


"""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,
)


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


__a			 =   namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    BertTokenizer.from_pretrained('''bert-base-uncased'''	,			do_lower_case=_UpperCamelCase )
						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=_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.
						UpperCAmelCase__		=    ''''''
						UpperCAmelCase__		=    ''''''

						UpperCAmelCase__		=    build_data_iterator(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    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 ):
												UpperCAmelCase__		=    predictor.translate_batch(_UpperCamelCase )
												UpperCAmelCase__		=    predictor.from_batch(_UpperCamelCase )
												UpperCAmelCase__		=    [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:
												UpperCAmelCase__		=    rouge_evaluator.get_scores(_UpperCamelCase	,			_UpperCamelCase )
												UpperCAmelCase__		=    format_rouge_scores(_UpperCamelCase )
												save_rouge_scores(_UpperCamelCase )
												print(_UpperCamelCase )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						for summary, document_name in zip(_UpperCamelCase	,			_UpperCamelCase ):
												# 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(_UpperCamelCase	,			_UpperCamelCase )
												with open(_UpperCamelCase	,			'''w''' ) as output:
																		output.write(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""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__ (       _UpperCamelCase ):





						"""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__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    load_and_cache_examples(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    SequentialSampler(_UpperCamelCase )

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

						UpperCAmelCase__		=    DataLoader(
						    _UpperCamelCase	,			sampler=_UpperCamelCase	,			batch_size=args.batch_size	,			collate_fn=_UpperCamelCase	,			)

						return iterator






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    CNNDMDataset(args.documents_dir )
						return dataset






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase ):





						"""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(_UpperCamelCase ) for _, _, summary_list in data]

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

						UpperCAmelCase__		=    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	,			_UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    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.'''	,			)
						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=_UpperCamelCase )

						evaluate(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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

						UpperCAmelCase__		=    os.listdir(_UpperCamelCase )
						if len(_UpperCamelCase ) == 0:
												return False

						return True


if __name__ == "__main__":
						main()



"""simple docstring"""







from collections import defaultdict






def 					a__ (       _UpperCamelCase = 5 ):





						"""simple docstring"""

						UpperCAmelCase__		=    defaultdict(_UpperCamelCase )
						UpperCAmelCase__		=    0

						while True:
												UpperCAmelCase__		=    get_digits(_UpperCamelCase )
												freqs[digits].append(_UpperCamelCase )

												if len(freqs[digits] ) == max_base:
																		UpperCAmelCase__		=    freqs[digits][0] ** 3
																		return base

												num += 1






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return "".join(sorted(str(num**3 ) ) )


if __name__ == "__main__":
						print(f'''{solution() = }''')