

_lowerCAmelCase      :    Any						       =    [0.33, 0.44, 0.55, 0.44, 0.33]
_lowerCAmelCase      :    int						       =    3
_lowerCAmelCase      :    Tuple						       =    5

# Buffer Space (with Parameters Space)
_lowerCAmelCase      :    list[float]						       =    []
_lowerCAmelCase      :    list[float]						       =    []

# Machine Time
_lowerCAmelCase      :    int						       =    0






def  UpperCamelCase_(							_snake_case     :						int			):




			"""simple docstring"""






			global buffer_space, params_space, machine_time, K, m, t

			# Choosing Dynamical Systems (All)
			for key, value in enumerate(_snake_case			):
						# Evolution Parameter
						__a						   =float(seed / value			)

						# Control Theory: Orbit Change
						__a						   =(buffer_space[(key + 1) % m] + e) % 1

						# Control Theory: Trajectory Change
						__a						   =(params_space[key] + e) % 1 + 3

						# Modification (Transition Function) - Jumps
						__a						   =round(float(r * value * (1 - value)			) ,  10			)
						__a						   =r  # Saving to Parameters Space

			# Logistic Map
			assert max(_snake_case			) < 1
			assert max(_snake_case			) < 4

			# Machine Time
			machine_time += 1






def  UpperCamelCase_(							):




			"""simple docstring"""






			global buffer_space, params_space, machine_time, K, m, t

			# PRNG (Xorshift by George Marsaglia)
			def xorshift(_snake_case     :						Dict ,  _snake_case     :						List[str]			):
						x ^= y >> 13
						y ^= x << 17
						x ^= y >> 5
						return x

			# Choosing Dynamical Systems (Increment)
			__a						   =machine_time % m

			# Evolution (Time Length)
			for _ in range(0 ,  _snake_case			):
						# Variables (Position + Parameters)
						__a						   =params_space[key]
						__a						   =buffer_space[key]

						# Modification (Transition Function) - Flow
						__a						   =round(float(r * value * (1 - value)			) ,  10			)
						__a						   =(machine_time * 0.01 + r * 1.01) % 1 + 3

			# Choosing Chaotic Data
			__a						   =int(buffer_space[(key + 2) % m] * (10**10)			)
			__a						   =int(buffer_space[(key - 2) % m] * (10**10)			)

			# Machine Time
			machine_time += 1

			return xorshift(_snake_case ,  _snake_case			) % 0Xffff_ffff






def  UpperCamelCase_(							):




			"""simple docstring"""






			global buffer_space, params_space, machine_time, K, m, t

			__a						   =K
			__a						   =[0] * m
			__a						   =0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						_lowerCAmelCase      :    Tuple						       =    random.sample(range(0xF_F_F_F_F_F_F_F), 100)
						for chunk in message:
												push(chunk)

						# for controlling
						_lowerCAmelCase      :    Optional[int]						       =    ""

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f'''{format(pull(), "#04x")}''')
												print(buffer_space)
												print(params_space)
												_lowerCAmelCase      :    Dict						       =    input("(e)exit? ").strip()









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase      :    List[str]						       =    False






@nightly
@require_torch_gpu
class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						# remove text_unet
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )

						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(__snake_case      )
									__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained(__snake_case      )

						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   =generator.manual_seed(0      )
						__a						   =pipe(
						    prompt='first prompt'      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=2      ,						output_type='numpy'      ,						).images

						assert np.abs(image - new_image      ).sum() < 1e-5, "Models don't have the same forward pass"





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'      )
						pipe.remove_unused_weights()
						pipe.to(__snake_case      )
						pipe.set_progress_bar_config(disable=__snake_case      )

						__a						   ='cyberpunk 2077'
						__a						   =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'      )
						__a						   =torch.manual_seed(0      )
						__a						   =pipe(
						    prompt=__snake_case      ,						image=__snake_case      ,						text_to_image_strength=0.75      ,						generator=__snake_case      ,						guidance_scale=7.5      ,						num_inference_steps=50      ,						output_type='numpy'      ,						).images

						__a						   =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a						   =np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2






