


"""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
__lowercase    					=      data_utils.TransfoXLTokenizer
__lowercase    					=      data_utils.TransfoXLCorpus
__lowercase    					=      data_utils
__lowercase    					=      data_utils


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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

    a						:		List[Any]      							=			corpus.__dict__
    corpus_dict_no_vocab.pop("vocab"  ,   A_       )
    a						:		Union[str, Any]      							=			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
    a						:		int      							=			os.path.abspath(A_       )
    a						:		int      							=			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 == "":
      a						:		List[str]      							=			TransfoXLConfig()
    else:
      a						:		Optional[int]      							=			TransfoXLConfig.from_json_file(A_       )
    print(F'''Building PyTorch model from configuration: {config}'''       )
    a						:		Dict      							=			TransfoXLLMHeadModel(A_       )

    a						:		Union[str, Any]      							=			load_tf_weights_in_transfo_xl(A_  ,   A_  ,   A_       )
    # Save pytorch-model
    a						:		Optional[int]      							=			os.path.join(A_  ,   A_       )
    a						:		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__":
 __lowercase    					=      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.""",
 )
 __lowercase    					=      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,
 )








from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




