





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def A_     (		A__    )  ->		List[str]:
       a__						:     List[Any]			=    {}
       a__						:     Optional[Any]			=    tokenizer(example['content']		,				truncation=A__    )['input_ids']
       a__						:     Union[str, Any]			=    len(example['content']    ) / len(output['input_ids']    )
       return output


lowercase				:	Optional[Any]      						=  HfArgumentParser(PretokenizationArguments)
lowercase				:	str      						=  parser.parse_args()
if args.num_workers is None:
    lowercase				:	str      						=  multiprocessing.cpu_count()
lowercase				:	Any      						=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowercase				:	str      						=  time.time()
lowercase				:	Dict      						=  load_dataset(args.dataset_name, split="""train""")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowercase				:	Optional[Any]      						=  time.time()
lowercase				:	Dict      						=  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowercase				:	int      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")









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				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



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

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

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    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				:	List[Any]      						=  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				:	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,
    )


