'''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,
)


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


__SCREAMING_SNAKE_CASE	:					Optional[Any]					=  namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]   )   ->   Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase					:  List[Any]											=   BertTokenizer.from_pretrained("bert-base-uncased"      ,     do_lower_case=_UpperCAmelCase   )
       _UpperCAmelCase					:  List[str]											=   BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"   )
       model.to(args.device   )
       model.eval()

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

       if args.compute_rouge:
              _UpperCAmelCase					:  str											=   []
              _UpperCAmelCase					:  str											=   []

              import nltk
              import rouge

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

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

def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				List[str]   )   ->   Union[str, Any]:


       """simple docstring"""







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

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

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   Union[str, Any]:


       """simple docstring"""







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

       return summary

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[Any]   )   ->   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      UpperCamelCase_							( _UpperCAmelCase  :				Any   )   ->   List[str]:


       """simple docstring"""







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

def      UpperCamelCase_							( _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				int   )   ->   Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   load_and_cache_examples(_UpperCAmelCase      ,     _UpperCAmelCase   )
       _UpperCAmelCase					:  List[Any]											=   SequentialSampler(_UpperCAmelCase   )

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

       _UpperCAmelCase					:  Union[str, Any]											=   DataLoader(
           _UpperCAmelCase      ,     sampler=_UpperCAmelCase      ,     batch_size=args.batch_size      ,     collate_fn=_UpperCAmelCase      ,     )

       return iterator

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


       """simple docstring"""







       _UpperCAmelCase					:  Union[str, Any]											=   CNNDMDataset(args.documents_dir   )
       return dataset

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				Optional[Any]      ,     _UpperCAmelCase  :				Union[str, Any]   )   ->   List[Any]:


       """simple docstring"""







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

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

       _UpperCAmelCase					:  int											=   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      UpperCamelCase_							( _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				Union[str, Any]   )   ->   List[Any]:


       """simple docstring"""







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

def      UpperCamelCase_							( )   ->   Tuple:


       """simple docstring"""







       _UpperCAmelCase					:  List[Any]											=   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=0.9_5      ,     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."      ,     )
       _UpperCAmelCase					:  int											=   parser.parse_args()

       # Select device (distibuted not available)
       _UpperCAmelCase					:  List[Any]											=   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					:  Dict											=   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      UpperCamelCase_							( _UpperCAmelCase  :				Dict   )   ->   Union[str, Any]:


       """simple docstring"""







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

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

       return True


if __name__ == "__main__":
  main()


'''simple docstring'''




from itertools import compress, repeat
from math import ceil, sqrt

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   list[int]:


       """simple docstring"""







       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       _UpperCAmelCase					:  Optional[int]											=   bytearray(b"\x01"   ) * ((num >> 1) - 1)

       for i in range(3      ,     int(sqrt(_UpperCAmelCase   )   ) + 1      ,     2   ):
              if sieve[(i >> 1) - 1]:
                     _UpperCAmelCase					:  List[Any]											=   i**2
                     _UpperCAmelCase					:  Optional[int]											=   repeat(
                         0      ,     ceil((num - i_squared) / (i << 1)   )   )

       return [2] + list(compress(range(3      ,     _UpperCAmelCase      ,     2   )      ,     _UpperCAmelCase   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

