
'''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,
)


__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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Tuple							)       ->	int:
 SCREAMING_SNAKE_CASE  :  str								=		BertTokenizer.from_pretrained('bert-base-uncased'   ,     do_lower_case=__UpperCamelCase							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'							)
 model.to(args.device							)
 model.eval()

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

 if args.compute_rouge:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[]
  SCREAMING_SNAKE_CASE  :  Tuple								=		[]

  import nltk
  import rouge

  nltk.download('punkt'							)
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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.
 SCREAMING_SNAKE_CASE  :  str								=		""""""
 SCREAMING_SNAKE_CASE  :  Dict								=		""""""

 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		build_data_iterator(__UpperCamelCase   ,     __UpperCamelCase							)
 SCREAMING_SNAKE_CASE  :  int								=		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							):
  SCREAMING_SNAKE_CASE  :  Dict								=		predictor.translate_batch(__UpperCamelCase							)
  SCREAMING_SNAKE_CASE  :  int								=		predictor.from_batch(__UpperCamelCase							)
  SCREAMING_SNAKE_CASE  :  Any								=		[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:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		rouge_evaluator.get_scores(__UpperCamelCase   ,     __UpperCamelCase							)
  SCREAMING_SNAKE_CASE  :  Any								=		format_rouge_scores(__UpperCamelCase							)
  save_rouge_scores(__UpperCamelCase							)
  print(__UpperCamelCase							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	List[str]   ,     snake_case_				:	List[Any]							)       ->	Tuple:
 for summary, document_name in zip(__UpperCamelCase   ,     __UpperCamelCase							):
  # Prepare the summary file's name
  if "." in document_name:
   SCREAMING_SNAKE_CASE  :  Dict								=		""".""".join(document_name.split('.'							)[:-1]							)
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		document_name.split('.'							)[-1]
   SCREAMING_SNAKE_CASE  :  Tuple								=		bare_document_name + """_summary.""" + extension
  else:
   SCREAMING_SNAKE_CASE  :  str								=		document_name + """_summary"""

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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]							)       ->	List[str]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		translation
 SCREAMING_SNAKE_CASE  :  str								=		(
     raw_summary.replace('[unused0]'   ,     ''							)
     .replace('[unused3]'   ,     ''							)
     .replace('[PAD]'   ,     ''							)
     .replace('[unused1]'   ,     ''							)
     .replace(R' +'   ,     ' '							)
     .replace(' [unused2] '   ,     '. '							)
     .replace('[unused2]'   ,     ''							)
     .strip()
 )

 return summary



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[Any]							)       ->	List[Any]:
 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_  (	snake_case_				:	Tuple							)       ->	str:
 with open('rouge_scores.txt'   ,     'w'							) as output:
  output.write(__UpperCamelCase							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	Union[str, Any]							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Tuple								=		load_and_cache_examples(__UpperCamelCase   ,     __UpperCamelCase							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		SequentialSampler(__UpperCamelCase							)

 def collate_fn(snake_case_				:	Optional[Any]							):
  return collate(__UpperCamelCase   ,     __UpperCamelCase   ,     block_size=512   ,     device=args.device							)

 SCREAMING_SNAKE_CASE  :  List[str]								=		DataLoader(
     __UpperCamelCase   ,     sampler=__UpperCamelCase   ,     batch_size=args.batch_size   ,     collate_fn=__UpperCamelCase   ,     )

 return iterator



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[Any]   ,     snake_case_				:	Optional[int]							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Dict								=		CNNDMDataset(args.documents_dir							)
 return dataset



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	Optional[int]   ,     snake_case_				:	Dict   ,     snake_case_				:	Union[str, Any]							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[x for x in data if not len(x[1]							) == 0]  # remove empty_files
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[name for name, _, _ in data]
 SCREAMING_SNAKE_CASE  :  Any								=		[""" """.join(__UpperCamelCase							) for _, _, summary_list in data]

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

 SCREAMING_SNAKE_CASE  :  List[Any]								=		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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	List[str]							)       ->	Union[str, Any]:
 SCREAMING_SNAKE_CASE  :  str								=		summary_tokens.to('cpu'							).numpy()
 SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.decode(__UpperCamelCase							)
 SCREAMING_SNAKE_CASE  :  List[Any]								=		summary.split('.'							)
 SCREAMING_SNAKE_CASE  :  int								=		[s + """.""" for s in sentences]
 return sentences



def        SCREAMING_SNAKE_CASE_  (	)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Any								=		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=0.95   ,     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.'   ,     )
 SCREAMING_SNAKE_CASE  :  str								=		parser.parse_args()

 # Select device (distibuted not available)
 SCREAMING_SNAKE_CASE  :  Any								=		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  :  Tuple								=		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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	List[str]:
 if not os.path.exists(__UpperCamelCase							):
  return False

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

 return True


if __name__ == "__main__":
   main()






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase   	=  {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TvltModel''',
           '''TvltForPreTraining''',
           '''TvltForAudioVisualClassification''',
           '''TvltPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''TvltImageProcessor''']


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

       __lowerCamelCase   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



