


'''simple docstring'''






from __future__ import annotations

from math import ceil, floor, sqrt






def 	_A				(lowerCAmelCase__  :int = 2_00_00_00		)   ->  int:




     '''simple docstring'''



     _a			      =							[0]
     _a			      =							42

     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
     _a			      =							0
     # the area corresponding to the grid that gives the product closest to target
     _a			      =							0
     # an estimate of b, using the quadratic formula
     _a			      =							42
     # the largest integer less than b_estimate
     _a			      =							42
     # the largest integer less than b_estimate
     _a			      =							42
     # the triangle number corresponding to b_floor
     _a			      =							42
     # the triangle number corresponding to b_ceil
     _a			      =							42

     for idx_a, triangle_a in enumerate(triangle_numbers[1:]				,						1		):
          _a			      =							(-1 + sqrt(1 + 8 * target / triangle_a		)) / 2
          _a			      =							floor(lowerCAmelCase__		)
          _a			      =							ceil(lowerCAmelCase__		)
          _a			      =							triangle_numbers[b_floor]
          _a			      =							triangle_numbers[b_ceil]

          if abs(target - triangle_b_first_guess * triangle_a		) < abs(
              target - best_product		):
               _a			      =							triangle_b_first_guess * triangle_a
               _a			      =							idx_a * b_floor

          if abs(target - triangle_b_second_guess * triangle_a		) < abs(
              target - best_product		):
               _a			      =							triangle_b_second_guess * triangle_a
               _a			      =							idx_a * b_ceil

     return area


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





'''simple docstring'''






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :str				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :Any		)   ->  str:




     '''simple docstring'''



     _a			      =							BertConfig.from_json_file(lowerCAmelCase__		)
     print(f'Building PyTorch model from configuration: {config}'		)
     _a			      =							BertForPreTraining(lowerCAmelCase__		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     torch.save(model.state_dict()				,						lowerCAmelCase__		)


if __name__ == "__main__":
 a_					:    Optional[int]          =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_					:    Optional[int]          =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

