




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       snake_case      (							A    )							->							str:




	'''simple docstring'''


	lowerCamelCase_							:	Optional[Any]							     =							BertTokenizer.from_pretrained('bert-base-uncased'		,     do_lower_case=A    )
	lowerCamelCase_							:	List[str]							     =							BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'    )
	model.to(args.device    )
	model.eval()

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

	if args.compute_rouge:
		lowerCamelCase_							:	str							     =							[]
		lowerCamelCase_							:	Optional[int]							     =							[]

		import nltk
		import rouge

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

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

	lowerCamelCase_							:	Optional[Any]							     =							build_data_iterator(A		,     A    )
	lowerCamelCase_							:	List[str]							     =							build_predictor(A		,     A		,     A		,     A    )

	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(A    ):
		lowerCamelCase_							:	Optional[Any]							     =							predictor.translate_batch(A    )
		lowerCamelCase_							:	Any							     =							predictor.from_batch(A    )
		lowerCamelCase_							:	str							     =							[format_summary(A    ) for t in translations]
		save_summaries(A		,     args.summaries_output_dir		,     batch.document_names    )

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

	if args.compute_rouge:
		lowerCamelCase_							:	Any							     =							rouge_evaluator.get_scores(A		,     A    )
		lowerCamelCase_							:	Optional[Any]							     =							format_rouge_scores(A    )
		save_rouge_scores(A    )
		print(A    )






def       snake_case      (							A		,     A		,     A    )							->							str:




	'''simple docstring'''


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

		lowerCamelCase_							:	Tuple							     =							os.path.join(A		,     A    )
		with open(A		,     'w'    ) as output:
			output.write(A    )






def       snake_case      (							A    )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:	Dict							     =							translation
	lowerCamelCase_							:	str							     =							(
	    raw_summary.replace('[unused0]'		,     ''    )
	    .replace('[unused3]'		,     ''    )
	    .replace('[PAD]'		,     ''    )
	    .replace('[unused1]'		,     ''    )
	    .replace(R' +'		,     ' '    )
	    .replace(' [unused2] '		,     '. '    )
	    .replace('[unused2]'		,     ''    )
	    .strip()
	)

	return summary






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




	'''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       snake_case      (							A    )							->							str:




	'''simple docstring'''


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






def       snake_case      (							A		,     A    )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:	str							     =							load_and_cache_examples(A		,     A    )
	lowerCamelCase_							:	Optional[Any]							     =							SequentialSampler(A    )

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

	lowerCamelCase_							:	List[Any]							     =							DataLoader(
	    A		,     sampler=A		,     batch_size=args.batch_size		,     collate_fn=A		,     )

	return iterator






def       snake_case      (							A		,     A    )							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:	Optional[Any]							     =							CNNDMDataset(args.documents_dir    )
	return dataset






def       snake_case      (							A		,     A		,     A		,     A    )							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:	Dict							     =							[x for x in data if not len(x[1]    ) == 0]  # remove empty_files
	lowerCamelCase_							:	str							     =							[name for name, _, _ in data]
	lowerCamelCase_							:	int							     =							[' '.join(A    ) for _, _, summary_list in data]

	lowerCamelCase_							:	List[Any]							     =							[encode_for_summarization(A		,     A		,     A    ) for _, story, summary in data]
	lowerCamelCase_							:	List[str]							     =							torch.tensor(
	    [truncate_or_pad(A		,     A		,     tokenizer.pad_token_id    ) for story, _ in encoded_text]    )
	lowerCamelCase_							:	Tuple							     =							compute_token_type_ids(A		,     tokenizer.cls_token_id    )
	lowerCamelCase_							:	Optional[int]							     =							build_mask(A		,     tokenizer.pad_token_id    )

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

	return batch






def       snake_case      (							A		,     A    )							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:	Optional[Any]							     =							summary_tokens.to('cpu'    ).numpy()
	lowerCamelCase_							:	str							     =							tokenizer.decode(A    )
	lowerCamelCase_							:	List[str]							     =							summary.split('.'    )
	lowerCamelCase_							:	Tuple							     =							[s + '.' for s in sentences]
	return sentences






def       snake_case      (							)							->							int:




	'''simple docstring'''


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

	# Select device (distibuted not available)
	lowerCamelCase_							:	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:
		lowerCamelCase_							:	Tuple							     =							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=A    )

	evaluate(A    )






def       snake_case      (							A    )							->							int:




	'''simple docstring'''


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

	lowerCamelCase_							:	List[Any]							     =							os.listdir(A    )
	if len(A    ) == 0:
		return False

	return True


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


UpperCamelCase__  =		{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		['CpmTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		['CpmTokenizerFast']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm import CpmTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm_fast import CpmTokenizerFast

else:
							import sys

							UpperCamelCase__  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
