

"""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      (   __UpperCamelCase						) ->      Optional[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      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Any:
 __magic_name__     =      {}

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

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

  __magic_name__     =      value.float()

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

 return upgrade





@torch.no_grad()
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=None						) ->      List[Any]:
 if config_path is not None:
  __magic_name__     =      FlavaConfig.from_pretrained(__UpperCamelCase						)
 else:
  __magic_name__     =      FlavaConfig()

 __magic_name__     =      FlavaForPreTraining(__UpperCamelCase						).eval()

 __magic_name__     =      convert_dalle_checkpoint(__UpperCamelCase      ,   __UpperCamelCase      ,   save_checkpoint=__UpperCamelCase						)

 if os.path.exists(__UpperCamelCase						):
  __magic_name__     =      torch.load(__UpperCamelCase      ,   map_location='''cpu'''						)
 else:
  __magic_name__     =      torch.hub.load_state_dict_from_url(__UpperCamelCase      ,   map_location='''cpu'''						)

 __magic_name__     =      upgrade_state_dict(__UpperCamelCase      ,   __UpperCamelCase						)
 hf_model.load_state_dict(__UpperCamelCase						)
 __magic_name__     =      hf_model.state_dict()
 __magic_name__     =      count_parameters(__UpperCamelCase						)
 __magic_name__     =      count_parameters(__UpperCamelCase						) + count_parameters(__UpperCamelCase						)

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

 hf_model.save_pretrained(__UpperCamelCase						)


if __name__ == "__main__":
 __lowerCamelCase										=		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										=		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_tokenizers_available, is_torch_available


__lowerCamelCase										=		{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCamelCase										=		[
     "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
     "M2M100ForConditionalGeneration",
     "M2M100Model",
     "M2M100PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
 from .tokenization_mam_aaa import MaMaaaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mam_aaa import (
      M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaMaaaForConditionalGeneration,
      MaMaaaModel,
      MaMaaaPreTrainedModel,
  )


else:
 import sys

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

