





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   int       ,			UpperCamelCase__   :   Optional[int]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   s.rsplit(UpperCamelCase__       ,			UpperCamelCase__					)
						return new.join(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Union[str, Any]:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Dict:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   {}

						__SCREAMING_SNAKE_CASE:				Optional[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:				Optional[int]           =   key.replace(F"""{group_key}."""       ,			F"""{group_key}.group."""					)

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

												if key.endswith('''.w'''					):
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   rreplace(UpperCamelCase__       ,			'''.w'''       ,			'''.weight'''       ,			1					)
												if key.endswith('''.b'''					):
																		__SCREAMING_SNAKE_CASE:				int           =   rreplace(UpperCamelCase__       ,			'''.b'''       ,			'''.bias'''       ,			1					)

												__SCREAMING_SNAKE_CASE:				str           =   value.float()

						return upgrade






@torch.no_grad()
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   int       ,			UpperCamelCase__   :   Any=None       ,			UpperCamelCase__   :   str=True					)      ->    str:

						"""simple docstring"""





						from dall_e import Encoder

						__SCREAMING_SNAKE_CASE:				Dict           =   Encoder()
						if os.path.exists(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.load(UpperCamelCase__					)
						else:
												__SCREAMING_SNAKE_CASE:				List[str]           =   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:				Optional[int]           =   FlavaImageCodebookConfig.from_pretrained(UpperCamelCase__					)
						else:
												__SCREAMING_SNAKE_CASE:				str           =   FlavaImageCodebookConfig()

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   FlavaImageCodebook(UpperCamelCase__					).eval()
						__SCREAMING_SNAKE_CASE:				str           =   encoder.state_dict()

						__SCREAMING_SNAKE_CASE:				Optional[int]           =   upgrade_state_dict(UpperCamelCase__					)
						hf_model.load_state_dict(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				str           =   hf_model.state_dict()
						__SCREAMING_SNAKE_CASE:				str           =   count_parameters(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   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							:       str					 =	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							:       List[str]					 =	parser.parse_args()

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










from ..utils import DummyObject, requires_backends





class 				a			(      metaclass=__lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ['''torch''', '''scipy''']






				def __init__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(self			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)



