



'''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,
)


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


lowercase_			=  namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  BertTokenizer.from_pretrained('''bert-base-uncased'''  ,     do_lower_case=__A)
     _a     =  BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''')
     model.to(args.device)
     model.eval()

     _a     =  {
         '''BOS''': tokenizer.vocab['''[unused0]'''],
         '''EOS''': tokenizer.vocab['''[unused1]'''],
         '''PAD''': tokenizer.vocab['''[PAD]'''],
     }

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

          import nltk
          import rouge

          nltk.download('''punkt''')
          _a     =  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.
     _a     =  ''''''
     _a     =  ''''''

     _a     =  build_data_iterator(__A  ,     __A)
     _a     =  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):
          _a     =  predictor.translate_batch(__A)
          _a     =  predictor.from_batch(__A)
          _a     =  [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:
          _a     =  rouge_evaluator.get_scores(__A  ,     __A)
          _a     =  format_rouge_scores(__A)
          save_rouge_scores(__A)
          print(__A)




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     for summary, document_name in zip(__A  ,     __A):
          # Prepare the summary file's name
          if "." in document_name:
               _a     =  '''.'''.join(document_name.split('''.''')[:-1])
               _a     =  document_name.split('''.''')[-1]
               _a     =  bare_document_name + '''_summary.''' + extension
          else:
               _a     =  document_name + '''_summary'''

          _a     =  os.path.join(__A  ,     __A)
          with open(__A  ,     '''w''') as output:
               output.write(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a  ,   _a  ,   _a     =  translation
     _a     =  (
         raw_summary.replace('''[unused0]'''  ,     '''''')
         .replace('''[unused3]'''  ,     '''''')
         .replace('''[PAD]'''  ,     '''''')
         .replace('''[unused1]'''  ,     '''''')
         .replace(r''' +'''  ,     ''' ''')
         .replace(''' [unused2] '''  ,     '''. ''')
         .replace('''[unused2]'''  ,     '''''')
         .strip()
     )

     return summary




def lowerCAmelCase  (__A):



     """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  (__A):



     """simple docstring"""



     with open('''rouge_scores.txt'''  ,     '''w''') as output:
          output.write(__A)




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  load_and_cache_examples(__A  ,     __A)
     _a     =  SequentialSampler(__A)

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

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

     return iterator




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  CNNDMDataset(args.documents_dir)
     return dataset




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A):



     """simple docstring"""



     _a     =  [x for x in data if not len(x[1]) == 0]  # remove empty_files
     _a     =  [name for name, _, _ in data]
     _a     =  [''' '''.join(__A) for _, _, summary_list in data]

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

     _a     =  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 lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  summary_tokens.to('''cpu''').numpy()
     _a     =  tokenizer.decode(__A)
     _a     =  summary.split('''.''')
     _a     =  [s + '''.''' for s in sentences]
     return sentences




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  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=0.95  ,     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.'''  ,     )
     _a     =  parser.parse_args()

     # Select device (distibuted not available)
     _a     =  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:
          _a     =  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 lowerCAmelCase  (__A):



     """simple docstring"""



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

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

     return True


if __name__ == "__main__":
  main()








'''simple docstring'''





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

lowercase_			=  0.25 + 0.0J
lowercase_			=  -0.4 + 0.6J
lowercase_			=  -0.1 + 0.651J
lowercase_			=  -2.0
lowercase_			=  56
lowercase_			=  2.0
lowercase_			=  666




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return numpy.exp(__A) + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return z_values * z_values + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  x.reshape((nb_pixels, 1))
     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  y.reshape((1, nb_pixels))
     return x + 1.0J * y




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A = None  ,     ):



     """simple docstring"""



     _a     =  z_a.astype('''complex64''')
     for _ in range(__A):
          _a     =  eval_function(__A  ,     __A)
          if infinity is not None:
               numpy.nan_to_num(__A  ,     copy=__A  ,     nan=__A)
               _a     =  infinity
     return z_n




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  (abs(__A)).transpose()
     _a     =  abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius)
     pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$''')
     pyplot.show()




def lowerCAmelCase  ():



     """simple docstring"""



     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in multiply''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''invalid value encountered in multiply'''  ,     )
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in absolute''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in exp''')


if __name__ == "__main__":
  lowercase_			=  prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  lowercase_			=  24
  lowercase_			=  2 * abs(c_cauliflower) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_cauliflower, escape_radius, z_final)

  lowercase_			=  64
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  161
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  12
  lowercase_			=  1_0000.0
  lowercase_			=  iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("e^z+c", c_exponential, escape_radius, z_final)



