





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


UpperCAmelCase_           =	logging.getLogger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[Any]							  =   """summarization"""
     __A	:  Union[str, Any]							  =   ["""loss"""]
     __A	:  Any							  =   ROUGE_KEYS
     __A	:  Any							  =   """rouge2"""





     def __init__(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        if hparams.sortish_sampler and hparams.gpus > 1:
           _UpperCAmelCase           =   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__(_UpperCamelCase				,							num_labels=_UpperCamelCase				,							mode=self.mode				,							**_UpperCamelCase					)
        use_task_specific_params(self.model				,							'''summarization'''					)
        save_git_info(self.hparams.output_dir					)
        _UpperCAmelCase           =   Path(self.output_dir					) / '''metrics.json'''
        _UpperCAmelCase           =   Path(self.output_dir					) / '''hparams.pkl'''
        pickle_save(self.hparams				,							self.hparams_save_path					)
        _UpperCAmelCase           =   0
        _UpperCAmelCase           =   defaultdict(_UpperCamelCase					)
        _UpperCAmelCase           =   self.config.model_type
        _UpperCAmelCase           =   self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

        _UpperCAmelCase           =   {
            "data_dir": self.hparams.data_dir,
            "max_source_length": self.hparams.max_source_length,
            "prefix": self.model.config.prefix or "",
        }
        _UpperCAmelCase           =   {
            '''train''': self.hparams.n_train,
            '''val''': self.hparams.n_val,
            '''test''': self.hparams.n_test,
        }
        _UpperCAmelCase           =   {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

        _UpperCAmelCase           =   {
            '''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()					)

        _UpperCAmelCase           =   get_git_info()['''repo_sha''']
        _UpperCAmelCase           =   hparams.num_workers
        _UpperCAmelCase           =   None  # default to config
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,							_UpperCamelCase					):
           _UpperCAmelCase           =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
           _UpperCAmelCase           =   self.decoder_start_token_id
        _UpperCAmelCase           =   (
            SeqaSeqDataset if hasattr(self.tokenizer				,							'''prepare_seq2seq_batch'''					) else LegacySeqaSeqDataset
        )
        _UpperCAmelCase           =   False
        _UpperCAmelCase           =   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:
           _UpperCAmelCase           =   self.hparams.eval_max_gen_length
        else:
           _UpperCAmelCase           =   self.model.config.max_length
        _UpperCAmelCase           =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   {
            k: self.tokenizer.batch_decode(v.tolist()					) if '''mask''' not in k else v.shape for k, v in batch.items()
        }
        save_json(_UpperCamelCase				,							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'''					)

        _UpperCAmelCase           =   True
        return readable_batch





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        return self.model(_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.tokenizer.batch_decode(
            _UpperCamelCase				,							skip_special_tokens=_UpperCamelCase				,							clean_up_tokenization_spaces=_UpperCamelCase					)
        return lmap(str.strip				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.tokenizer.pad_token_id
        _UpperCAmelCase       ,      _UpperCAmelCase           =   batch['''input_ids'''], batch['''attention_mask''']
        _UpperCAmelCase           =   batch['''labels''']
        if isinstance(self.model				,							_UpperCamelCase					):
           _UpperCAmelCase           =   self.model._shift_right(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   shift_tokens_right(_UpperCamelCase				,							_UpperCamelCase					)
        if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
           _UpperCAmelCase           =   decoder_input_ids
           self.save_readable_batch(_UpperCamelCase					)

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

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





     @property
     def 		UpperCamelCase(     self					):
        return self.tokenizer.pad_token_id





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self._step(_UpperCamelCase					)

