





"""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 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       BertTokenizer.from_pretrained('''bert-base-uncased'''			,		do_lower_case=_lowercase  )
						_UpperCamelCase     :   Optional[int]      =       BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''  )
						model.to(args.device  )
						model.eval()

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

						if args.compute_rouge:
												_UpperCamelCase     :   Dict      =       []
												_UpperCamelCase     :   Tuple      =       []

												import nltk
												import rouge

												nltk.download('''punkt'''  )
												_UpperCamelCase     :   List[Any]      =       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     :   List[Any]      =       ''''''
						_UpperCamelCase     :   Optional[Any]      =       ''''''

						_UpperCamelCase     :   Tuple      =       build_data_iterator(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[int]      =       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     :   Dict      =       predictor.translate_batch(_lowercase  )
												_UpperCamelCase     :   Union[str, Any]      =       predictor.from_batch(_lowercase  )
												_UpperCamelCase     :   List[Any]      =       [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     :   Optional[int]      =       rouge_evaluator.get_scores(_lowercase			,		_lowercase  )
												_UpperCamelCase     :   Optional[int]      =       format_rouge_scores(_lowercase  )
												save_rouge_scores(_lowercase  )
												print(_lowercase  )





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						for summary, document_name in zip(_lowercase			,		_lowercase  ):
												# Prepare the summary file's name
												if "." in document_name:
																		_UpperCamelCase     :   Union[str, Any]      =       '''.'''.join(document_name.split('''.'''  )[:-1]  )
																		_UpperCamelCase     :   int      =       document_name.split('''.'''  )[-1]
																		_UpperCamelCase     :   List[str]      =       bare_document_name + '''_summary.''' + extension
												else:
																		_UpperCamelCase     :   Any      =       document_name + '''_summary'''

												_UpperCamelCase     :   List[str]      =       os.path.join(_lowercase			,		_lowercase  )
												with open(_lowercase			,		'''w'''  ) as output:
																		output.write(_lowercase  )





def 			a_							(				_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       translation
						_UpperCamelCase     :   Any      =       (
						    raw_summary.replace('''[unused0]'''			,		''''''  )
						    .replace('''[unused3]'''			,		''''''  )
						    .replace('''[PAD]'''			,		''''''  )
						    .replace('''[unused1]'''			,		''''''  )
						    .replace(R''' +'''			,		''' '''  )
						    .replace(''' [unused2] '''			,		'''. '''  )
						    .replace('''[unused2]'''			,		''''''  )
						    .strip()
						)

						return summary





def 			a_							(				_lowercase  ):
						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 			a_							(				_lowercase  ):
						with open('''rouge_scores.txt'''			,		'''w'''  ) as output:
												output.write(_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       load_and_cache_examples(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Tuple      =       SequentialSampler(_lowercase  )

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

						_UpperCamelCase     :   Dict      =       DataLoader(
						    _lowercase			,		sampler=_lowercase			,		batch_size=args.batch_size			,		collate_fn=_lowercase			,		)

						return iterator





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       CNNDMDataset(args.documents_dir  )
						return dataset





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       [x for x in data if not len(x[1]  ) == 0]  # remove empty_files
						_UpperCamelCase     :   Union[str, Any]      =       [name for name, _, _ in data]
						_UpperCamelCase     :   Optional[Any]      =       [''' '''.join(_lowercase  ) for _, _, summary_list in data]

						_UpperCamelCase     :   Any      =       [encode_for_summarization(_lowercase			,		_lowercase			,		_lowercase  ) for _, story, summary in data]
						_UpperCamelCase     :   List[Any]      =       torch.tensor(
						    [truncate_or_pad(_lowercase			,		_lowercase			,		tokenizer.pad_token_id  ) for story, _ in encoded_text]  )
						_UpperCamelCase     :   Any      =       compute_token_type_ids(_lowercase			,		tokenizer.cls_token_id  )
						_UpperCamelCase     :   Dict      =       build_mask(_lowercase			,		tokenizer.pad_token_id  )

						_UpperCamelCase     :   Dict      =       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 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Tuple      =       summary_tokens.to('''cpu'''  ).numpy()
						_UpperCamelCase     :   Any      =       tokenizer.decode(_lowercase  )
						_UpperCamelCase     :   List[str]      =       summary.split('''.'''  )
						_UpperCamelCase     :   str      =       [s + '''.''' for s in sentences]
						return sentences





def 			a_							(				):
						_UpperCamelCase     :   List[Any]      =       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=200			,		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     :   int      =       parser.parse_args()

						# Select device (distibuted not available)
						_UpperCamelCase     :   List[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:
												_UpperCamelCase     :   List[Any]      =       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 			a_							(				_lowercase  ):
						if not os.path.exists(_lowercase  ):
												return False

						_UpperCamelCase     :   Optional[int]      =       os.listdir(_lowercase  )
						if len(_lowercase  ) == 0:
												return False

						return True


if __name__ == "__main__":
						main()










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

										with open(self.feature_extraction_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					return_tensors='''np'''				)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2				)






				def 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],					encoded_processor[key]				)






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										self.assertListEqual(
										    processor.model_input_names,					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)



