
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   :					List[Any]    =			logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


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


def  A			(   lowercase					)  ->    Dict:
		'''simple docstring'''

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

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

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

				import nltk
				import rouge

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

		# these (unused) arguments are defined to keep the compatibility
		# with the legacy code and will be deleted in a next iteration.
		UpperCamelCase            =	''
		UpperCamelCase            =	''

		UpperCamelCase            =	build_data_iterator(lowercase       ,			lowercase					)
		UpperCamelCase            =	build_predictor(lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)

		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(lowercase					):
				UpperCamelCase            =	predictor.translate_batch(lowercase					)
				UpperCamelCase            =	predictor.from_batch(lowercase					)
				UpperCamelCase            =	[format_summary(lowercase					) for t in translations]
				save_summaries(lowercase       ,			args.summaries_output_dir       ,			batch.document_names					)

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

		if args.compute_rouge:
				UpperCamelCase            =	rouge_evaluator.get_scores(lowercase       ,			lowercase					)
				UpperCamelCase            =	format_rouge_scores(lowercase					)
				save_rouge_scores(lowercase					)
				print(lowercase					)


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    List[Any]:
		'''simple docstring'''

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

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


def  A			(   lowercase					)  ->    Optional[int]:
		'''simple docstring'''

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

		return summary


def  A			(   lowercase					)  ->    List[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  A			(   lowercase					)  ->    str:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		UpperCamelCase            =	load_and_cache_examples(lowercase       ,			lowercase					)
		UpperCamelCase            =	SequentialSampler(lowercase					)

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

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

		return iterator


def  A			(   lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase            =	CNNDMDataset(args.documents_dir					)
		return dataset


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    str:
		'''simple docstring'''

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

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

		UpperCamelCase            =	Batch(
		    document_names=lowercase       ,			batch_size=len(lowercase					)       ,			src=encoded_stories.to(lowercase					)       ,			segs=encoder_token_type_ids.to(lowercase					)       ,			mask_src=encoder_mask.to(lowercase					)       ,			tgt_str=lowercase       ,			)

		return batch


def  A			(   lowercase       ,			lowercase					)  ->    Dict:
		'''simple docstring'''

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


def  A			(   )  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	argparse.ArgumentParser()
		parser.add_argument(
		    '--documents_dir'       ,			default=lowercase       ,			type=lowercase       ,			required=lowercase       ,			help='The folder where the documents to summarize are located.'       ,			)
		parser.add_argument(
		    '--summaries_output_dir'       ,			default=lowercase       ,			type=lowercase       ,			required=lowercase       ,			help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'       ,			)
		parser.add_argument(
		    '--compute_rouge'       ,			default=lowercase       ,			type=lowercase       ,			required=lowercase       ,			help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'       ,			)
		# EVALUATION options
		parser.add_argument(
		    '--no_cuda'       ,			default=lowercase       ,			type=lowercase       ,			help='Whether to force the execution on CPU.'       ,			)
		parser.add_argument(
		    '--batch_size'       ,			default=4       ,			type=lowercase       ,			help='Batch size per GPU/CPU for training.'       ,			)
		# BEAM SEARCH arguments
		parser.add_argument(
		    '--min_length'       ,			default=50       ,			type=lowercase       ,			help='Minimum number of tokens for the summaries.'       ,			)
		parser.add_argument(
		    '--max_length'       ,			default=200       ,			type=lowercase       ,			help='Maixmum number of tokens for the summaries.'       ,			)
		parser.add_argument(
		    '--beam_size'       ,			default=5       ,			type=lowercase       ,			help='The number of beams to start with for each example.'       ,			)
		parser.add_argument(
		    '--alpha'       ,			default=0.9_5       ,			type=lowercase       ,			help='The value of alpha for the length penalty in the beam search.'       ,			)
		parser.add_argument(
		    '--block_trigram'       ,			default=lowercase       ,			type=lowercase       ,			help='Whether to block the existence of repeating trigrams in the text generated by beam search.'       ,			)
		UpperCamelCase            =	parser.parse_args()

		# Select device (distibuted not available)
		UpperCamelCase            =	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            =	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=lowercase					)

		evaluate(lowercase					)


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

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

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

		return True


if __name__ == "__main__":
					main()


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		barcode //= 10  # exclude the last digit
		UpperCamelCase            =	False
		UpperCamelCase            =	0

		# extract and check each digit
		while barcode != 0:
				UpperCamelCase            =	1 if checker else 3
				s += mult * (barcode % 10)
				barcode //= 10
				UpperCamelCase            =	not checker

		return (10 - (s % 10)) % 10


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

		return len(str(lowercase					)					) == 13 and get_check_digit(lowercase					) == barcode % 10


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		if str(lowercase					).isalpha():
				UpperCamelCase            =	f'''Barcode \'{barcode}\' has alphabetic characters.'''
				raise ValueError(lowercase					)
		elif int(lowercase					) < 0:
				raise ValueError('The entered barcode has a negative value. Try again.'					)
		else:
				return int(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					_UpperCAmelCase   :					List[str]    =			get_barcode(input("Barcode: ").strip())

					if is_valid(barcode):
										print(F'''\'{barcode}\' is a valid barcode.''')
					else:
										print(F'''\'{barcode}\' is NOT a valid barcode.''')
