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
  
  
  _lowerCAmelCase					:Tuple  =      logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
  class 							_UpperCAmelCase							(				lowerCAmelCase_   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =4_2
       a__             =None
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Any  , UpperCamelCase__	:	Tuple=0.999  , UpperCamelCase__	:	Optional[int]="cosine"  , ):
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(UpperCamelCase__	:	List[str]							):
              return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(UpperCamelCase__	:	Optional[Any]							):
              return math.exp(t * -12.0							)
  
      else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'							)
  
      _UpperCAmelCase							:						Any       							=					[]
      for i in range(_SCREAMING_SNAKE_CASE							):
          _UpperCAmelCase							:						Optional[int]       							=					i / num_diffusion_timesteps
          _UpperCAmelCase							:						List[str]       							=					(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE							) / alpha_bar_fn(_SCREAMING_SNAKE_CASE							)  , _SCREAMING_SNAKE_CASE							)							)
      return torch.tensor(_SCREAMING_SNAKE_CASE  , dtype=torch.floataa							)
  
  
  class 							_UpperCAmelCase							(				lowerCAmelCase_	,lowerCAmelCase_   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =[e.name for e in KarrasDiffusionSchedulers]
       a__             =1
  
  
  
  
  
       @register_to_config
       def __init__(      self ,			A = 1_0_0_0 ,			A = 0.0_001 ,			A = 0.02 ,			A = "linear" ,			A = None ,			A = "epsilon" ,			A = "linear" ,			A = False ,			A = "linspace" ,			A = 0 ,			)							->      Union[str, Any]:
  
  
  
  
  
           if trained_betas is not None:
               _UpperCAmelCase							:						Any       							=					torch.tensor(__a ,			dtype=torch.floataa	)
           elif beta_schedule == "linear":
               _UpperCAmelCase							:						Dict       							=					torch.linspace(__a ,			__a ,			__a ,			dtype=torch.floataa	)
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase							:						Any       							=					(
                   torch.linspace(beta_start**0.5 ,			beta_end**0.5 ,			__a ,			dtype=torch.floataa	) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase							:						int       							=					betas_for_alpha_bar(__a	)
           else:
               raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'	)
  
           _UpperCAmelCase							:						List[Any]       							=					1.0 - self.betas
           _UpperCAmelCase							:						int       							=					torch.cumprod(self.alphas ,			dim=0	)
  
           _UpperCAmelCase							:						List[Any]       							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
           _UpperCAmelCase							:						List[Any]       							=					np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
           _UpperCAmelCase							:						Union[str, Any]       							=					torch.from_numpy(__a	)
  
           # setable values
           _UpperCAmelCase							:						List[str]       							=					None
           _UpperCAmelCase							:						List[Any]       							=					np.linspace(0 ,			num_train_timesteps - 1 ,			__a ,			dtype=__a	)[::-1].copy()
           _UpperCAmelCase							:						Optional[Any]       							=					torch.from_numpy(__a	)
           _UpperCAmelCase							:						Any       							=					False
           _UpperCAmelCase							:						str       							=					use_karras_sigmas
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Tuple:
  
  
  
  
  
           if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()
  
           return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      Union[str, Any]:
  
  
  
  
  
           if isinstance(__a ,			torch.Tensor	):
               _UpperCAmelCase							:						Optional[int]       							=					timestep.to(self.timesteps.device	)
           _UpperCAmelCase							:						Optional[Any]       							=					(self.timesteps == timestep).nonzero().item()
           _UpperCAmelCase							:						List[str]       							=					self.sigmas[step_index]
  
           _UpperCAmelCase							:						int       							=					sample / ((sigma**2 + 1) ** 0.5)
  
