



'''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 				__lowerCamelCase				(   A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  SwinaSRConfig()

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

   return config







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[Any]:






   """simple docstring"""







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

   if name == "norm.weight":
      UpperCamelCase					=  'layernorm.weight'
   if name == "norm.bias":
      UpperCamelCase					=  'layernorm.bias'

   if "conv_first" in name:
      UpperCamelCase					=  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:
         UpperCamelCase					=  name.replace('conv_last'    ,						'final_convolution'    )
      if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
         if "conv_before_upsample.0" in name:
            UpperCamelCase					=  name.replace('conv_before_upsample.0'    ,						'conv_before_upsample'    )
         if "upsample.0" in name:
            UpperCamelCase					=  name.replace('upsample.0'    ,						'upsample.convolution_0'    )
         if "upsample.2" in name:
            UpperCamelCase					=  name.replace('upsample.2'    ,						'upsample.convolution_1'    )
         UpperCamelCase					=  'upsample.' + name
      elif config.upsampler == "pixelshuffledirect":
         UpperCamelCase					=  name.replace('upsample.0.weight'    ,						'upsample.conv.weight'    )
         UpperCamelCase					=  name.replace('upsample.0.bias'    ,						'upsample.conv.bias'    )
      else:
         pass
   else:
      UpperCamelCase					=  'swin2sr.' + name

   return name







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Dict:






   """simple docstring"""







   for key in orig_state_dict.copy().keys():
      UpperCamelCase					=  orig_state_dict.pop(__UpperCamelCase    )

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

         if "weight" in key:
            UpperCamelCase					=  val[:dim, :]
            UpperCamelCase					=  val[dim : dim * 2, :]
            UpperCamelCase					=  val[-dim:, :]
         else:
            UpperCamelCase					=  val[:dim]
            UpperCamelCase					=  val[dim : dim * 2]
            UpperCamelCase					=  val[-dim:]
         pass
      else:
         UpperCamelCase					=  val

   return orig_state_dict







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  get_config(__UpperCamelCase    )
   UpperCamelCase					=  SwinaSRForImageSuperResolution(__UpperCamelCase    )
   model.eval()

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

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

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

   UpperCamelCase					=  model(__UpperCamelCase    )

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      UpperCamelCase					=  torch.Size([1, 3, 512, 512]    )
      UpperCamelCase					=  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:
      UpperCamelCase					=  torch.Size([1, 3, 1_024, 1_024]    )
      UpperCamelCase					=  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
      UpperCamelCase					=  torch.Size([1, 3, 1_024, 1_024]    )
      UpperCamelCase					=  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:
      UpperCamelCase					=  torch.Size([1, 3, 512, 512]    )
      UpperCamelCase					=  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:
      UpperCamelCase					=  torch.Size([1, 3, 1_024, 1_024]    )
      UpperCamelCase					=  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!'    )

   UpperCamelCase					=  {
       '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'
       ),
   }
   UpperCamelCase					=  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	:       Tuple      	=	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	:       int      	=	parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""


import re



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]'''		,       str_ )]



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= split_input(str_ )
  return "".join(
      [''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= split_input(__UpperCamelCase )
    if upper:
      __A									= ''''''.join(
          [
              separator.join([char.upper() for char in sub_str] )
              for sub_str in string_split
          ] )
    else:
      __A									= ''''''.join(
          [
              separator.join([char.lower() for char in sub_str] )
              for sub_str in string_split
          ] )
    return res_str
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  return to_simple_case(__UpperCamelCase )



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  try:
    __A									= to_simple_case(__UpperCamelCase )
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''_''' )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  return to_complex_case(__UpperCamelCase		,       __UpperCamelCase		,       '''-''' )


if __name__ == "__main__":
       __import__('doctest').testmod()




