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
  
  
  lowerCAmelCase   :List[str]								=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    torch.FloatTensor
  
  
  
  
  
  
  
  class _lowerCamelCase (		lowercase__	,  lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Tuple   					=       1
  
  
  
  
  
  
  
  		@register_to_config
  		def __init__(      self	:		int						,  _A	:		int = 40						,  _A	:		float = 0.002						,  _A	:		float = 80.0						,  _A	:		float = 0.5						,  _A	:		float = 1.0						,  _A	:		float = 7.0						,  _A	:		bool = True						,  )						->				str:
  			# standard deviation of the initial noise distribution
  			__magic_name__ :					Union[str, Any]  			=  sigma_max
  
  			__magic_name__ :					Any  			=  np.linspace(0						,  1						,  _A    )
  			__magic_name__ :					Dict  			=  self._convert_to_karras(_A    )
  			__magic_name__ :					Tuple  			=  self.sigma_to_t(_A    )
  
  			# setable values
  			__magic_name__ :					List[str]  			=  None
  			__magic_name__ :					Tuple  			=  torch.from_numpy(_A    )
  			__magic_name__ :					Any  			=  torch.from_numpy(_A    )
  			__magic_name__ :					Dict  			=  False
  			__magic_name__ :					Any  			=  False
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		Any						,  _A	:		Tuple=None    )						->				List[Any]:
  			if schedule_timesteps is None:
  				__magic_name__ :					Tuple  			=  self.timesteps
  
  			__magic_name__ :					Tuple  			=  (schedule_timesteps == timestep).nonzero()
  			return indices.item()
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		torch.FloatTensor						,  _A	:		Union[float, torch.FloatTensor]    )						->				torch.FloatTensor:
  			# Get sigma corresponding to timestep
  			if isinstance(_A						,  torch.Tensor    ):
  				__magic_name__ :					Any  			=  timestep.to(self.timesteps.device    )
  			__magic_name__ :					Dict  			=  self.index_for_timestep(_A    )
  			__magic_name__ :					List[str]  			=  self.sigmas[step_idx]
  
  			__magic_name__ :					Any  			=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
  			__magic_name__ :					Union[str, Any]  			=  True
  			return sample
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		Union[float, np.ndarray]    )						->				List[Any]:
  			if not isinstance(_A						,  np.ndarray    ):
  				__magic_name__ :					List[str]  			=  np.array(_A						,  dtype=np.floataa    )
  
  			__magic_name__ :					Dict  			=  1000 * 0.25 * np.log(sigmas + 1E-44    )
  
  			return timesteps
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int] = None						,  _A	:		Union[str, torch.device] = None						,  _A	:		Optional[List[int]] = None						,  )						->				List[Any]:
  			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(_A    )    ):
  					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}.'    )
  
  				__magic_name__ :					Optional[Any]  			=  np.array(_A						,  dtype=np.intaa    )
  				__magic_name__ :					Tuple  			=  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.'    )
  
  				__magic_name__ :					str  			=  num_inference_steps
  
  				__magic_name__ :					Union[str, Any]  			=  self.config.num_train_timesteps // self.num_inference_steps
  				__magic_name__ :					Optional[int]  			=  (np.arange(0						,  _A    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
  				__magic_name__ :					Tuple  			=  False
  
  			# Map timesteps to Karras sigmas directly for multistep sampling
  			# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  			__magic_name__ :					Union[str, Any]  			=  self.config.num_train_timesteps
  			__magic_name__ :					Optional[int]  			=  timesteps[::-1].copy()
  			__magic_name__ :					Optional[int]  			=  ramp / (num_train_timesteps - 1)
  			__magic_name__ :					Union[str, Any]  			=  self._convert_to_karras(_A    )
  			__magic_name__ :					str  			=  self.sigma_to_t(_A    )
  
