"""simple docstring"""







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Any													=	size
        lowercase_	:				Optional[int]													=	[0] * size
        lowercase_	:				Any													=	[0] * size
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        return index | (index + 1)
   @staticmethod
   def 			_UpperCAmelCase   (			__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        return (index & (index + 1)) - 1
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	value
        while index < self.size:
             lowercase_	:				List[str]													=	self.get_prev(A__			) + 1
             if current_left_border == index:
                  lowercase_	:				int													=	value
             else:
                  lowercase_	:				Optional[int]													=	max(A__     ,A__     ,A__			)
             lowercase_	:				List[str]													=	self.get_next(A__			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


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


if __name__ == "__main__":
       import doctest

       doctest.testmod()




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



