




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,
)


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


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



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =BertTokenizer.from_pretrained("""bert-base-uncased""",    do_lower_case=A__     )
    SCREAMING_SNAKE_CASE__       =BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""     )
    model.to(args.device     )
    model.eval()

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

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE__       =[]
        SCREAMING_SNAKE_CASE__       =[]

        import nltk
        import rouge

        nltk.download("""punkt"""     )
        SCREAMING_SNAKE_CASE__       =rouge.Rouge(
            metrics=["""rouge-n""", """rouge-l"""],    max_n=2,    limit_length=A__,    length_limit=args.beam_size,    length_limit_type="""words""",    apply_avg=A__,    apply_best=A__,    alpha=0.5,    weight_factor=1.2,    stemming=A__,    )

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    SCREAMING_SNAKE_CASE__       =''
    SCREAMING_SNAKE_CASE__       =''

    SCREAMING_SNAKE_CASE__       =build_data_iterator(A__,    A__     )
    SCREAMING_SNAKE_CASE__       =build_predictor(A__,    A__,    A__,    A__     )

    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(A__     ):
        SCREAMING_SNAKE_CASE__       =predictor.translate_batch(A__     )
        SCREAMING_SNAKE_CASE__       =predictor.from_batch(A__     )
        SCREAMING_SNAKE_CASE__       =[format_summary(A__     ) for t in translations]
        save_summaries(A__,    args.summaries_output_dir,    batch.document_names     )

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE__       =rouge_evaluator.get_scores(A__,    A__     )
        SCREAMING_SNAKE_CASE__       =format_rouge_scores(A__     )
        save_rouge_scores(A__     )
        print(A__     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    for summary, document_name in zip(A__,    A__     ):
        # Prepare the summary file's name
        if "." in document_name:
            SCREAMING_SNAKE_CASE__       ='.'.join(document_name.split("""."""     )[:-1]     )
            SCREAMING_SNAKE_CASE__       =document_name.split("""."""     )[-1]
            SCREAMING_SNAKE_CASE__       =bare_document_name + '_summary.' + extension
        else:
            SCREAMING_SNAKE_CASE__       =document_name + '_summary'

        SCREAMING_SNAKE_CASE__       =os.path.join(A__,    A__     )
        with open(A__,    """w"""     ) as output:
            output.write(A__     )



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =translation
    SCREAMING_SNAKE_CASE__       =(
        raw_summary.replace("""[unused0]""",    """"""     )
        .replace("""[unused3]""",    """"""     )
        .replace("""[PAD]""",    """"""     )
        .replace("""[unused1]""",    """"""     )
        .replace(R""" +""",    """ """     )
        .replace(""" [unused2] """,    """. """     )
        .replace("""[unused2]""",    """"""     )
        .strip()
    )

    return summary



def      UpperCAmelCase_   (     __UpperCamelCase     ):
    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     ):
    with open("""rouge_scores.txt""",    """w"""     ) as output:
        output.write(A__     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =load_and_cache_examples(A__,    A__     )
    SCREAMING_SNAKE_CASE__       =SequentialSampler(A__     )

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

    SCREAMING_SNAKE_CASE__       =DataLoader(
        A__,    sampler=A__,    batch_size=args.batch_size,    collate_fn=A__,    )

    return iterator



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =CNNDMDataset(args.documents_dir     )
    return dataset



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[x for x in data if not len(x[1]     ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE__       =[name for name, _, _ in data]
    SCREAMING_SNAKE_CASE__       =[' '.join(A__     ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE__       =[encode_for_summarization(A__,    A__,    A__     ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE__       =torch.tensor(
        [truncate_or_pad(A__,    A__,    tokenizer.pad_token_id     ) for story, _ in encoded_text]     )
    SCREAMING_SNAKE_CASE__       =compute_token_type_ids(A__,    tokenizer.cls_token_id     )
    SCREAMING_SNAKE_CASE__       =build_mask(A__,    tokenizer.pad_token_id     )

