






"""simple docstring"""





import numpy as np
import skfuzzy as fuzz

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

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

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



