import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       s.rsplit(__lowerCamelCase				,    __lowerCamelCase						)
					return new.join(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					# 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(     __lowerCamelCase						):
					__a		 =       {}

					__a		 =       ['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:
																				__a		 =       key.replace(f'''{group_key}.'''				,    f'''{group_key}.group.'''						)

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

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

										__a		 =       value.float()

					return upgrade
@torch.no_grad()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase=True						):
					from dall_e import Encoder

					__a		 =       Encoder()
					if os.path.exists(__lowerCamelCase						):
										__a		 =       torch.load(__lowerCamelCase						)
					else:
										__a		 =       torch.hub.load_state_dict_from_url(__lowerCamelCase						)

					if isinstance(__lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       ckpt.state_dict()
					encoder.load_state_dict(__lowerCamelCase						)

					if config_path is not None:
										__a		 =       FlavaImageCodebookConfig.from_pretrained(__lowerCamelCase						)
					else:
										__a		 =       FlavaImageCodebookConfig()

					__a		 =       FlavaImageCodebook(__lowerCamelCase						).eval()
					__a		 =       encoder.state_dict()

					__a		 =       upgrade_state_dict(__lowerCamelCase						)
					hf_model.load_state_dict(__lowerCamelCase						)
					__a		 =       hf_model.state_dict()
					__a		 =       count_parameters(__lowerCamelCase						)
					__a		 =       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__":
	lowerCamelCase_    :		Union[str, 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_    :		Optional[int]							=	parser.parse_args()

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





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


if __name__ == "__main__":
	from doctest import testmod

	testmod()




