

"""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


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        _UpperCamelCase	(						pl.LightningModule	):


  """simple docstring"""





  def __init__( self					: List[Any]       ,       snake_case					: Any       ,       snake_case					: int=None       ,       snake_case					: Union[str, Any]="base"       ,       snake_case					: List[str]=None       ,       snake_case					: Dict=None       ,       snake_case					: Dict=None       ,       **snake_case					: Dict       ,       )	->			Optional[int]:


         '''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(lowercase_      )
         __magic_name__			:		Union[str, Any]        =     0
         __magic_name__			:		Any        =     Path(self.hparams.output_dir      )
         __magic_name__			:		Dict        =     self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
                __magic_name__			:		Tuple        =     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:
                __magic_name__			:		PretrainedConfig        =     config

         __magic_name__			:		str        =     ("""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:
                __magic_name__			:		List[Any]        =     AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,       cache_dir=lowercase_       ,       )
         else:
                __magic_name__			:		PreTrainedTokenizer        =     tokenizer
         __magic_name__			:		List[str]        =     MODEL_MODES[mode]
         if model is None:
                __magic_name__			:		Any        =     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:
                __magic_name__			:		List[str]        =     model



  def 							_UpperCAmelCase      ( self					: Dict       ,       *snake_case					: List[Any]       ,       **snake_case					: List[Any]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     self.model_type.from_pretrained(*lowercase_       ,       **lowercase_      )



  def 							_UpperCAmelCase      ( self					: str      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		int        =     arg_to_scheduler[self.hparams.lr_scheduler]
         __magic_name__			:		List[Any]        =     get_schedule_func(
             self.opt       ,       num_warmup_steps=self.hparams.warmup_steps       ,       num_training_steps=self.total_steps()      )
         __magic_name__			:		List[Any]        =     {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
         return scheduler



  def 							_UpperCAmelCase      ( self					: Any      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self.model
         __magic_name__			:		Union[str, Any]        =     ["""bias""", """LayerNorm.weight"""]
         __magic_name__			:		Optional[Any]        =     [
             {
                 """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:
                __magic_name__			:		Tuple        =     Adafactor(
                    lowercase_       ,       lr=self.hparams.learning_rate       ,       scale_parameter=lowercase_       ,       relative_step=lowercase_      )

         else:
                __magic_name__			:		List[Any]        =     AdamW(
                    lowercase_       ,       lr=self.hparams.learning_rate       ,       eps=self.hparams.adam_epsilon      )
         __magic_name__			:		str        =     optimizer

         __magic_name__			:		Dict        =     self.get_lr_scheduler()

         return [optimizer], [scheduler]



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[int]       ,       snake_case					: Union[str, Any]      )	->			Optional[Any]:


         '''simple docstring'''





         return self.validation_step(lowercase_       ,       lowercase_      )



  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: Tuple      )	->			Dict:


         '''simple docstring'''





         return self.validation_end(lowercase_      )



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			int:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     max(1       ,       self.hparams.gpus      )  # TODO: consider num_tpu_cores
         __magic_name__			:		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					: List[Any]       ,       snake_case					: str      )	->			Optional[int]:


         '''simple docstring'''





         if mode == "test":
                __magic_name__			:		List[Any]        =     len(self.test_dataloader().dataset      )
         else:
                __magic_name__			:		Any        =     self.get_dataloader('''train'''       ,       self.hparams.train_batch_size       ,       shuffle=lowercase_      )
                __magic_name__			:		int        =     len(self.train_dataloader().dataset      )



  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: Optional[int]       ,       snake_case					: Optional[Any]       ,       snake_case					: Optional[Any] = False      )	->			List[Any]:


         '''simple docstring'''





         raise NotImplementedError('''You must implement this for your task'''      )



  def 							_UpperCAmelCase      ( self					: int      )	->			Any:


         '''simple docstring'''





         return self.train_loader



  def 							_UpperCAmelCase      ( self					: Dict      )	->			List[Any]:


         '''simple docstring'''





         return self.get_dataloader('''dev'''       ,       self.hparams.eval_batch_size       ,       shuffle=lowercase_      )



  def 							_UpperCAmelCase      ( self					: Tuple      )	->			int:


