


'''simple docstring'''

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 __snake_case					(      UpperCAmelCase_						:						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 __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_      =  {}

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

  lowerCamelCase_      =  key.replace("heads.cmd.mim_head.cls.predictions"					,				"mmm_image_head"		)
  lowerCamelCase_      =  key.replace("heads.cmd.mlm_head.cls.predictions"					,				"mmm_text_head"		)
  lowerCamelCase_      =  key.replace("heads.cmd.itm_head.cls"					,				"itm_head"		)
  lowerCamelCase_      =  key.replace("heads.cmd.itm_head.pooler"					,				"itm_head.pooler"		)
  lowerCamelCase_      =  key.replace("heads.cmd.clip_head.logit_scale"					,				"flava.logit_scale"		)
  lowerCamelCase_      =  key.replace("heads.fairseq_mlm.cls.predictions"					,				"mlm_head"		)
  lowerCamelCase_      =  key.replace("heads.imagenet.mim_head.cls.predictions"					,				"mim_head"		)
  lowerCamelCase_      =  key.replace("mm_text_projection"					,				"flava.text_to_mm_projection"		)
  lowerCamelCase_      =  key.replace("mm_image_projection"					,				"flava.image_to_mm_projection"		)
  lowerCamelCase_      =  key.replace("image_encoder.module"					,				"flava.image_model"		)
  lowerCamelCase_      =  key.replace("text_encoder.module"					,				"flava.text_model"		)
  lowerCamelCase_      =  key.replace("mm_encoder.module.encoder.cls_token"					,				"flava.multimodal_model.cls_token"		)
  lowerCamelCase_      =  key.replace("mm_encoder.module"					,				"flava.multimodal_model"		)
  lowerCamelCase_      =  key.replace("text_projection"					,				"flava.text_projection"		)
  lowerCamelCase_      =  key.replace("image_projection"					,				"flava.image_projection"		)

  lowerCamelCase_      =  value.float()

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

 return upgrade





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict=None		):
 if config_path is not None:
  lowerCamelCase_      =  FlavaConfig.from_pretrained(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  FlavaConfig()

 lowerCamelCase_      =  FlavaForPreTraining(UpperCAmelCase_		).eval()

 lowerCamelCase_      =  convert_dalle_checkpoint(UpperCAmelCase_					,				UpperCAmelCase_					,				save_checkpoint=UpperCAmelCase_		)

 if os.path.exists(UpperCAmelCase_		):
  lowerCamelCase_      =  torch.load(UpperCAmelCase_					,				map_location="cpu"		)
 else:
  lowerCamelCase_      =  torch.hub.load_state_dict_from_url(UpperCAmelCase_					,				map_location="cpu"		)

 lowerCamelCase_      =  upgrade_state_dict(UpperCAmelCase_					,				UpperCAmelCase_		)
 hf_model.load_state_dict(UpperCAmelCase_		)
 lowerCamelCase_      =  hf_model.state_dict()
 lowerCamelCase_      =  count_parameters(UpperCAmelCase_		)
 lowerCamelCase_      =  count_parameters(UpperCAmelCase_		) + count_parameters(UpperCAmelCase_		)

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

 hf_model.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Optional[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""")
     a_  :				Union[str, Any]						= parser.parse_args()

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







'''simple docstring'''

from __future__ import annotations





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  2
 lowerCamelCase_      =  []
 while i * i <= n:
  if n % i:
   i += 1
  else:
   n //= i
   factors.append(UpperCAmelCase_		)
 if n > 1:
  factors.append(UpperCAmelCase_		)
 return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()



