'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_	   = {
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "UniSpeechSatForAudioFrameClassification",
						    "UniSpeechSatForCTC",
						    "UniSpeechSatForPreTraining",
						    "UniSpeechSatForSequenceClassification",
						    "UniSpeechSatForXVector",
						    "UniSpeechSatModel",
						    "UniSpeechSatPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						A_	   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










import numpy as np


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	List[str]=5   ,							_a    :	Any=5      ):
													a__:							int									=depth
													a__:							Dict									=0
													a__:							str									=None
													a__:							List[Any]									=None
													a__:							List[Any]									=min_leaf_size
													a__:							Dict									=None


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Optional[int]      ):
													if labels.ndim != 1:
																				print("Error: Input labels must be one dimensional"      )

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


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	List[Any]   ,							_a    :	Dict      ):
													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:
																				a__:							List[str]									=np.mean(_a      )
																				return

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

													a__:							Any									=0
													a__:							List[Any]									=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:
																											a__:							List[str]									=self.mean_squared_error(x[:i]   ,							np.mean(y[:i]      )      )
																											a__:							str									=self.mean_squared_error(x[i:]   ,							np.mean(y[i:]      )      )
																											a__:							str									=error_left + error_right
																											if error < min_error:
																																		a__:							Optional[int]									=i
																																		a__:							Tuple									=error

													if best_split != 0:
																				a__:							List[Any]									=x[:best_split]
																				a__:							Any									=y[:best_split]
																				a__:							List[str]									=x[best_split:]
																				a__:							List[str]									=y[best_split:]

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

													return







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Tuple      ):
													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 						lowerCamelCase__			:


						@staticmethod
						def 	_lowerCamelCase      (			_a    :	Optional[int]   ,							_a    :	Optional[int]      ):
													a__:							Optional[int]									=float(0      )
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=np.arange(-1.0  ,			1.0  ,			0.005    )
							a__:							List[Any]									=np.sin(__magic_name__    )

							a__:							int									=DecisionTree(depth=10  ,			min_leaf_size=10    )
							tree.train(__magic_name__  ,			__magic_name__    )

							a__:							Union[str, Any]									=(np.random.rand(10    ) * 2) - 1
							a__:							Any									=np.array([tree.predict(__magic_name__    ) for x in test_cases]    )
							a__:							Dict									=np.mean((predictions - test_cases) ** 2    )

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


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

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





