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
  
  
  _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "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
  _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					{
      "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
  }
  _SCREAMING_SNAKE_CASE    :				str       =					sorted(arg_to_scheduler.keys())
  _SCREAMING_SNAKE_CASE    :				Any       =					"{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  class 						A__      (					pl.LightningModule			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case="base"    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=None    ,      **__snake_case    ,      ):
           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading
  
           self.save_hyperparameters(__snake_case					)
           snake_case					=       0
           snake_case					=       Path(self.hparams.output_dir					)
           snake_case					=       self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                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=__snake_case    ,      **__snake_case    ,      )
           else:
                snake_case					=       config
  
           snake_case					=       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
           for p in extra_model_params:
                if getattr(self.hparams    ,      __snake_case    ,      __snake_case					):
                     assert hasattr(self.config    ,      __snake_case					), F'''model config doesn\'t have a `{p}` attribute'''
                     setattr(self.config    ,      __snake_case    ,      getattr(self.hparams    ,      __snake_case					)					)
  
           if tokenizer is None:
                snake_case					=       AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,      cache_dir=__snake_case    ,      )
           else:
                snake_case					=       tokenizer
           snake_case					=       MODEL_MODES[mode]
           if model is None:
                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=__snake_case    ,      )
           else:
                snake_case					=       model
      def 	a_    ( self    ,      *__snake_case    ,      **__snake_case					):
           snake_case					=       self.model_type.from_pretrained(*__snake_case    ,      **__snake_case					)
      def 	a_    ( self					):
           snake_case					=       arg_to_scheduler[self.hparams.lr_scheduler]
           snake_case					=       get_schedule_func(
               self.opt    ,      num_warmup_steps=self.hparams.warmup_steps    ,      num_training_steps=self.total_steps()					)
           snake_case					=       {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
           return scheduler
      def 	a_    ( self					):
           snake_case					=       self.model
           snake_case					=       ['''bias''', '''LayerNorm.weight''']
           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:
                snake_case					=       Adafactor(
                    __snake_case    ,      lr=self.hparams.learning_rate    ,      scale_parameter=__snake_case    ,      relative_step=__snake_case					)
  
           else:
                snake_case					=       AdamW(
                    __snake_case    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon					)
           snake_case					=       optimizer
  
           snake_case					=       self.get_lr_scheduler()
  
           return [optimizer], [scheduler]
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           return self.validation_step(__snake_case    ,      __snake_case					)
      def 	a_    ( self    ,      __snake_case					):
           return self.validation_end(__snake_case					)
      def 	a_    ( self					):
           snake_case					=       max(1    ,      self.hparams.gpus					)  # TODO: consider num_tpu_cores
           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 	a_    ( self    ,      __snake_case					):
           if stage == "test":
                snake_case					=       len(self.test_dataloader().dataset					)
           else:
                snake_case					=       self.get_dataloader('''train'''    ,      self.hparams.train_batch_size    ,      shuffle=__snake_case					)
                snake_case					=       len(self.train_dataloader().dataset					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = False					):
           raise NotImplementedError('''You must implement this for your task'''					)
      def 	a_    ( self					):
           return self.train_loader
      def 	a_    ( self					):
           return self.get_dataloader('''dev'''    ,      self.hparams.eval_batch_size    ,      shuffle=__snake_case					)
      def 	a_    ( self					):
           return self.get_dataloader('''test'''    ,      self.hparams.eval_batch_size    ,      shuffle=__snake_case					)
      def 	a_    ( self    ,      __snake_case					):
           return os.path.join(
               self.hparams.data_dir    ,      '''cached_{}_{}_{}'''.format(
                   __snake_case    ,      list(filter(__snake_case    ,      self.hparams.model_name_or_path.split('''/'''					)					)					).pop()    ,      str(self.hparams.max_seq_length					)    ,      )    ,      )
      @pl.utilities.rank_zero_only
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       self.output_dir.joinpath('''best_tfmr'''					)
           snake_case					=       self.step_count
           self.model.save_pretrained(__snake_case					)
           self.tokenizer.save_pretrained(__snake_case					)
  
  
  