  			__magic_name__ :					List[str]  			=  np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
  			__magic_name__ :					List[Any]  			=  torch.from_numpy(_A    ).to(device=_A    )
  
  			if str(_A    ).startswith('mps'    ):
  				# mps does not support float64
  				__magic_name__ :					str  			=  torch.from_numpy(_A    ).to(_A						,  dtype=torch.floataa    )
  			else:
  				__magic_name__ :					Any  			=  torch.from_numpy(_A    ).to(device=_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Optional[int]    )						->				Any:
  			__magic_name__ :					float  			=  self.config.sigma_min
  			__magic_name__ :					float  			=  self.config.sigma_max
  
  			__magic_name__ :					Tuple  			=  self.config.rho
  			__magic_name__ :					List[str]  			=  sigma_min ** (1 / rho)
  			__magic_name__ :					int  			=  sigma_max ** (1 / rho)
  			__magic_name__ :					Union[str, Any]  			=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  			return sigmas
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		Dict    )						->				str:
  			__magic_name__ :					str  			=  self.config.sigma_data
  
  			__magic_name__ :					Any  			=  sigma_data**2 / (sigma**2 + sigma_data**2)
  			__magic_name__ :					Optional[int]  			=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  			return c_skip, c_out
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		str    )						->				List[str]:
  			__magic_name__ :					Dict  			=  self.config.sigma_min
  			__magic_name__ :					List[Any]  			=  self.config.sigma_data
  
  			__magic_name__ :					Optional[int]  			=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  			__magic_name__ :					Dict  			=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  			return c_skip, c_out
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		torch.FloatTensor						,  _A	:		Union[float, torch.FloatTensor]						,  _A	:		torch.FloatTensor						,  _A	:		Optional[torch.Generator] = None						,  _A	:		bool = True						,  )						->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
  			if (
  			    isinstance(_A						,  _A    )
  			    or isinstance(_A						,  torch.IntTensor    )
  			    or isinstance(_A						,  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(_A						,  torch.Tensor    ):
  				__magic_name__ :					List[str]  			=  timestep.to(self.timesteps.device    )
  
  			__magic_name__ :					Optional[Any]  			=  self.config.sigma_min
  			__magic_name__ :					Any  			=  self.config.sigma_max
  
  			__magic_name__ :					Any  			=  self.index_for_timestep(_A    )
  
  			# sigma_next corresponds to next_t in original implementation
  			__magic_name__ :					Tuple  			=  self.sigmas[step_index]
  			if step_index + 1 < self.config.num_train_timesteps:
  				__magic_name__ :					Tuple  			=  self.sigmas[step_index + 1]
  			else:
  				# Set sigma_next to sigma_min
  				__magic_name__ :					int  			=  self.sigmas[-1]
  
  			# Get scalings for boundary conditions
  			__magic_name__			,       __magic_name__ :					Optional[Any]  			=  self.get_scalings_for_boundary_condition(_A    )
  
  			# 1. Denoise model output using boundary conditions
  			__magic_name__ :					Dict  			=  c_out * model_output + c_skip * sample
  			if self.config.clip_denoised:
  				__magic_name__ :					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:
  				__magic_name__ :					Any  			=  randn_tensor(
  				    model_output.shape						,  dtype=model_output.dtype						,  device=model_output.device						,  generator=_A    )
  			else:
  				__magic_name__ :					List[Any]  			=  torch.zeros_like(_A    )
  			__magic_name__ :					Tuple  			=  noise * self.config.s_noise
  
  			__magic_name__ :					Union[str, Any]  			=  sigma_next.clamp(min=_A						,  max=_A    )
  
  			# 3. Return noisy sample
  			# tau = sigma_hat, eps = sigma_min
  			__magic_name__ :					Any  			=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
  			if not return_dict:
  				return (prev_sample,)
  
