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,
)


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


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







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						List[str]:
     a	:     Tuple						      =    BertTokenizer.from_pretrained('''bert-base-uncased'''    ,   do_lower_case=lowerCAmelCase_							)
     a	:     str						      =    BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''							)
     model.to(args.device							)
     model.eval()

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

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

          import nltk
          import rouge

          nltk.download('''punkt'''							)
          a	:     str						      =    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.
     a	:     Any						      =    ''''''
     a	:     Optional[Any]						      =    ''''''

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







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

          a	:     Any						      =    os.path.join(lowerCAmelCase_    ,   lowerCAmelCase_							)
          with open(lowerCAmelCase_    ,   '''w'''							) as output:
               output.write(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     a      ,				a      ,				a	:     str						      =    translation
     a	:     List[Any]						      =    (
         raw_summary.replace('''[unused0]'''    ,   ''''''							)
         .replace('''[unused3]'''    ,   ''''''							)
         .replace('''[PAD]'''    ,   ''''''							)
         .replace('''[unused1]'''    ,   ''''''							)
         .replace(R''' +'''    ,   ''' '''							)
         .replace(''' [unused2] '''    ,   '''. '''							)
         .replace('''[unused2]'''    ,   ''''''							)
         .strip()
     )

     return summary







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						List[str]:
     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							(		lowerCAmelCase_							)						->						str:
     with open('''rouge_scores.txt'''    ,   '''w'''							) as output:
          output.write(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[int]:
     a	:     Optional[Any]						      =    load_and_cache_examples(lowerCAmelCase_    ,   lowerCAmelCase_							)
     a	:     Tuple						      =    SequentialSampler(lowerCAmelCase_							)

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

     a	:     int						      =    DataLoader(
         lowerCAmelCase_    ,   sampler=lowerCAmelCase_    ,   batch_size=args.batch_size    ,   collate_fn=lowerCAmelCase_    ,   )

     return iterator







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     List[Any]						      =    CNNDMDataset(args.documents_dir							)
     return dataset







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Tuple:
     a	:     Tuple						      =    [x for x in data if not len(x[1]							) == 0]  # remove empty_files
     a	:     int						      =    [name for name, _, _ in data]
     a	:     Optional[Any]						      =    [''' '''.join(lowerCAmelCase_							) for _, _, summary_list in data]

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

     a	:     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 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Union[str, Any]:
     a	:     Tuple						      =    summary_tokens.to('''cpu'''							).numpy()
     a	:     List[Any]						      =    tokenizer.decode(lowerCAmelCase_							)
     a	:     int						      =    summary.split('''.'''							)
     a	:     int						      =    [s + '''.''' for s in sentences]
     return sentences







def 						UpperCAmelCase							(		)						->						int:
     a	:     List[str]						      =    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=5_0    ,   type=lowerCAmelCase_    ,   help='''Minimum number of tokens for the summaries.'''    ,   )
     parser.add_argument(
         '''--max_length'''    ,   default=2_0_0    ,   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=0.95    ,   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.'''    ,   )
     a	:     int						      =    parser.parse_args()

     # Select device (distibuted not available)
     a	:     Tuple						      =    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	:     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 						UpperCAmelCase							(		lowerCAmelCase_							)						->						Any:
     if not os.path.exists(lowerCAmelCase_							):
          return False

     a	:     Tuple						      =    os.listdir(lowerCAmelCase_							)
     if len(lowerCAmelCase_							) == 0:
          return False

     return True


if __name__ == "__main__":
  main()
import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     a	:     Optional[Any]						      =    min(re.search('''^ +'''    ,   lowerCAmelCase_							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase_							)]							)


UpperCAmelCase				 =   """\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

UpperCAmelCase				 =   """\
---
---
# Begin of markdown

Some cool dataset card
"""


UpperCAmelCase				 =   """\
# Begin of markdown

Some cool dataset card
"""






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def _lowercase(     self     )						->List[str]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             a	:     List[str]						      =    Path(lowerCAmelCase_     ) / '''README.md'''
             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     Dict						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[str]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[Any]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertEqual(lowerCAmelCase_				,  {}     )


   def _lowercase(     self     )						->Optional[Any]:
        a	:     Tuple						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Dict						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''     )
        with self.assertRaises(lowerCAmelCase_     ):
             DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Optional[int]						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )