
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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Any:




   """simple docstring"""
   _SCREAMING_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"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"
      _SCREAMING_SNAKE_CASE      =				[(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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     int:




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




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				dct.pop(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				val
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ViTMSNConfig()
   _SCREAMING_SNAKE_CASE      =				10_00

   _SCREAMING_SNAKE_CASE      =				"""datasets/huggingface/label-files"""
   _SCREAMING_SNAKE_CASE      =				"""imagenet-1k-id2label.json"""
   _SCREAMING_SNAKE_CASE      =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)    ,      """r"""						)						)
   _SCREAMING_SNAKE_CASE      =				{int(SCREAMING_SNAKE_CASE_						): v for k, v in idalabel.items()}
   _SCREAMING_SNAKE_CASE      =				idalabel
   _SCREAMING_SNAKE_CASE      =				{v: k for k, v in idalabel.items()}

   if "s16" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				3_84
      _SCREAMING_SNAKE_CASE      =				15_36
      _SCREAMING_SNAKE_CASE      =				6
   elif "l16" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				0.1
   elif "b4" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				4
   elif "l7" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				7
      _SCREAMING_SNAKE_CASE      =				10_24
      _SCREAMING_SNAKE_CASE      =				40_96
      _SCREAMING_SNAKE_CASE      =				24
      _SCREAMING_SNAKE_CASE      =				16
      _SCREAMING_SNAKE_CASE      =				0.1

   _SCREAMING_SNAKE_CASE      =				ViTMSNModel(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)["""target_encoder"""]

   _SCREAMING_SNAKE_CASE      =				ViTImageProcessor(size=config.image_size						)

   remove_projection_head(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				create_rename_keys(SCREAMING_SNAKE_CASE_    ,      base_model=SCREAMING_SNAKE_CASE_						)

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

   model.load_state_dict(SCREAMING_SNAKE_CASE_						)
   model.eval()

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

   _SCREAMING_SNAKE_CASE      =				Image.open(requests.get(SCREAMING_SNAKE_CASE_    ,      stream=SCREAMING_SNAKE_CASE_						).raw						)
   _SCREAMING_SNAKE_CASE      =				ViTImageProcessor(
       size=config.image_size    ,      image_mean=SCREAMING_SNAKE_CASE_    ,      image_std=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				image_processor(images=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""						)

   # forward pass
   torch.manual_seed(2						)
   _SCREAMING_SNAKE_CASE      =				model(**SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				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:
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[-1.0915, -1.4876, -1.1809]]						)
   elif "b16" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[14.2889, -18.9045, 11.7281]]						)
   elif "l16" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[41.5028, -22.8681, 45.6475]]						)
   elif "b4" in checkpoint_url:
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[-4.3868, 5.2932, -0.4137]]						)
   else:
      _SCREAMING_SNAKE_CASE      =				torch.tensor([[-0.1792, -0.6465, 2.4263]]						)

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

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

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


if __name__ == "__main__":
     UpperCamelCase__      :		int       							=  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."
     )

     UpperCamelCase__      :		List[Any]       							=  parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join([hex(SCREAMING_SNAKE_CASE_						)[2:].zfill(2						).upper() for byte in list(SCREAMING_SNAKE_CASE_						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bytes:




   """simple docstring"""
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(SCREAMING_SNAKE_CASE_						) % 2) != 0:
      raise ValueError(
          """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""						)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(SCREAMING_SNAKE_CASE_						) <= set("""0123456789ABCDEF"""						):
      raise ValueError(
          """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""						)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]    ,      16						) for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      2						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






