





'''simple docstring'''






import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							{}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            _lowerCAmelCase												=							key.replace("""heads.cmd.mim_head.cls.predictions"""							,     """mmm_image_head"""  )
            _lowerCAmelCase												=							key.replace("""heads.cmd.mlm_head.cls.predictions"""							,     """mmm_text_head"""  )
            _lowerCAmelCase												=							key.replace("""heads.cmd.itm_head.cls"""							,     """itm_head"""  )
            _lowerCAmelCase												=							key.replace("""heads.cmd.itm_head.pooler"""							,     """itm_head.pooler"""  )
            _lowerCAmelCase												=							key.replace("""heads.cmd.clip_head.logit_scale"""							,     """flava.logit_scale"""  )
            _lowerCAmelCase												=							key.replace("""heads.fairseq_mlm.cls.predictions"""							,     """mlm_head"""  )
            _lowerCAmelCase												=							key.replace("""heads.imagenet.mim_head.cls.predictions"""							,     """mim_head"""  )
            _lowerCAmelCase												=							key.replace("""mm_text_projection"""							,     """flava.text_to_mm_projection"""  )
            _lowerCAmelCase												=							key.replace("""mm_image_projection"""							,     """flava.image_to_mm_projection"""  )
            _lowerCAmelCase												=							key.replace("""image_encoder.module"""							,     """flava.image_model"""  )
            _lowerCAmelCase												=							key.replace("""text_encoder.module"""							,     """flava.text_model"""  )
            _lowerCAmelCase												=							key.replace("""mm_encoder.module.encoder.cls_token"""							,     """flava.multimodal_model.cls_token"""  )
            _lowerCAmelCase												=							key.replace("""mm_encoder.module"""							,     """flava.multimodal_model"""  )
            _lowerCAmelCase												=							key.replace("""text_projection"""							,     """flava.text_projection"""  )
            _lowerCAmelCase												=							key.replace("""image_projection"""							,     """flava.image_projection"""  )

            _lowerCAmelCase												=							value.float()

      for key, value in codebook_state_dict.items():
            _lowerCAmelCase												=							value

      return upgrade
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      if config_path is not None:
            _lowerCAmelCase												=							FlavaConfig.from_pretrained(lowerCAmelCase  )
      else:
            _lowerCAmelCase												=							FlavaConfig()

      _lowerCAmelCase												=							FlavaForPreTraining(lowerCAmelCase  ).eval()

      _lowerCAmelCase												=							convert_dalle_checkpoint(lowerCAmelCase							,     lowerCAmelCase							,     save_checkpoint=lowerCAmelCase  )

      if os.path.exists(lowerCAmelCase  ):
            _lowerCAmelCase												=							torch.load(lowerCAmelCase							,     map_location="""cpu"""  )
      else:
            _lowerCAmelCase												=							torch.hub.load_state_dict_from_url(lowerCAmelCase							,     map_location="""cpu"""  )

      _lowerCAmelCase												=							upgrade_state_dict(lowerCAmelCase							,     lowerCAmelCase  )
      hf_model.load_state_dict(lowerCAmelCase  )
      _lowerCAmelCase												=							hf_model.state_dict()
      _lowerCAmelCase												=							count_parameters(lowerCAmelCase  )
      _lowerCAmelCase												=							count_parameters(lowerCAmelCase  ) + count_parameters(lowerCAmelCase  )

      assert torch.allclose(lowerCAmelCase							,     lowerCAmelCase							,     atol=1e-3  )

      hf_model.save_pretrained(lowerCAmelCase  )


if __name__ == "__main__":
   A__    :       int              =argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
   parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   A__    :       Tuple              =parser.parse_args()

   convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_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()






