

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	int											= {}

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

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

  UpperCAmelCase       :	Any											= value.float()

 for key, value in codebook_state_dict.items():
  UpperCAmelCase       :	Union[str, Any]											= value

 return upgrade




@torch.no_grad()
def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=None	):





 if config_path is not None:
  UpperCAmelCase       :	Dict											= FlavaConfig.from_pretrained(_snake_case	)
 else:
  UpperCAmelCase       :	Optional[Any]											= FlavaConfig()

 UpperCAmelCase       :	List[Any]											= FlavaForPreTraining(_snake_case	).eval()

 UpperCAmelCase       :	Union[str, Any]											= convert_dalle_checkpoint(_snake_case  ,       _snake_case  ,       save_checkpoint=_snake_case	)

 if os.path.exists(_snake_case	):
  UpperCAmelCase       :	List[str]											= torch.load(_snake_case  ,       map_location='cpu'	)
 else:
  UpperCAmelCase       :	List[str]											= torch.hub.load_state_dict_from_url(_snake_case  ,       map_location='cpu'	)

 UpperCAmelCase       :	Any											= upgrade_state_dict(_snake_case  ,       _snake_case	)
 hf_model.load_state_dict(_snake_case	)
 UpperCAmelCase       :	int											= hf_model.state_dict()
 UpperCAmelCase       :	Union[str, Any]											= count_parameters(_snake_case	)
 UpperCAmelCase       :	List[Any]											= count_parameters(_snake_case	) + count_parameters(_snake_case	)

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

 hf_model.save_pretrained(_snake_case	)


if __name__ == "__main__":
 lowercase__			    =			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")
 lowercase__			    =			parser.parse_args()

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








"""simple docstring"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





