


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:Tuple											= min(re.search('''^ +'''				,						UpperCAmelCase_		).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(UpperCAmelCase_		)]		)


snake_case       :					Dict        =  '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case       :					Any        =  '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case       :					Union[str, Any]        =  '''\
# Begin of markdown

Some cool dataset card
'''







class 	_snake_case						(   unittest.TestCase							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       with tempfile.TemporaryDirectory() as tmp_dir:
             a		:Tuple											= Path(_lowerCamelCase					) / '''README.md'''
             with open(_lowerCamelCase      ,						'''w+'''					) as readme_file:
                   readme_file.write(_lowerCamelCase					)
             a		:str											= 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[Any]											= DatasetMetadata.from_readme(_lowerCamelCase					)
             self.assertDictEqual(_lowerCamelCase      ,						{}					)

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		: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:
            - 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		: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					)









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,
)


snake_case       :					Union[str, Any]        =  logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case       :					int        =  namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def        __lowerCamelCase		(				UpperCAmelCase_		:	str		):




      """simple docstring"""





      a		:Optional[Any]											= BertTokenizer.from_pretrained('''bert-base-uncased'''				,						do_lower_case=UpperCAmelCase_		)
      a		:int											= BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''		)
      model.to(args.device		)
      model.eval()

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

      if args.compute_rouge:
            a		:Dict											= []
            a		:List[Any]											= []

            import nltk
            import rouge

            nltk.download('''punkt'''		)
            a		:Tuple											= 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.
      a		:Union[str, Any]											= ''''''
      a		:List[Any]											= ''''''

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




      """simple docstring"""





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

            a		:Dict											= os.path.join(UpperCAmelCase_				,						UpperCAmelCase_		)
            with open(UpperCAmelCase_				,						'''w'''		) as output:
                  output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any		):




      """simple docstring"""





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

      return summary
def        __lowerCamelCase		(				UpperCAmelCase_		:	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        __lowerCamelCase		(				UpperCAmelCase_		:	int		):




      """simple docstring"""





      with open('''rouge_scores.txt'''				,						'''w'''		) as output:
            output.write(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Union[str, Any]		):




      """simple docstring"""





      a		:Any											= load_and_cache_examples(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Union[str, Any]											= SequentialSampler(UpperCAmelCase_		)

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

      a		:List[Any]											= DataLoader(
          UpperCAmelCase_				,						sampler=UpperCAmelCase_				,						batch_size=args.batch_size				,						collate_fn=UpperCAmelCase_				,						)

      return iterator
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Any		):




      """simple docstring"""





      a		:Union[str, Any]											= CNNDMDataset(args.documents_dir		)
      return dataset
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:Optional[int]											= [x for x in data if not len(x[1]		) == 0]  # remove empty_files
      a		:Tuple											= [name for name, _, _ in data]
      a		:str											= [''' '''.join(UpperCAmelCase_		) for _, _, summary_list in data]

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

      a		:List[Any]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]				,						UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a		:str											= summary_tokens.to('''cpu'''		).numpy()
      a		:Union[str, Any]											= tokenizer.decode(UpperCAmelCase_		)
      a		:Optional[Any]											= summary.split('''.'''		)
      a		:Any											= [s + '''.''' for s in sentences]
      return sentences
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		: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=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=0.95				,						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.'''				,						)
      a		:List[str]											= parser.parse_args()

      # Select device (distibuted not available)
      a		: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:
            a		:Union[str, 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        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]		):




      """simple docstring"""





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

      a		:Union[str, Any]											= os.listdir(UpperCAmelCase_		)
      if len(UpperCAmelCase_		) == 0:
            return False

      return True


if __name__ == "__main__":
     main()





