




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  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      4
            SCREAMING_SNAKE_CASE_     						=      48
            SCREAMING_SNAKE_CASE_     						=      'pixelshuffle_aux'
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      [6, 6, 6, 6]
            SCREAMING_SNAKE_CASE_     						=      60
            SCREAMING_SNAKE_CASE_     						=      [6, 6, 6, 6]
            SCREAMING_SNAKE_CASE_     						=      'pixelshuffledirect'
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      4
            SCREAMING_SNAKE_CASE_     						=      'nearest+conv'
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      1
            SCREAMING_SNAKE_CASE_     						=      1
            SCREAMING_SNAKE_CASE_     						=      1_26
            SCREAMING_SNAKE_CASE_     						=      7
            SCREAMING_SNAKE_CASE_     						=      2_5_5.0
            SCREAMING_SNAKE_CASE_     						=      ''

      return config

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

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

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

      return name

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Any							) ->			Optional[int]:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_     						=      orig_state_dict.pop(__UpperCAmelCase							)

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

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

      return orig_state_dict

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Tuple							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      get_config(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      SwinaSRForImageSuperResolution(__UpperCAmelCase							)
      model.eval()

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

      SCREAMING_SNAKE_CASE_     						=      1_26 if 'Jpeg' in checkpoint_url else 2_56
      SCREAMING_SNAKE_CASE_     						=      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]							),
          ]							)
      SCREAMING_SNAKE_CASE_     						=      transforms(__UpperCAmelCase							).unsqueeze(0							)

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

      SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase							)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 5_12, 5_12]							)
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 10_24, 10_24]							)
            SCREAMING_SNAKE_CASE_     						=      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
            SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 10_24, 10_24]							)
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 5_12, 5_12]							)
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      torch.Size([1, 3, 10_24, 10_24]							)
            SCREAMING_SNAKE_CASE_     						=      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]      ,      __UpperCAmelCase      ,      atol=1E-3							)
      print('Looks ok!'							)

      SCREAMING_SNAKE_CASE_     						=      {
          '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'
          ),
      }
      SCREAMING_SNAKE_CASE_     						=      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__			:       List[Any]         =							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__			:       Dict         =							parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
   import accelerate

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[int]							) ->			int:
      if not is_accelerate_available():
            return method
      SCREAMING_SNAKE_CASE_     						=      version.parse(accelerate.__version__							).base_version
      if version.parse(__UpperCAmelCase							) < version.parse('0.17.0'							):
            return method

      def wrapper(self						:     Optional[int]      ,      *__UpperCAmelCase						:     Optional[Any]      ,      **__UpperCAmelCase						:     Optional[Any]							):
            if hasattr(self      ,      '_hf_hook'							) and hasattr(self._hf_hook      ,      'pre_forward'							):
                  self._hf_hook.pre_forward(self							)
            return method(self      ,      *__UpperCAmelCase      ,      **__UpperCAmelCase							)

      return wrapper