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 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			4_2
   lowerCamelCase_          =			None
  
  
  
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=0.9_99						,				__snake_case="cosine"						,				)   -> str:
    if alpha_transform_type == "cosine":
  
      def alpha_bar_fn(__snake_case							):
        return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2
  
    elif alpha_transform_type == "exp":
  
      def alpha_bar_fn(__snake_case							):
        return math.exp(t * -12.0							)
  
    else:
      raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)
  
    _A           =    []
    for i in range(UpperCamelCase__							):
      _A           =    i / num_diffusion_timesteps
      _A           =    (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 		_snake_case      (			lowerCamelCase					,lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			[e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase_          =			1
   @register_to_config
   def __init__(						self	,    a = 1_0_0_0	,    a = 0.0_001	,    a = 0.02	,    a = "linear"	,    a = None	,    a = False	,    a = "epsilon"	,    a = "linspace"	,    a = 0	,    )	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if trained_betas is not None:
       _A           =    torch.tensor(__lowerCamelCase	,    dtype=torch.floataa					)
     elif beta_schedule == "linear":
       _A           =    torch.linspace(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase	,    dtype=torch.floataa					)
     elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       _A           =    (
           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           =    betas_for_alpha_bar(__lowerCamelCase					)
     else:
       raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)
  
     _A           =    1.0 - self.betas
     _A           =    torch.cumprod(self.alphas	,    dim=0					)
  
     _A           =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
     _A           =    np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
     _A           =    torch.from_numpy(__lowerCamelCase					)
  
     # setable values
     _A           =    None
     _A           =    use_karras_sigmas
     self.set_timesteps(__lowerCamelCase	,    __lowerCamelCase					)
     _A           =    []
     _A           =    False
   @property
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()
  
     return (self.sigmas.max() ** 2 + 1) ** 0.5
   def lowercase_		(						self	,    a	,    a					)	->							torch.FloatTensor:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if isinstance(__lowerCamelCase	,    torch.Tensor					):
       _A           =    timestep.to(self.timesteps.device					)
     _A           =    (self.timesteps == timestep).nonzero().item()
     _A           =    self.sigmas[step_index]
     _A           =    sample / ((sigma**2 + 1) ** 0.5)
     _A           =    True
     return sample
   def lowercase_		(						self	,    a	,    a	,    a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     def lms_derivative(a					):
       _A           =    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           =    integrate.quad(__lowerCamelCase	,    self.sigmas[t]	,    self.sigmas[t + 1]	,    epsrel=1e-4					)[0]
  
     return integrated_coeff
   def lowercase_		(						self	,    a	,    a = None					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    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           =    np.linspace(0	,    self.config.num_train_timesteps - 1	,    __lowerCamelCase	,    dtype=__lowerCamelCase					)[
           ::-1
       ].copy()
     elif self.config.timestep_spacing == "leading":
       _A           =    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           =    (np.arange(0	,    __lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase					)
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       _A           =    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           =    (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           =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
     _A           =    np.log(__lowerCamelCase					)
     _A           =    np.interp(__lowerCamelCase	,    np.arange(0	,    len(__lowerCamelCase					)					)	,    __lowerCamelCase					)
  
     if self.use_karras_sigmas:
       _A           =    self._convert_to_karras(in_sigmas=__lowerCamelCase					)
       _A           =    np.array([self._sigma_to_t(__lowerCamelCase	,    __lowerCamelCase					) for sigma in sigmas]					)
  
     _A           =    np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
  
     _A           =    torch.from_numpy(__lowerCamelCase					).to(device=__lowerCamelCase					)
     if str(__lowerCamelCase					).startswith('''mps'''					):
       # mps does not support float64
       _A           =    torch.from_numpy(__lowerCamelCase					).to(__lowerCamelCase	,    dtype=torch.floataa					)
     else:
       _A           =    torch.from_numpy(__lowerCamelCase					).to(device=__lowerCamelCase					)
  
     _A           =    []
   def lowercase_		(						self	,    a	,    a					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    np.log(__lowerCamelCase					)
  
     # get distribution
     _A           =    log_sigma - log_sigmas[:, np.newaxis]
  
     # get sigmas range
     _A           =    np.cumsum((dists >= 0)	,    axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
     _A           =    low_idx + 1
  
     _A           =    log_sigmas[low_idx]
     _A           =    log_sigmas[high_idx]
  
     # interpolate sigmas
     _A           =    (low - log_sigma) / (low - high)
     _A           =    np.clip(__lowerCamelCase	,    0	,    1					)
  
     # transform interpolation to time range
     _A           =    (1 - w) * low_idx + w * high_idx
     _A           =    t.reshape(sigma.shape					)
     return t
   def lowercase_		(						self	,    a					)	->							torch.FloatTensor:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    in_sigmas[-1].item()
     _A           =    in_sigmas[0].item()
  
     _A           =    7.0  # 7.0 is the value used in the paper
     _A           =    np.linspace(0	,    1	,    self.num_inference_steps					)
     _A           =    sigma_min ** (1 / rho)
     _A           =    sigma_max ** (1 / rho)
     _A           =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas
   def lowercase_		(						self	,    a	,    a	,    a	,    a = 4	,    a = True	,    )	->							Union[LMSDiscreteSchedulerOutput, Tuple]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     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           =    timestep.to(self.timesteps.device					)
     _A           =    (self.timesteps == timestep).nonzero().item()
     _A           =    self.sigmas[step_index]
  
     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
       _A           =    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       _A           =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
       _A           =    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           =    (sample - pred_original_sample) / sigma
     self.derivatives.append(__lowerCamelCase					)
     if len(self.derivatives					) > order:
       self.derivatives.pop(0					)
  
     # 3. Compute linear multistep coefficients
     _A           =    min(step_index + 1	,    __lowerCamelCase					)
     _A           =    [self.get_lms_coefficient(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					) for curr_order in range(__lowerCamelCase					)]
  
     # 4. Compute previous sample based on the derivatives path
     _A           =    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 lowercase_		(						self	,    a	,    a	,    a	,    )	->							torch.FloatTensor:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    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           =    self.timesteps.to(original_samples.device	,    dtype=torch.floataa					)
       _A           =    timesteps.to(original_samples.device	,    dtype=torch.floataa					)
     else:
       _A           =    self.timesteps.to(original_samples.device					)
       _A           =    timesteps.to(original_samples.device					)
  
     _A           =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
     _A           =    sigmas[step_indices].flatten()
     while len(sigma.shape					) < len(original_samples.shape					):
       _A           =    sigma.unsqueeze(-1					)
  
     _A           =    original_samples + noise * sigma
     return noisy_samples
  
  
  
   def __len__(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return self.config.num_train_timesteps
style_context: 
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  __lowerCamelCase  				=					"""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  __lowerCamelCase  				=					"""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
  class 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return datasets.MetricInfo(
         description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''					),
                 '''references''': datasets.Value('''int32'''					),
             }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }