





'''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       lowercase_      (		lowercase__						)   ->Optional[int]:
						# 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       lowercase_      (		lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case:   int  					=		{}

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

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

												_snake_case:   str  					=		value.float()

						for key, value in codebook_state_dict.items():
												_snake_case:   Optional[Any]  					=		value

						return upgrade





@torch.no_grad()
def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__=None						)   ->List[Any]:
						if config_path is not None:
												_snake_case:   List[Any]  					=		FlavaConfig.from_pretrained(lowercase__						)
						else:
												_snake_case:   Optional[int]  					=		FlavaConfig()

						_snake_case:   Union[str, Any]  					=		FlavaForPreTraining(lowercase__						).eval()

						_snake_case:   Dict  					=		convert_dalle_checkpoint(lowercase__				,  lowercase__				,  save_checkpoint=lowercase__						)

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

						_snake_case:   List[str]  					=		upgrade_state_dict(lowercase__				,  lowercase__						)
						hf_model.load_state_dict(lowercase__						)
						_snake_case:   str  					=		hf_model.state_dict()
						_snake_case:   Tuple  					=		count_parameters(lowercase__						)
						_snake_case:   Dict  					=		count_parameters(lowercase__						) + count_parameters(lowercase__						)

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

						hf_model.save_pretrained(lowercase__						)


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

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












'''simple docstring'''






import torch





def       lowercase_      (		)   ->Dict:
						if torch.cuda.is_available():
												_snake_case:   Any  					=		torch.cuda.device_count()
						else:
												_snake_case:   Optional[int]  					=		0
						print(F'''Successfully ran on {num_gpus} GPUs'''						)


if __name__ == "__main__":
		main()





