


'''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,
)


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


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

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					BertTokenizer.from_pretrained('''bert-base-uncased''',					do_lower_case=lowerCamelCase)
						__lowerCAmelCase											=					BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''')
						model.to(args.device)
						model.eval()

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

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

												import nltk
												import rouge

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

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

						__lowerCAmelCase											=					build_data_iterator(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					build_predictor(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

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

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

						if args.compute_rouge:
												__lowerCAmelCase											=					rouge_evaluator.get_scores(lowerCamelCase,					lowerCamelCase)
												__lowerCAmelCase											=					format_rouge_scores(lowerCamelCase)
												save_rouge_scores(lowerCamelCase)
												print(lowerCamelCase)

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

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

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					translation
						__lowerCAmelCase											=					(
						    raw_summary.replace('''[unused0]''',					'''''')
						    .replace('''[unused3]''',					'''''')
						    .replace('''[PAD]''',					'''''')
						    .replace('''[unused1]''',					'''''')
						    .replace(r''' +''',					''' ''')
						    .replace(''' [unused2] ''',					'''. ''')
						    .replace('''[unused2]''',					'''''')
						    .strip()
						)

						return summary

def 				__magic_name__(    lowerCamelCase):
						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 				__magic_name__(    lowerCamelCase):
						with open('''rouge_scores.txt''',					'''w''') as output:
												output.write(lowerCamelCase)

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					load_and_cache_examples(lowerCamelCase,					lowerCamelCase)
						__lowerCAmelCase											=					SequentialSampler(lowerCamelCase)

						def collate_fn(lowerCamelCase):
												return collate(lowerCamelCase,					lowerCamelCase,					block_size=5_1_2,					device=args.device)

						__lowerCAmelCase											=					DataLoader(
						    lowerCamelCase,					sampler=lowerCamelCase,					batch_size=args.batch_size,					collate_fn=lowerCamelCase,					)

						return iterator

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					CNNDMDataset(args.documents_dir)
						return dataset

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					[x for x in data if not len(x[1]) == 0]  # remove empty_files
						__lowerCAmelCase											=					[name for name, _, _ in data]
						__lowerCAmelCase											=					[''' '''.join(lowerCamelCase) for _, _, summary_list in data]

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

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

						return batch

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						__lowerCAmelCase											=					summary_tokens.to('''cpu''').numpy()
						__lowerCAmelCase											=					tokenizer.decode(lowerCamelCase)
						__lowerCAmelCase											=					summary.split('''.''')
						__lowerCAmelCase											=					[s + '''.''' for s in sentences]
						return sentences

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument(
						    '''--documents_dir''',					default=lowerCamelCase,					type=lowerCamelCase,					required=lowerCamelCase,					help='''The folder where the documents to summarize are located.''',					)
						parser.add_argument(
						    '''--summaries_output_dir''',					default=lowerCamelCase,					type=lowerCamelCase,					required=lowerCamelCase,					help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''',					)
						parser.add_argument(
						    '''--compute_rouge''',					default=lowerCamelCase,					type=lowerCamelCase,					required=lowerCamelCase,					help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''',					)
						# EVALUATION options
						parser.add_argument(
						    '''--no_cuda''',					default=lowerCamelCase,					type=lowerCamelCase,					help='''Whether to force the execution on CPU.''',					)
						parser.add_argument(
						    '''--batch_size''',					default=4,					type=lowerCamelCase,					help='''Batch size per GPU/CPU for training.''',					)
						# BEAM SEARCH arguments
						parser.add_argument(
						    '''--min_length''',					default=5_0,					type=lowerCamelCase,					help='''Minimum number of tokens for the summaries.''',					)
						parser.add_argument(
						    '''--max_length''',					default=2_0_0,					type=lowerCamelCase,					help='''Maixmum number of tokens for the summaries.''',					)
						parser.add_argument(
						    '''--beam_size''',					default=5,					type=lowerCamelCase,					help='''The number of beams to start with for each example.''',					)
						parser.add_argument(
						    '''--alpha''',					default=0.95,					type=lowerCamelCase,					help='''The value of alpha for the length penalty in the beam search.''',					)
						parser.add_argument(
						    '''--block_trigram''',					default=lowerCamelCase,					type=lowerCamelCase,					help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''',					)
						__lowerCAmelCase											=					parser.parse_args()

						# Select device (distibuted not available)
						__lowerCAmelCase											=					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											=					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=lowerCamelCase)

						evaluate(lowerCamelCase)

def 				__magic_name__(    lowerCamelCase):
						if not os.path.exists(lowerCamelCase):
												return False

						__lowerCAmelCase											=					os.listdir(lowerCamelCase)
						if len(lowerCamelCase) == 0:
												return False

						return True


if __name__ == "__main__":
	main()





'''simple docstring'''

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin


class    a__					(			__A				,    unittest.TestCase   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						SpeechaTextaTokenizer
				__UpperCamelCase     :		str						=						False




				def        _snake_case	(self		):
										super().setUp()

										__lowerCAmelCase											=					'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''		)
										__lowerCAmelCase											=					['''he re</w> 123''', '''here a 1456''']
										__lowerCAmelCase											=					dict(zip(__lowercase							,      range(len(__lowercase		)		)		)		)

										__lowerCAmelCase											=					{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__lowerCAmelCase											=					tempfile.mkdtemp()
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''vocab_file''']		)
										__lowerCAmelCase											=					os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES['''merges_file''']		)

										with open(self.vocab_file							,      '''w'''							,      encoding='''utf-8'''		) as fp:
																fp.write(json.dumps(__lowercase		) + '''\n'''		)

										with open(self.merges_file							,      '''w'''		) as fp:
																fp.write('''\n'''.join(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]							,      '''<s>'''		)
										self.assertEqual(vocab_keys[1]							,      '''<pad>'''		)
										self.assertEqual(vocab_keys[-1]							,      '''vocab'''		)
										self.assertEqual(len(__lowercase		)							,      14		)




				def        _snake_case	(self		):
										self.assertEqual(self.get_tokenizer().vocab_size							,      14		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										# make sure @@ is correctly concatenated
										__lowerCAmelCase											=					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__lowerCAmelCase											=					tokenizer.decode(__lowercase		)

										self.assertTrue(output_string == '''herecouple words ofthe'''		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(__lowercase		)
																os.remove(os.path.join(__lowercase							,      '''merges.txt'''		)		)

																# load tokenizer without merges file should not throw an error
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(__lowercase		)
																__lowerCAmelCase											=					SpeechaTextaTokenizer.from_pretrained(__lowercase		)

										self.assertIsNotNone(__lowercase		)




				def        _snake_case	(self		):
										if not self.test_slow_tokenizer:
																return

										__lowerCAmelCase											=					inspect.signature(self.tokenizer_class.__init__		)
										__lowerCAmelCase											=					self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(__lowercase							,      tokenizer.init_kwargs		)

