"""simple docstring"""


import numpy as np






class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5				):
										lowerCamelCase__ =depth
										lowerCamelCase__ =0
										lowerCamelCase__ =None
										lowerCamelCase__ =None
										lowerCamelCase__ =min_leaf_size
										lowerCamelCase__ =None

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

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

				def 		_a     (				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:
																lowerCamelCase__ =np.mean(_lowerCamelCase				)
																return

										if self.depth == 1:
																lowerCamelCase__ =np.mean(_lowerCamelCase				)
																return

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

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

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

										return






				def 		_a     (				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        __UpperCAmelCase						:

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

										return float(squared_error_sum / labels.size				)





def       lowerCamelCase_			( )							-> str:




						'''simple docstring'''



						lowerCamelCase__ =np.arange(-1.0       ,   1.0       ,   0.005    )
						lowerCamelCase__ =np.sin(__lowerCAmelCase    )

						lowerCamelCase__ =DecisionTree(depth=10       ,   min_leaf_size=10    )
						tree.train(__lowerCAmelCase       ,   __lowerCAmelCase    )

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

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


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

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



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


