





"""simple docstring"""




import heapq as hq
import math
from collections.abc import Iterator


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		List[str]	=				str(id_      )
								a      :		Optional[Any]	=				None
								a      :		Tuple	=				None
								a      :		str	=				[]
								a      :		Any	=				{}  # {vertex:distance}






							def __lt__(   self      ,					lowerCAmelCase__      )	-> Any:
								return self.key < other.key






							def __repr__(   self      )	-> Optional[Any]:
								return self.id






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								self.neighbors.append(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								a      :		Optional[Any]	=				weight






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	graph[a - 1].add_neighbor(graph[b - 1]					)
	graph[b - 1].add_neighbor(graph[a - 1]					)
	# add the edges:
	graph[a - 1].add_edge(graph[b - 1]    ,       _lowercase					)
	graph[b - 1].add_edge(graph[a - 1]    ,       _lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->list:






	'''simple docstring'''






	a      :		int	=				[]
	for u in graph:
		a      :		List[str]	=				math.inf
		a      :		int	=				None
	a      :		str	=				0
	a      :		Union[str, Any]	=				graph[:]
	while q:
		a      :		List[Any]	=				min(_lowercase					)
		q.remove(_lowercase					)
		for v in u.neighbors:
			if (v in q) and (u.edges[v.id] < v.key):
				a      :		List[Any]	=				u
				a      :		Optional[int]	=				u.edges[v.id]
	for i in range(1    ,       len(_lowercase					)					):
		a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
	return a






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->Iterator[tuple]:






	'''simple docstring'''






	for u in graph:
		a      :		str	=				math.inf
		a      :		Dict	=				None
	a      :		Dict	=				0

	a      :		List[Any]	=				list(_lowercase					)
	hq.heapify(_lowercase					)

	while h:
		a      :		Dict	=				hq.heappop(_lowercase					)
		for v in u.neighbors:
			if (v in h) and (u.edges[v.id] < v.key):
				a      :		Dict	=				u
				a      :		Optional[Any]	=				u.edges[v.id]
				hq.heapify(_lowercase					)

	for i in range(1    ,       len(_lowercase					)					):
		yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)






def       _SCREAMING_SNAKE_CASE		(	) ->None:






	'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""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						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		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=_lowercase    ,       default="data/dump.txt"    ,       help="The path to the data."					)
	parser.add_argument("--tokenizer_type"    ,       type=_lowercase    ,       default="bert"    ,       choices=["bert", "roberta", "gpt2"]					)
	parser.add_argument("--tokenizer_name"    ,       type=_lowercase    ,       default="bert-base-uncased"    ,       help="The tokenizer to use."					)
	parser.add_argument("--dump_file"    ,       type=_lowercase    ,       default="data/dump"    ,       help="The dump file prefix."					)
	a      :		Dict	=				parser.parse_args()

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

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

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

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

	a      :		Optional[int]	=				F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	a      :		Tuple	=				tokenizer.vocab_size
	if vocab_size < (1 << 16):
		a      :		Optional[int]	=				[np.uintaa(_lowercase					) for d in rslt]
	else:
		a      :		Optional[Any]	=				[np.intaa(_lowercase					) for d in rslt]
	random.shuffle(rslt_					)
	logger.info(F"""Dump to {dp_file}"""					)
	with open(_lowercase    ,       "wb"					) as handle:
		pickle.dump(rslt_    ,       _lowercase    ,       protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
		main()


