




"""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      __UpperCAmelCase     ( __UpperCamelCase	):
			# 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      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  Any								=       {}

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

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

						__lowercase			:  Tuple								=       value.float()

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

			return upgrade







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=None	):
			if config_path is not None:
						__lowercase			:  Union[str, Any]								=       FlavaConfig.from_pretrained(__UpperCamelCase	)
			else:
						__lowercase			:  Union[str, Any]								=       FlavaConfig()

			__lowercase			:  Any								=       FlavaForPreTraining(__UpperCamelCase	).eval()

			__lowercase			:  Any								=       convert_dalle_checkpoint(__UpperCamelCase	,				__UpperCamelCase	,				save_checkpoint=__UpperCamelCase	)

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

			__lowercase			:  Optional[int]								=       upgrade_state_dict(__UpperCamelCase	,				__UpperCamelCase	)
			hf_model.load_state_dict(__UpperCamelCase	)
			__lowercase			:  Union[str, Any]								=       hf_model.state_dict()
			__lowercase			:  Optional[Any]								=       count_parameters(__UpperCamelCase	)
			__lowercase			:  List[Any]								=       count_parameters(__UpperCamelCase	) + count_parameters(__UpperCamelCase	)

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

			hf_model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
			a_        =       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')
			a_        =       parser.parse_args()

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










'''simple docstring'''




from __future__ import annotations





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							int		)		->  Tuple:  # noqa: E741
   while r - l > 1:
      snake_case 						=   (l + r) // 2
      if v[m] >= key:
         snake_case 						=   m
      else:
         snake_case 						=   m  # noqa: E741
   return r





def 	__lowerCamelCase			(      __lowerCAmelCase       :							list[int]		)		->  int:
   if len(__lowerCAmelCase		) == 0:
      return 0

   snake_case 						=   [0] * len(__lowerCAmelCase		)
   snake_case 						=   1

   snake_case 						=   v[0]

   for i in range(1      ,    len(__lowerCAmelCase		)		):
      if v[i] < tail[0]:
         snake_case 						=   v[i]
      elif v[i] > tail[length - 1]:
         snake_case 						=   v[i]
         length += 1
      else:
         snake_case 						=   v[i]

   return length


if __name__ == "__main__":
 import doctest

 doctest.testmod()

