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							(UpperCAmelCase__     :							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 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							List[Any]  ):
					SCREAMING_SNAKE_CASE    			=       {}

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

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

										SCREAMING_SNAKE_CASE    			=       value.float()

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

					return upgrade





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							List[Any]=None  ):
					if config_path is not None:
										SCREAMING_SNAKE_CASE    			=       FlavaConfig.from_pretrained(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       FlavaConfig()

					SCREAMING_SNAKE_CASE    			=       FlavaForPreTraining(UpperCAmelCase__  ).eval()

					SCREAMING_SNAKE_CASE    			=       convert_dalle_checkpoint(UpperCAmelCase__      , UpperCAmelCase__      , save_checkpoint=UpperCAmelCase__  )

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

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

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

					hf_model.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
			_lowerCamelCase					:   List[str]							=		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					:   Dict							=		parser.parse_args()

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





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




