


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   __a			(    __UpperCamelCase      ):
 __lowercase :							Union[str, Any]        = 'summarization'
 __lowercase :							str        = ['loss']
 __lowercase :							List[str]        = ROUGE_KEYS
 __lowercase :							Optional[int]        = 'rouge2'
 def __init__(   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        if hparams.sortish_sampler and hparams.gpus > 1:
               lowercase__:   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__(lowerCAmelCase__				, num_labels=lowerCAmelCase__				, mode=self.mode				, **lowerCAmelCase__ )
        use_task_specific_params(self.model				, 'summarization' )
        save_git_info(self.hparams.output_dir )
        lowercase__:   Dict  			=   Path(self.output_dir ) / 'metrics.json'
        lowercase__:   int  			=   Path(self.output_dir ) / 'hparams.pkl'
        pickle_save(self.hparams				, self.hparams_save_path )
        lowercase__:   Optional[int]  			=   0
        lowercase__:   Optional[Any]  			=   defaultdict(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   self.config.model_type
        lowercase__:   List[str]  			=   self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

        lowercase__:   Optional[int]  			=   {
            '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() )

        lowercase__:   Optional[int]  			=   get_git_info()['repo_sha']
        lowercase__:   Tuple  			=   hparams.num_workers
        lowercase__:   List[str]  			=   None  # default to config
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				, lowerCAmelCase__ ):
               lowercase__:   Tuple  			=   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
               lowercase__:   Optional[Any]  			=   self.decoder_start_token_id
        lowercase__:   Union[str, Any]  			=   (
            SeqaSeqDataset if hasattr(self.tokenizer				, 'prepare_seq2seq_batch' ) else LegacySeqaSeqDataset
        )
        lowercase__:   Any  			=   False
        lowercase__:   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:
               lowercase__:   List[Any]  			=   self.hparams.eval_max_gen_length
        else:
               lowercase__:   Optional[Any]  			=   self.model.config.max_length
        lowercase__:   Dict  			=   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict[str, List[str]]:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   {
            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' )

        lowercase__:   List[Any]  			=   True
        return readable_batch
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return self.model(lowerCAmelCase__				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   str  			=   self.tokenizer.batch_decode(
            lowerCAmelCase__				, skip_special_tokens=lowerCAmelCase__				, clean_up_tokenization_spaces=lowerCAmelCase__ )
        return lmap(str.strip				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Tuple:




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

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

               assert lm_logits.shape[-1] == self.vocab_size
               lowercase__:   Optional[Any]  			=   ce_loss_fct(lm_logits.view(-1				, lm_logits.shape[-1] )				, tgt_ids.view(-1 ) )
        else:
               lowercase__:   str  			=   nn.functional.log_softmax(lowerCAmelCase__				, dim=-1 )
               lowercase__			,						lowercase__:   Optional[Any]  			=   label_smoothed_nll_loss(
                   lowerCAmelCase__				, lowerCAmelCase__				, self.hparams.label_smoothing				, ignore_index=lowerCAmelCase__ )
        return (loss,)
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return self.tokenizer.pad_token_id
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        lowercase__:   Dict  			=   self._step(lowerCAmelCase__ )

        lowercase__:   Optional[Any]  			=   dict(zip(self.loss_names				, lowerCAmelCase__ ) )
        # tokens per batch
        lowercase__:   Optional[Any]  			=   batch['input_ids'].ne(self.pad ).sum() + batch['labels'].ne(self.pad ).sum()
        lowercase__:   int  			=   batch['input_ids'].shape[0]
        lowercase__:   int  			=   batch['input_ids'].eq(self.pad ).sum()
        lowercase__:   List[Any]  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        return self._generative_step(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__="val" )					->				Dict:




        '''simple docstring'''
        self.step_count += 1
        lowercase__:   Optional[int]  			=   {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
        lowercase__:   List[Any]  			=   losses['loss']
        lowercase__:   Any  			=   {
            k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
        }
        lowercase__:   Any  			=   (
            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
        )
        lowercase__:   torch.FloatTensor  			=   torch.tensor(lowerCAmelCase__ ).type_as(lowerCAmelCase__ )
        generative_metrics.update({k: v.item() for k, v in losses.items()} )
        losses.update(lowerCAmelCase__ )
        lowercase__:   Dict  			=   {F'{prefix}_avg_{k}': x for k, x in losses.items()}
        lowercase__:   Tuple  			=   self.step_count
        self.metrics[prefix].append(lowerCAmelCase__ )  # callback writes this to self.metrics_save_path
        lowercase__:   Optional[int]  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Dict:




        '''simple docstring'''
        return calculate_rouge(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				dict:




        '''simple docstring'''
        lowercase__:   Dict  			=   time.time()

        # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
        lowercase__:   str  			=   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				, )
        lowercase__:   int  			=   (time.time() - ta) / batch['input_ids'].shape[0]
        lowercase__:   List[str]  			=   self.ids_to_clean_text(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   self.ids_to_clean_text(batch['labels'] )
        lowercase__:   Any  			=   self._step(lowerCAmelCase__ )
        lowercase__:   Any  			=   dict(zip(self.loss_names				, lowerCAmelCase__ ) )
        lowercase__:   Dict  			=   self.calc_generative_metrics(lowerCAmelCase__				, lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   np.mean(lmap(lowerCAmelCase__				, lowerCAmelCase__ ) )
        base_metrics.update(gen_time=lowerCAmelCase__				, gen_len=lowerCAmelCase__				, preds=lowerCAmelCase__				, target=lowerCAmelCase__				, **lowerCAmelCase__ )
        return base_metrics
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        return self._generative_step(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return self.validation_epoch_end(lowerCAmelCase__				, prefix='test' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				SeqaSeqDataset:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.n_obs[type_path]
        lowercase__:   List[str]  			=   self.target_lens[type_path]
        lowercase__:   int  			=   self.dataset_class(
            self.tokenizer				, type_path=lowerCAmelCase__				, n_obs=lowerCAmelCase__				, max_target_length=lowerCAmelCase__				, **self.dataset_kwargs				, )
        return dataset
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = False )					->				DataLoader:




        '''simple docstring'''
        lowercase__:   str  			=   self.get_dataset(lowerCAmelCase__ )

        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
               lowercase__:   Optional[Any]  			=   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":
               lowercase__:   List[str]  			=   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 		SCREAMING_SNAKE_CASE__   (   self )					->				DataLoader:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self.get_dataloader('train'				, batch_size=self.hparams.train_batch_size				, shuffle=lowerCAmelCase__ )
        return dataloader
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				DataLoader:




        '''simple docstring'''
        return self.get_dataloader('val'				, batch_size=self.hparams.eval_batch_size )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				DataLoader:




        '''simple docstring'''
        return self.get_dataloader('test'				, batch_size=self.hparams.eval_batch_size )
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        BaseTransformer.add_model_specific_args(lowerCAmelCase__				, lowerCAmelCase__ )
        add_generic_args(lowerCAmelCase__				, lowerCAmelCase__ )
        parser.add_argument(
            '--max_source_length'				, default=1_024				, 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   __a			(    __UpperCamelCase      ):
 __lowercase :							Any        = 'translation'
 __lowercase :							str        = ['loss']
 __lowercase :							Optional[Any]        = ['bleu']
 __lowercase :							Optional[int]        = 'bleu'
 def __init__(   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        super().__init__(lowerCAmelCase__				, **lowerCAmelCase__ )
        lowercase__:   Dict  			=   hparams.src_lang
        lowercase__:   List[Any]  			=   hparams.tgt_lang
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				dict:




        '''simple docstring'''
        return calculate_bleu(lowerCAmelCase__				, lowerCAmelCase__ )





def    snake_case_     (  snake_case			,							snake_case=None     )  ->				SummarizationModule:
       Path(args.output_dir     ).mkdir(exist_ok=snake_case     )
       check_output_dir(snake_case			,							expected_items=3     )

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

              lowercase__:   Union[str, Any]  			=   os.environ.get('WANDB_PROJECT'			,							snake_case     )
              lowercase__:   Tuple  			=   WandbLogger(name=model.output_dir.name			,							project=snake_case     )

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

              lowercase__:   Union[str, Any]  			=   WandbLogger(name=model.output_dir.name			,							project=f'hf_{dataset}'     )

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

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

       lowercase__:   Union[str, Any]  			=   ''
       lowercase__:   Union[str, Any]  			=   sorted(glob.glob(os.path.join(args.output_dir			,							'*.ckpt'     )			,							recursive=snake_case     )     )
       if checkpoints:
              lowercase__:   Optional[Any]  			=   checkpoints[-1]
              lowercase__:   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)






from typing import TYPE_CHECKING

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


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


