

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    :Any      =			logging.getLogger(__name__)

def       __snake_case						( ):



      """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=__UpperCamelCase				,default="data/dump.txt"				,help="The path to the data."    )
      parser.add_argument("--tokenizer_type"				,type=__UpperCamelCase				,default="bert"				,choices=["bert", "roberta", "gpt2"]    )
      parser.add_argument("--tokenizer_name"				,type=__UpperCamelCase				,default="bert-base-uncased"				,help="The tokenizer to use."    )
      parser.add_argument("--dump_file"				,type=__UpperCamelCase				,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(__UpperCamelCase    )} examples to process.'''    )

      A_						  =				[]
      A_						  =				0
      A_						  =				1_0000
      A_						  =				time.time()
      for text in data:
            A_						  =				f'''{bos} {text.strip()} {sep}'''
            A_						  =				tokenizer.encode(__UpperCamelCase				,add_special_tokens=__UpperCamelCase    )
            rslt.append(__UpperCamelCase    )

            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(__UpperCamelCase    )} examples processed.'''    )

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


if __name__ == "__main__":
      main()






'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

					warnings.warn(
					    """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use OwlViTImageProcessor instead."""  ,      __magic_name__  ,      )
					super().__init__(*__magic_name__  ,      **__magic_name__			)


