


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_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      UpperCAmelCase_							:      Tuple						=   4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      UpperCAmelCase_							:      List[str]						=   4
      UpperCAmelCase_							:      Union[str, Any]						=   4_8
      UpperCAmelCase_							:      Any						=   '''pixelshuffle_aux'''
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      UpperCAmelCase_							:      Any						=   [6, 6, 6, 6]
      UpperCAmelCase_							:      int						=   6_0
      UpperCAmelCase_							:      int						=   [6, 6, 6, 6]
      UpperCAmelCase_							:      Any						=   '''pixelshuffledirect'''
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      UpperCAmelCase_							:      Dict						=   4
      UpperCAmelCase_							:      Any						=   '''nearest+conv'''
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      UpperCAmelCase_							:      Union[str, Any]						=   1
      UpperCAmelCase_							:      str						=   1
      UpperCAmelCase_							:      Any						=   1_2_6
      UpperCAmelCase_							:      Tuple						=   7
      UpperCAmelCase_							:      Tuple						=   2_5_5.0
      UpperCAmelCase_							:      Optional[Any]						=   ''''''

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

   if name == "norm.weight":
      UpperCAmelCase_							:      int						=   '''layernorm.weight'''
   if name == "norm.bias":
      UpperCAmelCase_							:      int						=   '''layernorm.bias'''

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

   return name
def      snake_case_						( __lowercase			,       __lowercase       ):
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_							:      List[Any]						=   orig_state_dict.pop(__lowercase       )

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

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

   return orig_state_dict
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      int						=   get_config(__lowercase       )
   UpperCAmelCase_							:      Optional[int]						=   SwinaSRForImageSuperResolution(__lowercase       )
   model.eval()

   UpperCAmelCase_							:      Tuple						=   torch.hub.load_state_dict_from_url(__lowercase			,       map_location='''cpu'''       )
   UpperCAmelCase_							:      Tuple						=   convert_state_dict(__lowercase			,       __lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   model.load_state_dict(__lowercase			,       strict=__lowercase       )

   if len(__lowercase       ) > 0:
      raise ValueError('''Missing keys when converting: {}'''.format(__lowercase       )       )
   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_							:      List[Any]						=   '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
   UpperCAmelCase_							:      Union[str, Any]						=   Image.open(requests.get(__lowercase			,       stream=__lowercase       ).raw       ).convert('''RGB'''       )
   UpperCAmelCase_							:      Optional[int]						=   SwinaSRImageProcessor()
   # pixel_values = processor(image, return_tensors="pt").pixel_values

   UpperCAmelCase_							:      List[Any]						=   1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
   UpperCAmelCase_							:      int						=   Compose(
       [
           Resize((image_size, image_size)       ),
           ToTensor(),
           Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,       std=[0.2_2_9, 0.2_2_4, 0.2_2_5]       ),
       ]       )
   UpperCAmelCase_							:      Tuple						=   transforms(__lowercase       ).unsqueeze(0       )

   if config.num_channels == 1:
      UpperCAmelCase_							:      Optional[int]						=   pixel_values[:, 0, :, :].unsqueeze(1       )

   UpperCAmelCase_							:      List[Any]						=   model(__lowercase       )

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      UpperCAmelCase_							:      List[Any]						=   torch.Size([1, 3, 5_1_2, 5_1_2]       )
      UpperCAmelCase_							:      int						=   torch.tensor(
          [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]       )
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      UpperCAmelCase_							:      Optional[Any]						=   torch.Size([1, 3, 1_0_2_4, 1_0_2_4]       )
      UpperCAmelCase_							:      Union[str, Any]						=   torch.tensor(
          [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]       )
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      UpperCAmelCase_							:      int						=   torch.Size([1, 3, 1_0_2_4, 1_0_2_4]       )
      UpperCAmelCase_							:      Any						=   torch.tensor(
          [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]       )
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      UpperCAmelCase_							:      Optional[int]						=   torch.Size([1, 3, 5_1_2, 5_1_2]       )
      UpperCAmelCase_							:      Tuple						=   torch.tensor(
          [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]       )
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      UpperCAmelCase_							:      Optional[Any]						=   torch.Size([1, 3, 1_0_2_4, 1_0_2_4]       )
      UpperCAmelCase_							:      Dict						=   torch.tensor(
          [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]       )

   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]			,       __lowercase			,       atol=1e-3       )
   print('''Looks ok!'''       )

   UpperCAmelCase_							:      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'''
       ),
   }
   UpperCAmelCase_							:      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(__lowercase       )
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
      processor.save_pretrained(__lowercase       )

   if push_to_hub:
      model.push_to_hub(F'''caidas/{model_name}'''       )
      processor.push_to_hub(F'''caidas/{model_name}'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  Dict      					=							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.')

       __UpperCamelCase				:  Any      					=							parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))