"""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 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	Any     				=      BertTokenizer.from_pretrained('bert-base-uncased'						,	do_lower_case=_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'  )
  model.to(args.device  )
  model.eval()

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

  if args.compute_rouge:
    a__:	Optional[Any]     				=      []
    a__:	Dict     				=      []

    import nltk
    import rouge

    nltk.download('punkt'  )
    a__:	Optional[Any]     				=      rouge.Rouge(
        metrics=['rouge-n', 'rouge-l']						,	max_n=2						,	limit_length=_SCREAMING_SNAKE_CASE						,	length_limit=args.beam_size						,	length_limit_type='words'						,	apply_avg=_SCREAMING_SNAKE_CASE						,	apply_best=_SCREAMING_SNAKE_CASE						,	alpha=0.5						,	weight_factor=1.2						,	stemming=_SCREAMING_SNAKE_CASE						,	)

  # these (unused) arguments are defined to keep the compatibility
  # with the legacy code and will be deleted in a next iteration.
  a__:	Union[str, Any]     				=      ''
  a__:	Any     				=      ''

  a__:	Union[str, Any]     				=      build_data_iterator(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	int     				=      build_predictor(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  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(_SCREAMING_SNAKE_CASE  ):
    a__:	List[str]     				=      predictor.translate_batch(_SCREAMING_SNAKE_CASE  )
    a__:	Dict     				=      predictor.from_batch(_SCREAMING_SNAKE_CASE  )
    a__:	int     				=      [format_summary(_SCREAMING_SNAKE_CASE  ) for t in translations]
    save_summaries(_SCREAMING_SNAKE_CASE						,	args.summaries_output_dir						,	batch.document_names  )

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

  if args.compute_rouge:
    a__:	Dict     				=      rouge_evaluator.get_scores(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    a__:	Optional[Any]     				=      format_rouge_scores(_SCREAMING_SNAKE_CASE  )
    save_rouge_scores(_SCREAMING_SNAKE_CASE  )
    print(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  for summary, document_name in zip(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    # Prepare the summary file's name
    if "." in document_name:
      a__:	Any     				=      '.'.join(document_name.split('.'  )[:-1]  )
      a__:	str     				=      document_name.split('.'  )[-1]
      a__:	Optional[Any]     				=      bare_document_name + '_summary.' + extension
    else:
      a__:	Union[str, Any]     				=      document_name + '_summary'

    a__:	Union[str, Any]     				=      os.path.join(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    with open(_SCREAMING_SNAKE_CASE						,	'w'  ) as output:
      output.write(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__				,       a__				,       a__:	Optional[Any]     				=      translation
  a__:	Tuple     				=      (
      raw_summary.replace('[unused0]'						,	''  )
      .replace('[unused3]'						,	''  )
      .replace('[PAD]'						,	''  )
      .replace('[unused1]'						,	''  )
      .replace(r' +'						,	' '  )
      .replace(' [unused2] '						,	'. '  )
      .replace('[unused2]'						,	''  )
      .strip()
  )

  return summary







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  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 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  with open('rouge_scores.txt'						,	'w'  ) as output:
    output.write(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__:	List[Any]     				=      load_and_cache_examples(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      SequentialSampler(_SCREAMING_SNAKE_CASE  )

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

  a__:	Dict     				=      DataLoader(
      _SCREAMING_SNAKE_CASE						,	sampler=_SCREAMING_SNAKE_CASE						,	batch_size=args.batch_size						,	collate_fn=_SCREAMING_SNAKE_CASE						,	)

  return iterator







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  a__:	Dict     				=      CNNDMDataset(args.documents_dir  )
  return dataset







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	List[Any]     				=      [x for x in data if not len(x[1]  ) == 0]  # remove empty_files
  a__:	Any     				=      [name for name, _, _ in data]
  a__:	Dict     				=      [' '.join(_SCREAMING_SNAKE_CASE  ) for _, _, summary_list in data]

  a__:	Optional[int]     				=      [encode_for_summarization(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ) for _, story, summary in data]
  a__:	str     				=      torch.tensor(
      [truncate_or_pad(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	tokenizer.pad_token_id  ) for story, _ in encoded_text]  )
  a__:	int     				=      compute_token_type_ids(_SCREAMING_SNAKE_CASE						,	tokenizer.cls_token_id  )
  a__:	str     				=      build_mask(_SCREAMING_SNAKE_CASE						,	tokenizer.pad_token_id  )

  a__:	List[str]     				=      Batch(
      document_names=_SCREAMING_SNAKE_CASE						,	batch_size=len(_SCREAMING_SNAKE_CASE  )						,	src=encoded_stories.to(_SCREAMING_SNAKE_CASE  )						,	segs=encoder_token_type_ids.to(_SCREAMING_SNAKE_CASE  )						,	mask_src=encoder_mask.to(_SCREAMING_SNAKE_CASE  )						,	tgt_str=_SCREAMING_SNAKE_CASE						,	)

