




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


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




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		List[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  BertTokenizer.from_pretrained('''bert-base-uncased'''    ,   do_lower_case=lowercase_					)
    __UpperCamelCase     						=  BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''					)
    model.to(args.device					)
    model.eval()

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

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

        import nltk
        import rouge

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

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

    __UpperCamelCase     						=  build_data_iterator(lowercase_    ,   lowercase_					)
    __UpperCamelCase     						=  build_predictor(lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)

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

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

    if args.compute_rouge:
        __UpperCamelCase     						=  rouge_evaluator.get_scores(lowercase_    ,   lowercase_					)
        __UpperCamelCase     						=  format_rouge_scores(lowercase_					)
        save_rouge_scores(lowercase_					)
        print(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Any:

    """simple docstring"""


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

        __UpperCamelCase     						=  os.path.join(lowercase_    ,   lowercase_					)
        with open(lowercase_    ,   '''w'''					) as output:
            output.write(lowercase_					)




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

    """simple docstring"""


    __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  translation
    __UpperCamelCase     						=  (
        raw_summary.replace('''[unused0]'''    ,   ''''''					)
        .replace('''[unused3]'''    ,   ''''''					)
        .replace('''[PAD]'''    ,   ''''''					)
        .replace('''[unused1]'''    ,   ''''''					)
        .replace(r''' +'''    ,   ''' '''					)
        .replace(''' [unused2] '''    ,   '''. '''					)
        .replace('''[unused2]'''    ,   ''''''					)
        .strip()
    )

    return summary




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """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     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Dict:

    """simple docstring"""


    with open('''rouge_scores.txt'''    ,   '''w'''					) as output:
        output.write(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  load_and_cache_examples(lowercase_    ,   lowercase_					)
    __UpperCamelCase     						=  SequentialSampler(lowercase_					)

    def collate_fn(lowercase_					):
        return collate(lowercase_    ,   lowercase_    ,   block_size=5_12    ,   device=args.device					)

    __UpperCamelCase     						=  DataLoader(
        lowercase_    ,   sampler=lowercase_    ,   batch_size=args.batch_size    ,   collate_fn=lowercase_    ,   )

    return iterator




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  CNNDMDataset(args.documents_dir					)
    return dataset




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		str:

    """simple docstring"""


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

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

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

    return batch




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		List[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  summary_tokens.to('''cpu'''					).numpy()
    __UpperCamelCase     						=  tokenizer.decode(lowercase_					)
    __UpperCamelCase     						=  summary.split('''.'''					)
    __UpperCamelCase     						=  [s + '''.''' for s in sentences]
    return sentences




def     __SCREAMING_SNAKE_CASE      (     )				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  argparse.ArgumentParser()
    parser.add_argument(
        '''--documents_dir'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''The folder where the documents to summarize are located.'''    ,   )
    parser.add_argument(
        '''--summaries_output_dir'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''    ,   )
    parser.add_argument(
        '''--compute_rouge'''    ,   default=lowercase_    ,   type=lowercase_    ,   required=lowercase_    ,   help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''    ,   )
    # EVALUATION options
    parser.add_argument(
        '''--no_cuda'''    ,   default=lowercase_    ,   type=lowercase_    ,   help='''Whether to force the execution on CPU.'''    ,   )
    parser.add_argument(
        '''--batch_size'''    ,   default=4    ,   type=lowercase_    ,   help='''Batch size per GPU/CPU for training.'''    ,   )
    # BEAM SEARCH arguments
    parser.add_argument(
        '''--min_length'''    ,   default=50    ,   type=lowercase_    ,   help='''Minimum number of tokens for the summaries.'''    ,   )
    parser.add_argument(
        '''--max_length'''    ,   default=2_00    ,   type=lowercase_    ,   help='''Maixmum number of tokens for the summaries.'''    ,   )
    parser.add_argument(
        '''--beam_size'''    ,   default=5    ,   type=lowercase_    ,   help='''The number of beams to start with for each example.'''    ,   )
    parser.add_argument(
        '''--alpha'''    ,   default=0.95    ,   type=lowercase_    ,   help='''The value of alpha for the length penalty in the beam search.'''    ,   )
    parser.add_argument(
        '''--block_trigram'''    ,   default=lowercase_    ,   type=lowercase_    ,   help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''    ,   )
    __UpperCamelCase     						=  parser.parse_args()

    # Select device (distibuted not available)
    __UpperCamelCase     						=  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     						=  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=lowercase_					)

    evaluate(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Dict:

    """simple docstring"""


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

    __UpperCamelCase     						=  os.listdir(lowercase_					)
    if len(lowercase_					) == 0:
        return False

    return True


if __name__ == "__main__":
 main()












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       Any ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case       ,				'''image_mean''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''image_std''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_resize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)






