code: 
  
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  A_														=      logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  A_														=      {
      "base": AutoModel,
      "sequence-classification": AutoModelForSequenceClassification,
      "question-answering": AutoModelForQuestionAnswering,
      "pretraining": AutoModelForPreTraining,
      "token-classification": AutoModelForTokenClassification,
      "language-modeling": AutoModelWithLMHead,
      "summarization": AutoModelForSeqaSeqLM,
      "translation": AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  A_														=      {
      "linear": get_linear_schedule_with_warmup,
      "cosine": get_cosine_schedule_with_warmup,
      "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
      "polynomial": get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  A_														=      sorted(arg_to_scheduler.keys())
  A_														=      "{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  class 						snake_case			(       pl.LightningModule	):
  			'''simple docstring'''
  
  			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     argparse.Namespace							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Optional[int]="base"							,							lowerCAmelCase_     :     str=None							,							lowerCAmelCase_     :     Any=None							,							lowerCAmelCase_     :     Dict=None							,							**lowerCAmelCase_     :     int							,							)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__()
  								# TODO: move to self.save_hyperparameters()
  								# self.save_hyperparameters()
  								# can also expand arguments into trainer signature for easier reading
  
  								self.save_hyperparameters(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       0
  								SCREAMING_SNAKE_CASE_	      =       Path(self.hparams.output_dir					)
  								SCREAMING_SNAKE_CASE_	      =       self.hparams.cache_dir if self.hparams.cache_dir else None
  								if config is None:
  													SCREAMING_SNAKE_CASE_	      =       AutoConfig.from_pretrained(
  													    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,							**({'''num_labels''': num_labels} if num_labels is not None else {})							,							cache_dir=lowerCAmelCase_							,							**lowerCAmelCase_							,							)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       config
  
  								SCREAMING_SNAKE_CASE_	      =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
  								for p in extra_model_params:
  													if getattr(self.hparams							,							lowerCAmelCase_							,							lowerCAmelCase_					):
  																		assert hasattr(self.config							,							lowerCAmelCase_					), F'''model config doesn\'t have a `{p}` attribute'''
  																		setattr(self.config							,							lowerCAmelCase_							,							getattr(self.hparams							,							lowerCAmelCase_					)					)
  
  								if tokenizer is None:
  													SCREAMING_SNAKE_CASE_	      =       AutoTokenizer.from_pretrained(
  													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,							cache_dir=lowerCAmelCase_							,							)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       tokenizer
  								SCREAMING_SNAKE_CASE_	      =       MODEL_MODES[mode]
  								if model is None:
  													SCREAMING_SNAKE_CASE_	      =       self.model_type.from_pretrained(
  													    self.hparams.model_name_or_path							,							from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path					)							,							config=self.config							,							cache_dir=lowerCAmelCase_							,							)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       model
  
  			def _lowercase (	self     :     List[str]							,							*lowerCAmelCase_     :     List[str]							,							**lowerCAmelCase_     :     Optional[Any]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model_type.from_pretrained(*lowerCAmelCase_							,							**lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       arg_to_scheduler[self.hparams.lr_scheduler]
  								SCREAMING_SNAKE_CASE_	      =       get_schedule_func(
  								    self.opt							,							num_warmup_steps=self.hparams.warmup_steps							,							num_training_steps=self.total_steps()					)
  								SCREAMING_SNAKE_CASE_	      =       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
  								return scheduler
  
  			def _lowercase (	self     :     Optional[Any]					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.model
  								SCREAMING_SNAKE_CASE_	      =       ['''bias''', '''LayerNorm.weight''']
  								SCREAMING_SNAKE_CASE_	      =       [
  								    {
  								        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
  								        '''weight_decay''': self.hparams.weight_decay,
  								    },
  								    {
  								        '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
  								        '''weight_decay''': 0.0,
  								    },
  								]
  								if self.hparams.adafactor:
  													SCREAMING_SNAKE_CASE_	      =       Adafactor(
  													    lowerCAmelCase_							,							lr=self.hparams.learning_rate							,							scale_parameter=lowerCAmelCase_							,							relative_step=lowerCAmelCase_					)
  
  								else:
  													SCREAMING_SNAKE_CASE_	      =       AdamW(
  													    lowerCAmelCase_							,							lr=self.hparams.learning_rate							,							eps=self.hparams.adam_epsilon					)
  								SCREAMING_SNAKE_CASE_	      =       optimizer
  
  								SCREAMING_SNAKE_CASE_	      =       self.get_lr_scheduler()
  
  								return [optimizer], [scheduler]
  
