


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)




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"],
                )				,		)
