



'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				SCREAMING_SNAKE_CASE  :						str      					= SwinaSRConfig()

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

				return config






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				if "patch_embed.proj" in name and "layers" not in name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''patch_embed.proj'''    ,	'''embeddings.patch_embeddings.projection'''       )
				if "patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= name.replace('''patch_embed.norm'''    ,	'''embeddings.patch_embeddings.layernorm'''       )
				if "layers" in name:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 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  :						Optional[Any]      					= name.replace('''attn.proj'''    ,	'''attention.output.dense'''       )
				if "attn" in name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''attn'''    ,	'''attention.self'''       )
				if "norm1" in name:
								SCREAMING_SNAKE_CASE  :						List[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  :						str      					= name.replace('''mlp.fc1'''    ,	'''intermediate.dense'''       )
				if "mlp.fc2" in name:
								SCREAMING_SNAKE_CASE  :						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  :						Tuple      					= name.replace('''k_bias'''    ,	'''key.bias'''       )
				if "v_bias" in name:
								SCREAMING_SNAKE_CASE  :						Any      					= 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  :						List[str]      					= name.replace('''patch_embed.proj'''    ,	'''patch_embed.projection'''       )

				if name == "norm.weight":
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''layernorm.weight'''
				if name == "norm.bias":
								SCREAMING_SNAKE_CASE  :						str      					= '''layernorm.bias'''

				if "conv_first" in name:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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  :						List[str]      					= 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  :						Any      					= name.replace('''conv_before_upsample.0'''    ,	'''conv_before_upsample'''       )
												if "upsample.0" in name:
																SCREAMING_SNAKE_CASE  :						int      					= 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  :						str      					= '''upsample.''' + name
								elif config.upsampler == "pixelshuffledirect":
												SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''upsample.0.weight'''    ,	'''upsample.conv.weight'''       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''upsample.0.bias'''    ,	'''upsample.conv.bias'''       )
								else:
												pass
				else:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''swin2sr.''' + name

				return name






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :str       ):
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						Dict      					= orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE  :						List[Any]      					= key.split('''.'''       )
												SCREAMING_SNAKE_CASE  :						Dict      					= int(key_split[1]       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= int(key_split[4]       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= config.embed_dim

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

				return orig_state_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						int      					= get_config(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= SwinaSRForImageSuperResolution(_SCREAMING_SNAKE_CASE       )
				model.eval()

				SCREAMING_SNAKE_CASE  :						Tuple      					= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    ,	map_location='''cpu'''       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= convert_state_dict(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model.load_state_dict(_SCREAMING_SNAKE_CASE    ,	strict=_SCREAMING_SNAKE_CASE       )

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

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 1_26 if '''Jpeg''' in checkpoint_url else 2_56
				SCREAMING_SNAKE_CASE  :						int      					= Compose(
				    [
				        Resize((image_size, image_size)       ),
				        ToTensor(),
				        Normalize(mean=[0.485, 0.456, 0.406]    ,	std=[0.229, 0.224, 0.225]       ),
				    ]       )
				SCREAMING_SNAKE_CASE  :						Any      					= transforms(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )

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

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(_SCREAMING_SNAKE_CASE       )

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.Size([1, 3, 5_12, 5_12]       )
								SCREAMING_SNAKE_CASE  :						Dict      					= torch.tensor(
								    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]       )
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 3, 10_24, 10_24]       )
								SCREAMING_SNAKE_CASE  :						Dict      					= torch.tensor(
								    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]       )
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.Size([1, 3, 10_24, 10_24]       )
								SCREAMING_SNAKE_CASE  :						str      					= torch.tensor(
								    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]       )
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Tuple      					= torch.Size([1, 3, 5_12, 5_12]       )
								SCREAMING_SNAKE_CASE  :						int      					= torch.tensor(
								    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]       )
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 3, 10_24, 10_24]       )
								SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.tensor(
								    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]       )

				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]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       )
				print('''Looks ok!'''       )

				SCREAMING_SNAKE_CASE  :						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'''
				    ),
				}
				SCREAMING_SNAKE_CASE  :						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(_SCREAMING_SNAKE_CASE       )
								print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
								processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
	snake_case_			=     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.""")

	snake_case_			=     parser.parse_args()
	convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




