


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 OwlViTImageProcessor







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def __init__(  self				:     List[str]   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Any=7   , __lowerCamelCase				:     Optional[int]=3   , __lowerCamelCase				:     int=18   , __lowerCamelCase				:     List[str]=30   , __lowerCamelCase				:     Dict=400   , __lowerCamelCase				:     Tuple=True   , __lowerCamelCase				:     int=None   , __lowerCamelCase				:     Any=True   , __lowerCamelCase				:     str=None   , __lowerCamelCase				:     str=True   , __lowerCamelCase				:     Any=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   , __lowerCamelCase				:     int=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   , __lowerCamelCase				:     Tuple=True   , ):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    parent
             lowerCamelCase__						:							Optional[Any]       =    batch_size
             lowerCamelCase__						:							Dict       =    num_channels
             lowerCamelCase__						:							Dict       =    image_size
             lowerCamelCase__						:							Union[str, Any]       =    min_resolution
             lowerCamelCase__						:							Tuple       =    max_resolution
             lowerCamelCase__						:							Optional[int]       =    do_resize
             lowerCamelCase__						:							str       =    size if size is not None else {"""height""": 18, """width""": 18}
             lowerCamelCase__						:							Optional[Any]       =    do_center_crop
             lowerCamelCase__						:							Dict       =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
             lowerCamelCase__						:							List[str]       =    do_normalize
             lowerCamelCase__						:							List[str]       =    image_mean
             lowerCamelCase__						:							Optional[Any]       =    image_std
             lowerCamelCase__						:							Tuple       =    do_convert_rgb





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_convert_rgb": self.do_convert_rgb,
             }







@require_torch
@require_vision
class      _lowercase					(				snake_case_				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     OwlViTImageProcessor if is_vision_available() else None





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    OwlViTImageProcessingTester(self			)





       @property
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_   , "do_resize"			)			)
             self.assertTrue(hasattr(lowercase_   , "size"			)			)
             self.assertTrue(hasattr(lowercase_   , "do_center_crop"			)			)
             self.assertTrue(hasattr(lowercase_   , "center_crop"			)			)
             self.assertTrue(hasattr(lowercase_   , "do_normalize"			)			)
             self.assertTrue(hasattr(lowercase_   , "image_mean"			)			)
             self.assertTrue(hasattr(lowercase_   , "image_std"			)			)
             self.assertTrue(hasattr(lowercase_   , "do_convert_rgb"			)			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.image_processing_class.from_dict(self.image_processor_dict			)
             self.assertEqual(image_processor.size   , {"height": 18, "width": 18}			)
             self.assertEqual(image_processor.crop_size   , {"height": 18, "width": 18}			)

             lowerCamelCase__						:							Union[str, Any]       =    self.image_processing_class.from_dict(self.image_processor_dict   , size=42   , crop_size=84			)
             self.assertEqual(image_processor.size   , {"height": 42, "width": 42}			)
             self.assertEqual(image_processor.crop_size   , {"height": 84, "width": 84}			)





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             lowerCamelCase__						:							Any       =    prepare_image_inputs(self.image_processor_tester   , equal_resolution=lowercase_			)

             for image in image_inputs:
                   self.assertIsInstance(lowercase_   , Image.Image			)

             # Test not batched input
             lowerCamelCase__						:							Optional[int]       =    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
             lowerCamelCase__						:							Tuple       =    image_processing(lowercase_   , 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				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.image_processing_class(**self.image_processor_dict			)
             # create random numpy tensors
             lowerCamelCase__						:							List[Any]       =    prepare_image_inputs(self.image_processor_tester   , equal_resolution=lowercase_   , numpify=lowercase_			)
             for image in image_inputs:
                   self.assertIsInstance(lowercase_   , np.ndarray			)

             # Test not batched input
             lowerCamelCase__						:							List[Any]       =    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
             lowerCamelCase__						:							List[Any]       =    image_processing(lowercase_   , 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			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.image_processing_class(**self.image_processor_dict			)
             # create random PyTorch tensors
             lowerCamelCase__						:							Any       =    prepare_image_inputs(self.image_processor_tester   , equal_resolution=lowercase_   , torchify=lowercase_			)
             for image in image_inputs:
                   self.assertIsInstance(lowercase_   , torch.Tensor			)

             # Test not batched input
             lowerCamelCase__						:							Dict       =    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
             lowerCamelCase__						:							Optional[Any]       =    image_processing(lowercase_   , 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"],
                 )   , )







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)

