"""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
UpperCAmelCase__  		=						data_utils.TransfoXLTokenizer
UpperCAmelCase__  		=						data_utils.TransfoXLCorpus
UpperCAmelCase__  		=						data_utils
UpperCAmelCase__  		=						data_utils






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(lowercase   ,"""rb"""		) as fp:
      _UpperCAmelCase						 =      pickle.load(lowercase   ,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(lowercase   ,lowercase		)

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

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

    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(lowercase		)
    print(f'''Building PyTorch model from configuration: {config}'''		)
    _UpperCAmelCase						 =      TransfoXLLMHeadModel(lowercase		)

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


if __name__ == "__main__":
    UpperCAmelCase__  		=						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.""",
    )
    UpperCAmelCase__  		=						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"""


from __future__ import annotations

from random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
