

'''simple docstring'''
import numpy as np







class 							A__					:





  def __init__(	self   :			Tuple							,      _a   :			int=5							,      _a   :			Any=5				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=depth
       _SCREAMING_SNAKE_CASE												=0
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=min_leaf_size
       _SCREAMING_SNAKE_CASE												=None





  def 		A						(	self   :			Dict							,      _a   :			Dict							,      _a   :			List[Any]				)			->					Any:





       '''simple docstring'''






       if labels.ndim != 1:
            print('Error: Input labels must be one dimensional'				)

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





  def 		A						(	self   :			Any							,      _a   :			List[Any]							,      _a   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       if x.ndim != 1:
            print('Error: Input data set must be one dimensional'				)
            return
       if len(_a				) != len(_a				):
            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(_a				) < 2 * self.min_leaf_size:
            _SCREAMING_SNAKE_CASE												=np.mean(_a				)
            return

       if self.depth == 1:
            _SCREAMING_SNAKE_CASE												=np.mean(_a				)
            return

       _SCREAMING_SNAKE_CASE												=0
       _SCREAMING_SNAKE_CASE												=self.mean_squared_error(_a							,      np.mean(_a				)				) * 2
       for i in range(len(_a				)				):
            if len(x[:i]				) < self.min_leaf_size:
                 continue
            elif len(x[i:]				) < self.min_leaf_size:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE												=self.mean_squared_error(x[:i]							,      np.mean(y[:i]				)				)
                 _SCREAMING_SNAKE_CASE												=self.mean_squared_error(x[i:]							,      np.mean(y[i:]				)				)
                 _SCREAMING_SNAKE_CASE												=error_left + error_right
                 if error < min_error:
                      _SCREAMING_SNAKE_CASE												=i
                      _SCREAMING_SNAKE_CASE												=error

       if best_split != 0:
            _SCREAMING_SNAKE_CASE												=x[:best_split]
            _SCREAMING_SNAKE_CASE												=y[:best_split]
            _SCREAMING_SNAKE_CASE												=x[best_split:]
            _SCREAMING_SNAKE_CASE												=y[best_split:]

            _SCREAMING_SNAKE_CASE												=x[best_split]
            _SCREAMING_SNAKE_CASE												=DecisionTree(
                depth=self.depth - 1							,      min_leaf_size=self.min_leaf_size				)
            _SCREAMING_SNAKE_CASE												=DecisionTree(
                depth=self.depth - 1							,      min_leaf_size=self.min_leaf_size				)
            self.left.train(_a							,      _a				)
            self.right.train(_a							,      _a				)
       else:
            _SCREAMING_SNAKE_CASE												=np.mean(_a				)

       return





  def 		A						(	self   :			int							,      _a   :			Any				)			->					int:





       '''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(_a				)
            else:
                 return self.left.predict(_a				)
       else:
            print('Error: Decision tree not yet trained'				)
            return None







class 							A__					:





  @staticmethod
  def 		A						(	_a   :			int							,      _a   :			int				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=float(0				)
       for label in labels:
            squared_error_sum += (label - prediction) ** 2

       return float(squared_error_sum / labels.size				)


def  _lowerCAmelCase      (		)   ->    Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=np.arange(-1.0							,				1.0							,				0.0_05       )
     _SCREAMING_SNAKE_CASE												=np.sin(_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=DecisionTree(depth=10							,				min_leaf_size=10       )
     tree.train(_UpperCamelCase							,				_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=(np.random.rand(10       ) * 2) - 1
     _SCREAMING_SNAKE_CASE												=np.array([tree.predict(_UpperCamelCase       ) for x in test_cases]       )
     _SCREAMING_SNAKE_CASE												=np.mean((predictions - test_cases) ** 2       )

     print('Test values: ' + str(_UpperCamelCase       )       )
     print('Predictions: ' + str(_UpperCamelCase       )       )
     print('Average error: ' + str(_UpperCamelCase       )       )


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

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



'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase				:   int      				=  logging.getLogger(__name__)







@dataclass
class 							A__					:
  A__					=       field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  A__					=       field(
      default=A__  ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
  A__					=       field(default=A__  ,     metadata={'help': 'Set this flag to use fast tokenization.'}						)
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  A__					=       field(
      default=A__  ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,     )


def  _lowerCAmelCase      (		)   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

     if len(sys.argv       ) == 2 and sys.argv[1].endswith('.json'       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir       )
         and os.listdir(training_args.output_dir       )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
              ' --overwrite_output_dir to overcome.'       )

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,				)
     logger.warning(
         'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,				training_args.local_rank							,				training_args.device							,				training_args.n_gpu							,				bool(training_args.local_rank != -1       )							,				training_args.fpaa							,				)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'							,				_UpperCamelCase       )

     # Prepare Question-Answering task
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _SCREAMING_SNAKE_CASE												=AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				)
     _SCREAMING_SNAKE_CASE												=AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				use_fast=_UpperCamelCase							,				)
     _SCREAMING_SNAKE_CASE												=AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path							,				from_tf=bool('.ckpt' in model_args.model_name_or_path       )							,				config=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)

     # Get datasets
     _SCREAMING_SNAKE_CASE												=hasattr(model.config							,				'lang2id'       )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir       )
         if training_args.do_train
         else None
     )
     _SCREAMING_SNAKE_CASE												=(
         SquadDataset(
             _UpperCamelCase							,				tokenizer=_UpperCamelCase							,				mode='dev'							,				is_language_sensitive=_UpperCamelCase							,				cache_dir=model_args.cache_dir							,				)
         if training_args.do_eval
         else None
     )

     # Data collator
     _SCREAMING_SNAKE_CASE												=DataCollatorWithPadding(_UpperCamelCase							,				pad_to_multiple_of=8       ) if training_args.fpaa else None

     # Initialize our Trainer
     _SCREAMING_SNAKE_CASE												=Trainer(
         model=_UpperCamelCase							,				args=_UpperCamelCase							,				train_dataset=_UpperCamelCase							,				eval_dataset=_UpperCamelCase							,				data_collator=_UpperCamelCase							,				)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple       )   ->    List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
