






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,
)


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


__snake_case     :     Dict       =  namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])







def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    BertTokenizer.from_pretrained('bert-base-uncased'     ,							do_lower_case=UpperCamelCase_		)
						lowerCAmelCase__         =    BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'		)
						model.to(args.device		)
						model.eval()

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

						if args.compute_rouge:
												lowerCAmelCase__         =    []
												lowerCAmelCase__         =    []

												import nltk
												import rouge

												nltk.download('punkt'		)
												lowerCAmelCase__         =    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.
						lowerCAmelCase__         =    ''
						lowerCAmelCase__         =    ''

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







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Optional[Any]		)	-> List[Any]:

						"""simple docstring"""



						for summary, document_name in zip(UpperCamelCase_     ,							UpperCamelCase_		):
												# Prepare the summary file's name
												if "." in document_name:
																		lowerCAmelCase__         =    '.'.join(document_name.split('.'		)[:-1]		)
																		lowerCAmelCase__         =    document_name.split('.'		)[-1]
																		lowerCAmelCase__         =    bare_document_name + '_summary.' + extension
												else:
																		lowerCAmelCase__         =    document_name + '_summary'

												lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)
												with open(UpperCamelCase_     ,							'w'		) as output:
																		output.write(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       int		)	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    translation
						lowerCAmelCase__         =    (
						    raw_summary.replace('[unused0]'     ,							''		)
						    .replace('[unused3]'     ,							''		)
						    .replace('[PAD]'     ,							''		)
						    .replace('[unused1]'     ,							''		)
						    .replace(r' +'     ,							' '		)
						    .replace(' [unused2] '     ,							'. '		)
						    .replace('[unused2]'     ,							''		)
						    .strip()
						)

						return summary







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple		)	-> Optional[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 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]		)	-> Union[str, Any]:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Optional[int]		)	-> str:

						"""simple docstring"""



						lowerCAmelCase__         =    load_and_cache_examples(UpperCamelCase_     ,							UpperCamelCase_		)
						lowerCAmelCase__         =    SequentialSampler(UpperCamelCase_		)

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

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

						return iterator







def 						_UpperCamelCase (     UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       List[Any]		)	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    CNNDMDataset(args.documents_dir		)
						return dataset







def 						_UpperCamelCase (     UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[Any]     ,							UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Tuple		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    [x for x in data if not len(x[1]		) == 0]  # remove empty_files
						lowerCAmelCase__         =    [name for name, _, _ in data]
						lowerCAmelCase__         =    [' '.join(UpperCamelCase_		) for _, _, summary_list in data]

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

						lowerCAmelCase__         =    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 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> int:

						"""simple docstring"""



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







def 						_UpperCamelCase (     )	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    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.'     ,							)
						lowerCAmelCase__         =    parser.parse_args()

						# Select device (distibuted not available)
						lowerCAmelCase__         =    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:
												lowerCAmelCase__         =    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 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]		)	-> Tuple:

						"""simple docstring"""



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

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

						return True


if __name__ == "__main__":
					main()













import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


__snake_case     :     Optional[int]       =  """\
@inproceedings{ramshaw-marcus-1995-text,
    title = \"Text Chunking using Transformation-Based Learning\",
    author = \"Ramshaw, Lance  and
      Marcus, Mitch\",
    booktitle = \"Third Workshop on Very Large Corpora\",
    year = \"1995\",
    url = \"https://www.aclweb.org/anthology/W95-0107\",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
"""

__snake_case     :     Any       =  """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

__snake_case     :     Optional[int]       =  """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION)
class      __SCREAMING_SNAKE_CASE       ( datasets.Metric):






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,    citation=_CITATION  ,    homepage='https://github.com/chakki-works/seqeval'  ,    inputs_description=_KWARGS_DESCRIPTION  ,    features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												            'references': datasets.Sequence(datasets.Value('string'  ,    id='label' )  ,    id='sequence' ),
												        } )  ,    codebase_urls=['https://github.com/chakki-works/seqeval']  ,    reference_urls=['https://github.com/chakki-works/seqeval']  ,    )







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = "warn"  ,    ):






												"""simple docstring"""
												if scheme is not None:
																		try:
																								lowerCAmelCase__         =    importlib.import_module('seqeval.scheme' )
																								lowerCAmelCase__         =    getattr(_UpperCamelCase  ,    _UpperCamelCase )
																		except AttributeError:
																								raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}" )
												lowerCAmelCase__         =    classification_report(
												    y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase  ,    suffix=_UpperCamelCase  ,    output_dict=_UpperCamelCase  ,    scheme=_UpperCamelCase  ,    mode=_UpperCamelCase  ,    sample_weight=_UpperCamelCase  ,    zero_division=_UpperCamelCase  ,    )
												report.pop('macro avg' )
												report.pop('weighted avg' )
												lowerCAmelCase__         =    report.pop('micro avg' )

												lowerCAmelCase__         =    {
												    type_name: {
												        'precision': score['precision'],
												        'recall': score['recall'],
												        'f1': score['f1-score'],
												        'number': score['support'],
												    }
												    for type_name, score in report.items()
												}
												lowerCAmelCase__         =    overall_score['precision']
												lowerCAmelCase__         =    overall_score['recall']
												lowerCAmelCase__         =    overall_score['f1-score']
												lowerCAmelCase__         =    accuracy_score(y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase )

												return scores





