





'''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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     SwinaSRConfig()

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

      return config




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if "patch_embed.proj" in name and "layers" not in name:
            _snake_case		:	List[str]					     =     name.replace('''patch_embed.proj'''			,      '''embeddings.patch_embeddings.projection'''	)
      if "patch_embed.norm" in name:
            _snake_case		:	Optional[Any]					     =     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		:	Union[str, Any]					     =     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		:	int					     =     name.replace('''attn'''			,      '''attention.self'''	)
      if "norm1" in name:
            _snake_case		:	Union[str, Any]					     =     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		:	Dict					     =     name.replace('''mlp.fc1'''			,      '''intermediate.dense'''	)
      if "mlp.fc2" in name:
            _snake_case		:	List[str]					     =     name.replace('''mlp.fc2'''			,      '''output.dense'''	)
      if "q_bias" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''q_bias'''			,      '''query.bias'''	)
      if "k_bias" in name:
            _snake_case		:	Any					     =     name.replace('''k_bias'''			,      '''key.bias'''	)
      if "v_bias" in name:
            _snake_case		:	Tuple					     =     name.replace('''v_bias'''			,      '''value.bias'''	)
      if "cpb_mlp" in name:
            _snake_case		:	Optional[Any]					     =     name.replace('''cpb_mlp'''			,      '''continuous_position_bias_mlp'''	)
      if "patch_embed.proj" in name:
            _snake_case		:	Dict					     =     name.replace('''patch_embed.proj'''			,      '''patch_embed.projection'''	)

      if name == "norm.weight":
            _snake_case		:	List[Any]					     =     '''layernorm.weight'''
      if name == "norm.bias":
            _snake_case		:	Optional[Any]					     =     '''layernorm.bias'''

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

      return name




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            _snake_case		:	Optional[int]					     =     orig_state_dict.pop(lowerCAmelCase_	)

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

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

      return orig_state_dict




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Tuple					     =     get_config(lowerCAmelCase_	)
      _snake_case		:	Optional[Any]					     =     SwinaSRForImageSuperResolution(lowerCAmelCase_	)
      model.eval()

      _snake_case		:	Optional[int]					     =     torch.hub.load_state_dict_from_url(lowerCAmelCase_			,      map_location='''cpu'''	)
      _snake_case		:	int					     =     convert_state_dict(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     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		:	List[str]					     =     '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
      _snake_case		:	Union[str, Any]					     =     Image.open(requests.get(lowerCAmelCase_			,      stream=lowerCAmelCase_	).raw	).convert('''RGB'''	)
      _snake_case		:	List[Any]					     =     SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      _snake_case		:	Optional[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		:	Optional[int]					     =     pixel_values[:, 0, :, :].unsqueeze(1	)

      _snake_case		:	Optional[int]					     =     model(lowerCAmelCase_	)

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

      _snake_case		:	Dict					     =     {
          '''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		:	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(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__":
   UpperCAmelCase					:		Union[str, 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.')

   UpperCAmelCase					:		Dict		=							parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     ArgumentParser('''Transformers CLI tool'''			,      usage='''transformers-cli <command> [<args>]'''	)
      _snake_case		:	List[str]					     =     parser.add_subparsers(help='''transformers-cli command helpers'''	)

      # Register commands
      ConvertCommand.register_subcommand(lowerCAmelCase_	)
      DownloadCommand.register_subcommand(lowerCAmelCase_	)
      EnvironmentCommand.register_subcommand(lowerCAmelCase_	)
      RunCommand.register_subcommand(lowerCAmelCase_	)
      ServeCommand.register_subcommand(lowerCAmelCase_	)
      UserCommands.register_subcommand(lowerCAmelCase_	)
      AddNewModelCommand.register_subcommand(lowerCAmelCase_	)
      AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_	)
      LfsCommands.register_subcommand(lowerCAmelCase_	)
      PTtoTFCommand.register_subcommand(lowerCAmelCase_	)

      # Let's go
      _snake_case		:	str					     =     parser.parse_args()

      if not hasattr(lowerCAmelCase_			,      '''func'''	):
            parser.print_help()
            exit(1	)

      # Run
      _snake_case		:	Union[str, Any]					     =     args.func(lowerCAmelCase_	)
      service.run()


if __name__ == "__main__":
   main()


