





"""simple docstring"""





def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  for n in range(1  ,			1_00_00_00  ):
    yield n * (n + 1) // 2


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						1
  __UpperCAmelCase    :						Any       =						2
  while i * i <= n:
    __UpperCAmelCase    :						Tuple       =						0
    while n % i == 0:
      n //= i
      multiplicity += 1
    divisors_count *= multiplicity + 1
    i += 1
  if n > 1:
    divisors_count *= 2
  return divisors_count


def       lowerCamelCase_       (   )						->Optional[int]:





  """simple docstring"""


  return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase_  ) > 5_00  )


if __name__ == "__main__":
  print(solution())






"""simple docstring"""





import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						LxmertConfig.from_json_file(UpperCAmelCase_  )
  print(f'''Building PyTorch model from configuration: {config}'''  )
  __UpperCAmelCase    :						Tuple       =						LxmertForPreTraining(UpperCAmelCase_  )

  # Load weights from tf checkpoint
  load_tf_weights_in_lxmert(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

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


if __name__ == "__main__":
  lowercase__				: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(
      '--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.'
  )
  lowercase__				:Union[str, Any]				   =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)