"""simple docstring"""




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Dict			) ->   List[Any]:
  _lowerCAmelCase		: Dict						    =  s.rsplit(_lowerCamelCase    ,_lowerCamelCase			)
  return new.join(_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   Dict:
  # 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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]			) ->   Dict:
  _lowerCAmelCase		: str						    =  {}

  _lowerCAmelCase		: Optional[int]						    =  ["""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:
        _lowerCAmelCase		: Optional[Any]						    =  key.replace(f"{group_key}."    ,f"{group_key}.group."			)

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

    if key.endswith(""".w"""			):
      _lowerCAmelCase		: Optional[int]						    =  rreplace(_lowerCamelCase    ,""".w"""    ,""".weight"""    ,1			)
    if key.endswith(""".b"""			):
      _lowerCAmelCase		: Dict						    =  rreplace(_lowerCamelCase    ,""".b"""    ,""".bias"""    ,1			)

    _lowerCAmelCase		: Tuple						    =  value.float()

  return upgrade





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: int    ,_lowerCamelCase		: Optional[Any]=None    ,_lowerCamelCase		: List[str]=True			) ->   Optional[Any]:
  from dall_e import Encoder

  _lowerCAmelCase		: Optional[Any]						    =  Encoder()
  if os.path.exists(_lowerCamelCase			):
    _lowerCAmelCase		: int						    =  torch.load(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: List[str]						    =  torch.hub.load_state_dict_from_url(_lowerCamelCase			)

  if isinstance(_lowerCamelCase    ,_lowerCamelCase			):
    _lowerCAmelCase		: Any						    =  ckpt.state_dict()
  encoder.load_state_dict(_lowerCamelCase			)

  if config_path is not None:
    _lowerCAmelCase		: Tuple						    =  FlavaImageCodebookConfig.from_pretrained(_lowerCamelCase			)
  else:
    _lowerCAmelCase		: Union[str, Any]						    =  FlavaImageCodebookConfig()

  _lowerCAmelCase		: Optional[int]						    =  FlavaImageCodebook(_lowerCamelCase			).eval()
  _lowerCAmelCase		: Any						    =  encoder.state_dict()

  _lowerCAmelCase		: List[str]						    =  upgrade_state_dict(_lowerCamelCase			)
  hf_model.load_state_dict(_lowerCamelCase			)
  _lowerCAmelCase		: Any						    =  hf_model.state_dict()
  _lowerCAmelCase		: Optional[Any]						    =  count_parameters(_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  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   :	List[str] 					=     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   :	Optional[Any] 					=     parser.parse_args()

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





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




