






'''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
SCREAMING_SNAKE_CASE						=	data_utils.TransfoXLTokenizer
SCREAMING_SNAKE_CASE						=	data_utils.TransfoXLCorpus
SCREAMING_SNAKE_CASE						=	data_utils
SCREAMING_SNAKE_CASE						=	data_utils


def 							lowercase_				(    __A :       Dict					,      __A :       Any					,      __A :       Optional[int]					,      __A :       List[str]			)       ->      Tuple:



 """simple docstring"""




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

  lowercase					:   Optional[int]				  =corpus.__dict__
  corpus_dict_no_vocab.pop('''vocab'''					,      __A			)
  lowercase					:   Dict				  =pytorch_dump_folder_path + '''/''' + CORPUS_NAME
  print(F'Save dataset to {pytorch_dataset_dump_path}'			)
  torch.save(__A					,      __A			)

 if tf_checkpoint_path:
  # Convert a pre-trained TensorFlow model
  lowercase					:   Dict				  =os.path.abspath(__A			)
  lowercase					:   str				  =os.path.abspath(__A			)

  print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'			)
  # Initialise PyTorch model
  if transfo_xl_config_file == "":
   lowercase					:   Any				  =TransfoXLConfig()
  else:
   lowercase					:   str				  =TransfoXLConfig.from_json_file(__A			)
  print(F'Building PyTorch model from configuration: {config}'			)
  lowercase					:   List[str]				  =TransfoXLLMHeadModel(__A			)

  lowercase					:   Dict				  =load_tf_weights_in_transfo_xl(__A					,      __A					,      __A			)
  # Save pytorch-model
  lowercase					:   int				  =os.path.join(__A					,      __A			)
  lowercase					:   Union[str, Any]				  =os.path.join(__A					,      __A			)
  print(F'Save PyTorch model to {os.path.abspath(__A			)}'			)
  torch.save(model.state_dict()					,      __A			)
  print(F'Save configuration file to {os.path.abspath(__A			)}'			)
  with open(__A					,      '''w'''					,      encoding='''utf-8'''			) as f:
   f.write(config.to_json_string()			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	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.',
      )
      SCREAMING_SNAKE_CASE						=	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'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


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



