



from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
		from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					List[Any]:
									lowerCAmelCase	:   Tuple					     =					self.config_class(**self.inputs_dict )
									self.parent.assertTrue(hasattr(lowercase_    ,  """hidden_sizes""" ) )
									self.parent.assertTrue(hasattr(lowercase_    ,  """neck_hidden_sizes""" ) )
									self.parent.assertTrue(hasattr(lowercase_    ,  """num_attention_heads""" ) )






class 	_a			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=13    ,  lowercase_=32    ,  lowercase_=2    ,  lowercase_=3    ,  lowercase_=640    ,  lowercase_=4    ,  lowercase_="silu"    ,  lowercase_=3    ,  lowercase_=32    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=0.0_2    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=10    ,  lowercase_=None    ,  )	->					str:
									lowerCAmelCase	:   Any					     =					parent
									lowerCAmelCase	:   Union[str, Any]					     =					batch_size
									lowerCAmelCase	:   int					     =					image_size
									lowerCAmelCase	:   Optional[Any]					     =					patch_size
									lowerCAmelCase	:   str					     =					num_channels
									lowerCAmelCase	:   List[Any]					     =					last_hidden_size
									lowerCAmelCase	:   Optional[int]					     =					num_attention_heads
									lowerCAmelCase	:   Dict					     =					hidden_act
									lowerCAmelCase	:   Optional[int]					     =					conv_kernel_size
									lowerCAmelCase	:   List[Any]					     =					output_stride
									lowerCAmelCase	:   Dict					     =					hidden_dropout_prob
									lowerCAmelCase	:   List[Any]					     =					attention_probs_dropout_prob
									lowerCAmelCase	:   List[str]					     =					classifier_dropout_prob
									lowerCAmelCase	:   Union[str, Any]					     =					use_labels
									lowerCAmelCase	:   Dict					     =					is_training
									lowerCAmelCase	:   Optional[Any]					     =					num_labels
									lowerCAmelCase	:   List[Any]					     =					initializer_range
									lowerCAmelCase	:   str					     =					scope






			def 							_snake_case   (    self )	->					str:
									lowerCAmelCase	:   List[str]					     =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									lowerCAmelCase	:   int					     =					None
									lowerCAmelCase	:   Tuple					     =					None
									if self.use_labels:
															lowerCAmelCase	:   Any					     =					ids_tensor([self.batch_size]    ,  self.num_labels )
															lowerCAmelCase	:   Tuple					     =					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,  self.num_labels )

									lowerCAmelCase	:   Tuple					     =					self.get_config()

									return config, pixel_values, labels, pixel_labels






			def 							_snake_case   (    self )	->					Union[str, Any]:
									return MobileViTConfig(
									    image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  num_attention_heads=self.num_attention_heads    ,  hidden_act=self.hidden_act    ,  conv_kernel_size=self.conv_kernel_size    ,  output_stride=self.output_stride    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  classifier_dropout_prob=self.classifier_dropout_prob    ,  initializer_range=self.initializer_range    ,  )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Any					     =					TFMobileViTModel(config=lowercase_ )
									lowerCAmelCase	:   int					     =					model(lowercase_    ,  training=lowercase_ )
									lowerCAmelCase	:   List[str]					     =					self.image_size // self.output_stride
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,  (self.batch_size, self.last_hidden_size, expected_height, expected_width) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Dict					     =					self.num_labels
									lowerCAmelCase	:   Optional[Any]					     =					TFMobileViTForImageClassification(lowercase_ )
									lowerCAmelCase	:   Dict					     =					model(lowercase_    ,  labels=lowercase_    ,  training=lowercase_ )
									self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels) )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Any:
									lowerCAmelCase	:   List[str]					     =					self.num_labels
									lowerCAmelCase	:   List[str]					     =					TFMobileViTForSemanticSegmentation(lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					self.image_size // self.output_stride

									lowerCAmelCase	:   Optional[Any]					     =					model(lowercase_    ,  training=lowercase_ )
									self.parent.assertEqual(
									    result.logits.shape    ,  (self.batch_size, self.num_labels, expected_height, expected_width) )

									lowerCAmelCase	:   Optional[Any]					     =					model(lowercase_    ,  labels=lowercase_    ,  training=lowercase_ )
									self.parent.assertEqual(
									    result.logits.shape    ,  (self.batch_size, self.num_labels, expected_height, expected_width) )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Tuple					     =					config_and_inputs
									lowerCAmelCase	:   Optional[int]					     =					{"""pixel_values""": pixel_values}
									return config, inputs_dict






@require_tf
class 	_a			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   Union[str, Any]	     =		(
			    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
			    if is_tf_available()
			    else ()
			)
			_UpperCamelCase:   Dict	     =		(
			    {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
			    if is_tf_available()
			    else {}
			)

