'''simple docstring'''
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       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Union[str, Any]=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      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"
            UpperCAmelCase_            =  [(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       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any=False					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase							,    _UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  [
          '''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(_UpperCamelCase							,    _UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


      UpperCAmelCase_            =  dct.pop(_UpperCamelCase					)
      UpperCAmelCase_            =  val



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ViTMSNConfig()
      UpperCAmelCase_            =  1000

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

      if "s16" in checkpoint_url:
            UpperCAmelCase_            =  384
            UpperCAmelCase_            =  1536
            UpperCAmelCase_            =  6
      elif "l16" in checkpoint_url:
            UpperCAmelCase_            =  1024
            UpperCAmelCase_            =  4096
            UpperCAmelCase_            =  24
            UpperCAmelCase_            =  16
            UpperCAmelCase_            =  0.1
      elif "b4" in checkpoint_url:
            UpperCAmelCase_            =  4
      elif "l7" in checkpoint_url:
            UpperCAmelCase_            =  7
            UpperCAmelCase_            =  1024
            UpperCAmelCase_            =  4096
            UpperCAmelCase_            =  24
            UpperCAmelCase_            =  16
            UpperCAmelCase_            =  0.1

      UpperCAmelCase_            =  ViTMSNModel(_UpperCamelCase					)

      UpperCAmelCase_            =  torch.hub.load_state_dict_from_url(_UpperCamelCase							,    map_location='''cpu'''					)['''target_encoder''']

      UpperCAmelCase_            =  ViTImageProcessor(size=config.image_size					)

      remove_projection_head(_UpperCamelCase					)
      UpperCAmelCase_            =  create_rename_keys(_UpperCamelCase							,    base_model=_UpperCamelCase					)

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

      model.load_state_dict(_UpperCamelCase					)
      model.eval()

      UpperCAmelCase_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)
      UpperCAmelCase_            =  ViTImageProcessor(
          size=config.image_size							,    image_mean=_UpperCamelCase							,    image_std=_UpperCamelCase					)
      UpperCAmelCase_            =  image_processor(images=_UpperCamelCase							,    return_tensors='''pt'''					)

      # forward pass
      torch.manual_seed(2					)
      UpperCAmelCase_            =  model(**_UpperCamelCase					)
      UpperCAmelCase_            =  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:
            UpperCAmelCase_            =  torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]					)
      elif "b16" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[14.2_889, -18.9_045, 11.7_281]]					)
      elif "l16" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[41.5_028, -22.8_681, 45.6_475]]					)
      elif "b4" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]					)
      else:
            UpperCAmelCase_            =  torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]					)

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

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

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


if __name__ == "__main__":
      lowercase__  :      Dict        =					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."
      )

      lowercase__  :      str        =					parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''
import re



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]'''							,    str_					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  split_input(str_					)
      return "".join(
          [''''''.join([char.capitalize() for char in sub_str]					) for sub_str in string_split]					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  split_input(_UpperCamelCase					)
            if upper:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.upper() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            else:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.lower() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            return res_str
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return to_simple_case(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  to_simple_case(_UpperCamelCase					)
            return res_str[0].lower() + res_str[1:]
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''_'''					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''-'''					)


if __name__ == "__main__":
      __import__("doctest").testmod()





