



'''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,
)


A_				:					Any							=      logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A_				:					List[str]							=      namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])




def        snake_case_   (	lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					BertTokenizer.from_pretrained("""bert-base-uncased"""			,  do_lower_case=lowerCAmelCase_						)
       _UpperCAmelCase    :		Dict     	=					BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""						)
       model.to(args.device						)
       model.eval()

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

       if args.compute_rouge:
              _UpperCAmelCase    :		Optional[Any]     	=					[]
              _UpperCAmelCase    :		Optional[int]     	=					[]

              import nltk
              import rouge

              nltk.download("""punkt"""						)
              _UpperCAmelCase    :		Optional[Any]     	=					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.
       _UpperCAmelCase    :		Optional[int]     	=					""""""
       _UpperCAmelCase    :		Any     	=					""""""

       _UpperCAmelCase    :		List[Any]     	=					build_data_iterator(lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Any     	=					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_						):
              _UpperCAmelCase    :		Optional[int]     	=					predictor.translate_batch(lowerCAmelCase_						)
              _UpperCAmelCase    :		Dict     	=					predictor.from_batch(lowerCAmelCase_						)
              _UpperCAmelCase    :		Union[str, Any]     	=					[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:
              _UpperCAmelCase    :		Dict     	=					rouge_evaluator.get_scores(lowerCAmelCase_			,  lowerCAmelCase_						)
              _UpperCAmelCase    :		Optional[int]     	=					format_rouge_scores(lowerCAmelCase_						)
              save_rouge_scores(lowerCAmelCase_						)
              print(lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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

              _UpperCAmelCase    :		Union[str, Any]     	=					os.path.join(lowerCAmelCase_			,  lowerCAmelCase_						)
              with open(lowerCAmelCase_			,  """w"""						) as output:
                     output.write(lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


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

       return summary




def        snake_case_   (	lowerCAmelCase_						)->						Tuple:





       '''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        snake_case_   (	lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					load_and_cache_examples(lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					SequentialSampler(lowerCAmelCase_						)

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

       _UpperCAmelCase    :		Dict     	=					DataLoader(
           lowerCAmelCase_			,  sampler=lowerCAmelCase_			,  batch_size=args.batch_size			,  collate_fn=lowerCAmelCase_			,  )

       return iterator




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					CNNDMDataset(args.documents_dir						)
       return dataset




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					[x for x in data if not len(x[1]						) == 0]  # remove empty_files
       _UpperCAmelCase    :		str     	=					[name for name, _, _ in data]
       _UpperCAmelCase    :		List[str]     	=					[""" """.join(lowerCAmelCase_						) for _, _, summary_list in data]

       _UpperCAmelCase    :		Optional[int]     	=					[encode_for_summarization(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						) for _, story, summary in data]
       _UpperCAmelCase    :		Union[str, Any]     	=					torch.tensor(
           [truncate_or_pad(lowerCAmelCase_			,  lowerCAmelCase_			,  tokenizer.pad_token_id						) for story, _ in encoded_text]						)
       _UpperCAmelCase    :		Tuple     	=					compute_token_type_ids(lowerCAmelCase_			,  tokenizer.cls_token_id						)
       _UpperCAmelCase    :		Tuple     	=					build_mask(lowerCAmelCase_			,  tokenizer.pad_token_id						)

       _UpperCAmelCase    :		List[Any]     	=					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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						int:





       '''simple docstring'''


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




def        snake_case_   (	)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		int     	=					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=200			,  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."""			,  )
       _UpperCAmelCase    :		Any     	=					parser.parse_args()

       # Select device (distibuted not available)
       _UpperCAmelCase    :		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:
              _UpperCAmelCase    :		Optional[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=lowerCAmelCase_						)

       evaluate(lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


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

       _UpperCAmelCase    :		Any     	=					os.listdir(lowerCAmelCase_						)
       if len(lowerCAmelCase_						) == 0:
              return False

       return True


if __name__ == "__main__":
   main()








'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

        return np.mean((labels - prediction) ** 2				)







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               print("""Error: X and y have different lengths"""				)
               return
        if y.ndim != 1:
               print("""Error: Data set labels must be one dimensional"""				)
               return

        if len(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        if self.prediction is not None:
               return self.prediction
        elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                      return self.right.predict(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


if __name__ == "__main__":
   main()
   import doctest

   doctest.testmod(name="""mean_squarred_error""", verbose=True)



