
"""simple docstring"""




import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def     __magic_name__		(					UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Dict							,		UpperCamelCase     :		Tuple     )     ->						Tuple:
    # Construct model
    if openai_config_file == "":
        a__		=			OpenAIGPTConfig()
    else:
        a__		=			OpenAIGPTConfig.from_json_file(UpperCamelCase     )
    a__		=			OpenAIGPTModel(UpperCamelCase     )

    # Load weights from numpy
    load_tf_weights_in_openai_gpt(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # Save pytorch-model
    a__		=			pytorch_dump_folder_path + '/' + WEIGHTS_NAME
    a__		=			pytorch_dump_folder_path + '/' + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path}'     )
    torch.save(model.state_dict()							,		UpperCamelCase     )
    print(f'Save configuration file to {pytorch_config_dump_path}'     )
    with open(UpperCamelCase							,		'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(
        '--openai_checkpoint_folder_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(
        '--openai_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_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )




"""simple docstring"""




from math import ceil



def     __magic_name__		(					UpperCamelCase     :		int = 1001     )     ->						int:
    a__		=			1

    for i in range(1							,		int(ceil(n / 2.0     )     )     ):
        a__		=			2 * i + 1
        a__		=			2 * i
        a__		=			total + 4 * odd**2 - 6 * even

    return total


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution())
    else:
        try:
            a		:       Any     						=       int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number')


