"""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__ (       lowerCAmelCase__  :int	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            lowercase														=			4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            lowercase														=			4
            lowercase														=			4_8
            lowercase														=			"""pixelshuffle_aux"""
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            lowercase														=			[6, 6, 6, 6]
            lowercase														=			6_0
            lowercase														=			[6, 6, 6, 6]
            lowercase														=			"""pixelshuffledirect"""
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            lowercase														=			4
            lowercase														=			"""nearest+conv"""
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            lowercase														=			1
            lowercase														=			1
            lowercase														=			1_2_6
            lowercase														=			7
            lowercase														=			255.0
            lowercase														=			""""""

      return config


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Any	)				->					Optional[Any]:
      '''simple docstring'''


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

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

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

      return name


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Optional[int]	)				->					Union[str, Any]:
      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            lowercase														=			orig_state_dict.pop(lowerCAmelCase__	)

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

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

      return orig_state_dict


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			get_config(lowerCAmelCase__	)
      lowercase														=			SwinaSRForImageSuperResolution(lowerCAmelCase__	)
      model.eval()

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

      lowercase														=			1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
      lowercase														=			Compose(
          [
              Resize((image_size, image_size)	),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]      ,	std=[0.229, 0.224, 0.225]	),
          ]	)
      lowercase														=			transforms(lowerCAmelCase__	).unsqueeze(0	)

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

      lowercase														=			model(lowerCAmelCase__	)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            lowercase														=			torch.Size([1, 3, 5_1_2, 5_1_2]	)
            lowercase														=			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:
            lowercase														=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
            lowercase														=			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
            lowercase														=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
            lowercase														=			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:
            lowercase														=			torch.Size([1, 3, 5_1_2, 5_1_2]	)
            lowercase														=			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:
            lowercase														=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
            lowercase														=			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!"""	)

      lowercase														=			{
          """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"""
          ),
      }
      lowercase														=			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__":
       __lowerCAmelCase					: Dict        =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					: Dict        =parser.parse_args()
       convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            warnings.warn(
                """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use YolosImageProcessor instead."""	, __lowerCAmelCase	, )
            super().__init__(*__lowerCAmelCase	, **__lowerCAmelCase		)



