


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  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__   ):
   __a					=  {}

   __a					=  ["""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:
            __a					=  key.replace(f'''{group_key}.'''		,				f'''{group_key}.group.'''   )

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

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

      __a					=  value.float()

   return upgrade




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

   __a					=  Encoder()
   if os.path.exists(lowerCAmelCase__   ):
      __a					=  torch.load(lowerCAmelCase__   )
   else:
      __a					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__   )

   if isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      __a					=  ckpt.state_dict()
   encoder.load_state_dict(lowerCAmelCase__   )

   if config_path is not None:
      __a					=  FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase__   )
   else:
      __a					=  FlavaImageCodebookConfig()

   __a					=  FlavaImageCodebook(lowerCAmelCase__   ).eval()
   __a					=  encoder.state_dict()

   __a					=  upgrade_state_dict(lowerCAmelCase__   )
   hf_model.load_state_dict(lowerCAmelCase__   )
   __a					=  hf_model.state_dict()
   __a					=  count_parameters(lowerCAmelCase__   )
   __a					=  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__":
  SCREAMING_SNAKE_CASE							    =							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')
  SCREAMING_SNAKE_CASE							    =							parser.parse_args()

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





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						(   lowercase_ )		->						Union[str, 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						(   lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: int     		=   {}

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

    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mim_head.cls.predictions"""			,			"""mmm_image_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.mlm_head.cls.predictions"""			,			"""mmm_text_head""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.itm_head.cls"""			,			"""itm_head""" )
    lowercase__	: Tuple     		=   key.replace("""heads.cmd.itm_head.pooler"""			,			"""itm_head.pooler""" )
    lowercase__	: Optional[Any]     		=   key.replace("""heads.cmd.clip_head.logit_scale"""			,			"""flava.logit_scale""" )
    lowercase__	: Optional[int]     		=   key.replace("""heads.fairseq_mlm.cls.predictions"""			,			"""mlm_head""" )
    lowercase__	: List[Any]     		=   key.replace("""heads.imagenet.mim_head.cls.predictions"""			,			"""mim_head""" )
    lowercase__	: int     		=   key.replace("""mm_text_projection"""			,			"""flava.text_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_image_projection"""			,			"""flava.image_to_mm_projection""" )
    lowercase__	: Optional[Any]     		=   key.replace("""image_encoder.module"""			,			"""flava.image_model""" )
    lowercase__	: Any     		=   key.replace("""text_encoder.module"""			,			"""flava.text_model""" )
    lowercase__	: Optional[Any]     		=   key.replace("""mm_encoder.module.encoder.cls_token"""			,			"""flava.multimodal_model.cls_token""" )
    lowercase__	: Tuple     		=   key.replace("""mm_encoder.module"""			,			"""flava.multimodal_model""" )
    lowercase__	: Any     		=   key.replace("""text_projection"""			,			"""flava.text_projection""" )
    lowercase__	: List[Any]     		=   key.replace("""image_projection"""			,			"""flava.image_projection""" )

    lowercase__	: str     		=   value.float()

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

  return upgrade

@torch.no_grad()
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None )		->						Union[str, Any]:


  '''simple docstring'''






  if config_path is not None:
    lowercase__	: int     		=   FlavaConfig.from_pretrained(lowercase_ )
  else:
    lowercase__	: Optional[int]     		=   FlavaConfig()

  lowercase__	: List[Any]     		=   FlavaForPreTraining(lowercase_ ).eval()

  lowercase__	: Dict     		=   convert_dalle_checkpoint(lowercase_			,			lowercase_			,			save_checkpoint=lowercase_ )

  if os.path.exists(lowercase_ ):
    lowercase__	: Dict     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  else:
    lowercase__	: Dict     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )

  lowercase__	: int     		=   upgrade_state_dict(lowercase_			,			lowercase_ )
  hf_model.load_state_dict(lowercase_ )
  lowercase__	: Optional[int]     		=   hf_model.state_dict()
  lowercase__	: Optional[int]     		=   count_parameters(lowercase_ )
  lowercase__	: Any     		=   count_parameters(lowercase_ ) + count_parameters(lowercase_ )

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

  hf_model.save_pretrained(lowercase_ )


if __name__ == "__main__":
     lowerCamelCase__     :		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""")
     lowerCamelCase__     :		List[str]        = parser.parse_args()

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






