






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	List[Any]  ,					a						:	Dict  ,					a						:	str ):
							snake_case__	  =       s.rsplit(a  ,					a )
							return new.join(a )

def _UpperCAmelCase   (	a						:	Dict ):
							# 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 _UpperCAmelCase   (	a						:	Union[str, Any] ):
							snake_case__	  =       {}

							snake_case__	  =       ["""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__	  =       key.replace(F'''{group_key}.'''  ,					F'''{group_key}.group.''' )

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

														if key.endswith(""".w""" ):
																					snake_case__	  =       rreplace(a  ,					""".w"""  ,					""".weight"""  ,					1 )
														if key.endswith(""".b""" ):
																					snake_case__	  =       rreplace(a  ,					""".b"""  ,					""".bias"""  ,					1 )

														snake_case__	  =       value.float()

							return upgrade

@torch.no_grad()
def _UpperCAmelCase   (	a						:	int  ,					a						:	Union[str, Any]  ,					a						:	int=None  ,					a						:	Dict=True ):
							from dall_e import Encoder

							snake_case__	  =       Encoder()
							if os.path.exists(a ):
														snake_case__	  =       torch.load(a )
							else:
														snake_case__	  =       torch.hub.load_state_dict_from_url(a )

							if isinstance(a  ,					a ):
														snake_case__	  =       ckpt.state_dict()
							encoder.load_state_dict(a )

							if config_path is not None:
														snake_case__	  =       FlavaImageCodebookConfig.from_pretrained(a )
							else:
														snake_case__	  =       FlavaImageCodebookConfig()

							snake_case__	  =       FlavaImageCodebook(a ).eval()
							snake_case__	  =       encoder.state_dict()

							snake_case__	  =       upgrade_state_dict(a )
							hf_model.load_state_dict(a )
							snake_case__	  =       hf_model.state_dict()
							snake_case__	  =       count_parameters(a )
							snake_case__	  =       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


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       data
											snake_case__	  =       None
											snake_case__	  =       None


def _UpperCAmelCase   (	a						:	Node | None ):  # In Order traversal of the tree
							if tree:
														display(tree.left )
														print(tree.data )
														display(tree.right )

def _UpperCAmelCase   (	a						:	Node | None ):
							return 1 + max(depth_of_tree(tree.left )  ,					depth_of_tree(tree.right ) ) if tree else 0

def _UpperCAmelCase   (	a						:	Node ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
							else:
														return not tree.left and not tree.right

def _UpperCAmelCase   (	):  # Main function for testing.
							snake_case__	  =       Node(1 )
							snake_case__	  =       Node(2 )
							snake_case__	  =       Node(3 )
							snake_case__	  =       Node(4 )
							snake_case__	  =       Node(5 )
							snake_case__	  =       Node(6 )
							snake_case__	  =       Node(7 )
							snake_case__	  =       Node(8 )
							snake_case__	  =       Node(9 )

							print(is_full_binary_tree(a ) )
							print(depth_of_tree(a ) )
							print("""Tree is: """ )
							display(a )


if __name__ == "__main__":
	main()
