



'''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__  :					List[Any]				= logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


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





def    a_					(						_UpperCAmelCase  :	Dict							)  ->   List[Any]:
      __snake_case      :					int						=  BertTokenizer.from_pretrained('bert-base-uncased'      ,do_lower_case=lowercase_							)
      __snake_case      :					int						=  BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'							)
      model.to(args.device							)
      model.eval()

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

      if args.compute_rouge:
            __snake_case      :					Tuple						=  []
            __snake_case      :					Union[str, Any]						=  []

            import nltk
            import rouge

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

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      __snake_case      :					Optional[Any]						=  ''
      __snake_case      :					Tuple						=  ''

      __snake_case      :					str						=  build_data_iterator(lowercase_      ,lowercase_							)
      __snake_case      :					List[str]						=  build_predictor(lowercase_      ,lowercase_      ,lowercase_      ,lowercase_							)

      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(lowercase_							):
            __snake_case      :					Optional[Any]						=  predictor.translate_batch(lowercase_							)
            __snake_case      :					List[str]						=  predictor.from_batch(lowercase_							)
            __snake_case      :					List[str]						=  [format_summary(lowercase_							) for t in translations]
            save_summaries(lowercase_      ,args.summaries_output_dir      ,batch.document_names							)

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

      if args.compute_rouge:
            __snake_case      :					Tuple						=  rouge_evaluator.get_scores(lowercase_      ,lowercase_							)
            __snake_case      :					Any						=  format_rouge_scores(lowercase_							)
            save_rouge_scores(lowercase_							)
            print(lowercase_							)





def    a_					(						_UpperCAmelCase  :	Optional[int]      ,_UpperCAmelCase  :	List[str]      ,_UpperCAmelCase  :	Optional[int]							)  ->   Any:


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

            __snake_case      :					int						=  os.path.join(lowercase_      ,lowercase_							)
            with open(lowercase_      ,'w'							) as output:
                  output.write(lowercase_							)





def    a_					(						_UpperCAmelCase  :	Tuple							)  ->   Union[str, Any]:
      __snake_case				,			__snake_case				,			__snake_case      :					Union[str, Any]						=  translation
      __snake_case      :					Union[str, Any]						=  (
          raw_summary.replace('[unused0]'      ,''							)
          .replace('[unused3]'      ,''							)
          .replace('[PAD]'      ,''							)
          .replace('[unused1]'      ,''							)
          .replace(r' +'      ,' '							)
          .replace(' [unused2] '      ,'. '							)
          .replace('[unused2]'      ,''							)
          .strip()
      )

      return summary





def    a_					(						_UpperCAmelCase  :	Optional[Any]							)  ->   int:
      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_					(						_UpperCAmelCase  :	str							)  ->   Dict:


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





def    a_					(						_UpperCAmelCase  :	int      ,_UpperCAmelCase  :	str							)  ->   Optional[Any]:
      __snake_case      :					Dict						=  load_and_cache_examples(lowercase_      ,lowercase_							)
      __snake_case      :					str						=  SequentialSampler(lowercase_							)

      def collate_fn(_UpperCAmelCase  :	Optional[Any]							):
            return collate(lowercase_      ,lowercase_      ,block_size=5_12      ,device=args.device							)

      __snake_case      :					List[Any]						=  DataLoader(
          lowercase_      ,sampler=lowercase_      ,batch_size=args.batch_size      ,collate_fn=lowercase_      ,)

      return iterator





def    a_					(						_UpperCAmelCase  :	List[Any]      ,_UpperCAmelCase  :	List[Any]							)  ->   Dict:
      __snake_case      :					Tuple						=  CNNDMDataset(args.documents_dir							)
      return dataset





def    a_					(						_UpperCAmelCase  :	Tuple      ,_UpperCAmelCase  :	List[str]      ,_UpperCAmelCase  :	str      ,_UpperCAmelCase  :	int							)  ->   str:
      __snake_case      :					List[str]						=  [x for x in data if not len(x[1]							) == 0]  # remove empty_files
      __snake_case      :					str						=  [name for name, _, _ in data]
      __snake_case      :					Optional[int]						=  [' '.join(lowercase_							) for _, _, summary_list in data]

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

      __snake_case      :					int						=  Batch(
          document_names=lowercase_      ,batch_size=len(lowercase_							)      ,src=encoded_stories.to(lowercase_							)      ,segs=encoder_token_type_ids.to(lowercase_							)      ,mask_src=encoder_mask.to(lowercase_							)      ,tgt_str=lowercase_      ,)

      return batch





def    a_					(						_UpperCAmelCase  :	Optional[int]      ,_UpperCAmelCase  :	Optional[Any]							)  ->   List[Any]:
      __snake_case      :					Optional[Any]						=  summary_tokens.to('cpu'							).numpy()
      __snake_case      :					List[Any]						=  tokenizer.decode(lowercase_							)
      __snake_case      :					Optional[int]						=  summary.split('.'							)
      __snake_case      :					Optional[int]						=  [s + '.' for s in sentences]
      return sentences





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

      # Select device (distibuted not available)
      __snake_case      :					List[str]						=  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:
            __snake_case      :					List[str]						=  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=lowercase_							)

      evaluate(lowercase_							)





def    a_					(						_UpperCAmelCase  :	Tuple							)  ->   Dict:


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

      __snake_case      :					Optional[int]						=  os.listdir(lowercase_							)
      if len(lowercase_							) == 0:
            return False

      return True


if __name__ == "__main__":
    main()









import random

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       float       ,				snake_case    :       float ):
          __UpperCamelCase     						=  x
          __UpperCamelCase     						=  y







      def 					snake_case		(   self    :       List[Any] ):
          return (self.x**2 + self.y**2) <= 1







      @classmethod
      def 					snake_case		(   cls    :       List[str] ):
          return cls(x=random.random()       ,				y=random.random() )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		float:

    """simple docstring"""


    if number_of_simulations < 1:
        raise ValueError('''At least one simulation is necessary to estimate PI.'''					)

    __UpperCamelCase     						=  0
    for _ in range(lowercase_					):
        __UpperCamelCase     						=  Point.random_unit_square()

        if random_point.is_in_unit_circle():
            number_in_unit_circle += 1

    return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
 # import doctest

 # doctest.testmod()
 from math import pi

 a_      						=   "Please enter the desired number of Monte Carlo simulations: "
 a_      						=   estimate_pi(int(input(prompt).strip()))
 print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")






