






import numpy as np
import skfuzzy as fuzz

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

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