  			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Optional[Any]					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.validation_step(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     int					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.validation_end(lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Tuple					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       max(1							,							self.hparams.gpus					)  # TODO: consider num_tpu_cores
  								SCREAMING_SNAKE_CASE_	      =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Optional[int]					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if stage == "test":
  													SCREAMING_SNAKE_CASE_	      =       len(self.test_dataloader().dataset					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       self.get_dataloader('''train'''							,							self.hparams.train_batch_size							,							shuffle=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       len(self.train_dataloader().dataset					)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     bool = False					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								raise NotImplementedError('''You must implement this for your task'''					)
  
  			def _lowercase (	self     :     int					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.train_loader
  
  			def _lowercase (	self     :     List[str]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_dataloader('''dev'''							,							self.hparams.eval_batch_size							,							shuffle=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Union[str, Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_dataloader('''test'''							,							self.hparams.eval_batch_size							,							shuffle=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     str					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return os.path.join(
  								    self.hparams.data_dir							,							'''cached_{}_{}_{}'''.format(
  								        lowerCAmelCase_							,							list(filter(lowerCAmelCase_							,							self.hparams.model_name_or_path.split('''/'''					)					)					).pop()							,							str(self.hparams.max_seq_length					)							,							)							,							)
  
  			@pl.utilities.rank_zero_only
  			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Dict[str, Any]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.output_dir.joinpath('''best_tfmr'''					)
  								SCREAMING_SNAKE_CASE_	      =       self.step_count
  								self.model.save_pretrained(lowerCAmelCase_					)
  								self.tokenizer.save_pretrained(lowerCAmelCase_					)
  
  
  
  
  			@staticmethod
  			def _lowercase (	lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     Optional[Any]					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								parser.add_argument(
  								    '''--model_name_or_path'''							,							default=lowerCAmelCase_							,							type=lowerCAmelCase_							,							required=lowerCAmelCase_							,							help='''Path to pretrained model or model identifier from huggingface.co/models'''							,							)
  								parser.add_argument(
  								    '''--config_name'''							,							default=''''''							,							type=lowerCAmelCase_							,							help='''Pretrained config name or path if not the same as model_name'''					)
  								parser.add_argument(
  								    '''--tokenizer_name'''							,							default=lowerCAmelCase_							,							type=lowerCAmelCase_							,							help='''Pretrained tokenizer name or path if not the same as model_name'''							,							)
  								parser.add_argument(
  								    '''--cache_dir'''							,							default=''''''							,							type=lowerCAmelCase_							,							help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''							,							)
  								parser.add_argument(
  								    '''--encoder_layerdrop'''							,							type=lowerCAmelCase_							,							help='''Encoder layer dropout probability (Optional). Goes into model.config'''							,							)
  								parser.add_argument(
  								    '''--decoder_layerdrop'''							,							type=lowerCAmelCase_							,							help='''Decoder layer dropout probability (Optional). Goes into model.config'''							,							)
  								parser.add_argument(
  								    '''--dropout'''							,							type=lowerCAmelCase_							,							help='''Dropout probability (Optional). Goes into model.config'''							,							)
  								parser.add_argument(
  								    '''--attention_dropout'''							,							type=lowerCAmelCase_							,							help='''Attention dropout probability (Optional). Goes into model.config'''							,							)
  								parser.add_argument('''--learning_rate'''							,							default=5e-5							,							type=lowerCAmelCase_							,							help='''The initial learning rate for Adam.'''					)
  								parser.add_argument(
  								    '''--lr_scheduler'''							,							default='''linear'''							,							choices=lowerCAmelCase_							,							metavar=lowerCAmelCase_							,							type=lowerCAmelCase_							,							help='''Learning rate scheduler'''							,							)
  								parser.add_argument('''--weight_decay'''							,							default=0.0							,							type=lowerCAmelCase_							,							help='''Weight decay if we apply some.'''					)
  								parser.add_argument('''--adam_epsilon'''							,							default=1e-8							,							type=lowerCAmelCase_							,							help='''Epsilon for Adam optimizer.'''					)
  								parser.add_argument('''--warmup_steps'''							,							default=0							,							type=lowerCAmelCase_							,							help='''Linear warmup over warmup_steps.'''					)
  								parser.add_argument('''--num_workers'''							,							default=4							,							type=lowerCAmelCase_							,							help='''kwarg passed to DataLoader'''					)
  								parser.add_argument('''--num_train_epochs'''							,							dest='''max_epochs'''							,							default=3							,							type=lowerCAmelCase_					)
  								parser.add_argument('''--train_batch_size'''							,							default=32							,							type=lowerCAmelCase_					)
  								parser.add_argument('''--eval_batch_size'''							,							default=32							,							type=lowerCAmelCase_					)
  								parser.add_argument('''--adafactor'''							,							action='''store_true'''					)
  
  class 						snake_case			(       pl.Callback	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Dict					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if (
  								    trainer.is_global_zero and trainer.global_rank == 0
  								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
  													pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  class 						snake_case			(       pl.Callback	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     Tuple					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       trainer.lr_schedulers[0]['''scheduler''']
  								SCREAMING_SNAKE_CASE_	      =       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
  								pl_module.logger.log_metrics(lowerCAmelCase_					)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								rank_zero_info('''***** Validation results *****'''					)
  								SCREAMING_SNAKE_CASE_	      =       trainer.callback_metrics
  								# Log results
  								for key in sorted(lowerCAmelCase_					):
  													if key not in ["log", "progress_bar"]:
  																		rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase_							,							str(metrics[key]					)					)					)
  
