



'''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 						__snake_case(				_lowerCAmelCase		)		-> Any:
      snake_case__  :    Any    						=      SwinaSRConfig()

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

      return config





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

      if name == "norm.weight":
            snake_case__  :    str    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Tuple    						=      """layernorm.bias"""

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

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Tuple    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "qkv" in key:
                  snake_case__  :    Optional[int]    						=      key.split("""."""		)
                  snake_case__  :    Tuple    						=      int(key_split[1]		)
                  snake_case__  :    List[Any]    						=      int(key_split[4]		)
                  snake_case__  :    List[Any]    						=      config.embed_dim

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

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Tuple    						=      get_config(_lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      SwinaSRForImageSuperResolution(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    Union[str, Any]    						=      torch.hub.load_state_dict_from_url(_lowerCAmelCase     ,   map_location="""cpu"""		)
      snake_case__  :    Optional[int]    						=      convert_state_dict(_lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Union[str, Any]    						=      model.load_state_dict(_lowerCAmelCase     ,   strict=_lowerCAmelCase		)

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

      snake_case__  :    List[Any]    						=      126 if """Jpeg""" in checkpoint_url else 256
      snake_case__  :    Tuple    						=      Compose(
          [
              Resize((image_size, image_size)		),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]     ,   std=[0.229, 0.224, 0.225]		),
          ]		)
      snake_case__  :    Union[str, Any]    						=      transforms(_lowerCAmelCase		).unsqueeze(0		)

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

      snake_case__  :    Optional[int]    						=      model(_lowerCAmelCase		)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            snake_case__  :    Optional[Any]    						=      torch.Size([1, 3, 512, 512]		)
            snake_case__  :    Any    						=      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:
            snake_case__  :    List[str]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Any    						=      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
            snake_case__  :    List[Any]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Tuple    						=      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:
            snake_case__  :    Any    						=      torch.Size([1, 3, 512, 512]		)
            snake_case__  :    Dict    						=      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:
            snake_case__  :    Union[str, Any]    						=      torch.Size([1, 3, 1_024, 1_024]		)
            snake_case__  :    Optional[Any]    						=      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]     ,   _lowerCAmelCase     ,   atol=1e-3		)
      print("""Looks ok!"""		)

      snake_case__  :    Optional[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"""
          ),
      }
      snake_case__  :    Union[str, 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(_lowerCAmelCase		)
            print(f"Saving image processor to {pytorch_dump_folder_path}"		)
            processor.save_pretrained(_lowerCAmelCase		)

      if push_to_hub:
            model.push_to_hub(f"caidas/{model_name}"		)
            processor.push_to_hub(f"caidas/{model_name}"		)


if __name__ == "__main__":
       __a          =		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.")

       __a          =		parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Dict    						=      SwinConfig()
      snake_case__  :    Optional[Any]    						=      swin_name.split("""_"""		)

      snake_case__  :    Any    						=      name_split[1]
      snake_case__  :    List[Any]    						=      int(name_split[4]		)
      snake_case__  :    int    						=      int(name_split[3][-1]		)

      if model_size == "tiny":
            snake_case__  :    List[Any]    						=      96
            snake_case__  :    int    						=      (2, 2, 6, 2)
            snake_case__  :    int    						=      (3, 6, 12, 24)
      elif model_size == "small":
            snake_case__  :    Union[str, Any]    						=      96
            snake_case__  :    Optional[Any]    						=      (2, 2, 18, 2)
            snake_case__  :    str    						=      (3, 6, 12, 24)
      elif model_size == "base":
            snake_case__  :    Dict    						=      128
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    Dict    						=      (4, 8, 16, 32)
      else:
            snake_case__  :    List[str]    						=      192
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    List[Any]    						=      (6, 12, 24, 48)

      if "in22k" in swin_name:
            snake_case__  :    str    						=      21_841
      else:
            snake_case__  :    List[str]    						=      1_000
            snake_case__  :    int    						=      """huggingface/label-files"""
            snake_case__  :    Any    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    List[Any]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            snake_case__  :    Optional[int]    						=      idalabel
            snake_case__  :    List[Any]    						=      {v: k for k, v in idalabel.items()}

      snake_case__  :    List[Any]    						=      img_size
      snake_case__  :    Dict    						=      num_classes
      snake_case__  :    Dict    						=      embed_dim
      snake_case__  :    Optional[int]    						=      depths
      snake_case__  :    int    						=      num_heads
      snake_case__  :    Optional[int]    						=      window_size

      return config





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""		)
      if "layers" in name:
            snake_case__  :    str    						=      """encoder.""" + name
      if "attn.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Tuple    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    Union[str, Any]    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    Dict    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)

      if name == "norm.weight":
            snake_case__  :    Tuple    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      """layernorm.bias"""

      if "head" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""head"""     ,   """classifier"""		)
      else:
            snake_case__  :    List[str]    						=      """swin.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Optional[int]    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__  :    Dict    						=      key.split("""."""		)
                  snake_case__  :    Optional[int]    						=      int(key_split[1]		)
                  snake_case__  :    Union[str, Any]    						=      int(key_split[3]		)
                  snake_case__  :    List[Any]    						=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      snake_case__  :    Optional[int]    						=      get_swin_config(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SwinForImageClassification(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    str    						=      convert_state_dict(timm_model.state_dict()     ,   _lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)

      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

      snake_case__  :    Dict    						=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""     ,   """-"""		)		)		)
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      snake_case__  :    Optional[int]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		)

      snake_case__  :    Optional[Any]    						=      timm_model(inputs["""pixel_values"""]		)
      snake_case__  :    str    						=      model(**_lowerCAmelCase		).logits

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase     ,   atol=1e-3		)

      print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)

      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swin_name",
           default="swin_tiny_patch4_window7_224",
           type=str,
           help="Name of the Swin timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __a          =		parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
