"""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
)
lowerCAmelCase__  :  Dict						     =	logging.getLogger(__name__)



def   a_						(					):
				UpperCAmelCase__             =							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.'       )
				UpperCAmelCase__             =							parser.parse_args()

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

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

				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							0
				UpperCAmelCase__             =							1_0_0_0_0
				UpperCAmelCase__             =							time.time()
				for text in data:
								UpperCAmelCase__             =							f'''{bos} {text.strip()} {sep}'''
								UpperCAmelCase__             =							tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
								rslt.append(lowerCamelCase       )

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

				UpperCAmelCase__             =							f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
				UpperCAmelCase__             =							tokenizer.vocab_size
				if vocab_size < (1 << 1_6):
								UpperCAmelCase__             =							[np.uintaa(lowerCamelCase       ) for d in rslt]
				else:
								UpperCAmelCase__             =							[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"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



