



"""simple docstring"""







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig

def     UpperCamelCase_   (   lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]						)						->       List[str]:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			s.rsplit(lowerCAmelCase__   ,       lowerCAmelCase__						)
  return new.join(lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]						)						->       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_   (   lowerCAmelCase__				:    Union[str, Any]						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			{}

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

    if "res_path" in key:
      lowerCAmelCase_       :    List[str]								=			key.replace('res_path.'   ,       'res_path.path.'						)

    if key.endswith('.w'						):
      lowerCAmelCase_       :    Optional[int]								=			rreplace(lowerCAmelCase__   ,       '.w'   ,       '.weight'   ,       1						)
    if key.endswith('.b'						):
      lowerCAmelCase_       :    int								=			rreplace(lowerCAmelCase__   ,       '.b'   ,       '.bias'   ,       1						)

    lowerCAmelCase_       :    int								=			value.float()

  return upgrade

@torch.no_grad()
def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int=None   ,       lowerCAmelCase__				:    str=True						)						->       str:

  """simple docstring"""




  from dall_e import Encoder

  lowerCAmelCase_       :    Any								=			Encoder()
  if os.path.exists(lowerCAmelCase__						):
    lowerCAmelCase_       :    str								=			torch.load(lowerCAmelCase__						)
  else:
    lowerCAmelCase_       :    int								=			torch.hub.load_state_dict_from_url(lowerCAmelCase__						)

  if isinstance(lowerCAmelCase__   ,       lowerCAmelCase__						):
    lowerCAmelCase_       :    Union[str, Any]								=			ckpt.state_dict()
  encoder.load_state_dict(lowerCAmelCase__						)

  if config_path is not None:
    lowerCAmelCase_       :    Any								=			FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase__						)
  else:
    lowerCAmelCase_       :    Union[str, Any]								=			FlavaImageCodebookConfig()

  lowerCAmelCase_       :    str								=			FlavaImageCodebook(lowerCAmelCase__						).eval()
  lowerCAmelCase_       :    Dict								=			encoder.state_dict()

  lowerCAmelCase_       :    Tuple								=			upgrade_state_dict(lowerCAmelCase__						)
  hf_model.load_state_dict(lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			hf_model.state_dict()
  lowerCAmelCase_       :    List[str]								=			count_parameters(lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[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__":
     lowercase__		:    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""")
     lowercase__		:    List[str]				   =     parser.parse_args()

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










"""simple docstring"""







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase__		:    int				   =     HUGGINGFACE_HUB_CACHE


lowercase__		:    Tuple				   =     """config.json"""
lowercase__		:    Union[str, Any]				   =     """diffusion_pytorch_model.bin"""
lowercase__		:    List[Any]				   =     """diffusion_flax_model.msgpack"""
lowercase__		:    List[str]				   =     """model.onnx"""
lowercase__		:    List[Any]				   =     """diffusion_pytorch_model.safetensors"""
lowercase__		:    Dict				   =     """weights.pb"""
lowercase__		:    List[Any]				   =     """https://huggingface.co"""
lowercase__		:    List[Any]				   =     default_cache_path
lowercase__		:    Tuple				   =     """diffusers_modules"""
lowercase__		:    Tuple				   =     os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase__		:    List[str]				   =     ["""fp16""", """non-ema"""]
lowercase__		:    Optional[Any]				   =     """.self_attn"""





