
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a__				:      List[Any]    		=     logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a__				:      Any    		=     {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a__				:      Optional[Any]    		=     {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a__				:      List[Any]    		=     sorted(arg_to_scheduler.keys())
a__				:      str    		=     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''






class     a_       (			pl.LightningModule							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=None				,       _lowerCamelCase="base"				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       _lowerCamelCase=None				,       **_lowerCamelCase				,       )					->Tuple:
           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = 0
           SCREAMING_SNAKE_CASE				:				Dict				      = Path(self.hparams.output_dir   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
               SCREAMING_SNAKE_CASE				:				Any				      = AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,       **({'''num_labels''': num_labels} if num_labels is not None else {})				,       cache_dir=_lowerCamelCase				,       **_lowerCamelCase				,       )
           else:
               SCREAMING_SNAKE_CASE				:				PretrainedConfig				      = config

           SCREAMING_SNAKE_CASE				:				str				      = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
           for p in extra_model_params:
               if getattr(self.hparams				,       _lowerCamelCase				,       _lowerCamelCase   ):
                   assert hasattr(self.config				,       _lowerCamelCase   ), F"""model config doesn't have a `{p}` attribute"""
                   setattr(self.config				,       _lowerCamelCase				,       getattr(self.hparams				,       _lowerCamelCase   )   )

           if tokenizer is None:
               SCREAMING_SNAKE_CASE				:				List[str]				      = AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,       cache_dir=_lowerCamelCase				,       )
           else:
               SCREAMING_SNAKE_CASE				:				PreTrainedTokenizer				      = tokenizer
           SCREAMING_SNAKE_CASE				:				int				      = MODEL_MODES[mode]
           if model is None:
               SCREAMING_SNAKE_CASE				:				Dict				      = self.model_type.from_pretrained(
                   self.hparams.model_name_or_path				,       from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path   )				,       config=self.config				,       cache_dir=_lowerCamelCase				,       )
           else:
               SCREAMING_SNAKE_CASE				:				Any				      = model






       def 	__lowerCAmelCase     (		self				,       *_lowerCamelCase				,       **_lowerCamelCase   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				int				      = self.model_type.from_pretrained(*_lowerCamelCase				,       **_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				str				      = arg_to_scheduler[self.hparams.lr_scheduler]
           SCREAMING_SNAKE_CASE				:				Any				      = get_schedule_func(
               self.opt				,       num_warmup_steps=self.hparams.warmup_steps				,       num_training_steps=self.total_steps()   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
           return scheduler






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Dict				      = self.model
           SCREAMING_SNAKE_CASE				:				Tuple				      = ['''bias''', '''LayerNorm.weight''']
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
               {
                   '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
                   '''weight_decay''': self.hparams.weight_decay,
               },
               {
                   '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
                   '''weight_decay''': 0.0,
               },
           ]
           if self.hparams.adafactor:
               SCREAMING_SNAKE_CASE				:				str				      = Adafactor(
                   _lowerCamelCase				,       lr=self.hparams.learning_rate				,       scale_parameter=_lowerCamelCase				,       relative_step=_lowerCamelCase   )

           else:
               SCREAMING_SNAKE_CASE				:				Dict				      = AdamW(
                   _lowerCamelCase				,       lr=self.hparams.learning_rate				,       eps=self.hparams.adam_epsilon   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = optimizer

           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_lr_scheduler()

           return [optimizer], [scheduler]






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           return self.validation_step(_lowerCamelCase				,       _lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->List[str]:
           return self.validation_end(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Tuple				      = max(1				,       self.hparams.gpus   )  # TODO: consider num_tpu_cores
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
           return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Optional[Any]:
           if mode == "test":
               SCREAMING_SNAKE_CASE				:				Tuple				      = len(self.test_dataloader().dataset   )
           else:
               SCREAMING_SNAKE_CASE				:				str				      = self.get_dataloader('''train'''				,       self.hparams.train_batch_size				,       shuffle=_lowerCamelCase   )
               SCREAMING_SNAKE_CASE				:				Optional[int]				      = len(self.train_dataloader().dataset   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase = False   )					->str:
           raise NotImplementedError('''You must implement this for your task'''   )






       def 	__lowerCAmelCase     (		self   )					->int:
           return self.train_loader






