





'''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,
)


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


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple )			->						Dict:
						UpperCAmelCase_      :   Any      =	BertTokenizer.from_pretrained('''bert-base-uncased''',      do_lower_case=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization''' )
						model.to(args.device )
						model.eval()

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

						if args.compute_rouge:
												UpperCAmelCase_      :   Union[str, Any]      =	[]
												UpperCAmelCase_      :   Optional[int]      =	[]

												import nltk
												import rouge

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

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

						UpperCAmelCase_      :   Any      =	build_data_iterator(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	build_predictor(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						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(SCREAMING_SNAKE_CASE__ ):
												UpperCAmelCase_      :   List[Any]      =	predictor.translate_batch(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Union[str, Any]      =	predictor.from_batch(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Tuple      =	[format_summary(SCREAMING_SNAKE_CASE__ ) for t in translations]
												save_summaries(SCREAMING_SNAKE_CASE__,      args.summaries_output_dir,      batch.document_names )

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

						if args.compute_rouge:
												UpperCAmelCase_      :   Union[str, Any]      =	rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Any      =	format_rouge_scores(SCREAMING_SNAKE_CASE__ )
												save_rouge_scores(SCREAMING_SNAKE_CASE__ )
												print(SCREAMING_SNAKE_CASE__ )





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

												UpperCAmelCase_      :   Any      =	os.path.join(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												with open(SCREAMING_SNAKE_CASE__,      '''w''' ) as output:
																		output.write(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Optional[int]:
						UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   int      =	translation
						UpperCAmelCase_      :   Any      =	(
						    raw_summary.replace('''[unused0]''',      '''''' )
						    .replace('''[unused3]''',      '''''' )
						    .replace('''[PAD]''',      '''''' )
						    .replace('''[unused1]''',      '''''' )
						    .replace(R''' +''',      ''' ''' )
						    .replace(''' [unused2] ''',      '''. ''' )
						    .replace('''[unused2]''',      '''''' )
						    .strip()
						)

						return summary





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str )			->						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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any )			->						Tuple:
						with open('''rouge_scores.txt''',      '''w''' ) as output:
												output.write(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[int]:
						UpperCAmelCase_      :   Dict      =	load_and_cache_examples(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	SequentialSampler(SCREAMING_SNAKE_CASE__ )

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

						UpperCAmelCase_      :   Any      =	DataLoader(
						    SCREAMING_SNAKE_CASE__,      sampler=SCREAMING_SNAKE_CASE__,      batch_size=args.batch_size,      collate_fn=SCREAMING_SNAKE_CASE__,      )

						return iterator





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						str:
						UpperCAmelCase_      :   List[Any]      =	CNNDMDataset(args.documents_dir )
						return dataset





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      int )			->						Optional[int]:
						UpperCAmelCase_      :   Optional[int]      =	[x for x in data if not len(x[1] ) == 0]  # remove empty_files
						UpperCAmelCase_      :   Optional[int]      =	[name for name, _, _ in data]
						UpperCAmelCase_      :   List[Any]      =	[''' '''.join(SCREAMING_SNAKE_CASE__ ) for _, _, summary_list in data]

						UpperCAmelCase_      :   List[Any]      =	[encode_for_summarization(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ) for _, story, summary in data]
						UpperCAmelCase_      :   List[str]      =	torch.tensor(
						    [truncate_or_pad(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      tokenizer.pad_token_id ) for story, _ in encoded_text] )
						UpperCAmelCase_      :   str      =	compute_token_type_ids(SCREAMING_SNAKE_CASE__,      tokenizer.cls_token_id )
						UpperCAmelCase_      :   str      =	build_mask(SCREAMING_SNAKE_CASE__,      tokenizer.pad_token_id )

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

						return batch





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Dict      =	summary_tokens.to('''cpu''' ).numpy()
						UpperCAmelCase_      :   Optional[int]      =	tokenizer.decode(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	summary.split('''.''' )
						UpperCAmelCase_      :   Dict      =	[s + '''.''' for s in sentences]
						return sentences





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

						# Select device (distibuted not available)
						UpperCAmelCase_      :   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:
												UpperCAmelCase_      :   Optional[Any]      =	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=SCREAMING_SNAKE_CASE__ )

						evaluate(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Optional[Any]:
						if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
												return False

						UpperCAmelCase_      :   str      =	os.listdir(SCREAMING_SNAKE_CASE__ )
						if len(SCREAMING_SNAKE_CASE__ ) == 0:
												return False

						return True


if __name__ == "__main__":
					main()












"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbartaa import MBartaaTokenizer
else:
		_snake_case	:						Dict					=					None


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[Any]					=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case	:						str					=					{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case	:						Union[str, Any]					=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
_snake_case	:						int					=					['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']
					UpperCamelCase						=				MBartaaTokenizer

					UpperCamelCase						=				[]
					UpperCamelCase						=				[]


					def __init__(   self     :Any	,			__UpperCamelCase     :str=None	,			__UpperCamelCase     :Any=None	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Any]="</s>"	,			__UpperCamelCase     :Tuple="</s>"	,			__UpperCamelCase     :Any="<s>"	,			__UpperCamelCase     :Optional[int]="<unk>"	,			__UpperCamelCase     :str="<pad>"	,			__UpperCamelCase     :int="<mask>"	,			**__UpperCamelCase     :List[str]	,			):
												# Mask token behave like a normal word, i.e. include the space before it
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else mask_token

												A						=    kwargs.get("additional_special_tokens"	,			[]						)
												kwargs["additional_special_tokens"] += [
												    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    __UpperCamelCase	,			src_lang=__UpperCamelCase	,			tgt_lang=__UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			**__UpperCamelCase	,			)

												A						=    vocab_file
												A						=    False if not self.vocab_file else True

												A						=    {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												A						=    src_lang if src_lang is not None else "en_XX"
												A						=    tgt_lang
												A						=    self.lang_code_to_id[self._src_lang]
												self.set_src_lang_special_tokens(self._src_lang						)


					@property
					def       lowerCamelCase			(   self     :int						):
												return self._src_lang


					@src_lang.setter
					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None						):
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :str = "en_XX"	,			__UpperCamelCase     :Optional[List[str]] = None	,			__UpperCamelCase     :str = "ro_RO"	,			**__UpperCamelCase     :Tuple	,			):
												A						=    src_lang
												A						=    tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												return self.set_src_lang_special_tokens(self.src_lang						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str]	,			__UpperCamelCase     :Optional[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
												A						=    src_lang
												A						=    self(__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    tgt_lang_id
												return inputs


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str] = None						):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."						)

												if not os.path.isdir(__UpperCamelCase						):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
																			return
												A						=    os.path.join(
												    __UpperCamelCase	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						):
																			copyfile(self.vocab_file	,			__UpperCamelCase						)

												return (out_vocab_file,)


