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


__A         =				namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:
   lowercase__:     int      =   BertTokenizer.from_pretrained('''bert-base-uncased''' ,					do_lower_case=__UpperCAmelCase				)
   lowercase__:     Optional[int]      =   BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''				)
   model.to(args.device				)
   model.eval()

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

   if args.compute_rouge:
      lowercase__:     Optional[int]      =   []
      lowercase__:     str      =   []

      import nltk
      import rouge

      nltk.download('''punkt'''				)
      lowercase__:     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.
   lowercase__:     str      =   ''''''
   lowercase__:     List[Any]      =   ''''''

   lowercase__:     str      =   build_data_iterator(__UpperCAmelCase ,					__UpperCAmelCase				)
   lowercase__:     Union[str, 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				):
      lowercase__:     Optional[Any]      =   predictor.translate_batch(__UpperCAmelCase				)
      lowercase__:     Dict      =   predictor.from_batch(__UpperCAmelCase				)
      lowercase__:     List[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:
      lowercase__:     Optional[Any]      =   rouge_evaluator.get_scores(__UpperCAmelCase ,					__UpperCAmelCase				)
      lowercase__:     Dict      =   format_rouge_scores(__UpperCAmelCase				)
      save_rouge_scores(__UpperCAmelCase				)
      print(__UpperCAmelCase				)



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

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



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Optional[int]:
   lowercase__,				lowercase__,				lowercase__:     Optional[Any]      =   translation
   lowercase__:     Any      =   (
       raw_summary.replace('''[unused0]''' ,					''''''				)
       .replace('''[unused3]''' ,					''''''				)
       .replace('''[PAD]''' ,					''''''				)
       .replace('''[unused1]''' ,					''''''				)
       .replace(R''' +''' ,					''' '''				)
       .replace(''' [unused2] ''' ,					'''. '''				)
       .replace('''[unused2]''' ,					''''''				)
       .strip()
   )

   return summary



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Any:
   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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Union[str, Any]:
   with open('''rouge_scores.txt''' ,					'''w'''				) as output:
      output.write(__UpperCAmelCase				)



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						List[Any]:
   lowercase__:     Optional[Any]      =   load_and_cache_examples(__UpperCAmelCase ,					__UpperCAmelCase				)
   lowercase__:     Optional[int]      =   SequentialSampler(__UpperCAmelCase				)

   def collate_fn(__UpperCAmelCase				):
      return collate(__UpperCAmelCase ,					__UpperCAmelCase ,					block_size=5_1_2 ,					device=args.device				)

   lowercase__:     Optional[Any]      =   DataLoader(
       __UpperCAmelCase ,					sampler=__UpperCAmelCase ,					batch_size=args.batch_size ,					collate_fn=__UpperCAmelCase ,					)

   return iterator



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						List[str]:
   lowercase__:     Union[str, Any]      =   CNNDMDataset(args.documents_dir				)
   return dataset



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Any:
   lowercase__:     Any      =   [x for x in data if not len(x[1]				) == 0]  # remove empty_files
   lowercase__:     Tuple      =   [name for name, _, _ in data]
   lowercase__:     Any      =   [''' '''.join(__UpperCAmelCase				) for _, _, summary_list in data]

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

   lowercase__:     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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase				)						->						Optional[int]:
   lowercase__:     Optional[Any]      =   summary_tokens.to('''cpu'''				).numpy()
   lowercase__:     List[Any]      =   tokenizer.decode(__UpperCAmelCase				)
   lowercase__:     str      =   summary.split('''.'''				)
   lowercase__:     Tuple      =   [s + '''.''' for s in sentences]
   return sentences



def SCREAMING_SNAKE_CASE__    (     )						->						List[Any]:
   lowercase__:     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=5_0 ,					type=__UpperCAmelCase ,					help='''Minimum number of tokens for the summaries.''' ,					)
   parser.add_argument(
       '''--max_length''' ,					default=2_0_0 ,					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.''' ,					)
   lowercase__:     str      =   parser.parse_args()

   # Select device (distibuted not available)
   lowercase__:     Union[str, 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:
      lowercase__:     List[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 SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						Union[str, Any]:
   if not os.path.exists(__UpperCAmelCase				):
      return False

   lowercase__:     List[str]      =   os.listdir(__UpperCAmelCase				)
   if len(__UpperCAmelCase				) == 0:
      return False

   return True


if __name__ == "__main__":
       main()

"""simple docstring"""






class 					UpperCAmelCase		:



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase=None							):
       lowercase__:     Optional[int]      =   data
       lowercase__:     Dict      =   None






    def __repr__(     self							):
       lowercase__:     int      =   ''''''
       lowercase__:     Any      =   self
       while temp:
          string_rep += F"""<{temp.data}> ---> """
          lowercase__:     Any      =   temp.next
       string_rep += "<END>"
       return string_rep



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase				)						->						str:

   # if elements_list is empty
   if not elements_list:
      raise Exception('''The Elements List is empty'''				)

   # Set first element as Head
   lowercase__:     Union[str, Any]      =   Node(elements_list[0]				)
   lowercase__:     List[str]      =   head
   # Loop through elements from position 1
   for data in elements_list[1:]:
      lowercase__:     Tuple      =   Node(__UpperCAmelCase				)
      lowercase__:     Tuple      =   current.next
   return head


__A         =				[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(f'''List: {list_data}''')
print("Creating Linked List from List.")
__A         =				make_linked_list(list_data)
print("Linked List:")




print(linked_list)
