

"""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,
)


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


__lowerCAmelCase				      =   namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def 		A_	(   __UpperCamelCase     :				List[Any]				):
		lowercase										=							BertTokenizer.from_pretrained('''bert-base-uncased'''       ,	do_lower_case=__UpperCamelCase				)
		lowercase										=							BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''				)
		model.to(args.device				)
		model.eval()

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

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

				import nltk
				import rouge

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

		lowercase										=							build_data_iterator(__UpperCamelCase       ,	__UpperCamelCase				)
		lowercase										=							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				):
				lowercase										=							predictor.translate_batch(__UpperCamelCase				)
				lowercase										=							predictor.from_batch(__UpperCamelCase				)
				lowercase										=							[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:
				lowercase										=							rouge_evaluator.get_scores(__UpperCamelCase       ,	__UpperCamelCase				)
				lowercase										=							format_rouge_scores(__UpperCamelCase				)
				save_rouge_scores(__UpperCamelCase				)
				print(__UpperCamelCase				)
def 		A_	(   __UpperCamelCase     :				Union[str, Any]       ,	__UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				List[Any]				):
		for summary, document_name in zip(__UpperCamelCase       ,	__UpperCamelCase				):
				# Prepare the summary file's name
				if "." in document_name:
						lowercase										=							'''.'''.join(document_name.split('''.'''				)[:-1]				)
						lowercase										=							document_name.split('''.'''				)[-1]
						lowercase										=							bare_document_name + '''_summary.''' + extension
				else:
						lowercase										=							document_name + '''_summary'''

				lowercase										=							os.path.join(__UpperCamelCase       ,	__UpperCamelCase				)
				with open(__UpperCamelCase       ,	'''w'''				) as output:
						output.write(__UpperCamelCase				)
def 		A_	(   __UpperCamelCase     :				Optional[Any]				):
		lowercase					,  lowercase					,  lowercase										=							translation
		lowercase										=							(
		    raw_summary.replace('''[unused0]'''       ,	''''''				)
		    .replace('''[unused3]'''       ,	''''''				)
		    .replace('''[PAD]'''       ,	''''''				)
		    .replace('''[unused1]'''       ,	''''''				)
		    .replace(R''' +'''       ,	''' '''				)
		    .replace(''' [unused2] '''       ,	'''. '''				)
		    .replace('''[unused2]'''       ,	''''''				)
		    .strip()
		)

		return summary
def 		A_	(   __UpperCamelCase     :				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 		A_	(   __UpperCamelCase     :				Any				):
		with open('''rouge_scores.txt'''       ,	'''w'''				) as output:
				output.write(__UpperCamelCase				)
def 		A_	(   __UpperCamelCase     :				int       ,	__UpperCamelCase     :				List[str]				):
		lowercase										=							load_and_cache_examples(__UpperCamelCase       ,	__UpperCamelCase				)
		lowercase										=							SequentialSampler(__UpperCamelCase				)

		def collate_fn(__UpperCamelCase     :				Dict				):
				return collate(__UpperCamelCase       ,	__UpperCamelCase       ,	block_size=5_12       ,	device=args.device				)

		lowercase										=							DataLoader(
		    __UpperCamelCase       ,	sampler=__UpperCamelCase       ,	batch_size=args.batch_size       ,	collate_fn=__UpperCamelCase       ,	)

		return iterator
def 		A_	(   __UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				int				):
		lowercase										=							CNNDMDataset(args.documents_dir				)
		return dataset
def 		A_	(   __UpperCamelCase     :				Optional[Any]       ,	__UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				List[Any]				):
		lowercase										=							[x for x in data if not len(x[1]				) == 0]  # remove empty_files
		lowercase										=							[name for name, _, _ in data]
		lowercase										=							[''' '''.join(__UpperCamelCase				) for _, _, summary_list in data]

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

		lowercase										=							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 		A_	(   __UpperCamelCase     :				Optional[int]       ,	__UpperCamelCase     :				Optional[int]				):
		lowercase										=							summary_tokens.to('''cpu'''				).numpy()
		lowercase										=							tokenizer.decode(__UpperCamelCase				)
		lowercase										=							summary.split('''.'''				)
		lowercase										=							[s + '''.''' for s in sentences]
		return sentences
def 		A_	(   ):
		lowercase										=							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=2_00       ,	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.'''       ,	)
		lowercase										=							parser.parse_args()

		# Select device (distibuted not available)
		lowercase										=							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										=							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 		A_	(   __UpperCamelCase     :				int				):
		if not os.path.exists(__UpperCamelCase				):
				return False

		lowercase										=							os.listdir(__UpperCamelCase				)
		if len(__UpperCamelCase				) == 0:
				return False

		return True


if __name__ == "__main__":
							main()


"""simple docstring"""






from typing import Callable
def 		A_	(   __UpperCamelCase     :				Callable				):

		def wrapper(__UpperCamelCase     :				Optional[Any]				):
				lowercase										=							function_with_docstring.__doc__
				return target_function

		return wrapper