'''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    __UpperCAmelCase			(  a_:       int							):
   _UpperCAmelCase							:	Optional[Any]							=   SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      _UpperCAmelCase							:	Dict							=   4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      _UpperCAmelCase							:	Optional[int]							=   4
      _UpperCAmelCase							:	Dict							=   48
      _UpperCAmelCase							:	Optional[Any]							=   "pixelshuffle_aux"
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      _UpperCAmelCase							:	Optional[int]							=   [6, 6, 6, 6]
      _UpperCAmelCase							:	int							=   60
      _UpperCAmelCase							:	Optional[int]							=   [6, 6, 6, 6]
      _UpperCAmelCase							:	Tuple							=   "pixelshuffledirect"
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      _UpperCAmelCase							:	Tuple							=   4
      _UpperCAmelCase							:	Optional[Any]							=   "nearest+conv"
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      _UpperCAmelCase							:	Dict							=   1
      _UpperCAmelCase							:	List[Any]							=   1
      _UpperCAmelCase							:	Optional[Any]							=   126
      _UpperCAmelCase							:	Dict							=   7
      _UpperCAmelCase							:	Union[str, Any]							=   2_55.0
      _UpperCAmelCase							:	Optional[int]							=   ""

   return config






def    __UpperCAmelCase			(  a_:       List[Any],					a_:       Optional[Any]							):




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

   if name == "norm.weight":
      _UpperCAmelCase							:	Any							=   "layernorm.weight"
   if name == "norm.bias":
      _UpperCAmelCase							:	Any							=   "layernorm.bias"

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

   return name






def    __UpperCAmelCase			(  a_:       int,					a_:       Optional[Any]							):




   for key in orig_state_dict.copy().keys():
      _UpperCAmelCase							:	str							=   orig_state_dict.pop(snake_case_							)

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

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

   return orig_state_dict






def    __UpperCAmelCase			(  a_:       int,					a_:       Dict,					a_:       Any							):
   _UpperCAmelCase							:	Optional[int]							=   get_config(snake_case_							)
   _UpperCAmelCase							:	List[str]							=   SwinaSRForImageSuperResolution(snake_case_							)
   model.eval()

   _UpperCAmelCase							:	List[str]							=   torch.hub.load_state_dict_from_url(snake_case_,					map_location="cpu"							)
   _UpperCAmelCase							:	List[str]							=   convert_state_dict(snake_case_,					snake_case_							)
   _UpperCAmelCase							,       _UpperCAmelCase							:	List[Any]							=   model.load_state_dict(snake_case_,					strict=snake_case_							)

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

   _UpperCAmelCase							:	int							=   126 if "Jpeg" in checkpoint_url else 256
   _UpperCAmelCase							:	Optional[Any]							=   Compose(
       [
           Resize((image_size, image_size)							),
           ToTensor(),
           Normalize(mean=[0.4_85, 0.4_56, 0.4_06],					std=[0.2_29, 0.2_24, 0.2_25]							),
       ]							)
   _UpperCAmelCase							:	List[str]							=   transforms(snake_case_							).unsqueeze(0							)

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

   _UpperCAmelCase							:	Tuple							=   model(snake_case_							)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      _UpperCAmelCase							:	int							=   torch.Size([1, 3, 512, 512]							)
      _UpperCAmelCase							:	Optional[int]							=   torch.tensor(
          [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]							)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      _UpperCAmelCase							:	Tuple							=   torch.Size([1, 3, 1_024, 1_024]							)
      _UpperCAmelCase							:	Optional[int]							=   torch.tensor(
          [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]							)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      _UpperCAmelCase							:	Union[str, Any]							=   torch.Size([1, 3, 1_024, 1_024]							)
      _UpperCAmelCase							:	Tuple							=   torch.tensor(
          [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]							)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      _UpperCAmelCase							:	str							=   torch.Size([1, 3, 512, 512]							)
      _UpperCAmelCase							:	int							=   torch.tensor(
          [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]							)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      _UpperCAmelCase							:	Dict							=   torch.Size([1, 3, 1_024, 1_024]							)
      _UpperCAmelCase							:	Optional[Any]							=   torch.tensor(
          [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]							)

   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],					snake_case_,					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[str]							=   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(snake_case_							)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
      processor.save_pretrained(snake_case_							)

   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 math





def 	lowerCAmelCase_				(					)    ->      None:


     '''simple docstring'''




     UpperCAmelCase_           =   input("Enter message: "			)
     UpperCAmelCase_           =   int(input(f"""Enter key [2-{len(snake_case_			) - 1}]: """			)			)
     UpperCAmelCase_           =   input("Encryption/Decryption [e/d]: "			)

     if mode.lower().startswith("e"			):
          UpperCAmelCase_           =   encrypt_message(snake_case_					,    snake_case_			)
     elif mode.lower().startswith("d"			):
          UpperCAmelCase_           =   decrypt_message(snake_case_					,    snake_case_			)

     # Append pipe symbol (vertical bar) to identify spaces at the end.
     print(f"""Output:\n{text + "|"}"""			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   [""] * key
     for col in range(snake_case_			):
          UpperCAmelCase_           =   col
          while pointer < len(snake_case_			):
               cipher_text[col] += message[pointer]
               pointer += key
     return "".join(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   math.ceil(len(snake_case_			) / key			)
     UpperCAmelCase_           =   key
     UpperCAmelCase_           =   (num_cols * num_rows) - len(snake_case_			)
     UpperCAmelCase_           =   [""] * num_cols
     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0

     for symbol in message:
          plain_text[col] += symbol
          col += 1

          if (
              (col == num_cols)
              or (col == num_cols - 1)
              and (row >= num_rows - num_shaded_boxes)
          ):
               UpperCAmelCase_           =   0
               row += 1

     return "".join(snake_case_			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()




