






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       :       Optional[int]        =				logging.getLogger(__name__)
def 					snake_case_						(   ):
							__lowercase					:							Dict			=   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."""					)
							__lowercase					:							Tuple			=   parser.parse_args()

							logger.info(F"Loading Tokenizer ({args.tokenizer_name})"					)
							if args.tokenizer_type == "bert":
														__lowercase					:							str			=   BertTokenizer.from_pretrained(args.tokenizer_name					)
														__lowercase					:							Any			=   tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
														__lowercase					:							Any			=   tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														__lowercase					:							List[str]			=   RobertaTokenizer.from_pretrained(args.tokenizer_name					)
														__lowercase					:							List[str]			=   tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
														__lowercase					:							Optional[int]			=   tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
							elif args.tokenizer_type == "gpt2":
														__lowercase					:							int			=   GPTaTokenizer.from_pretrained(args.tokenizer_name					)
														__lowercase					:							Dict			=   tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
														__lowercase					:							List[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					:							List[str]			=   fp.readlines()

							logger.info("""Start encoding"""					)
							logger.info(F"{len(lowerCAmelCase_					)} examples to process."					)

							__lowercase					:							Any			=   []
							__lowercase					:							Optional[int]			=   0
							__lowercase					:							List[str]			=   10000
							__lowercase					:							Union[str, Any]			=   time.time()
							for text in data:
														__lowercase					:							Union[str, Any]			=   F"{bos} {text.strip()} {sep}"
														__lowercase					:							Tuple			=   tokenizer.encode(lowerCAmelCase_				,				add_special_tokens=lowerCAmelCase_					)
														rslt.append(lowerCAmelCase_					)

														iter += 1
														if iter % interval == 0:
																					__lowercase					:							str			=   time.time()
																					logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"					)
																					__lowercase					:							Optional[int]			=   time.time()
							logger.info("""Finished binarization"""					)
							logger.info(F"{len(lowerCAmelCase_					)} examples processed."					)

							__lowercase					:							Optional[int]			=   F"{args.dump_file}.{args.tokenizer_name}.pickle"
							__lowercase					:							List[Any]			=   tokenizer.vocab_size
							if vocab_size < (1 << 16):
														__lowercase					:							Dict			=   [np.uintaa(lowerCAmelCase_					) for d in rslt]
							else:
														__lowercase					:							Dict			=   [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()







lowerCamelCase       :       Tuple        =				{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase       :       int        =				['''a''', '''b''', '''c''', '''d''', '''e''']
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Dict			=   start
							# add current to visited
							visited.append(lowerCAmelCase_					)
							__lowercase					:							Dict			=   edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					__lowercase					:							List[Any]			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # if all neighbors visited add current to sort
							sort.append(lowerCAmelCase_					)
							# if all vertices haven't been visited select a new one to visit
							if len(lowerCAmelCase_					) != len(lowerCAmelCase_					):
														for vertice in vertices:
																					if vertice not in visited:
																												__lowercase					:							Tuple			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # return sort
							return sort


if __name__ == "__main__":
	lowerCamelCase       :       Any        =				topological_sort('''a''', [], [])
	print(sort)