


import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import NatBackbone, NatForImageClassification, NatModel
				from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__=1_3 ,	lowerCAmelCase__=6_4 ,	lowerCAmelCase__=4 ,	lowerCAmelCase__=3 ,	lowerCAmelCase__=1_6 ,	lowerCAmelCase__=[1, 2, 1] ,	lowerCAmelCase__=[2, 4, 8] ,	lowerCAmelCase__=3 ,	lowerCAmelCase__=2.0 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=0.0 ,	lowerCAmelCase__=0.0 ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__="gelu" ,	lowerCAmelCase__=True ,	lowerCAmelCase__=0.02 ,	lowerCAmelCase__=1E-5 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=None ,	lowerCAmelCase__=True ,	lowerCAmelCase__=1_0 ,	lowerCAmelCase__=["stage1", "stage2"] ,	lowerCAmelCase__=[1, 2] ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				parent
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				batch_size
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				image_size
										SCREAMING_SNAKE_CASE_		:    str 		=				patch_size
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				num_channels
										SCREAMING_SNAKE_CASE_		:    int 		=				embed_dim
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				depths
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				num_heads
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				kernel_size
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				mlp_ratio
										SCREAMING_SNAKE_CASE_		:    str 		=				qkv_bias
										SCREAMING_SNAKE_CASE_		:    int 		=				hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:    str 		=				attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				drop_path_rate
										SCREAMING_SNAKE_CASE_		:    int 		=				hidden_act
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				patch_norm
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				layer_norm_eps
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				initializer_range
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				is_training
										SCREAMING_SNAKE_CASE_		:    int 		=				scope
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				use_labels
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				num_labels
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				out_features
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				out_indices






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										SCREAMING_SNAKE_CASE_		:    int 		=				None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				ids_tensor([self.batch_size] ,	self.num_labels					)

										SCREAMING_SNAKE_CASE_		:    Any 		=				self.get_config()

										return config, pixel_values, labels






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return NatConfig(
										    num_labels=self.num_labels ,	image_size=self.image_size ,	patch_size=self.patch_size ,	num_channels=self.num_channels ,	embed_dim=self.embed_dim ,	depths=self.depths ,	num_heads=self.num_heads ,	kernel_size=self.kernel_size ,	mlp_ratio=self.mlp_ratio ,	qkv_bias=self.qkv_bias ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	drop_path_rate=self.drop_path_rate ,	hidden_act=self.hidden_act ,	patch_norm=self.patch_norm ,	layer_norm_eps=self.layer_norm_eps ,	initializer_range=self.initializer_range ,	out_features=self.out_features ,	out_indices=self.out_indices ,	)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				NatModel(config=lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    str 		=				(config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

										self.parent.assertEqual(
										    result.last_hidden_state.shape ,	(self.batch_size, expected_height, expected_width, expected_dim)					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				NatForImageClassification(lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model(lowerCAmelCase__ ,	labels=lowerCAmelCase__					)
										self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)					)

										# test greyscale images
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				1
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				NatForImageClassification(lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				model(lowerCAmelCase__					)
										self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				NatBackbone(config=lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(lowerCAmelCase__					)

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps					) ,	len(config.out_features					)					)
										self.parent.assertListEqual(list(result.feature_maps[0].shape					) ,	[self.batch_size, model.channels[0], 1_6, 1_6]					)

										# verify channels
										self.parent.assertEqual(len(model.channels					) ,	len(config.out_features					)					)

										# verify backbone works with out_features=None
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				None
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				NatBackbone(config=lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()
										SCREAMING_SNAKE_CASE_		:    Dict 		=				model(lowerCAmelCase__					)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps					) ,	1					)
										self.parent.assertListEqual(list(result.feature_maps[0].shape					) ,	[self.batch_size, model.channels[-1], 4, 4]					)

										# verify channels
										self.parent.assertEqual(len(model.channels					) ,	1					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				config_and_inputs
										SCREAMING_SNAKE_CASE_		:    Any 		=				{'pixel_values': pixel_values}
										return config, inputs_dict







@require_natten
@require_torch
class   __lowercase						(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					unittest.TestCase     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	(
							    (
							        NatModel,
							        NatForImageClassification,
							        NatBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase						     =	(
							    {"""feature-extraction""": NatModel, """image-classification""": NatForImageClassification}
							    if is_torch_available()
							    else {}
							)
							_UpperCAmelCase						     =	False

							_UpperCAmelCase						     =	False
							_UpperCAmelCase						     =	False
							_UpperCAmelCase						     =	False
							_UpperCAmelCase						     =	False






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				NatModelTester(self					)
										SCREAMING_SNAKE_CASE_		:    str 		=				ConfigTester(self ,	config_class=lowerCAmelCase__ ,	embed_dim=3_7					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase__					)






							@unittest.skip(reason='Nat does not use inputs_embeds'					)
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										pass






