code: 
  
  
  '''simple docstring'''
  
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  A__	:      str				=						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      torch.FloatTensor
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    1
  
  
  
  
  
  
  
      @register_to_config
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 40    ,  SCREAMING_SNAKE_CASE_ = 0.0_0_2    ,  SCREAMING_SNAKE_CASE_ = 8_0.0    ,  SCREAMING_SNAKE_CASE_ = 0.5    ,  SCREAMING_SNAKE_CASE_ = 1.0    ,  SCREAMING_SNAKE_CASE_ = 7.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  List[Any]:
           # standard deviation of the initial noise distribution
           __lowerCamelCase     :			Tuple              =	sigma_max
  
           __lowerCamelCase     :			Optional[Any]              =	np.linspace(0    ,  1    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	self._convert_to_karras(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Dict              =	self.sigma_to_t(SCREAMING_SNAKE_CASE_			)
  
           # setable values
           __lowerCamelCase     :			Optional[Any]              =	None
           __lowerCamelCase     :			Optional[int]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	False
           __lowerCamelCase     :			List[Any]              =	False
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=None			)       ->  int:
           if schedule_timesteps is None:
                __lowerCamelCase     :			int              =	self.timesteps
  
           __lowerCamelCase     :			str              =	(schedule_timesteps == timestep).nonzero()
           return indices.item()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  torch.FloatTensor:
           # Get sigma corresponding to timestep
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			str              =	timestep.to(self.timesteps.device			)
           __lowerCamelCase     :			Union[str, Any]              =	self.index_for_timestep(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	self.sigmas[step_idx]
  
           __lowerCamelCase     :			Union[str, Any]              =	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
           __lowerCamelCase     :			Optional[int]              =	True
           return sample
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           if not isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			):
                __lowerCamelCase     :			Any              =	np.array(SCREAMING_SNAKE_CASE_    ,  dtype=np.floataa			)
  
           __lowerCamelCase     :			Tuple              =	10_00 * 0.2_5 * np.log(sigmas + 1E-44			)
  
           return timesteps
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  List[str]:
           if num_inference_steps is None and timesteps is None:
                raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'			)
  
           if num_inference_steps is not None and timesteps is not None:
                raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'			)
  
           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
                for i in range(1    ,  len(SCREAMING_SNAKE_CASE_			)			):
                     if timesteps[i] >= timesteps[i - 1]:
                          raise ValueError('`timesteps` must be in descending order.'			)
  
                if timesteps[0] >= self.config.num_train_timesteps:
                     raise ValueError(
                         f'`timesteps` must start before `self.config.train_timesteps`:'
                         f' {self.config.num_train_timesteps}.'			)
  
                __lowerCamelCase     :			Dict              =	np.array(SCREAMING_SNAKE_CASE_    ,  dtype=np.intaa			)
                __lowerCamelCase     :			Union[str, Any]              =	True
           else:
                if num_inference_steps > self.config.num_train_timesteps:
                     raise ValueError(
                         f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                         f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                         f' maximal {self.config.num_train_timesteps} timesteps.'			)
  
                __lowerCamelCase     :			int              =	num_inference_steps
  
                __lowerCamelCase     :			int              =	self.config.num_train_timesteps // self.num_inference_steps
                __lowerCamelCase     :			Union[str, Any]              =	(np.arange(0    ,  SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
                __lowerCamelCase     :			Union[str, Any]              =	False
  
           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           __lowerCamelCase     :			List[Any]              =	self.config.num_train_timesteps
           __lowerCamelCase     :			Optional[Any]              =	timesteps[::-1].copy()
           __lowerCamelCase     :			List[Any]              =	ramp / (num_train_timesteps - 1)
           __lowerCamelCase     :			str              =	self._convert_to_karras(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	self.sigma_to_t(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[int]              =	np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
           __lowerCamelCase     :			Tuple              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
  
           if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
                # mps does not support float64
                __lowerCamelCase     :			Dict              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           else:
                __lowerCamelCase     :			int              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Any:
           __lowerCamelCase     :			float              =	self.config.sigma_min
           __lowerCamelCase     :			float              =	self.config.sigma_max
  
