code: 
  
  
  
  
  
  '''simple docstring'''
  
  import math
  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 KarrasDiffusionSchedulers, SchedulerMixin
  
  
  lowercase_			       =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				None
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Any=0.999      , __lowerCamelCase				:					Optional[int]="cosine"      , )		->List[str]:
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(__lowerCamelCase				:					Dict					):
              return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(__lowerCamelCase				:					List[Any]					):
              return math.exp(t * -12.0					)
  
      else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'					)
  
      _SCREAMING_SNAKE_CASE             =    []
      for i in range(__lowerCamelCase					):
          _SCREAMING_SNAKE_CASE             =    i / num_diffusion_timesteps
          _SCREAMING_SNAKE_CASE             =    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__lowerCamelCase					) / alpha_bar_fn(__lowerCamelCase					)      , __lowerCamelCase					)					)
      return torch.tensor(__lowerCamelCase      , dtype=torch.floataa					)
  
  
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				[e.name for e in KarrasDiffusionSchedulers]
      UpperCamelCase									=				1
  
  
      @register_to_config
      def __init__(	self				,	A = 1000				,	A = 0.0001				,	A = 0.02				,	A = "linear"				,	A = None				,	A = "epsilon"				,	A = "linspace"				,	A = 0				,	)	->     List[Any]:
          if trained_betas is not None:
              _SCREAMING_SNAKE_CASE             =    torch.tensor(A				,	dtype=torch.floataa )
          elif beta_schedule == "linear":
              _SCREAMING_SNAKE_CASE             =    torch.linspace(A				,	A				,	A				,	dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _SCREAMING_SNAKE_CASE             =    (
                  torch.linspace(beta_start**0.5				,	beta_end**0.5				,	A				,	dtype=torch.floataa ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _SCREAMING_SNAKE_CASE             =    betas_for_alpha_bar(A )
          else:
              raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
  
          _SCREAMING_SNAKE_CASE             =    1.0 - self.betas
          _SCREAMING_SNAKE_CASE             =    torch.cumprod(self.alphas				,	dim=0 )
  
          _SCREAMING_SNAKE_CASE             =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          _SCREAMING_SNAKE_CASE             =    np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
          _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A )
  
          # setable values
          _SCREAMING_SNAKE_CASE             =    None
          _SCREAMING_SNAKE_CASE             =    np.linspace(0				,	num_train_timesteps - 1				,	A				,	dtype=A )[::-1].copy()
          _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A )
          _SCREAMING_SNAKE_CASE             =    False
  
  
      @property
      def     snake_case_(	self )	->     Tuple:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()
  
          return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
      def     snake_case_(	self				,	A				,	A )	->     torch.FloatTensor:
          if isinstance(A				,	torch.Tensor ):
              _SCREAMING_SNAKE_CASE             =    timestep.to(self.timesteps.device )
          _SCREAMING_SNAKE_CASE             =    (self.timesteps == timestep).nonzero().item()
          _SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
          _SCREAMING_SNAKE_CASE             =    sample / ((sigma**2 + 1) ** 0.5)
          _SCREAMING_SNAKE_CASE             =    True
          return sample
  
  
      def     snake_case_(	self				,	A				,	A = None )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    num_inference_steps
  
          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
              _SCREAMING_SNAKE_CASE             =    np.linspace(0				,	self.config.num_train_timesteps - 1				,	A				,	dtype=A )[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _SCREAMING_SNAKE_CASE             =    (np.arange(0				,	A ) * step_ratio).round()[::-1].copy().astype(A )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              _SCREAMING_SNAKE_CASE             =    self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              _SCREAMING_SNAKE_CASE             =    (np.arange(self.config.num_train_timesteps				,	0				,	-step_ratio )).round().copy().astype(A )
              timesteps -= 1
          else:
              raise ValueError(
                  f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
  
          _SCREAMING_SNAKE_CASE             =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          _SCREAMING_SNAKE_CASE             =    np.interp(A				,	np.arange(0				,	len(A ) )				,	A )
          _SCREAMING_SNAKE_CASE             =    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
          _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(device=A )
          if str(A ).startswith("""mps""" ):
              # mps does not support float64
              _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(A				,	dtype=torch.floataa )
          else:
              _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A ).to(device=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A = None				,	A = True				,	)	->     Union[EulerAncestralDiscreteSchedulerOutput, 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"""
                      """ `EulerDiscreteScheduler.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 ):
              _SCREAMING_SNAKE_CASE             =    timestep.to(self.timesteps.device )
  
          _SCREAMING_SNAKE_CASE             =    (self.timesteps == timestep).nonzero().item()
          _SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
  
          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              _SCREAMING_SNAKE_CASE             =    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              _SCREAMING_SNAKE_CASE             =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError("""prediction_type not implemented yet: sample""" )
          else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
  
