




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      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  Tuple		       =     SwinaSRConfig()

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

  return config



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

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

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

  return name



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__				:  List[Any]		       =     orig_state_dict.pop(UpperCamelCase      )

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

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

  return orig_state_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[int]		       =     get_config(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     SwinaSRForImageSuperResolution(UpperCamelCase      )
  model.eval()

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

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

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

  lowerCAmelCase__				:  Dict		       =     model(UpperCamelCase      )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    lowerCAmelCase__				:  Any		       =     torch.Size([1, 3, 512, 512]      )
    lowerCAmelCase__				:  List[str]		       =     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:
    lowerCAmelCase__				:  Union[str, Any]		       =     torch.Size([1, 3, 1024, 1024]      )
    lowerCAmelCase__				:  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
    lowerCAmelCase__				:  Optional[Any]		       =     torch.Size([1, 3, 1024, 1024]      )
    lowerCAmelCase__				:  Union[str, Any]		       =     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:
    lowerCAmelCase__				:  Optional[Any]		       =     torch.Size([1, 3, 512, 512]      )
    lowerCAmelCase__				:  List[Any]		       =     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:
    lowerCAmelCase__				:  List[str]		       =     torch.Size([1, 3, 1024, 1024]      )
    lowerCAmelCase__				:  List[str]		       =     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]	,      UpperCamelCase	,      atol=1E-3      )
  print('''Looks ok!'''      )

  lowerCAmelCase__				:  List[str]		       =     {
      '''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'''
      ),
  }
  lowerCAmelCase__				:  Optional[int]		       =     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)







def      __lowerCAmelCase			(					UpperCamelCase      )     ->	bool:
  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''      )
  if len(UpperCamelCase      ) == 0:
    raise ValueError('''Input list must be a non empty list'''      )
  if len(UpperCamelCase      ) == 1:
    return True
  lowerCAmelCase__				:  Tuple		       =     series[1] - series[0]
  for index in range(len(UpperCamelCase      ) - 1      ):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	float:
  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''      )
  if len(UpperCamelCase      ) == 0:
    raise ValueError('''Input list must be a non empty list'''      )
  lowerCAmelCase__				:  int		       =     0
  for val in series:
    answer += val
  return answer / len(UpperCamelCase      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

