
"""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



"""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)