    SCREAMING_SNAKE_CASE__       =Batch(
        document_names=A__,    batch_size=len(A__     ),    src=encoded_stories.to(A__     ),    segs=encoder_token_type_ids.to(A__     ),    mask_src=encoder_mask.to(A__     ),    tgt_str=A__,    )

    return batch



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =summary_tokens.to("""cpu"""     ).numpy()
    SCREAMING_SNAKE_CASE__       =tokenizer.decode(A__     )
    SCREAMING_SNAKE_CASE__       =summary.split("""."""     )
    SCREAMING_SNAKE_CASE__       =[s + '.' for s in sentences]
    return sentences



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       =argparse.ArgumentParser()
    parser.add_argument(
        """--documents_dir""",    default=A__,    type=A__,    required=A__,    help="""The folder where the documents to summarize are located.""",    )
    parser.add_argument(
        """--summaries_output_dir""",    default=A__,    type=A__,    required=A__,    help="""The folder in wich the summaries should be written. Defaults to the folder where the documents are""",    )
    parser.add_argument(
        """--compute_rouge""",    default=A__,    type=A__,    required=A__,    help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.""",    )
    # EVALUATION options
    parser.add_argument(
        """--no_cuda""",    default=A__,    type=A__,    help="""Whether to force the execution on CPU.""",    )
    parser.add_argument(
        """--batch_size""",    default=4,    type=A__,    help="""Batch size per GPU/CPU for training.""",    )
    # BEAM SEARCH arguments
    parser.add_argument(
        """--min_length""",    default=50,    type=A__,    help="""Minimum number of tokens for the summaries.""",    )
    parser.add_argument(
        """--max_length""",    default=200,    type=A__,    help="""Maixmum number of tokens for the summaries.""",    )
    parser.add_argument(
        """--beam_size""",    default=5,    type=A__,    help="""The number of beams to start with for each example.""",    )
    parser.add_argument(
        """--alpha""",    default=0.95,    type=A__,    help="""The value of alpha for the length penalty in the beam search.""",    )
    parser.add_argument(
        """--block_trigram""",    default=A__,    type=A__,    help="""Whether to block the existence of repeating trigrams in the text generated by beam search.""",    )
    SCREAMING_SNAKE_CASE__       =parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE__       =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:
        SCREAMING_SNAKE_CASE__       =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=A__     )

    evaluate(A__     )



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

    SCREAMING_SNAKE_CASE__       =os.listdir(A__     )
    if len(A__     ) == 0:
        return False

    return True


if __name__ == "__main__":
    main()







import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Optional[Any]=False						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(
             filename_skip_take["""filename"""],
             filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
             filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
         )
         open(os.path.join(_UpperCamelCase						)    ,"""wb"""						).close()
         SCREAMING_SNAKE_CASE__       =pa.Table.from_pydict({"""filename""": [Path(_UpperCamelCase						).name] * 1_0_0}						)
         if take == -1:
             SCREAMING_SNAKE_CASE__       =len(_UpperCamelCase						) - skip
         if skip is not None and take is not None:
             SCREAMING_SNAKE_CASE__       =pa_table.slice(_UpperCamelCase    ,_UpperCamelCase						)
         return pa_table






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =[train_info, test_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""test[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-test"""						)
             self.assertEqual(dset.num_rows    ,3_3						)
             self.assertEqual(dset.num_columns    ,1						)

             SCREAMING_SNAKE_CASE__       =["""train""", """test[:33%]"""]
             SCREAMING_SNAKE_CASE__       =[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
             for instructions in [instructionsa, instructionsa]:
                 SCREAMING_SNAKE_CASE__       =[reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						) for instr in instructions]
                 SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =(Dataset(**_UpperCamelCase						) for dataset_kwargs in datasets_kwargs)
                 self.assertEqual(train_dset["""filename"""][0]    ,f"""{name}-train"""						)
                 self.assertEqual(train_dset.num_rows    ,1_0_0						)
                 self.assertEqual(train_dset.num_columns    ,1						)
                 self.assertIsInstance(train_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(train_dset.split						)    ,"""train"""						)
                 self.assertEqual(test_dset["""filename"""][0]    ,f"""{name}-test"""						)
                 self.assertEqual(test_dset.num_rows    ,3_3						)
                 self.assertEqual(test_dset.num_columns    ,1						)
                 self.assertIsInstance(test_dset.split    ,_UpperCamelCase						)
                 self.assertEqual(str(test_dset.split						)    ,"""test[:33%]"""						)
                 del train_dset, test_dset