        _UpperCAmelCase           =   dict(zip(self.loss_names				,							_UpperCamelCase					)					)
        # tokens per batch
        _UpperCAmelCase           =   batch['''input_ids'''].ne(self.pad					).sum() + batch['''labels'''].ne(self.pad					).sum()
        _UpperCAmelCase           =   batch['''input_ids'''].shape[0]
        _UpperCAmelCase           =   batch['''input_ids'''].eq(self.pad					).sum()
        _UpperCAmelCase           =   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				,							_UpperCamelCase				,							_UpperCamelCase					):
        return self._generative_step(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase="val"					):
        self.step_count += 1
        _UpperCAmelCase           =   {k: torch.stack([x[k] for x in outputs]					).mean() for k in self.loss_names}
        _UpperCAmelCase           =   losses['''loss''']
        _UpperCAmelCase           =   {
            k: np.array([x[k] for x in outputs]					).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
        }
        _UpperCAmelCase           =   (
            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
        )
        _UpperCAmelCase           =   torch.tensor(_UpperCamelCase					).type_as(_UpperCamelCase					)
        generative_metrics.update({k: v.item() for k, v in losses.items()}					)
        losses.update(_UpperCamelCase					)
        _UpperCAmelCase           =   {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
        _UpperCAmelCase           =   self.step_count
        self.metrics[prefix].append(_UpperCamelCase					)  # callback writes this to self.metrics_save_path
        _UpperCAmelCase           =   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				,							_UpperCamelCase				,							_UpperCamelCase					):
        return calculate_rouge(_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   time.time()

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        return self._generative_step(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        return self.validation_epoch_end(_UpperCamelCase				,							prefix='''test'''					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.n_obs[type_path]
        _UpperCAmelCase           =   self.target_lens[type_path]
        _UpperCAmelCase           =   self.dataset_class(
            self.tokenizer				,							type_path=_UpperCamelCase				,							n_obs=_UpperCamelCase				,							max_target_length=_UpperCamelCase				,							**self.dataset_kwargs				,							)
        return dataset





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = False					):
        _UpperCAmelCase           =   self.get_dataset(_UpperCamelCase					)

        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           _UpperCAmelCase           =   dataset.make_sortish_sampler(_UpperCamelCase				,							distributed=self.hparams.gpus > 1					)
           return DataLoader(
               _UpperCamelCase				,							batch_size=_UpperCamelCase				,							collate_fn=dataset.collate_fn				,							shuffle=_UpperCamelCase				,							num_workers=self.num_workers				,							sampler=_UpperCamelCase				,							)

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_dataloader('''train'''				,							batch_size=self.hparams.train_batch_size				,							shuffle=_UpperCamelCase					)
        return dataloader





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





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





     @staticmethod
     def 		UpperCamelCase(     _UpperCamelCase				,							_UpperCamelCase					):
        BaseTransformer.add_model_specific_args(_UpperCamelCase				,							_UpperCamelCase					)
        add_generic_args(_UpperCamelCase				,							_UpperCamelCase					)
        parser.add_argument(
            '''--max_source_length'''				,							default=1024				,							type=_UpperCamelCase				,							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=_UpperCamelCase				,							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=142				,							type=_UpperCamelCase				,							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=142				,							type=_UpperCamelCase				,							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=_UpperCamelCase					)
        parser.add_argument('''--overwrite_output_dir'''				,							action='''store_true'''				,							default=_UpperCamelCase					)
        parser.add_argument('''--max_tokens_per_batch'''				,							type=_UpperCamelCase				,							default=_UpperCamelCase					)
        parser.add_argument('''--logger_name'''				,							type=_UpperCamelCase				,							choices=['''default''', '''wandb''', '''wandb_shared''']				,							default='''default'''					)
        parser.add_argument('''--n_train'''				,							type=_UpperCamelCase				,							default=-1				,							required=_UpperCamelCase				,							help='''# examples. -1 means use all.'''					)
        parser.add_argument('''--n_val'''				,							type=_UpperCamelCase				,							default=500				,							required=_UpperCamelCase				,							help='''# examples. -1 means use all.'''					)
        parser.add_argument('''--n_test'''				,							type=_UpperCamelCase				,							default=-1				,							required=_UpperCamelCase				,							help='''# examples. -1 means use all.'''					)
        parser.add_argument(
            '''--task'''				,							type=_UpperCamelCase				,							default='''summarization'''				,							required=_UpperCamelCase				,							help='''# examples. -1 means use all.'''					)
        parser.add_argument('''--label_smoothing'''				,							type=_UpperCamelCase				,							default=0.0				,							required=_UpperCamelCase					)
        parser.add_argument('''--src_lang'''				,							type=_UpperCamelCase				,							default=''''''				,							required=_UpperCamelCase					)
        parser.add_argument('''--tgt_lang'''				,							type=_UpperCamelCase				,							default=''''''				,							required=_UpperCamelCase					)
        parser.add_argument('''--eval_beams'''				,							type=_UpperCamelCase				,							default=_UpperCamelCase				,							required=_UpperCamelCase					)
        parser.add_argument(
            '''--val_metric'''				,							type=_UpperCamelCase				,							default=_UpperCamelCase				,							required=_UpperCamelCase				,							choices=['''bleu''', '''rouge2''', '''loss''', None]					)
        parser.add_argument('''--eval_max_gen_length'''				,							type=_UpperCamelCase				,							default=_UpperCamelCase				,							help='''never generate more than n tokens'''					)
        parser.add_argument('''--save_top_k'''				,							type=_UpperCamelCase				,							default=1				,							required=_UpperCamelCase				,							help='''How many checkpoints to save'''					)
        parser.add_argument(
            '''--early_stopping_patience'''				,							type=_UpperCamelCase				,							default=-1				,							required=_UpperCamelCase				,							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 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   """translation"""
     __A	:  Dict							  =   ["""loss"""]
     __A	:  str							  =   ["""bleu"""]
     __A	:  Tuple							  =   """bleu"""





