code: 
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  
  
  
  @dataclass
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					42
  			UpperCamelCase__					   =					42
  
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[str]   ,     _snake_case   :						str=0.999   ,     _snake_case   :						List[Any]="cosine"   ,     )       ->	Optional[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if alpha_transform_type == "cosine":
  
  		def alpha_bar_fn(_snake_case   :						Dict     ):
  			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2
  
  	elif alpha_transform_type == "exp":
  
  		def alpha_bar_fn(_snake_case   :						Optional[Any]     ):
  			return math.exp(t * -12.0     )
  
  	else:
  		raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''     )
  
  	__magic_name__	:	Optional[int]			     =  []
  	for i in range(_snake_case     ):
  		__magic_name__	:	Any			     =  i / num_diffusion_timesteps
  		__magic_name__	:	int			     =  (i + 1) / num_diffusion_timesteps
  		betas.append(min(1 - alpha_bar_fn(_snake_case     ) / alpha_bar_fn(_snake_case     )   ,     _snake_case     )     )
  	return torch.tensor(_snake_case   ,     dtype=torch.floataa     )
  
  
  
  
  
  class    _snake_case      (  snake_case       ,		snake_case       ):
  			UpperCamelCase__					   =					1
  
  
  
  
  
  
  
  			@register_to_config
  			def __init__(       self		,     _a = 1_000		,     _a = 0.00_01		,     _a = 0.02		,     _a = "linear"		,     _a = 0.0		,     _a = None		,     _a = True		,     ):
  				if trained_betas is not None:
  					__magic_name__	:	Any			     =  torch.from_numpy(_a	)
  				elif beta_schedule == "linear":
  					__magic_name__	:	Tuple			     =  torch.linspace(_a		,     _a		,     _a		,     dtype=torch.floataa	)
  				elif beta_schedule == "scaled_linear":
  					# this schedule is very specific to the latent diffusion model.
  					__magic_name__	:	Union[str, Any]			     =  (
  					    torch.linspace(beta_start**0.5		,     beta_end**0.5		,     _a		,     dtype=torch.floataa	) ** 2
  					)
  				elif beta_schedule == "squaredcos_cap_v2":
  					# Glide cosine schedule
  					__magic_name__	:	Dict			     =  betas_for_alpha_bar(_a	)
  				elif beta_schedule == "sigmoid":
  					# GeoDiff sigmoid schedule
  					__magic_name__	:	Any			     =  torch.linspace(-6		,     6		,     _a	)
  					__magic_name__	:	Tuple			     =  torch.sigmoid(_a	) * (beta_end - beta_start) + beta_start
  				else:
  					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)
  
  				__magic_name__	:	Dict			     =  1.0 - self.betas
  				__magic_name__	:	Union[str, Any]			     =  torch.cumprod(self.alphas		,     dim=0	)
  				__magic_name__	:	Optional[Any]			     =  torch.tensor(1.0	)
  
  				__magic_name__	:	str			     =  torch.tensor(1.0	)
  
  				# standard deviation of the initial noise distribution
  				__magic_name__	:	Dict			     =  1.0
  
  				# setable values
  				__magic_name__	:	Dict			     =  None
  				__magic_name__	:	List[str]			     =  torch.from_numpy(np.arange(0		,     _a	)[::-1].copy()	)
  
  				__magic_name__	:	Dict			     =  eta
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				return sample
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = 10		,     _a = 10		,     _a = None		,     ):
  				__magic_name__	:	List[Any]			     =  min(self.config.num_train_timesteps		,     _a	)
  				__magic_name__	:	int			     =  num_inference_steps
  
  				__magic_name__	:	List[str]			     =  []
  
  				__magic_name__	:	List[Any]			     =  {}
  				for j in range(0		,     num_inference_steps - jump_length		,     _a	):
  					__magic_name__	:	str			     =  jump_n_sample - 1
  