     def   __A    (     self      :			Tuple						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ="""my_name"""
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0_0    ,shard_lengths=[1_0_0] * 1_0						)
         SCREAMING_SNAKE_CASE__       =[train_info]
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)						)
             self.assertEqual(dset["""filename"""][0]    ,f"""{name}-train-00000-of-00010"""						)
             self.assertEqual(dset["""filename"""][-1]    ,f"""{name}-train-00003-of-00010"""						)
             self.assertEqual(dset.num_rows    ,3_3_0						)
             self.assertEqual(dset.num_columns    ,1						)




     def   __A    (     self      :			Dict						)		->       Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""train"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitInfo(name="""test"""    ,num_examples=1_0_0						)
         SCREAMING_SNAKE_CASE__       =SplitDict()
         split_dict.add(_UpperCamelCase						)
         split_dict.add(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =DatasetInfo(splits=_UpperCamelCase						)

         with tempfile.TemporaryDirectory() as tmp_dir:
             SCREAMING_SNAKE_CASE__       =ReaderTest(_UpperCamelCase    ,_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__       =[
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""train"""						)},
                 {"""filename""": os.path.join(_UpperCamelCase    ,"""test"""						), """skip""": 1_0, """take""": 1_0},
             ]
             SCREAMING_SNAKE_CASE__       =Dataset(**reader.read_files(_UpperCamelCase    ,original_instructions="""train+test[10:20]"""						)						)
             self.assertEqual(dset.num_rows    ,1_1_0						)
             self.assertEqual(dset.num_columns    ,1						)
             del dset






@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =ArrowReader.read_table(__UpperCamelCase,    in_memory=__UpperCamelCase     )
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



@pytest.mark.parametrize("""in_memory""",    [False, True]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =arrow_file
    SCREAMING_SNAKE_CASE__       =ArrowReader("""""",    __UpperCamelCase     )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE__       =reader.read_files([{"""filename""": filename}],    in_memory=__UpperCamelCase     )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    SCREAMING_SNAKE_CASE__       =dataset_kwargs["""arrow_table"""]
    assert table.shape == dataset.data.shape
    assert set(table.column_names     ) == set(dataset.data.column_names     )
    assert dict(table.to_pydict()     ) == dict(dataset.data.to_pydict()     )  # to_pydict returns OrderedDict



def      UpperCAmelCase_   (     ):
    assert ReadInstruction("""train""",    to=10,    unit="""abs"""     ).to_spec() == "train[:10]"
    assert ReadInstruction("""train""",    from_=-80,    to=10,    unit="""%"""     ).to_spec() == "train[-80%:10%]"

    SCREAMING_SNAKE_CASE__       ="""train+test"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test

    SCREAMING_SNAKE_CASE__       ="""train[2:10]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_abs

    SCREAMING_SNAKE_CASE__       ="""train[15%:-20%]"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct

    SCREAMING_SNAKE_CASE__       ="""train[:10%](closest)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == "train[:10%]"

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_pct_rounding

    SCREAMING_SNAKE_CASE__       ="""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
    assert ReadInstruction.from_spec(__UpperCamelCase     ).to_spec() == spec_train_test_pct_rounding



def      UpperCAmelCase_   (     ):
    SCREAMING_SNAKE_CASE__       ="""dummy"""
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train[:33%]"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix"""

    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train.arrow"""     ), "skip": 0, "take": 33}
    ]

    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100,    shard_lengths=[10] * 10     )]
    SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )
    assert isinstance(__UpperCamelCase,    __UpperCamelCase     )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00000-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00001-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00002-of-00010.arrow"""     ), "skip": 0, "take": -1},
        {"filename": os.path.join(__UpperCamelCase,    f"""{name}-train-00003-of-00010.arrow"""     ), "skip": 0, "take": 3},
    ]




@pytest.mark.parametrize("""name, expected_exception""",    [(None, TypeError), ("""""", ValueError)]     )
def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =[SplitInfo(name="""train""",    num_examples=100     )]
    SCREAMING_SNAKE_CASE__       ="""train"""
    SCREAMING_SNAKE_CASE__       ="""arrow"""
    SCREAMING_SNAKE_CASE__       ="""prefix_path"""
    with pytest.raises(__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__       =make_file_instructions(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )






