


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,
)


SCREAMING_SNAKE_CASE		:Tuple											=       logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


SCREAMING_SNAKE_CASE		:List[str]											=       namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   BertTokenizer.from_pretrained("bert-base-uncased"						,  do_lower_case=lowerCAmelCase_							)
   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=lowerCAmelCase_						,  length_limit=args.beam_size						,  length_limit_type="words"						,  apply_avg=lowerCAmelCase_						,  apply_best=lowerCAmelCase_						,  alpha=0.5						,  weight_factor=1.2						,  stemming=lowerCAmelCase_						,  )

   # 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(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   build_predictor(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

   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(lowerCAmelCase_							):
      snake_case_         =   predictor.translate_batch(lowerCAmelCase_							)
      snake_case_         =   predictor.from_batch(lowerCAmelCase_							)
      snake_case_         =   [format_summary(lowerCAmelCase_							) for t in translations]
      save_summaries(lowerCAmelCase_						,  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(lowerCAmelCase_						,  lowerCAmelCase_							)
      snake_case_         =   format_rouge_scores(lowerCAmelCase_							)
      save_rouge_scores(lowerCAmelCase_							)
      print(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Any							)->int:
   '''simple docstring'''






   for summary, document_name in zip(lowerCAmelCase_						,  lowerCAmelCase_							):
      # 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(lowerCAmelCase_						,  lowerCAmelCase_							)
      with open(lowerCAmelCase_						,  "w"							) as output:
         output.write(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]							)->Any:
   '''simple docstring'''






   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      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]							)->List[str]:
   '''simple docstring'''






   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      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple							)->List[Any]:
   '''simple docstring'''






   with open("rouge_scores.txt"						,  "w"							) as output:
      output.write(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:int							)->Optional[Any]:
   '''simple docstring'''






   snake_case_         =   load_and_cache_examples(lowerCAmelCase_						,  lowerCAmelCase_							)
   snake_case_         =   SequentialSampler(lowerCAmelCase_							)

   def collate_fn(lowerCAmelCase_							:List[Any]							):
      return collate(lowerCAmelCase_						,  lowerCAmelCase_						,  block_size=512						,  device=args.device							)

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

   return iterator



def      _lowerCAmelCase  (							lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Any							)->List[str]:
   '''simple docstring'''






   snake_case_         =   CNNDMDataset(args.documents_dir							)
   return dataset



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:str							)->Optional[Any]:
   '''simple docstring'''






   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(lowerCAmelCase_							) for _, _, summary_list in data]

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

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

   return batch



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[int]							)->List[str]:
   '''simple docstring'''






   snake_case_         =   summary_tokens.to("cpu"							).numpy()
   snake_case_         =   tokenizer.decode(lowerCAmelCase_							)
   snake_case_         =   summary.split("."							)
   snake_case_         =   [s + "." for s in sentences]
   return sentences



def      _lowerCAmelCase  (							)->Dict:
   '''simple docstring'''






   snake_case_         =   argparse.ArgumentParser()
   parser.add_argument(
       "--documents_dir"						,  default=lowerCAmelCase_						,  type=lowerCAmelCase_						,  required=lowerCAmelCase_						,  help="The folder where the documents to summarize are located."						,  )
   parser.add_argument(
       "--summaries_output_dir"						,  default=lowerCAmelCase_						,  type=lowerCAmelCase_						,  required=lowerCAmelCase_						,  help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"						,  )
   parser.add_argument(
       "--compute_rouge"						,  default=lowerCAmelCase_						,  type=lowerCAmelCase_						,  required=lowerCAmelCase_						,  help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."						,  )
   # EVALUATION options
   parser.add_argument(
       "--no_cuda"						,  default=lowerCAmelCase_						,  type=lowerCAmelCase_						,  help="Whether to force the execution on CPU."						,  )
   parser.add_argument(
       "--batch_size"						,  default=4						,  type=lowerCAmelCase_						,  help="Batch size per GPU/CPU for training."						,  )
   # BEAM SEARCH arguments
   parser.add_argument(
       "--min_length"						,  default=50						,  type=lowerCAmelCase_						,  help="Minimum number of tokens for the summaries."						,  )
   parser.add_argument(
       "--max_length"						,  default=200						,  type=lowerCAmelCase_						,  help="Maixmum number of tokens for the summaries."						,  )
   parser.add_argument(
       "--beam_size"						,  default=5						,  type=lowerCAmelCase_						,  help="The number of beams to start with for each example."						,  )
   parser.add_argument(
       "--alpha"						,  default=0.9_5						,  type=lowerCAmelCase_						,  help="The value of alpha for the length penalty in the beam search."						,  )
   parser.add_argument(
       "--block_trigram"						,  default=lowerCAmelCase_						,  type=lowerCAmelCase_						,  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=lowerCAmelCase_							)

   evaluate(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->Dict:
   '''simple docstring'''






   if not os.path.exists(lowerCAmelCase_							):
      return False

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

   return True


if __name__ == "__main__":
   main()








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

     if not self.min_threshold < threshold < self.max_threshold:
        snake_case_         =   F'''Factor value should be from 0 to {self.max_threshold}'''
        raise ValueError(_lowerCAmelCase					)

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       Burkes(imread('''image_data/lena.jpg''', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F'''Original image with dithering threshold: {burkes.threshold}''',
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()




