

import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union







class 	_snake_case						(   enum.Enum							):
 SCREAMING_SNAKE_CASE__   = '''timm'''
 SCREAMING_SNAKE_CASE__   = '''transformers'''







def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      if stage_names is None:
            raise ValueError('''Stage_names must be set for transformers backbones'''		)

      if out_features is not None:
            if not isinstance(__lowerCAmelCase				,						(list,)		):
                  raise ValueError(F'''out_features must be a list {type(__lowerCAmelCase		)}'''		)
            if any(feat not in stage_names for feat in out_features		):
                  raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''		)

      if out_indices is not None:
            if not isinstance(__lowerCAmelCase				,						(list, tuple)		):
                  raise ValueError(F'''out_indices must be a list or tuple, got {type(__lowerCAmelCase		)}'''		)
            if any(idx >= len(__lowerCAmelCase		) for idx in out_indices		):
                  raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''		)

      if out_features is not None and out_indices is not None:
            if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
                  raise ValueError('''out_features and out_indices should have the same length if both are set'''		)
            if out_features != [stage_names[idx] for idx in out_indices]:
                  raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	str				,						):




      """simple docstring"""





      if out_indices is None and out_features is None:
            a		:Dict											= [len(__lowerCAmelCase		) - 1]
            a		:Optional[int]											= [stage_names[-1]]
      elif out_indices is None and out_features is not None:
            a		:List[Any]											= [stage_names.index(__lowerCAmelCase		) for layer in out_features]
      elif out_features is None and out_indices is not None:
            a		:Any											= [stage_names[idx] for idx in out_indices]
      return out_features, out_indices
def        __lowerCamelCase		(				UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	List[str]				,						):




      """simple docstring"""





      verify_out_features_out_indices(out_features=__lowerCAmelCase				,						out_indices=__lowerCAmelCase				,						stage_names=__lowerCAmelCase		)
      a		:Tuple											= _align_output_features_output_indices(
          out_features=__lowerCAmelCase				,						out_indices=__lowerCAmelCase				,						stage_names=__lowerCAmelCase		)
      # Verify that the aligned out_features and out_indices are valid
      verify_out_features_out_indices(out_features=__lowerCAmelCase				,						out_indices=__lowerCAmelCase				,						stage_names=__lowerCAmelCase		)
      return output_features, output_indices







class 	_snake_case						:
 SCREAMING_SNAKE_CASE__   = None




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):






       if getattr(self      ,						'''_backbone'''      ,						_lowerCamelCase					) is None:
             raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''					)

       # These will diagree with the defaults for the transformers models e.g. for resnet50
       # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
       # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
       a		:Any											= [stage["module"] for stage in self._backbone.feature_info.info]
       a		:List[str]											= [stage["num_chs"] for stage in self._backbone.feature_info.info]
       a		:Union[str, Any]											= self._backbone.feature_info.out_indices
       a		:Union[str, Any]											= self._backbone.feature_info.module_name()

       # We verify the out indices and out features are valid
       verify_out_features_out_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=self.stage_names					)
       a		:Union[str, Any]											= out_features, out_indices




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:List[str]											= getattr(_lowerCamelCase      ,						'''stage_names'''					)
       a		:List[str]											= getattr(_lowerCamelCase      ,						'''out_features'''      ,						_lowerCamelCase					)
       a		:List[Any]											= getattr(_lowerCamelCase      ,						'''out_indices'''      ,						_lowerCamelCase					)

       a		:Any											= stage_names
       a		:str											= get_aligned_output_features_output_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=_lowerCamelCase					)
       # Number of channels for each stage. This is set in the transformer backbone model init
       a		:Tuple											= None




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:Any											= config

       a		:str											= getattr(_lowerCamelCase      ,						'''use_timm_backbone'''      ,						_lowerCamelCase					)
       a		:Optional[int]											= BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

       if self.backbone_type == BackboneType.TIMM:
             self._init_timm_backbone(_lowerCamelCase					)
       elif self.backbone_type == BackboneType.TRANSFORMERS:
             self._init_transformers_backbone(_lowerCamelCase					)
       else:
             raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self._out_features




 @out_features.setter
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:str											= get_aligned_output_features_output_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=self.stage_names					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self._out_indices




