

'''simple docstring'''







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__lowerCamelCase		       =    data_utils.TransfoXLTokenizer
__lowerCamelCase		       =    data_utils.TransfoXLCorpus
__lowerCamelCase		       =    data_utils
__lowerCamelCase		       =    data_utils
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(_SCREAMING_SNAKE_CASE,       """rb"""				) as fp:
                     A_						=      pickle.load(_SCREAMING_SNAKE_CASE,       encoding="""latin1"""				)
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              A_						=      pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
              print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''				)
              A_						=      corpus.vocab.__dict__
              torch.save(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE				)

              A_						=      corpus.__dict__
              corpus_dict_no_vocab.pop("""vocab""",       _SCREAMING_SNAKE_CASE				)
              A_						=      pytorch_dump_folder_path + """/""" + CORPUS_NAME
              print(F'''Save dataset to {pytorch_dataset_dump_path}'''				)
              torch.save(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE				)

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              A_						=      os.path.abspath(_SCREAMING_SNAKE_CASE				)
              A_						=      os.path.abspath(_SCREAMING_SNAKE_CASE				)

              print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''				)
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     A_						=      TransfoXLConfig()
              else:
                     A_						=      TransfoXLConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
              print(F'''Building PyTorch model from configuration: {config}'''				)
              A_						=      TransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE				)

              A_						=      load_tf_weights_in_transfo_xl(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE				)
              # Save pytorch-model
              A_						=      os.path.join(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE				)
              A_						=      os.path.join(_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE				)
              print(F'''Save PyTorch model to {os.path.abspath(_SCREAMING_SNAKE_CASE				)}'''				)
              torch.save(model.state_dict(),       _SCREAMING_SNAKE_CASE				)
              print(F'''Save configuration file to {os.path.abspath(_SCREAMING_SNAKE_CASE				)}'''				)
              with open(_SCREAMING_SNAKE_CASE,       """w""",       encoding="""utf-8"""				) as f:
                     f.write(config.to_json_string()				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default=None,
          type=str,
          required=True,
          help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
      )
      parser.add_argument(
          '''--tf_checkpoint_path''',
          default='''''',
          type=str,
          help='''An optional path to a TensorFlow checkpoint path to be converted.''',
      )
      parser.add_argument(
          '''--transfo_xl_config_file''',
          default='''''',
          type=str,
          help=(
              '''An optional config json file corresponding to the pre-trained BERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--transfo_xl_dataset_file''',
          default='''''',
          type=str,
          help='''An optional dataset file to be converted in a vocabulary.''',
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(
          args.tf_checkpoint_path,
          args.transfo_xl_config_file,
          args.pytorch_dump_folder_path,
          args.transfo_xl_dataset_file,
      )



'''simple docstring'''







import os

__lowerCamelCase		       =    {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      0

       A_						=      0
       while index < len(UpperCAmelCase__				) - 1:
              A_						=      SYMBOLS[numerals[index]]
              A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
       A_						=      """"""

       A_						=      num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       A_						=      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

       A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__ = "/p089_roman.txt"				)			->			int:
       A_						=      0

       with open(os.path.dirname(UpperCAmelCase__				) + roman_numerals_filename				) as filea:
              A_						=      filea.readlines()

       for line in lines:
              A_						=      line.strip()
              A_						=      parse_roman_numerals(UpperCAmelCase__				)
              A_						=      generate_roman_numerals(UpperCAmelCase__				)
              savings += len(UpperCAmelCase__				) - len(UpperCAmelCase__				)

       return savings


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