





"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


lowerCamelCase_       =		logging.getLogger(__name__)






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  '''summarization'''
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  ['''loss''']
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  ROUGE_KEYS
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  '''rouge2'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             if hparams.sortish_sampler and hparams.gpus > 1:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  False
             elif hparams.max_tokens_per_batch is not None:
                   if hparams.gpus > 1:
                         raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''							)
                   if hparams.sortish_sampler:
                         raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''							)

             super().__init__(SCREAMING_SNAKE_CASE__		,num_labels=SCREAMING_SNAKE_CASE__		,mode=self.mode		,**SCREAMING_SNAKE_CASE__							)
             use_task_specific_params(self.model		,'''summarization'''							)
             save_git_info(self.hparams.output_dir							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  Path(self.output_dir							) / '''metrics.json'''
             __SCREAMING_SNAKE_CASE       :Any		    =  Path(self.output_dir							) / '''hparams.pkl'''
             pickle_save(self.hparams		,self.hparams_save_path							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  0
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  defaultdict(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.config.model_type
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

             __SCREAMING_SNAKE_CASE       :dict		    =  {
                 "data_dir": self.hparams.data_dir,
                 "max_source_length": self.hparams.max_source_length,
                 "prefix": self.model.config.prefix or "",
             }
             __SCREAMING_SNAKE_CASE       :List[str]		    =  {
                 '''train''': self.hparams.n_train,
                 '''val''': self.hparams.n_val,
                 '''test''': self.hparams.n_test,
             }
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {
                 '''train''': self.hparams.max_target_length,
                 '''val''': self.hparams.val_max_target_length,
                 '''test''': self.hparams.test_max_target_length,
             }
             assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
             assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
             if self.hparams.freeze_embeds:
                   freeze_embeds(self.model							)
             if self.hparams.freeze_encoder:
                   freeze_params(self.model.get_encoder()							)
                   assert_all_frozen(self.model.get_encoder()							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  get_git_info()['''repo_sha''']
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hparams.num_workers
             __SCREAMING_SNAKE_CASE       :Dict		    =  None  # default to config
             if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                   __SCREAMING_SNAKE_CASE       :int		    =  self.decoder_start_token_id
             __SCREAMING_SNAKE_CASE       :List[str]		    =  (
                 SeqaSeqDataset if hasattr(self.tokenizer		,'''prepare_seq2seq_batch'''							) else LegacySeqaSeqDataset
             )
             __SCREAMING_SNAKE_CASE       :Any		    =  False
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
             if self.hparams.eval_max_gen_length is not None:
                   __SCREAMING_SNAKE_CASE       :str		    =  self.hparams.eval_max_gen_length
             else:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model.config.max_length
             __SCREAMING_SNAKE_CASE       :Any		    =  self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Dict[str, List[str]]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  {
                 k: self.tokenizer.batch_decode(v.tolist()							) if '''mask''' not in k else v.shape for k, v in batch.items()
             }
             save_json(SCREAMING_SNAKE_CASE__		,Path(self.output_dir							) / '''text_batch.json'''							)
             save_json({k: v.tolist() for k, v in batch.items()}		,Path(self.output_dir							) / '''tok_batch.json'''							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  True
             return readable_batch




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             return self.model(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.tokenizer.batch_decode(
                 SCREAMING_SNAKE_CASE__		,skip_special_tokens=SCREAMING_SNAKE_CASE__		,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)
             return lmap(str.strip		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.tokenizer.pad_token_id
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  batch['''input_ids'''], batch['''attention_mask''']
             __SCREAMING_SNAKE_CASE       :List[str]		    =  batch['''labels''']
             if isinstance(self.model		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model._shift_right(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  shift_tokens_right(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_input_ids
                   self.save_readable_batch(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  self(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,decoder_input_ids=SCREAMING_SNAKE_CASE__		,use_cache=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  outputs['''logits''']
             if self.hparams.label_smoothing == 0:
                   # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                   __SCREAMING_SNAKE_CASE       :Any		    =  nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__							)

                   assert lm_logits.shape[-1] == self.vocab_size
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ce_loss_fct(lm_logits.view(-1		,lm_logits.shape[-1]							)		,tgt_ids.view(-1							)							)
             else:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  nn.functional.log_softmax(SCREAMING_SNAKE_CASE__		,dim=-1							)
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  label_smoothed_nll_loss(
                       SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.hparams.label_smoothing		,ignore_index=SCREAMING_SNAKE_CASE__							)
             return (loss,)




