


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCAmelCase		       =							logging.getLogger(__name__)




def    snake_case_     (  )  ->				int:
       lowercase__:   Union[str, Any]  			=   argparse.ArgumentParser(
           description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'     )
       parser.add_argument('--file_path'			,							type=a_			,							default='data/dump.txt'			,							help='The path to the data.'     )
       parser.add_argument('--tokenizer_type'			,							type=a_			,							default='bert'			,							choices=['bert', 'roberta', 'gpt2']     )
       parser.add_argument('--tokenizer_name'			,							type=a_			,							default='bert-base-uncased'			,							help='The tokenizer to use.'     )
       parser.add_argument('--dump_file'			,							type=a_			,							default='data/dump'			,							help='The dump file prefix.'     )
       lowercase__:   Any  			=   parser.parse_args()

       logger.info(f'Loading Tokenizer ({args.tokenizer_name})'     )
       if args.tokenizer_type == "bert":
              lowercase__:   Union[str, Any]  			=   BertTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__:   Optional[int]  			=   tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
              lowercase__:   Union[str, Any]  			=   tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
       elif args.tokenizer_type == "roberta":
              lowercase__:   str  			=   RobertaTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__:   str  			=   tokenizer.special_tokens_map['''cls_token''']  # `<s>`
              lowercase__:   str  			=   tokenizer.special_tokens_map['''sep_token''']  # `</s>`
       elif args.tokenizer_type == "gpt2":
              lowercase__:   Union[str, Any]  			=   GPTaTokenizer.from_pretrained(args.tokenizer_name     )
              lowercase__:   Optional[int]  			=   tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
              lowercase__:   str  			=   tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

       logger.info(f'Loading text from {args.file_path}'     )
       with open(args.file_path			,							'r'			,							encoding='utf8'     ) as fp:
              lowercase__:   Union[str, Any]  			=   fp.readlines()

       logger.info('Start encoding'     )
       logger.info(f'{len(a_     )} examples to process.'     )

       lowercase__:   Optional[int]  			=   []
       lowercase__:   List[str]  			=   0
       lowercase__:   Optional[Any]  			=   1_00_00
       lowercase__:   List[Any]  			=   time.time()
       for text in data:
              lowercase__:   Any  			=   f'{bos} {text.strip()} {sep}'
              lowercase__:   int  			=   tokenizer.encode(a_			,							add_special_tokens=a_     )
              rslt.append(a_     )

              iter += 1
              if iter % interval == 0:
                     lowercase__:   Any  			=   time.time()
                     logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'     )
                     lowercase__:   Any  			=   time.time()
       logger.info('Finished binarization'     )
       logger.info(f'{len(a_     )} examples processed.'     )

       lowercase__:   Optional[int]  			=   f'{args.dump_file}.{args.tokenizer_name}.pickle'
       lowercase__:   str  			=   tokenizer.vocab_size
       if vocab_size < (1 << 16):
              lowercase__:   Union[str, Any]  			=   [np.uintaa(a_     ) for d in rslt]
       else:
              lowercase__:   List[Any]  			=   [np.intaa(a_     ) for d in rslt]
       random.shuffle(rslt_     )
       logger.info(f'Dump to {dp_file}'     )
       with open(a_			,							'wb'     ) as handle:
              pickle.dump(rslt_			,							a_			,							protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
 main()









"""simple docstring"""
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''' ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42 ,   dataset_name='''my_dataset'''							)}							),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42							)}							),
        SplitDict({'''train''': SplitInfo()}							),
    ] ,   )
def      __lowerCamelCase   (							a_     :		SplitDict							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  split_dict._to_yaml_list()
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SplitDict._from_yaml_list(a_							)
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            __SCREAMING_SNAKE_CASE   :int						=  None
            # the split name of split_dict takes over the name of the split info object
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  split_name
      assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info''' ,   [SplitInfo(), SplitInfo(dataset_name=a_							), SplitInfo(dataset_name='''my_dataset'''							)]							)
def      __lowerCamelCase   (							a_     :		Dict							)			->      Any:
      # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
      # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  asdict(SplitDict({'''train''': split_info}							)							)
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name