

import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  int ,  A__      :  int=False	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=[]
      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'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
            rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
            rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
            rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
            rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
            rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
            rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
            rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
            rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')	)
            rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')	)

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

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

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            _lowercase      			=[(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 	a (     A__      :  str ,  A__      :  Dict ,  A__      :  Dict=False	)       -> Optional[Any]:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase      			=['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(A__ ,  A__	)
def 	a (     A__      :  Tuple ,  A__      :  List[Any] ,  A__      :  Optional[Any]	)       -> Any:





      """simple docstring"""



      _lowercase      			=dct.pop(A__	)
      _lowercase      			=val
def 	a (     )       -> Dict:





      """simple docstring"""



      _lowercase      			='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase      			=Image.open(requests.get(A__ ,  stream=A__	).raw	)
      return im
@torch.no_grad()
def 	a (     A__      :  Dict ,  A__      :  Optional[int] ,  A__      :  Optional[Any]=True	)       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			=ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _lowercase      			=8
      # set labels if required
      if not base_model:
            _lowercase      			=1000
            _lowercase      			='huggingface/label-files'
            _lowercase      			='imagenet-1k-id2label.json'
            _lowercase      			=json.load(open(hf_hub_download(A__ ,  A__ ,  repo_type='dataset'	) ,  'r'	)	)
            _lowercase      			={int(A__	): v for k, v in idalabel.items()}
            _lowercase      			=idalabel
            _lowercase      			={v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _lowercase      			=384
            _lowercase      			=1536
            _lowercase      			=12
            _lowercase      			=6

      # load original model from torch hub
      _lowercase      			=torch.hub.load('facebookresearch/dino:main' ,  A__	)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowercase      			=original_model.state_dict()
      if base_model:
            remove_classification_head_(A__	)
      _lowercase      			=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__ ,  A__	)

      # load HuggingFace model
      if base_model:
            _lowercase      			=ViTModel(A__ ,  add_pooling_layer=A__	).eval()
      else:
            _lowercase      			=ViTForImageClassification(A__	).eval()
      model.load_state_dict(A__	)

      # Check outputs on an image, prepared by ViTImageProcessor
      _lowercase      			=ViTImageProcessor()
      _lowercase      			=image_processor(images=prepare_img() ,  return_tensors='pt'	)
      _lowercase      			=encoding['pixel_values']
      _lowercase      			=model(A__	)

      if base_model:
            _lowercase      			=original_model(A__	)
            assert torch.allclose(A__ ,  outputs.last_hidden_state[:, 0, :] ,  atol=1e-1	)
      else:
            _lowercase      			=original_model(A__	)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(A__ ,  outputs.logits ,  atol=1e-3	)

      Path(A__	).mkdir(exist_ok=A__	)
      print(F'''Saving model {model_name} 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__":
  lowercase_   							=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='dino_vitb16',
      type=str,
      help='Name of the model trained with DINO 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(
      '--base_model',
      action='store_true',
      help='Whether to only convert the base model (no projection head weights).',
  )

  parser.set_defaults(base_model=True)
  lowercase_   							=  parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """timesformer"""




  def __init__(							self				,  lowerCAmelCase=224				,  lowerCAmelCase=16				,  lowerCAmelCase=3				,  lowerCAmelCase=8				,  lowerCAmelCase=768				,  lowerCAmelCase=12				,  lowerCAmelCase=12				,  lowerCAmelCase=3_072				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.02				,  lowerCAmelCase=1e-6				,  lowerCAmelCase=True				,  lowerCAmelCase="divided_space_time"				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=num_frames

        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=initializer_range
        _lowercase      			=layer_norm_eps
        _lowercase      			=qkv_bias

        _lowercase      			=attention_type
        _lowercase      			=drop_path_rate


