






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        a__      (	unittest.TestCase		):






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






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				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 					_lowerCamelCase					(      self							, lowercase__=False							, lowercase__=False							, lowercase__=False						)				->				str:
				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

				if torchify:
						__A    =							[torch.from_numpy(lowercase__						) for x in image_inputs]

				return image_inputs





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       List[Any] 					= BlipImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							BlipImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				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						)				->				Tuple:
				pass






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

				# Test not batched input
				__A    =							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
				__A    =							image_processor(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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							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
				__A    =							image_processor(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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							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
				__A    =							image_processor(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.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       List[str] 					= BlipImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							BlipImageProcessingTester(self							, num_channels=4						)
				__A    =							3






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






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				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]:
				pass






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

				# Test not batched input
				__A    =							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
				__A    =							image_processor(lowercase__							, 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)

