






"""simple docstring"""





UpperCamelCase					=     [chr(i) for i in range(32, 1_26)]
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(reversed(range(len(alphabets))))
UpperCamelCase					=     []
UpperCamelCase					=     UpperCamelCase					=     UpperCamelCase					=     0






def 							A(    ):
       """simple docstring"""





       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       lowercase__:							int         =     gear_one[0]
       gear_one.append(snake_case_						)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(snake_case_						)						) == 0:
              lowercase__:							List[str]         =     gear_two[0]
              gear_two.append(snake_case_						)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(snake_case_						)						) == 0:
                     lowercase__:							Dict         =     gear_three[0]
                     gear_three.append(snake_case_						)
                     del gear_three[0]
                     gear_three_pos += 1






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     alphabets.index(snake_case_						)
       lowercase__:							Dict         =     gear_one[target]
       lowercase__:							Tuple         =     gear_two[target]
       lowercase__:							str         =     gear_three[target]
       lowercase__:							Any         =     reflector[target]
       lowercase__:							Any         =     gear_three.index(snake_case_						)
       lowercase__:							Tuple         =     gear_two.index(snake_case_						)
       lowercase__:							Union[str, Any]         =     gear_one.index(snake_case_						)
       code.append(alphabets[target]						)
       rotator()


if __name__ == "__main__":
  UpperCamelCase					=     list(input("""Type your message:\n"""))
  while True:
    try:
      UpperCamelCase					=     int(input("""Please set token:(must be only digits)\n"""))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("""\n""" + """""".join(code))
  print(
      F"\nYour Token is {token} please write it down.\nIf you want to decode "
      """this message again you should input same digits as token!"""
  )








'''simple docstring'''





import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


A							     = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A							     = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				BertTokenizer.from_pretrained("bert-base-uncased"			,       do_lower_case=_a      )
      snake_case_    							=				BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"      )
      model.to(args.device      )
      model.eval()

      snake_case_    							=				{
          "BOS": tokenizer.vocab["[unused0]"],
          "EOS": tokenizer.vocab["[unused1]"],
          "PAD": tokenizer.vocab["[PAD]"],
      }

      if args.compute_rouge:
            snake_case_    							=				[]
            snake_case_    							=				[]

            import nltk
            import rouge

            nltk.download("punkt"      )
            snake_case_    							=				rouge.Rouge(
                metrics=["rouge-n", "rouge-l"]			,       max_n=2			,       limit_length=_a			,       length_limit=args.beam_size			,       length_limit_type="words"			,       apply_avg=_a			,       apply_best=_a			,       alpha=0.5			,       weight_factor=1.2			,       stemming=_a			,       )

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      snake_case_    							=				""
      snake_case_    							=				""

      snake_case_    							=				build_data_iterator(_a			,       _a      )
      snake_case_    							=				build_predictor(_a			,       _a			,       _a			,       _a      )

      logger.info("***** Running evaluation *****"      )
      logger.info("  Number examples = %d"			,       len(data_iterator.dataset      )      )
      logger.info("  Batch size = %d"			,       args.batch_size      )
      logger.info(""      )
      logger.info("***** Beam Search parameters *****"      )
      logger.info("  Beam size = %d"			,       args.beam_size      )
      logger.info("  Minimum length = %d"			,       args.min_length      )
      logger.info("  Maximum length = %d"			,       args.max_length      )
      logger.info("  Alpha (length penalty) = %.2f"			,       args.alpha      )
      logger.info("  Trigrams %s be blocked"			,       ("will" if args.block_trigram else "will NOT")      )

      for batch in tqdm(_a      ):
            snake_case_    							=				predictor.translate_batch(_a      )
            snake_case_    							=				predictor.from_batch(_a      )
            snake_case_    							=				[format_summary(_a      ) for t in translations]
            save_summaries(_a			,       args.summaries_output_dir			,       batch.document_names      )

            if args.compute_rouge:
                  reference_summaries += batch.tgt_str
                  generated_summaries += summaries

      if args.compute_rouge:
            snake_case_    							=				rouge_evaluator.get_scores(_a			,       _a      )
            snake_case_    							=				format_rouge_scores(_a      )
            save_rouge_scores(_a      )
            print(_a      )
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				List[str]			,       _a  :				Any      ):
      for summary, document_name in zip(_a			,       _a      ):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case_    							=				".".join(document_name.split("."      )[:-1]      )
                  snake_case_    							=				document_name.split("."      )[-1]
                  snake_case_    							=				bare_document_name + "_summary." + extension
            else:
                  snake_case_    							=				document_name + "_summary"

