






import numpy as np
import skfuzzy as fuzz

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

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














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	a_							=    CLIPImageProcessor()
	a_							=    CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	a_							=    UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)






