"""simple docstring"""




import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     _a      :		Tuple           =   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      :		Tuple           =   [0, 25, 50]
     _a      :		List[str]           =   [25, 50, 75]
     _a      :		List[Any]           =   fuzz.membership.trimf(X, abca)
     _a      :		List[str]           =   fuzz.membership.trimf(X, abca)

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


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 (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