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             return self.tokenizer.pad_token_id




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self._step(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  dict(zip(self.loss_names		,SCREAMING_SNAKE_CASE__							)							)
             # tokens per batch
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  batch['''input_ids'''].ne(self.pad							).sum() + batch['''labels'''].ne(self.pad							).sum()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  batch['''input_ids'''].shape[0]
             __SCREAMING_SNAKE_CASE       :Dict		    =  batch['''input_ids'''].eq(self.pad							).sum()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  batch['''input_ids'''].eq(self.pad							).float().mean()
             # TODO(SS): make a wandb summary metric for this
             return {"loss": loss_tensors[0], "log": logs}




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             return self._generative_step(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="val"							)      ->				Dict:





             """simple docstring"""






             self.step_count += 1
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  losses['''loss''']
             __SCREAMING_SNAKE_CASE       :Dict		    =  {
                 k: np.array([x[k] for x in outputs]							).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
             }
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  (
                 generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
             )
             __SCREAMING_SNAKE_CASE       :torch.FloatTensor		    =  torch.tensor(SCREAMING_SNAKE_CASE__							).type_as(SCREAMING_SNAKE_CASE__							)
             generative_metrics.update({k: v.item() for k, v in losses.items()}							)
             losses.update(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :str		    =  {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
             __SCREAMING_SNAKE_CASE       :str		    =  self.step_count
             self.metrics[prefix].append(SCREAMING_SNAKE_CASE__							)  # callback writes this to self.metrics_save_path
             __SCREAMING_SNAKE_CASE       :str		    =  flatten_list([x['''preds'''] for x in outputs]							)
             return {
                 "log": all_metrics,
                 "preds": preds,
                 f'''{prefix}_loss''': loss,
                 f'''{prefix}_{self.val_metric}''': metric_tensor,
             }




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             return calculate_rouge(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  time.time()

             # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.model.generate(
                 batch['''input_ids''']		,attention_mask=batch['''attention_mask''']		,use_cache=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=self.decoder_start_token_id		,num_beams=self.eval_beams		,max_length=self.eval_max_length		,)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  (time.time() - ta) / batch['''input_ids'''].shape[0]
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.ids_to_clean_text(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.ids_to_clean_text(batch['''labels''']							)
             __SCREAMING_SNAKE_CASE       :int		    =  self._step(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  dict(zip(self.loss_names		,SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.calc_generative_metrics(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  np.mean(lmap(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)							)
             base_metrics.update(gen_time=SCREAMING_SNAKE_CASE__		,gen_len=SCREAMING_SNAKE_CASE__		,preds=SCREAMING_SNAKE_CASE__		,target=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             return base_metrics




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             return self._generative_step(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             return self.validation_epoch_end(SCREAMING_SNAKE_CASE__		,prefix='''test'''							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				SeqaSeqDataset:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.n_obs[type_path]
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.target_lens[type_path]
             __SCREAMING_SNAKE_CASE       :int		    =  self.dataset_class(
                 self.tokenizer		,type_path=SCREAMING_SNAKE_CASE__		,n_obs=SCREAMING_SNAKE_CASE__		,max_target_length=SCREAMING_SNAKE_CASE__		,**self.dataset_kwargs		,)
             return dataset




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = False							)      ->				DataLoader:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.get_dataset(SCREAMING_SNAKE_CASE__							)

             if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE__		,distributed=self.hparams.gpus > 1							)
                   return DataLoader(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,collate_fn=dataset.collate_fn		,shuffle=SCREAMING_SNAKE_CASE__		,num_workers=self.num_workers		,sampler=SCREAMING_SNAKE_CASE__		,)

             elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  dataset.make_dynamic_sampler(
                       self.hparams.max_tokens_per_batch		,distributed=self.hparams.gpus > 1							)
                   return DataLoader(
                       SCREAMING_SNAKE_CASE__		,batch_sampler=SCREAMING_SNAKE_CASE__		,collate_fn=dataset.collate_fn		,num_workers=self.num_workers		,)
             else:
                   return DataLoader(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,collate_fn=dataset.collate_fn		,shuffle=SCREAMING_SNAKE_CASE__		,num_workers=self.num_workers		,sampler=SCREAMING_SNAKE_CASE__		,)




       def    _UpperCamelCase					(						self							)      ->				DataLoader:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.get_dataloader('''train'''		,batch_size=self.hparams.train_batch_size		,shuffle=SCREAMING_SNAKE_CASE__							)
             return dataloader




       def    _UpperCamelCase					(						self							)      ->				DataLoader:





             """simple docstring"""






             return self.get_dataloader('''val'''		,batch_size=self.hparams.eval_batch_size							)




       def    _UpperCamelCase					(						self							)      ->				DataLoader:





             """simple docstring"""






             return self.get_dataloader('''test'''		,batch_size=self.hparams.eval_batch_size							)




       @staticmethod
       def    _UpperCamelCase					(						SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             add_generic_args(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             parser.add_argument(
                 '''--max_source_length'''		,default=10_24		,type=SCREAMING_SNAKE_CASE__		,help=(
                     '''The maximum total input sequence length after tokenization. Sequences longer '''
                     '''than this will be truncated, sequences shorter will be padded.'''
                 )		,)
             parser.add_argument(
                 '''--max_target_length'''		,default=56		,type=SCREAMING_SNAKE_CASE__		,help=(
                     '''The maximum total input sequence length after tokenization. Sequences longer '''
                     '''than this will be truncated, sequences shorter will be padded.'''
                 )		,)
             parser.add_argument(
                 '''--val_max_target_length'''		,default=1_42		,type=SCREAMING_SNAKE_CASE__		,help=(
                     '''The maximum total input sequence length after tokenization. Sequences longer '''
                     '''than this will be truncated, sequences shorter will be padded.'''
                 )		,)
             parser.add_argument(
                 '''--test_max_target_length'''		,default=1_42		,type=SCREAMING_SNAKE_CASE__		,help=(
                     '''The maximum total input sequence length after tokenization. Sequences longer '''
                     '''than this will be truncated, sequences shorter will be padded.'''
                 )		,)
             parser.add_argument('''--freeze_encoder'''		,action='''store_true'''							)
             parser.add_argument('''--freeze_embeds'''		,action='''store_true'''							)
             parser.add_argument('''--sortish_sampler'''		,action='''store_true'''		,default=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--overwrite_output_dir'''		,action='''store_true'''		,default=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--max_tokens_per_batch'''		,type=SCREAMING_SNAKE_CASE__		,default=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--logger_name'''		,type=SCREAMING_SNAKE_CASE__		,choices=['''default''', '''wandb''', '''wandb_shared''']		,default='''default'''							)
             parser.add_argument('''--n_train'''		,type=SCREAMING_SNAKE_CASE__		,default=-1		,required=SCREAMING_SNAKE_CASE__		,help='''# examples. -1 means use all.'''							)
             parser.add_argument('''--n_val'''		,type=SCREAMING_SNAKE_CASE__		,default=5_00		,required=SCREAMING_SNAKE_CASE__		,help='''# examples. -1 means use all.'''							)
             parser.add_argument('''--n_test'''		,type=SCREAMING_SNAKE_CASE__		,default=-1		,required=SCREAMING_SNAKE_CASE__		,help='''# examples. -1 means use all.'''							)
             parser.add_argument(
                 '''--task'''		,type=SCREAMING_SNAKE_CASE__		,default='''summarization'''		,required=SCREAMING_SNAKE_CASE__		,help='''# examples. -1 means use all.'''							)
             parser.add_argument('''--label_smoothing'''		,type=SCREAMING_SNAKE_CASE__		,default=0.0		,required=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--src_lang'''		,type=SCREAMING_SNAKE_CASE__		,default=''''''		,required=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--tgt_lang'''		,type=SCREAMING_SNAKE_CASE__		,default=''''''		,required=SCREAMING_SNAKE_CASE__							)
             parser.add_argument('''--eval_beams'''		,type=SCREAMING_SNAKE_CASE__		,default=SCREAMING_SNAKE_CASE__		,required=SCREAMING_SNAKE_CASE__							)
             parser.add_argument(
                 '''--val_metric'''		,type=SCREAMING_SNAKE_CASE__		,default=SCREAMING_SNAKE_CASE__		,required=SCREAMING_SNAKE_CASE__		,choices=['''bleu''', '''rouge2''', '''loss''', None]							)
             parser.add_argument('''--eval_max_gen_length'''		,type=SCREAMING_SNAKE_CASE__		,default=SCREAMING_SNAKE_CASE__		,help='''never generate more than n tokens'''							)
             parser.add_argument('''--save_top_k'''		,type=SCREAMING_SNAKE_CASE__		,default=1		,required=SCREAMING_SNAKE_CASE__		,help='''How many checkpoints to save'''							)
             parser.add_argument(
                 '''--early_stopping_patience'''		,type=SCREAMING_SNAKE_CASE__		,default=-1		,required=SCREAMING_SNAKE_CASE__		,help=(
                     '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
                     ''' val_check_interval will effect it.'''
                 )		,)
             return parser






