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
  
  
  __snake_case							      =						logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  __snake_case							      =						{
      """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
  __snake_case							      =						{
      """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
  }
  __snake_case							      =						sorted(arg_to_scheduler.keys())
  __snake_case							      =						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      pl.LightningModule				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_="base"					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   List[str]:
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading
  
            self.save_hyperparameters(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :str										=     0
            UpperCamelCase :int										=     Path(self.hparams.output_dir				)
            UpperCamelCase :List[str]										=     self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                UpperCamelCase :List[str]										=     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=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )
            else:
                UpperCamelCase :PretrainedConfig										=     config
  
            UpperCamelCase :List[str]										=     ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
            for p in extra_model_params:
                if getattr(self.hparams					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                    assert hasattr(self.config					,       SCREAMING_SNAKE_CASE_				), F'''model config doesn\'t have a `{p}` attribute'''
                    setattr(self.config					,       SCREAMING_SNAKE_CASE_					,       getattr(self.hparams					,       SCREAMING_SNAKE_CASE_				)				)
  
            if tokenizer is None:
                UpperCamelCase :str										=     AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,       cache_dir=SCREAMING_SNAKE_CASE_					,       )
            else:
                UpperCamelCase :PreTrainedTokenizer										=     tokenizer
            UpperCamelCase :Tuple										=     MODEL_MODES[mode]
            if model is None:
                UpperCamelCase :Tuple										=     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=SCREAMING_SNAKE_CASE_					,       )
            else:
                UpperCamelCase :List[Any]										=     model
        def 					UpperCAmelCase		(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Dict:
            UpperCamelCase :Tuple										=     self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   str:
            UpperCamelCase :Union[str, Any]										=     arg_to_scheduler[self.hparams.lr_scheduler]
            UpperCamelCase :Optional[int]										=     get_schedule_func(
                self.opt					,       num_warmup_steps=self.hparams.warmup_steps					,       num_training_steps=self.total_steps()				)
            UpperCamelCase :Any										=     {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
            return scheduler
        def 					UpperCAmelCase		(       self				)       ->   Tuple:
            UpperCamelCase :Optional[Any]										=     self.model
            UpperCamelCase :Dict										=     ['''bias''', '''LayerNorm.weight''']
            UpperCamelCase :str										=     [
                {
                    '''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:
                UpperCamelCase :int										=     Adafactor(
                    SCREAMING_SNAKE_CASE_					,       lr=self.hparams.learning_rate					,       scale_parameter=SCREAMING_SNAKE_CASE_					,       relative_step=SCREAMING_SNAKE_CASE_				)
  
            else:
                UpperCamelCase :Optional[int]										=     AdamW(
                    SCREAMING_SNAKE_CASE_					,       lr=self.hparams.learning_rate					,       eps=self.hparams.adam_epsilon				)
            UpperCamelCase :Union[str, Any]										=     optimizer
  
            UpperCamelCase :str										=     self.get_lr_scheduler()
  
            return [optimizer], [scheduler]
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            return self.validation_step(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
            return self.validation_end(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :Any										=     max(1					,       self.hparams.gpus				)  # TODO: consider num_tpu_cores
            UpperCamelCase :Optional[int]										=     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            if stage == "test":
                UpperCamelCase :Tuple										=     len(self.test_dataloader().dataset				)
            else:
                UpperCamelCase :Dict										=     self.get_dataloader('''train'''					,       self.hparams.train_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Tuple										=     len(self.train_dataloader().dataset				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = False				)       ->   Any:
            raise NotImplementedError('''You must implement this for your task'''				)
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            return self.train_loader
        def 					UpperCAmelCase		(       self				)       ->   Any:
            return self.get_dataloader('''dev'''					,       self.hparams.eval_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Tuple:
            return self.get_dataloader('''test'''					,       self.hparams.eval_batch_size					,       shuffle=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[int]:
            return os.path.join(
                self.hparams.data_dir					,       '''cached_{}_{}_{}'''.format(
                    SCREAMING_SNAKE_CASE_					,       list(filter(SCREAMING_SNAKE_CASE_					,       self.hparams.model_name_or_path.split('''/'''				)				)				).pop()					,       str(self.hparams.max_seq_length				)					,       )					,       )
        @pl.utilities.rank_zero_only
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            UpperCamelCase :Dict										=     self.output_dir.joinpath('''best_tfmr'''				)
            UpperCamelCase :str										=     self.step_count
            self.model.save_pretrained(SCREAMING_SNAKE_CASE_				)
            self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_				)
  
