






'''simple docstring'''







import numpy as np
import skfuzzy as fuzz

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

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







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

						# extract state_dict for VQVAE
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

    # extract state_dict for UNetLDM
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  DDIMScheduler(
						    timesteps=config.model.params.timesteps   ,    beta_schedule="""scaled_linear"""   ,    beta_start=config.model.params.linear_start   ,    beta_end=config.model.params.linear_end   ,    clip_sample=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		argparse.ArgumentParser()
							parser.add_argument('--checkpoint_path', type=str, required=True)
							parser.add_argument('--config_path', type=str, required=True)
							parser.add_argument('--output_path', type=str, required=True)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

