




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     a     =				SwinaSRConfig()

     if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          a     =				4
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          a     =				4
          a     =				48
          a     =				"pixelshuffle_aux"
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          a     =				[6, 6, 6, 6]
          a     =				60
          a     =				[6, 6, 6, 6]
          a     =				"pixelshuffledirect"
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          a     =				4
          a     =				"nearest+conv"
     elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
          a     =				1
          a     =				1
          a     =				1_26
          a     =				7
          a     =				255.0
          a     =				""

     return config

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

     if name == "norm.weight":
          a     =				"layernorm.weight"
     if name == "norm.bias":
          a     =				"layernorm.bias"

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

     return name

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     for key in orig_state_dict.copy().keys():
          a     =				orig_state_dict.pop(__UpperCamelCase)

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

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

     return orig_state_dict

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     a     =				get_config(__UpperCamelCase)
     a     =				SwinaSRForImageSuperResolution(__UpperCamelCase)
     model.eval()

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

     a     =				1_26 if "Jpeg" in checkpoint_url else 2_56
     a     =				Compose(
         [
             Resize((image_size, image_size)),
             ToTensor(),
             Normalize(mean=[0.485, 0.456, 0.406]		, std=[0.229, 0.224, 0.225]),
         ])
     a     =				transforms(__UpperCamelCase).unsqueeze(0)

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

     a     =				model(__UpperCamelCase)

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

     a     =				{
         "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"
         ),
     }
     a     =				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__":
   lowercase__				:      Optional[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.")

   lowercase__				:      int	   = parser.parse_args()
   convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import math
import sys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     if number != int(__UpperCamelCase):
          raise ValueError("the value of input must be a natural number")
     if number < 0:
          raise ValueError("the value of input must not be a negative number")
     if number == 0:
          return 1
     a     =				[-1] * (number + 1)
     a     =				0
     for i in range(1		, number + 1):
          a     =				sys.maxsize
          a     =				int(math.sqrt(__UpperCamelCase))
          for j in range(1		, root + 1):
               a     =				1 + answers[i - (j**2)]
               a     =				min(__UpperCamelCase		, __UpperCamelCase)
          a     =				answer
     return answers[number]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

