

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


__A	:   str	=					logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__):
						_UpperCamelCase:torch.FloatTensor
						_UpperCamelCase:Optional[torch.FloatTensor]	=		None




def        __UpperCamelCase     (      _A					:							Any      ,  _A					:							Dict=0.9_9_9      ,  _A					:							Any="cosine"      ,  )				->Union[str, Any]:



							"""simple docstring"""
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_A					:							Optional[int]		):
																					return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_A					:							Any		):
																					return math.exp(t * -1_2.0		)

							else:
														raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

							lowerCamelCase_								=[]
							for i in range(_A		):
														lowerCamelCase_								=i / num_diffusion_timesteps
														lowerCamelCase_								=(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_A		) / alpha_bar_fn(_A		)      ,  _A		)		)
							return torch.tensor(_A      ,  dtype=torch.floataa		)






class 			_SCREAMING_SNAKE_CASE      (  lowerCAmelCase__ ,    lowerCAmelCase__):
						_UpperCamelCase:List[str]	=		[e.name for e in KarrasDiffusionSchedulers]
						_UpperCamelCase:Tuple	=		1







						@register_to_config
						def __init__(			self     ,						_SCREAMING_SNAKE_CASE = 1000     ,						_SCREAMING_SNAKE_CASE = 0.0_0_0_1     ,						_SCREAMING_SNAKE_CASE = 0.0_2     ,						_SCREAMING_SNAKE_CASE = "linear"     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = "epsilon"     ,						_SCREAMING_SNAKE_CASE = "linspace"     ,						_SCREAMING_SNAKE_CASE = 0     ,						)->							Optional[int]:
													if trained_betas is not None:
																				lowerCamelCase_								=torch.tensor(_SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							)
													elif beta_schedule == "linear":
																				lowerCamelCase_								=torch.linspace(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							)
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				lowerCamelCase_								=(
																				    torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						_SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							) ** 2
																				)
													elif beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				lowerCamelCase_								=betas_for_alpha_bar(_SCREAMING_SNAKE_CASE							)
													else:
																				raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'							)

													lowerCamelCase_								=1.0 - self.betas
													lowerCamelCase_								=torch.cumprod(self.alphas     ,						dim=0							)

													lowerCamelCase_								=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
													lowerCamelCase_								=np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
													lowerCamelCase_								=torch.from_numpy(_SCREAMING_SNAKE_CASE							)

													# setable values
													lowerCamelCase_								=None
													lowerCamelCase_								=np.linspace(0     ,						num_train_timesteps - 1     ,						_SCREAMING_SNAKE_CASE     ,						dtype=_SCREAMING_SNAKE_CASE							)[::-1].copy()
													lowerCamelCase_								=torch.from_numpy(_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=False







						@property
						def        _snake_case				(			self							)->							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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							torch.FloatTensor:
													if isinstance(_SCREAMING_SNAKE_CASE     ,						torch.Tensor							):
																				lowerCamelCase_								=timestep.to(self.timesteps.device							)
													lowerCamelCase_								=(self.timesteps == timestep).nonzero().item()
													lowerCamelCase_								=self.sigmas[step_index]
													lowerCamelCase_								=sample / ((sigma**2 + 1) ** 0.5)
													lowerCamelCase_								=True
													return sample







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							Any:
													lowerCamelCase_								=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":
																				lowerCamelCase_								=np.linspace(0     ,						self.config.num_train_timesteps - 1     ,						_SCREAMING_SNAKE_CASE     ,						dtype=_SCREAMING_SNAKE_CASE							)[
																				    ::-1
																				].copy()
													elif self.config.timestep_spacing == "leading":
																				lowerCamelCase_								=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
																				lowerCamelCase_								=(np.arange(0     ,						_SCREAMING_SNAKE_CASE							) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE							)
																				timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																				lowerCamelCase_								=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
																				lowerCamelCase_								=(np.arange(self.config.num_train_timesteps     ,						0     ,						-step_ratio							)).round().copy().astype(_SCREAMING_SNAKE_CASE							)
																				timesteps -= 1
													else:
																				raise ValueError(
																				    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'							)

													lowerCamelCase_								=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
													lowerCamelCase_								=np.interp(_SCREAMING_SNAKE_CASE     ,						np.arange(0     ,						len(_SCREAMING_SNAKE_CASE							)							)     ,						_SCREAMING_SNAKE_CASE							)
													lowerCamelCase_								=np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
													lowerCamelCase_								=torch.from_numpy(_SCREAMING_SNAKE_CASE							).to(device=_SCREAMING_SNAKE_CASE							)
													if str(_SCREAMING_SNAKE_CASE							).startswith("""mps"""							):
																				# mps does not support float64
																				lowerCamelCase_								=torch.from_numpy(_SCREAMING_SNAKE_CASE							).to(_SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							)
													else:
																				lowerCamelCase_								=torch.from_numpy(_SCREAMING_SNAKE_CASE							).to(device=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = True     ,						)->							Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

													if (
													    isinstance(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
													    or isinstance(_SCREAMING_SNAKE_CASE     ,						torch.IntTensor							)
													    or isinstance(_SCREAMING_SNAKE_CASE     ,						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(_SCREAMING_SNAKE_CASE     ,						torch.Tensor							):
																				lowerCamelCase_								=timestep.to(self.timesteps.device							)

													lowerCamelCase_								=(self.timesteps == timestep).nonzero().item()
													lowerCamelCase_								=self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				lowerCamelCase_								=sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				lowerCamelCase_								=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'							)

													lowerCamelCase_								=self.sigmas[step_index]
													lowerCamelCase_								=self.sigmas[step_index + 1]
													lowerCamelCase_								=(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													lowerCamelCase_								=(sigma_to**2 - sigma_up**2) ** 0.5

													# 2. Convert to an ODE derivative
													lowerCamelCase_								=(sample - pred_original_sample) / sigma

													lowerCamelCase_								=sigma_down - sigma

													lowerCamelCase_								=sample + derivative * dt

													lowerCamelCase_								=model_output.device
													lowerCamelCase_								=randn_tensor(model_output.shape     ,						dtype=model_output.dtype     ,						device=_SCREAMING_SNAKE_CASE     ,						generator=_SCREAMING_SNAKE_CASE							)

													lowerCamelCase_								=prev_sample + noise * sigma_up

													if not return_dict:
																				return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=_SCREAMING_SNAKE_CASE     ,						pred_original_sample=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						)->							torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													lowerCamelCase_								=self.sigmas.to(device=original_samples.device     ,						dtype=original_samples.dtype							)
													if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE							):
																				# mps does not support float64
																				lowerCamelCase_								=self.timesteps.to(original_samples.device     ,						dtype=torch.floataa							)
																				lowerCamelCase_								=timesteps.to(original_samples.device     ,						dtype=torch.floataa							)
													else:
																				lowerCamelCase_								=self.timesteps.to(original_samples.device							)
																				lowerCamelCase_								=timesteps.to(original_samples.device							)

													lowerCamelCase_								=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

													lowerCamelCase_								=sigmas[step_indices].flatten()
													while len(sigma.shape							) < len(original_samples.shape							):
																				lowerCamelCase_								=sigma.unsqueeze(-1							)

													lowerCamelCase_								=original_samples + noise * sigma
													return noisy_samples







						def __len__(			self							)->							Tuple:
													return self.config.num_train_timesteps



from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