			_UpperCamelCase:   Optional[Any]	     =		False
			_UpperCamelCase:   Tuple	     =		False
			_UpperCamelCase:   Tuple	     =		False
			_UpperCamelCase:   List[Any]	     =		False
			_UpperCamelCase:   List[Any]	     =		False






			def 							_snake_case   (    self )	->					Any:
									lowerCAmelCase	:   List[Any]					     =					TFMobileViTModelTester(self )
									lowerCAmelCase	:   Optional[int]					     =					TFMobileViTConfigTester(self    ,  config_class=lowercase_    ,  has_text_modality=lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									self.config_tester.run_common_tests()






			@unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
			def 							_snake_case   (    self )	->					Tuple:
									pass






			@unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
			def 							_snake_case   (    self )	->					str:
									pass






			@unittest.skip(reason="""MobileViT does not output attentions""" )
			def 							_snake_case   (    self )	->					Optional[int]:
									pass






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCAmelCase	:   List[Any]					     =					["""pixel_values"""]
															self.assertListEqual(arg_names[:1]    ,  lowercase_ )






			def 							_snake_case   (    self )	->					List[str]:
									lowerCAmelCase	:   Any					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_ )






			def 							_snake_case   (    self )	->					List[str]:
									def check_hidden_states_output(lowercase_    ,  lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   List[str]					     =					model_class(lowercase_ )

															lowerCAmelCase	:   int					     =					model(**self._prepare_for_class(lowercase_    ,  lowercase_ ) )

															lowerCAmelCase	:   List[str]					     =					outputs.hidden_states

															lowerCAmelCase	:   List[str]					     =					5
															self.assertEqual(len(lowercase_ )    ,  lowercase_ )

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															lowerCAmelCase	:   str					     =					2
															for i in range(len(lowercase_ ) ):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:] )    ,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,  )
																					divisor *= 2

															self.assertEqual(self.model_tester.output_stride    ,  divisor // 2 )

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

									for model_class in self.all_model_classes:
															lowerCAmelCase	:   Optional[int]					     =					True
															check_hidden_states_output(lowercase_    ,  lowercase_    ,  lowercase_ )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCAmelCase	:   Optional[int]					     =					True

															check_hidden_states_output(lowercase_    ,  lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase	:   Union[str, Any]					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_ )






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase	:   Optional[Any]					     =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_ )






			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0    ,  reason="""TF does not support backprop for grouped convolutions on CPU."""    ,  )
			def 							_snake_case   (    self )	->					Dict:
									super().test_dataset_conversion()






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_=2e-1    ,  lowercase_=2e-1 )	->					List[Any]:
									self.assertTrue(np.allclose(lowercase_    ,  lowercase_    ,  atol=lowercase_    ,  rtol=lowercase_ ) )






			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0    ,  reason="""TF does not support backprop for grouped convolutions on CPU."""    ,  )
			@slow
			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
															if model_class.__name__ != "TFMobileViTModel":
																					lowerCAmelCase	:   List[str]					     =					model_class(lowercase_ )
																					if getattr(lowercase_    ,  """hf_compute_loss"""    ,  lowercase_ ):
																											super().test_keras_fit()






			def 							_snake_case   (    self )	->					int:
									lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															# set an ignore index to correctly test the masked loss used in
															# `TFMobileViTForSemanticSegmentation`.
															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																					lowerCAmelCase	:   str					     =					5

															lowerCAmelCase	:   str					     =					model_class(lowercase_ )
															if getattr(lowercase_    ,  """hf_compute_loss"""    ,  lowercase_ ):
																					# The number of elements in the loss should be the same as the number of elements in the label
																					lowerCAmelCase	:   Any					     =					self._prepare_for_class(inputs_dict.copy()    ,  lowercase_    ,  return_labels=lowercase_ )
																					lowerCAmelCase	:   str					     =					prepared_for_class[
																					    sorted(prepared_for_class.keys() - inputs_dict.keys()    ,  reverse=lowercase_ )[0]
																					]
																					lowerCAmelCase	:   List[Any]					     =					added_label.shape.as_list()[:1]

																					# Test that model correctly compute the loss with kwargs
																					lowerCAmelCase	:   Optional[int]					     =					self._prepare_for_class(inputs_dict.copy()    ,  lowercase_    ,  return_labels=lowercase_ )
																					lowerCAmelCase	:   str					     =					{"""input_ids""", """pixel_values""", """input_features"""}
																					lowerCAmelCase	:   Optional[int]					     =					possible_input_names.intersection(set(lowercase_ ) ).pop()
																					lowerCAmelCase	:   int					     =					prepared_for_class.pop(lowercase_ )

																					lowerCAmelCase	:   List[Any]					     =					model(lowercase_    ,  **lowercase_ )[0]
																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

