




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .pipeline_cycle_diffusion import CycleDiffusionPipeline
						from .pipeline_stable_diffusion import StableDiffusionPipeline
						from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
						from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
						from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
						from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
						from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
						from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
						from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
						from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
						from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
						from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
						from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
						from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
						from .pipeline_stable_unclip import StableUnCLIPPipeline
						from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
						from .safety_checker import StableDiffusionSafetyChecker
						from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
						from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    StableDiffusionDepthaImgPipeline,
						    StableDiffusionDiffEditPipeline,
						    StableDiffusionPixaPixZeroPipeline,
						)
else:
						from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
						from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
						from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
						if not (
						    is_torch_available()
						    and is_transformers_available()
						    and is_k_diffusion_available()
						    and is_k_diffusion_version('''>=''', '''0.0.12''')
						):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
						from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
						if not (is_transformers_available() and is_onnx_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_onnx_objects import *  # noqa F403
else:
						from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
						from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
						from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
						from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
						from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
						import flax



						@flax.struct.dataclass
						class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
												lowerCAmelCase__			      =42
												lowerCAmelCase__			      =42

						from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
						from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
						from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
						from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
						from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





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


def 					_a							(					lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase )



class    lowercase_		:



						def __init__(			self	:   int					,							*,
						    snake_case__	:   float = np.inf					,							snake_case__	:   str = "linear"					,							snake_case__	:   float = 0.0					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							regularization
								SCREAMING_SNAKE_CASE_					      =							gamma
								if kernel == "linear":
										SCREAMING_SNAKE_CASE_					      =							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_					      =							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_					      =							f'''Unknown kernel: {kernel}'''
										raise ValueError(snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.dot(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int					,							snake_case__	:   list[ndarray]					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							observations
								SCREAMING_SNAKE_CASE_					      =							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_)      ,				)					      =							np.shape(snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							LinearConstraint(snake_case__					,							0					,							0  )
								SCREAMING_SNAKE_CASE_					      =							Bounds(0					,							self.regularization  )

								SCREAMING_SNAKE_CASE_					      =							minimize(
								    snake_case__					,							np.ones(snake_case__  )					,							bounds=snake_case__					,							constraints=[ly_contraint]  ).x
								SCREAMING_SNAKE_CASE_					      =							l_star

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






						def 	__a					(			self	:   Any					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()