





'''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


lowercase__		:     Union[str, Any]			=							logging.getLogger(__name__)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str									=       'summarization'
       _snake_case      :	Union[str, Any]									=       ['loss']
       _snake_case      :	Dict									=       ROUGE_KEYS
       _snake_case      :	List[Any]									=       'rouge2'





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						List[Any]			,					**lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




            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__(lowerCAmelCase__			,					num_labels=lowerCAmelCase__			,					mode=self.mode			,					**lowerCAmelCase__	)
            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(lowerCAmelCase__	)
            _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			,					lowerCAmelCase__	):
                 _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 snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Dict[str, torch.Tensor]	)       ->	Dict[str, List[str]]:

            '''simple docstring'''




            _UpperCamelCase							       =			{
                k: self.tokenizer.batch_decode(v.tolist()	) if '''mask''' not in k else v.shape for k, v in batch.items()
            }
            save_json(lowerCAmelCase__			,					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 snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Tuple			,					**lowerCAmelCase__  :						List[str]	)       ->	str:

            '''simple docstring'''




            return self.model(lowerCAmelCase__			,					**lowerCAmelCase__	)





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						List[int]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.batch_decode(
                lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__			,					clean_up_tokenization_spaces=lowerCAmelCase__	)
            return lmap(str.strip			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						dict	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.pad_token_id
            _UpperCamelCase       ,							_UpperCamelCase							       =			batch['''input_ids'''], batch['''attention_mask''']
            _UpperCamelCase							       =			batch['''labels''']
            if isinstance(self.model			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			self.model._shift_right(lowerCAmelCase__	)
            else:
                 _UpperCamelCase							       =			shift_tokens_right(lowerCAmelCase__			,					lowerCAmelCase__	)
            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(lowerCAmelCase__	)

            _UpperCamelCase							       =			self(lowerCAmelCase__			,					attention_mask=lowerCAmelCase__			,					decoder_input_ids=lowerCAmelCase__			,					use_cache=lowerCAmelCase__	)
            _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=lowerCAmelCase__	)

                 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(lowerCAmelCase__			,					dim=-1	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			label_smoothed_nll_loss(
                     lowerCAmelCase__			,					lowerCAmelCase__			,					self.hparams.label_smoothing			,					ignore_index=lowerCAmelCase__	)
            return (loss,)





       @property
       def snake_case__		(				self  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            return self.tokenizer.pad_token_id





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Optional[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self._step(lowerCAmelCase__	)

            _UpperCamelCase							       =			dict(zip(self.loss_names			,					lowerCAmelCase__	)	)
            # 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 snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Any]	)       ->	Dict:

            '''simple docstring'''




            return self._generative_step(lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Any="val"	)       ->	Dict:

            '''simple docstring'''




            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(lowerCAmelCase__	).type_as(lowerCAmelCase__	)
            generative_metrics.update({k: v.item() for k, v in losses.items()}	)
            losses.update(lowerCAmelCase__	)
            _UpperCamelCase							       =			{f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
            _UpperCamelCase							       =			self.step_count
            self.metrics[prefix].append(lowerCAmelCase__	)  # 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 snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Any	)       ->	Dict:

            '''simple docstring'''




            return calculate_rouge(lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						dict	)       ->	dict:

            '''simple docstring'''




            _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=lowerCAmelCase__			,					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(lowerCAmelCase__	)
            _UpperCamelCase							       =			self.ids_to_clean_text(batch['''labels''']	)
            _UpperCamelCase							       =			self._step(lowerCAmelCase__	)
            _UpperCamelCase							       =			dict(zip(self.loss_names			,					lowerCAmelCase__	)	)
            _UpperCamelCase							       =			self.calc_generative_metrics(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			np.mean(lmap(lowerCAmelCase__			,					lowerCAmelCase__	)	)
            base_metrics.update(gen_time=lowerCAmelCase__			,					gen_len=lowerCAmelCase__			,					preds=lowerCAmelCase__			,					target=lowerCAmelCase__			,					**lowerCAmelCase__	)
            return base_metrics





       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            return self._generative_step(lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Tuple	)       ->	List[str]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						str	)       ->	SeqaSeqDataset:

            '''simple docstring'''




            _UpperCamelCase							       =			self.n_obs[type_path]
            _UpperCamelCase							       =			self.target_lens[type_path]
            _UpperCamelCase							       =			self.dataset_class(
                self.tokenizer			,					type_path=lowerCAmelCase__			,					n_obs=lowerCAmelCase__			,					max_target_length=lowerCAmelCase__			,					**self.dataset_kwargs			,					)
            return dataset





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						bool = False	)       ->	DataLoader:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_dataset(lowerCAmelCase__	)

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

            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(
                     lowerCAmelCase__			,					batch_sampler=lowerCAmelCase__			,					collate_fn=dataset.collate_fn			,					num_workers=self.num_workers			,					)
            else:
                 return DataLoader(
                     lowerCAmelCase__			,					batch_size=lowerCAmelCase__			,					collate_fn=dataset.collate_fn			,					shuffle=lowerCAmelCase__			,					num_workers=self.num_workers			,					sampler=lowerCAmelCase__			,					)





       def snake_case__		(				self  :						Optional[Any]	)       ->	DataLoader:

            '''simple docstring'''




