





import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
 # Create universe of discourse in Python using linspace ()
 __A							:			Any         =			np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

 # Create two fuzzy sets by defining any membership function
 # (trapmf(), gbellmf(), gaussmf(), etc).
 __A							:			List[str]         =			[0, 25, 50]
 __A							:			Union[str, Any]         =			[25, 50, 75]
 __A							:			Optional[int]         =			fuzz.membership.trimf(X, abca)
 __A							:			int         =			fuzz.membership.trimf(X, abca)

 # Compute the different operations using inbuilt functions.
 __A							:			Any         =			np.ones(75)
 __A							:			Union[str, Any]         =			np.zeros((75,))
 # 1. Union = max(µA(x), µB(x))
 __A							:			List[Any]         =			fuzz.fuzzy_or(X, young, X, middle_aged)[1]
 # 2. Intersection = min(µA(x), µB(x))
 __A							:			str         =			fuzz.fuzzy_and(X, young, X, middle_aged)[1]
 # 3. Complement (A) = (1- min(µA(x))
 __A							:			Optional[int]         =			fuzz.fuzzy_not(young)
 # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
 __A							:			Optional[Any]         =			fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
 # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
 __A							:			Tuple         =			young + middle_aged - (young * middle_aged)
 # 6. Algebraic Product = (µA(x) * µB(x))
 __A							:			int         =			young * middle_aged
 # 7. Bounded Sum = min[1,(µA(x), µB(x))]
 __A							:			Optional[int]         =			fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
 # 8. Bounded difference = min[0,(µA(x), µB(x))]
 __A							:			List[Any]         =			fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

 # max-min composition
 # max-product composition

 # Plot each set A, set B and each operation result using plot() and subplot().
 from matplotlib import pyplot as plt

 plt.figure()

 plt.subplot(4, 3, 1)
 plt.plot(X, young)
 plt.title("Young")
 plt.grid(True)

 plt.subplot(4, 3, 2)
 plt.plot(X, middle_aged)
 plt.title("Middle aged")
 plt.grid(True)

 plt.subplot(4, 3, 3)
 plt.plot(X, union)
 plt.title("union")
 plt.grid(True)

 plt.subplot(4, 3, 4)
 plt.plot(X, intersection)
 plt.title("intersection")
 plt.grid(True)

 plt.subplot(4, 3, 5)
 plt.plot(X, complement_a)
 plt.title("complement_a")
 plt.grid(True)

 plt.subplot(4, 3, 6)
 plt.plot(X, difference)
 plt.title("difference a/b")
 plt.grid(True)

 plt.subplot(4, 3, 7)
 plt.plot(X, alg_sum)
 plt.title("alg_sum")
 plt.grid(True)

 plt.subplot(4, 3, 8)
 plt.plot(X, alg_product)
 plt.title("alg_product")
 plt.grid(True)

 plt.subplot(4, 3, 9)
 plt.plot(X, bdd_sum)
 plt.title("bdd_sum")
 plt.grid(True)

 plt.subplot(4, 3, 10)
 plt.plot(X, bdd_difference)
 plt.title("bdd_difference")
 plt.grid(True)

 plt.subplots_adjust(hspace=0.5)
 plt.show()









'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


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 .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


