'''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,
)


_lowercase		:					Optional[int]													=		logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


_lowercase		:					List[str]													=		namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])




def 				lowerCamelCase      ( UpperCAmelCase__			:   Any					)     -> Union[str, Any]:
							lowercase_   :     Optional[int]									=			BertTokenizer.from_pretrained("""bert-base-uncased""" , do_lower_case=__snake_case					)
							lowercase_   :     Optional[int]									=			BertAbs.from_pretrained("""remi/bertabs-finetuned-extractive-abstractive-summarization"""					)
							model.to(args.device					)
							model.eval()

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

							if args.compute_rouge:
														lowercase_   :     List[Any]									=			[]
														lowercase_   :     Any									=			[]

														import nltk
														import rouge

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

							# these (unused) arguments are defined to keep the compatibility
							# with the legacy code and will be deleted in a next iteration.
							lowercase_   :     Optional[int]									=			""""""
							lowercase_   :     Dict									=			""""""

							lowercase_   :     Optional[Any]									=			build_data_iterator(__snake_case , __snake_case					)
							lowercase_   :     List[str]									=			build_predictor(__snake_case , __snake_case , __snake_case , __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(__snake_case					):
														lowercase_   :     List[str]									=			predictor.translate_batch(__snake_case					)
														lowercase_   :     Tuple									=			predictor.from_batch(__snake_case					)
														lowercase_   :     str									=			[format_summary(__snake_case					) for t in translations]
														save_summaries(__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:
														lowercase_   :     Dict									=			rouge_evaluator.get_scores(__snake_case , __snake_case					)
														lowercase_   :     str									=			format_rouge_scores(__snake_case					)
														save_rouge_scores(__snake_case					)
														print(__snake_case					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   Optional[Any]					)     -> Tuple:


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

														lowercase_   :     List[str]									=			os.path.join(__snake_case , __snake_case					)
														with open(__snake_case , """w"""					) as output:
																					output.write(__snake_case					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   Any					)     -> Any:
							lowercase_		,		lowercase_		,		lowercase_   :     Dict									=			translation
							lowercase_   :     Optional[Any]									=			(
							    raw_summary.replace("""[unused0]""" , """"""					)
							    .replace("""[unused3]""" , """"""					)
							    .replace("""[PAD]""" , """"""					)
							    .replace("""[unused1]""" , """"""					)
							    .replace(R""" +""" , """ """					)
							    .replace(""" [unused2] """ , """. """					)
							    .replace("""[unused2]""" , """"""					)
							    .strip()
							)

							return summary




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict					)     -> 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 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> Any:


							with open("""rouge_scores.txt""" , """w"""					) as output:
														output.write(__snake_case					)




def 				lowerCamelCase      ( UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   str					)     -> Optional[int]:
							lowercase_   :     Any									=			load_and_cache_examples(__snake_case , __snake_case					)
							lowercase_   :     Any									=			SequentialSampler(__snake_case					)

							def collate_fn(UpperCAmelCase__			:   List[str]					):
														return collate(__snake_case , __snake_case , block_size=512 , device=args.device					)

							lowercase_   :     List[str]									=			DataLoader(
							    __snake_case , sampler=__snake_case , batch_size=args.batch_size , collate_fn=__snake_case , )

							return iterator




def 				lowerCamelCase      ( UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   List[Any]					)     -> List[str]:
							lowercase_   :     Optional[Any]									=			CNNDMDataset(args.documents_dir					)
							return dataset




def 				lowerCamelCase      ( UpperCAmelCase__			:   Any , UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   Optional[Any]					)     -> Tuple:
							lowercase_   :     Union[str, Any]									=			[x for x in data if not len(x[1]					) == 0]  # remove empty_files
							lowercase_   :     Optional[int]									=			[name for name, _, _ in data]
							lowercase_   :     int									=			[""" """.join(__snake_case					) for _, _, summary_list in data]

