"""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
_a				:		int	   =       data_utils.TransfoXLTokenizer
_a				:		Any	   =       data_utils.TransfoXLCorpus
_a				:		str	   =       data_utils
_a				:		int	   =       data_utils





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

				_lowerCAmelCase						:	Optional[int]        =					corpus.__dict__
				corpus_dict_no_vocab.pop("""vocab"""      ,_lowerCamelCase     )
				_lowerCAmelCase						:	List[Any]        =					pytorch_dump_folder_path + """/""" + CORPUS_NAME
				print(f"Save dataset to {pytorch_dataset_dump_path}"     )
				torch.save(_lowerCamelCase      ,_lowerCamelCase     )

		if tf_checkpoint_path:
				# Convert a pre-trained TensorFlow model
				_lowerCAmelCase						:	Dict        =					os.path.abspath(_lowerCamelCase     )
				_lowerCAmelCase						:	Union[str, Any]        =					os.path.abspath(_lowerCamelCase     )

				print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."     )
				# Initialise PyTorch model
				if transfo_xl_config_file == "":
						_lowerCAmelCase						:	Tuple        =					TransfoXLConfig()
				else:
						_lowerCAmelCase						:	List[str]        =					TransfoXLConfig.from_json_file(_lowerCamelCase     )
				print(f"Building PyTorch model from configuration: {config}"     )
				_lowerCAmelCase						:	Optional[int]        =					TransfoXLLMHeadModel(_lowerCamelCase     )

				_lowerCAmelCase						:	Optional[int]        =					load_tf_weights_in_transfo_xl(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
				# Save pytorch-model
				_lowerCAmelCase						:	List[Any]        =					os.path.join(_lowerCamelCase      ,_lowerCamelCase     )
				_lowerCAmelCase						:	str        =					os.path.join(_lowerCamelCase      ,_lowerCamelCase     )
				print(f"Save PyTorch model to {os.path.abspath(_lowerCamelCase     )}"     )
				torch.save(model.state_dict()      ,_lowerCamelCase     )
				print(f"Save configuration file to {os.path.abspath(_lowerCamelCase     )}"     )
				with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""     ) as f:
						f.write(config.to_json_string()     )


if __name__ == "__main__":
					_a				:		str	   =       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.',
					)
					_a				:		List[Any]	   =       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 math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




