lowerCAmelCase__	:  List[str]						=  [0.33, 0.44, 0.55, 0.44, 0.33]
lowerCAmelCase__	:  int						=  3
lowerCAmelCase__	:  Dict						=  5

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

# Machine Time
lowerCAmelCase__	:  Optional[int]						=  0
def 					UpperCamelCase__		(							A__       )  ->   Optional[int]:
				global buffer_space, params_space, machine_time, K, m, t

				# Choosing Dynamical Systems (All)
				for key, value in enumerate(A__       ):
								# Evolution Parameter
								snake_case__   :      List[str]   				= float(seed / value       )

								# Control Theory: Orbit Change
								snake_case__   :      Union[str, Any]   				= (buffer_space[(key + 1) % m] + e) % 1

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

								# Modification (Transition Function) - Jumps
								snake_case__   :      Dict   				= round(float(r * value * (1 - value)       )     ,					10       )
								snake_case__   :      List[str]   				= r  # Saving to Parameters Space

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

				# Machine Time
				machine_time += 1
def 					UpperCamelCase__		(							)  ->   int:
				global buffer_space, params_space, machine_time, K, m, t

				# PRNG (Xorshift by George Marsaglia)
				def xorshift(A__     ,					A__       ):
								x ^= y >> 13
								y ^= x << 17
								x ^= y >> 5
								return x

				# Choosing Dynamical Systems (Increment)
				snake_case__   :      Optional[Any]   				= machine_time % m

				# Evolution (Time Length)
				for _ in range(0     ,					A__       ):
								# Variables (Position + Parameters)
								snake_case__   :      List[Any]   				= params_space[key]
								snake_case__   :      Tuple   				= buffer_space[key]

								# Modification (Transition Function) - Flow
								snake_case__   :      Optional[Any]   				= round(float(r * value * (1 - value)       )     ,					10       )
								snake_case__   :      Optional[int]   				= (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

				# Choosing Chaotic Data
				snake_case__   :      Dict   				= int(buffer_space[(key + 2) % m] * (10**10)       )
				snake_case__   :      Union[str, Any]   				= int(buffer_space[(key - 2) % m] * (10**10)       )

				# Machine Time
				machine_time += 1

				return xorshift(A__     ,					A__       ) % 0xFF_FF_FF_FF
def 					UpperCamelCase__		(							)  ->   Optional[int]:
				global buffer_space, params_space, machine_time, K, m, t

				snake_case__   :      Optional[Any]   				= K
				snake_case__   :      Dict   				= [0] * m
				snake_case__   :      List[Any]   				= 0


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

				# Pushing Data (Input)
				import random

				lowerCAmelCase__	:  Any						=  random.sample(range(0XFF_FF_FF_FF), 1_00)
				for chunk in message:
								push(chunk)

				# for controlling
				lowerCAmelCase__	:  List[str]						=  ''''''

				# Pulling Data (Output)
				while inp in ("e", "E"):
								print(F'''{format(pull(), '#04x')}''')
								print(buffer_space)
								print(params_space)
								lowerCAmelCase__	:  Optional[Any]						=  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__	:  Union[str, Any]						=  False





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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

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

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			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      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


