code: 
  
  
  '''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,
  )
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  logging.basicConfig(stream=sys.stdout, level=logging.INFO)
  
  
  UpperCAmelCase__      =							namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  BertTokenizer.from_pretrained('bert-base-uncased',do_lower_case=_SCREAMING_SNAKE_CASE   )
  			__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=_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=  ''
  			__A=  ''
  
  			__A=  build_data_iterator(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			__A=  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=  predictor.translate_batch(_SCREAMING_SNAKE_CASE   )
  						__A=  predictor.from_batch(_SCREAMING_SNAKE_CASE   )
  						__A=  [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=  rouge_evaluator.get_scores(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  						__A=  format_rouge_scores(_SCREAMING_SNAKE_CASE   )
  						save_rouge_scores(_SCREAMING_SNAKE_CASE   )
  						print(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			for summary, document_name in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ):
  						# 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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  						with open(_SCREAMING_SNAKE_CASE,'w'   ) as output:
  									output.write(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Dict   ):
  
  
  
  
  
  
  			"""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 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   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 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			with open('rouge_scores.txt','w'   ) as output:
  						output.write(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Union[str, Any],_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  load_and_cache_examples(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  			__A=  SequentialSampler(_SCREAMING_SNAKE_CASE   )
  
  			def collate_fn(_SCREAMING_SNAKE_CASE				:   Dict   ):
  						return collate(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,block_size=512,device=args.device   )
  
  			__A=  DataLoader(
  			    _SCREAMING_SNAKE_CASE,sampler=_SCREAMING_SNAKE_CASE,batch_size=args.batch_size,collate_fn=_SCREAMING_SNAKE_CASE,)
  
  			return iterator
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   int   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  CNNDMDataset(args.documents_dir   )
  			return dataset
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  
  
  
  
  
  
  			"""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(_SCREAMING_SNAKE_CASE   ) for _, _, summary_list in data]
  
  			__A=  [encode_for_summarization(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   ) for _, story, summary in data]
  			__A=  torch.tensor(
  			    [truncate_or_pad(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
  			__A=  compute_token_type_ids(_SCREAMING_SNAKE_CASE,tokenizer.cls_token_id   )
  			__A=  build_mask(_SCREAMING_SNAKE_CASE,tokenizer.pad_token_id   )
  
  			__A=  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 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   int   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  summary_tokens.to('cpu'   ).numpy()
  			__A=  tokenizer.decode(_SCREAMING_SNAKE_CASE   )
  			__A=  summary.split('.'   )
  			__A=  [s + '.' for s in sentences]
  			return sentences
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  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=  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=_SCREAMING_SNAKE_CASE   )
  
  			evaluate(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if not os.path.exists(_SCREAMING_SNAKE_CASE   ):
  						return False
  
  			__A=  os.listdir(_SCREAMING_SNAKE_CASE   )
  			if len(_SCREAMING_SNAKE_CASE   ) == 0:
  						return False
  
  			return True
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Any
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any  )			->		int:
  						__A=  []
  						__A=  0
  			def __str__(     self       : Tuple  )			->		Dict:
  						__A=  '<' + str(self.stack  )[1:-1] + '>'
  						return printed
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
  						self.stack.append(lowerCAmelCase_  )
  						__A=  self.length + 1
  			def 				lowerCAmelCase    (     self       : int  )			->		Any:
  						self.rotate(1  )
  						__A=  self.stack[self.length - 1]
  						__A=  self.stack[:-1]
  						self.rotate(self.length - 1  )
  						__A=  self.length - 1
  						return dequeued
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
  						for _ in range(lowerCAmelCase_  ):
  									__A=  self.stack[0]
  									__A=  self.stack[1:]
  									self.put(lowerCAmelCase_  )
  									__A=  self.length - 1
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
  						__A=  self.get()
  						self.put(lowerCAmelCase_  )
  						self.rotate(self.length - 1  )
  						return front
  			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
  						return self.length
  
  
  
  