


'''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()







'''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