  				__magic_name__	:	List[Any]			     =  num_inference_steps
  				while t >= 1:
  					__magic_name__	:	Any			     =  t - 1
  					timesteps.append(_a	)
  
  					if jumps.get(_a		,     0	) > 0:
  						__magic_name__	:	int			     =  jumps[t] - 1
  						for _ in range(_a	):
  							__magic_name__	:	Optional[Any]			     =  t + 1
  							timesteps.append(_a	)
  
  				__magic_name__	:	Optional[Any]			     =  np.array(_a	) * (self.config.num_train_timesteps // self.num_inference_steps)
  				__magic_name__	:	Tuple			     =  torch.from_numpy(_a	).to(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  t - self.config.num_train_timesteps // self.num_inference_steps
  
  				__magic_name__	:	str			     =  self.alphas_cumprod[t]
  				__magic_name__	:	Optional[Any]			     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				__magic_name__	:	Tuple			     =  1 - alpha_prod_t
  				__magic_name__	:	Any			     =  1 - alpha_prod_t_prev
  
  				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  				# variance to pred_sample
  				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  				# without eta.
  				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  				__magic_name__	:	List[Any]			     =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  				return variance
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     _a = True		,     ):
  				__magic_name__	:	Any			     =  timestep
  				__magic_name__	:	Union[str, Any]			     =  timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  				# 1. compute alphas, betas
  				__magic_name__	:	List[str]			     =  self.alphas_cumprod[t]
  				__magic_name__	:	List[str]			     =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				__magic_name__	:	str			     =  1 - alpha_prod_t
  
  				# 2. compute predicted original sample from predicted noise also called
  				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  				__magic_name__	:	str			     =  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  				# 3. Clip "predicted x_0"
  				if self.config.clip_sample:
  					__magic_name__	:	List[Any]			     =  torch.clamp(_a		,     -1		,     1	)
  
  				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  				# substitute formula (7) in the algorithm coming from DDPM paper
  				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  				# DDIM schedule gives the same results as DDPM with eta = 1.0
  				# Noise is being reused in 7. and 8., but no impact on quality has
  				# been observed.
  
  				# 5. Add noise
  				__magic_name__	:	Dict			     =  model_output.device
  				__magic_name__	:	Tuple			     =  randn_tensor(model_output.shape		,     generator=_a		,     device=_a		,     dtype=model_output.dtype	)
  				__magic_name__	:	List[Any]			     =  self.eta * self._get_variance(_a	) ** 0.5
  
  				__magic_name__	:	Optional[int]			     =  0
  				if t > 0 and self.eta > 0:
  					__magic_name__	:	Any			     =  std_dev_t * noise
  
  				# 6. compute "direction pointing to x_t" of formula (12)
  				# from https://arxiv.org/pdf/2010.02502.pdf
  				__magic_name__	:	str			     =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
  				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  				__magic_name__	:	str			     =  alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  				__magic_name__	:	List[Any]			     =  (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  				__magic_name__	:	str			     =  mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  				if not return_dict:
  					return (
  					    pred_prev_sample,
  					    pred_original_sample,
  					)
  
  				return RePaintSchedulerOutput(prev_sample=_a		,     pred_original_sample=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a=None	):
  				__magic_name__	:	List[Any]			     =  self.config.num_train_timesteps // self.num_inference_steps
  
  				for i in range(_a	):
  					__magic_name__	:	List[Any]			     =  self.betas[timestep + i]
  					if sample.device.type == "mps":
  						# randn does not work reproducibly on mps
  						__magic_name__	:	List[str]			     =  randn_tensor(sample.shape		,     dtype=sample.dtype		,     generator=_a	)
  						__magic_name__	:	int			     =  noise.to(sample.device	)
  					else:
  						__magic_name__	:	Dict			     =  randn_tensor(sample.shape		,     generator=_a		,     device=sample.device		,     dtype=sample.dtype	)
  
  					# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  					__magic_name__	:	Tuple			     =  (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  				return sample
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     ):
  				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."	)
  
  
  
  
  
  
  
  			def __len__(       self	):
  				return self.config.num_train_timesteps
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  