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  '''translation'''
       SCREAMING_SNAKE_CASE_       :				Dict     						=  ['''loss''']
       SCREAMING_SNAKE_CASE_       :				Any     						=  ['''bleu''']
       SCREAMING_SNAKE_CASE_       :				int     						=  '''bleu'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             super().__init__(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  hparams.src_lang
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hparams.tgt_lang




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				dict:





             """simple docstring"""






             return calculate_bleu(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Any=None							)      ->				SummarizationModule:
      Path(args.output_dir							).mkdir(exist_ok=a_							)
      check_output_dir(a_			,       expected_items=3							)

      if model is None:
            if "summarization" in args.task:
                  __SCREAMING_SNAKE_CASE       :SummarizationModule		    =  SummarizationModule(a_							)
            else:
                  __SCREAMING_SNAKE_CASE       :SummarizationModule		    =  TranslationModule(a_							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  Path(args.data_dir							).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir							).startswith('''/tmp'''							)
          or str(args.output_dir							).startswith('''/var'''							)
      ):
            __SCREAMING_SNAKE_CASE       :int		    =  True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            __SCREAMING_SNAKE_CASE       :str		    =  os.environ.get('''WANDB_PROJECT'''			,       a_							)
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  WandbLogger(name=model.output_dir.name			,       project=a_							)

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            __SCREAMING_SNAKE_CASE       :str		    =  WandbLogger(name=model.output_dir.name			,       project=f'''hf_{dataset}'''							)

      if args.early_stopping_patience >= 0:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  get_early_stopping_callback(model.val_metric			,       args.early_stopping_patience							)
      else:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  False

      __SCREAMING_SNAKE_CASE       :str		    =  args.val_metric == '''loss'''
      __SCREAMING_SNAKE_CASE       :pl.Trainer		    =  generic_train(
          a_			,       a_			,       logging_callback=SeqaSeqLoggingCallback()			,       checkpoint_callback=get_checkpoint_callback(
              args.output_dir			,       model.val_metric			,       args.save_top_k			,       a_							)			,       early_stopping_callback=a_			,       logger=a_			,       )
      pickle_save(model.hparams			,       model.output_dir / '''hparams.pkl'''							)
      if not args.do_predict:
            return model

      __SCREAMING_SNAKE_CASE       :List[Any]		    =  ''''''
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  sorted(glob.glob(os.path.join(args.output_dir			,       '''*.ckpt'''							)			,       recursive=a_							)							)
      if checkpoints:
            __SCREAMING_SNAKE_CASE       :Any		    =  checkpoints[-1]
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams							)

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
  lowerCamelCase_       =		argparse.ArgumentParser()
  lowerCamelCase_       =		pl.Trainer.add_argparse_args(parser)
  lowerCamelCase_       =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

  lowerCamelCase_       =		parser.parse_args()

  main(args)






"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  '''bart'''
       SCREAMING_SNAKE_CASE_       :				str     						=  ['''past_key_values''']
       SCREAMING_SNAKE_CASE_       :				str     						=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




       def __init__(						self		,SCREAMING_SNAKE_CASE__=5_02_65		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=10_24		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=2		,**SCREAMING_SNAKE_CASE__		,)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  max_position_embeddings
             __SCREAMING_SNAKE_CASE       :Any		    =  d_model
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  encoder_attention_heads
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  decoder_ffn_dim
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layers
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  decoder_attention_heads
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  dropout
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  attention_dropout
             __SCREAMING_SNAKE_CASE       :Dict		    =  activation_dropout
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  activation_function
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  init_std
             __SCREAMING_SNAKE_CASE       :int		    =  encoder_layerdrop
             __SCREAMING_SNAKE_CASE       :Any		    =  decoder_layerdrop
             __SCREAMING_SNAKE_CASE       :str		    =  classifier_dropout
             __SCREAMING_SNAKE_CASE       :List[str]		    =  use_cache
             __SCREAMING_SNAKE_CASE       :List[str]		    =  encoder_layers
             __SCREAMING_SNAKE_CASE       :Tuple		    =  scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 num_labels=SCREAMING_SNAKE_CASE__		,pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,is_encoder_decoder=SCREAMING_SNAKE_CASE__		,decoder_start_token_id=SCREAMING_SNAKE_CASE__		,forced_eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)

