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