



'''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_				:					Any							=      logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A_				:					List[str]							=      namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])




def        snake_case_   (	lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					BertTokenizer.from_pretrained("""bert-base-uncased"""			,  do_lower_case=lowerCAmelCase_						)
       _UpperCAmelCase    :		Dict     	=					BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""						)
       model.to(args.device						)
       model.eval()

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

       if args.compute_rouge:
              _UpperCAmelCase    :		Optional[Any]     	=					[]
              _UpperCAmelCase    :		Optional[int]     	=					[]

              import nltk
              import rouge

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

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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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

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




def        snake_case_   (	lowerCAmelCase_						)->						int:





       '''simple docstring'''


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

       return summary




def        snake_case_   (	lowerCAmelCase_						)->						Tuple:





       '''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        snake_case_   (	lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					load_and_cache_examples(lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					SequentialSampler(lowerCAmelCase_						)

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

       _UpperCAmelCase    :		Dict     	=					DataLoader(
           lowerCAmelCase_			,  sampler=lowerCAmelCase_			,  batch_size=args.batch_size			,  collate_fn=lowerCAmelCase_			,  )

       return iterator




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					CNNDMDataset(args.documents_dir						)
       return dataset




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						List[str]:





       '''simple docstring'''


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

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

       _UpperCAmelCase    :		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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						int:





       '''simple docstring'''


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




def        snake_case_   (	)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		int     	=					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=50			,  type=lowerCAmelCase_			,  help="""Minimum number of tokens for the summaries."""			,  )
       parser.add_argument(
           """--max_length"""			,  default=200			,  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=0.9_5			,  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."""			,  )
       _UpperCAmelCase    :		Any     	=					parser.parse_args()

       # Select device (distibuted not available)
       _UpperCAmelCase    :		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:
              _UpperCAmelCase    :		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        snake_case_   (	lowerCAmelCase_						)->						Union[str, Any]:





       '''simple docstring'''


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

       _UpperCAmelCase    :		Any     	=					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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).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(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







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

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

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

