code: 
  """simple docstring"""
  
  
  
  
  
  
  
  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_     				=   logging.getLogger(__name__)
  
  require_version('pytorch_lightning>=1.0.4')
  
  SCREAMING_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
  SCREAMING_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
  }
  SCREAMING_SNAKE_CASE_     				=   sorted(arg_to_scheduler.keys())
  SCREAMING_SNAKE_CASE_     				=   '{' + ', '.join(arg_to_scheduler_choices) + '}'
  
  
  
  
  
  
  class     __a			(					pl.LightningModule			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=None							,lowerCamelCase_="base"							,lowerCamelCase_=None							,lowerCamelCase_=None							,lowerCamelCase_=None							,**lowerCamelCase_							,):
  
  
  
            '''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_		)
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					Path(self.hparams.output_dir		)
            _UpperCAmelCase   	=					self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
               _UpperCAmelCase   	=					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:
               _UpperCAmelCase   	=					config
  
            _UpperCAmelCase   	=					("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:
               _UpperCAmelCase   	=					AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,cache_dir=lowerCamelCase_							,)
            else:
               _UpperCAmelCase   	=					tokenizer
            _UpperCAmelCase   	=					MODEL_MODES[mode]
            if model is None:
               _UpperCAmelCase   	=					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:
               _UpperCAmelCase   	=					model
  
  
  
  
  
  
  
         def    _snake_case			(					self							,*lowerCamelCase_							,**lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_type.from_pretrained(*lowerCamelCase_							,**lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					arg_to_scheduler[self.hparams.lr_scheduler]
            _UpperCAmelCase   	=					get_schedule_func(
                self.opt							,num_warmup_steps=self.hparams.warmup_steps							,num_training_steps=self.total_steps()		)
            _UpperCAmelCase   	=					{"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model
            _UpperCAmelCase   	=					["bias", "LayerNorm.weight"]
            _UpperCAmelCase   	=					[
                {
                    "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   	=					Adafactor(
                   lowerCamelCase_							,lr=self.hparams.learning_rate							,scale_parameter=lowerCamelCase_							,relative_step=lowerCamelCase_		)
  
            else:
               _UpperCAmelCase   	=					AdamW(
                   lowerCamelCase_							,lr=self.hparams.learning_rate							,eps=self.hparams.adam_epsilon		)
            _UpperCAmelCase   	=					optimizer
  
            _UpperCAmelCase   	=					self.get_lr_scheduler()
  
            return [optimizer], [scheduler]
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            return self.validation_step(lowerCamelCase_							,lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            return self.validation_end(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					max(1							,self.hparams.gpus		)  # TODO: consider num_tpu_cores
            _UpperCAmelCase   	=					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if stage == "test":
               _UpperCAmelCase   	=					len(self.test_dataloader().dataset		)
            else:
               _UpperCAmelCase   	=					self.get_dataloader("train"							,self.hparams.train_batch_size							,shuffle=lowerCamelCase_		)
               _UpperCAmelCase   	=					len(self.train_dataloader().dataset		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = False		):
  
  
  
            '''simple docstring'''
  
            raise NotImplementedError("You must implement this for your task"		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return self.train_loader
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return self.get_dataloader("dev"							,self.hparams.eval_batch_size							,shuffle=lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return self.get_dataloader("test"							,self.hparams.eval_batch_size							,shuffle=lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''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    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.output_dir.joinpath("best_tfmr"		)
            _UpperCAmelCase   	=					self.step_count
            self.model.save_pretrained(lowerCamelCase_		)
            self.tokenizer.save_pretrained(lowerCamelCase_		)
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''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     __a			(					pl.Callback			):
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''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     __a			(					pl.Callback			):
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					trainer.lr_schedulers[0]["scheduler"]
            _UpperCAmelCase   	=					{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
            pl_module.logger.log_metrics(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            rank_zero_info("***** Validation results *****"		)
            _UpperCAmelCase   	=					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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            rank_zero_info("***** Test results *****"		)
            _UpperCAmelCase   	=					trainer.callback_metrics
            # Log and save results to file
            _UpperCAmelCase   	=					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		(		a_			:		Optional[Any]    ,  a_			:		Optional[Any]				)     ->       None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir"    ,  default=a_    ,  type=a_    ,  required=a_    ,  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=a_    ,  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=a_				)
     parser.add_argument("--max_grad_norm"    ,  dest="gradient_clip_val"    ,  default=1.0    ,  type=a_    ,  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=a_    ,  default=1    ,  help="Number of updates steps to accumulate before performing a backward/update pass."    ,  )
     parser.add_argument("--seed"    ,  type=a_    ,  default=42    ,  help="random seed for initialization"				)
     parser.add_argument(
         "--data_dir"    ,  default=a_    ,  type=a_    ,  required=a_    ,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."    ,  )
  
