

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


__snake_case							:     Union[str, Any] 						=	logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    A__(a_    ):
						"""simple docstring"""






						_A  :							torch.FloatTensor
						_A  :							Optional[torch.FloatTensor]   			=	None







def 			_UpperCAmelCase							(		a__       ,					a__=0.999       ,					a__="cosine"       ,					):

			'''simple docstring'''



			if alpha_transform_type == "cosine":

						def alpha_bar_fn(a__):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(a__):
									return math.exp(t * -12.0)

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

			a_		:		Union[str, Any]				    =				[]
			for i in range(a__):
						a_		:		Tuple				    =				i / num_diffusion_timesteps
						a_		:		Tuple				    =				(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    A__(a_,       a_    ):
						"""simple docstring"""






						_A  :							Any   			=	[e.name for e in KarrasDiffusionSchedulers]
						_A  :							Optional[Any]   			=	1





						@register_to_config
						def __init__(  self       ,     _lowercase = 1_000       ,     _lowercase = 0.0_0_0_1       ,     _lowercase = 0.0_2       ,     _lowercase = "linear"       ,     _lowercase = None       ,     _lowercase = "epsilon"       ,     _lowercase = "linspace"       ,     _lowercase = 0       ,     )      ->				str:
									if trained_betas is not None:
												a_		:		Optional[int]				    =				torch.tensor(_lowercase       ,     dtype=torch.floataa  )
									elif beta_schedule == "linear":
												a_		:		Any				    =				torch.linspace(_lowercase       ,     _lowercase       ,     _lowercase       ,     dtype=torch.floataa  )
									elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												a_		:		int				    =				(
												    torch.linspace(beta_start**0.5       ,     beta_end**0.5       ,     _lowercase       ,     dtype=torch.floataa  ) ** 2
												)
									elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												a_		:		List[str]				    =				betas_for_alpha_bar(_lowercase  )
									else:
												raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

									a_		:		Union[str, Any]				    =				1.0 - self.betas
									a_		:		int				    =				torch.cumprod(self.alphas       ,     dim=0  )

									a_		:		Union[str, Any]				    =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
									a_		:		str				    =				np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
									a_		:		Union[str, Any]				    =				torch.from_numpy(_lowercase  )

									# setable values
									a_		:		Dict				    =				None
									a_		:		Dict				    =				np.linspace(0       ,     num_train_timesteps - 1       ,     _lowercase       ,     dtype=_lowercase  )[::-1].copy()
									a_		:		Any				    =				torch.from_numpy(_lowercase  )
									a_		:		Union[str, Any]				    =				False





						@property
						def 	UpperCamelCase__			(  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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				torch.FloatTensor:
									if isinstance(_lowercase       ,     torch.Tensor  ):
												a_		:		Optional[int]				    =				timestep.to(self.timesteps.device  )
									a_		:		str				    =				(self.timesteps == timestep).nonzero().item()
									a_		:		Union[str, Any]				    =				self.sigmas[step_index]
									a_		:		Optional[Any]				    =				sample / ((sigma**2 + 1) ** 0.5)
									a_		:		Optional[int]				    =				True
									return sample





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[Any]:
									a_		:		int				    =				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_		:		List[Any]				    =				np.linspace(0       ,     self.config.num_train_timesteps - 1       ,     _lowercase       ,     dtype=_lowercase  )[
												    ::-1
												].copy()
									elif self.config.timestep_spacing == "leading":
												a_		:		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
												a_		:		Tuple				    =				(np.arange(0       ,     _lowercase  ) * step_ratio).round()[::-1].copy().astype(_lowercase  )
												timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
												a_		:		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
												a_		:		Any				    =				(np.arange(self.config.num_train_timesteps       ,     0       ,     -step_ratio  )).round().copy().astype(_lowercase  )
												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_		:		str				    =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
									a_		:		Tuple				    =				np.interp(_lowercase       ,     np.arange(0       ,     len(_lowercase  )  )       ,     _lowercase  )
									a_		:		Dict				    =				np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
									a_		:		Optional[Any]				    =				torch.from_numpy(_lowercase  ).to(device=_lowercase  )
									if str(_lowercase  ).startswith("""mps"""  ):
												# mps does not support float64
												a_		:		Union[str, Any]				    =				torch.from_numpy(_lowercase  ).to(_lowercase       ,     dtype=torch.floataa  )
									else:
												a_		:		int				    =				torch.from_numpy(_lowercase  ).to(device=_lowercase  )





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

									if (
									    isinstance(_lowercase       ,     _lowercase  )
									    or isinstance(_lowercase       ,     torch.IntTensor  )
									    or isinstance(_lowercase       ,     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(_lowercase       ,     torch.Tensor  ):
												a_		:		Dict				    =				timestep.to(self.timesteps.device  )

									a_		:		List[Any]				    =				(self.timesteps == timestep).nonzero().item()
									a_		:		Optional[Any]				    =				self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
												a_		:		str				    =				sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												a_		:		Optional[int]				    =				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`'''  )

									a_		:		Any				    =				self.sigmas[step_index]
									a_		:		Optional[Any]				    =				self.sigmas[step_index + 1]
									a_		:		str				    =				(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
									a_		:		int				    =				(sigma_to**2 - sigma_up**2) ** 0.5

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

									a_		:		Dict				    =				sigma_down - sigma

									a_		:		Optional[int]				    =				sample + derivative * dt

									a_		:		Optional[Any]				    =				model_output.device
									a_		:		Optional[int]				    =				randn_tensor(model_output.shape       ,     dtype=model_output.dtype       ,     device=_lowercase       ,     generator=_lowercase  )

									a_		:		Dict				    =				prev_sample + noise * sigma_up

									if not return_dict:
												return (prev_sample,)

									return EulerAncestralDiscreteSchedulerOutput(
									    prev_sample=_lowercase       ,     pred_original_sample=_lowercase  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									a_		:		str				    =				self.sigmas.to(device=original_samples.device       ,     dtype=original_samples.dtype  )
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase  ):
												# mps does not support float64
												a_		:		List[Any]				    =				self.timesteps.to(original_samples.device       ,     dtype=torch.floataa  )
												a_		:		Union[str, Any]				    =				timesteps.to(original_samples.device       ,     dtype=torch.floataa  )
									else:
												a_		:		Tuple				    =				self.timesteps.to(original_samples.device  )
												a_		:		Tuple				    =				timesteps.to(original_samples.device  )

									a_		:		str				    =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									a_		:		Dict				    =				original_samples + noise * sigma
									return noisy_samples





						def __len__(  self  )      ->				List[str]:
									return self.config.num_train_timesteps





def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			return 1 if input_a == 0 else 0


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			assert not_gate(0) == 1
			assert not_gate(1) == 0


if __name__ == "__main__":
	print(not_gate(0))
	print(not_gate(1))


