



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


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


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Tuple:
				snake_case__      :   Optional[int]      =    BertTokenizer.from_pretrained('''bert-base-uncased'''   ,				do_lower_case=UpperCamelCase_			)
				snake_case__      :   List[Any]      =    BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''			)
				model.to(args.device			)
				model.eval()

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

				if args.compute_rouge:
								snake_case__      :   List[Any]      =    []
								snake_case__      :   List[str]      =    []

								import nltk
								import rouge

								nltk.download('''punkt'''			)
								snake_case__      :   Tuple      =    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.
				snake_case__      :   Tuple      =    ''''''
				snake_case__      :   Any      =    ''''''

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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:Optional[Any]			)		->						List[Any]:



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

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


def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						List[Any]:
				snake_case__  ,      snake_case__  ,      snake_case__      :   Optional[int]      =    translation
				snake_case__      :   Union[str, Any]      =    (
				    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]:
				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]:



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


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Union[str, Any]      =    load_and_cache_examples(UpperCamelCase_   ,				UpperCamelCase_			)
				snake_case__      :   str      =    SequentialSampler(UpperCamelCase_			)

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

				snake_case__      :   Optional[Any]      =    DataLoader(
				    UpperCamelCase_   ,				sampler=UpperCamelCase_   ,				batch_size=args.batch_size   ,				collate_fn=UpperCamelCase_   ,				)

				return iterator


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:List[Any]			)		->						Tuple:
				snake_case__      :   int      =    CNNDMDataset(args.documents_dir			)
				return dataset


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Tuple			)		->						Any:
				snake_case__      :   Optional[Any]      =    [x for x in data if not len(x[1]			) == 0]  # remove empty_files
				snake_case__      :   Tuple      =    [name for name, _, _ in data]
				snake_case__      :   int      =    [''' '''.join(UpperCamelCase_			) for _, _, summary_list in data]

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

				snake_case__      :   Optional[int]      =    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:
				snake_case__      :   Optional[Any]      =    summary_tokens.to('''cpu'''			).numpy()
				snake_case__      :   List[Any]      =    tokenizer.decode(UpperCamelCase_			)
				snake_case__      :   Optional[int]      =    summary.split('''.'''			)
				snake_case__      :   Dict      =    [s + '''.''' for s in sentences]
				return sentences


def 							__UpperCAmelCase				(				)		->						Optional[Any]:
				snake_case__      :   List[str]      =    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.'''   ,				)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()

				# Select device (distibuted not available)
				snake_case__      :   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:
								snake_case__      :   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=UpperCamelCase_			)

				evaluate(UpperCamelCase_			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[str, Any]			)		->						Tuple:



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

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

				return True


if __name__ == "__main__":
				main()












from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

__snake_case     :     str       =  TypeVar("""T""", bound=bool)


class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    value
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return self._value







						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		lowerCAmelCase__    ,							lowerCAmelCase__         =    roota, roota

												lowerCAmelCase__         =    roota
												lowerCAmelCase__         =    roota.right
												lowerCAmelCase__         =    roota.left
												lowerCAmelCase__         =    SkewNode.merge(_UpperCamelCase  ,    _UpperCamelCase )

												return result




class      __SCREAMING_SNAKE_CASE       ( Generic[T]):






						def __init__(						self  ,    _UpperCamelCase = () ):






												"""simple docstring"""
												lowerCAmelCase__         =    None
												if data:
																		for item in data:
																								self.insert(_UpperCamelCase )






						def __bool__(						self ):






												"""simple docstring"""
												return self._root is not None






						def __iter__(						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    []
												while self:
																		result.append(self.pop() )

												# Pushing items back to the heap not to clear it.
												for item in result:
																		self.insert(_UpperCamelCase )

												return iter(_UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    SkewNode.merge(self._root  ,    SkewNode(_UpperCamelCase ) )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.top()
												lowerCAmelCase__         =    (
												    SkewNode.merge(self._root.left  ,    self._root.right ) if self._root else None
												)

												return result






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.' )
												return self._root.value







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    None


if __name__ == "__main__":
					import doctest

					doctest.testmod()