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

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

							return batch




def 				lowerCamelCase      ( UpperCAmelCase__			:   str , UpperCAmelCase__			:   int					)     -> Optional[int]:
							lowercase_   :     List[Any]									=			summary_tokens.to("""cpu"""					).numpy()
							lowercase_   :     Tuple									=			tokenizer.decode(__snake_case					)
							lowercase_   :     int									=			summary.split("""."""					)
							lowercase_   :     Dict									=			[s + """.""" for s in sentences]
							return sentences




def 				lowerCamelCase      ( )     -> int:
							lowercase_   :     Optional[Any]									=			argparse.ArgumentParser()
							parser.add_argument(
							    """--documents_dir""" , default=__snake_case , type=__snake_case , required=__snake_case , help="""The folder where the documents to summarize are located.""" , )
							parser.add_argument(
							    """--summaries_output_dir""" , default=__snake_case , type=__snake_case , required=__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=__snake_case , type=__snake_case , required=__snake_case , help="""Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.""" , )
							# EVALUATION options
							parser.add_argument(
							    """--no_cuda""" , default=__snake_case , type=__snake_case , help="""Whether to force the execution on CPU.""" , )
							parser.add_argument(
							    """--batch_size""" , default=4 , type=__snake_case , help="""Batch size per GPU/CPU for training.""" , )
							# BEAM SEARCH arguments
							parser.add_argument(
							    """--min_length""" , default=50 , type=__snake_case , help="""Minimum number of tokens for the summaries.""" , )
							parser.add_argument(
							    """--max_length""" , default=200 , type=__snake_case , help="""Maixmum number of tokens for the summaries.""" , )
							parser.add_argument(
							    """--beam_size""" , default=5 , type=__snake_case , help="""The number of beams to start with for each example.""" , )
							parser.add_argument(
							    """--alpha""" , default=0.95 , type=__snake_case , help="""The value of alpha for the length penalty in the beam search.""" , )
							parser.add_argument(
							    """--block_trigram""" , default=__snake_case , type=__snake_case , help="""Whether to block the existence of repeating trigrams in the text generated by beam search.""" , )
							lowercase_   :     str									=			parser.parse_args()

							# Select device (distibuted not available)
							lowercase_   :     Optional[int]									=			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:
														lowercase_   :     Optional[int]									=			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=__snake_case					)

							evaluate(__snake_case					)




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


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

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

							return True


if __name__ == "__main__":
						main()









import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class  lowerCamelCase__  (			unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     SCREAMING_SNAKE_CASE__      =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      if isinstance(model.config						,	UpperCAmelCase			):
       _lowercase ,       _lowercase				 =list(tokenizer.lang_code_to_id.keys()			)[:2]
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang=UpperCAmelCase						,	tgt_lang=UpperCAmelCase			)
      else:
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      return translator, ["Some string", "Some other text"]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =translator('''Some string'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

      _lowercase				 =translator(['''Some string''', '''other string''']			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}, {'''translation_text''': ANY(UpperCAmelCase			)}]			)





     @require_torch
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)







class  lowerCamelCase__  (			unittest.TestCase):





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      # We don't provide a default for this pair
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation_cn_to_ar'''			)

      # but we do for this one
      _lowercase				 =pipeline(task='''translation_en_to_de'''			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''en'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''de'''			)





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)
      _lowercase				 =MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)

      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      # Missing src_lang, tgt_lang
      with self.assertRaises(UpperCAmelCase			):
       translator('''This is a test'''			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)

      _lowercase				 =translator('''This is a test'''						,	src_lang='''en_XX'''						,	tgt_lang='''hi_IN'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''यह एक परीक्षण है'''}]			)

      # src_lang, tgt_lang can be defined at pipeline call time
      _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang='''en_XX'''						,	tgt_lang='''ar_AR'''			)
      _lowercase				 =translator('''This is a test'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': '''هذا إختبار'''}]			)





     @require_torch
     def 	__A   (self			)	->  Tuple:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      _lowercase				 =pipeline(task='''translation_cn_to_ar'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''cn'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''ar'''			)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      with pytest.warns(UpperCAmelCase						,	match=R'''.*translation_en_to_de.*'''			):
       _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator.task						,	'''translation_en_to_de'''			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''en'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''de'''			)





     @require_torch
     def 	__A   (self			)	->  List[str]:
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation'''			)