							@unittest.skip(reason='Nat does not use feedforward chunking'					)
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										pass






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    Any 		=				model_class(lowerCAmelCase__					)
													self.assertIsInstance(model.get_input_embeddings() ,	(nn.Module)					)
													SCREAMING_SNAKE_CASE_		:    Any 		=				model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__ ,	nn.Linear					)					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Dict 		=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model_class(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				inspect.signature(model.forward					)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				[*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				['pixel_values']
													self.assertListEqual(arg_names[:1] ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model_class(lowerCAmelCase__					)
										model.to(lowerCAmelCase__					)
										model.eval()

										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(**self._prepare_for_class(lowerCAmelCase__ ,	lowerCAmelCase__					)					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				outputs.hidden_states

										SCREAMING_SNAKE_CASE_		:    Dict 		=				getattr(
										    self.model_tester ,	'expected_num_hidden_layers' ,	len(self.model_tester.depths					) + 1					)
										self.assertEqual(len(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										# Nat has a different seq_length
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				(
										    config.patch_size
										    if isinstance(config.patch_size ,	collections.abc.Iterable					)
										    else (config.patch_size, config.patch_size)
										)

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				image_size[0] // patch_size[0]
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				image_size[1] // patch_size[1]

										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]					) ,	[height, width, self.model_tester.embed_dim] ,	)

										if model_class.__name__ != "NatBackbone":
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				outputs.reshaped_hidden_states
													self.assertEqual(len(lowerCAmelCase__					) ,	lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				reshaped_hidden_states[0].shape
													SCREAMING_SNAKE_CASE_		:    str 		=				(
													    reshaped_hidden_states[0].view(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).permute(0 ,	2 ,	3 ,	1					)
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-3:]					) ,	[height, width, self.model_tester.embed_dim] ,	)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_		:    Any 		=				(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size ,	collections.abc.Iterable					)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    Any 		=				True
													self.check_hidden_states_output(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_		:    Dict 		=				True

													self.check_hidden_states_output(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					)






							@slow
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				NatModel.from_pretrained(lowerCAmelCase__					)
													self.assertIsNotNone(lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				_config_zero_init(lowerCAmelCase__					)
										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:    str 		=				model_class(config=lowerCAmelCase__					)
													for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1E9).round() / 1E9).item() ,	[0.0, 1.0] ,	msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,	)







@require_natten
@require_vision
@require_torch
class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							@cached_property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'					) if is_vision_available() else None






							@slow
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Dict 		=				NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'					).to(lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.default_image_processor

										SCREAMING_SNAKE_CASE_		:    Dict 		=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image_processor(images=lowerCAmelCase__ ,	return_tensors='pt'					).to(lowerCAmelCase__					)

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				model(**lowerCAmelCase__					)

										# verify the logits
										SCREAMING_SNAKE_CASE_		:    int 		=				torch.Size((1, 1_0_0_0)					)
										self.assertEqual(outputs.logits.shape ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				torch.tensor([0.3_805, -0.8_676, -0.3_912]					).to(lowerCAmelCase__					)
										self.assertTrue(torch.allclose(outputs.logits[0, :3] ,	lowerCAmelCase__ ,	atol=1E-4					)					)







@require_torch
@require_natten
class   __lowercase						(unittest.TestCase  ,					__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	(NatBackbone,) if is_torch_available() else ()
							_UpperCAmelCase						     =	NatConfig






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				NatModelTester(self					)









import numpy as np







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__=5 ,	lowerCAmelCase__=5					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				depth
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				0
										SCREAMING_SNAKE_CASE_		:    int 		=				None
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				None
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				min_leaf_size
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				None






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




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

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""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:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean(lowerCAmelCase__					)
													return

										if self.depth == 1:
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				np.mean(lowerCAmelCase__					)
													return

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

										if best_split != 0:
													SCREAMING_SNAKE_CASE_		:    int 		=				x[:best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				y[:best_split]
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				x[best_split:]
													SCREAMING_SNAKE_CASE_		:    str 		=				y[best_split:]

													SCREAMING_SNAKE_CASE_		:    str 		=				x[best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													SCREAMING_SNAKE_CASE_		:    int 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													self.left.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
													self.right.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
										else:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.mean(lowerCAmelCase__					)

										return






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""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   __lowercase						:





							"""simple docstring"""







							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				float(0					)
										for label in labels:
													squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size					)







def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.arange(-1.0,			1.0,			0.0_05							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				np.sin(A__							)

			SCREAMING_SNAKE_CASE_		:    str 		=				DecisionTree(depth=1_0,			min_leaf_size=1_0							)
			tree.train(A__,			A__							)

			SCREAMING_SNAKE_CASE_		:    Dict 		=				(np.random.rand(1_0							) * 2) - 1
			SCREAMING_SNAKE_CASE_		:    Any 		=				np.array([tree.predict(A__							) for x in test_cases]							)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				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)





