


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     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       {}

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

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

          lowercase_       :int     =       value.float()

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

     return upgrade




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a=None		)					->							List[Any]:
     '''simple docstring'''





     if config_path is not None:
          lowercase_       :List[Any]     =       FlavaConfig.from_pretrained(_a		)
     else:
          lowercase_       :int     =       FlavaConfig()

     lowercase_       :Tuple     =       FlavaForPreTraining(_a		).eval()

     lowercase_       :Any     =       convert_dalle_checkpoint(_a       ,		_a       ,		save_checkpoint=_a		)

     if os.path.exists(_a		):
          lowercase_       :Tuple     =       torch.load(_a       ,		map_location='''cpu'''		)
     else:
          lowercase_       :Optional[int]     =       torch.hub.load_state_dict_from_url(_a       ,		map_location='''cpu'''		)

     lowercase_       :List[Any]     =       upgrade_state_dict(_a       ,		_a		)
     hf_model.load_state_dict(_a		)
     lowercase_       :List[str]     =       hf_model.state_dict()
     lowercase_       :List[str]     =       count_parameters(_a		)
     lowercase_       :Dict     =       count_parameters(_a		) + count_parameters(_a		)

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

     hf_model.save_pretrained(_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Any         =				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")
   SCREAMING_SNAKE_CASE       :		List[Any]         =				parser.parse_args()

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





from torch import nn




def     UpperCamelCase    (      _a		)					->							str:
     '''simple docstring'''





     if act_fn in ["swish", "silu"]:
          return nn.SiLU()
     elif act_fn == "mish":
          return nn.Mish()
     elif act_fn == "gelu":
          return nn.GELU()
     else:
          raise ValueError(f"Unsupported activation function: {act_fn}"		)