          _SCREAMING_SNAKE_CASE             =    self.sigmas[step_index]
          _SCREAMING_SNAKE_CASE             =    self.sigmas[step_index + 1]
          _SCREAMING_SNAKE_CASE             =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          _SCREAMING_SNAKE_CASE             =    (sigma_to**2 - sigma_up**2) ** 0.5
  
          # 2. Convert to an ODE derivative
          _SCREAMING_SNAKE_CASE             =    (sample - pred_original_sample) / sigma
  
          _SCREAMING_SNAKE_CASE             =    sigma_down - sigma
  
          _SCREAMING_SNAKE_CASE             =    sample + derivative * dt
  
          _SCREAMING_SNAKE_CASE             =    model_output.device
          _SCREAMING_SNAKE_CASE             =    randn_tensor(model_output.shape				,	dtype=model_output.dtype				,	device=A				,	generator=A )
  
          _SCREAMING_SNAKE_CASE             =    prev_sample + noise * sigma_up
  
          if not return_dict:
              return (prev_sample,)
  
          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=A				,	pred_original_sample=A )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	)	->     torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _SCREAMING_SNAKE_CASE             =    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
              _SCREAMING_SNAKE_CASE             =    self.timesteps.to(original_samples.device				,	dtype=torch.floataa )
              _SCREAMING_SNAKE_CASE             =    timesteps.to(original_samples.device				,	dtype=torch.floataa )
          else:
              _SCREAMING_SNAKE_CASE             =    self.timesteps.to(original_samples.device )
              _SCREAMING_SNAKE_CASE             =    timesteps.to(original_samples.device )
  
          _SCREAMING_SNAKE_CASE             =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
          _SCREAMING_SNAKE_CASE             =    sigmas[step_indices].flatten()
          while len(sigma.shape ) < len(original_samples.shape ):
              _SCREAMING_SNAKE_CASE             =    sigma.unsqueeze(-1 )
  
          _SCREAMING_SNAKE_CASE             =    original_samples + noise * sigma
          return noisy_samples
  
  
  
  
  
      def __len__(	self )	->     Optional[int]:
          return self.config.num_train_timesteps
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					float      , __lowerCamelCase				:					float      , __lowerCamelCase				:					float					)		->list[int]:
      if hue < 0 or hue > 360:
          raise Exception("""hue should be between 0 and 360"""					)
  
      if saturation < 0 or saturation > 1:
          raise Exception("""saturation should be between 0 and 1"""					)
  
      if value < 0 or value > 1:
          raise Exception("""value should be between 0 and 1"""					)
  
      _SCREAMING_SNAKE_CASE             =    value * saturation
      _SCREAMING_SNAKE_CASE             =    hue / 60
      _SCREAMING_SNAKE_CASE             =    chroma * (1 - abs(hue_section % 2 - 1					))
      _SCREAMING_SNAKE_CASE             =    value - chroma
  
      if hue_section >= 0 and hue_section <= 1:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 1 and hue_section <= 2:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 2 and hue_section <= 3:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
      elif hue_section > 3 and hue_section <= 4:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      elif hue_section > 4 and hue_section <= 5:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      else:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
  
      return [red, green, blue]
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int					)		->list[float]:
      if red < 0 or red > 255:
          raise Exception("""red should be between 0 and 255"""					)
  
      if green < 0 or green > 255:
          raise Exception("""green should be between 0 and 255"""					)
  
      if blue < 0 or blue > 255:
          raise Exception("""blue should be between 0 and 255"""					)
  
      _SCREAMING_SNAKE_CASE             =    red / 255
      _SCREAMING_SNAKE_CASE             =    green / 255
      _SCREAMING_SNAKE_CASE             =    blue / 255
      _SCREAMING_SNAKE_CASE             =    max(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    value - min(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    0 if value == 0 else chroma / value
  
      if chroma == 0:
          _SCREAMING_SNAKE_CASE             =    0.0
      elif value == float_red:
          _SCREAMING_SNAKE_CASE             =    60 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
          _SCREAMING_SNAKE_CASE             =    60 * (2 + (float_blue - float_red) / chroma)
      else:
          _SCREAMING_SNAKE_CASE             =    60 * (4 + (float_red - float_green) / chroma)
  
      _SCREAMING_SNAKE_CASE             =    (hue + 360) % 360
  
      return [hue, saturation, value]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					list[float]      , __lowerCamelCase				:					list[float]					)		->bool:
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[0] - hsv_a[0]					) < 0.2
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[1] - hsv_a[1]					) < 0.002
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[2] - hsv_a[2]					) < 0.002
  
      return check_hue and check_saturation and check_value
  
  
  
  