            _UpperCamelCase							       =			self.get_dataloader('''train'''			,					batch_size=self.hparams.train_batch_size			,					shuffle=lowerCAmelCase__	)
            return dataloader





       def snake_case__		(				self  :						int	)       ->	DataLoader:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Tuple	)       ->	DataLoader:

            '''simple docstring'''




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







       @staticmethod
       def snake_case__		(				lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						str	)       ->	int:

            '''simple docstring'''




            BaseTransformer.add_model_specific_args(lowerCAmelCase__			,					lowerCAmelCase__	)
            add_generic_args(lowerCAmelCase__			,					lowerCAmelCase__	)
            parser.add_argument(
                '''--max_source_length'''			,					default=1024			,					type=lowerCAmelCase__			,					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=lowerCAmelCase__			,					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=lowerCAmelCase__			,					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=lowerCAmelCase__			,					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=lowerCAmelCase__	)
            parser.add_argument('''--overwrite_output_dir'''			,					action='''store_true'''			,					default=lowerCAmelCase__	)
            parser.add_argument('''--max_tokens_per_batch'''			,					type=lowerCAmelCase__			,					default=lowerCAmelCase__	)
            parser.add_argument('''--logger_name'''			,					type=lowerCAmelCase__			,					choices=['''default''', '''wandb''', '''wandb_shared''']			,					default='''default'''	)
            parser.add_argument('''--n_train'''			,					type=lowerCAmelCase__			,					default=-1			,					required=lowerCAmelCase__			,					help='''# examples. -1 means use all.'''	)
            parser.add_argument('''--n_val'''			,					type=lowerCAmelCase__			,					default=500			,					required=lowerCAmelCase__			,					help='''# examples. -1 means use all.'''	)
            parser.add_argument('''--n_test'''			,					type=lowerCAmelCase__			,					default=-1			,					required=lowerCAmelCase__			,					help='''# examples. -1 means use all.'''	)
            parser.add_argument(
                '''--task'''			,					type=lowerCAmelCase__			,					default='''summarization'''			,					required=lowerCAmelCase__			,					help='''# examples. -1 means use all.'''	)
            parser.add_argument('''--label_smoothing'''			,					type=lowerCAmelCase__			,					default=0.0			,					required=lowerCAmelCase__	)
            parser.add_argument('''--src_lang'''			,					type=lowerCAmelCase__			,					default=''''''			,					required=lowerCAmelCase__	)
            parser.add_argument('''--tgt_lang'''			,					type=lowerCAmelCase__			,					default=''''''			,					required=lowerCAmelCase__	)
            parser.add_argument('''--eval_beams'''			,					type=lowerCAmelCase__			,					default=lowerCAmelCase__			,					required=lowerCAmelCase__	)
            parser.add_argument(
                '''--val_metric'''			,					type=lowerCAmelCase__			,					default=lowerCAmelCase__			,					required=lowerCAmelCase__			,					choices=['''bleu''', '''rouge2''', '''loss''', None]	)
            parser.add_argument('''--eval_max_gen_length'''			,					type=lowerCAmelCase__			,					default=lowerCAmelCase__			,					help='''never generate more than n tokens'''	)
            parser.add_argument('''--save_top_k'''			,					type=lowerCAmelCase__			,					default=1			,					required=lowerCAmelCase__			,					help='''How many checkpoints to save'''	)
            parser.add_argument(
                '''--early_stopping_patience'''			,					type=lowerCAmelCase__			,					default=-1			,					required=lowerCAmelCase__			,					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 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str									=       'translation'
       _snake_case      :	Optional[Any]									=       ['loss']
       _snake_case      :	List[Any]									=       ['bleu']
       _snake_case      :	Dict									=       'bleu'





       def __init__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Dict			,					**lowerCAmelCase__  :						List[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__			,					**lowerCAmelCase__	)
            _UpperCamelCase							       =			hparams.src_lang
            _UpperCamelCase							       =			hparams.tgt_lang







       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Any]	)       ->	dict:

            '''simple docstring'''




            return calculate_bleu(lowerCAmelCase__			,					lowerCAmelCase__	)







def    a__							(							lowercase   :	Tuple,   lowercase   :	List[Any]=None )						->						SummarizationModule:





     """simple docstring"""

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

     if model is None:
          if "summarization" in args.task:
               _UpperCamelCase							       =			SummarizationModule(lowercase )
          else:
               _UpperCamelCase							       =			TranslationModule(lowercase )
     _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''',   lowercase )
          _UpperCamelCase							       =			WandbLogger(name=model.output_dir.name,   project=lowercase )

     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(
         lowercase,   lowercase,   logging_callback=SeqaSeqLoggingCallback(),   checkpoint_callback=get_checkpoint_callback(
             args.output_dir,   model.val_metric,   args.save_top_k,   lowercase ),   early_stopping_callback=lowercase,   logger=lowercase,   )
     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=lowercase ) )
     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__":
   lowercase__		:     int			=							argparse.ArgumentParser()
   lowercase__		:     Any			=							pl.Trainer.add_argparse_args(parser)
   lowercase__		:     int			=							SummarizationModule.add_model_specific_args(parser, os.getcwd())

   lowercase__		:     int			=							parser.parse_args()

   main(args)










'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:     Tuple			=							{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     int			=							['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   lowercase__		:     List[Any]			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