  
  
  
  			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								rank_zero_info('''***** Test results *****'''					)
  								SCREAMING_SNAKE_CASE_	      =       trainer.callback_metrics
  								# Log and save results to file
  								SCREAMING_SNAKE_CASE_	      =       os.path.join(pl_module.hparams.output_dir							,							'''test_results.txt'''					)
  								with open(lowerCAmelCase_							,							'''w'''					) as writer:
  													for key in sorted(lowerCAmelCase_					):
  																		if key not in ["log", "progress_bar"]:
  																							rank_zero_info('''{} = {}\n'''.format(lowerCAmelCase_							,							str(metrics[key]					)					)					)
  																							writer.write('''{} = {}\n'''.format(lowerCAmelCase_							,							str(metrics[key]					)					)					)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					parser.add_argument(
  					    '''--output_dir'''				,default=UpperCAmelCase				,type=UpperCAmelCase				,required=UpperCAmelCase				,help='''The output directory where the model predictions and checkpoints will be written.'''				,)
  					parser.add_argument(
  					    '''--fp16'''				,action='''store_true'''				,help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''				,)
  
  					parser.add_argument(
  					    '''--fp16_opt_level'''				,type=UpperCAmelCase				,default='''O2'''				,help=(
  					        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
  					        '''See details at https://nvidia.github.io/apex/amp.html'''
  					    )				,)
  					parser.add_argument('''--n_tpu_cores'''				,dest='''tpu_cores'''				,type=UpperCAmelCase      )
  					parser.add_argument('''--max_grad_norm'''				,dest='''gradient_clip_val'''				,default=1.0				,type=UpperCAmelCase				,help='''Max gradient norm'''      )
  					parser.add_argument('''--do_train'''				,action='''store_true'''				,help='''Whether to run training.'''      )
  					parser.add_argument('''--do_predict'''				,action='''store_true'''				,help='''Whether to run predictions on the test set.'''      )
  					parser.add_argument(
  					    '''--gradient_accumulation_steps'''				,dest='''accumulate_grad_batches'''				,type=UpperCAmelCase				,default=1				,help='''Number of updates steps to accumulate before performing a backward/update pass.'''				,)
  					parser.add_argument('''--seed'''				,type=UpperCAmelCase				,default=42				,help='''random seed for initialization'''      )
  					parser.add_argument(
  					    '''--data_dir'''				,default=UpperCAmelCase				,type=UpperCAmelCase				,required=UpperCAmelCase				,help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=None				,UpperCAmelCase=True				,UpperCAmelCase=None				,UpperCAmelCase=[]				,UpperCAmelCase=None				,UpperCAmelCase=None				,**UpperCAmelCase				,)->		Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					pl.seed_everything(args.seed      )
  
  					# init model
  					SCREAMING_SNAKE_CASE_	      =       Path(model.hparams.output_dir      )
  					odir.mkdir(exist_ok=UpperCAmelCase      )
  
  					# add custom checkpoints
  					if checkpoint_callback is None:
  										SCREAMING_SNAKE_CASE_	      =       pl.callbacks.ModelCheckpoint(
  										    filepath=args.output_dir				,prefix='''checkpoint'''				,monitor='''val_loss'''				,mode='''min'''				,save_top_k=1      )
  					if early_stopping_callback:
  										extra_callbacks.append(UpperCAmelCase      )
  					if logging_callback is None:
  										SCREAMING_SNAKE_CASE_	      =       LoggingCallback()
  
  					SCREAMING_SNAKE_CASE_	      =       {}
  
  					# TODO: remove with PyTorch 1.6 since pl uses native amp
  					if args.fpaa:
  										SCREAMING_SNAKE_CASE_	      =       16
  										# train_params["amp_level"] = args.fp16_opt_level
  
  					if args.gpus > 1:
  										SCREAMING_SNAKE_CASE_	      =       '''auto'''  # "ddp"
  										SCREAMING_SNAKE_CASE_	      =       '''ddp'''
  
  					SCREAMING_SNAKE_CASE_	      =       args.accumulate_grad_batches
  					SCREAMING_SNAKE_CASE_	      =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  					SCREAMING_SNAKE_CASE_	      =       '''auto'''
  
  					SCREAMING_SNAKE_CASE_	      =       pl.Trainer.from_argparse_args(
  					    UpperCAmelCase				,weights_summary=UpperCAmelCase				,callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]				,logger=UpperCAmelCase				,**UpperCAmelCase				,)
  
  					if args.do_train:
  										trainer.fit(UpperCAmelCase      )
  
  					return trainer
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["MarianTokenizer"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "MarianForCausalLM",
  		    "MarianModel",
  		    "MarianMTModel",
  		    "MarianPreTrainedModel",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  