

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	):



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



      def 		UpperCamelCase_						(     self :							int						)       ->	Optional[Any]:
        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 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							int=False     ,   A__ :							str=False     ,   A__ :							Tuple=False						)       ->	Optional[Any]:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
          _snake_case		=				[]
          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:
          _snake_case		=				[]
          for i in range(self.batch_size						):
            _snake_case,	_snake_case		=				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
          _snake_case		=				[Image.fromarray(np.moveaxis(A__     ,   0     ,   -1						)						) for x in image_inputs]

        if torchify:
          _snake_case		=				[torch.from_numpy(A__						) for x in image_inputs]

        return image_inputs



@require_torch
@require_vision
class     lowercase_       (    __lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Any		 =							BlipImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Union[str, Any]:
        _snake_case		=				BlipImageProcessingTester(self						)



      @property
      def 		UpperCamelCase_						(     self :							List[str]						)       ->	str:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	int:
        _snake_case		=				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 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Optional[int]:
        pass



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

        # Test not batched input
        _snake_case		=				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
        _snake_case		=				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 		UpperCamelCase_						(     self :							Optional[int]						)       ->	Optional[Any]:
        # Initialize image_processor
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _snake_case		=				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
        _snake_case		=				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
        _snake_case		=				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 		UpperCamelCase_						(     self :							Dict						)       ->	str:
        # Initialize image_processor
        _snake_case		=				self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _snake_case		=				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
        _snake_case		=				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
        _snake_case		=				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_       (    __lowercase  ,				unittest.TestCase	):
      UpperCamelCase_     :						Optional[Any]		 =							BlipImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Dict:
        _snake_case		=				BlipImageProcessingTester(self     ,   num_channels=4						)
        _snake_case		=				3



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



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Tuple:
        _snake_case		=				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 		UpperCamelCase_						(     self :							Tuple						)       ->	int:
        pass







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

        # Test not batched input
        _snake_case		=				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
        _snake_case		=				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'''],
            )     ,   )




import numpy as np



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							Optional[int]=5     ,   A__ :							Any=5						)       ->	Any:
        _snake_case		=				depth
        _snake_case		=				0
        _snake_case		=				None
        _snake_case		=				None
        _snake_case		=				min_leaf_size
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	int:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''						)

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



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							str     ,   A__ :							int						)       ->	Optional[Any]:
        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:
          _snake_case		=				np.mean(A__						)
          return

        if self.depth == 1:
          _snake_case		=				np.mean(A__						)
          return

        _snake_case		=				0
        _snake_case		=				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:
            _snake_case		=				self.mean_squared_error(x[:i]     ,   np.mean(y[:i]						)						)
            _snake_case		=				self.mean_squared_error(x[i:]     ,   np.mean(y[i:]						)						)
            _snake_case		=				error_left + error_right
            if error < min_error:
              _snake_case		=				i
              _snake_case		=				error

        if best_split != 0:
          _snake_case		=				x[:best_split]
          _snake_case		=				y[:best_split]
          _snake_case		=				x[best_split:]
          _snake_case		=				y[best_split:]

          _snake_case		=				x[best_split]
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          self.left.train(A__     ,   A__						)
          self.right.train(A__     ,   A__						)
        else:
          _snake_case		=				np.mean(A__						)

        return







      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Tuple						)       ->	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_       :



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

        return float(squared_error_sum / labels.size						)



def  snake_case_()				->      Any:



  """simple docstring"""






  _snake_case		=				np.arange(-1.0					,			1.0					,			0.005	)
  _snake_case		=				np.sin(_UpperCamelCase	)

  _snake_case		=				DecisionTree(depth=10					,			min_leaf_size=10	)
  tree.train(_UpperCamelCase					,			_UpperCamelCase	)

  _snake_case		=				(np.random.rand(10	) * 2) - 1
  _snake_case		=				np.array([tree.predict(_UpperCamelCase	) for x in test_cases]	)
  _snake_case		=				np.mean((predictions - test_cases) ** 2	)

  print('''Test values: ''' + str(_UpperCamelCase	)	)
  print('''Predictions: ''' + str(_UpperCamelCase	)	)
  print('''Average error: ''' + str(_UpperCamelCase	)	)


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

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

