


'''simple docstring'''






from math import ceil






def 		_A (lowerCAmelCase__     :Any = 10_01						)      ->     int:




     '''simple docstring'''



     _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_    :			Optional[Any]      				=			int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print("Invalid entry - please enter a number")


"""simple docstring"""






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   # Initialise PyTorch model
   lowercase__:   Optional[Any]					=      FunnelConfig.from_json_file(__UpperCAmelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   lowercase__:   List[Any]					=      FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict()       ,    __UpperCAmelCase )


if __name__ == "__main__":
       __A			   = 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(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __A			   = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )
