code: 
  
  
  import math
  import warnings
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				42
       __SCREAMING_SNAKE_CASE						    =				None
  
  
  
  
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]       ,							UpperCamelCase__  :						Any=0.9_99       ,							UpperCamelCase__  :						Optional[int]="cosine"       ,							):
         if alpha_transform_type == "cosine":
  
                def alpha_bar_fn(UpperCamelCase__  :						Dict		):
                       return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2
  
         elif alpha_transform_type == "exp":
  
                def alpha_bar_fn(UpperCamelCase__  :						Union[str, Any]		):
                       return math.exp(t * -12.0		)
  
         else:
                raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)
  
         _A   : int       					=				[]
         for i in range(UpperCamelCase__		):
                _A   : str       					=				i / num_diffusion_timesteps
                _A   : Dict       					=				(i + 1) / num_diffusion_timesteps
                betas.append(min(1 - alpha_bar_fn(UpperCamelCase__		) / alpha_bar_fn(UpperCamelCase__		)       ,							UpperCamelCase__		)		)
         return torch.tensor(UpperCamelCase__       ,							dtype=torch.floataa		)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a  ,					a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				[e.name for e in KarrasDiffusionSchedulers]
       __SCREAMING_SNAKE_CASE						    =				1
  
  
       @register_to_config
       def __init__(		self				,		__lowerCamelCase = 1_0_0_0				,		__lowerCamelCase = 0.0_0_0_1				,		__lowerCamelCase = 0.0_2				,		__lowerCamelCase = "linear"				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		__lowerCamelCase = "epsilon"				,		__lowerCamelCase = "linspace"				,		__lowerCamelCase = 0				,		)    ->       List[Any]:
              if trained_betas is not None:
                     _A   : int       					=				torch.tensor(__lowerCamelCase				,		dtype=torch.floataa)
              elif beta_schedule == "linear":
                     _A   : str       					=				torch.linspace(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		dtype=torch.floataa)
              elif beta_schedule == "scaled_linear":
                     # this schedule is very specific to the latent diffusion model.
                     _A   : int       					=				(
                         torch.linspace(beta_start**0.5				,		beta_end**0.5				,		__lowerCamelCase				,		dtype=torch.floataa) ** 2
                     )
              elif beta_schedule == "squaredcos_cap_v2":
                     # Glide cosine schedule
                     _A   : List[str]       					=				betas_for_alpha_bar(__lowerCamelCase)
              else:
                     raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")
  
              _A   : Dict       					=				1.0 - self.betas
              _A   : List[str]       					=				torch.cumprod(self.alphas				,		dim=0)
  
              _A   : Optional[Any]       					=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
              _A   : str       					=				np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
              _A   : str       					=				torch.from_numpy(__lowerCamelCase)
  
              # setable values
              _A   : Union[str, Any]       					=				None
              _A   : Dict       					=				use_karras_sigmas
              self.set_timesteps(__lowerCamelCase				,		__lowerCamelCase)
              _A   : Tuple       					=				[]
              _A   : Union[str, Any]       					=				False
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              # 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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       torch.FloatTensor:
              if isinstance(__lowerCamelCase				,		torch.Tensor):
                     _A   : Tuple       					=				timestep.to(self.timesteps.device)
              _A   : Any       					=				(self.timesteps == timestep).nonzero().item()
              _A   : List[str]       					=				self.sigmas[step_index]
              _A   : List[Any]       					=				sample / ((sigma**2 + 1) ** 0.5)
              _A   : Union[str, Any]       					=				True
              return sample
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[int]:
  
              def lms_derivative(__lowerCamelCase):
                     _A   : Optional[Any]       					=				1.0
                     for k in range(__lowerCamelCase):
                            if current_order == k:
                                   continue
                            prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
                     return prod
  
              _A   : Optional[Any]       					=				integrate.quad(__lowerCamelCase				,		self.sigmas[t]				,		self.sigmas[t + 1]				,		epsrel=1e-4)[0]
  
              return integrated_coeff
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Optional[Any]:
              _A   : Dict       					=				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":
                     _A   : Optional[int]       					=				np.linspace(0				,		self.config.num_train_timesteps - 1				,		__lowerCamelCase				,		dtype=__lowerCamelCase)[
                         ::-1
                     ].copy()
              elif self.config.timestep_spacing == "leading":
                     _A   : Optional[int]       					=				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
                     _A   : Optional[Any]       					=				(np.arange(0				,		__lowerCamelCase) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase)
                     timesteps += self.config.steps_offset
              elif self.config.timestep_spacing == "trailing":
                     _A   : Union[str, Any]       					=				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
                     _A   : Union[str, Any]       					=				(np.arange(self.config.num_train_timesteps				,		0				,		-step_ratio)).round().copy().astype(__lowerCamelCase)
                     timesteps -= 1
              else:
                     raise ValueError(
                         F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")
  
              _A   : List[Any]       					=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
              _A   : List[Any]       					=				np.log(__lowerCamelCase)
              _A   : Optional[Any]       					=				np.interp(__lowerCamelCase				,		np.arange(0				,		len(__lowerCamelCase))				,		__lowerCamelCase)
  
              if self.use_karras_sigmas:
                     _A   : Union[str, Any]       					=				self._convert_to_karras(in_sigmas=__lowerCamelCase)
                     _A   : List[Any]       					=				np.array([self._sigma_to_t(__lowerCamelCase				,		__lowerCamelCase) for sigma in sigmas])
  
