




'''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,
)


lowerCAmelCase_			:     str							    =     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


lowerCAmelCase_			:     int							    =     namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]							):




     """simple docstring"""


     a_    :       List[str]    	=     BertTokenizer.from_pretrained("""bert-base-uncased"""   ,						do_lower_case=UpperCamelCase__							)
     a_    :       Union[str, Any]    	=     BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""							)
     model.to(args.device							)
     model.eval()

     a_    :       str    	=     {
         """BOS""": tokenizer.vocab["""[unused0]"""],
         """EOS""": tokenizer.vocab["""[unused1]"""],
         """PAD""": tokenizer.vocab["""[PAD]"""],
     }

     if args.compute_rouge:
          a_    :       List[Any]    	=     []
          a_    :       Dict    	=     []

          import nltk
          import rouge

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

     # these (unused) arguments are defined to keep the compatibility
     # with the legacy code and will be deleted in a next iteration.
     a_    :       Any    	=     """"""
     a_    :       Dict    	=     """"""

     a_    :       List[Any]    	=     build_data_iterator(UpperCamelCase__   ,						UpperCamelCase__							)
     a_    :       str    	=     build_predictor(UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__							)

     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(UpperCamelCase__							):
          a_    :       Tuple    	=     predictor.translate_batch(UpperCamelCase__							)
          a_    :       Tuple    	=     predictor.from_batch(UpperCamelCase__							)
          a_    :       List[Any]    	=     [format_summary(UpperCamelCase__							) for t in translations]
          save_summaries(UpperCamelCase__   ,						args.summaries_output_dir   ,						batch.document_names							)

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

     if args.compute_rouge:
          a_    :       Tuple    	=     rouge_evaluator.get_scores(UpperCamelCase__   ,						UpperCamelCase__							)
          a_    :       Optional[int]    	=     format_rouge_scores(UpperCamelCase__							)
          save_rouge_scores(UpperCamelCase__							)
          print(UpperCamelCase__							)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     for summary, document_name in zip(UpperCamelCase__   ,						UpperCamelCase__							):
          # Prepare the summary file's name
          if "." in document_name:
               a_    :       List[Any]    	=     """.""".join(document_name.split("""."""							)[:-1]							)
               a_    :       Tuple    	=     document_name.split("""."""							)[-1]
               a_    :       str    	=     bare_document_name + """_summary.""" + extension
          else:
               a_    :       Dict    	=     document_name + """_summary"""

          a_    :       List[str]    	=     os.path.join(UpperCamelCase__   ,						UpperCamelCase__							)
          with open(UpperCamelCase__   ,						"""w"""							) as output:
               output.write(UpperCamelCase__							)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_	,    a_	,    a_    :       List[Any]    	=     translation
     a_    :       int    	=     (
         raw_summary.replace("""[unused0]"""   ,						""""""							)
         .replace("""[unused3]"""   ,						""""""							)
         .replace("""[PAD]"""   ,						""""""							)
         .replace("""[unused1]"""   ,						""""""							)
         .replace(R""" +"""   ,						""" """							)
         .replace(""" [unused2] """   ,						""". """							)
         .replace("""[unused2]"""   ,						""""""							)
         .strip()
     )

     return summary



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Any							):




     """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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     with open("""rouge_scores.txt"""   ,						"""w"""							) as output:
          output.write(UpperCamelCase__							)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				List[str]   ,						UpperCamelCase__						:				Optional[Any]							):




     """simple docstring"""


     a_    :       Dict    	=     load_and_cache_examples(UpperCamelCase__   ,						UpperCamelCase__							)
     a_    :       Any    	=     SequentialSampler(UpperCamelCase__							)

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

     a_    :       List[str]    	=     DataLoader(
         UpperCamelCase__   ,						sampler=UpperCamelCase__   ,						batch_size=args.batch_size   ,						collate_fn=UpperCamelCase__   ,						)

     return iterator



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple   ,						UpperCamelCase__						:				Optional[int]							):




     """simple docstring"""


     a_    :       Any    	=     CNNDMDataset(args.documents_dir							)
     return dataset



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int   ,						UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     [x for x in data if not len(x[1]							) == 0]  # remove empty_files
     a_    :       List[Any]    	=     [name for name, _, _ in data]
     a_    :       Tuple    	=     [""" """.join(UpperCamelCase__							) for _, _, summary_list in data]

     a_    :       List[Any]    	=     [encode_for_summarization(UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__							) for _, story, summary in data]
     a_    :       Tuple    	=     torch.tensor(
         [truncate_or_pad(UpperCamelCase__   ,						UpperCamelCase__   ,						tokenizer.pad_token_id							) for story, _ in encoded_text]							)
     a_    :       Dict    	=     compute_token_type_ids(UpperCamelCase__   ,						tokenizer.cls_token_id							)
     a_    :       Tuple    	=     build_mask(UpperCamelCase__   ,						tokenizer.pad_token_id							)

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

     return batch



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple   ,						UpperCamelCase__						:				List[Any]							):




     """simple docstring"""


     a_    :       Optional[Any]    	=     summary_tokens.to("""cpu"""							).numpy()
     a_    :       Union[str, Any]    	=     tokenizer.decode(UpperCamelCase__							)
     a_    :       List[Any]    	=     summary.split("""."""							)
     a_    :       Any    	=     [s + """.""" for s in sentences]
     return sentences



def    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


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

     evaluate(UpperCamelCase__							)



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Tuple							):




     """simple docstring"""


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

     a_    :       Any    	=     os.listdir(UpperCamelCase__							)
     if len(UpperCamelCase__							) == 0:
          return False

     return True


if __name__ == "__main__":
 main()






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


if __name__ == "__main__":
 print(f"{solution() = }")
