code: 
  
  
  import numpy as np
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase=5				,		__lowerCamelCase=5)    ->       Dict:
              _A   : Tuple       					=				depth
              _A   : Tuple       					=				0
              _A   : int       					=				None
              _A   : Union[str, Any]       					=				None
              _A   : int       					=				min_leaf_size
              _A   : Any       					=				None
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Any:
              if labels.ndim != 1:
                     print("Error: Input labels must be one dimensional")
  
              return np.mean((labels - prediction) ** 2)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[int]:
              if x.ndim != 1:
                     print("Error: Input data set must be one dimensional")
                     return
              if len(SCREAMING_SNAKE_CASE_) != len(SCREAMING_SNAKE_CASE_):
                     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(SCREAMING_SNAKE_CASE_) < 2 * self.min_leaf_size:
                     _A   : Optional[Any]       					=				np.mean(SCREAMING_SNAKE_CASE_)
                     return
  
              if self.depth == 1:
                     _A   : Any       					=				np.mean(SCREAMING_SNAKE_CASE_)
                     return
  
              _A   : Tuple       					=				0
              _A   : Optional[Any]       					=				self.mean_squared_error(SCREAMING_SNAKE_CASE_				,		np.mean(SCREAMING_SNAKE_CASE_)) * 2
              for i in range(len(SCREAMING_SNAKE_CASE_)):
                     if len(x[:i]) < self.min_leaf_size:
                            continue
                     elif len(x[i:]) < self.min_leaf_size:
                            continue
                     else:
                            _A   : Tuple       					=				self.mean_squared_error(x[:i]				,		np.mean(y[:i]))
                            _A   : Union[str, Any]       					=				self.mean_squared_error(x[i:]				,		np.mean(y[i:]))
                            _A   : List[Any]       					=				error_left + error_right
                            if error < min_error:
                                   _A   : List[str]       					=				i
                                   _A   : Optional[int]       					=				error
  
              if best_split != 0:
                     _A   : List[Any]       					=				x[:best_split]
                     _A   : Dict       					=				y[:best_split]
                     _A   : List[str]       					=				x[best_split:]
                     _A   : Tuple       					=				y[best_split:]
  
                     _A   : Dict       					=				x[best_split]
                     _A   : Union[str, Any]       					=				DecisionTree(
                         depth=self.depth - 1				,		min_leaf_size=self.min_leaf_size)
                     _A   : Optional[int]       					=				DecisionTree(
                         depth=self.depth - 1				,		min_leaf_size=self.min_leaf_size)
                     self.left.train(SCREAMING_SNAKE_CASE_				,		SCREAMING_SNAKE_CASE_)
                     self.right.train(SCREAMING_SNAKE_CASE_				,		SCREAMING_SNAKE_CASE_)
              else:
                     _A   : List[Any]       					=				np.mean(SCREAMING_SNAKE_CASE_)
  
              return
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Optional[Any]:
              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(SCREAMING_SNAKE_CASE_)
                     else:
                            return self.left.predict(SCREAMING_SNAKE_CASE_)
              else:
                     print("Error: Decision tree not yet trained")
                     return None
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       @staticmethod
       def 				_lowerCamelCase				(		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[int]:
              _A   : List[Any]       					=				float(0)
              for label in labels:
                     squared_error_sum += (label - prediction) ** 2
  
              return float(squared_error_sum / labels.size)
  
  
  
  
  def 							_UpperCAmelCase       ():
         _A   : Optional[Any]       					=				np.arange(-1.0       ,							1.0       ,							0.0_05		)
         _A   : Dict       					=				np.sin(snake_case__		)
  
         _A   : List[Any]       					=				DecisionTree(depth=10       ,							min_leaf_size=10		)
         tree.train(snake_case__       ,							snake_case__		)
  
         _A   : Optional[Any]       					=				(np.random.rand(10		) * 2) - 1
         _A   : Tuple       					=				np.array([tree.predict(snake_case__		) for x in test_cases]		)
         _A   : Tuple       					=				np.mean((predictions - test_cases) ** 2		)
  
         print("Test values: " + str(snake_case__		)		)
         print("Predictions: " + str(snake_case__		)		)
         print("Average error: " + str(snake_case__		)		)
  
  
  if __name__ == "__main__":
      main()
      import doctest
  
      doctest.testmod(name='mean_squarred_error', verbose=True)
  
style_context: 
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import MobileNetVaImageProcessor
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
              _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
              _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
              _A   : str       					=				parent
              _A   : Any       					=				batch_size
              _A   : Dict       					=				num_channels
              _A   : int       					=				image_size
              _A   : Union[str, Any]       					=				min_resolution
              _A   : Tuple       					=				max_resolution
              _A   : Any       					=				do_resize
              _A   : Optional[int]       					=				size
              _A   : Tuple       					=				do_center_crop
              _A   : List[str]       					=				crop_size
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
              }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowerCAmelCase__ (       a  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              _A   : Dict       					=				MobileNetVaImageProcessingTester(self)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Dict:
              return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : int       					=				self.image_processing_class.from_dict(self.image_processor_dict)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 2_0})
              self.assertEqual(image_processor.crop_size				,		{"height": 1_8, "width": 1_8})
  
              _A   : Any       					=				self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 4_2})
              self.assertEqual(image_processor.crop_size				,		{"height": 8_4, "width": 8_4})
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              pass
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              # Initialize image_processing
              _A   : Optional[int]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PIL images
              _A   : List[Any]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		Image.Image)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Dict       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              # Initialize image_processing
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random numpy tensors
              _A   : int       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		numpify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		np.ndarray)
  
              # Test not batched input
              _A   : Tuple       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : str       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              # Initialize image_processing
              _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PyTorch tensors
              _A   : List[str]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		torchify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		torch.Tensor)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Any       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  