





"""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_     :		str							)			->      Optional[Any]:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :List[str]						=  4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  4
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  48
            __SCREAMING_SNAKE_CASE   :str						=  '''pixelshuffle_aux'''
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :Any						=  [6, 6, 6, 6]
            __SCREAMING_SNAKE_CASE   :int						=  60
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [6, 6, 6, 6]
            __SCREAMING_SNAKE_CASE   :Tuple						=  '''pixelshuffledirect'''
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :int						=  4
            __SCREAMING_SNAKE_CASE   :List[str]						=  '''nearest+conv'''
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :Dict						=  1
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1
            __SCREAMING_SNAKE_CASE   :str						=  1_26
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  7
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  255.0
            __SCREAMING_SNAKE_CASE   :int						=  ''''''

      return config



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

      if name == "norm.weight":
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  '''layernorm.weight'''
      if name == "norm.bias":
            __SCREAMING_SNAKE_CASE   :str						=  '''layernorm.bias'''

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

      return name



def      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		List[str]							)			->      Any:
      for key in orig_state_dict.copy().keys():
            __SCREAMING_SNAKE_CASE   :List[Any]						=  orig_state_dict.pop(a_							)

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

                  if "weight" in key:
                        __SCREAMING_SNAKE_CASE   :Optional[int]						=  val[:dim, :]
                        __SCREAMING_SNAKE_CASE   :Dict						=  val[dim : dim * 2, :]
                        __SCREAMING_SNAKE_CASE   :int						=  val[-dim:, :]
                  else:
                        __SCREAMING_SNAKE_CASE   :int						=  val[:dim]
                        __SCREAMING_SNAKE_CASE   :Optional[Any]						=  val[dim : dim * 2]
                        __SCREAMING_SNAKE_CASE   :Optional[int]						=  val[-dim:]
                  pass
            else:
                  __SCREAMING_SNAKE_CASE   :Tuple						=  val

      return orig_state_dict



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		List[Any] ,   a_     :		Union[str, Any]							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  get_config(a_							)
      __SCREAMING_SNAKE_CASE   :str						=  SwinaSRForImageSuperResolution(a_							)
      model.eval()

      __SCREAMING_SNAKE_CASE   :Dict						=  torch.hub.load_state_dict_from_url(a_ ,   map_location='''cpu'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  convert_state_dict(a_ ,   a_							)
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[Any]						=  model.load_state_dict(a_ ,   strict=a_							)

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

      __SCREAMING_SNAKE_CASE   :Dict						=  1_26 if '''Jpeg''' in checkpoint_url else 2_56
      __SCREAMING_SNAKE_CASE   :List[Any]						=  Compose(
          [
              Resize((image_size, image_size)							),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406] ,   std=[0.229, 0.224, 0.225]							),
          ]							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  transforms(a_							).unsqueeze(0							)

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

      __SCREAMING_SNAKE_CASE   :str						=  model(a_							)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.Size([1, 3, 5_12, 5_12]							)
            __SCREAMING_SNAKE_CASE   :int						=  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:
            __SCREAMING_SNAKE_CASE   :Dict						=  torch.Size([1, 3, 10_24, 10_24]							)
            __SCREAMING_SNAKE_CASE   :str						=  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
            __SCREAMING_SNAKE_CASE   :List[str]						=  torch.Size([1, 3, 10_24, 10_24]							)
            __SCREAMING_SNAKE_CASE   :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:
            __SCREAMING_SNAKE_CASE   :Any						=  torch.Size([1, 3, 5_12, 5_12]							)
            __SCREAMING_SNAKE_CASE   :str						=  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:
            __SCREAMING_SNAKE_CASE   :Any						=  torch.Size([1, 3, 10_24, 10_24]							)
            __SCREAMING_SNAKE_CASE   :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] ,   a_ ,   atol=1e-3							)
      print('''Looks ok!'''							)

      __SCREAMING_SNAKE_CASE   :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'''
          ),
      }
      __SCREAMING_SNAKE_CASE   :Optional[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(a_							)
            print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
            processor.save_pretrained(a_							)

      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)






"""simple docstring"""
def      __lowerCamelCase   (							a_     :		list							)			->      list:

      if len(a_							) < 2:
            return collection

      def circle_sort_util(a_     :		list ,   a_     :		int ,   a_     :		int							) -> bool:
            __SCREAMING_SNAKE_CASE   :int						=  False

            if low == high:
                  return swapped

            __SCREAMING_SNAKE_CASE   :int						=  low
            __SCREAMING_SNAKE_CASE   :Tuple						=  high

            while left < right:
                  if collection[left] > collection[right]:
                        __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  (
                            collection[right],
                            collection[left],
                        )
                        __SCREAMING_SNAKE_CASE   :Optional[int]						=  True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  (
                      collection[right + 1],
                      collection[left],
                  )

                  __SCREAMING_SNAKE_CASE   :Tuple						=  True

            __SCREAMING_SNAKE_CASE   :int						=  low + int((high - low) / 2							)
            __SCREAMING_SNAKE_CASE   :int						=  circle_sort_util(a_ ,   a_ ,   a_							)
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  circle_sort_util(a_ ,   mid + 1 ,   a_							)

            return swapped or left_swap or right_swap

      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  True

      while is_not_sorted is True:
            __SCREAMING_SNAKE_CASE   :Tuple						=  circle_sort_util(a_ ,   0 ,   len(a_							) - 1							)

      return collection


if __name__ == "__main__":
  lowerCamelCase_								=		input("Enter numbers separated by a comma:\n").strip()
  lowerCamelCase_								=		[int(item) for item in user_input.split(",")]
  print(circle_sort(unsorted))