           _UpperCAmelCase							:						Union[str, Any]       							=					True
           return sample
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Dict:
           _UpperCAmelCase							:						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":
               _UpperCAmelCase							:						Optional[Any]       							=					np.linspace(0 ,			self.config.num_train_timesteps - 1 ,			__a ,			dtype=__a	)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase							:						str       							=					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
               _UpperCAmelCase							:						int       							=					(np.arange(0 ,			__a	) * step_ratio).round()[::-1].copy().astype(__a	)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase							:						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
               _UpperCAmelCase							:						str       							=					(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\'.'	)
  
           _UpperCAmelCase							:						Tuple       							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
           _UpperCAmelCase							:						Tuple       							=					np.log(__a	)
  
           if self.config.interpolation_type == "linear":
               _UpperCAmelCase							:						Optional[Any]       							=					np.interp(__a ,			np.arange(0 ,			len(__a	)	) ,			__a	)
           elif self.config.interpolation_type == "log_linear":
               _UpperCAmelCase							:						Optional[Any]       							=					torch.linspace(np.log(sigmas[-1]	) ,			np.log(sigmas[0]	) ,			num_inference_steps + 1	).exp()
           else:
               raise ValueError(
                   f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                   ''' \'linear\' or \'log_linear\''''	)
  
           if self.use_karras_sigmas:
               _UpperCAmelCase							:						Any       							=					self._convert_to_karras(in_sigmas=__a ,			num_inference_steps=self.num_inference_steps	)
               _UpperCAmelCase							:						Tuple       							=					np.array([self._sigma_to_t(__a ,			__a	) for sigma in sigmas]	)
  
           _UpperCAmelCase							:						str       							=					np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
           _UpperCAmelCase							:						List[Any]       							=					torch.from_numpy(__a	).to(device=__a	)
           if str(__a	).startswith('''mps'''	):
               # mps does not support float64
               _UpperCAmelCase							:						int       							=					torch.from_numpy(__a	).to(__a ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						int       							=					torch.from_numpy(__a	).to(device=__a	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      List[str]:
           _UpperCAmelCase							:						Any       							=					np.log(__a	)
  
           # get distribution
           _UpperCAmelCase							:						Dict       							=					log_sigma - log_sigmas[:, np.newaxis]
  
           # get sigmas range
           _UpperCAmelCase							:						Optional[Any]       							=					np.cumsum((dists >= 0) ,			axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
           _UpperCAmelCase							:						int       							=					low_idx + 1
  
           _UpperCAmelCase							:						Tuple       							=					log_sigmas[low_idx]
           _UpperCAmelCase							:						Any       							=					log_sigmas[high_idx]
  
           # interpolate sigmas
           _UpperCAmelCase							:						Dict       							=					(low - log_sigma) / (low - high)
           _UpperCAmelCase							:						List[Any]       							=					np.clip(__a ,			0 ,			1	)
  
           # transform interpolation to time range
           _UpperCAmelCase							:						Union[str, Any]       							=					(1 - w) * low_idx + w * high_idx
           _UpperCAmelCase							:						Optional[Any]       							=					t.reshape(sigma.shape	)
           return t
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      Dict:
           _UpperCAmelCase							:						float       							=					in_sigmas[-1].item()
           _UpperCAmelCase							:						float       							=					in_sigmas[0].item()
  
           _UpperCAmelCase							:						Union[str, Any]       							=					7.0  # 7.0 is the value used in the paper
           _UpperCAmelCase							:						int       							=					np.linspace(0 ,			1 ,			__a	)
           _UpperCAmelCase							:						int       							=					sigma_min ** (1 / rho)
           _UpperCAmelCase							:						Optional[Any]       							=					sigma_max ** (1 / rho)
           _UpperCAmelCase							:						Optional[Any]       							=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A = 0.0 ,			A = 0.0 ,			A = float('''inf'''	) ,			A = 1.0 ,			A = None ,			A = True ,			)							->      int:
  
  
  
  
  
           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	):
               _UpperCAmelCase							:						Optional[int]       							=					timestep.to(self.timesteps.device	)
  
