
"""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,
)


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


_a									=				namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       BertTokenizer.from_pretrained("bert-base-uncased",						do_lower_case=__lowerCamelCase    )
   UpperCAmelCase_   :   Optional[int]     	=       BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"    )
   model.to(args.device    )
   model.eval()

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

   if args.compute_rouge:
      UpperCAmelCase_   :   List[Any]     	=       []
      UpperCAmelCase_   :   int     	=       []

      import nltk
      import rouge

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

   # these (unused) arguments are defined to keep the compatibility
   # with the legacy code and will be deleted in a next iteration.
   UpperCAmelCase_   :   str     	=       ''
   UpperCAmelCase_   :   Union[str, Any]     	=       ''

   UpperCAmelCase_   :   Tuple     	=       build_data_iterator(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       build_predictor(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

   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(__lowerCamelCase    ):
      UpperCAmelCase_   :   List[Any]     	=       predictor.translate_batch(__lowerCamelCase    )
      UpperCAmelCase_   :   str     	=       predictor.from_batch(__lowerCamelCase    )
      UpperCAmelCase_   :   List[str]     	=       [format_summary(__lowerCamelCase    ) for t in translations]
      save_summaries(__lowerCamelCase,						args.summaries_output_dir,						batch.document_names    )

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

   if args.compute_rouge:
      UpperCAmelCase_   :   str     	=       rouge_evaluator.get_scores(__lowerCamelCase,						__lowerCamelCase    )
      UpperCAmelCase_   :   Any     	=       format_rouge_scores(__lowerCamelCase    )
      save_rouge_scores(__lowerCamelCase    )
      print(__lowerCamelCase    )







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

      UpperCAmelCase_   :   Union[str, Any]     	=       os.path.join(__lowerCamelCase,						__lowerCamelCase    )
      with open(__lowerCamelCase,						"w"    ) as output:
         output.write(__lowerCamelCase    )







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[str]     	=       translation
   UpperCAmelCase_   :   Any     	=       (
       raw_summary.replace("[unused0]",						""    )
       .replace("[unused3]",						""    )
       .replace("[PAD]",						""    )
       .replace("[unused1]",						""    )
       .replace(r" +",						" "    )
       .replace(" [unused2] ",						". "    )
       .replace("[unused2]",						""    )
       .strip()
   )

   return summary







def       __a	(						__lowerCamelCase    ):
   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	(						__lowerCamelCase    ):
   with open("rouge_scores.txt",						"w"    ) as output:
      output.write(__lowerCamelCase    )







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[str]     	=       load_and_cache_examples(__lowerCamelCase,						__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       SequentialSampler(__lowerCamelCase    )

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

   UpperCAmelCase_   :   List[str]     	=       DataLoader(
       __lowerCamelCase,						sampler=__lowerCamelCase,						batch_size=args.batch_size,						collate_fn=__lowerCamelCase,						)

   return iterator







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   List[str]     	=       CNNDMDataset(args.documents_dir    )
   return dataset







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Any     	=       [x for x in data if not len(x[1]    ) == 0]  # remove empty_files
   UpperCAmelCase_   :   int     	=       [name for name, _, _ in data]
   UpperCAmelCase_   :   int     	=       [' '.join(__lowerCamelCase    ) for _, _, summary_list in data]

   UpperCAmelCase_   :   Any     	=       [encode_for_summarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ) for _, story, summary in data]
   UpperCAmelCase_   :   Optional[Any]     	=       torch.tensor(
       [truncate_or_pad(__lowerCamelCase,						__lowerCamelCase,						tokenizer.pad_token_id    ) for story, _ in encoded_text]    )
   UpperCAmelCase_   :   Optional[int]     	=       compute_token_type_ids(__lowerCamelCase,						tokenizer.cls_token_id    )
   UpperCAmelCase_   :   Optional[int]     	=       build_mask(__lowerCamelCase,						tokenizer.pad_token_id    )

   UpperCAmelCase_   :   Optional[int]     	=       Batch(
       document_names=__lowerCamelCase,						batch_size=len(__lowerCamelCase    ),						src=encoded_stories.to(__lowerCamelCase    ),						segs=encoder_token_type_ids.to(__lowerCamelCase    ),						mask_src=encoder_mask.to(__lowerCamelCase    ),						tgt_str=__lowerCamelCase,						)

   return batch







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Union[str, Any]     	=       summary_tokens.to("cpu"    ).numpy()
   UpperCAmelCase_   :   Dict     	=       tokenizer.decode(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       summary.split("."    )
   UpperCAmelCase_   :   Dict     	=       [s + '.' for s in sentences]
   return sentences