     def __init__(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							**_UpperCamelCase					)
        _UpperCAmelCase           =   hparams.src_lang
        _UpperCAmelCase           =   hparams.tgt_lang





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        return calculate_bleu(_UpperCamelCase				,							_UpperCamelCase					)




def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Any=None						)  ->  SummarizationModule:






   """simple docstring"""







   Path(args.output_dir						).mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
   check_output_dir(SCREAMING_SNAKE_CASE_				,	expected_items=3						)

   if model is None:
      if "summarization" in args.task:
         _UpperCAmelCase           =   SummarizationModule(SCREAMING_SNAKE_CASE_						)
      else:
         _UpperCAmelCase           =   TranslationModule(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   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'''						)
   ):
      _UpperCAmelCase           =   True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      _UpperCAmelCase           =   os.environ.get('''WANDB_PROJECT'''				,	SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   WandbLogger(name=model.output_dir.name				,	project=SCREAMING_SNAKE_CASE_						)

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

      _UpperCAmelCase           =   WandbLogger(name=model.output_dir.name				,	project=F'''hf_{dataset}'''						)

   if args.early_stopping_patience >= 0:
      _UpperCAmelCase           =   get_early_stopping_callback(model.val_metric				,	args.early_stopping_patience						)
   else:
      _UpperCAmelCase           =   False

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

   _UpperCAmelCase           =   ''''''
   _UpperCAmelCase           =   sorted(glob.glob(os.path.join(args.output_dir				,	'''*.ckpt'''						)				,	recursive=SCREAMING_SNAKE_CASE_						)						)
   if checkpoints:
      _UpperCAmelCase           =   checkpoints[-1]
      _UpperCAmelCase           =   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__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   UpperCAmelCase_           =	pl.Trainer.add_argparse_args(parser)
   UpperCAmelCase_           =	SummarizationModule.add_model_specific_args(parser, os.getcwd())

   UpperCAmelCase_           =	parser.parse_args()

   main(args)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Tuple							  =   """rwkv"""
     __A	:  Any							  =   {"""max_position_embeddings""": """context_length"""}





     def __init__(     self				,							_UpperCamelCase=50277				,							_UpperCamelCase=1024				,							_UpperCamelCase=4096				,							_UpperCamelCase=32				,							_UpperCamelCase=None				,							_UpperCamelCase=None				,							_UpperCamelCase=1e-5				,							_UpperCamelCase=0				,							_UpperCamelCase=0				,							_UpperCamelCase=6				,							_UpperCamelCase=False				,							_UpperCamelCase=True				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   context_length
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   attention_hidden_size if attention_hidden_size is not None else hidden_size
        _UpperCAmelCase           =   intermediate_size if intermediate_size is not None else 4 * hidden_size
        _UpperCAmelCase           =   layer_norm_epsilon
        _UpperCAmelCase           =   rescale_every
        _UpperCAmelCase           =   use_cache

        _UpperCAmelCase           =   bos_token_id
        _UpperCAmelCase           =   eos_token_id

        super().__init__(
            tie_word_embeddings=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)