





'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      if "cls_token" in name:
            _lowerCAmelCase												=							name.replace("""cls_token"""							,     """vit.embeddings.cls_token"""  )
      if "mask_token" in name:
            _lowerCAmelCase												=							name.replace("""mask_token"""							,     """decoder.mask_token"""  )
      if "decoder_pos_embed" in name:
            _lowerCAmelCase												=							name.replace("""decoder_pos_embed"""							,     """decoder.decoder_pos_embed"""  )
      if "pos_embed" in name and "decoder" not in name:
            _lowerCAmelCase												=							name.replace("""pos_embed"""							,     """vit.embeddings.position_embeddings"""  )
      if "patch_embed.proj" in name:
            _lowerCAmelCase												=							name.replace("""patch_embed.proj"""							,     """vit.embeddings.patch_embeddings.projection"""  )
      if "patch_embed.norm" in name:
            _lowerCAmelCase												=							name.replace("""patch_embed.norm"""							,     """vit.embeddings.norm"""  )
      if "decoder_blocks" in name:
            _lowerCAmelCase												=							name.replace("""decoder_blocks"""							,     """decoder.decoder_layers"""  )
      if "blocks" in name:
            _lowerCAmelCase												=							name.replace("""blocks"""							,     """vit.encoder.layer"""  )
      if "attn.proj" in name:
            _lowerCAmelCase												=							name.replace("""attn.proj"""							,     """attention.output.dense"""  )
      if "attn" in name:
            _lowerCAmelCase												=							name.replace("""attn"""							,     """attention.self"""  )
      if "norm1" in name:
            _lowerCAmelCase												=							name.replace("""norm1"""							,     """layernorm_before"""  )
      if "norm2" in name:
            _lowerCAmelCase												=							name.replace("""norm2"""							,     """layernorm_after"""  )
      if "mlp.fc1" in name:
            _lowerCAmelCase												=							name.replace("""mlp.fc1"""							,     """intermediate.dense"""  )
      if "mlp.fc2" in name:
            _lowerCAmelCase												=							name.replace("""mlp.fc2"""							,     """output.dense"""  )
      if "decoder_embed" in name:
            _lowerCAmelCase												=							name.replace("""decoder_embed"""							,     """decoder.decoder_embed"""  )
      if "decoder_norm" in name:
            _lowerCAmelCase												=							name.replace("""decoder_norm"""							,     """decoder.decoder_norm"""  )
      if "decoder_pred" in name:
            _lowerCAmelCase												=							name.replace("""decoder_pred"""							,     """decoder.decoder_pred"""  )
      if "norm.weight" in name and "decoder" not in name:
            _lowerCAmelCase												=							name.replace("""norm.weight"""							,     """vit.layernorm.weight"""  )
      if "norm.bias" in name and "decoder" not in name:
            _lowerCAmelCase												=							name.replace("""norm.bias"""							,     """vit.layernorm.bias"""  )

      return name
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase												=							orig_state_dict.pop(lowerCAmelCase  )

            if "qkv" in key:
                  _lowerCAmelCase												=							key.split("""."""  )
                  _lowerCAmelCase												=							int(key_split[1]  )
                  if "decoder_blocks" in key:
                        _lowerCAmelCase												=							config.decoder_hidden_size
                        _lowerCAmelCase												=							"""decoder.decoder_layers."""
                        if "weight" in key:
                              _lowerCAmelCase												=							val[:dim, :]
                              _lowerCAmelCase												=							val[dim : dim * 2, :]
                              _lowerCAmelCase												=							val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase												=							val[:dim]
                              _lowerCAmelCase												=							val[dim : dim * 2]
                              _lowerCAmelCase												=							val[-dim:]
                  else:
                        _lowerCAmelCase												=							config.hidden_size
                        _lowerCAmelCase												=							"""vit.encoder.layer."""
                        if "weight" in key:
                              _lowerCAmelCase												=							val[:dim, :]
                              _lowerCAmelCase												=							val[dim : dim * 2, :]
                              _lowerCAmelCase												=							val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase												=							val[:dim]
                              _lowerCAmelCase												=							val[dim : dim * 2]
                              _lowerCAmelCase												=							val[-dim:]

            else:
                  _lowerCAmelCase												=							val

      return orig_state_dict
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCAmelCase												=							10_24
            _lowerCAmelCase												=							40_96
            _lowerCAmelCase												=							24
            _lowerCAmelCase												=							16
      elif "huge" in checkpoint_url:
            _lowerCAmelCase												=							14
            _lowerCAmelCase												=							12_80
            _lowerCAmelCase												=							51_20
            _lowerCAmelCase												=							32
            _lowerCAmelCase												=							16

      _lowerCAmelCase												=							ViTMAEForPreTraining(lowerCAmelCase  )

      _lowerCAmelCase												=							torch.hub.load_state_dict_from_url(lowerCAmelCase							,     map_location="""cpu"""  )["""model"""]

      _lowerCAmelCase												=							ViTMAEImageProcessor(size=config.image_size  )

      _lowerCAmelCase												=							convert_state_dict(lowerCAmelCase							,     lowerCAmelCase  )

      model.load_state_dict(lowerCAmelCase  )
      model.eval()

      _lowerCAmelCase												=							"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

      _lowerCAmelCase												=							Image.open(requests.get(lowerCAmelCase							,     stream=lowerCAmelCase  ).raw  )
      _lowerCAmelCase												=							ViTMAEImageProcessor(size=config.image_size  )
      _lowerCAmelCase												=							image_processor(images=lowerCAmelCase							,     return_tensors="""pt"""  )

      # forward pass
      torch.manual_seed(2  )
      _lowerCAmelCase												=							model(**lowerCAmelCase  )
      _lowerCAmelCase												=							outputs.logits

      if "large" in checkpoint_url:
            _lowerCAmelCase												=							torch.tensor(
                [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]  )
      elif "huge" in checkpoint_url:
            _lowerCAmelCase												=							torch.tensor(
                [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]  )
      else:
            _lowerCAmelCase												=							torch.tensor(
                [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]  )

      # verify logits
      assert torch.allclose(logits[0, :3, :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__":
      A__    :       Optional[Any]              =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
          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.'''
      )

      A__    :       str              =parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							cva.getAffineTransform(lowerCAmelCase							,     lowerCAmelCase  )
      return cva.warpAffine(lowerCAmelCase							,     lowerCAmelCase							,     (rows, cols)  )


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

      # set different points to rotate image
      A__    :       Optional[int]              =np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
      A__    :       Tuple              =np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
      A__    :       List[str]              =np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
      A__    :       Dict              =np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

      # add all rotated images in a list
      A__    :       List[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
      A__    :       Tuple              =plt.figure(1)
      A__    :       int              =['''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()