           __lowerCamelCase     :			Tuple              =	self.config.rho
           __lowerCamelCase     :			Dict              =	sigma_min ** (1 / rho)
           __lowerCamelCase     :			List[Any]              =	sigma_max ** (1 / rho)
           __lowerCamelCase     :			List[str]              =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
           __lowerCamelCase     :			List[Any]              =	self.config.sigma_data
  
           __lowerCamelCase     :			int              =	sigma_data**2 / (sigma**2 + sigma_data**2)
           __lowerCamelCase     :			int              =	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           __lowerCamelCase     :			List[Any]              =	self.config.sigma_min
           __lowerCamelCase     :			Union[str, Any]              =	self.config.sigma_data
  
           __lowerCamelCase     :			List[str]              =	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           __lowerCamelCase     :			List[str]              =	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
           if (
               isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
               or isinstance(SCREAMING_SNAKE_CASE_    ,  torch.IntTensor			)
               or isinstance(SCREAMING_SNAKE_CASE_    ,  torch.LongTensor			)
           ):
                raise ValueError(
                    (
                        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                        f' `{self.__class__}.step()` is not supported. Make sure to pass'
                        ' one of the `scheduler.timesteps` as a timestep.'
                    )    ,  )
  
           if not self.is_scale_input_called:
                logger.warning(
                    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                    'See `StableDiffusionPipeline` for a usage example.'			)
  
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			Dict              =	timestep.to(self.timesteps.device			)
  
           __lowerCamelCase     :			str              =	self.config.sigma_min
           __lowerCamelCase     :			Tuple              =	self.config.sigma_max
  
           __lowerCamelCase     :			List[Any]              =	self.index_for_timestep(SCREAMING_SNAKE_CASE_			)
  
           # sigma_next corresponds to next_t in original implementation
           __lowerCamelCase     :			Optional[int]              =	self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
                __lowerCamelCase     :			Tuple              =	self.sigmas[step_index + 1]
           else:
                # Set sigma_next to sigma_min
                __lowerCamelCase     :			Optional[Any]              =	self.sigmas[-1]
  
           # Get scalings for boundary conditions
           __lowerCamelCase       , __lowerCamelCase     :			str              =	self.get_scalings_for_boundary_condition(SCREAMING_SNAKE_CASE_			)
  
           # 1. Denoise model output using boundary conditions
           __lowerCamelCase     :			List[Any]              =	c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
                __lowerCamelCase     :			Tuple              =	denoised.clamp(-1    ,  1			)
  
           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps			) > 1:
                __lowerCamelCase     :			Optional[Any]              =	randn_tensor(
                    model_output.shape    ,  dtype=model_output.dtype    ,  device=model_output.device    ,  generator=SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			int              =	torch.zeros_like(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	noise * self.config.s_noise
  
           __lowerCamelCase     :			Dict              =	sigma_next.clamp(min=SCREAMING_SNAKE_CASE_    ,  max=SCREAMING_SNAKE_CASE_			)
  
           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           __lowerCamelCase     :			Optional[int]              =	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
           if not return_dict:
                return (prev_sample,)
  
           return CMStochasticIterativeSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  )       ->  torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           __lowerCamelCase     :			int              =	self.sigmas.to(device=original_samples.device    ,  dtype=original_samples.dtype			)
           if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_			):
                # mps does not support float64
                __lowerCamelCase     :			List[str]              =	self.timesteps.to(original_samples.device    ,  dtype=torch.floataa			)
                __lowerCamelCase     :			Optional[Any]              =	timesteps.to(original_samples.device    ,  dtype=torch.floataa			)
           else:
                __lowerCamelCase     :			Any              =	self.timesteps.to(original_samples.device			)
                __lowerCamelCase     :			str              =	timesteps.to(original_samples.device			)
  
           __lowerCamelCase     :			int              =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           __lowerCamelCase     :			Any              =	sigmas[step_indices].flatten()
           while len(sigma.shape			) < len(original_samples.shape			):
                __lowerCamelCase     :			Any              =	sigma.unsqueeze(-1			)
  
           __lowerCamelCase     :			Dict              =	original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
  
  
      def __len__(							self			)       ->  Optional[Any]:
           return self.config.num_train_timesteps
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  