

"""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  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      # 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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{}

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

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

            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	value.float()

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

      return upgrade




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None							)       ->				List[Any]:
      if config_path is not None:
            SCREAMING_SNAKE_CASE__   :      str      		=	FlavaConfig.from_pretrained(__lowerCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE__   :      str      		=	FlavaConfig()

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlavaForPreTraining(__lowerCAmelCase							).eval()

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	convert_dalle_checkpoint(__lowerCAmelCase  ,					__lowerCAmelCase  ,					save_checkpoint=__lowerCAmelCase							)

      if os.path.exists(__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.hub.load_state_dict_from_url(__lowerCAmelCase  ,					map_location="""cpu"""							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	upgrade_state_dict(__lowerCAmelCase  ,					__lowerCAmelCase							)
      hf_model.load_state_dict(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	hf_model.state_dict()
      SCREAMING_SNAKE_CASE__   :      str      		=	count_parameters(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	count_parameters(__lowerCAmelCase							) + count_parameters(__lowerCAmelCase							)

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

      hf_model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
  a					: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("--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					:Optional[Any]    = parser.parse_args()

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







"""simple docstring"""


a					:List[str]    = [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
      while place < len(__lowerCAmelCase							):
            if (place + 1 < len(__lowerCAmelCase							)) and (vals[roman[place]] < vals[roman[place + 1]]):
                  total += vals[roman[place + 1]] - vals[roman[place]]
                  place += 2
            else:
                  total += vals[roman[place]]
                  place += 1
      return total




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for arabic, roman in ROMAN:
            ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      List[str]      		=	divmod(__lowerCAmelCase  ,					__lowerCAmelCase							)
            result.append(roman * factor							)
            if number == 0:
                  break
      return "".join(__lowerCAmelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




