






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 				_UpperCAmelCase (	A__		,A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	StableDiffusionControlNetInpaintPipeline
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase__    							=	frozenset({"""control_image"""}     )  # skip `image` and `mask` for now, only test for control_image
				lowercase__    							=	TEXT_TO_IMAGE_IMAGE_PARAMS




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__       			=UNetaDConditionModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=9,  out_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),  cross_attention_dim=32,  )
								torch.manual_seed(0  )
								lowercase__       			=ControlNetModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  in_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  cross_attention_dim=32,  conditioning_embedding_out_channels=(16, 32),  )
								torch.manual_seed(0  )
								lowercase__       			=DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__       			=AutoencoderKL(
								    block_out_channels=[32, 64],  in_channels=3,  out_channels=3,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],  latent_channels=4,  )
								torch.manual_seed(0  )
								lowercase__       			=CLIPTextConfig(
								    bos_token_id=0,  eos_token_id=2,  hidden_size=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=1_000,  )
								lowercase__       			=CLIPTextModel(lowerCamelCase  )
								lowercase__       			=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__       			={
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




				def     lowercase__       (					self						:   int,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__       			=torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__       			=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowercase__       			=2
								lowercase__       			=randn_tensor(
								    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),  generator=lowerCamelCase,  device=torch.device(lowerCamelCase  ),  )
								lowercase__       			=floats_tensor((1, 3, 32, 32),  rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowercase__       			=init_image.cpu().permute(0,  2,  3,  1  )[0]

								lowercase__       			=Image.fromarray(np.uinta(lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )
								lowercase__       			=Image.fromarray(np.uinta(init_image + 4  )  ).convert('''RGB'''  ).resize((64, 64)  )

								lowercase__       			={
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''mask_image''': mask_image,
								    '''control_image''': control_image,
								}

								return inputs




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),  reason='''XFormers attention is only available with CUDA and `xformers` installed''',  )
				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )



class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	StableDiffusionControlNetInpaintPipeline
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase__    							=	frozenset([]     )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__       			=UNetaDConditionModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=4,  out_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),  cross_attention_dim=32,  )
								torch.manual_seed(0  )
								lowercase__       			=ControlNetModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  in_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  cross_attention_dim=32,  conditioning_embedding_out_channels=(16, 32),  )
								torch.manual_seed(0  )
								lowercase__       			=DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__       			=AutoencoderKL(
								    block_out_channels=[32, 64],  in_channels=3,  out_channels=3,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],  latent_channels=4,  )
								torch.manual_seed(0  )
								lowercase__       			=CLIPTextConfig(
								    bos_token_id=0,  eos_token_id=2,  hidden_size=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=1_000,  )
								lowercase__       			=CLIPTextModel(lowerCamelCase  )
								lowercase__       			=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__       			={
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components



class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	StableDiffusionControlNetInpaintPipeline
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase__    							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__       			=UNetaDConditionModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=9,  out_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),  cross_attention_dim=32,  )
								torch.manual_seed(0  )

								def init_weights(lowerCamelCase						:   Optional[Any]  ):
												if isinstance(lowerCamelCase,  torch.nn.Convad  ):
																torch.nn.init.normal(m.weight  )
																m.bias.data.fill_(1.0  )

								lowercase__       			=ControlNetModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  in_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  cross_attention_dim=32,  conditioning_embedding_out_channels=(16, 32),  )
								controlneta.controlnet_down_blocks.apply(lowerCamelCase  )

								torch.manual_seed(0  )
								lowercase__       			=ControlNetModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  in_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  cross_attention_dim=32,  conditioning_embedding_out_channels=(16, 32),  )
								controlneta.controlnet_down_blocks.apply(lowerCamelCase  )

								torch.manual_seed(0  )
								lowercase__       			=DDIMScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0  )
								lowercase__       			=AutoencoderKL(
								    block_out_channels=[32, 64],  in_channels=3,  out_channels=3,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],  latent_channels=4,  )
								torch.manual_seed(0  )
								lowercase__       			=CLIPTextConfig(
								    bos_token_id=0,  eos_token_id=2,  hidden_size=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=1_000,  )
								lowercase__       			=CLIPTextModel(lowerCamelCase  )
								lowercase__       			=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__       			=MultiControlNetModel([controlneta, controlneta]  )

								lowercase__       			={
								    '''unet''': unet,
								    '''controlnet''': controlnet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Dict=0  ):

								'''simple docstring'''
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__       			=torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__       			=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowercase__       			=2

								lowercase__       			=[
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),  generator=lowerCamelCase,  device=torch.device(lowerCamelCase  ),  ),
								    randn_tensor(
								        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),  generator=lowerCamelCase,  device=torch.device(lowerCamelCase  ),  ),
								]
								lowercase__       			=floats_tensor((1, 3, 32, 32),  rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowercase__       			=init_image.cpu().permute(0,  2,  3,  1  )[0]

								lowercase__       			=Image.fromarray(np.uinta(lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )
								lowercase__       			=Image.fromarray(np.uinta(init_image + 4  )  ).convert('''RGB'''  ).resize((64, 64)  )

								lowercase__       			={
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								    '''image''': image,
								    '''mask_image''': mask_image,
								    '''control_image''': control_image,
								}

								return inputs




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=self.get_dummy_components()
								lowercase__       			=self.pipeline_class(**lowerCamelCase  )
								pipe.to(lowerCamelCase  )

