



'''simple docstring'''






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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BlipImageProcessor



class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def __init__(				self    ,a_    ,a_=7    ,a_=3    ,a_=18    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=False    ,a_=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,a_=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,a_=True    ,)						->  Optional[int]:
        _UpperCAmelCase    :		Dict     	=					size if size is not None else {"""height""": 20, """width""": 20}
        _UpperCAmelCase    :		Dict     	=					parent
        _UpperCAmelCase    :		Tuple     	=					batch_size
        _UpperCAmelCase    :		Optional[int]     	=					num_channels
        _UpperCAmelCase    :		int     	=					image_size
        _UpperCAmelCase    :		List[Any]     	=					min_resolution
        _UpperCAmelCase    :		str     	=					max_resolution
        _UpperCAmelCase    :		Tuple     	=					do_resize
        _UpperCAmelCase    :		Union[str, Any]     	=					size
        _UpperCAmelCase    :		int     	=					do_normalize
        _UpperCAmelCase    :		List[str]     	=					image_mean
        _UpperCAmelCase    :		Any     	=					image_std
        _UpperCAmelCase    :		Union[str, Any]     	=					do_pad
        _UpperCAmelCase    :		Tuple     	=					do_convert_rgb







 def _snake_case					(				self				)						->  List[str]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
            "do_pad": self.do_pad,
        }







 def _snake_case					(				self    ,a_=False    ,a_=False    ,a_=False				)						->  Dict:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
               _UpperCAmelCase    :		Union[str, Any]     	=					[]
               for i in range(self.batch_size				):
                      image_inputs.append(
                          np.random.randint(
                              255    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta				)				)
        else:
               _UpperCAmelCase    :		Optional[int]     	=					[]
               for i in range(self.batch_size				):
                      _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					np.random.choice(np.arange(self.min_resolution    ,self.max_resolution				)    ,2				)
                      image_inputs.append(np.random.randint(255    ,size=(self.num_channels, width, height)    ,dtype=np.uinta				)				)

        if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               _UpperCAmelCase    :		Any     	=					[Image.fromarray(np.moveaxis(a_    ,0    ,-1				)				) for x in image_inputs]

        if torchify:
               _UpperCAmelCase    :		Optional[int]     	=					[torch.from_numpy(a_				) for x in image_inputs]

        return image_inputs







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		Dict     	=					BlipImageProcessingTester(self				)







 @property
 def _snake_case					(				self				)						->  str:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Any:
        pass







 def _snake_case					(				self				)						->  Union[str, Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		List[str]     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[str]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		List[str]     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  Optional[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		str     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        _UpperCAmelCase    :		str     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,numpify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,np.ndarray				)

        # Test not batched input
        _UpperCAmelCase    :		Dict     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		int     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







 def _snake_case					(				self				)						->  List[str]:
        # Initialize image_processor
        _UpperCAmelCase    :		Dict     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        _UpperCAmelCase    :		Union[str, Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_    ,torchify=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,torch.Tensor				)

        # Test not batched input
        _UpperCAmelCase    :		str     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Optional[Any]     	=					image_processor(a_    ,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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)







@require_torch
@require_vision
class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    BlipImageProcessor if is_vision_available() else None







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					BlipImageProcessingTester(self    ,num_channels=4				)
        _UpperCAmelCase    :		List[Any]     	=					3







 @property
 def _snake_case					(				self				)						->  int:
        return self.image_processor_tester.prepare_image_processor_dict()







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(a_    ,"""do_resize"""				)				)
        self.assertTrue(hasattr(a_    ,"""size"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_normalize"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_mean"""				)				)
        self.assertTrue(hasattr(a_    ,"""image_std"""				)				)
        self.assertTrue(hasattr(a_    ,"""do_convert_rgb"""				)				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        pass







 def _snake_case					(				self				)						->  List[Any]:
        # Initialize image_processor
        _UpperCAmelCase    :		Any     	=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        _UpperCAmelCase    :		Optional[Any]     	=					self.image_processor_tester.prepare_inputs(equal_resolution=a_				)
        for image in image_inputs:
               self.assertIsInstance(a_    ,Image.Image				)

        # Test not batched input
        _UpperCAmelCase    :		List[Any]     	=					image_processor(image_inputs[0]    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                1,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)

        # Test batched
        _UpperCAmelCase    :		Dict     	=					image_processor(a_    ,return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape    ,(
                self.image_processor_tester.batch_size,
                self.expected_encoded_image_num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )    ,)








'''simple docstring'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

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







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               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(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

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

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

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

        return







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







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

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


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

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



