


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 	a_      (    SCREAMING_SNAKE_CASE__					:      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 	a_      (    SCREAMING_SNAKE_CASE__					:      Dict		,			SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		int		={}

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

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

              _lowerCamelCase       :		Dict		=value.float()

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

       return upgrade




@torch.no_grad()
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]=None     ):
       '''simple docstring'''





       if config_path is not None:
              _lowerCamelCase       :		Tuple		=FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__     )
       else:
              _lowerCamelCase       :		Optional[int]		=FlavaConfig()

       _lowerCamelCase       :		int		=FlavaForPreTraining(SCREAMING_SNAKE_CASE__     ).eval()

       _lowerCamelCase       :		List[Any]		=convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			save_checkpoint=SCREAMING_SNAKE_CASE__     )

       if os.path.exists(SCREAMING_SNAKE_CASE__     ):
              _lowerCamelCase       :		List[Any]		=torch.load(SCREAMING_SNAKE_CASE__		,			map_location='cpu'     )
       else:
              _lowerCamelCase       :		List[Any]		=torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__		,			map_location='cpu'     )

       _lowerCamelCase       :		Tuple		=upgrade_state_dict(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       hf_model.load_state_dict(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		str		=hf_model.state_dict()
       _lowerCamelCase       :		Dict		=count_parameters(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		int		=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__":
 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)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


