
'''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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	SwinaSRConfig()

						if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												lowercase_     :					str													=	4
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												lowercase_     :					Union[str, Any]													=	4
												lowercase_     :					Any													=	48
												lowercase_     :					Tuple													=	'''pixelshuffle_aux'''
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												lowercase_     :					Any													=	[6, 6, 6, 6]
												lowercase_     :					List[Any]													=	60
												lowercase_     :					Optional[int]													=	[6, 6, 6, 6]
												lowercase_     :					Dict													=	'''pixelshuffledirect'''
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												lowercase_     :					Any													=	4
												lowercase_     :					Dict													=	'''nearest+conv'''
						elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
												lowercase_     :					List[str]													=	1
												lowercase_     :					List[str]													=	1
												lowercase_     :					List[Any]													=	126
												lowercase_     :					List[Any]													=	7
												lowercase_     :					Optional[int]													=	255.0
												lowercase_     :					List[Any]													=	''''''

						return config
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,      __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





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

						if name == "norm.weight":
												lowercase_     :					List[Any]													=	'''layernorm.weight'''
						if name == "norm.bias":
												lowercase_     :					str													=	'''layernorm.bias'''

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

						return name
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,      __SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												lowercase_     :					int													=	orig_state_dict.pop(__SCREAMING_SNAKE_CASE							)

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

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

						return orig_state_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,      __SCREAMING_SNAKE_CASE :			int				,      __SCREAMING_SNAKE_CASE :			Optional[int]							):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	get_config(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Optional[int]													=	SwinaSRForImageSuperResolution(__SCREAMING_SNAKE_CASE							)
						model.eval()

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

						lowercase_     :					Any													=	126 if '''Jpeg''' in checkpoint_url else 256
						lowercase_     :					int													=	Compose(
						    [
						        Resize((image_size, image_size)							),
						        ToTensor(),
						        Normalize(mean=[0.485, 0.456, 0.406]				,      std=[0.229, 0.224, 0.225]							),
						    ]							)
						lowercase_     :					List[Any]													=	transforms(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)

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

						lowercase_     :					Any													=	model(__SCREAMING_SNAKE_CASE							)

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												lowercase_     :					Optional[Any]													=	torch.Size([1, 3, 512, 512]							)
												lowercase_     :					Any													=	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:
												lowercase_     :					Optional[int]													=	torch.Size([1, 3, 1024, 1024]							)
												lowercase_     :					Tuple													=	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
												lowercase_     :					Any													=	torch.Size([1, 3, 1024, 1024]							)
												lowercase_     :					Tuple													=	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:
												lowercase_     :					Dict													=	torch.Size([1, 3, 512, 512]							)
												lowercase_     :					str													=	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:
												lowercase_     :					Optional[int]													=	torch.Size([1, 3, 1024, 1024]							)
												lowercase_     :					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!'''							)

						lowercase_     :					Union[str, Any]													=	{
						    '''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_     :					Dict													=	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__":
							_lowercase			:  List[str]   							=	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.")

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





'''simple docstring'''







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if version.parse(hfh.__version__							).release < version.parse('''0.11.0'''							).release:
												# old versions of hfh don't url-encode the file path
												lowercase_     :					int													=	quote(__SCREAMING_SNAKE_CASE							)
						return hfh.hf_hub_url(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''				,				revision=__SCREAMING_SNAKE_CASE							)



