



"""simple docstring"""

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					s.rsplit(A__				,    A__			)
					return new.join(A__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					{}

					snake_case_					:		Union[str, Any] 					=					["""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:
																				snake_case_					:		Union[str, Any] 					=					key.replace(f'{group_key}.'				,    f'{group_key}.group.'			)

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

										if key.endswith(""".w"""			):
															snake_case_					:		Dict 					=					rreplace(A__				,    """.w"""				,    """.weight"""				,    1			)
										if key.endswith(""".b"""			):
															snake_case_					:		Any 					=					rreplace(A__				,    """.b"""				,    """.bias"""				,    1			)

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

					return upgrade







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  int=None				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=True			):



					"""simple docstring"""





					from dall_e import Encoder

					snake_case_					:		Tuple 					=					Encoder()
					if os.path.exists(A__			):
										snake_case_					:		Any 					=					torch.load(A__			)
					else:
										snake_case_					:		Dict 					=					torch.hub.load_state_dict_from_url(A__			)

					if isinstance(A__				,    A__			):
										snake_case_					:		Tuple 					=					ckpt.state_dict()
					encoder.load_state_dict(A__			)

					if config_path is not None:
										snake_case_					:		Any 					=					FlavaImageCodebookConfig.from_pretrained(A__			)
					else:
										snake_case_					:		Tuple 					=					FlavaImageCodebookConfig()

					snake_case_					:		Optional[int] 					=					FlavaImageCodebook(A__			).eval()
					snake_case_					:		Union[str, Any] 					=					encoder.state_dict()

					snake_case_					:		Tuple 					=					upgrade_state_dict(A__			)
					hf_model.load_state_dict(A__			)
					snake_case_					:		Optional[int] 					=					hf_model.state_dict()
					snake_case_					:		Any 					=					count_parameters(A__			)
					snake_case_					:		Tuple 					=					count_parameters(A__			)

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

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


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

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









"""simple docstring"""

def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()




