

"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Union[str, Any]							,      _lowercase       :		Optional[Any]							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[int]      =				s.rsplit(__lowerCAmelCase							,      __lowerCAmelCase	)
					return new.join(__lowerCAmelCase	)



def   lowercase_	(    _lowercase       :		Optional[Any]	):




					'''simple docstring'''






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



def   lowercase_	(    _lowercase       :		Dict	):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				{}

					UpperCAmelCase			:   str      =				["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:
																				UpperCAmelCase			:   int      =				key.replace(F"""{group_key}."""							,      F"""{group_key}.group."""	)

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

										if key.endswith(".w"	):
															UpperCAmelCase			:   int      =				rreplace(__lowerCAmelCase							,      ".w"							,      ".weight"							,      1	)
										if key.endswith(".b"	):
															UpperCAmelCase			:   Dict      =				rreplace(__lowerCAmelCase							,      ".b"							,      ".bias"							,      1	)

										UpperCAmelCase			:   Optional[int]      =				value.float()

					return upgrade



@torch.no_grad()
def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		int							,      _lowercase       :		Union[str, Any]=None							,      _lowercase       :		Optional[Any]=True	):




					'''simple docstring'''






					from dall_e import Encoder

					UpperCAmelCase			:   Tuple      =				Encoder()
					if os.path.exists(__lowerCAmelCase	):
										UpperCAmelCase			:   str      =				torch.load(__lowerCAmelCase	)
					else:
										UpperCAmelCase			:   Any      =				torch.hub.load_state_dict_from_url(__lowerCAmelCase	)

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

					if config_path is not None:
										UpperCAmelCase			:   Dict      =				FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase	)
					else:
										UpperCAmelCase			:   Optional[int]      =				FlavaImageCodebookConfig()

					UpperCAmelCase			:   Tuple      =				FlavaImageCodebook(__lowerCAmelCase	).eval()
					UpperCAmelCase			:   List[str]      =				encoder.state_dict()

					UpperCAmelCase			:   Dict      =				upgrade_state_dict(__lowerCAmelCase	)
					hf_model.load_state_dict(__lowerCAmelCase	)
					UpperCAmelCase			:   Optional[int]      =				hf_model.state_dict()
					UpperCAmelCase			:   Union[str, Any]      =				count_parameters(__lowerCAmelCase	)
					UpperCAmelCase			:   List[str]      =				count_parameters(__lowerCAmelCase	)

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

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


if __name__ == "__main__":
	snake_case_   :   Optional[int]        =       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""")
	snake_case_   :   List[Any]        =       parser.parse_args()

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





"""simple docstring"""






snake_case_   :   List[str]        =       6_5_5_2_1



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				1
					UpperCAmelCase			:   Optional[int]      =				0
					for plain_chr in plain_text:
										UpperCAmelCase			:   int      =				(a + ord(_lowercase	)) % MOD_ADLER
										UpperCAmelCase			:   str      =				(b + a) % MOD_ADLER
					return (b << 16) | a


