import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42

class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =CustomOutput(images=np.random.rand(1       ,  3       ,  4       ,  4       )       )

		# check every way of getting the attribute
		assert isinstance(outputs.images       ,  np.ndarray       )
		assert outputs.images.shape == (1, 3, 4, 4)
		assert isinstance(outputs["images"]       ,  np.ndarray       )
		assert outputs["images"].shape == (1, 3, 4, 4)
		assert isinstance(outputs[0]       ,  np.ndarray       )
		assert outputs[0].shape == (1, 3, 4, 4)

		# test with a non-tensor attribute
		_lowercase   =CustomOutput(images=[PIL.Image.new("RGB"       ,  (4, 4)       )]       )

		# check every way of getting the attribute
		assert isinstance(outputs.images       ,  lowerCAmelCase_       )
		assert isinstance(outputs.images[0]       ,  PIL.Image.Image       )
		assert isinstance(outputs["images"]       ,  lowerCAmelCase_       )
		assert isinstance(outputs["images"][0]       ,  PIL.Image.Image       )
		assert isinstance(outputs[0]       ,  lowerCAmelCase_       )
		assert isinstance(outputs[0][0]       ,  PIL.Image.Image       )


	def __lowerCAmelCase      (   self       ):
		# test output reinitialization with a `dict` for compatibility with `accelerate`
		_lowercase   =CustomOutput({"images": np.random.rand(1       ,  3       ,  4       ,  4       )}       )

		# check every way of getting the attribute
		assert isinstance(outputs.images       ,  np.ndarray       )
		assert outputs.images.shape == (1, 3, 4, 4)
		assert isinstance(outputs["images"]       ,  np.ndarray       )
		assert outputs["images"].shape == (1, 3, 4, 4)
		assert isinstance(outputs[0]       ,  np.ndarray       )
		assert outputs[0].shape == (1, 3, 4, 4)

		# test with a non-tensor attribute
		_lowercase   =CustomOutput({"images": [PIL.Image.new("RGB"       ,  (4, 4)       )]}       )

		# check every way of getting the attribute
		assert isinstance(outputs.images       ,  lowerCAmelCase_       )
		assert isinstance(outputs.images[0]       ,  PIL.Image.Image       )
		assert isinstance(outputs["images"]       ,  lowerCAmelCase_       )
		assert isinstance(outputs["images"][0]       ,  PIL.Image.Image       )
		assert isinstance(outputs[0]       ,  lowerCAmelCase_       )
		assert isinstance(outputs[0][0]       ,  PIL.Image.Image       )





import numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


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

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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		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:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

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

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

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

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

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		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 	_a  :



	"""simple docstring"""



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

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

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

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


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

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




