




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,
)


snake_case_					:					Optional[int]       						=			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case_					:					Union[str, Any]       						=			namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])


def   A		(__A  :       Union[str, Any]			)  -> Union[str, Any]:







     """simple docstring"""


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

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

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

          import nltk
          import rouge

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

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

     UpperCAmelCase_		=				build_data_iterator(__A  ,							__A			)
     UpperCAmelCase_		=				build_predictor(__A  ,							__A  ,							__A  ,							__A			)

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

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

     if args.compute_rouge:
          UpperCAmelCase_		=				rouge_evaluator.get_scores(__A  ,							__A			)
          UpperCAmelCase_		=				format_rouge_scores(__A			)
          save_rouge_scores(__A			)
          print(__A			)


def   A		(__A  :       List[str]  ,							__A  :       Optional[Any]  ,							__A  :       Optional[int]			)  -> List[Any]:







     """simple docstring"""


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

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


def   A		(__A  :       str			)  -> str:







     """simple docstring"""


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

     return summary


def   A		(__A  :       Union[str, Any]			)  -> Optional[int]:







     """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		(__A  :       List[Any]			)  -> Any:







     """simple docstring"""


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


def   A		(__A  :       Any  ,							__A  :       Dict			)  -> Tuple:







     """simple docstring"""


     UpperCAmelCase_		=				load_and_cache_examples(__A  ,							__A			)
     UpperCAmelCase_		=				SequentialSampler(__A			)

     def collate_fn(__A  :       List[str]			):
          return collate(__A  ,							__A  ,							block_size=512  ,							device=args.device			)

     UpperCAmelCase_		=				DataLoader(
         __A  ,							sampler=__A  ,							batch_size=args.batch_size  ,							collate_fn=__A  ,							)

     return iterator


def   A		(__A  :       List[Any]  ,							__A  :       List[str]			)  -> Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_		=				CNNDMDataset(args.documents_dir			)
     return dataset


def   A		(__A  :       int  ,							__A  :       Tuple  ,							__A  :       Union[str, Any]  ,							__A  :       int			)  -> Any:







     """simple docstring"""


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

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

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

     return batch


def   A		(__A  :       Optional[Any]  ,							__A  :       Optional[int]			)  -> int:







     """simple docstring"""


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


def   A		()  -> List[Any]:







     """simple docstring"""


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

     # Select device (distibuted not available)
     UpperCAmelCase_		=				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:
          UpperCAmelCase_		=				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=__A			)

     evaluate(__A			)


def   A		(__A  :       Optional[Any]			)  -> str:







     """simple docstring"""


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

     UpperCAmelCase_		=				os.listdir(__A			)
     if len(__A			) == 0:
          return False

     return True


if __name__ == "__main__":
    main()












def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 491.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 32) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 32) * 5 / 9) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 273.15) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 491.67) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 5 / 9)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25 + 273.15)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32 + 459.67)  ,							__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






