

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_				(__A					:						Tuple					)      ->     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_				(__A					:						str ,       __A					:						List[Any]					)      ->     List[Any]:
   __lowerCAmelCase  :      int    =						{}

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

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

      __lowerCAmelCase  :      str    =						value.float()

   for key, value in codebook_state_dict.items():
      __lowerCAmelCase  :      Optional[int]    =						value

   return upgrade





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

   __lowerCAmelCase  :      List[str]    =						FlavaForPreTraining(__A					).eval()

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

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

   __lowerCAmelCase  :      Optional[int]    =						upgrade_state_dict(__A ,       __A					)
   hf_model.load_state_dict(__A					)
   __lowerCAmelCase  :      str    =						hf_model.state_dict()
   __lowerCAmelCase  :      Optional[int]    =						count_parameters(__A					)
   __lowerCAmelCase  :      Any    =						count_parameters(__A					) + count_parameters(__A					)

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   hf_model.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			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""")
   __UpperCAmelCase								=			parser.parse_args()

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





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


