





'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE = 1000   ):


  """simple docstring"""

  return sum(e for e in range(3  ,      __SCREAMING_SNAKE_CASE   ) if e % 3 == 0 or e % 5 == 0   )


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








'''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						: List[Any]		=   data_utils.TransfoXLTokenizer
__lowerCamelCase						: str		=   data_utils.TransfoXLCorpus
__lowerCamelCase						: Optional[int]		=   data_utils
__lowerCamelCase						: int		=   data_utils


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(__SCREAMING_SNAKE_CASE  ,      '''rb'''   ) as fp:
      _UpperCamelCase       			=pickle.load(__SCREAMING_SNAKE_CASE  ,      encoding='''latin1'''   )
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    _UpperCamelCase       			=pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
    print(f'''Save vocabulary to {pytorch_vocab_dump_path}'''   )
    _UpperCamelCase       			=corpus.vocab.__dict__
    torch.save(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

    _UpperCamelCase       			=corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=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
    _UpperCamelCase       			=os.path.abspath(__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=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 == "":
      _UpperCamelCase       			=TransfoXLConfig()
    else:
      _UpperCamelCase       			=TransfoXLConfig.from_json_file(__SCREAMING_SNAKE_CASE   )
    print(f'''Building PyTorch model from configuration: {config}'''   )
    _UpperCamelCase       			=TransfoXLLMHeadModel(__SCREAMING_SNAKE_CASE   )

    _UpperCamelCase       			=load_tf_weights_in_transfo_xl(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    # Save pytorch-model
    _UpperCamelCase       			=os.path.join(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=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						: Any		=   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						: Optional[int]		=   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,
      )