  
  
  
        @staticmethod
        def 					UpperCAmelCase		(       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
            parser.add_argument(
                '''--model_name_or_path'''					,       default=SCREAMING_SNAKE_CASE_					,       type=SCREAMING_SNAKE_CASE_					,       required=SCREAMING_SNAKE_CASE_					,       help='''Path to pretrained model or model identifier from huggingface.co/models'''					,       )
            parser.add_argument(
                '''--config_name'''					,       default=''''''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Pretrained config name or path if not the same as model_name'''				)
            parser.add_argument(
                '''--tokenizer_name'''					,       default=SCREAMING_SNAKE_CASE_					,       type=SCREAMING_SNAKE_CASE_					,       help='''Pretrained tokenizer name or path if not the same as model_name'''					,       )
            parser.add_argument(
                '''--cache_dir'''					,       default=''''''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''					,       )
            parser.add_argument(
                '''--encoder_layerdrop'''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''					,       )
            parser.add_argument(
                '''--decoder_layerdrop'''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''					,       )
            parser.add_argument(
                '''--dropout'''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Dropout probability (Optional). Goes into model.config'''					,       )
            parser.add_argument(
                '''--attention_dropout'''					,       type=SCREAMING_SNAKE_CASE_					,       help='''Attention dropout probability (Optional). Goes into model.config'''					,       )
            parser.add_argument('''--learning_rate'''					,       default=5e-5					,       type=SCREAMING_SNAKE_CASE_					,       help='''The initial learning rate for Adam.'''				)
            parser.add_argument(
                '''--lr_scheduler'''					,       default='''linear'''					,       choices=SCREAMING_SNAKE_CASE_					,       metavar=SCREAMING_SNAKE_CASE_					,       type=SCREAMING_SNAKE_CASE_					,       help='''Learning rate scheduler'''					,       )
            parser.add_argument('''--weight_decay'''					,       default=0.0					,       type=SCREAMING_SNAKE_CASE_					,       help='''Weight decay if we apply some.'''				)
            parser.add_argument('''--adam_epsilon'''					,       default=1e-8					,       type=SCREAMING_SNAKE_CASE_					,       help='''Epsilon for Adam optimizer.'''				)
            parser.add_argument('''--warmup_steps'''					,       default=0					,       type=SCREAMING_SNAKE_CASE_					,       help='''Linear warmup over warmup_steps.'''				)
            parser.add_argument('''--num_workers'''					,       default=4					,       type=SCREAMING_SNAKE_CASE_					,       help='''kwarg passed to DataLoader'''				)
            parser.add_argument('''--num_train_epochs'''					,       dest='''max_epochs'''					,       default=3					,       type=SCREAMING_SNAKE_CASE_				)
            parser.add_argument('''--train_batch_size'''					,       default=32					,       type=SCREAMING_SNAKE_CASE_				)
            parser.add_argument('''--eval_batch_size'''					,       default=32					,       type=SCREAMING_SNAKE_CASE_				)
            parser.add_argument('''--adafactor'''					,       action='''store_true'''				)
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      pl.Callback				):
  
  
  
        """simple docstring"""
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   int:
            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     UpperCAmelCase_		(      pl.Callback				):
  
  
  
