


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,
)


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


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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

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

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

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

            import nltk
            import rouge

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

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

      snake_case_			= build_data_iterator(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
      snake_case_			= build_predictor(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

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

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

      if args.compute_rouge:
            snake_case_			= rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
            snake_case_			= format_rouge_scores(SCREAMING_SNAKE_CASE   )
            save_rouge_scores(SCREAMING_SNAKE_CASE   )
            print(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Union[str, Any]:




      """simple docstring"""

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

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    List[Any]:




      """simple docstring"""

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

      return summary






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Union[str, 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       (SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    List[str]:




      """simple docstring"""

      snake_case_			= load_and_cache_examples(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
      snake_case_			= SequentialSampler(SCREAMING_SNAKE_CASE   )

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

      snake_case_			= DataLoader(
          SCREAMING_SNAKE_CASE   ,   sampler=SCREAMING_SNAKE_CASE   ,   batch_size=args.batch_size   ,   collate_fn=SCREAMING_SNAKE_CASE   ,   )

      return iterator






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[int]:




      """simple docstring"""

      snake_case_			= CNNDMDataset(args.documents_dir   )
      return dataset






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[int]:




      """simple docstring"""

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

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

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

      return batch






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Dict:




      """simple docstring"""

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






def 			__lowerCAmelCase       ()->    Optional[int]:




      """simple docstring"""

      snake_case_			= argparse.ArgumentParser()
      parser.add_argument(
          '''--documents_dir'''   ,   default=SCREAMING_SNAKE_CASE   ,   type=SCREAMING_SNAKE_CASE   ,   required=SCREAMING_SNAKE_CASE   ,   help='''The folder where the documents to summarize are located.'''   ,   )
      parser.add_argument(
          '''--summaries_output_dir'''   ,   default=SCREAMING_SNAKE_CASE   ,   type=SCREAMING_SNAKE_CASE   ,   required=SCREAMING_SNAKE_CASE   ,   help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are'''   ,   )
      parser.add_argument(
          '''--compute_rouge'''   ,   default=SCREAMING_SNAKE_CASE   ,   type=SCREAMING_SNAKE_CASE   ,   required=SCREAMING_SNAKE_CASE   ,   help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'''   ,   )
      # EVALUATION options
      parser.add_argument(
          '''--no_cuda'''   ,   default=SCREAMING_SNAKE_CASE   ,   type=SCREAMING_SNAKE_CASE   ,   help='''Whether to force the execution on CPU.'''   ,   )
      parser.add_argument(
          '''--batch_size'''   ,   default=4   ,   type=SCREAMING_SNAKE_CASE   ,   help='''Batch size per GPU/CPU for training.'''   ,   )
      # BEAM SEARCH arguments
      parser.add_argument(
          '''--min_length'''   ,   default=50   ,   type=SCREAMING_SNAKE_CASE   ,   help='''Minimum number of tokens for the summaries.'''   ,   )
      parser.add_argument(
          '''--max_length'''   ,   default=200   ,   type=SCREAMING_SNAKE_CASE   ,   help='''Maixmum number of tokens for the summaries.'''   ,   )
      parser.add_argument(
          '''--beam_size'''   ,   default=5   ,   type=SCREAMING_SNAKE_CASE   ,   help='''The number of beams to start with for each example.'''   ,   )
      parser.add_argument(
          '''--alpha'''   ,   default=0.9_5   ,   type=SCREAMING_SNAKE_CASE   ,   help='''The value of alpha for the length penalty in the beam search.'''   ,   )
      parser.add_argument(
          '''--block_trigram'''   ,   default=SCREAMING_SNAKE_CASE   ,   type=SCREAMING_SNAKE_CASE   ,   help='''Whether to block the existence of repeating trigrams in the text generated by beam search.'''   ,   )
      snake_case_			= parser.parse_args()

      # Select device (distibuted not available)
      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:
            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=SCREAMING_SNAKE_CASE   )

      evaluate(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

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

      snake_case_			= os.listdir(SCREAMING_SNAKE_CASE   )
      if len(SCREAMING_SNAKE_CASE   ) == 0:
            return False

      return True


if __name__ == "__main__":
   main()



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image







class lowerCAmelCase_		(					unittest.TestCase   ,		lowerCamelCase__			):

 '''simple docstring'''

 def 		UpperCamelCase__	(      self   ):
       snake_case_			= load_tool('''image-question-answering'''   )
       self.tool.setup()
       snake_case_			= load_tool('''image-question-answering'''						, remote=_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(_UpperCAmelCase						, '''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.tool(image=_UpperCAmelCase						, question='''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
       snake_case_			= self.remote_tool(image=_UpperCAmelCase						, question='''How many cats are sleeping on the couch?'''   )
       self.assertEqual(_UpperCAmelCase						, '''2'''   )