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           return self.get_dataloader('''dev'''				,       self.hparams.eval_batch_size				,       shuffle=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           return self.get_dataloader('''test'''				,       self.hparams.eval_batch_size				,       shuffle=_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->Any:
           return os.path.join(
               self.hparams.data_dir				,       '''cached_{}_{}_{}'''.format(
                   _lowerCamelCase				,       list(filter(_lowerCamelCase				,       self.hparams.model_name_or_path.split('''/'''   )   )   ).pop()				,       str(self.hparams.max_seq_length   )				,       )				,       )






       @pl.utilities.rank_zero_only
       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase   )					->None:
           SCREAMING_SNAKE_CASE				:				Dict				      = self.output_dir.joinpath('''best_tfmr'''   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.step_count
           self.model.save_pretrained(_lowerCamelCase   )
           self.tokenizer.save_pretrained(_lowerCamelCase   )






       @staticmethod
       def 	__lowerCAmelCase     (		_lowerCamelCase				,       _lowerCamelCase   )					->str:
           parser.add_argument(
               '''--model_name_or_path'''				,       default=_lowerCamelCase				,       type=_lowerCamelCase				,       required=_lowerCamelCase				,       help='''Path to pretrained model or model identifier from huggingface.co/models'''				,       )
           parser.add_argument(
               '''--config_name'''				,       default=''''''				,       type=_lowerCamelCase				,       help='''Pretrained config name or path if not the same as model_name'''   )
           parser.add_argument(
               '''--tokenizer_name'''				,       default=_lowerCamelCase				,       type=_lowerCamelCase				,       help='''Pretrained tokenizer name or path if not the same as model_name'''				,       )
           parser.add_argument(
               '''--cache_dir'''				,       default=''''''				,       type=_lowerCamelCase				,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''				,       )
           parser.add_argument(
               '''--encoder_layerdrop'''				,       type=_lowerCamelCase				,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''				,       )
           parser.add_argument(
               '''--decoder_layerdrop'''				,       type=_lowerCamelCase				,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''				,       )
           parser.add_argument(
               '''--dropout'''				,       type=_lowerCamelCase				,       help='''Dropout probability (Optional). Goes into model.config'''				,       )
           parser.add_argument(
               '''--attention_dropout'''				,       type=_lowerCamelCase				,       help='''Attention dropout probability (Optional). Goes into model.config'''				,       )
           parser.add_argument('''--learning_rate'''				,       default=5e-5				,       type=_lowerCamelCase				,       help='''The initial learning rate for Adam.'''   )
           parser.add_argument(
               '''--lr_scheduler'''				,       default='''linear'''				,       choices=_lowerCamelCase				,       metavar=_lowerCamelCase				,       type=_lowerCamelCase				,       help='''Learning rate scheduler'''				,       )
           parser.add_argument('''--weight_decay'''				,       default=0.0				,       type=_lowerCamelCase				,       help='''Weight decay if we apply some.'''   )
           parser.add_argument('''--adam_epsilon'''				,       default=1e-8				,       type=_lowerCamelCase				,       help='''Epsilon for Adam optimizer.'''   )
           parser.add_argument('''--warmup_steps'''				,       default=0				,       type=_lowerCamelCase				,       help='''Linear warmup over warmup_steps.'''   )
           parser.add_argument('''--num_workers'''				,       default=4				,       type=_lowerCamelCase				,       help='''kwarg passed to DataLoader'''   )
           parser.add_argument('''--num_train_epochs'''				,       dest='''max_epochs'''				,       default=3				,       type=_lowerCamelCase   )
           parser.add_argument('''--train_batch_size'''				,       default=32				,       type=_lowerCamelCase   )
           parser.add_argument('''--eval_batch_size'''				,       default=32				,       type=_lowerCamelCase   )
           parser.add_argument('''--adafactor'''				,       action='''store_true'''   )






class     a_       (			pl.Callback							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = trainer.lr_schedulers[0]['''scheduler''']
           SCREAMING_SNAKE_CASE				:				List[str]				      = {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
           pl_module.logger.log_metrics(_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Any:
           rank_zero_info('''***** Validation results *****'''   )
           SCREAMING_SNAKE_CASE				:				str				      = trainer.callback_metrics
           # Log results
           for key in sorted(_lowerCamelCase   ):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info('''{} = {}\n'''.format(_lowerCamelCase				,       str(metrics[key]   )   )   )






