


import math
import sys

import cva
import numpy as np
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    math.sqrt(UpperCamelCase_    )
     snake_case					=    1 / (sigma * math.sqrt(2 * math.pi    ))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    np.zeros((kernel_size, kernel_size)    )
     for i in range(0							,UpperCamelCase_    ):
          for j in range(0							,UpperCamelCase_    ):
               snake_case					=    math.sqrt(
                   abs(i - kernel_size // 2    ) ** 2 + abs(j - kernel_size // 2    ) ** 2    )
     return vec_gaussian(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):



     """simple docstring"""






     snake_case					=    np.zeros(img.shape    )
     snake_case					=    get_gauss_kernel(UpperCamelCase_							,UpperCamelCase_    )
     snake_case			,  snake_case					=    img.shape
     for i in range(kernel_size // 2							,size_x - kernel_size // 2    ):
          for j in range(kernel_size // 2							,size_y - kernel_size // 2    ):
               snake_case					=    get_slice(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
               snake_case					=    img_s - img_s[kernel_size // 2, kernel_size // 2]
               snake_case					=    vec_gaussian(UpperCamelCase_							,UpperCamelCase_    )
               snake_case					=    np.multiply(UpperCamelCase_							,UpperCamelCase_    )
               snake_case					=    np.multiply(UpperCamelCase_							,UpperCamelCase_    )
               snake_case					=    np.sum(UpperCamelCase_    ) / np.sum(UpperCamelCase_    )
               snake_case					=    val
     return imga
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    args[1] if args[1:] else '''../image_data/lena.jpg'''
     snake_case					=    float(args[2]    ) if args[2:] else 1.0
     snake_case					=    float(args[3]    ) if args[3:] else 1.0
     if args[4:]:
          snake_case					=    int(args[4]    )
          snake_case					=    kernel_size + abs(kernel_size % 2 - 1    )
     else:
          snake_case					=    5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   parse_args(sys.argv)
 _SCREAMING_SNAKE_CASE      :			Tuple      				=   cva.imread(filename, 0)
 cva.imshow("input image", img)

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   img / 2_55
 _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   out.astype("float32")
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 _SCREAMING_SNAKE_CASE      :			int      				=   out * 2_55
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   np.uinta(out)
 cva.imshow("output image", out)
 cva.waitKey(0)
 cva.destroyAllWindows()








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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     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'''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"
          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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






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



     """simple docstring"""






     snake_case					=    ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    [
         '''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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    dct.pop(UpperCamelCase_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ViTMSNConfig()
     snake_case					=    10_00

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

     if "s16" in checkpoint_url:
          snake_case					=    3_84
          snake_case					=    15_36
          snake_case					=    6
     elif "l16" in checkpoint_url:
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1
     elif "b4" in checkpoint_url:
          snake_case					=    4
     elif "l7" in checkpoint_url:
          snake_case					=    7
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1

     snake_case					=    ViTMSNModel(UpperCamelCase_    )

     snake_case					=    torch.hub.load_state_dict_from_url(UpperCamelCase_							,map_location='''cpu'''    )['''target_encoder''']

     snake_case					=    ViTImageProcessor(size=config.image_size    )

     remove_projection_head(UpperCamelCase_    )
     snake_case					=    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()

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

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

     # forward pass
     torch.manual_seed(2    )
     snake_case					=    model(**UpperCamelCase_    )
     snake_case					=    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:
          snake_case					=    torch.tensor([[-1.0915, -1.4876, -1.1809]]    )
     elif "b16" in checkpoint_url:
          snake_case					=    torch.tensor([[14.2889, -18.9045, 11.7281]]    )
     elif "l16" in checkpoint_url:
          snake_case					=    torch.tensor([[41.5028, -22.8681, 45.6475]]    )
     elif "b4" in checkpoint_url:
          snake_case					=    torch.tensor([[-4.3868, 5.2932, -0.4137]]    )
     else:
          snake_case					=    torch.tensor([[-0.1792, -0.6465, 2.4263]]    )

     # 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__":
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   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."
 )

 _SCREAMING_SNAKE_CASE      :			Any      				=   parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