              _A   : Union[str, Any]       					=				np.concatenate([sigmas, [0.0]]).astype(np.floataa)
  
              _A   : List[Any]       					=				torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)
              if str(__lowerCamelCase).startswith("mps"):
                     # mps does not support float64
                     _A   : Dict       					=				torch.from_numpy(__lowerCamelCase).to(__lowerCamelCase				,		dtype=torch.floataa)
              else:
                     _A   : Any       					=				torch.from_numpy(__lowerCamelCase).to(device=__lowerCamelCase)
  
              _A   : str       					=				[]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       List[Any]:
              # get log sigma
              _A   : Dict       					=				np.log(__lowerCamelCase)
  
              # get distribution
              _A   : Any       					=				log_sigma - log_sigmas[:, np.newaxis]
  
              # get sigmas range
              _A   : List[str]       					=				np.cumsum((dists >= 0)				,		axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
              _A   : int       					=				low_idx + 1
  
              _A   : Optional[int]       					=				log_sigmas[low_idx]
              _A   : int       					=				log_sigmas[high_idx]
  
              # interpolate sigmas
              _A   : Optional[int]       					=				(low - log_sigma) / (low - high)
              _A   : Optional[int]       					=				np.clip(__lowerCamelCase				,		0				,		1)
  
              # transform interpolation to time range
              _A   : Any       					=				(1 - w) * low_idx + w * high_idx
              _A   : int       					=				t.reshape(sigma.shape)
              return t
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       torch.FloatTensor:
              _A   : float       					=				in_sigmas[-1].item()
              _A   : float       					=				in_sigmas[0].item()
  
              _A   : str       					=				7.0  # 7.0 is the value used in the paper
              _A   : Dict       					=				np.linspace(0				,		1				,		self.num_inference_steps)
              _A   : int       					=				sigma_min ** (1 / rho)
              _A   : Optional[int]       					=				sigma_max ** (1 / rho)
              _A   : Tuple       					=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
              return sigmas
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 4				,		__lowerCamelCase = True				,		)    ->       Union[LMSDiscreteSchedulerOutput, Tuple]:
              if not self.is_scale_input_called:
                     warnings.warn(
                         "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                         "See `StableDiffusionPipeline` for a usage example.")
  
              if isinstance(__lowerCamelCase				,		torch.Tensor):
                     _A   : Optional[int]       					=				timestep.to(self.timesteps.device)
              _A   : str       					=				(self.timesteps == timestep).nonzero().item()
              _A   : Union[str, Any]       					=				self.sigmas[step_index]
  
              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              if self.config.prediction_type == "epsilon":
                     _A   : Optional[int]       					=				sample - sigma * model_output
              elif self.config.prediction_type == "v_prediction":
                     # * c_out + input * c_skip
                     _A   : List[str]       					=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
              elif self.config.prediction_type == "sample":
                     _A   : int       					=				model_output
              else:
                     raise ValueError(
                         F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`")
  
              # 2. Convert to an ODE derivative
              _A   : List[Any]       					=				(sample - pred_original_sample) / sigma
              self.derivatives.append(__lowerCamelCase)
              if len(self.derivatives) > order:
                     self.derivatives.pop(0)
  
              # 3. Compute linear multistep coefficients
              _A   : List[str]       					=				min(step_index + 1				,		__lowerCamelCase)
              _A   : Optional[Any]       					=				[self.get_lms_coefficient(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase) for curr_order in range(__lowerCamelCase)]
  
              # 4. Compute previous sample based on the derivatives path
              _A   : Union[str, Any]       					=				sample + sum(
                  coeff * derivative for coeff, derivative in zip(__lowerCamelCase				,		reversed(self.derivatives)))
  
              if not return_dict:
                     return (prev_sample,)
  
              return LMSDiscreteSchedulerOutput(prev_sample=__lowerCamelCase				,		pred_original_sample=__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       torch.FloatTensor:
              # Make sure sigmas and timesteps have the same device and dtype as original_samples
              _A   : Dict       					=				self.sigmas.to(device=original_samples.device				,		dtype=original_samples.dtype)
              if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase):
                     # mps does not support float64
                     _A   : Union[str, Any]       					=				self.timesteps.to(original_samples.device				,		dtype=torch.floataa)
                     _A   : Tuple       					=				timesteps.to(original_samples.device				,		dtype=torch.floataa)
              else:
                     _A   : Optional[int]       					=				self.timesteps.to(original_samples.device)
                     _A   : Union[str, Any]       					=				timesteps.to(original_samples.device)
  
              _A   : str       					=				[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
              _A   : Optional[Any]       					=				sigmas[step_indices].flatten()
              while len(sigma.shape) < len(original_samples.shape):
                     _A   : str       					=				sigma.unsqueeze(-1)
  
              _A   : List[Any]       					=				original_samples + noise * sigma
              return noisy_samples
  
  
       def __len__(		self)    ->       Dict:
              return self.config.num_train_timesteps
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  