




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,
)


SCREAMING_SNAKE_CASE_						:					str			   =       logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


SCREAMING_SNAKE_CASE_						:					Tuple			   =       namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       BertTokenizer.from_pretrained("""bert-base-uncased"""   ,    do_lower_case=UpperCAmelCase_		)
    a__      =       BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""		)
    model.to(args.device		)
    model.eval()

    a__      =       {
        """BOS""": tokenizer.vocab["""[unused0]"""],
        """EOS""": tokenizer.vocab["""[unused1]"""],
        """PAD""": tokenizer.vocab["""[PAD]"""],
    }

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

        import nltk
        import rouge

        nltk.download("""punkt"""		)
        a__      =       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__      =       """"""
    a__      =       """"""

    a__      =       build_data_iterator(UpperCAmelCase_   ,    UpperCAmelCase_		)
    a__      =       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__      =       predictor.translate_batch(UpperCAmelCase_		)
        a__      =       predictor.from_batch(UpperCAmelCase_		)
        a__      =       [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__      =       rouge_evaluator.get_scores(UpperCAmelCase_   ,    UpperCAmelCase_		)
        a__      =       format_rouge_scores(UpperCAmelCase_		)
        save_rouge_scores(UpperCAmelCase_		)
        print(UpperCAmelCase_		)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		):
    for summary, document_name in zip(UpperCAmelCase_   ,    UpperCAmelCase_		):
        # Prepare the summary file's name
        if "." in document_name:
            a__      =       """.""".join(document_name.split("""."""		)[:-1]		)
            a__      =       document_name.split("""."""		)[-1]
            a__      =       bare_document_name + """_summary.""" + extension
        else:
            a__      =       document_name + """_summary"""

        a__      =       os.path.join(UpperCAmelCase_   ,    UpperCAmelCase_		)
        with open(UpperCAmelCase_   ,    """w"""		) as output:
            output.write(UpperCAmelCase_		)
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__ ,		a__ ,		a__      =       translation
    a__      =       (
        raw_summary.replace("""[unused0]"""   ,    """"""		)
        .replace("""[unused3]"""   ,    """"""		)
        .replace("""[PAD]"""   ,    """"""		)
        .replace("""[unused1]"""   ,    """"""		)
        .replace(r""" +"""   ,    """ """		)
        .replace(""" [unused2] """   ,    """. """		)
        .replace("""[unused2]"""   ,    """"""		)
        .strip()
    )

    return summary
def        __lowerCAmelCase (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        __lowerCAmelCase (UpperCAmelCase_		):
    with open("""rouge_scores.txt"""   ,    """w"""		) as output:
        output.write(UpperCAmelCase_		)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       load_and_cache_examples(UpperCAmelCase_   ,    UpperCAmelCase_		)
    a__      =       SequentialSampler(UpperCAmelCase_		)

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

    a__      =       DataLoader(
        UpperCAmelCase_   ,    sampler=UpperCAmelCase_   ,    batch_size=args.batch_size   ,    collate_fn=UpperCAmelCase_   ,    )

    return iterator
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       CNNDMDataset(args.documents_dir		)
    return dataset
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       [x for x in data if not len(x[1]		) == 0]  # remove empty_files
    a__      =       [name for name, _, _ in data]
    a__      =       [""" """.join(UpperCAmelCase_		) for _, _, summary_list in data]

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

    a__      =       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_   ,    UpperCAmelCase_		):
    a__      =       summary_tokens.to("""cpu"""		).numpy()
    a__      =       tokenizer.decode(UpperCAmelCase_		)
    a__      =       summary.split("""."""		)
    a__      =       [s + """.""" for s in sentences]
    return sentences
def        __lowerCAmelCase ():
    a__      =       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=0.9_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."""   ,    )
    a__      =       parser.parse_args()

    # Select device (distibuted not available)
    a__      =       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__      =       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_		):
    if not os.path.exists(UpperCAmelCase_		):
        return False

    a__      =       os.listdir(UpperCAmelCase_		)
    if len(UpperCAmelCase_		) == 0:
        return False

    return True


if __name__ == "__main__":
 main()












import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 		__UpperCAmelCase:





      """simple docstring"""



      @staticmethod
      def 				__SCREAMING_SNAKE_CASE		(      *A :       str						,				**A :       int    )			->			Union[str, Any]:






          """simple docstring"""
          pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Tuple						=    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     __UpperCAmelCase	: Dict						=    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       Optional[int]						,				A :       Tuple						,				A :       int    )			->			str:






         """simple docstring"""
         a__      =       ImageClassificationPipeline(model=A						,				image_processor=A						,				top_k=2    )
         a__      =       [
             Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    ),
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]
         return image_classifier, examples

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       List[str]						,				A :       Dict    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )

         self.assertEqual(
             A						,				[
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
             ]						,				)

         import datasets

         a__      =       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,				"""image"""						,				split="""test"""    )

         # Accepts URL + PIL.Image + lists
         a__      =       image_classifier(
             [
                 Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    ),
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 # RGBA
                 dataset[0]["""file"""],
                 # LA
                 dataset[1]["""file"""],
                 # L
                 dataset[2]["""file"""],
             ]    )
         self.assertEqual(
             A						,				[
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
             ]						,				)

     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

     @require_tf
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			str:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A						,				framework="""tf"""    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

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






         """simple docstring"""
         a__      =       PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         a__      =       pipeline(
             """image-classification"""						,				model="""hf-internal-testing/tiny-random-vit"""						,				tokenizer=A    )

         self.assertIs(image_classifier.tokenizer						,				A    )






     @slow
     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Dict:






         """simple docstring"""
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-conv"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.3_2_1, """label""": """tiger cat"""},
                 {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
                 {"""score""": 0.0_0_7, """label""": """radiator"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-fourier"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
                 {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-learned"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
                 {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
             ]						,				)






