"""simple docstring"""







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				s.rsplit(A_	,  A_		)
	return new.join(A_		)
def    __lowerCAmelCase    (_UpperCamelCase		):
	# 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    (_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[Any]						      =				{}

	__lowerCAmelCase      :						Tuple						      =				['''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:
				__lowerCAmelCase      :						Union[str, Any]						      =				key.replace(F"{group_key}."	,  F"{group_key}.group."		)

		if "res_path" in key:
			__lowerCAmelCase      :						Optional[Any]						      =				key.replace('res_path.'	,  'res_path.path.'		)

		if key.endswith('.w'		):
			__lowerCAmelCase      :						Any						      =				rreplace(A_	,  '.w'	,  '.weight'	,  1		)
		if key.endswith('.b'		):
			__lowerCAmelCase      :						int						      =				rreplace(A_	,  '.b'	,  '.bias'	,  1		)

		__lowerCAmelCase      :						List[str]						      =				value.float()

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

	__lowerCAmelCase      :						Optional[Any]						      =				Encoder()
	if os.path.exists(A_		):
		__lowerCAmelCase      :						List[Any]						      =				torch.load(A_		)
	else:
		__lowerCAmelCase      :						Union[str, Any]						      =				torch.hub.load_state_dict_from_url(A_		)

	if isinstance(A_	,  A_		):
		__lowerCAmelCase      :						Optional[int]						      =				ckpt.state_dict()
	encoder.load_state_dict(A_		)

	if config_path is not None:
		__lowerCAmelCase      :						Dict						      =				FlavaImageCodebookConfig.from_pretrained(A_		)
	else:
		__lowerCAmelCase      :						Any						      =				FlavaImageCodebookConfig()

	__lowerCAmelCase      :						str						      =				FlavaImageCodebook(A_		).eval()
	__lowerCAmelCase      :						Dict						      =				encoder.state_dict()

	__lowerCAmelCase      :						List[str]						      =				upgrade_state_dict(A_		)
	hf_model.load_state_dict(A_		)
	__lowerCAmelCase      :						List[str]						      =				hf_model.state_dict()
	__lowerCAmelCase      :						str						      =				count_parameters(A_		)
	__lowerCAmelCase      :						str						      =				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__":
							lowerCamelCase__        =		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__        =		parser.parse_args()

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

"""simple docstring"""







class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				size
			__lowerCAmelCase      :						str						      =				[0] * size
			__lowerCAmelCase      :						Any						      =				[0] * size

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return index | (index + 1)

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return (index & (index + 1)) - 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				value
			while index < self.size:
				__lowerCAmelCase      :						Dict						      =				self.get_prev(_SCREAMING_SNAKE_CASE			) + 1
				if current_left_border == index:
					__lowerCAmelCase      :						Any						      =				value
				else:
					__lowerCAmelCase      :						Any						      =				max(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				self.get_next(_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			right -= 1  # Because of right is exclusive
			__lowerCAmelCase      :						Optional[int]						      =				0
			while left <= right:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_prev(_SCREAMING_SNAKE_CASE			)
				if left <= current_left:
					__lowerCAmelCase      :						Optional[Any]						      =				max(_SCREAMING_SNAKE_CASE	,		self.tree[right]			)
					__lowerCAmelCase      :						Optional[Any]						      =				current_left
				else:
					__lowerCAmelCase      :						List[str]						      =				max(_SCREAMING_SNAKE_CASE	,		self.arr[right]			)
					right -= 1
			return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()