


"""simple docstring"""



from __future__ import annotations







def   __lowerCAmelCase						(						lowercase  :  list[int]						,							lowercase  :  list[int]						,							lowercase  :  int			)     ->						tuple[float, list[float]]:


    """simple docstring"""





    snake_case   :				Tuple				 =   list(range(len(lowercase			)			)			)
    snake_case   :				Optional[int]				 =   [v / w for v, w in zip(lowercase						,							lowercase			)]
    index.sort(key=lambda lowercase			: ratio[i]						,							reverse=lowercase			)

    snake_case   :				float				 =   0
    snake_case   :				list[float]				 =   [0] * len(lowercase			)
    for i in index:
        if weight[i] <= capacity:
            snake_case   :				int				 =   1
            max_value += value[i]
            capacity -= weight[i]
        else:
            snake_case   :				List[Any]				 =   capacity / weight[i]
            max_value += value[i] * capacity / weight[i]
            break

    return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()









"""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
__snake_case             =   data_utils.TransfoXLTokenizer
__snake_case             =   data_utils.TransfoXLCorpus
__snake_case             =   data_utils
__snake_case             =   data_utils







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





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

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

        snake_case   :				str				 =   load_tf_weights_in_transfo_xl(lowercase						,							lowercase						,							lowercase			)
        # Save pytorch-model
        snake_case   :				Union[str, Any]				 =   os.path.join(lowercase						,							lowercase			)
        snake_case   :				Optional[Any]				 =   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__":
  __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.""",
  )
  __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,
  )





