





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	lowerCAmelCase_					(			__a      ,       __a=False						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: Dict         =[]
  for i in range(config.num_hidden_layers						):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")						)
    rename_keys.append(
        (F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")						)
    rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")						)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("module.cls_token", "vit.embeddings.cls_token"),
          ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
          ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
          ("module.pos_embed", "vit.embeddings.position_embeddings"),
      ]						)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("module.norm.weight", "layernorm.weight"),
            ("module.norm.bias", "layernorm.bias"),
        ]						)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCamelCase__: int         =[(pair[0], pair[1][4:]) if pair[1].startswith("vit"						) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("norm.weight", "vit.layernorm.weight"),
            ("norm.bias", "vit.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]						)

  return rename_keys





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=False						)	->						Any:







  """simple docstring"""





  for i in range(config.num_hidden_layers						):
    if base_model:
      lowerCamelCase__: List[Any]         =""
    else:
      lowerCamelCase__: Dict         ="vit."
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCamelCase__: Tuple         =state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight"""						)
    lowerCamelCase__: int         =state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias"""						)
    # next, add query, keys and values (in that order) to the state dict
    lowerCamelCase__: int         =in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase__: Optional[int]         =in_proj_bias[: config.hidden_size]
    lowerCamelCase__: Union[str, Any]         =in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase__: str         =in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCamelCase__: Dict         =in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase__: str         =in_proj_bias[-config.hidden_size :]





def 	lowerCAmelCase_					(			__a						)	->						Optional[Any]:







  """simple docstring"""





  lowerCamelCase__: Tuple         =["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a						)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Any         =[
      "module.fc.fc1.weight",
      "module.fc.fc1.bias",
      "module.fc.bn1.weight",
      "module.fc.bn1.bias",
      "module.fc.bn1.running_mean",
      "module.fc.bn1.running_var",
      "module.fc.bn1.num_batches_tracked",
      "module.fc.fc2.weight",
      "module.fc.fc2.bias",
      "module.fc.bn2.weight",
      "module.fc.bn2.bias",
      "module.fc.bn2.running_mean",
      "module.fc.bn2.running_var",
      "module.fc.bn2.num_batches_tracked",
      "module.fc.fc3.weight",
      "module.fc.fc3.bias",
  ]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: int         =dct.pop(__a						)
  lowerCamelCase__: Tuple         =val





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						str:







  """simple docstring"""





  lowerCamelCase__: List[str]         =ViTMSNConfig()
  lowerCamelCase__: Optional[Any]         =1000

  lowerCamelCase__: Dict         ="datasets/huggingface/label-files"
  lowerCamelCase__: List[str]         ="imagenet-1k-id2label.json"
  lowerCamelCase__: List[str]         =json.load(open(hf_hub_download(__a      ,       __a						)      ,       "r"						)						)
  lowerCamelCase__: int         ={int(__a						): v for k, v in idalabel.items()}
  lowerCamelCase__: List[str]         =idalabel
  lowerCamelCase__: Union[str, Any]         ={v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    lowerCamelCase__: Dict         =384
    lowerCamelCase__: Union[str, Any]         =1536
    lowerCamelCase__: Dict         =6
  elif "l16" in checkpoint_url:
    lowerCamelCase__: str         =1024
    lowerCamelCase__: List[Any]         =4096
    lowerCamelCase__: List[str]         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Any         =0.1
  elif "b4" in checkpoint_url:
    lowerCamelCase__: List[str]         =4
  elif "l7" in checkpoint_url:
    lowerCamelCase__: Optional[Any]         =7
    lowerCamelCase__: int         =1024
    lowerCamelCase__: int         =4096
    lowerCamelCase__: Tuple         =24
    lowerCamelCase__: List[Any]         =16
    lowerCamelCase__: Tuple         =0.1

  lowerCamelCase__: Union[str, Any]         =ViTMSNModel(__a						)

  lowerCamelCase__: Tuple         =torch.hub.load_state_dict_from_url(__a      ,       map_location="cpu"						)["target_encoder"]

  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(size=config.image_size						)

  remove_projection_head(__a						)
  lowerCamelCase__: Optional[Any]         =create_rename_keys(__a      ,       base_model=__a						)

  for src, dest in rename_keys:
    rename_key(__a      ,       __a      ,       __a						)
  read_in_q_k_v(__a      ,       __a      ,       base_model=__a						)

  model.load_state_dict(__a						)
  model.eval()

  lowerCamelCase__: Dict         ="http://images.cocodataset.org/val2017/000000039769.jpg"

  lowerCamelCase__: Any         =Image.open(requests.get(__a      ,       stream=__a						).raw						)
  lowerCamelCase__: Union[str, Any]         =ViTImageProcessor(
      size=config.image_size      ,       image_mean=__a      ,       image_std=__a						)
  lowerCamelCase__: List[str]         =image_processor(images=__a      ,       return_tensors="pt"						)

  # forward pass
  torch.manual_seed(2						)
  lowerCamelCase__: Union[str, Any]         =model(**__a						)
  lowerCamelCase__: List[str]         =outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    lowerCamelCase__: str         =torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]						)
  elif "b16" in checkpoint_url:
    lowerCamelCase__: Tuple         =torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]						)
  elif "l16" in checkpoint_url:
    lowerCamelCase__: List[Any]         =torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]						)
  elif "b4" in checkpoint_url:
    lowerCamelCase__: Union[str, Any]         =torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]						)
  else:
    lowerCamelCase__: Dict         =torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]						)

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]      ,       __a      ,       atol=1e-4						)

  print(F"""Saving model to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(__a						)

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
  image_processor.save_pretrained(__a						)


if __name__ == "__main__":
     __A							   =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
         type=str,
         help="URL of the 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."
     )

     __A							   =							parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


if __name__ == "__main__":
    from doctest import testmod

    testmod()


