

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def 					_lowercase     (				a_				:					Any					,a_				:					List[Any]					,a_				:					List[Any]					,a_				:					Tuple				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    s.rsplit(a_					,a_				)
					return new.join(a_				)




def 					_lowercase     (				a_				:					List[Any]				)				->	str:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					List[Any]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    {}

					__magic_name__							=    ['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:
																				__magic_name__							=    key.replace(F'{group_key}.'					,F'{group_key}.group.'				)

										if "res_path" in key:
															__magic_name__							=    key.replace('res_path.'					,'res_path.path.'				)

										if key.endswith('.w'				):
															__magic_name__							=    rreplace(a_					,'.w'					,'.weight'					,1				)
										if key.endswith('.b'				):
															__magic_name__							=    rreplace(a_					,'.b'					,'.bias'					,1				)

										__magic_name__							=    value.float()

					return upgrade




@torch.no_grad()
def 					_lowercase     (				a_				:					List[str]					,a_				:					str					,a_				:					List[str]=None					,a_				:					Optional[int]=True				)				->	Optional[int]:




					'''simple docstring'''







					from dall_e import Encoder

					__magic_name__							=    Encoder()
					if os.path.exists(a_				):
										__magic_name__							=    torch.load(a_				)
					else:
										__magic_name__							=    torch.hub.load_state_dict_from_url(a_				)

					if isinstance(a_					,a_				):
										__magic_name__							=    ckpt.state_dict()
					encoder.load_state_dict(a_				)

					if config_path is not None:
										__magic_name__							=    FlavaImageCodebookConfig.from_pretrained(a_				)
					else:
										__magic_name__							=    FlavaImageCodebookConfig()

					__magic_name__							=    FlavaImageCodebook(a_				).eval()
					__magic_name__							=    encoder.state_dict()

					__magic_name__							=    upgrade_state_dict(a_				)
					hf_model.load_state_dict(a_				)
					__magic_name__							=    hf_model.state_dict()
					__magic_name__							=    count_parameters(a_				)
					__magic_name__							=    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)







from __future__ import annotations




def 					_lowercase     (				a_				:					list[int]				)				->	int:




					'''simple docstring'''







					if not nums:
										return 0
					__magic_name__							=    nums[0]
					__magic_name__							=    0
					for num in nums[1:]:
										__magic_name__,	__magic_name__							=    (
										    max_excluding + num,
										    max(a_					,a_				),
										)
					return max(a_					,a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




