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


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





def  lowercase(  UpperCamelCase_					)		->  List[Any]:







 '''simple docstring'''
 UpperCamelCase      =			BertTokenizer.from_pretrained("""bert-base-uncased"""       ,				do_lower_case=UpperCamelCase_					)
 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=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.
 UpperCamelCase      =			""""""
 UpperCamelCase      =			""""""

 UpperCamelCase      =			build_data_iterator(UpperCamelCase_       ,				UpperCamelCase_					)
 UpperCamelCase      =			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_					):
  UpperCamelCase      =			predictor.translate_batch(UpperCamelCase_					)
  UpperCamelCase      =			predictor.from_batch(UpperCamelCase_					)
  UpperCamelCase      =			[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:
  UpperCamelCase      =			rouge_evaluator.get_scores(UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			format_rouge_scores(UpperCamelCase_					)
  save_rouge_scores(UpperCamelCase_					)
  print(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 for summary, document_name in zip(UpperCamelCase_       ,				UpperCamelCase_					):
  # 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(UpperCamelCase_       ,				UpperCamelCase_					)
  with open(UpperCamelCase_       ,				"""w"""					) as output:
   output.write(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''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  lowercase(  UpperCamelCase_					)		->  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  lowercase(  UpperCamelCase_					)		->  Union[str, Any]:







 '''simple docstring'''
 with open("""rouge_scores.txt"""       ,				"""w"""					) as output:
  output.write(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			load_and_cache_examples(UpperCamelCase_       ,				UpperCamelCase_					)
 UpperCamelCase      =			SequentialSampler(UpperCamelCase_					)

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

 UpperCamelCase      =			DataLoader(
     UpperCamelCase_       ,				sampler=UpperCamelCase_       ,				batch_size=args.batch_size       ,				collate_fn=UpperCamelCase_       ,				)

 return iterator





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			CNNDMDataset(args.documents_dir					)
 return dataset





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  Any:







 '''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(UpperCamelCase_					) for _, _, summary_list in data]

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

 UpperCamelCase      =			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  lowercase(  UpperCamelCase_       ,				UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 UpperCamelCase      =			summary_tokens.to("""cpu"""					).numpy()
 UpperCamelCase      =			tokenizer.decode(UpperCamelCase_					)
 UpperCamelCase      =			summary.split("""."""					)
 UpperCamelCase      =			[s + """.""" for s in sentences]
 return sentences





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







 '''simple docstring'''
 UpperCamelCase      =			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."""       ,				)
 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=UpperCamelCase_					)

 evaluate(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_					)		->  str:







 '''simple docstring'''
 if not os.path.exists(UpperCamelCase_					):
  return False

 UpperCamelCase      =			os.listdir(UpperCamelCase_					)
 if len(UpperCamelCase_					) == 0:
  return False

 return True


if __name__ == "__main__":
     main()

import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase     ,    __lowerCAmelCase		):







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_tool("""document-question-answering"""							)
    self.tool.setup()
    UpperCamelCase      =			load_tool("""document-question-answering"""      ,      remote=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    self.tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)