        """simple docstring"""
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
            UpperCamelCase :Dict										=     trainer.lr_schedulers[0]['''scheduler''']
            UpperCamelCase :List[Any]										=     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
            pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            rank_zero_info('''***** Validation results *****'''				)
            UpperCamelCase :Any										=     trainer.callback_metrics
            # Log results
            for key in sorted(SCREAMING_SNAKE_CASE_				):
                if key not in ["log", "progress_bar"]:
                    rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_					,       str(metrics[key]				)				)				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            rank_zero_info('''***** Test results *****'''				)
            UpperCamelCase :List[str]										=     trainer.callback_metrics
            # Log and save results to file
            UpperCamelCase :Any										=     os.path.join(pl_module.hparams.output_dir					,       '''test_results.txt'''				)
            with open(SCREAMING_SNAKE_CASE_					,       '''w'''				) as writer:
                for key in sorted(SCREAMING_SNAKE_CASE_				):
                    if key not in ["log", "progress_bar"]:
                        rank_zero_info('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_					,       str(metrics[key]				)				)				)
                        writer.write('''{} = {}\n'''.format(SCREAMING_SNAKE_CASE_					,       str(metrics[key]				)				)				)
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Dict ):
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          '''--output_dir'''  ,						default=SCREAMING_SNAKE_CASE__  ,						type=SCREAMING_SNAKE_CASE__  ,						required=SCREAMING_SNAKE_CASE__  ,						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=SCREAMING_SNAKE_CASE__  ,						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=SCREAMING_SNAKE_CASE__ )
      parser.add_argument('''--max_grad_norm'''  ,						dest='''gradient_clip_val'''  ,						default=1.0  ,						type=SCREAMING_SNAKE_CASE__  ,						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=SCREAMING_SNAKE_CASE__  ,						default=1  ,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,						)
      parser.add_argument('''--seed'''  ,						type=SCREAMING_SNAKE_CASE__  ,						default=42  ,						help='''random seed for initialization''' )
      parser.add_argument(
          '''--data_dir'''  ,						default=SCREAMING_SNAKE_CASE__  ,						type=SCREAMING_SNAKE_CASE__  ,						required=SCREAMING_SNAKE_CASE__  ,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,						)
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			BaseTransformer  ,						SCREAMING_SNAKE_CASE__						:			argparse.Namespace  ,						SCREAMING_SNAKE_CASE__						:			Tuple=None  ,						SCREAMING_SNAKE_CASE__						:			str=True  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]=None  ,						SCREAMING_SNAKE_CASE__						:			Tuple=[]  ,						SCREAMING_SNAKE_CASE__						:			List[str]=None  ,						SCREAMING_SNAKE_CASE__						:			str=None  ,						**SCREAMING_SNAKE_CASE__						:			int  ,						):
      pl.seed_everything(args.seed )
  
      # init model
      UpperCamelCase :Optional[Any]										=     Path(model.hparams.output_dir )
      odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
  
      # add custom checkpoints
      if checkpoint_callback is None:
          UpperCamelCase :Tuple										=     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(SCREAMING_SNAKE_CASE__ )
      if logging_callback is None:
          UpperCamelCase :Any										=     LoggingCallback()
  
      UpperCamelCase :Any										=     {}
  
      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
          UpperCamelCase :Optional[int]										=     16
          # train_params["amp_level"] = args.fp16_opt_level
  
      if args.gpus > 1:
          UpperCamelCase :Optional[int]										=     '''auto'''  # "ddp"
          UpperCamelCase :Optional[int]										=     '''ddp'''
  
      UpperCamelCase :int										=     args.accumulate_grad_batches
      UpperCamelCase :Tuple										=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      UpperCamelCase :List[str]										=     '''auto'''
  
      UpperCamelCase :Optional[int]										=     pl.Trainer.from_argparse_args(
          SCREAMING_SNAKE_CASE__  ,						weights_summary=SCREAMING_SNAKE_CASE__  ,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,						logger=SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__  ,						)
  
      if args.do_train:
          trainer.fit(SCREAMING_SNAKE_CASE__ )
  
      return trainer
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  