 @out_indices.setter
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:str											= get_aligned_output_features_output_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=self.stage_names					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return [self.out_feature_channels[name] for name in self.out_features]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       a		:List[str]											= dict(inspect.signature(self.forward					).parameters					)
       a		:Optional[int]											= {k: v for k, v in kwargs.items() if k in signature}
       return self(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						):
       raise NotImplementedError('''This method should be implemented by the derived class.'''					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= super().to_dict()
       a		:Optional[int]											= output.pop('''_out_features'''					)
       a		:int											= output.pop('''_out_indices'''					)
       return output







class 	_snake_case						:




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self._out_features




 @out_features.setter
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:str											= get_aligned_output_features_output_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=self.stage_names					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self._out_indices




 @out_indices.setter
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:Dict											= get_aligned_output_features_output_indices(
           out_features=_lowerCamelCase      ,						out_indices=_lowerCamelCase      ,						stage_names=self.stage_names					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= super().to_dict()
       a		:str											= output.pop('''_out_features'''					)
       a		:List[Any]											= output.pop('''_out_indices'''					)
       return output









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       :					Union[str, Any]        =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case       :					int        =  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:Optional[Any]											= BertTokenizer.from_pretrained('''bert-base-uncased'''				,						do_lower_case=UpperCAmelCase_		)
      a		:int											= BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''		)
      model.to(args.device		)
      model.eval()

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

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

            import nltk
            import rouge

            nltk.download('''punkt'''		)
            a		:Tuple											= 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		:Union[str, Any]											= ''''''
      a		:List[Any]											= ''''''

      a		:Optional[int]											= build_data_iterator(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Optional[Any]											= 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		:Any											= predictor.translate_batch(UpperCAmelCase_		)
            a		:List[Any]											= predictor.from_batch(UpperCAmelCase_		)
            a		:Tuple											= [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		:Union[str, Any]											= rouge_evaluator.get_scores(UpperCAmelCase_				,						UpperCAmelCase_		)
            a		:Dict											= format_rouge_scores(UpperCAmelCase_		)
            save_rouge_scores(UpperCAmelCase_		)
            print(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]				,						UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Dict		):




      """simple docstring"""





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

            a		:Dict											= os.path.join(UpperCAmelCase_				,						UpperCAmelCase_		)
            with open(UpperCAmelCase_				,						'''w'''		) as output:
                  output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a							,       a							,       a		:List[str]											= translation
      a		:int											= (
          raw_summary.replace('''[unused0]'''				,						''''''		)
          .replace('''[unused3]'''				,						''''''		)
          .replace('''[PAD]'''				,						''''''		)
          .replace('''[unused1]'''				,						''''''		)
          .replace(R''' +'''				,						''' '''		)
          .replace(''' [unused2] '''				,						'''. '''		)
          .replace('''[unused2]'''				,						''''''		)
          .strip()
      )

      return summary
def        __lowerCamelCase		(				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        __lowerCamelCase		(				UpperCAmelCase_		:	int		):




      """simple docstring"""





      with open('''rouge_scores.txt'''				,						'''w'''		) as output:
            output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





      a		:Any											= load_and_cache_examples(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Union[str, Any]											= SequentialSampler(UpperCAmelCase_		)

      def collate_fn(UpperCAmelCase_		:	str		):
            return collate(UpperCAmelCase_				,						UpperCAmelCase_				,						block_size=512				,						device=args.device		)

      a		:List[Any]											= DataLoader(
          UpperCAmelCase_				,						sampler=UpperCAmelCase_				,						batch_size=args.batch_size				,						collate_fn=UpperCAmelCase_				,						)

      return iterator
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Union[str, Any]											= CNNDMDataset(args.documents_dir		)
      return dataset
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:Optional[int]											= [x for x in data if not len(x[1]		) == 0]  # remove empty_files
      a		:Tuple											= [name for name, _, _ in data]
      a		:str											= [''' '''.join(UpperCAmelCase_		) for _, _, summary_list in data]

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

      a		:List[Any]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:str											= summary_tokens.to('''cpu'''		).numpy()
      a		:Union[str, Any]											= tokenizer.decode(UpperCAmelCase_		)
      a		:Optional[Any]											= summary.split('''.'''		)
      a		:Any											= [s + '''.''' for s in sentences]
      return sentences
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:Dict											= 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		:List[str]											= parser.parse_args()

      # Select device (distibuted not available)
      a		:Optional[int]											= 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		:Union[str, 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        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]		):




      """simple docstring"""





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

      a		:Union[str, Any]											= os.listdir(UpperCAmelCase_		)
      if len(UpperCAmelCase_		) == 0:
            return False

      return True


if __name__ == "__main__":
     main()





