



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'''




							_lowerCAmelCase     :							List[Any]    =    SwinaSRConfig()

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

							return config






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




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

							if name == "norm.weight":
														_lowerCAmelCase     :							Union[str, Any]    =    "layernorm.weight"
							if name == "norm.bias":
														_lowerCAmelCase     :							Optional[int]    =    "layernorm.bias"

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

							return name






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase     :							List[Any]    =    orig_state_dict.pop(_lowerCamelCase  )

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

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

							return orig_state_dict






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    SwinaSRForImageSuperResolution(_lowerCamelCase  )
							model.eval()

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

							_lowerCAmelCase     :							List[Any]    =    126 if "Jpeg" in checkpoint_url else 256
							_lowerCAmelCase     :							int    =    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]  ),
							    ]  )
							_lowerCAmelCase     :							List[str]    =    transforms(_lowerCamelCase  ).unsqueeze(0  )

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

							_lowerCAmelCase     :							Optional[Any]    =    model(_lowerCamelCase  )

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

							_lowerCAmelCase     :							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"
							    ),
							}
							_lowerCAmelCase     :							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(_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__":
						_snake_case		=  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.")

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


"""simple docstring"""



import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  for i in range(config.num_hidden_layers		):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]		)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]		)

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  print(f'''Saving model {deit_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__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT 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.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



