
									A_	:						List[str]	=     int(key_split[4]    )
									A_	:						Optional[int]	=     config.embed_dim

									if "weight" in key:
												A_	:						List[Any]	=     val[:dim, :]
												A_	:						Optional[int]	=     val[dim : dim * 2, :]
												A_	:						Any	=     val[-dim:, :]
									else:
												A_	:						Dict	=     val[:dim]
												A_	:						int	=     val[dim : dim * 2]
												A_	:						List[Any]	=     val[-dim:]
									pass
						else:
									A_	:						Optional[Any]	=     val

			return orig_state_dict



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						int	=     get_config(lowerCamelCase__    )
			A_	:						Any	=     SwinaSRForImageSuperResolution(lowerCamelCase__    )
			model.eval()

			A_	:						Any	=     torch.hub.load_state_dict_from_url(lowerCamelCase__						,      map_location="""cpu"""    )
			A_	:						Union[str, Any]	=     convert_state_dict(lowerCamelCase__						,      lowerCamelCase__    )
			A_,    A_	:						str	=     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
			A_	:						Tuple	=     """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
			A_	:						Union[str, Any]	=     Image.open(requests.get(lowerCamelCase__						,      stream=lowerCamelCase__    ).raw    ).convert("""RGB"""    )
			A_	:						str	=     SwinaSRImageProcessor()
			# pixel_values = processor(image, return_tensors="pt").pixel_values

			A_	:						Tuple	=     1_26 if """Jpeg""" in checkpoint_url else 2_56
			A_	:						Dict	=     Compose(
			    [
			        Resize((image_size, image_size)    ),
			        ToTensor(),
			        Normalize(mean=[0.485, 0.456, 0.406]						,      std=[0.229, 0.224, 0.225]    ),
			    ]    )
			A_	:						str	=     transforms(lowerCamelCase__    ).unsqueeze(0    )

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

			A_	:						List[Any]	=     model(lowerCamelCase__    )

			# assert values
			if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
						A_	:						str	=     torch.Size([1, 3, 5_12, 5_12]    )
						A_	:						Optional[Any]	=     torch.tensor(
						    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]    )
			elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						A_	:						str	=     torch.Size([1, 3, 10_24, 10_24]    )
						A_	:						Any	=     torch.tensor(
						    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]    )
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						# TODO values didn't match exactly here
						A_	:						List[Any]	=     torch.Size([1, 3, 10_24, 10_24]    )
						A_	:						Dict	=     torch.tensor(
						    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]    )
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						A_	:						Union[str, Any]	=     torch.Size([1, 3, 5_12, 5_12]    )
						A_	:						int	=     torch.tensor(
						    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]    )
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						A_	:						Any	=     torch.Size([1, 3, 10_24, 10_24]    )
						A_	:						List[str]	=     torch.tensor(
						    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]    )

			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!"""    )

			A_	:						int	=     {
			    """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"""
			    ),
			}
			A_	:						str	=     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__":
						lowerCamelCase							: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.''')

						lowerCamelCase							:int				    =					parser.parse_args()
						convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')