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase   )					->Optional[int]:
           rank_zero_info('''***** Test results *****'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = trainer.callback_metrics
           # Log and save results to file
           SCREAMING_SNAKE_CASE				:				int				      = os.path.join(pl_module.hparams.output_dir				,       '''test_results.txt'''   )
           with open(_lowerCamelCase				,       '''w'''   ) as writer:
               for key in sorted(_lowerCamelCase   ):
                   if key not in ["log", "progress_bar"]:
                       rank_zero_info('''{} = {}\n'''.format(_lowerCamelCase				,       str(metrics[key]   )   )   )
                       writer.write('''{} = {}\n'''.format(_lowerCamelCase				,       str(metrics[key]   )   )   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    parser.add_argument(
        '''--output_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The output directory where the model predictions and checkpoints will be written.''' ,						)
    parser.add_argument(
        '''--fp16''' ,						action='''store_true''' ,						help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' ,						)

    parser.add_argument(
        '''--fp16_opt_level''' ,						type=a__ ,						default='''O2''' ,						help=(
            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
            '''See details at https://nvidia.github.io/apex/amp.html'''
        ) ,						)
    parser.add_argument('''--n_tpu_cores''' ,						dest='''tpu_cores''' ,						type=a__   )
    parser.add_argument('''--max_grad_norm''' ,						dest='''gradient_clip_val''' ,						default=1.0 ,						type=a__ ,						help='''Max gradient norm'''   )
    parser.add_argument('''--do_train''' ,						action='''store_true''' ,						help='''Whether to run training.'''   )
    parser.add_argument('''--do_predict''' ,						action='''store_true''' ,						help='''Whether to run predictions on the test set.'''   )
    parser.add_argument(
        '''--gradient_accumulation_steps''' ,						dest='''accumulate_grad_batches''' ,						type=a__ ,						default=1 ,						help='''Number of updates steps to accumulate before performing a backward/update pass.''' ,						)
    parser.add_argument('''--seed''' ,						type=a__ ,						default=42 ,						help='''random seed for initialization'''   )
    parser.add_argument(
        '''--data_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' ,						)







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__=None ,						a__=True ,						a__=[] ,						a__=None ,						a__=None ,						**a__ ,						):



    """simple docstring"""



    pl.seed_everything(args.seed   )

    # init model
    SCREAMING_SNAKE_CASE				:				Optional[int]				      = Path(model.hparams.output_dir   )
    odir.mkdir(exist_ok=a__   )

    # add custom checkpoints
    if checkpoint_callback is None:
        SCREAMING_SNAKE_CASE				:				Tuple				      = pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir ,						prefix='''checkpoint''' ,						monitor='''val_loss''' ,						mode='''min''' ,						save_top_k=1   )
    if early_stopping_callback:
        extra_callbacks.append(a__   )
    if logging_callback is None:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = LoggingCallback()

    SCREAMING_SNAKE_CASE				:				Tuple				      = {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        SCREAMING_SNAKE_CASE				:				Any				      = 16
        SCREAMING_SNAKE_CASE				:				Tuple				      = args.fpaa_opt_level

    if args.gpus > 1:
        SCREAMING_SNAKE_CASE				:				str				      = '''ddp'''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = args.accumulate_grad_batches
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = extra_train_kwargs.get('''accelerator''' ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = extra_train_kwargs.get('''profiler''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				List[str]				      = pl.Trainer.from_argparse_args(
        a__ ,						weights_summary=a__ ,						callbacks=[logging_callback] + extra_callbacks ,						logger=a__ ,						checkpoint_callback=a__ ,						**a__ ,						)

    if args.do_train:
        trainer.fit(a__   )

    return trainer


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


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

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

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = 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.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				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__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[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:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



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

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        raw_summary.replace('''[unused0]''' ,						''''''   )
        .replace('''[unused3]''' ,						''''''   )
        .replace('''[PAD]''' ,						''''''   )
        .replace('''[unused1]''' ,						''''''   )
        .replace(r''' +''' ,						''' '''   )
        .replace(''' [unused2] ''' ,						'''. '''   )
        .replace('''[unused2]''' ,						''''''   )
        .strip()
    )

    return summary







def 				UpperCAmelCase_(    a__   ):



    """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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

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

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



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

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				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 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = 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.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[Any]				      = 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:
        SCREAMING_SNAKE_CASE				:				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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