             # ensure backward compatibility for BART CNN models
             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  self.bos_token_id
                   warnings.warn(
                       f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                       '''The config can simply be saved and uploaded again to be fixed.'''							)






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ]							)

                   if self.use_past:
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch'''}
                         __SCREAMING_SNAKE_CASE       :int		    =  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                   else:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  {0: '''batch''', 1: '''decoder_sequence'''}
                         __SCREAMING_SNAKE_CASE       :Any		    =  {0: '''batch''', 1: '''decoder_sequence'''}

                   if self.use_past:
                         self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__		,direction='''inputs'''							)
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ]							)
                   if self.use_past:
                         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  self.num_layers
                         for i in range(SCREAMING_SNAKE_CASE__							):
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                               __SCREAMING_SNAKE_CASE       :List[Any]		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                           ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                           ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ]							)

             return common_inputs




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :str		    =  super().outputs
             else:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  super(SCREAMING_SNAKE_CASE__		,self							).outputs
                   if self.use_past:
                         __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  self.num_layers
                         for i in range(SCREAMING_SNAKE_CASE__							):
                               __SCREAMING_SNAKE_CASE       :str		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
                               __SCREAMING_SNAKE_CASE       :Any		    =  {0: '''batch''', 2: '''past_sequence + sequence'''}
             return common_outputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # Generate decoder inputs
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  seq_length if not self.use_past else 1
             __SCREAMING_SNAKE_CASE       :Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             __SCREAMING_SNAKE_CASE       :Any		    =  dict(**SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
                   else:
                         import torch
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  common_inputs['''input_ids'''].shape
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  common_inputs['''decoder_input_ids'''].shape[1]
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.num_attention_heads
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  (
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  decoder_seq_length + 3
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  (
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.cat(
                       [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)]		,dim=1							)

                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  []
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  self.num_layers
                   __SCREAMING_SNAKE_CASE       :int		    =  min(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE       :Any		    =  max(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) - min_num_layers
                   __SCREAMING_SNAKE_CASE       :int		    =  '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                   for _ in range(SCREAMING_SNAKE_CASE__							):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                                 torch.zeros(SCREAMING_SNAKE_CASE__							),
                             )							)
                   # TODO: test this.
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                   for _ in range(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                         common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__							), torch.zeros(SCREAMING_SNAKE_CASE__							))							)
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
                   else:
                         import torch
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :str		    =  common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  seqlen + 2
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.num_layers
                   __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :int		    =  self.num_attention_heads
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  (
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  common_inputs['''attention_mask'''].dtype
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.cat(
                       [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,dtype=SCREAMING_SNAKE_CASE__							)]		,dim=1							)
                   __SCREAMING_SNAKE_CASE       :str		    =  [
                       (torch.zeros(SCREAMING_SNAKE_CASE__							), torch.zeros(SCREAMING_SNAKE_CASE__							)) for _ in range(SCREAMING_SNAKE_CASE__							)
                   ]
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_batch		,num_token_to_add=0							)

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__		,fixed_dimension=OnnxConfig.default_fixed_sequence		,num_token_to_add=SCREAMING_SNAKE_CASE__							)

             # Generate dummy inputs according to compute batch and sequence
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
             __SCREAMING_SNAKE_CASE       :str		    =  dict(tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)							)
             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = -1		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :str		    =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)

             elif self.task == "causal-lm":
                   __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_causal_lm(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :int		    =  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,seq_length=SCREAMING_SNAKE_CASE__		,is_pair=SCREAMING_SNAKE_CASE__		,framework=SCREAMING_SNAKE_CASE__							)

             return common_inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             if self.task in ["default", "seq2seq-lm"]:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  super(SCREAMING_SNAKE_CASE__		,self							)._flatten_past_key_values_(
                       SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)