



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




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					)					)
   }