





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()
_A					:				Any				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						List[str]:
    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'''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"
        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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    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'''blocks.{i}.attn.qkv.weight'''	)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''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 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase_ ,							lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    )				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=True	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        SCREAMING_SNAKE_CASE__											=			8
    # set labels if required
    if not base_model:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
        SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
        SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        SCREAMING_SNAKE_CASE__											=			3_8_4
        SCREAMING_SNAKE_CASE__											=			1_5_3_6
        SCREAMING_SNAKE_CASE__											=			1_2
        SCREAMING_SNAKE_CASE__											=			6

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

    # load state_dict of original model, remove and rename some keys
    SCREAMING_SNAKE_CASE__											=			original_model.state_dict()
    if base_model:
        remove_classification_head_(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			create_rename_keys(lowerCAmelCase_ ,							base_model=lowerCAmelCase_	)
    for src, dest in rename_keys:
        rename_key(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    read_in_q_k_v(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

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

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

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

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    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}'''	)
    image_processor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				Optional[Any]				=			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)
  _A					:				Optional[Any]				=			parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)


'''simple docstring'''

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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Tuple	=			"""van"""


 def __init__(    self  : List[Any]			,							a__  : Dict=224			,							a__  : Dict=3			,							a__  : Union[str, Any]=[7, 3, 3, 3]			,							a__  : Optional[Any]=[4, 2, 2, 2]			,							a__  : Optional[Any]=[64, 128, 320, 512]			,							a__  : List[str]=[3, 3, 12, 3]			,							a__  : Any=[8, 8, 4, 4]			,							a__  : Optional[int]="gelu"			,							a__  : List[Any]=0.02			,							a__  : Tuple=1e-6			,							a__  : List[str]=1e-2			,							a__  : List[str]=0.0			,							a__  : List[Any]=0.0			,							**a__  : Tuple			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   Any     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	patch_sizes
  lowerCAmelCase__     :   Dict     		=	strides
  lowerCAmelCase__     :   List[str]     		=	hidden_sizes
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Tuple     		=	mlp_ratios
  lowerCAmelCase__     :   Optional[Any]     		=	hidden_act
  lowerCAmelCase__     :   Dict     		=	initializer_range
  lowerCAmelCase__     :   int     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[Any]     		=	layer_scale_init_value
  lowerCAmelCase__     :   List[str]     		=	drop_path_rate
  lowerCAmelCase__     :   Any     		=	dropout_rate





