


"""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,
)


__snake_case												=							logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


__snake_case												=							namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def    A_ ( _lowerCAmelCase  :       Dict							):






     """simple docstring"""

     _a     						=					BertTokenizer.from_pretrained('''bert-base-uncased''',  do_lower_case=_lowerCAmelCase							)
     _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=_lowerCAmelCase,  length_limit=args.beam_size,  length_limit_type='''words''',  apply_avg=_lowerCAmelCase,  apply_best=_lowerCAmelCase,  alpha=0.5,  weight_factor=1.2,  stemming=_lowerCAmelCase,  )

     # 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(_lowerCAmelCase,  _lowerCAmelCase							)
     _a     						=					build_predictor(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     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(_lowerCAmelCase							):
          _a     						=					predictor.translate_batch(_lowerCAmelCase							)
          _a     						=					predictor.from_batch(_lowerCAmelCase							)
          _a     						=					[format_summary(_lowerCAmelCase							) for t in translations]
          save_summaries(_lowerCAmelCase,  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(_lowerCAmelCase,  _lowerCAmelCase							)
          _a     						=					format_rouge_scores(_lowerCAmelCase							)
          save_rouge_scores(_lowerCAmelCase							)
          print(_lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     for summary, document_name in zip(_lowerCAmelCase,  _lowerCAmelCase							):
          # 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(_lowerCAmelCase,  _lowerCAmelCase							)
          with open(_lowerCAmelCase,  '''w'''							) as output:
               output.write(_lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       str							):






     """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    A_ ( _lowerCAmelCase  :       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_ ( _lowerCAmelCase  :       int							):






     """simple docstring"""

     with open('''rouge_scores.txt''',  '''w'''							) as output:
          output.write(_lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       Union[str, Any],  _lowerCAmelCase  :       Optional[int]							):






     """simple docstring"""

     _a     						=					load_and_cache_examples(_lowerCAmelCase,  _lowerCAmelCase							)
     _a     						=					SequentialSampler(_lowerCAmelCase							)

     def collate_fn(_lowerCAmelCase  :       List[str]							):
          return collate(_lowerCAmelCase,  _lowerCAmelCase,  block_size=5_12,  device=args.device							)

     _a     						=					DataLoader(
         _lowerCAmelCase,  sampler=_lowerCAmelCase,  batch_size=args.batch_size,  collate_fn=_lowerCAmelCase,  )

     return iterator
def    A_ ( _lowerCAmelCase  :       int,  _lowerCAmelCase  :       Union[str, Any]							):






     """simple docstring"""

     _a     						=					CNNDMDataset(args.documents_dir							)
     return dataset
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       List[str],  _lowerCAmelCase  :       Optional[Any],  _lowerCAmelCase  :       Any							):






     """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(_lowerCAmelCase							) for _, _, summary_list in data]

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

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

     return batch
def    A_ ( _lowerCAmelCase  :       List[Any],  _lowerCAmelCase  :       Dict							):






     """simple docstring"""

     _a     						=					summary_tokens.to('''cpu'''							).numpy()
     _a     						=					tokenizer.decode(_lowerCAmelCase							)
     _a     						=					summary.split('''.'''							)
     _a     						=					[s + '''.''' for s in sentences]
     return sentences
def    A_ ( ):






     """simple docstring"""

     _a     						=					argparse.ArgumentParser()
     parser.add_argument(
         '''--documents_dir''',  default=_lowerCAmelCase,  type=_lowerCAmelCase,  required=_lowerCAmelCase,  help='''The folder where the documents to summarize are located.''',  )
     parser.add_argument(
         '''--summaries_output_dir''',  default=_lowerCAmelCase,  type=_lowerCAmelCase,  required=_lowerCAmelCase,  help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''',  )
     parser.add_argument(
         '''--compute_rouge''',  default=_lowerCAmelCase,  type=_lowerCAmelCase,  required=_lowerCAmelCase,  help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''',  )
     # EVALUATION options
     parser.add_argument(
         '''--no_cuda''',  default=_lowerCAmelCase,  type=_lowerCAmelCase,  help='''Whether to force the execution on CPU.''',  )
     parser.add_argument(
         '''--batch_size''',  default=4,  type=_lowerCAmelCase,  help='''Batch size per GPU/CPU for training.''',  )
     # BEAM SEARCH arguments
     parser.add_argument(
         '''--min_length''',  default=50,  type=_lowerCAmelCase,  help='''Minimum number of tokens for the summaries.''',  )
     parser.add_argument(
         '''--max_length''',  default=2_00,  type=_lowerCAmelCase,  help='''Maixmum number of tokens for the summaries.''',  )
     parser.add_argument(
         '''--beam_size''',  default=5,  type=_lowerCAmelCase,  help='''The number of beams to start with for each example.''',  )
     parser.add_argument(
         '''--alpha''',  default=0.9_5,  type=_lowerCAmelCase,  help='''The value of alpha for the length penalty in the beam search.''',  )
     parser.add_argument(
         '''--block_trigram''',  default=_lowerCAmelCase,  type=_lowerCAmelCase,  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=_lowerCAmelCase							)

     evaluate(_lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

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

     _a     						=					os.listdir(_lowerCAmelCase							)
     if len(_lowerCAmelCase							) == 0:
          return False

     return True


if __name__ == "__main__":
   main()





"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims


