



"""simple docstring"""







import numpy as np





class    UpperCamelCase     :




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_=5							,UpperCAmelCase_=5					):
			_lowercase							:      Optional[Any]			=					depth
			_lowercase							:      str			=					0
			_lowercase							:      List[Any]			=					None
			_lowercase							:      Union[str, Any]			=					None
			_lowercase							:      Dict			=					min_leaf_size
			_lowercase							:      Optional[Any]			=					None






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			if labels.ndim != 1:
					print("""Error: Input labels must be one dimensional"""					)

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






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			if x.ndim != 1:
					print("""Error: Input data set must be one dimensional"""					)
					return
			if len(UpperCAmelCase_					) != len(UpperCAmelCase_					):
					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(UpperCAmelCase_					) < 2 * self.min_leaf_size:
					_lowercase							:      str			=					np.mean(UpperCAmelCase_					)
					return

			if self.depth == 1:
					_lowercase							:      Union[str, Any]			=					np.mean(UpperCAmelCase_					)
					return

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

			if best_split != 0:
					_lowercase							:      Tuple			=					x[:best_split]
					_lowercase							:      int			=					y[:best_split]
					_lowercase							:      int			=					x[best_split:]
					_lowercase							:      Optional[int]			=					y[best_split:]

					_lowercase							:      Optional[int]			=					x[best_split]
					_lowercase							:      Any			=					DecisionTree(
					    depth=self.depth - 1							,min_leaf_size=self.min_leaf_size					)
					_lowercase							:      Tuple			=					DecisionTree(
					    depth=self.depth - 1							,min_leaf_size=self.min_leaf_size					)
					self.left.train(UpperCAmelCase_							,UpperCAmelCase_					)
					self.right.train(UpperCAmelCase_							,UpperCAmelCase_					)
			else:
					_lowercase							:      Tuple			=					np.mean(UpperCAmelCase_					)

			return






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			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(UpperCAmelCase_					)
					else:
							return self.left.predict(UpperCAmelCase_					)
			else:
					print("""Error: Decision tree not yet trained"""					)
					return None







class    UpperCamelCase     :




	"""simple docstring"""






	@staticmethod
	def 					lowerCamelCase__ (			UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      str			=					float(0					)
			for label in labels:
					squared_error_sum += (label - prediction) ** 2

			return float(squared_error_sum / labels.size					)







def       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      int			=					np.arange(-1.0					,							1.0					,							0.0_0_5	)
		_lowercase							:      int			=					np.sin(__UpperCAmelCase	)

		_lowercase							:      Any			=					DecisionTree(depth=10					,							min_leaf_size=10	)
		tree.train(__UpperCAmelCase					,							__UpperCAmelCase	)

		_lowercase							:      Any			=					(np.random.rand(10	) * 2) - 1
		_lowercase							:      Union[str, Any]			=					np.array([tree.predict(__UpperCAmelCase	) for x in test_cases]	)
		_lowercase							:      Optional[Any]			=					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"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase:   Dict       			=							logging.get_logger(__name__)

UpperCAmelCase:   Optional[Any]       			=							{
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Dict		    =   "opt"
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   ["past_key_values"]






	def __init__(			self							,UpperCAmelCase_=5_02_72							,UpperCAmelCase_=7_68							,UpperCAmelCase_=12							,UpperCAmelCase_=30_72							,UpperCAmelCase_=20_48							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=None							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.0							,UpperCAmelCase_=12							,UpperCAmelCase_="relu"							,UpperCAmelCase_=0.0							,UpperCAmelCase_=0.02							,UpperCAmelCase_=True							,UpperCAmelCase_=1							,UpperCAmelCase_=2							,UpperCAmelCase_=2							,UpperCAmelCase_=True							,UpperCAmelCase_=True							,**UpperCAmelCase_							,):
			super().__init__(
			    pad_token_id=UpperCAmelCase_							,bos_token_id=UpperCAmelCase_							,eos_token_id=UpperCAmelCase_							,**UpperCAmelCase_							,)
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Tuple			=					max_position_embeddings
			_lowercase							:      Union[str, Any]			=					num_attention_heads
			_lowercase							:      List[str]			=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
			_lowercase							:      List[str]			=					ffn_dim
			_lowercase							:      List[str]			=					hidden_size
			_lowercase							:      List[Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					dropout
			_lowercase							:      str			=					attention_dropout
			_lowercase							:      List[Any]			=					activation_function
			_lowercase							:      int			=					init_std
			_lowercase							:      List[Any]			=					layerdrop
			_lowercase							:      Tuple			=					use_cache
			_lowercase							:      Any			=					do_layer_norm_before
			# We keep these variables at `True` for backward compatibility.
			_lowercase							:      int			=					enable_bias
			_lowercase							:      str			=					layer_norm_elementwise_affine

			# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
			# with checkpoints that have been fine-tuned before transformers v4.20.1
			# see https://github.com/facebookresearch/metaseq/pull/164
			_lowercase							:      Optional[Any]			=					_remove_final_layer_norm