           _UpperCAmelCase							:						str       							=					(self.timesteps == timestep).nonzero().item()
           _UpperCAmelCase							:						List[Any]       							=					self.sigmas[step_index]
  
           _UpperCAmelCase							:						List[Any]       							=					min(s_churn / (len(self.sigmas	) - 1) ,			2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0
  
           _UpperCAmelCase							:						Optional[Any]       							=					randn_tensor(
               model_output.shape ,			dtype=model_output.dtype ,			device=model_output.device ,			generator=__a	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					noise * s_noise
           _UpperCAmelCase							:						int       							=					sigma * (gamma + 1)
  
           if gamma > 0:
               _UpperCAmelCase							:						Optional[Any]       							=					sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
  
           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           # NOTE: "original_sample" should not be an expected prediction_type but is left in for
           # backwards compatibility
           if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
               _UpperCAmelCase							:						Optional[Any]       							=					model_output
           elif self.config.prediction_type == "epsilon":
               _UpperCAmelCase							:						Tuple       							=					sample - sigma_hat * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               _UpperCAmelCase							:						List[Any]       							=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           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
           _UpperCAmelCase							:						List[Any]       							=					(sample - pred_original_sample) / sigma_hat
  
           _UpperCAmelCase							:						Union[str, Any]       							=					self.sigmas[step_index + 1] - sigma_hat
  
           _UpperCAmelCase							:						Tuple       							=					sample + derivative * dt
  
           if not return_dict:
               return (prev_sample,)
  
           return EulerDiscreteSchedulerOutput(prev_sample=__a ,			pred_original_sample=__a	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			)							->      Optional[Any]:
           _UpperCAmelCase							:						Optional[int]       							=					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
               _UpperCAmelCase							:						int       							=					self.timesteps.to(original_samples.device ,			dtype=torch.floataa	)
               _UpperCAmelCase							:						int       							=					timesteps.to(original_samples.device ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						Optional[Any]       							=					self.timesteps.to(original_samples.device	)
               _UpperCAmelCase							:						Any       							=					timesteps.to(original_samples.device	)
  
           _UpperCAmelCase							:						Tuple       							=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           _UpperCAmelCase							:						str       							=					sigmas[step_indices].flatten()
           while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase							:						str       							=					sigma.unsqueeze(-1	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
       def __len__(      self	)							->      Union[str, Any]:
           return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						Tuple       							=					target
           if len(self.target	) == 0:
               raise ValueError('''Target data can not be empty'''	)
           if len(self.sample	) != len(self.target	):
               raise ValueError('''Sample data and Target data do not have matching lengths'''	)
           _UpperCAmelCase							:						List[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      None:
           for sample in self.sample:
               sample.insert(0 ,			self.bias	)
  
           for _ in range(self.col_sample	):
               self.weight.append(random.random()	)
  
           self.weight.insert(0 ,			self.bias	)
  
           _UpperCAmelCase							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						int       							=					epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                   print(('''\nEpoch:\n''', epoch_count)	)
                   print('''------------------------\n'''	)
                   # if epoch_count > self.epoch_number or not error:
                   break
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      None:
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           sample.insert(0 ,			self.bias	)
           _UpperCAmelCase							:						str       							=					0
           for i in range(self.col_sample + 1	):
               _UpperCAmelCase							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					:Optional[int]  =      [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _lowerCAmelCase					:Dict  =      [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
    _lowerCAmelCase					:int  =      Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
    )
    network.training()
    print('Finished training perceptron')
    print('Enter values to predict or q to exit')
    while True:
      _lowerCAmelCase					:list  =      []
      for i in range(len(samples[0])):
        _lowerCAmelCase					:List[Any]  =      input('value: ').strip()
        if user_input == "q":
          break
        _lowerCAmelCase					:Any  =      float(user_input)
        sample.insert(i, observation)
      network.sort(sample)
  
  
  