code: 
  
  
  
  import math
  from collections import defaultdict
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=0.9_99						,				__snake_case="cosine"						,				)   -> Union[str, Any]:
    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(__snake_case							):
      _A           =    i / num_diffusion_timesteps
      _A           =    (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      (			lowerCamelCase					,lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			[e.name for e in KarrasDiffusionSchedulers]
   lowerCamelCase_          =			2
   @register_to_config
   def __init__(						self	,    a = 1_0_0_0	,    a = 0.00_085	,    a = 0.012	,    a = "linear"	,    a = None	,    a = "epsilon"	,    a = "linspace"	,    a = 0	,    )	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if trained_betas is not None:
       _A           =    torch.tensor(a	,    dtype=torch.floataa					)
     elif beta_schedule == "linear":
       _A           =    torch.linspace(a	,    a	,    a	,    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	,    a	,    dtype=torch.floataa					) ** 2
       )
     elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       _A           =    betas_for_alpha_bar(a					)
     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					)
  
     #  set all values
     self.set_timesteps(a	,    a	,    a					)
   def lowercase_		(						self	,    a	,    a=None					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if schedule_timesteps is None:
       _A           =    self.timesteps
  
     _A           =    (schedule_timesteps == timestep).nonzero()
  
     # The sigma index that is taken for the **very** first `step`
     # is always the second index (or the last index if there is only 1)
     # This way we can ensure we don't accidentally skip a sigma in
     # case we start in the middle of the denoising schedule (e.g. for image-to-image)
     if len(self._index_counter					) == 0:
       _A           =    1 if len(a					) > 1 else 0
     else:
       _A           =    timestep.cpu().item() if torch.is_tensor(a					) else timestep
       _A           =    self._index_counter[timestep_int]
  
     return indices[pos].item()
   @property
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """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"""
  
  
  
  
  
  
  
     _A           =    self.index_for_timestep(a					)
  
     if self.state_in_first_order:
       _A           =    self.sigmas[step_index]
     else:
       _A           =    self.sigmas_interpol[step_index - 1]
  
     _A           =    sample / ((sigma**2 + 1) ** 0.5)
     return sample
   def lowercase_		(						self	,    a	,    a = None	,    a = None	,    )	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    num_inference_steps
  
     _A           =    num_train_timesteps or self.config.num_train_timesteps
  
     # "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	,    num_train_timesteps - 1	,    a	,    dtype=a					)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
       _A           =    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	,    a					) * step_ratio).round()[::-1].copy().astype(a					)
       timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
       _A           =    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(a	,    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\'.'''					)
  
     _A           =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
     _A           =    torch.from_numpy(np.log(a					)					).to(a					)
  
     _A           =    np.interp(a	,    np.arange(0	,    len(a					)					)	,    a					)
     _A           =    np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
     _A           =    torch.from_numpy(a					).to(device=a					)
  
     # compute up and down sigmas
     _A           =    sigmas.roll(-1					)
     _A           =    0.0
     _A           =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
     _A           =    (sigmas_next**2 - sigmas_up**2) ** 0.5
     _A           =    0.0
  
     # compute interpolated sigmas
     _A           =    sigmas.log().lerp(sigmas_down.log()	,    0.5					).exp()
     _A           =    0.0
  
     # set sigmas
     _A           =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
     _A           =    torch.cat(
         [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
     _A           =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
     _A           =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)
  
     if str(a					).startswith('''mps'''					):
       # mps does not support float64
       _A           =    torch.from_numpy(a					).to(a	,    dtype=torch.floataa					)
     else:
       _A           =    torch.from_numpy(a					).to(a					)
  
     _A           =    self.sigma_to_t(a					).to(a	,    dtype=timesteps.dtype					)
     _A           =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,    dim=-1					).flatten()
  
     _A           =    torch.cat([timesteps[:1], interleaved_timesteps]					)
  
     _A           =    None
  
     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     _A           =    defaultdict(a					)
   def lowercase_		(						self	,    a					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    sigma.log()
  
     # get distribution
     _A           =    log_sigma - self.log_sigmas[:, None]
  
     # get sigmas range
     _A           =    dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
     _A           =    low_idx + 1
  
     _A           =    self.log_sigmas[low_idx]
     _A           =    self.log_sigmas[high_idx]
  
     # interpolate sigmas
     _A           =    (low - log_sigma) / (low - high)
     _A           =    w.clamp(0	,    1					)
  
     # transform interpolation to time range
     _A           =    (1 - w) * low_idx + w * high_idx
     _A           =    t.view(sigma.shape					)
     return t
   @property
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return self.sample is None
   def lowercase_		(						self	,    a	,    a	,    a	,    a = None	,    a = True	,    )	->							Union[SchedulerOutput, Tuple]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.index_for_timestep(a					)
  
     # advance index counter by 1
     _A           =    timestep.cpu().item() if torch.is_tensor(a					) else timestep
     self._index_counter[timestep_int] += 1
  
     if self.state_in_first_order:
       _A           =    self.sigmas[step_index]
       _A           =    self.sigmas_interpol[step_index]
       _A           =    self.sigmas_up[step_index]
       _A           =    self.sigmas_down[step_index - 1]
     else:
       # 2nd order / KPDM2's method
       _A           =    self.sigmas[step_index - 1]
       _A           =    self.sigmas_interpol[step_index - 1]
       _A           =    self.sigmas_up[step_index - 1]
       _A           =    self.sigmas_down[step_index - 1]
  
     # currently only gamma=0 is supported. This usually works best anyways.
     # We can support gamma in the future but then need to scale the timestep before
     # passing it to the model which requires a change in API
     _A           =    0
     _A           =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now
  
     _A           =    model_output.device
     _A           =    randn_tensor(model_output.shape	,    dtype=model_output.dtype	,    device=a	,    generator=a					)
  
     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
       _A           =    sigma_hat if self.state_in_first_order else sigma_interpol
       _A           =    sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
       _A           =    sigma_hat if self.state_in_first_order else sigma_interpol
       _A           =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**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`'''					)
  
     if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       _A           =    (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       _A           =    sigma_interpol - sigma_hat
  
       # store for 2nd order step
       _A           =    sample
       _A           =    dt
       _A           =    sample + derivative * dt
     else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       _A           =    (sample - pred_original_sample) / sigma_interpol
       # 3. delta timestep
       _A           =    sigma_down - sigma_hat
  
       _A           =    self.sample
       _A           =    None
  
       _A           =    sample + derivative * dt
       _A           =    prev_sample + noise * sigma_up
  
     if not return_dict:
       return (prev_sample,)
  
     return SchedulerOutput(prev_sample=a					)
   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(a					):
       # 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           =    [self.index_for_timestep(a	,    a					) 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					)	->							Optional[int]:
  
  
  
  
     """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					)					)
     }