																					# Test that model correctly compute the loss when we mask some positions
																					lowerCAmelCase	:   Dict					     =					self._prepare_for_class(inputs_dict.copy()    ,  lowercase_    ,  return_labels=lowercase_ )
																					lowerCAmelCase	:   str					     =					{"""input_ids""", """pixel_values""", """input_features"""}
																					lowerCAmelCase	:   Any					     =					possible_input_names.intersection(set(lowercase_ ) ).pop()
																					lowerCAmelCase	:   Union[str, Any]					     =					prepared_for_class.pop(lowercase_ )
																					if "labels" in prepared_for_class:
																											lowerCAmelCase	:   List[str]					     =					prepared_for_class["""labels"""].numpy()
																											if len(labels.shape ) > 1 and labels.shape[1] != 1:
																																	# labels[0] = -100
																																	lowerCAmelCase	:   Optional[int]					     =					tf.convert_to_tensor(lowercase_ )
																																	lowerCAmelCase	:   List[str]					     =					model(lowercase_    ,  **lowercase_ )[0]
																																	self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
																																	self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )

                # Test that model correctly compute the loss with a dict
																					lowerCAmelCase	:   Dict					     =					self._prepare_for_class(inputs_dict.copy()    ,  lowercase_    ,  return_labels=lowercase_ )
																					lowerCAmelCase	:   Optional[int]					     =					model(lowercase_ )[0]
																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

																					# Test that model correctly compute the loss with a tuple
																					lowerCAmelCase	:   Union[str, Any]					     =					self._prepare_for_class(inputs_dict.copy()    ,  lowercase_    ,  return_labels=lowercase_ )

																					# Get keys that were added with the _prepare_for_class function
																					lowerCAmelCase	:   Any					     =					prepared_for_class.keys() - inputs_dict.keys()
																					lowerCAmelCase	:   Optional[int]					     =					inspect.signature(model.call ).parameters
																					lowerCAmelCase	:   Dict					     =					list(signature.keys() )

																					# Create a dictionary holding the location of the tensors in the tuple
																					lowerCAmelCase	:   int					     =					{0: input_name}
																					for label_key in label_keys:
																											lowerCAmelCase	:   List[Any]					     =					signature_names.index(lowercase_ )
																											lowerCAmelCase	:   List[str]					     =					label_key
																					lowerCAmelCase	:   List[Any]					     =					sorted(tuple_index_mapping.items() )
																					# Initialize a list with their default values, update the values and convert to a tuple
																					lowerCAmelCase	:   Optional[Any]					     =					[]

																					for name in signature_names:
																											if name != "kwargs":
																																	list_input.append(signature[name].default )

																					for index, value in sorted_tuple_index_mapping:
																											lowerCAmelCase	:   Optional[Any]					     =					prepared_for_class[value]

																					lowerCAmelCase	:   List[Any]					     =					tuple(lowercase_ )

																					# Send to model
																					lowerCAmelCase	:   str					     =					model(tuple_input[:-1] )[0]

																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )






			@slow
			def 							_snake_case   (    self )	->					str:
									for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase	:   str					     =					TFMobileViTModel.from_pretrained(lowercase_ )
															self.assertIsNotNone(lowercase_ )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   Any					     =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
						return image






@require_tf
class 	_a			(      unittest.TestCase  ):






			@slow
			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   List[str]					     =					TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" )

									lowerCAmelCase	:   List[Any]					     =					MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" )
									lowerCAmelCase	:   List[str]					     =					prepare_img()
									lowerCAmelCase	:   List[str]					     =					image_processor(images=lowercase_    ,  return_tensors="""tf""" )

									# forward pass
									lowerCAmelCase	:   List[str]					     =					model(**lowercase_    ,  training=lowercase_ )

									# verify the logits
									lowerCAmelCase	:   Any					     =					tf.TensorShape((1, 1000) )
									self.assertEqual(outputs.logits.shape    ,  lowercase_ )

									lowerCAmelCase	:   Dict					     =					tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3] )

									tf.debugging.assert_near(outputs.logits[0, :3]    ,  lowercase_    ,  atol=1e-4    ,  rtol=1e-04 )






			@slow
			def 							_snake_case   (    self )	->					Any:
									# `from_pt` will be removed
									lowerCAmelCase	:   Optional[Any]					     =					TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

									lowerCAmelCase	:   Dict					     =					MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )

									lowerCAmelCase	:   int					     =					prepare_img()
									lowerCAmelCase	:   List[str]					     =					image_processor(images=lowercase_    ,  return_tensors="""tf""" )

									# forward pass
									lowerCAmelCase	:   List[Any]					     =					model(inputs.pixel_values    ,  training=lowercase_ )
									lowerCAmelCase	:   Dict					     =					outputs.logits

									# verify the logits
									lowerCAmelCase	:   str					     =					tf.TensorShape((1, 21, 32, 32) )
									self.assertEqual(logits.shape    ,  lowercase_ )

									lowerCAmelCase	:   str					     =					tf.constant(
									    [
									        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
									        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
									        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
									    ] )

									tf.debugging.assert_near(logits[0, :3, :3, :3]    ,  lowercase_    ,  rtol=1e-4    ,  atol=1e-4 )






import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															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(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

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

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									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(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

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


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

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

