



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 (       )     ->     Dict:






      '''simple docstring'''







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

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

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

      UpperCAmelCase_  :	List[str]				      =		[]
      UpperCAmelCase_  :	str				      =		0
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0_0
      UpperCAmelCase_  :	int				      =		time.time()
      for text in data:
            UpperCAmelCase_  :	str				      =		F"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase_  :	Any				      =		tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case    )
            rslt.append(__snake_case    )

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

      UpperCAmelCase_  :	List[Any]				      =		F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase_  :	Optional[int]				      =		tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            UpperCAmelCase_  :	List[Any]				      =		[np.uintaa(__snake_case    ) for d in rslt]
      else:
            UpperCAmelCase_  :	str				      =		[np.intaa(__snake_case    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(F"""Dump to {dp_file}"""    )
      with open(__snake_case					,  "wb"    ) as handle:
            pickle.dump(rslt_					,  __snake_case					,  protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
   main()





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	list[list[Edge]]				      =		[[] for _ in range(lowercase)]
             UpperCAmelCase_  :	str				      =		size



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             return iter(self._graph[vertex])



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._size



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1.")

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size).")

             self._graph[from_vertex].append(Edge(lowercase     ,lowercase))



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		deque([start_vertex])
             UpperCAmelCase_  :	list[int | None]				      =		[None] * self.size
             UpperCAmelCase_  :	Dict				      =		0

             while queue:
                   UpperCAmelCase_  :	List[Any]				      =		queue.popleft()
                   UpperCAmelCase_  :	Any				      =		distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         UpperCAmelCase_  :	Tuple				      =		current_distance + edge.weight
                         UpperCAmelCase_  :	Optional[Any]				      =		distances[edge.destination_vertex]
                         if (
                             isinstance(lowercase     ,lowercase)
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         UpperCAmelCase_  :	Tuple				      =		new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex)
                         else:
                               queue.append(edge.destination_vertex)

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex.")

             return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
