


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      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		int=False       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[Any]   	=		[]
       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"
              UpperCamelCase    :	Tuple   	=		[(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	(							SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		str=False       ):






       """simple docstring"""





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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		['''head.weight''', '''head.bias''']
       for k in ignore_keys:
              state_dict.pop(lowerCamelCase__							,			lowerCamelCase__       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		[
           '''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(lowerCamelCase__							,			lowerCamelCase__       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dct.pop(lowerCamelCase__       )
       UpperCamelCase    :	Union[str, Any]   	=		val


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		ViTMSNConfig()
       UpperCamelCase    :	Dict   	=		1_0_0_0

       UpperCamelCase    :	List[str]   	=		'''datasets/huggingface/label-files'''
       UpperCamelCase    :	Optional[int]   	=		'''imagenet-1k-id2label.json'''
       UpperCamelCase    :	Dict   	=		json.load(open(hf_hub_download(lowerCamelCase__							,			lowerCamelCase__       )							,			'''r'''       )       )
       UpperCamelCase    :	List[Any]   	=		{int(lowerCamelCase__       ): v for k, v in idalabel.items()}
       UpperCamelCase    :	List[str]   	=		idalabel
       UpperCamelCase    :	Optional[Any]   	=		{v: k for k, v in idalabel.items()}

       if "s16" in checkpoint_url:
              UpperCamelCase    :	List[str]   	=		3_8_4
              UpperCamelCase    :	Any   	=		1_5_3_6
              UpperCamelCase    :	str   	=		6
       elif "l16" in checkpoint_url:
              UpperCamelCase    :	Any   	=		1_0_2_4
              UpperCamelCase    :	Union[str, Any]   	=		4_0_9_6
              UpperCamelCase    :	str   	=		2_4
              UpperCamelCase    :	Optional[Any]   	=		1_6
              UpperCamelCase    :	int   	=		0.1
       elif "b4" in checkpoint_url:
              UpperCamelCase    :	Optional[int]   	=		4
       elif "l7" in checkpoint_url:
              UpperCamelCase    :	Union[str, Any]   	=		7
              UpperCamelCase    :	Union[str, Any]   	=		1_0_2_4
              UpperCamelCase    :	Dict   	=		4_0_9_6
              UpperCamelCase    :	Optional[int]   	=		2_4
              UpperCamelCase    :	int   	=		1_6
              UpperCamelCase    :	str   	=		0.1

       UpperCamelCase    :	Tuple   	=		ViTMSNModel(lowerCamelCase__       )

       UpperCamelCase    :	Tuple   	=		torch.hub.load_state_dict_from_url(lowerCamelCase__							,			map_location='''cpu'''       )['''target_encoder''']

       UpperCamelCase    :	Union[str, Any]   	=		ViTImageProcessor(size=config.image_size       )

       remove_projection_head(lowerCamelCase__       )
       UpperCamelCase    :	Optional[int]   	=		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__							,			base_model=lowerCamelCase__       )

       model.load_state_dict(lowerCamelCase__       )
       model.eval()

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

       UpperCamelCase    :	List[Any]   	=		Image.open(requests.get(lowerCamelCase__							,			stream=lowerCamelCase__       ).raw       )
       UpperCamelCase    :	List[str]   	=		ViTImageProcessor(
           size=config.image_size							,			image_mean=lowerCamelCase__							,			image_std=lowerCamelCase__       )
       UpperCamelCase    :	Tuple   	=		image_processor(images=lowerCamelCase__							,			return_tensors='''pt'''       )

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

       # verify logits
       assert torch.allclose(last_hidden_state[:, 0, :3]							,			lowerCamelCase__							,			atol=1E-4       )

       print(F"""Saving model 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__":
   __UpperCAmelCase : str 			=				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."
   )

   __UpperCAmelCase : List[Any] 			=				parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