  return batch







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Optional[int]     				=      summary_tokens.to('cpu'  ).numpy()
  a__:	List[str]     				=      tokenizer.decode(_SCREAMING_SNAKE_CASE  )
  a__:	Any     				=      summary.split('.'  )
  a__:	int     				=      [s + '.' for s in sentences]
  return sentences







def 		__a  (      )      ->Union[str, Any]:
  a__:	Optional[int]     				=      argparse.ArgumentParser()
  parser.add_argument(
      '--documents_dir'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='The folder where the documents to summarize are located.'						,	)
  parser.add_argument(
      '--summaries_output_dir'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'						,	)
  parser.add_argument(
      '--compute_rouge'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'						,	)
  # EVALUATION options
  parser.add_argument(
      '--no_cuda'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	help='Whether to force the execution on CPU.'						,	)
  parser.add_argument(
      '--batch_size'						,	default=4						,	type=_SCREAMING_SNAKE_CASE						,	help='Batch size per GPU/CPU for training.'						,	)
  # BEAM SEARCH arguments
  parser.add_argument(
      '--min_length'						,	default=50						,	type=_SCREAMING_SNAKE_CASE						,	help='Minimum number of tokens for the summaries.'						,	)
  parser.add_argument(
      '--max_length'						,	default=200						,	type=_SCREAMING_SNAKE_CASE						,	help='Maixmum number of tokens for the summaries.'						,	)
  parser.add_argument(
      '--beam_size'						,	default=5						,	type=_SCREAMING_SNAKE_CASE						,	help='The number of beams to start with for each example.'						,	)
  parser.add_argument(
      '--alpha'						,	default=0.95						,	type=_SCREAMING_SNAKE_CASE						,	help='The value of alpha for the length penalty in the beam search.'						,	)
  parser.add_argument(
      '--block_trigram'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	help='Whether to block the existence of repeating trigrams in the text generated by beam search.'						,	)
  a__:	int     				=      parser.parse_args()

  # Select device (distibuted not available)
  a__:	Union[str, Any]     				=      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__:	Dict     				=      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=_SCREAMING_SNAKE_CASE  )

  evaluate(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Any:
  if not os.path.exists(_SCREAMING_SNAKE_CASE  ):
    return False

  a__:	Optional[Any]     				=      os.listdir(_SCREAMING_SNAKE_CASE  )
  if len(_SCREAMING_SNAKE_CASE  ) == 0:
    return False

  return True


if __name__ == "__main__":
 main()






'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
     import torch

     from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer







@require_torch
class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					Tuple						)->	Optional[int]:
          snake_case__					:	Any          =					XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(lowerCamelCase						)

          # encoder-decoder outputs
          snake_case__					:	Optional[int]          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          snake_case__					:	Optional[int]          =					torch.tensor(
              [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]						).to(lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # encoder outputs
          snake_case__					:	Dict          =					model.prophetnet.encoder(lowerCamelCase						)[0]
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]						).to(lowerCamelCase						)
          snake_case__					:	str          =					torch.Size((1, 4, 1_024)						)
          self.assertEqual(encoder_outputs.shape					, lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # decoder outputs
          snake_case__					:	int          =					model.prophetnet.decoder(
              lowerCamelCase					, encoder_hidden_states=lowerCamelCase					, )
          snake_case__					:	Any          =					decoder_outputs[1].view(1					, model.config.ngram					, 14					, -1						)
          snake_case__					:	Optional[Any]          =					model.lm_head(lowerCamelCase						)
          snake_case__					:	str          =					predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Tuple          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)

          snake_case__					:	str          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	List[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[str]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case__					:	List[str]          =					torch.tensor(
              [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]						).to(lowerCamelCase						)

          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          snake_case__					:	Dict          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					512

          snake_case__					:	Any          =					XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

          snake_case__					:	Optional[int]          =					(
              """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
              """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
              """ system will not be able to receive security updates, which could make their computers vulnerable to"""
              """ cyber attacks."""
          )
          snake_case__					:	Union[str, Any]          =					(
              """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
              """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
              """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
              """ к кибератакам."""
          )
          snake_case__					:	int          =					(
              """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
          )

          snake_case__					:	int          =					tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					, padding=lowerCamelCase					, max_length=255					, return_tensors="""pt"""						).input_ids
          snake_case__					:	Optional[int]          =					input_ids.to(lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					model.generate(
              lowerCamelCase					, num_beams=10					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					[tokenizer.decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids]
          snake_case__					:	Optional[Any]          =					"""Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case__					:	Dict          =					"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case__					:	Optional[int]          =					"""微软打算终止对Windows 7操作系统的免费支持"""
          self.assertListEqual(
              [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]					, lowerCamelCase					, )

          snake_case__					:	Optional[int]          =					model.generate(
              lowerCamelCase					, num_beams=1					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Tuple          =					[
              tokenizer.convert_ids_to_tokens(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids_beama
          ]
          snake_case__					:	Any          =					"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
          snake_case__					:	Union[str, Any]          =					"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """						)
          snake_case__					:	Tuple          =					"""微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """						)
          self.assertListEqual(
              [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]					, lowerCamelCase					, )