  			return CMStochasticIterativeSchedulerOutput(prev_sample=_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		torch.FloatTensor						,  _A	:		torch.FloatTensor						,  _A	:		torch.FloatTensor						,  )						->				torch.FloatTensor:
  			# Make sure sigmas and timesteps have the same device and dtype as original_samples
  			__magic_name__ :					Tuple  			=  self.sigmas.to(device=original_samples.device						,  dtype=original_samples.dtype    )
  			if original_samples.device.type == "mps" and torch.is_floating_point(_A    ):
  				# mps does not support float64
  				__magic_name__ :					Optional[int]  			=  self.timesteps.to(original_samples.device						,  dtype=torch.floataa    )
  				__magic_name__ :					Optional[Any]  			=  timesteps.to(original_samples.device						,  dtype=torch.floataa    )
  			else:
  				__magic_name__ :					Dict  			=  self.timesteps.to(original_samples.device    )
  				__magic_name__ :					List[str]  			=  timesteps.to(original_samples.device    )
  
  			__magic_name__ :					Union[str, Any]  			=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  			__magic_name__ :					int  			=  sigmas[step_indices].flatten()
  			while len(sigma.shape    ) < len(original_samples.shape    ):
  				__magic_name__ :					List[Any]  			=  sigma.unsqueeze(-1    )
  
  			__magic_name__ :					Any  			=  original_samples + noise * sigma
  			return noisy_samples
  
  
  
  
  
  
  
  		def __len__(      self	:		str    )						->				Optional[Any]:
  			return self.config.num_train_timesteps
style_context: 
  
  
  '''simple docstring'''
  
  from .number_theory.prime_numbers import next_prime
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		Dict						,  _A	:		int						,  _A	:		int | None = None						,  _A	:		float | None = None						,  )						->				None:
  			__magic_name__ :					Optional[int]  			=  size_table
  			__magic_name__ :					int  			=  [None] * self.size_table
  			__magic_name__ :					int  			=  0.75 if lim_charge is None else lim_charge
  			__magic_name__ :					int  			=  1 if charge_factor is None else charge_factor
  			__magic_name__ :					list  			=  []
  			__magic_name__ :					dict  			=  {}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				str:
  			return self._keys
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			return sum(1 for slot in self.values if slot is not None    ) / (
  			    self.size_table * self.charge_factor
  			)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		int    )						->				List[Any]:
  			return key % self.size_table
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Tuple    )						->				Optional[int]:
  			print(F'step {step_ord}'    )
  			print(list(range(len(self.values    )    )    )    )
  			print(self.values    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					int  			=  1
  			__magic_name__ :					Optional[int]  			=  values
  			for value in values:
  				self.insert_data(_A    )
  				self._step_by_step(_A    )
  				i += 1
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		List[str]						,  _A	:		List[Any]    )						->				Dict:
  			__magic_name__ :					str  			=  data
  			__magic_name__ :					Optional[int]  			=  data
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[Any]						,  _A	:		Union[str, Any]=None    )						->				int:
  			__magic_name__ :					int  			=  self.hash_function(key + 1    )
  
  			while self.values[new_key] is not None and self.values[new_key] != key:
  				if self.values.count(_A    ) > 0:
  					__magic_name__ :					Dict  			=  self.hash_function(new_key + 1    )
  				else:
  					__magic_name__ :					Tuple  			=  None
  					break
  
  			return new_key
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Tuple:
  			__magic_name__ :					Dict  			=  [value for value in self.values if value is not None]
  			__magic_name__ :					int  			=  next_prime(self.size_table						,  factor=2    )
  			self._keys.clear()
  			__magic_name__ :					Tuple  			=  [None] * self.size_table  # hell's pointers D: don't DRY ;/
  			for value in survivor_values:
  				self.insert_data(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		Union[str, Any]    )						->				Tuple:
  			__magic_name__ :					Tuple  			=  self.hash_function(_A    )
  
  			if self.values[key] is None:
  				self._set_value(_A						,  _A    )
  
  			elif self.values[key] == data:
  				pass
  
  			else:
  				__magic_name__ :					str  			=  self._collision_resolution(_A						,  _A    )
  				if collision_resolution is not None:
  					self._set_value(_A						,  _A    )
  				else:
  					self.rehashing()
  					self.insert_data(_A    )