


'''simple docstring'''







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

# Buffer Space (with Parameters Space)
snake_case_       :    List[Any]          =					[]
snake_case_       :    Union[str, Any]          =					[]

# Machine Time
snake_case_       :    List[Any]          =					0





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Optional[Any]				)	->		Any:
						global buffer_space, params_space, machine_time, K, m, t

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

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

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

												# Modification (Transition Function) - Jumps
												UpperCAmelCase_     : int         =					round(float(r * value * (1 - value)				),     10				)
												UpperCAmelCase_     : Optional[int]         =					r  # Saving to Parameters Space

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

						# Machine Time
						machine_time += 1





def     lowerCamelCase_ (					)	->		str:
						global buffer_space, params_space, machine_time, K, m, t

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

						# Choosing Dynamical Systems (Increment)
						UpperCAmelCase_     : Any         =					machine_time % m

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

												# Modification (Transition Function) - Flow
												UpperCAmelCase_     : Tuple         =					round(float(r * value * (1 - value)				),     10				)
												UpperCAmelCase_     : Optional[int]         =					(machine_time * 0.01 + r * 1.01) % 1 + 3

						# Choosing Chaotic Data
						UpperCAmelCase_     : List[str]         =					int(buffer_space[(key + 2) % m] * (10**10)				)
						UpperCAmelCase_     : Tuple         =					int(buffer_space[(key - 2) % m] * (10**10)				)

						# Machine Time
						machine_time += 1

						return xorshift(_A,     _A				) % 0xFF_FFF_FFF





def     lowerCamelCase_ (					)	->		Optional[int]:
						global buffer_space, params_space, machine_time, K, m, t

						UpperCAmelCase_     : Any         =					K
						UpperCAmelCase_     : Any         =					[0] * m
						UpperCAmelCase_     : List[str]         =					0


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

					# Pushing Data (Input)
					import random

					snake_case_       :    Dict          =					random.sample(range(0xffffffff), 1_00)
					for chunk in message:
										push(chunk)

					# for controlling
					snake_case_       :    str          =					""

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












'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __a (lowerCamelCase       ):
							__a   :				UNetaDModel
							__a   :				PNDMScheduler

							def __init__(							self							:       Optional[Any]    ,       __magic_name__							:       UNetaDModel    ,       __magic_name__							:       PNDMScheduler				)   ->    Tuple:







													"""simple docstring"""
													super().__init__()

													UpperCAmelCase_     : Optional[int]         =					PNDMScheduler.from_config(scheduler.config				)

													self.register_modules(unet=__magic_name__    ,       scheduler=__magic_name__				)



							@torch.no_grad()
							def __call__(							self							:       Union[str, Any]    ,       __magic_name__							:       int = 1    ,       __magic_name__							:       int = 50    ,       __magic_name__							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       __magic_name__							:       Optional[str] = "pil"    ,       __magic_name__							:       bool = True    ,       **__magic_name__							:       Dict    ,       )   ->    Union[ImagePipelineOutput, Tuple]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					randn_tensor(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,       generator=__magic_name__    ,       device=self.device    ,       )

													self.scheduler.set_timesteps(__magic_name__				)
													for t in self.progress_bar(self.scheduler.timesteps				):
																			UpperCAmelCase_     : Optional[int]         =					self.unet(__magic_name__    ,       __magic_name__				).sample

																			UpperCAmelCase_     : str         =					self.scheduler.step(__magic_name__    ,       __magic_name__    ,       __magic_name__				).prev_sample

													UpperCAmelCase_     : int         =					(image / 2 + 0.5).clamp(0    ,       1				)
													UpperCAmelCase_     : Any         =					image.cpu().permute(0    ,       2    ,       3    ,       1				).numpy()
													if output_type == "pil":
																			UpperCAmelCase_     : Optional[int]         =					self.numpy_to_pil(__magic_name__				)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__magic_name__				)





