





'''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


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "summarization"
	a      :			List[str]     =    ["loss"]
	a      :			Union[str, Any]     =    ROUGE_KEYS
	a      :			str     =    "rouge2"


	def __init__(self       ,_lowerCamelCase       ,**_lowerCamelCase	)							->							int:







				'''simple docstring'''





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

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

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict[str, List[str]]:







				'''simple docstring'''





				__lowercase			  =							{
				    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			  =							True
				return readable_batch


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,**_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				return self.model(_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self.tokenizer.batch_decode(
				    _lowerCamelCase       ,skip_special_tokens=_lowerCamelCase       ,clean_up_tokenization_spaces=_lowerCamelCase	)
				return lmap(str.strip       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





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

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

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


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return self.tokenizer.pad_token_id


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							self._step(_lowerCamelCase	)

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







				'''simple docstring'''





				return self._generative_step(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase="val"	)							->							Dict:







				'''simple docstring'''





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







				'''simple docstring'''





				return calculate_rouge(_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							dict:







				'''simple docstring'''





				__lowercase			  =							time.time()

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				return self._generative_step(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							SeqaSeqDataset:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = False	)							->							DataLoader:







				'''simple docstring'''





				__lowercase			  =							self.get_dataset(_lowerCamelCase	)

				if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
							__lowercase			  =							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			  =							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   _UpperCAmelCase       (self	)							->							DataLoader:







				'''simple docstring'''





				__lowercase			  =							self.get_dataloader('''train'''       ,batch_size=self.hparams.train_batch_size       ,shuffle=_lowerCamelCase	)
				return dataloader


	def   _UpperCAmelCase       (self	)							->							DataLoader:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							DataLoader:







				'''simple docstring'''





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


	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase       ,_lowerCamelCase	)							->							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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Any     =    "translation"
	a      :			Union[str, Any]     =    ["loss"]
	a      :			Optional[int]     =    ["bleu"]
	a      :			Optional[Any]     =    "bleu"


	def __init__(self       ,_lowerCamelCase       ,**_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							hparams.src_lang
				__lowercase			  =							hparams.tgt_lang


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							dict:







				'''simple docstring'''





				return calculate_bleu(_lowerCamelCase       ,_lowerCamelCase	)





def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					List[str]=None			):
			Path(args.output_dir			).mkdir(exist_ok=lowerCamelCase_			)
			check_output_dir(lowerCamelCase_						,    expected_items=3			)

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

						__lowercase			  =							os.environ.get('''WANDB_PROJECT'''						,    lowerCamelCase_			)
						__lowercase			  =							WandbLogger(name=model.output_dir.name						,    project=lowerCamelCase_			)

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

						__lowercase			  =							WandbLogger(name=model.output_dir.name						,    project=f"hf_{dataset}"			)

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

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

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

	_SCREAMING_SNAKE_CASE   =      parser.parse_args()

	main(args)












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





