





"""simple docstring"""





import os

lowercase__				:Any				   =       {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}


def       lowerCamelCase_       (   UpperCAmelCase_  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						0

  __UpperCAmelCase    :						Union[str, Any]       =						0
  while index < len(UpperCAmelCase_  ) - 1:
    __UpperCAmelCase    :						Dict       =						SYMBOLS[numerals[index]]
    __UpperCAmelCase    :						Any       =						SYMBOLS[numerals[index + 1]]
    if current_value < next_value:
      total_value -= current_value
    else:
      total_value += current_value
    index += 1
  total_value += SYMBOLS[numerals[index]]

  return total_value


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						''''''

  __UpperCAmelCase    :						Union[str, Any]       =						num // 10_00
  numerals += m_count * "M"
  num %= 10_00

  __UpperCAmelCase    :						List[str]       =						num // 1_00
  if c_count == 9:
    numerals += "CM"
    c_count -= 9
  elif c_count == 4:
    numerals += "CD"
    c_count -= 4
  if c_count >= 5:
    numerals += "D"
    c_count -= 5
  numerals += c_count * "C"
  num %= 1_00

  __UpperCAmelCase    :						List[Any]       =						num // 10
  if x_count == 9:
    numerals += "XC"
    x_count -= 9
  elif x_count == 4:
    numerals += "XL"
    x_count -= 4
  if x_count >= 5:
    numerals += "L"
    x_count -= 5
  numerals += x_count * "X"
  num %= 10

  if num == 9:
    numerals += "IX"
    num -= 9
  elif num == 4:
    numerals += "IV"
    num -= 4
  if num >= 5:
    numerals += "V"
    num -= 5
  numerals += num * "I"

  return numerals


def       lowerCamelCase_       (   UpperCAmelCase_ = "/p089_roman.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						0

  with open(os.path.dirname(UpperCAmelCase_  ) + roman_numerals_filename  ) as filea:
    __UpperCAmelCase    :						Dict       =						filea.readlines()

  for line in lines:
    __UpperCAmelCase    :						Union[str, Any]       =						line.strip()
    __UpperCAmelCase    :						Optional[Any]       =						parse_roman_numerals(UpperCAmelCase_  )
    __UpperCAmelCase    :						List[Any]       =						generate_roman_numerals(UpperCAmelCase_  )
    savings += len(UpperCAmelCase_  ) - len(UpperCAmelCase_  )

  return savings


if __name__ == "__main__":
  print(f"""{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)