




'''simple docstring'''



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			(_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  int						)-> Any:




     '''simple docstring'''





     if gpta_config_file == "":
          __snake_case            =	GPTaConfig()
     else:
          __snake_case            =	GPTaConfig.from_json_file(_lowerCamelCase						)
     __snake_case            =	GPTaModel(_lowerCamelCase						)

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

     # Save pytorch-model
     __snake_case            =	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     __snake_case            =	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''						)
     torch.save(model.state_dict() ,						_lowerCamelCase						)
     print(f'''Save configuration file to {pytorch_config_dump_path}'''						)
     with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as f:
          f.write(config.to_json_string()						)


if __name__ == "__main__":
     UpperCAmelCase_							:	int	     =		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.'''
         ),
     )
     UpperCAmelCase_							:	Optional[Any]	     =		parser.parse_args()
     convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)








'''simple docstring'''
from __future__ import annotations

from math import ceil, floor, sqrt




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = 200_0000			)  ->							int:


		'''simple docstring'''


		snake_case  :       list[int]     			=						[0]
		snake_case  :       int

		for idx in range(1 ,       ceil(sqrt(target * 2			) * 1.1			)			):
				triangle_numbers.append(triangle_numbers[-1] + idx			)

		# we want this to be as close as possible to target
		snake_case  :       int     			=						0
		# the area corresponding to the grid that gives the product closest to target
		snake_case  :       int     			=						0
		# an estimate of b, using the quadratic formula
		snake_case  :       float
		# the largest integer less than b_estimate
		snake_case  :       int
		# the largest integer less than b_estimate
		snake_case  :       int
		# the triangle number corresponding to b_floor
		snake_case  :       int
		# the triangle number corresponding to b_ceil
		snake_case  :       int

		for idx_a, triangle_a in enumerate(triangle_numbers[1:] ,       1			):
				snake_case  :       int     			=						(-1 + sqrt(1 + 8 * target / triangle_a			)) / 2
				snake_case  :       Optional[int]     			=						floor(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						ceil(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[Any]     			=						triangle_numbers[b_floor]
				snake_case  :       Union[str, Any]     			=						triangle_numbers[b_ceil]

				if abs(target - triangle_b_first_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       str     			=						triangle_b_first_guess * triangle_a
						snake_case  :       int     			=						idx_a * b_floor

				if abs(target - triangle_b_second_guess * triangle_a			) < abs(
				    target - best_product			):
						snake_case  :       List[Any]     			=						triangle_b_second_guess * triangle_a
						snake_case  :       Any     			=						idx_a * b_ceil

		return area


if __name__ == "__main__":
						print(f"{solution() = }")


