code: 
  
  
  import numpy as np
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       Optional[Any]=5	,      UpperCamelCase       :       Tuple=5		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			depth
              _lowercase					:				Tuple           =			0
              _lowercase					:				int           =			None
              _lowercase					:				Union[str, Any]           =			None
              _lowercase					:				int           =			min_leaf_size
              _lowercase					:				Any           =			None
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if labels.ndim != 1:
                     print('''Error: Input labels must be one dimensional'''		)
  
              return np.mean((labels - prediction) ** 2		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if x.ndim != 1:
                     print('''Error: Input data set must be one dimensional'''		)
                     return
              if len(SCREAMING_SNAKE_CASE_		) != len(SCREAMING_SNAKE_CASE_		):
                     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(SCREAMING_SNAKE_CASE_		) < 2 * self.min_leaf_size:
                     _lowercase					:				Optional[Any]           =			np.mean(SCREAMING_SNAKE_CASE_		)
                     return
  
              if self.depth == 1:
                     _lowercase					:				Any           =			np.mean(SCREAMING_SNAKE_CASE_		)
                     return
  
              _lowercase					:				Tuple           =			0
              _lowercase					:				Optional[Any]           =			self.mean_squared_error(SCREAMING_SNAKE_CASE_	,      np.mean(SCREAMING_SNAKE_CASE_		)		) * 2
              for i in range(len(SCREAMING_SNAKE_CASE_		)		):
                     if len(x[:i]		) < self.min_leaf_size:
                            continue
                     elif len(x[i:]		) < self.min_leaf_size:
                            continue
                     else:
                            _lowercase					:				Tuple           =			self.mean_squared_error(x[:i]	,      np.mean(y[:i]		)		)
                            _lowercase					:				Union[str, Any]           =			self.mean_squared_error(x[i:]	,      np.mean(y[i:]		)		)
                            _lowercase					:				List[Any]           =			error_left + error_right
                            if error < min_error:
                                   _lowercase					:				List[str]           =			i
                                   _lowercase					:				Optional[int]           =			error
  
              if best_split != 0:
                     _lowercase					:				List[Any]           =			x[:best_split]
                     _lowercase					:				Dict           =			y[:best_split]
                     _lowercase					:				List[str]           =			x[best_split:]
                     _lowercase					:				Tuple           =			y[best_split:]
  
                     _lowercase					:				Dict           =			x[best_split]
                     _lowercase					:				Union[str, Any]           =			DecisionTree(
                         depth=self.depth - 1	,      min_leaf_size=self.min_leaf_size		)
                     _lowercase					:				Optional[int]           =			DecisionTree(
                         depth=self.depth - 1	,      min_leaf_size=self.min_leaf_size		)
                     self.left.train(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_		)
                     self.right.train(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_		)
              else:
                     _lowercase					:				List[Any]           =			np.mean(SCREAMING_SNAKE_CASE_		)
  
              return
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any	,      UpperCamelCase       :       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(SCREAMING_SNAKE_CASE_		)
                     else:
                            return self.left.predict(SCREAMING_SNAKE_CASE_		)
              else:
                     print('''Error: Decision tree not yet trained'''		)
                     return None
  
  
  
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       @staticmethod
       def   lowerCAmelCase_   ( UpperCamelCase       :       Tuple	,      UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			float(0		)
              for label in labels:
                     squared_error_sum += (label - prediction) ** 2
  
              return float(squared_error_sum / labels.size		)
  
  
  
  
  def   UpperCamelCase__   (    )						->							Optional[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			np.arange(-1.0    ,     1.0    ,     0.005			)
         _lowercase					:				Dict           =			np.sin(snake_case__			)
  
         _lowercase					:				List[Any]           =			DecisionTree(depth=10    ,     min_leaf_size=10			)
         tree.train(snake_case__    ,     snake_case__			)
  
         _lowercase					:				Optional[Any]           =			(np.random.rand(10			) * 2) - 1
         _lowercase					:				Tuple           =			np.array([tree.predict(snake_case__			) for x in test_cases]			)
         _lowercase					:				Tuple           =			np.mean((predictions - test_cases) ** 2			)
  
         print('''Test values: ''' + str(snake_case__			)			)
         print('''Predictions: ''' + str(snake_case__			)			)
         print('''Average error: ''' + str(snake_case__			)			)
  
  
  if __name__ == "__main__":
     main()
     import doctest
  
     doctest.testmod(name='mean_squarred_error', verbose=True)
style_context: 
  
  
  import os
  from pathlib import Path
  from typing import Dict, List
  
  import fire
  import torch
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from transformers.utils.logging import get_logger
  
  
  UpperCamelCase__				       =			get_logger(__name__)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if text.startswith(UpperCAmelCase_			):
                return text[len(UpperCAmelCase_			) :]
         return text  # or whatever
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			{}
         for k in state_dicts[0].keys():
                _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
                _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
                assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
                _lowercase					:				Union[str, Any]           =			new_t
         return new_sd
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
         else:
                assert os.path.isdir(UpperCAmelCase_			)
                _lowercase					:				List[str]           =			list(Path(UpperCAmelCase_			).glob('''*.ckpt'''			)			)
                assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'
  
         if len(UpperCAmelCase_			) > 1:
                logger.info(F'averaging the weights of {ckpt_files}'			)
  
         _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
         _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
         assert not missing, F'missing keys: {missing}'
         hf_model.save_pretrained(UpperCAmelCase_			)
         try:
                _lowercase					:				Union[str, Any]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_			)
                tok.save_pretrained(UpperCAmelCase_			)
         except Exception:
                pass
                # dont copy tokenizer if cant
  
  
  if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)