





"""simple docstring"""


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()
lowerCamelCase__				  =   logging.get_logger(__name__)




def 					lowercase__    (					lowercase_		,lowercase_=False			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       []
      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"
            _UpperCamelCase      :	List[str]						=       [(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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=False			)					->		Any:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       ["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       dct.pop(lowercase_			)
      _UpperCamelCase      :	List[str]						=       val




def 					lowercase__    (					)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase      :	Any						=       Image.open(requests.get(lowercase_		,stream=lowercase_			).raw			)
      return im




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=True			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _UpperCamelCase      :	List[Any]						=       8
      # set labels if required
      if not base_model:
            _UpperCamelCase      :	Tuple						=       1_000
            _UpperCamelCase      :	Union[str, Any]						=       "huggingface/label-files"
            _UpperCamelCase      :	Optional[Any]						=       "imagenet-1k-id2label.json"
            _UpperCamelCase      :	int						=       json.load(open(hf_hub_download(lowercase_		,lowercase_		,repo_type="dataset"			)		,"r"			)			)
            _UpperCamelCase      :	int						=       {int(lowercase_			): v for k, v in idalabel.items()}
            _UpperCamelCase      :	int						=       idalabel
            _UpperCamelCase      :	List[str]						=       {v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _UpperCamelCase      :	Dict						=       384
            _UpperCamelCase      :	Any						=       1_536
            _UpperCamelCase      :	List[Any]						=       12
            _UpperCamelCase      :	Union[str, Any]						=       6

      # load original model from torch hub
      _UpperCamelCase      :	List[str]						=       torch.hub.load("facebookresearch/dino:main"		,lowercase_			)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _UpperCamelCase      :	int						=       original_model.state_dict()
      if base_model:
            remove_classification_head_(lowercase_			)
      _UpperCamelCase      :	Optional[Any]						=       create_rename_keys(lowercase_		,base_model=lowercase_			)
      for src, dest in rename_keys:
            rename_key(lowercase_		,lowercase_		,lowercase_			)
      read_in_q_k_v(lowercase_		,lowercase_		,lowercase_			)

      # load HuggingFace model
      if base_model:
            _UpperCamelCase      :	Dict						=       ViTModel(lowercase_		,add_pooling_layer=lowercase_			).eval()
      else:
            _UpperCamelCase      :	List[str]						=       ViTForImageClassification(lowercase_			).eval()
      model.load_state_dict(lowercase_			)

      # Check outputs on an image, prepared by ViTImageProcessor
      _UpperCamelCase      :	Tuple						=       ViTImageProcessor()
      _UpperCamelCase      :	str						=       image_processor(images=prepare_img()		,return_tensors="pt"			)
      _UpperCamelCase      :	str						=       encoding["pixel_values"]
      _UpperCamelCase      :	Union[str, Any]						=       model(lowercase_			)

      if base_model:
            _UpperCamelCase      :	Union[str, Any]						=       original_model(lowercase_			)
            assert torch.allclose(lowercase_		,outputs.last_hidden_state[:, 0, :]		,atol=1e-1			)
      else:
            _UpperCamelCase      :	str						=       original_model(lowercase_			)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(lowercase_		,outputs.logits		,atol=1e-3			)

      Path(lowercase_			).mkdir(exist_ok=lowercase_			)
      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(lowercase_			)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
      image_processor.save_pretrained(lowercase_			)


if __name__ == "__main__":
  lowerCamelCase__				  =   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)
  lowerCamelCase__				  =   parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
  with open(file_name, "wb") as fp:
    fp.write(image_data)
  print(f"""Done. Image saved to disk as {file_name}.""")
