
"""simple docstring"""





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    s.rsplit(__lowerCamelCase,		__lowerCamelCase  )
   return new.join(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase  ):
   # encoder.embeddings are double copied in original FLAVA
   return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()  )







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[int]			     =    {}

   UpperCAmelCase_					:					List[str]			     =    ["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_					:					Tuple			     =    key.replace(f"""{group_key}.""",		f"""{group_key}.group."""  )

      if "res_path" in key:
         UpperCAmelCase_					:					Optional[Any]			     =    key.replace("res_path.",		"res_path.path."  )

      if key.endswith(".w"  ):
         UpperCAmelCase_					:					Any			     =    rreplace(__lowerCamelCase,		".w",		".weight",		1  )
      if key.endswith(".b"  ):
         UpperCAmelCase_					:					Optional[Any]			     =    rreplace(__lowerCamelCase,		".b",		".bias",		1  )

      UpperCAmelCase_					:					Optional[Any]			     =    value.float()

   return upgrade







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None,		__lowerCamelCase=True  ):
   from dall_e import Encoder

   UpperCAmelCase_					:					Any			     =    Encoder()
   if os.path.exists(__lowerCamelCase  ):
      UpperCAmelCase_					:					str			     =    torch.load(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					List[str]			     =    torch.hub.load_state_dict_from_url(__lowerCamelCase  )

   if isinstance(__lowerCamelCase,		__lowerCamelCase  ):
      UpperCAmelCase_					:					Any			     =    ckpt.state_dict()
   encoder.load_state_dict(__lowerCamelCase  )

   if config_path is not None:
      UpperCAmelCase_					:					int			     =    FlavaImageCodebookConfig.from_pretrained(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					List[Any]			     =    FlavaImageCodebookConfig()

   UpperCAmelCase_					:					str			     =    FlavaImageCodebook(__lowerCamelCase  ).eval()
   UpperCAmelCase_					:					Dict			     =    encoder.state_dict()

   UpperCAmelCase_					:					List[str]			     =    upgrade_state_dict(__lowerCamelCase  )
   hf_model.load_state_dict(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    hf_model.state_dict()
   UpperCAmelCase_					:					List[Any]			     =    count_parameters(__lowerCamelCase  )
   UpperCAmelCase_					:					List[Any]			     =    count_parameters(__lowerCamelCase  )

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

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


if __name__ == "__main__":
    _a		       =							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		       =							parser.parse_args()

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




"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime


@require_onnxruntime
class A_	:




  '''simple docstring'''
  pass


