





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 ChineseCLIPImageProcessor





class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=7					,  lowercase=3					,  lowercase=18					,  lowercase=30					,  lowercase=400					,  lowercase=True					,  lowercase=None					,  lowercase=True					,  lowercase=None					,  lowercase=True					,  lowercase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,  lowercase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,  lowercase=True					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	str =				size if size is not None else {'height': 224, 'width': 224}
          a__						:	Tuple =				crop_size if crop_size is not None else {'height': 18, 'width': 18}
          a__						:	Dict =				parent
          a__						:	Dict =				batch_size
          a__						:	Any =				num_channels
          a__						:	Any =				image_size
          a__						:	Optional[int] =				min_resolution
          a__						:	Tuple =				max_resolution
          a__						:	int =				do_resize
          a__						:	Union[str, Any] =				size
          a__						:	Optional[int] =				do_center_crop
          a__						:	Optional[int] =				crop_size
          a__						:	str =				do_normalize
          a__						:	List[str] =				image_mean
          a__						:	str =				image_std
          a__						:	Union[str, Any] =				do_convert_rgb

   def 							__lowercase		(					self)			->						int:







          '''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,
          }

   def 							__lowercase		(					self					,  lowercase=False					,  lowercase=False					,  lowercase=False)			->						Any:







          '''simple docstring'''

          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
                 a__						:	Optional[int] =				[]
                 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__						:	Tuple =				[]
                 for i in range(self.batch_size):
                        a__ ,							a__						:	Tuple =				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__						:	List[Any] =				[Image.fromarray(np.moveaxis(lowercase					,  0					,  -1)) for x in image_inputs]

          if torchify:
                 a__						:	Any =				[torch.from_numpy(lowercase) for x in image_inputs]

          return image_inputs





@require_torch
@require_vision
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Tuple         =  ChineseCLIPImageProcessor if is_vision_available() else None

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Union[str, Any] =				ChineseCLIPImageProcessingTester(self					,  do_center_crop=lowercase)

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          return self.image_processor_tester.prepare_image_processor_dict()

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Dict =				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 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size					,  {'height': 224, 'width': 224})
          self.assertEqual(image_processor.crop_size					,  {'height': 18, 'width': 18})

          a__						:	List[str] =				self.image_processing_class.from_dict(self.image_processor_dict					,  size=42					,  crop_size=84)
          self.assertEqual(image_processor.size					,  {'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size					,  {'height': 84, 'width': 84})

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__						:	Optional[Any] =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  Image.Image)

          # Test not batched input
          a__						:	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
          a__						:	str =				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 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Tuple =				self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          a__						:	Tuple =				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__						:	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
          a__						:	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 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	int =				self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          a__						:	List[str] =				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__						:	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
          a__						:	str =				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'],
              )					,  )





@require_torch
@require_vision
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  ChineseCLIPImageProcessor if is_vision_available() else None

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Union[str, Any] =				ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=lowercase)
          a__						:	List[Any] =				3

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return self.image_processor_tester.prepare_image_processor_dict()

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				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 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

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







          '''simple docstring'''

          a__						:	Optional[int] =				self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__						:	Optional[int] =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  Image.Image)

          # Test not batched input
          a__						:	List[str] =				image_processing(image_inputs[0]					,  return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape					,  (
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )					,  )

          # Test batched
          a__						:	Optional[Any] =				image_processing(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.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )					,  )









import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				self.__linear
          elif kernel == "rbf":
                 if self.gamma == 0:
                        raise ValueError('rbf kernel requires gamma')
                 if not isinstance(self.gamma					,  (float, int)):
                        raise ValueError('gamma must be float or int')
                 if not self.gamma > 0:
                        raise ValueError('gamma must be > 0')
                 a__						:	str =				self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
          else:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				classes

          # using Wolfe's Dual to calculate w.
          # Primal problem: minimize 1/2*norm_squared(w)
          #   constraint: yn(w . xn + b) >= 1
          #
          # With l a vector
          # Dual problem: maximize sum_n(ln) -
          #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
          #   constraint: self.C >= ln >= 0
          #           and sum_n(ln*yn) = 0
          # Then we get w using w = sum_n(ln*yn*xn)
          # At the end we can get b ~= mean(yn - w . xn)
          #
          # Since we use kernels, we only need l_star to calculate b
          # and to classify observations

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)
                 for i in range(lowercase):
                        for j in range(lowercase):
                               s += (
                                   candidate[i]
                                   * candidate[j]
                                   * classes[i]
                                   * classes[j]
                                   * self.kernel(observations[i]					,  observations[j])
                               )
                 return 1 / 2 * s - sum(lowercase)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				0
          for i in range(lowercase):
                 for j in range(lowercase):
                        s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                            observations[i]					,  observations[j])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	int =				sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n]					,  lowercase)
              for n in range(len(self.classes)))
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


