






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 						A_					(						lowercase_			)				->Optional[int]:







 """simple docstring"""

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

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

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

  import nltk
  import rouge

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

 # these (unused) arguments are defined to keep the compatibility
 # with the legacy code and will be deleted in a next iteration.
 SCREAMING_SNAKE_CASE				    =		''
 SCREAMING_SNAKE_CASE				    =		''

 SCREAMING_SNAKE_CASE				    =		build_data_iterator(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		build_predictor(lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

 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(lowercase_			):
  SCREAMING_SNAKE_CASE				    =		predictor.translate_batch(lowercase_			)
  SCREAMING_SNAKE_CASE				    =		predictor.from_batch(lowercase_			)
  SCREAMING_SNAKE_CASE				    =		[format_summary(lowercase_			) for t in translations]
  save_summaries(lowercase_	,  args.summaries_output_dir	,  batch.document_names			)

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

 if args.compute_rouge:
  SCREAMING_SNAKE_CASE				    =		rouge_evaluator.get_scores(lowercase_	,  lowercase_			)
  SCREAMING_SNAKE_CASE				    =		format_rouge_scores(lowercase_			)
  save_rouge_scores(lowercase_			)
  print(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Union[str, Any]:







 """simple docstring"""

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

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





def 						A_					(						lowercase_			)				->List[str]:







 """simple docstring"""

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

 return summary





def 						A_					(						lowercase_			)				->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 						A_					(						lowercase_			)				->int:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		load_and_cache_examples(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		SequentialSampler(lowercase_			)

 def collate_fn(lowercase_			):
  return collate(lowercase_	,  lowercase_	,  block_size=5_1_2	,  device=args.device			)

 SCREAMING_SNAKE_CASE				    =		DataLoader(
     lowercase_	,  sampler=lowercase_	,  batch_size=args.batch_size	,  collate_fn=lowercase_	,  )

 return iterator





def 						A_					(						lowercase_	,  lowercase_			)				->Any:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		CNNDMDataset(args.documents_dir			)
 return dataset





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

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

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

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

 return batch





def 						A_					(						lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

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





def 						A_					(						)				->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		argparse.ArgumentParser()
 parser.add_argument(
     '--documents_dir'	,  default=lowercase_	,  type=lowercase_	,  required=lowercase_	,  help='The folder where the documents to summarize are located.'	,  )
 parser.add_argument(
     '--summaries_output_dir'	,  default=lowercase_	,  type=lowercase_	,  required=lowercase_	,  help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'	,  )
 parser.add_argument(
     '--compute_rouge'	,  default=lowercase_	,  type=lowercase_	,  required=lowercase_	,  help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'	,  )
 # EVALUATION options
 parser.add_argument(
     '--no_cuda'	,  default=lowercase_	,  type=lowercase_	,  help='Whether to force the execution on CPU.'	,  )
 parser.add_argument(
     '--batch_size'	,  default=4	,  type=lowercase_	,  help='Batch size per GPU/CPU for training.'	,  )
 # BEAM SEARCH arguments
 parser.add_argument(
     '--min_length'	,  default=5_0	,  type=lowercase_	,  help='Minimum number of tokens for the summaries.'	,  )
 parser.add_argument(
     '--max_length'	,  default=2_0_0	,  type=lowercase_	,  help='Maixmum number of tokens for the summaries.'	,  )
 parser.add_argument(
     '--beam_size'	,  default=5	,  type=lowercase_	,  help='The number of beams to start with for each example.'	,  )
 parser.add_argument(
     '--alpha'	,  default=0.9_5	,  type=lowercase_	,  help='The value of alpha for the length penalty in the beam search.'	,  )
 parser.add_argument(
     '--block_trigram'	,  default=lowercase_	,  type=lowercase_	,  help='Whether to block the existence of repeating trigrams in the text generated by beam search.'	,  )
 SCREAMING_SNAKE_CASE				    =		parser.parse_args()

 # Select device (distibuted not available)
 SCREAMING_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:
  SCREAMING_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=lowercase_			)

 evaluate(lowercase_			)





def 						A_					(						lowercase_			)				->Tuple:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE				    =		os.listdir(lowercase_			)
 if len(lowercase_			) == 0:
  return False

 return True


if __name__ == "__main__":
      main()












import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase      =				logging.getLogger(__name__)
class 	a_:



     """simple docstring"""



     def __init__(		self					:	Any)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any])							->	Optional[Any]:

      """simple docstring"""
      if not self.initialized:
       SCREAMING_SNAKE_CASE				    =		RagRetriever(
           lowerCAmelCase__  ,  question_encoder_tokenizer=lowerCAmelCase__  ,  generator_tokenizer=lowerCAmelCase__  ,  index=lowerCAmelCase__  ,  init_retrieval=lowerCAmelCase__  ,  )
       SCREAMING_SNAKE_CASE				    =		True





     def     __UpperCamelCase						(		self					:	List[str])							->	Any:

      """simple docstring"""
      self.retriever.index.init_index()





     def     __UpperCamelCase						(		self					:	int)							->	Dict:

      """simple docstring"""
      # delete the old self.retriever object before assigning the new index
      del self.retriever
      SCREAMING_SNAKE_CASE				    =		False





     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Optional[int])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.retriever._main_retrieve(lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		self.retriever.index.get_doc_dicts(lowerCAmelCase__)
      return doc_ids, retrieved_doc_embeds, doc_dicts

class 	a_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	List[str]  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Optional[Any]=None)							->	Tuple:

      """simple docstring"""
      if index is not None and index.is_initialized() and len(lowerCAmelCase__) > 0:
       raise ValueError(
           'When using Ray for distributed fine-tuning, '
           'you\'ll need to provide the paths instead, '
           'as the dataset and the index are loaded '
           'separately. More info in examples/rag/use_own_knowledge_dataset.py ')

      super().__init__(
          lowerCAmelCase__  ,  question_encoder_tokenizer=lowerCAmelCase__  ,  generator_tokenizer=lowerCAmelCase__  ,  index=lowerCAmelCase__  ,  init_retrieval=lowerCAmelCase__  ,  )

      SCREAMING_SNAKE_CASE				    =		retrieval_workers
      SCREAMING_SNAKE_CASE				    =		question_encoder_tokenizer
      SCREAMING_SNAKE_CASE				    =		generator_tokenizer
      if len(self.retrieval_workers) > 0:
       ray.get(
           [
               worker.create_rag_retriever.remote(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
               for worker in self.retrieval_workers
           ])





     def     __UpperCamelCase						(		self					:	Any)							->	Dict:

      """simple docstring"""
      logger.info('initializing retrieval')

      if len(self.retrieval_workers) > 0:
       ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
      else:
       # Non-distributed training. Load index into this same process.
       self.index.init_index()





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Union[str, Any])							->	List[str]:

      """simple docstring"""
      if len(self.retrieval_workers) > 0:
       # Select a random retrieval actor.
       SCREAMING_SNAKE_CASE				    =		self.retrieval_workers[random.randint(0  ,  len(self.retrieval_workers) - 1)]
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		ray.get(
           random_worker.retrieve.remote(lowerCAmelCase__  ,  lowerCAmelCase__))
      else:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._main_retrieve(lowerCAmelCase__  ,  lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		self.index.get_doc_dicts(lowerCAmelCase__)
      return retrieved_doc_embeds, doc_ids, doc_dicts





     @classmethod
     def     __UpperCamelCase						(		cls					:	List[str]  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Any=None  ,  **lowerCAmelCase__					:	Optional[Any])							->	Union[str, Any]:

      """simple docstring"""
      return super(lowerCAmelCase__  ,  cls).get_tokenizers(lowerCAmelCase__  ,  lowerCAmelCase__  ,  **lowerCAmelCase__)





     @classmethod
     def     __UpperCamelCase						(		cls					:	Any  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any]=None  ,  **lowerCAmelCase__					:	str)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('config'  ,  lowerCAmelCase__) or RagConfig.from_pretrained(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		RagTokenizer.from_pretrained(lowerCAmelCase__  ,  config=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		rag_tokenizer.question_encoder
      SCREAMING_SNAKE_CASE				    =		rag_tokenizer.generator

      if indexed_dataset is not None:
       SCREAMING_SNAKE_CASE				    =		'custom'
       SCREAMING_SNAKE_CASE				    =		CustomHFIndex(config.retrieval_vector_size  ,  lowerCAmelCase__)
      else:
       SCREAMING_SNAKE_CASE				    =		cls._build_index(lowerCAmelCase__)

      return cls(
          lowerCAmelCase__  ,  question_encoder_tokenizer=lowerCAmelCase__  ,  generator_tokenizer=lowerCAmelCase__  ,  retrieval_workers=lowerCAmelCase__  ,  index=lowerCAmelCase__  ,  )





     def     __UpperCamelCase						(		self					:	List[Any])							->	Union[str, Any]:

      """simple docstring"""
      logger.info('re-loading the new dataset with embeddings')
      # access from the training loop

      ray.get([worker.clear_object.remote() for worker in self.retrieval_workers])

      # build the index object again
      SCREAMING_SNAKE_CASE				    =		self._build_index(self.config)

      ray.get(
          [
              worker.create_rag_retriever.remote(
                  self.config  ,  self.question_encoder_tokenizer  ,  self.generator_tokenizer  ,  lowerCAmelCase__)
              for worker in self.retrieval_workers
          ])




