





'''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()
lowercase__    :      Dict						=    logging.get_logger(__name__)






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






def        _lowerCAmelCase			(  __snake_case  : Union[str, Any]			)     ->     Optional[Any]:
     __A						: Dict      =       ['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(__snake_case ,							__snake_case			)






def        _lowerCAmelCase			(  __snake_case  : Optional[int] ,							__snake_case  : List[str] ,							__snake_case  : Tuple			)     ->     str:
     __A						: Dict      =       dct.pop(__snake_case			)
     __A						: Any      =       val






def        _lowerCAmelCase			(  )     ->     str:
     __A						: str      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						: Dict      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)
     return im






@torch.no_grad()
def        _lowerCAmelCase			(  __snake_case  : Any ,							__snake_case  : int ,							__snake_case  : Optional[Any]=True			)     ->     List[str]:
     __A						: List[str]      =       ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          __A						: Optional[int]      =       8
     # set labels if required
     if not base_model:
          __A						: Optional[Any]      =       10_00
          __A						: Union[str, Any]      =       'huggingface/label-files'
          __A						: Union[str, Any]      =       'imagenet-1k-id2label.json'
          __A						: List[Any]      =       json.load(open(hf_hub_download(__snake_case ,							__snake_case ,							repo_type='dataset'			) ,							'r'			)			)
          __A						: int      =       {int(__snake_case			): v for k, v in idalabel.items()}
          __A						: Optional[int]      =       idalabel
          __A						: Dict      =       {v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          __A						: List[Any]      =       3_84
          __A						: str      =       15_36
          __A						: Optional[Any]      =       12
          __A						: List[str]      =       6

     # load original model from torch hub
     __A						: List[Any]      =       torch.hub.load('facebookresearch/dino:main' ,							__snake_case			)
     original_model.eval()

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

     # load HuggingFace model
     if base_model:
          __A						: Optional[int]      =       ViTModel(__snake_case ,							add_pooling_layer=__snake_case			).eval()
     else:
          __A						: Any      =       ViTForImageClassification(__snake_case			).eval()
     model.load_state_dict(__snake_case			)

     # Check outputs on an image, prepared by ViTImageProcessor
     __A						: int      =       ViTImageProcessor()
     __A						: str      =       image_processor(images=prepare_img() ,							return_tensors='pt'			)
     __A						: Any      =       encoding['pixel_values']
     __A						: Dict      =       model(__snake_case			)

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

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


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






'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : Optional[int]			)     ->     List[str]:
     __A						: List[str]      =       k_size // 2
     __A ,__A						: List[Any]      =       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						: List[Any]      =       1 / (2 * pi * sigma) * exp(-(square(__snake_case			) + square(__snake_case			)) / (2 * square(__snake_case			))			)
     return g






def        _lowerCAmelCase			(  __snake_case  : List[str] ,							__snake_case  : List[str] ,							__snake_case  : int			)     ->     Union[str, Any]:
     __A ,__A						: Tuple      =       image.shape[0], image.shape[1]
     # dst image height and width
     __A						: Tuple      =       height - k_size + 1
     __A						: Optional[Any]      =       width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						: str      =       zeros((dst_height * dst_width, k_size * k_size)			)
     __A						: Optional[Any]      =       0
     for i, j in product(range(__snake_case			) ,							range(__snake_case			)			):
          __A						: int      =       ravel(image[i : i + k_size, j : j + k_size]			)
          __A						: List[str]      =       window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						: List[Any]      =       gen_gaussian_kernel(__snake_case ,							__snake_case			)
     __A						: Any      =       ravel(__snake_case			)

     # reshape and get the dst image
     __A						: Dict      =       dot(__snake_case ,							__snake_case			).reshape(__snake_case ,							__snake_case			).astype(__snake_case			)

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__    :      List[Any]						=    imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__    :      Dict						=    cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__    :      Any						=    gaussian_filter(gray, 3, sigma=1)
  lowercase__    :      str						=    gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()