								lowercase__       			=10.0
								lowercase__       			=4

								lowercase__       			=self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=steps
								lowercase__       			=scale
								lowercase__       			=pipe(**lowerCamelCase  )[0]

								lowercase__       			=self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=steps
								lowercase__       			=scale
								lowercase__       			=pipe(**lowerCamelCase,  control_guidance_start=0.1,  control_guidance_end=0.2  )[0]

								lowercase__       			=self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=steps
								lowercase__       			=scale
								lowercase__       			=pipe(**lowerCamelCase,  control_guidance_start=[0.1, 0.3],  control_guidance_end=[0.2, 0.7]  )[0]

								lowercase__       			=self.get_dummy_inputs(lowerCamelCase  )
								lowercase__       			=steps
								lowercase__       			=scale
								lowercase__       			=pipe(**lowerCamelCase,  control_guidance_start=0.4,  control_guidance_end=[0.5, 0.8]  )[0]

								# make sure that all outputs are different
								assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
								assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3
								assert np.sum(np.abs(output_a - output_a  )  ) > 1E-3




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3  )




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),  reason='''XFormers attention is only available with CUDA and `xformers` installed''',  )
				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								self._test_inference_batch_single_identical(expected_max_diff=2E-3  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=self.get_dummy_components()
								lowercase__       			=self.pipeline_class(**lowerCamelCase  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								with tempfile.TemporaryDirectory() as tmpdir:
												try:
																# save_pretrained is not implemented for Multi-ControlNet
																pipe.save_pretrained(lowerCamelCase  )
												except NotImplementedError:
																pass



@slow
@require_torch_gpu
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''  )

								lowercase__       			=StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting''',  safety_checker=lowerCamelCase,  controlnet=lowerCamelCase  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__       			=torch.Generator(device='''cpu'''  ).manual_seed(0  )
								lowercase__       			=load_image(
								    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''  ).resize((512, 512)  )

								lowercase__       			=load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
								    '''/stable_diffusion_inpaint/input_bench_mask.png'''  ).resize((512, 512)  )

								lowercase__       			='''pitch black hole'''

								lowercase__       			=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''  ).resize((512, 512)  )

								lowercase__       			=pipe(
								    lowerCamelCase,  image=lowerCamelCase,  mask_image=lowerCamelCase,  control_image=lowerCamelCase,  generator=lowerCamelCase,  output_type='''np''',  num_inference_steps=3,  )

								lowercase__       			=output.images[0]

								assert image.shape == (512, 512, 3)

								lowercase__       			=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''  )

								assert np.abs(expected_image - image  ).max() < 9E-2




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''  )

								lowercase__       			=StableDiffusionControlNetInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  safety_checker=lowerCamelCase,  controlnet=lowerCamelCase  )
								lowercase__       			=DDIMScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__       			=torch.Generator(device='''cpu'''  ).manual_seed(33  )

								lowercase__       			=load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''  )
								lowercase__       			=init_image.resize((512, 512)  )

								lowercase__       			=load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''  )
								lowercase__       			=mask_image.resize((512, 512)  )

								lowercase__       			='''a handsome man with ray-ban sunglasses'''

								def make_inpaint_condition(lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple  ):
												lowercase__       			=np.array(image.convert('''RGB'''  )  ).astype(np.floataa  ) / 255.0
												lowercase__       			=np.array(image_mask.convert('''L'''  )  ).astype(np.floataa  ) / 255.0

												assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
												lowercase__       			=-1.0  # set as masked pixel
												lowercase__       			=np.expand_dims(lowerCamelCase,  0  ).transpose(0,  3,  1,  2  )
												lowercase__       			=torch.from_numpy(lowerCamelCase  )
												return image

								lowercase__       			=make_inpaint_condition(lowerCamelCase,  lowerCamelCase  )

								lowercase__       			=pipe(
								    lowerCamelCase,  image=lowerCamelCase,  mask_image=lowerCamelCase,  control_image=lowerCamelCase,  guidance_scale=9.0,  eta=1.0,  generator=lowerCamelCase,  num_inference_steps=20,  output_type='''np''',  )
								lowercase__       			=output.images[0]

								assert image.shape == (512, 512, 3)

								lowercase__       			=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''  )

								assert np.abs(expected_image - image  ).max() < 9E-2












import numpy as np
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   int=5,  lowerCamelCase						:   Optional[Any]=5  ):

								'''simple docstring'''
								lowercase__       			=      depth
								lowercase__       			=      0
								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      min_leaf_size
								lowercase__       			=      None




				def     lowercase__       (					self						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict  ):

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

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




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple  ):

								'''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:
												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     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   Tuple  ):

								'''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						:   int,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      float(0  )
								for label in labels:
												squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size  )






def   a      (						):






				'''simple docstring'''
				lowercase__       			=      np.arange(-1.0		,       1.0		,       0.0_05    )
				lowercase__       			=      np.sin(lowerCamelCase_    )

				lowercase__       			=      DecisionTree(depth=10		,       min_leaf_size=10    )
				tree.train(lowerCamelCase_		,       lowerCamelCase_    )

				lowercase__       			=      (np.random.rand(10    ) * 2) - 1
				lowercase__       			=      np.array([tree.predict(lowerCamelCase_    ) for x in test_cases]    )
				lowercase__       			=      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)