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

   # Select device (distibuted not available)
   UpperCAmelCase_   :   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:
      UpperCAmelCase_   :   Dict     	=       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=__lowerCamelCase    )

   evaluate(__lowerCamelCase    )







def       __a	(						__lowerCamelCase    ):
   if not os.path.exists(__lowerCamelCase    ):
      return False

   UpperCAmelCase_   :   Tuple     	=       os.listdir(__lowerCamelCase    )
   if len(__lowerCamelCase    ) == 0:
      return False

   return True


if __name__ == "__main__":
    main()










'''simple docstring'''

import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A:   Dict						=					get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class 				__magic_name__  (						UpperCAmelCase_,    unittest.TestCase					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   int   =	ErnieMTokenizer
	SCREAMING_SNAKE_CASE_       :   Optional[Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	True
	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	False
	SCREAMING_SNAKE_CASE_       :   Dict   =	False
	def 							lowerCamelCase__		(				self							)    ->	Any:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase_   : List[Any]   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)
							tokenizer.save_pretrained(self.tmpdirname							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      'this is a test'
							lowercase_   : Dict   	=      'this is a test'
							return input_text, output_text
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : str   	=      '<pad>'
							lowercase_   : Optional[Any]   	=      0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)	,   _lowercase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							lowercase_   : List[str]   	=      list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]	,   '<pad>'							)
							self.assertEqual(vocab_keys[1]	,   '<unk>'							)
							self.assertEqual(vocab_keys[-1]	,   '▁eloquent'							)
							self.assertEqual(len(_lowercase							)	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							self.assertEqual(self.get_tokenizer().vocab_size	,   3_0000							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							if not self.test_rust_tokenizer:
													return

							lowercase_   : Optional[int]   	=      self.get_tokenizer()
							lowercase_   : Optional[Any]   	=      self.get_rust_tokenizer()

							lowercase_   : Tuple   	=      'I was born in 92000, and this is falsé.'

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize(_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.tokenize(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Any   	=      tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							lowercase_   : List[Any]   	=      rust_tokenizer.encode(_lowercase	,   add_special_tokens=_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)

							lowercase_   : Union[str, Any]   	=      self.get_rust_tokenizer()
							lowercase_   : Optional[Any]   	=      tokenizer.encode(_lowercase							)
							lowercase_   : str   	=      rust_tokenizer.encode(_lowercase							)
							self.assertListEqual(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Optional[int]   	=      ErnieMTokenizer(_lowercase	,   do_lower_case=_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : Tuple   	=      tokenizer.tokenize('This is a test'							)
							self.assertListEqual(_lowercase	,   ['▁this', '▁is', '▁a', '▁test']							)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase							)	,   [48, 25, 21, 1289]							)

							lowercase_   : Union[str, Any]   	=      tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
							# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']							)
							lowercase_   : int   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
							self.assertListEqual(_lowercase	,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]							)

							lowercase_   : Optional[int]   	=      tokenizer.convert_ids_to_tokens(_lowercase							)
							self.assertListEqual(
							    _lowercase	,   ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,   )
	def 							lowerCamelCase__		(				self							)    ->	Any:
							lowercase_   : Tuple   	=      ErnieMTokenizer(_lowercase	,   unk_token='<unk>'	,   pad_token='<pad>'							)

							lowercase_   : List[Any]   	=      tokenizer.encode('sequence builders'							)
							lowercase_   : Optional[int]   	=      tokenizer.encode('multi-sequence build'							)

							lowercase_   : Union[str, Any]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase							)
							lowercase_   : Optional[int]   	=      tokenizer.build_inputs_with_special_tokens(_lowercase	,   _lowercase							)

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
							    tokenizer.sep_token_id
							] + text_a + [tokenizer.sep_token_id]







	@slow
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							# fmt: off
							lowercase_   : Dict   	=      {'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_lowercase	,   model_name='susnato/ernie-m-base_pytorch'	,   sequences=[
							        'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
							        'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
							        'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
							        'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
							        'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
							        'conditioning on both left and right context in all layers.',
							        'The quick brown fox jumps over the lazy dog.',
							    ]	,   )
