



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


__snake_case						:Optional[Any]						=					logging.getLogger(__name__)

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Dict						 =						'''summarization'''
       UpperCamelCase__  :   Any						 =						['''loss''']
       UpperCamelCase__  :   Optional[int]						 =						ROUGE_KEYS
       UpperCamelCase__  :   Optional[Any]						 =						'''rouge2'''



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           if hparams.sortish_sampler and hparams.gpus > 1:
               __a							=    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)
           __a							=    Path(self.output_dir) / '''metrics.json'''
           __a							=    Path(self.output_dir) / '''hparams.pkl'''
           pickle_save(self.hparams					,		self.hparams_save_path)
           __a							=    0
           __a							=    defaultdict(__SCREAMING_SNAKE_CASE)
           __a							=    self.config.model_type
           __a							=    self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

           __a							=    {
               '''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())

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



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict[str, torch.Tensor]):


           '''simple docstring'''

           __a							=    {
               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''')

           __a							=    True
           return readable_batch



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.model(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[int]):


           '''simple docstring'''

           __a							=    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 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					dict):


           '''simple docstring'''

           __a							=    self.tokenizer.pad_token_id
           __a	,       __a							=    batch['''input_ids'''], batch['''attention_mask''']
           __a							=    batch['''labels''']
           if isinstance(self.model					,		__SCREAMING_SNAKE_CASE):
               __a							=    self.model._shift_right(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    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
               __a							=    decoder_input_ids
               self.save_readable_batch(__SCREAMING_SNAKE_CASE)

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

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



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.tokenizer.pad_token_id



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self._step(__SCREAMING_SNAKE_CASE)

           __a							=    dict(zip(self.loss_names					,		__SCREAMING_SNAKE_CASE))
           # tokens per batch
           __a							=    batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
           __a							=    batch['''input_ids'''].shape[0]
           __a							=    batch['''input_ids'''].eq(self.pad).sum()
           __a							=    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 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           return self._generative_step(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					List[Any]="val"):


           '''simple docstring'''

           self.step_count += 1
           __a							=    {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
           __a							=    losses['''loss''']
           __a							=    {
               k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
           }
           __a							=    (
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           __a							=    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)
           __a							=    {F'{prefix}_avg_{k}': x for k, x in losses.items()}
           __a							=    self.step_count
           self.metrics[prefix].append(__SCREAMING_SNAKE_CASE)  # callback writes this to self.metrics_save_path
           __a							=    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 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return calculate_rouge(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					dict):


           '''simple docstring'''

           __a							=    time.time()

           # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
           __a							=    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					,		)
           __a							=    (time.time() - ta) / batch['''input_ids'''].shape[0]
           __a							=    self.ids_to_clean_text(__SCREAMING_SNAKE_CASE)
           __a							=    self.ids_to_clean_text(batch['''labels'''])
           __a							=    self._step(__SCREAMING_SNAKE_CASE)
           __a							=    dict(zip(self.loss_names					,		__SCREAMING_SNAKE_CASE))
           __a							=    self.calc_generative_metrics(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    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 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           return self._generative_step(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    self.n_obs[type_path]
           __a							=    self.target_lens[type_path]
           __a							=    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 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

           __a							=    self.get_dataset(__SCREAMING_SNAKE_CASE)

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
               __a							=    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":
               __a							=    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 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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





       @staticmethod
       def 		_lowerCamelCase      (		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''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=1_024					,		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=142					,		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=142					,		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=500					,		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    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Optional[int]						 =						'''translation'''
       UpperCamelCase__  :   Union[str, Any]						 =						['''loss''']
       UpperCamelCase__  :   Dict						 =						['''bleu''']
       UpperCamelCase__  :   str						 =						'''bleu'''



       def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    hparams.src_lang
           __a							=    hparams.tgt_lang





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           return calculate_bleu(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    Path(args.output_dir    ).mkdir(exist_ok=_UpperCAmelCase    )
    check_output_dir(_UpperCAmelCase       ,			expected_items=3    )

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

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

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

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

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

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

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

 __snake_case						:str						=					parser.parse_args()

 main(args)










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


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[Any]						=					{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						'''gptsan-japanese'''
       UpperCamelCase__  :   Dict						 =						[
           '''past_key_values''',
       ]
       UpperCamelCase__  :   Dict						 =						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }



       def __init__(		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=36_000					,		__SCREAMING_SNAKE_CASE							:					Tuple=1_280					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1_024					,		__SCREAMING_SNAKE_CASE							:					List[Any]=8_192					,		__SCREAMING_SNAKE_CASE							:					str=4_096					,		__SCREAMING_SNAKE_CASE							:					Any=128					,		__SCREAMING_SNAKE_CASE							:					int=10					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=16					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=128					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.0					,		__SCREAMING_SNAKE_CASE							:					List[Any]=1E-5					,		__SCREAMING_SNAKE_CASE							:					str=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.0					,		__SCREAMING_SNAKE_CASE							:					List[str]="float32"					,		__SCREAMING_SNAKE_CASE							:					List[str]=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					int=0.0_02					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					Tuple=True					,		__SCREAMING_SNAKE_CASE							:					int=35_998					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=35_995					,		__SCREAMING_SNAKE_CASE							:					List[str]=35_999					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           __a							=    vocab_size
           __a							=    max_position_embeddings
           __a							=    d_model
           __a							=    d_ff
           __a							=    d_ext
           __a							=    d_spout
           __a							=    num_switch_layers
           __a							=    num_ext_layers
           __a							=    num_switch_layers + num_ext_layers
           __a							=    num_heads
           __a							=    num_experts
           __a							=    expert_capacity
           __a							=    dropout_rate
           __a							=    layer_norm_epsilon
           __a							=    router_bias
           __a							=    router_jitter_noise
           __a							=    router_dtype
           __a							=    router_ignore_padding_tokens
           __a							=    output_hidden_states
           __a							=    output_attentions
           __a							=    initializer_factor
           __a							=    output_router_logits
           __a							=    use_cache

           super().__init__(
               separator_token_id=__SCREAMING_SNAKE_CASE					,		pad_token_id=__SCREAMING_SNAKE_CASE					,		eos_token_id=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





