





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  (    _lowerCAmelCase       :       List[Any]		)       ->  Tuple:
   A_    : Dict								=	SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      A_    : Optional[Any]								=	4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      A_    : Optional[int]								=	4
      A_    : List[Any]								=	48
      A_    : Optional[Any]								=	'pixelshuffle_aux'
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      A_    : Any								=	[6, 6, 6, 6]
      A_    : List[Any]								=	60
      A_    : Union[str, Any]								=	[6, 6, 6, 6]
      A_    : str								=	'pixelshuffledirect'
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      A_    : List[str]								=	4
      A_    : Union[str, Any]								=	'nearest+conv'
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      A_    : Any								=	1
      A_    : List[str]								=	1
      A_    : Optional[Any]								=	126
      A_    : Dict								=	7
      A_    : int								=	2_55.0
      A_    : Dict								=	''

   return config

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Tuple		)       ->  Dict:
   if "patch_embed.proj" in name and "layers" not in name:
      A_    : Optional[int]								=	name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection"		)
   if "patch_embed.norm" in name:
      A_    : Any								=	name.replace("patch_embed.norm" , "embeddings.patch_embeddings.layernorm"		)
   if "layers" in name:
      A_    : Any								=	name.replace("layers" , "encoder.stages"		)
   if "residual_group.blocks" in name:
      A_    : Optional[int]								=	name.replace("residual_group.blocks" , "layers"		)
   if "attn.proj" in name:
      A_    : Tuple								=	name.replace("attn.proj" , "attention.output.dense"		)
   if "attn" in name:
      A_    : Optional[int]								=	name.replace("attn" , "attention.self"		)
   if "norm1" in name:
      A_    : List[Any]								=	name.replace("norm1" , "layernorm_before"		)
   if "norm2" in name:
      A_    : Optional[int]								=	name.replace("norm2" , "layernorm_after"		)
   if "mlp.fc1" in name:
      A_    : Optional[int]								=	name.replace("mlp.fc1" , "intermediate.dense"		)
   if "mlp.fc2" in name:
      A_    : Optional[int]								=	name.replace("mlp.fc2" , "output.dense"		)
   if "q_bias" in name:
      A_    : Optional[int]								=	name.replace("q_bias" , "query.bias"		)
   if "k_bias" in name:
      A_    : List[Any]								=	name.replace("k_bias" , "key.bias"		)
   if "v_bias" in name:
      A_    : Any								=	name.replace("v_bias" , "value.bias"		)
   if "cpb_mlp" in name:
      A_    : List[str]								=	name.replace("cpb_mlp" , "continuous_position_bias_mlp"		)
   if "patch_embed.proj" in name:
      A_    : Union[str, Any]								=	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_    : 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:
         A_    : 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:
            A_    : Any								=	name.replace("conv_before_upsample.0" , "conv_before_upsample"		)
         if "upsample.0" in name:
            A_    : Optional[int]								=	name.replace("upsample.0" , "upsample.convolution_0"		)
         if "upsample.2" in name:
            A_    : Any								=	name.replace("upsample.2" , "upsample.convolution_1"		)
         A_    : List[str]								=	'upsample.' + name
      elif config.upsampler == "pixelshuffledirect":
         A_    : Optional[int]								=	name.replace("upsample.0.weight" , "upsample.conv.weight"		)
         A_    : List[Any]								=	name.replace("upsample.0.bias" , "upsample.conv.bias"		)
      else:
         pass
   else:
      A_    : Optional[Any]								=	'swin2sr.' + name

   return name

def    __snake_case  (    _lowerCAmelCase       :       Optional[Any] , _lowerCAmelCase       :       str		)       ->  int:
   for key in orig_state_dict.copy().keys():
      A_    : List[str]								=	orig_state_dict.pop(_lowerCAmelCase		)

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

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

   return orig_state_dict

def    __snake_case  (    _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Optional[Any]		)       ->  Any:
   A_    : Optional[int]								=	get_config(_lowerCAmelCase		)
   A_    : str								=	SwinaSRForImageSuperResolution(_lowerCAmelCase		)
   model.eval()

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

   A_    : Any								=	126 if 'Jpeg' in checkpoint_url else 256
   A_    : List[str]								=	Compose(
       [
           Resize((image_size, image_size)		),
           ToTensor(),
           Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25]		),
       ]		)
   A_    : Tuple								=	transforms(_lowerCAmelCase		).unsqueeze(0		)

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

   A_    : str								=	model(_lowerCAmelCase		)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      A_    : List[Any]								=	torch.Size([1, 3, 512, 512]		)
      A_    : Optional[int]								=	torch.tensor(
          [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]		)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      A_    : List[str]								=	torch.Size([1, 3, 1024, 1024]		)
      A_    : Optional[int]								=	torch.tensor(
          [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]		)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      A_    : Union[str, Any]								=	torch.Size([1, 3, 1024, 1024]		)
      A_    : Optional[int]								=	torch.tensor(
          [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]		)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      A_    : Optional[Any]								=	torch.Size([1, 3, 512, 512]		)
      A_    : Dict								=	torch.tensor(
          [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]		)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      A_    : Optional[int]								=	torch.Size([1, 3, 1024, 1024]		)
      A_    : Any								=	torch.tensor(
          [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]		)

   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_    : 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'
       ),
   }
   A_    : List[Any]								=	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							:					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.''')

 _lowerCAmelCase							:					Optional[Any]		    =		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




