


'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def _UpperCamelCase       (     __A      ,			__A      ,			__A      ,			__A    )							->		str:


      '''simple docstring'''



      UpperCamelCase__										=				s.rsplit(__A      ,			__A    )
      return new.join(__A    )







def _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



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







def _UpperCamelCase       (     __A    )							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__										=				{}

      UpperCamelCase__										=				["group_1", "group_2", "group_3", "group_4"]
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        UpperCamelCase__										=				key.replace(F'''{group_key}.'''      ,			F'''{group_key}.group.'''    )

            if "res_path" in key:
                  UpperCamelCase__										=				key.replace("res_path."      ,			"res_path.path."    )

            if key.endswith(".w"    ):
                  UpperCamelCase__										=				rreplace(__A      ,			".w"      ,			".weight"      ,			1    )
            if key.endswith(".b"    ):
                  UpperCamelCase__										=				rreplace(__A      ,			".b"      ,			".bias"      ,			1    )

            UpperCamelCase__										=				value.float()

      return upgrade







@torch.no_grad()
def _UpperCamelCase       (     __A      ,			__A      ,			__A=None      ,			__A=True    )							->		Optional[int]:


      '''simple docstring'''



      from dall_e import Encoder

      UpperCamelCase__										=				Encoder()
      if os.path.exists(__A    ):
            UpperCamelCase__										=				torch.load(__A    )
      else:
            UpperCamelCase__										=				torch.hub.load_state_dict_from_url(__A    )

      if isinstance(__A      ,			__A    ):
            UpperCamelCase__										=				ckpt.state_dict()
      encoder.load_state_dict(__A    )

      if config_path is not None:
            UpperCamelCase__										=				FlavaImageCodebookConfig.from_pretrained(__A    )
      else:
            UpperCamelCase__										=				FlavaImageCodebookConfig()

      UpperCamelCase__										=				FlavaImageCodebook(__A    ).eval()
      UpperCamelCase__										=				encoder.state_dict()

      UpperCamelCase__										=				upgrade_state_dict(__A    )
      hf_model.load_state_dict(__A    )
      UpperCamelCase__										=				hf_model.state_dict()
      UpperCamelCase__										=				count_parameters(__A    )
      UpperCamelCase__										=				count_parameters(__A    )

      assert torch.allclose(__A      ,			__A      ,			atol=1E-3    )

      if save_checkpoint:
            hf_model.save_pretrained(__A    )
      else:
            return hf_state_dict


if __name__ == "__main__":
  a__  :					Dict        =       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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  a__  :					Any        =       parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)













def 						_a    (      a :int = 100 )							->			int:
 a		  =      n * (n + 1) * (2 * n + 1) / 6
 a		  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares )


if __name__ == "__main__":
     print(f"""{solution() = }""")