  
      @staticmethod
      def 	a_    ( __snake_case    ,      __snake_case					):
           parser.add_argument(
               '''--model_name_or_path'''    ,      default=__snake_case    ,      type=__snake_case    ,      required=__snake_case    ,      help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,      )
           parser.add_argument(
               '''--config_name'''    ,      default=''''''    ,      type=__snake_case    ,      help='''Pretrained config name or path if not the same as model_name'''					)
           parser.add_argument(
               '''--tokenizer_name'''    ,      default=__snake_case    ,      type=__snake_case    ,      help='''Pretrained tokenizer name or path if not the same as model_name'''    ,      )
           parser.add_argument(
               '''--cache_dir'''    ,      default=''''''    ,      type=__snake_case    ,      help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''    ,      )
           parser.add_argument(
               '''--encoder_layerdrop'''    ,      type=__snake_case    ,      help='''Encoder layer dropout probability (Optional). Goes into model.config'''    ,      )
           parser.add_argument(
               '''--decoder_layerdrop'''    ,      type=__snake_case    ,      help='''Decoder layer dropout probability (Optional). Goes into model.config'''    ,      )
           parser.add_argument(
               '''--dropout'''    ,      type=__snake_case    ,      help='''Dropout probability (Optional). Goes into model.config'''    ,      )
           parser.add_argument(
               '''--attention_dropout'''    ,      type=__snake_case    ,      help='''Attention dropout probability (Optional). Goes into model.config'''    ,      )
           parser.add_argument('''--learning_rate'''    ,      default=5E-5    ,      type=__snake_case    ,      help='''The initial learning rate for Adam.'''					)
           parser.add_argument(
               '''--lr_scheduler'''    ,      default='''linear'''    ,      choices=__snake_case    ,      metavar=__snake_case    ,      type=__snake_case    ,      help='''Learning rate scheduler'''    ,      )
           parser.add_argument('''--weight_decay'''    ,      default=0.0    ,      type=__snake_case    ,      help='''Weight decay if we apply some.'''					)
           parser.add_argument('''--adam_epsilon'''    ,      default=1E-8    ,      type=__snake_case    ,      help='''Epsilon for Adam optimizer.'''					)
           parser.add_argument('''--warmup_steps'''    ,      default=0    ,      type=__snake_case    ,      help='''Linear warmup over warmup_steps.'''					)
           parser.add_argument('''--num_workers'''    ,      default=4    ,      type=__snake_case    ,      help='''kwarg passed to DataLoader'''					)
           parser.add_argument('''--num_train_epochs'''    ,      dest='''max_epochs'''    ,      default=3    ,      type=__snake_case					)
           parser.add_argument('''--train_batch_size'''    ,      default=3_2    ,      type=__snake_case					)
           parser.add_argument('''--eval_batch_size'''    ,      default=3_2    ,      type=__snake_case					)
           parser.add_argument('''--adafactor'''    ,      action='''store_true'''					)
  
  
  
  
  
  
  class 						A__      (					pl.Callback			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           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 						A__      (					pl.Callback			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           snake_case					=       trainer.lr_schedulers[0]['''scheduler''']
           snake_case					=       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
           pl_module.logger.log_metrics(__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           rank_zero_info('''***** Validation results *****'''					)
           snake_case					=       trainer.callback_metrics
           # Log results
           for key in sorted(__snake_case					):
                if key not in ["log", "progress_bar"]:
                     rank_zero_info('''{} = {}\n'''.format(__snake_case    ,      str(metrics[key]					)					)					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           rank_zero_info('''***** Test results *****'''					)
           snake_case					=       trainer.callback_metrics
           # Log and save results to file
           snake_case					=       os.path.join(pl_module.hparams.output_dir    ,      '''test_results.txt'''					)
           with open(__snake_case    ,      '''w'''					) as writer:
                for key in sorted(__snake_case					):
                     if key not in ["log", "progress_bar"]:
                          rank_zero_info('''{} = {}\n'''.format(__snake_case    ,      str(metrics[key]					)					)					)
                          writer.write('''{} = {}\n'''.format(__snake_case    ,      str(metrics[key]					)					)					)
  
  
  
  
  
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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_							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       pl.seed_everything(args.seed       )
  
       # init model
       snake_case					=       Path(model.hparams.output_dir       )
       odir.mkdir(exist_ok=UpperCamelCase_       )
  
       # add custom checkpoints
       if checkpoint_callback is None:
            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:
            snake_case					=       LoggingCallback()
  
       snake_case					=       {}
  
       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
            snake_case					=       16
            # train_params["amp_level"] = args.fp16_opt_level
  
       if args.gpus > 1:
            snake_case					=       '''auto'''  # "ddp"
            snake_case					=       '''ddp'''
  
       snake_case					=       args.accumulate_grad_batches
       snake_case					=       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       snake_case					=       '''auto'''
  
       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_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  