


'''simple docstring'''






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
)
a_   :      Optional[Any]				    =      logging.getLogger(__name__)






def 		_A			()				->					Tuple:




     '''simple docstring'''



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

     logger.info(f'Loading Tokenizer ({args.tokenizer_name})'						)
     if args.tokenizer_type == "bert":
          _a      =							BertTokenizer.from_pretrained(args.tokenizer_name						)
          _a      =							tokenizer.special_tokens_map['cls_token']  # `[CLS]`
          _a      =							tokenizer.special_tokens_map['sep_token']  # `[SEP]`
     elif args.tokenizer_type == "roberta":
          _a      =							RobertaTokenizer.from_pretrained(args.tokenizer_name						)
          _a      =							tokenizer.special_tokens_map['cls_token']  # `<s>`
          _a      =							tokenizer.special_tokens_map['sep_token']  # `</s>`
     elif args.tokenizer_type == "gpt2":
          _a      =							GPTaTokenizer.from_pretrained(args.tokenizer_name						)
          _a      =							tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
          _a      =							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:
          _a      =							fp.readlines()

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

     _a      =							[]
     _a      =							0
     _a      =							1_00_00
     _a      =							time.time()
     for text in data:
          _a      =							f'{bos} {text.strip()} {sep}'
          _a      =							tokenizer.encode(lowerCamelCase_			,  add_special_tokens=lowerCamelCase_						)
          rslt.append(lowerCamelCase_						)

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

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


if __name__ == "__main__":
 main()





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

