


"""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      (    A_	)->				List[Any]:
  '''simple docstring'''







  return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()	)


def 					lowercase      (    A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Any											=      {}

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

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

    a						:		Union[str, Any]											=      value.float()

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

  return upgrade


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=None	)->				List[str]:
  '''simple docstring'''







  if config_path is not None:
    a						:		List[str]											=      FlavaConfig.from_pretrained(A_	)
  else:
    a						:		Dict											=      FlavaConfig()

  a						:		Optional[Any]											=      FlavaForPreTraining(A_	).eval()

  a						:		List[Any]											=      convert_dalle_checkpoint(A_  ,       A_  ,       save_checkpoint=A_	)

  if os.path.exists(A_	):
    a						:		Union[str, Any]											=      torch.load(A_  ,       map_location="cpu"	)
  else:
    a						:		Dict											=      torch.hub.load_state_dict_from_url(A_  ,       map_location="cpu"	)

  a						:		int											=      upgrade_state_dict(A_  ,       A_	)
  hf_model.load_state_dict(A_	)
  a						:		Tuple											=      hf_model.state_dict()
  a						:		Optional[Any]											=      count_parameters(A_	)
  a						:		List[Any]											=      count_parameters(A_	) + count_parameters(A_	)

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

  hf_model.save_pretrained(A_	)


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

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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