            snake_case_    							=				os.path.join(_a			,       _a      )
            with open(_a			,       "w"      ) as output:
                  output.write(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				translation
      snake_case_    							=				(
          raw_summary.replace("[unused0]"			,       ""      )
          .replace("[unused3]"			,       ""      )
          .replace("[PAD]"			,       ""      )
          .replace("[unused1]"			,       ""      )
          .replace(R" +"			,       " "      )
          .replace(" [unused2] "			,       ". "      )
          .replace("[unused2]"			,       ""      )
          .strip()
      )

      return summary
def 	snake_case_							(				_a  :				str      ):
      return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
          scores["rouge-1"]["f"]			,       scores["rouge-1"]["p"]			,       scores["rouge-1"]["r"]			,       scores["rouge-2"]["f"]			,       scores["rouge-2"]["p"]			,       scores["rouge-2"]["r"]			,       scores["rouge-l"]["f"]			,       scores["rouge-l"]["p"]			,       scores["rouge-l"]["r"]			,       )
def 	snake_case_							(				_a  :				str      ):
      with open("rouge_scores.txt"			,       "w"      ) as output:
            output.write(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				load_and_cache_examples(_a			,       _a      )
      snake_case_    							=				SequentialSampler(_a      )

      def collate_fn(_a  :				Tuple      ):
            return collate(_a			,       _a			,       block_size=512			,       device=args.device      )

      snake_case_    							=				DataLoader(
          _a			,       sampler=_a			,       batch_size=args.batch_size			,       collate_fn=_a			,       )

      return iterator
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      snake_case_    							=				CNNDMDataset(args.documents_dir      )
      return dataset
def 	snake_case_							(				_a  :				int			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				[x for x in data if not len(x[1]      ) == 0]  # remove empty_files
      snake_case_    							=				[name for name, _, _ in data]
      snake_case_    							=				[" ".join(_a      ) for _, _, summary_list in data]

      snake_case_    							=				[encode_for_summarization(_a			,       _a			,       _a      ) for _, story, summary in data]
      snake_case_    							=				torch.tensor(
          [truncate_or_pad(_a			,       _a			,       tokenizer.pad_token_id      ) for story, _ in encoded_text]      )
      snake_case_    							=				compute_token_type_ids(_a			,       tokenizer.cls_token_id      )
      snake_case_    							=				build_mask(_a			,       tokenizer.pad_token_id      )

      snake_case_    							=				Batch(
          document_names=_a			,       batch_size=len(_a      )			,       src=encoded_stories.to(_a      )			,       segs=encoder_token_type_ids.to(_a      )			,       mask_src=encoder_mask.to(_a      )			,       tgt_str=_a			,       )

      return batch
def 	snake_case_							(				_a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				summary_tokens.to("cpu"      ).numpy()
      snake_case_    							=				tokenizer.decode(_a      )
      snake_case_    							=				summary.split("."      )
      snake_case_    							=				[s + "." for s in sentences]
      return sentences
def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser()
      parser.add_argument(
          "--documents_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder where the documents to summarize are located."			,       )
      parser.add_argument(
          "--summaries_output_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"			,       )
      parser.add_argument(
          "--compute_rouge"			,       default=_a			,       type=_a			,       required=_a			,       help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."			,       )
      # EVALUATION options
      parser.add_argument(
          "--no_cuda"			,       default=_a			,       type=_a			,       help="Whether to force the execution on CPU."			,       )
      parser.add_argument(
          "--batch_size"			,       default=4			,       type=_a			,       help="Batch size per GPU/CPU for training."			,       )
      # BEAM SEARCH arguments
      parser.add_argument(
          "--min_length"			,       default=50			,       type=_a			,       help="Minimum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--max_length"			,       default=200			,       type=_a			,       help="Maixmum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--beam_size"			,       default=5			,       type=_a			,       help="The number of beams to start with for each example."			,       )
      parser.add_argument(
          "--alpha"			,       default=0.9_5			,       type=_a			,       help="The value of alpha for the length penalty in the beam search."			,       )
      parser.add_argument(
          "--block_trigram"			,       default=_a			,       type=_a			,       help="Whether to block the existence of repeating trigrams in the text generated by beam search."			,       )
      snake_case_    							=				parser.parse_args()

      # Select device (distibuted not available)
      snake_case_    							=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )

      # Check the existence of directories
      if not args.summaries_output_dir:
            snake_case_    							=				args.documents_dir

      if not documents_dir_is_valid(args.documents_dir      ):
            raise FileNotFoundError(
                "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
                " specify a valid path."      )
      os.makedirs(args.summaries_output_dir			,       exist_ok=_a      )

      evaluate(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      if not os.path.exists(_a      ):
            return False

      snake_case_    							=				os.listdir(_a      )
      if len(_a      ) == 0:
            return False

      return True


if __name__ == "__main__":
     main()