  
  def 			__UpperCAmelCase		(		a_			:		BaseTransformer    ,  a_			:		argparse.Namespace    ,  a_			:		int=None    ,  a_			:		str=True    ,  a_			:		List[Any]=None    ,  a_			:		List[str]=[]    ,  a_			:		Any=None    ,  a_			:		List[str]=None    ,  **a_			:		int    ,  )     ->       Optional[Any]:
     pl.seed_everything(args.seed				)
  
     # init model
     _UpperCAmelCase   	=					Path(model.hparams.output_dir				)
     odir.mkdir(exist_ok=a_				)
  
     # add custom checkpoints
     if checkpoint_callback is None:
        _UpperCAmelCase   	=					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(a_				)
     if logging_callback is None:
        _UpperCAmelCase   	=					LoggingCallback()
  
     _UpperCAmelCase   	=					{}
  
     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
        _UpperCAmelCase   	=					16
        # train_params["amp_level"] = args.fp16_opt_level
  
     if args.gpus > 1:
        _UpperCAmelCase   	=					"auto"  # "ddp"
        _UpperCAmelCase   	=					"ddp"
  
     _UpperCAmelCase   	=					args.accumulate_grad_batches
     _UpperCAmelCase   	=					None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _UpperCAmelCase   	=					"auto"
  
     _UpperCAmelCase   	=					pl.Trainer.from_argparse_args(
         a_    ,  weights_summary=a_    ,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]    ,  logger=a_    ,  **a_    ,  )
  
     if args.do_train:
        trainer.fit(a_				)
  
     return trainer
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					depth
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					min_leaf_size
            _UpperCAmelCase   	=					None
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if labels.ndim != 1:
               print("Error: Input labels must be one dimensional"		)
  
            return np.mean((labels - prediction) ** 2		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if x.ndim != 1:
               print("Error: Input data set must be one dimensional"		)
               return
            if len(lowerCamelCase_		) != len(lowerCamelCase_		):
               print("Error: X and y have different lengths"		)
               return
            if y.ndim != 1:
               print("Error: Data set labels must be one dimensional"		)
               return
  
            if len(lowerCamelCase_		) < 2 * self.min_leaf_size:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            if self.depth == 1:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					self.mean_squared_error(lowerCamelCase_							,np.mean(lowerCamelCase_		)		) * 2
            for i in range(len(lowerCamelCase_		)		):
               if len(x[:i]		) < self.min_leaf_size:
                  continue
               elif len(x[i:]		) < self.min_leaf_size:
                  continue
               else:
                  _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                  _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                  _UpperCAmelCase   	=					error_left + error_right
                  if error < min_error:
                     _UpperCAmelCase   	=					i
                     _UpperCAmelCase   	=					error
  
            if best_split != 0:
               _UpperCAmelCase   	=					x[:best_split]
               _UpperCAmelCase   	=					y[:best_split]
               _UpperCAmelCase   	=					x[best_split:]
               _UpperCAmelCase   	=					y[best_split:]
  
               _UpperCAmelCase   	=					x[best_split]
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               self.left.train(lowerCamelCase_							,lowerCamelCase_		)
               self.right.train(lowerCamelCase_							,lowerCamelCase_		)
            else:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if self.prediction is not None:
               return self.prediction
            elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                  return self.right.predict(lowerCamelCase_		)
               else:
                  return self.left.predict(lowerCamelCase_		)
            else:
               print("Error: Decision tree not yet trained"		)
               return None
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					float(0		)
            for label in labels:
               squared_error_sum += (label - prediction) ** 2
  
            return float(squared_error_sum / labels.size		)
  
  
  
  def 			__UpperCAmelCase		(		)     ->       Dict:
     _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
     _UpperCAmelCase   	=					np.sin(a_				)
  
     _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
     tree.train(a_    ,  a_				)
  
     _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
     _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
     _UpperCAmelCase   	=					np.mean((predictions - test_cases) ** 2				)
  
     print("Test values: " + str(a_				)				)
     print("Predictions: " + str(a_				)				)
     print("Average error: " + str(a_				)				)
  
  
  if __name__ == "__main__":
    main()
    import doctest
  
    doctest.testmod(name='mean_squarred_error', verbose=True)
  
  