




"""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 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : str      ,       snake_case__   : List[Any] )     ->  Any:


				'''simple docstring'''






				if gpta_config_file == "":
								lowerCAmelCase  =				GPTaConfig()
				else:
								lowerCAmelCase  =				GPTaConfig.from_json_file(snake_case__ )
				lowerCAmelCase  =				GPTaModel(snake_case__ )

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

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


if __name__ == "__main__":
		a      =							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      =							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 decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 							lowercase					(snake_case__   : str      ,       snake_case__   : float | Decimal      ,       snake_case__   : float = 10**-10 )     ->  float:


				'''simple docstring'''






				lowerCAmelCase  =				a
				while True:
								lowerCAmelCase  =				Decimal(snake_case__ ) - (
								    Decimal(eval(snake_case__ ) ) / Decimal(eval(str(diff(snake_case__ ) ) ) )  # noqa: S307
								)
								# This number dictates the accuracy of the answer
								if abs(eval(snake_case__ ) ) < precision:  # noqa: S307
												return float(snake_case__ )


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")
		# Find root of polynomial
		print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}""")
		# Find Square Root of 5
		print(f"""The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}""")
		# Exponential Roots
		print(f"""The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}""")





