






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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  return np.dot(lowercase						,     lowercase				)


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Optional[Any]					, *,
         UpperCAmelCase_       :	float = np.inf					, UpperCAmelCase_       :	str = "linear"					, UpperCAmelCase_       :	float = 0.0					, ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    regularization
       SCREAMING_SNAKE_CASE       :      int							=    gamma
       if kernel == "linear":
         SCREAMING_SNAKE_CASE       :      List[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"      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    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:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    f'''Unknown kernel: {kernel}'''
         raise ValueError(UpperCAmelCase_      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	ndarray					, UpperCAmelCase_       :	ndarray      ):
       return np.dot(UpperCAmelCase_					, UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	ndarray					, UpperCAmelCase_       :	ndarray      ):
       return np.exp(-(self.gamma * norm_squared(vectora - vectora      ))      )


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	list[ndarray]					, UpperCAmelCase_       :	ndarray      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    observations
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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

       ((SCREAMING_SNAKE_CASE)						,  )       :      Dict							=    np.shape(UpperCAmelCase_      )

       def to_minimize(UpperCAmelCase_       :	ndarray      ) -> float:
         SCREAMING_SNAKE_CASE       :      Dict							=    0
         ((SCREAMING_SNAKE_CASE)						,  )       :      str							=    np.shape(UpperCAmelCase_      )
         for i in range(UpperCAmelCase_      ):
           for j in range(UpperCAmelCase_      ):
             s += (
                 candidate[i]
                 * candidate[j]
                 * classes[i]
                 * classes[j]
                 * self.kernel(observations[i]					, observations[j]      )
             )
         return 1 / 2 * s - sum(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    LinearConstraint(UpperCAmelCase_					, 0					, 0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    Bounds(0					, self.regularization      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    minimize(
           UpperCAmelCase_					, np.ones(UpperCAmelCase_      )					, bounds=UpperCAmelCase_					, constraints=[ly_contraint]      ).x
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    l_star

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






     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	ndarray      ):
       SCREAMING_SNAKE_CASE       :      int							=    sum(
           self.optimum[n]
           * self.classes[n]
           * self.kernel(self.observations[n]					, UpperCAmelCase_      )
           for n in range(len(self.classes      )      )      )
       return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()














import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	IFImgaImgSuperResolutionPipeline
     UpperCamelCase_							:		Any			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCamelCase_							:		Optional[Any]			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}					)
     UpperCamelCase_							:		str			=	PipelineTesterMixin.required_optional_params - {'''latents'''}


     def 				_A       (				self       :	List[Any]      ):
       return self._get_superresolution_dummy_components()


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]=0      ):
       if str(UpperCAmelCase_      ).startswith("mps"      ):
         SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 32, 32)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 16, 16)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }

       return inputs


     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()					, reason="XFormers attention is only available with CUDA and `xformers` installed"					, )
     def 				_A       (				self       :	Tuple      ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )


     def 				_A       (				self       :	Optional[Any]      ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != "cuda"					, reason="float16 requires CUDA"      )
     def 				_A       (				self       :	List[Any]      ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1      )


     def 				_A       (				self       :	Optional[int]      ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )


     def 				_A       (				self       :	Tuple      ):
       self._test_save_load_local()






     def 				_A       (				self       :	List[str]      ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2					, )






