


import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class      UpperCamelCase					(		lowercase__						,      lowercase__						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Optional[int]									=IFInpaintingSuperResolutionPipeline
   lowercase					:	Any									=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
   lowercase					:	List[Any]									=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}     )
   lowercase					:	Union[str, Any]									=PipelineTesterMixin.required_optional_params - {"""latents"""}
   def 		UpperCamelCase  (     self		):
        return self._get_superresolution_dummy_components()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=0		):
        if str(UpperCamelCase_		).startswith('''mps'''		):
             lowercase_       :int     =       torch.manual_seed(UpperCamelCase_		)
        else:
             lowercase_       :List[Any]     =       torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       floats_tensor((1, 3, 16, 16)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
        lowercase_       :Optional[int]     =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)
        lowercase_       :int     =       floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(UpperCamelCase_		)		).to(UpperCamelCase_		)

        lowercase_       :Optional[Any]     =       {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''original_image''': original_image,
            '''mask_image''': mask_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 		UpperCamelCase  (     self		):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)
   def 		UpperCamelCase  (     self		):
        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != '''cuda'''   ,							reason='''float16 requires CUDA'''		)
   def 		UpperCamelCase  (     self		):
        # 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 		UpperCamelCase  (     self		):
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)
   def 		UpperCamelCase  (     self		):
        self._test_save_load_local()




   def 		UpperCamelCase  (     self		):
        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2   ,							)





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




def     UpperCamelCase    (      _a		)					->							float:
     '''simple docstring'''





     return np.dot(_a       ,		_a		)






class      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							*,
       UpperCamelCase_ = np.inf   ,							UpperCamelCase_ = "linear"   ,							UpperCamelCase_ = 0.0   ,							):
        lowercase_       :Dict     =       regularization
        lowercase_       :Optional[Any]     =       gamma
        if kernel == "linear":
             lowercase_       :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'''		)
             lowercase_       :Union[str, 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:
             lowercase_       :str     =       f"Unknown kernel: {kernel}"
             raise ValueError(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.dot(UpperCamelCase_   ,							UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Tuple     =       observations
        lowercase_       :Union[str, 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

        ((lowercase_)    ,      )       :Optional[Any]     =       np.shape(UpperCamelCase_		)

        def to_minimize(UpperCamelCase_		) -> float:
             lowercase_       :Dict     =       0
             ((lowercase_)    ,      )       :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_		)

        lowercase_       :Dict     =       LinearConstraint(UpperCamelCase_   ,							0   ,							0		)
        lowercase_       :Optional[Any]     =       Bounds(0   ,							self.regularization		)

        lowercase_       :Any     =       minimize(
            UpperCamelCase_   ,							np.ones(UpperCamelCase_		)   ,							bounds=UpperCamelCase_   ,							constraints=[ly_contraint]		).x
        lowercase_       :Tuple     =       l_star

        # calculating mean offset of separation plane to points
        lowercase_       :str     =       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]		)
        lowercase_       :List[str]     =       s / n




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Union[str, Any]     =       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()

