


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				{}

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

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

            snake_case_	=				value.float()

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

      return upgrade






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None )->  int:




      """simple docstring"""

      if config_path is not None:
            snake_case_	=				FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE )
      else:
            snake_case_	=				FlavaConfig()

      snake_case_	=				FlavaForPreTraining(SCREAMING_SNAKE_CASE ).eval()

      snake_case_	=				convert_dalle_checkpoint(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , save_checkpoint=SCREAMING_SNAKE_CASE )

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

      snake_case_	=				upgrade_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      hf_model.load_state_dict(SCREAMING_SNAKE_CASE )
      snake_case_	=				hf_model.state_dict()
      snake_case_	=				count_parameters(SCREAMING_SNAKE_CASE )
      snake_case_	=				count_parameters(SCREAMING_SNAKE_CASE ) + count_parameters(SCREAMING_SNAKE_CASE )

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

      hf_model.save_pretrained(SCREAMING_SNAKE_CASE )


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)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase      =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ViTMSNModel""",
       """ViTMSNForImageClassification""",
       """ViTMSNPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

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