

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	s.rsplit(_UpperCAmelCase    ,     _UpperCAmelCase   )
						return new.join(_UpperCAmelCase   )







def       A_				(   _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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	{}

						SCREAMING_SNAKE_CASE_:			int  		=	["group_1", "group_2", "group_3", "group_4"]
						for key, value in state_dict.items():
												for group_key in group_keys:
																		if group_key in key:
																								SCREAMING_SNAKE_CASE_:			Any  		=	key.replace(f"{group_key}."    ,     f"{group_key}.group."   )

												if "res_path" in key:
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	key.replace("res_path."    ,     "res_path.path."   )

												if key.endswith(".w"   ):
																		SCREAMING_SNAKE_CASE_:			Tuple  		=	rreplace(_UpperCAmelCase    ,     ".w"    ,     ".weight"    ,     1   )
												if key.endswith(".b"   ):
																		SCREAMING_SNAKE_CASE_:			str  		=	rreplace(_UpperCAmelCase    ,     ".b"    ,     ".bias"    ,     1   )

												SCREAMING_SNAKE_CASE_:			List[str]  		=	value.float()

						return upgrade







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=None    ,     _UpperCAmelCase=True   ):
						from dall_e import Encoder

						SCREAMING_SNAKE_CASE_:			Tuple  		=	Encoder()
						if os.path.exists(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			int  		=	torch.load(_UpperCAmelCase   )
						else:
												SCREAMING_SNAKE_CASE_:			int  		=	torch.hub.load_state_dict_from_url(_UpperCAmelCase   )

						if isinstance(_UpperCAmelCase    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			List[str]  		=	ckpt.state_dict()
						encoder.load_state_dict(_UpperCAmelCase   )

						if config_path is not None:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	FlavaImageCodebookConfig.from_pretrained(_UpperCAmelCase   )
						else:
												SCREAMING_SNAKE_CASE_:			Dict  		=	FlavaImageCodebookConfig()

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	FlavaImageCodebook(_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	encoder.state_dict()

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	upgrade_state_dict(_UpperCAmelCase   )
						hf_model.load_state_dict(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	hf_model.state_dict()
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	count_parameters(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	count_parameters(_UpperCAmelCase   )

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

						if save_checkpoint:
												hf_model.save_pretrained(_UpperCAmelCase   )
						else:
												return hf_state_dict


if __name__ == "__main__":
						lowerCAmelCase							:  Optional[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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						lowerCAmelCase							:  Dict            =						parser.parse_args()

						convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






