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      :			List[str]							       =		logging.getLogger(__name__)



def      __SCREAMING_SNAKE_CASE  ( )							-> List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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."""     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()

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

  logger.info("""Start encoding"""     )
  logger.info(f"""{len(__UpperCamelCase     )} examples to process."""     )

  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				1_00_00
  SCREAMING_SNAKE_CASE__      							=				time.time()
  for text in data:
    SCREAMING_SNAKE_CASE__      							=				f"""{bos} {text.strip()} {sep}"""
    SCREAMING_SNAKE_CASE__      							=				tokenizer.encode(__UpperCamelCase			,					add_special_tokens=__UpperCamelCase     )
    rslt.append(__UpperCamelCase     )

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

  SCREAMING_SNAKE_CASE__      							=				f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
  SCREAMING_SNAKE_CASE__      							=				tokenizer.vocab_size
  if vocab_size < (1 << 16):
    SCREAMING_SNAKE_CASE__      							=				[np.uintaa(__UpperCamelCase     ) for d in rslt]
  else:
    SCREAMING_SNAKE_CASE__      							=				[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()







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


__lowerCamelCase      :			str							       =		logging.get_logger(__name__)







class __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    List[str]	,     *_lowercase      :    Union[str, Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






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