         '''simple docstring'''





         return self.get_dataloader('''test'''       ,       self.hparams.eval_batch_size       ,       shuffle=lowercase_      )



  def 							_UpperCAmelCase      ( self					: int       ,       snake_case					: Any      )	->			List[Any]:


         '''simple docstring'''





         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					: Dict       ,       snake_case					: int      )	->			None:


         '''simple docstring'''





         __magic_name__			:		Dict        =     self.output_dir.joinpath('''best_tfmr'''      )
         __magic_name__			:		Tuple        =     self.step_count
         self.model.save_pretrained(lowercase_      )
         self.tokenizer.save_pretrained(lowercase_      )




  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: Tuple       ,       snake_case					: Union[str, Any]      )	->			str:


         '''simple docstring'''





         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					: Union[str, Any]       ,       snake_case					: Dict       ,       snake_case					: Union[str, Any]      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		Dict        =     trainer.lr_schedulers[0]["""scheduler"""]
         __magic_name__			:		Optional[int]        =     {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
         pl_module.logger.log_metrics(lowercase_      )



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: Optional[int]       ,       snake_case					: List[Any]      )	->			Union[str, Any]:


         '''simple docstring'''





         rank_zero_info('''***** Validation results *****'''      )
         __magic_name__			:		str        =     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					: str       ,       snake_case					: List[Any]       ,       snake_case					: Optional[int]      )	->			str:


         '''simple docstring'''





         rank_zero_info('''***** Test results *****'''      )
         __magic_name__			:		Tuple        =     trainer.callback_metrics
         # Log and save results to file
         __magic_name__			:		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       UpperCamelCase_					(     lowerCamelCase							:					int     , lowerCamelCase							:					Optional[Any]   )						->     Optional[Any]:



       """simple docstring"""







       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       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Dict     , lowerCamelCase							:					List[Any]=None     , lowerCamelCase							:					Union[str, Any]=True     , lowerCamelCase							:					Optional[Any]=[]     , lowerCamelCase							:					Optional[Any]=None     , lowerCamelCase							:					Tuple=None     , **lowerCamelCase							:					List[str]     , )						->     str:



       """simple docstring"""







       pl.seed_everything(args.seed   )

       # init model
       __magic_name__			:		List[str]        =     Path(model.hparams.output_dir   )
       odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__   )

       # add custom checkpoints
       if checkpoint_callback is None:
              __magic_name__			:		Any        =     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:
              __magic_name__			:		int        =     LoggingCallback()

       __magic_name__			:		Union[str, Any]        =     {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              __magic_name__			:		Optional[int]        =     16
              __magic_name__			:		Optional[Any]        =     args.fpaa_opt_level

       if args.gpus > 1:
              __magic_name__			:		str        =     """ddp"""

       __magic_name__			:		Union[str, Any]        =     args.accumulate_grad_batches
       __magic_name__			:		str        =     extra_train_kwargs.get('''accelerator'''     , SCREAMING_SNAKE_CASE__   )
       __magic_name__			:		Any        =     extra_train_kwargs.get('''profiler'''     , SCREAMING_SNAKE_CASE__   )

       __magic_name__			:		List[str]        =     pl.Trainer.from_argparse_args(
           SCREAMING_SNAKE_CASE__     , weights_summary=SCREAMING_SNAKE_CASE__     , callbacks=[logging_callback] + extra_callbacks     , logger=SCREAMING_SNAKE_CASE__     , checkpoint_callback=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )

       if args.do_train:
              trainer.fit(SCREAMING_SNAKE_CASE__   )

       return trainer





import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

									return np.mean((labels - prediction) ** 2 )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															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(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

															lowerCAmelCase	:   Any					     =					x[best_split]
															lowerCAmelCase	:   str					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															lowerCAmelCase	:   List[str]					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															self.left.train(lowercase_    ,  lowercase_ )
															self.right.train(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					np.mean(lowercase_ )

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									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(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

						print("""Test values: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Predictions: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Average error: """ + str(SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='mean_squarred_error', verbose=True)

