





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      lowerCamelCase_						(				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    Optional[Any]				=  SwinaSRConfig()

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

				return config






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


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

				if name == "norm.weight":
								snake_case_						:    str				=  '''layernorm.weight'''
				if name == "norm.bias":
								snake_case_						:    Any				=  '''layernorm.bias'''

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

				return name






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								snake_case_						:    List[str]				=  orig_state_dict.pop(_UpperCamelCase )

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

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

				return orig_state_dict






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						List[str]:






				"""simple docstring"""


				snake_case_						:    Optional[Any]				=  get_config(_UpperCamelCase )
				snake_case_						:    Optional[int]				=  SwinaSRForImageSuperResolution(_UpperCamelCase )
				model.eval()

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

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

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

				snake_case_						:    Dict				=  model(_UpperCamelCase )

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								snake_case_						:    List[str]				=  torch.Size([1, 3, 512, 512] )
								snake_case_						:    Optional[int]				=  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:
								snake_case_						:    List[Any]				=  torch.Size([1, 3, 1_024, 1_024] )
								snake_case_						:    Optional[int]				=  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
								snake_case_						:    Dict				=  torch.Size([1, 3, 1_024, 1_024] )
								snake_case_						:    List[Any]				=  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:
								snake_case_						:    Tuple				=  torch.Size([1, 3, 512, 512] )
								snake_case_						:    Dict				=  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:
								snake_case_						:    int				=  torch.Size([1, 3, 1_024, 1_024] )
								snake_case_						:    Optional[Any]				=  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] ,				_UpperCamelCase ,				atol=1E-3 )
				print('''Looks ok!''' )

				snake_case_						:    Optional[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'''
				    ),
				}
				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(_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__":
	lowerCAmelCase_    =				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_    =				parser.parse_args()
	convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
