




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



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	float:
  return np.dot(UpperCamelCase	,      UpperCamelCase      )






class  _lowerCAmelCase     :

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return np.dot(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     observations
        lowerCAmelCase__				:  Optional[int]		       =     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

        ((lowerCAmelCase__)       ,			)				:  Optional[Any]		       =     np.shape(__UpperCAmelCase		)

        def to_minimize(__UpperCAmelCase		) -> float:
          lowerCAmelCase__				:  Tuple		       =     0
          ((lowerCAmelCase__)       ,			)				:  Tuple		       =     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		)

        lowerCAmelCase__				:  Union[str, Any]		       =     LinearConstraint(__UpperCAmelCase      ,				0      ,				0		)
        lowerCAmelCase__				:  str		       =     Bounds(0      ,				self.regularization		)

        lowerCAmelCase__				:  List[str]		       =     minimize(
            __UpperCAmelCase      ,				np.ones(__UpperCAmelCase		)      ,				bounds=__UpperCAmelCase      ,				constraints=[ly_contraint]		).x
        lowerCAmelCase__				:  Any		       =     l_star

        # calculating mean offset of separation plane to points
        lowerCAmelCase__				:  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]		)
        lowerCAmelCase__				:  Dict		       =     s / n







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[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 unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort






class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      # FIXME: add fast tests
      pass






@nightly
@require_onnxruntime
@require_torch_gpu
class  _lowerCAmelCase     (  unittest.TestCase	):

      @property
      def 	__magic_name__(   self		):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

      @property
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     ort.SessionOptions()
        lowerCAmelCase__				:  List[Any]		       =     False
        return options

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  Optional[int]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  List[Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  List[Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  int		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=10      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Any		       =     output.images
        lowerCAmelCase__				:  Any		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Any		       =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo.png'''		)
        lowerCAmelCase__				:  str		       =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     LMSDiscreteScheduler.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				subfolder='''scheduler'''      ,				revision='''onnx'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     OnnxStableDiffusionInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''      ,				revision='''onnx'''      ,				scheduler=__UpperCAmelCase      ,				safety_checker=__UpperCAmelCase      ,				feature_extractor=__UpperCAmelCase      ,				provider=self.gpu_provider      ,				sess_options=self.gpu_options      ,				)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        lowerCAmelCase__				:  str		       =     '''A red cat sitting on a park bench'''

        lowerCAmelCase__				:  Union[str, Any]		       =     np.random.RandomState(0		)
        lowerCAmelCase__				:  Optional[Any]		       =     pipe(
            prompt=__UpperCAmelCase      ,				image=__UpperCAmelCase      ,				mask_image=__UpperCAmelCase      ,				guidance_scale=7.5      ,				num_inference_steps=20      ,				generator=__UpperCAmelCase      ,				output_type='''np'''      ,				)
        lowerCAmelCase__				:  Tuple		       =     output.images
        lowerCAmelCase__				:  Optional[Any]		       =     images[0, 255:258, 255:258, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase__				:  Optional[int]		       =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3

