



'''simple docstring'''


import numpy as np







class 						UpperCAmelCase						:


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_=5				,	lowerCAmelCase_=5)			->   str:



      """simple docstring"""







      a_									=depth
      a_									=0
      a_									=None
      a_									=None
      a_									=min_leaf_size
      a_									=None




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







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

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




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Optional[int]:



      """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:
           a_									=np.mean(lowerCAmelCase_)
           return

      if self.depth == 1:
           a_									=np.mean(lowerCAmelCase_)
           return

      a_									=0
      a_									=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:
                a_									=self.mean_squared_error(x[:i]				,	np.mean(y[:i]))
                a_									=self.mean_squared_error(x[i:]				,	np.mean(y[i:]))
                a_									=error_left + error_right
                if error < min_error:
                     a_									=i
                     a_									=error

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

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

      return




 def    lowercase_	( self				,	lowerCAmelCase_)			->   List[Any]:



      """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 						UpperCAmelCase						:


 '''simple docstring'''




 @staticmethod
 def    lowercase_	( lowerCAmelCase_				,	lowerCAmelCase_)			->   Optional[Any]:



      """simple docstring"""







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

      return float(squared_error_sum / labels.size)






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									=np.arange(-1.0							,						1.0							,						0.005							)
     a_									=np.sin(lowercase__							)

     a_									=DecisionTree(depth=1_0							,						min_leaf_size=1_0							)
     tree.train(lowercase__							,						lowercase__							)

     a_									=(np.random.rand(1_0							) * 2) - 1
     a_									=np.array([tree.predict(lowercase__							) for x in test_cases]							)
     a_									=np.mean((predictions - test_cases) ** 2							)

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


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

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







'''simple docstring'''


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase											=      logging.get_logger(__name__)

lowercase											=      {
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			str	=   "gpt2"
 __magic_name__       :			str	=   ["past_key_values"]
 __magic_name__       :			Dict	=   {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__( self				,	lowerCAmelCase_=5_0_2_5_7				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=7_6_8				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=None				,	lowerCAmelCase_="gelu_new"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=1e-5				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_="cls_index"				,	lowerCAmelCase_=True				,	lowerCAmelCase_=None				,	lowerCAmelCase_=True				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=False				,	lowerCAmelCase_=False				,	**lowerCAmelCase_				,	)			->   Dict:



      """simple docstring"""







      a_									=vocab_size
      a_									=n_positions
      a_									=n_embd
      a_									=n_layer
      a_									=n_head
      a_									=n_inner
      a_									=activation_function
      a_									=resid_pdrop
      a_									=embd_pdrop
      a_									=attn_pdrop
      a_									=layer_norm_epsilon
      a_									=initializer_range
      a_									=summary_type
      a_									=summary_use_proj
      a_									=summary_activation
      a_									=summary_first_dropout
      a_									=summary_proj_to_labels
      a_									=scale_attn_weights
      a_									=use_cache
      a_									=scale_attn_by_inverse_layer_idx
      a_									=reorder_and_upcast_attn

      a_									=bos_token_id
      a_									=eos_token_id

      super().__init__(bos_token_id=lowerCAmelCase_				,	eos_token_id=lowerCAmelCase_				,	**lowerCAmelCase_)







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = "default"				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = False				,	)			->   List[str]:



      """simple docstring"""







      super().__init__(lowerCAmelCase_				,	task=lowerCAmelCase_				,	patching_specs=lowerCAmelCase_				,	use_past=lowerCAmelCase_)
      if not getattr(self._config				,	"pad_token_id"				,	lowerCAmelCase_):
           # TODO: how to do that better?
           a_									=0




 @property
 def    lowercase_	( self)			->   Mapping[str, Mapping[int, str]]:



      """simple docstring"""







      a_									=OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
      if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase_				,	direction="inputs")
           a_									={0: "batch", 1: "past_sequence + sequence"}
      else:
           a_									={0: "batch", 1: "sequence"}

      return common_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_layer




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_head




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = None				,	)			->   Mapping[str, Any]:



      """simple docstring"""







      a_									=super(lowerCAmelCase_				,	self).generate_dummy_inputs(
          lowerCAmelCase_				,	batch_size=lowerCAmelCase_				,	seq_length=lowerCAmelCase_				,	is_pair=lowerCAmelCase_				,	framework=lowerCAmelCase_)

      # We need to order the input in the way they appears in the forward()
      a_									=OrderedDict({"input_ids": common_inputs["input_ids"]})

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
           else:
                import torch

                a_		,    a_									=common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                a_									=seqlen + 2
                a_									=(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                a_									=[
                    (torch.zeros(lowerCAmelCase_), torch.zeros(lowerCAmelCase_)) for _ in range(self.num_layers)
                ]

      a_									=common_inputs["attention_mask"]
      if self.use_past:
           a_									=ordered_inputs["attention_mask"].dtype
           a_									=torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase_				,	lowerCAmelCase_				,	dtype=lowerCAmelCase_)]				,	dim=1)

      return ordered_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return 1_3


