





"""simple docstring"""




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 SchedulerMixin


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




@dataclass
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42




class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase       ):
				UpperCamelCase			=					1






				@register_to_config
				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					int = 4_0,				lowerCAmelCase__	:					float = 0.002,				lowerCAmelCase__	:					float = 80.0,				lowerCAmelCase__	:					float = 0.5,				lowerCAmelCase__	:					float = 1.0,				lowerCAmelCase__	:					float = 7.0,				lowerCAmelCase__	:					bool = True,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					sigma_max

										_UpperCamelCase    :      Dict						     =					np.linspace(0,				1,				lowerCAmelCase__							)
										_UpperCamelCase    :      int						     =					self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					self.sigma_to_t(lowerCAmelCase__							)

										# setable values
										_UpperCamelCase    :      Union[str, Any]						     =					None
										_UpperCamelCase    :      List[Any]						     =					torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					torch.from_numpy(lowerCAmelCase__							)
										_UpperCamelCase    :      List[Any]						     =					False
										_UpperCamelCase    :      int						     =					False






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Tuple=None							)			->   List[Any]:




										'''simple docstring'''



										if schedule_timesteps is None:
																_UpperCamelCase    :      Optional[int]						     =					self.timesteps

										_UpperCamelCase    :      int						     =					(schedule_timesteps == timestep).nonzero()
										return indices.item()






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor]							)			->   torch.FloatTensor:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Optional[int]						     =					timestep.to(self.timesteps.device							)
										_UpperCamelCase    :      List[str]						     =					self.index_for_timestep(lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[Any]						     =					self.sigmas[step_idx]

										_UpperCamelCase    :      str						     =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										_UpperCamelCase    :      int						     =					True
										return sample






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					Union[float, np.ndarray]							)			->   str:




										'''simple docstring'''



										if not isinstance(lowerCAmelCase__,				np.ndarray							):
																_UpperCamelCase    :      Optional[int]						     =					np.array(lowerCAmelCase__,				dtype=np.floataa							)

										_UpperCamelCase    :      List[str]						     =					1_0_0_0 * 0.25 * np.log(sigmas + 1e-4_4							)

										return timesteps






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					Optional[int] = None,				lowerCAmelCase__	:					Union[str, torch.device] = None,				lowerCAmelCase__	:					Optional[List[int]] = None,				)			->   Tuple:




										'''simple docstring'''



										if num_inference_steps is None and timesteps is None:
																raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''							)

										if num_inference_steps is not None and timesteps is not None:
																raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''							)

										# Follow DDPMScheduler custom timesteps logic
										if timesteps is not None:
																for i in range(1,				len(lowerCAmelCase__							)							):
																						if timesteps[i] >= timesteps[i - 1]:
																												raise ValueError('''`timesteps` must be in descending order.'''							)

																if timesteps[0] >= self.config.num_train_timesteps:
																						raise ValueError(
																						    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																						    f""" {self.config.num_train_timesteps}."""							)

																_UpperCamelCase    :      int						     =					np.array(lowerCAmelCase__,				dtype=np.intaa							)
																_UpperCamelCase    :      List[Any]						     =					True
										else:
																if num_inference_steps > self.config.num_train_timesteps:
																						raise ValueError(
																						    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																						    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																						    f""" maximal {self.config.num_train_timesteps} timesteps."""							)

																_UpperCamelCase    :      Union[str, Any]						     =					num_inference_steps

																_UpperCamelCase    :      Any						     =					self.config.num_train_timesteps // self.num_inference_steps
																_UpperCamelCase    :      Tuple						     =					(np.arange(0,				lowerCAmelCase__							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																_UpperCamelCase    :      Union[str, Any]						     =					False

										# Map timesteps to Karras sigmas directly for multistep sampling
										# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
										_UpperCamelCase    :      Optional[Any]						     =					self.config.num_train_timesteps
										_UpperCamelCase    :      Dict						     =					timesteps[::-1].copy()
										_UpperCamelCase    :      Dict						     =					ramp / (num_train_timesteps - 1)
										_UpperCamelCase    :      List[Any]						     =					self._convert_to_karras(lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					self.sigma_to_t(lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[Any]						     =					np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
										_UpperCamelCase    :      List[str]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)

										if str(lowerCAmelCase__							).startswith('''mps'''							):
																# mps does not support float64
																_UpperCamelCase    :      Union[str, Any]						     =					torch.from_numpy(lowerCAmelCase__							).to(lowerCAmelCase__,				dtype=torch.floataa							)
										else:
																_UpperCamelCase    :      List[str]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					str							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      float						     =					self.config.sigma_min
										_UpperCamelCase    :      float						     =					self.config.sigma_max

										_UpperCamelCase    :      List[str]						     =					self.config.rho
										_UpperCamelCase    :      str						     =					sigma_min ** (1 / rho)
										_UpperCamelCase    :      List[Any]						     =					sigma_max ** (1 / rho)
										_UpperCamelCase    :      Union[str, Any]						     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					self.config.sigma_data

										_UpperCamelCase    :      Optional[Any]						     =					sigma_data**2 / (sigma**2 + sigma_data**2)
										_UpperCamelCase    :      int						     =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out






				def        snake_case			(				self	:					str,				lowerCAmelCase__	:					Union[str, Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.config.sigma_min
										_UpperCamelCase    :      int						     =					self.config.sigma_data

										_UpperCamelCase    :      Tuple						     =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										_UpperCamelCase    :      List[str]						     =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Optional[torch.Generator] = None,				lowerCAmelCase__	:					bool = True,				)			->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:




										'''simple docstring'''



										if (
										    isinstance(lowerCAmelCase__,				lowerCAmelCase__							)
										    or isinstance(lowerCAmelCase__,				torch.IntTensor							)
										    or isinstance(lowerCAmelCase__,				torch.LongTensor							)
										):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        f""" `{self.__class__}.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(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Any						     =					timestep.to(self.timesteps.device							)

										_UpperCamelCase    :      int						     =					self.config.sigma_min
										_UpperCamelCase    :      List[Any]						     =					self.config.sigma_max

										_UpperCamelCase    :      Tuple						     =					self.index_for_timestep(lowerCAmelCase__							)

										# sigma_next corresponds to next_t in original implementation
										_UpperCamelCase    :      Union[str, Any]						     =					self.sigmas[step_index]
										if step_index + 1 < self.config.num_train_timesteps:
																_UpperCamelCase    :      List[Any]						     =					self.sigmas[step_index + 1]
										else:
																# Set sigma_next to sigma_min
																_UpperCamelCase    :      Tuple						     =					self.sigmas[-1]

										# Get scalings for boundary conditions
										_UpperCamelCase     ,							_UpperCamelCase    :      List[Any]						     =					self.get_scalings_for_boundary_condition(lowerCAmelCase__							)

										# 1. Denoise model output using boundary conditions
										_UpperCamelCase    :      Any						     =					c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
																_UpperCamelCase    :      List[Any]						     =					denoised.clamp(-1,				1							)

										# 2. Sample z ~ N(0, s_noise^2 * I)
										# Noise is not used for onestep sampling.
										if len(self.timesteps							) > 1:
																_UpperCamelCase    :      Union[str, Any]						     =					randn_tensor(
																    model_output.shape,				dtype=model_output.dtype,				device=model_output.device,				generator=lowerCAmelCase__							)
										else:
																_UpperCamelCase    :      Optional[int]						     =					torch.zeros_like(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					noise * self.config.s_noise

										_UpperCamelCase    :      List[Any]						     =					sigma_next.clamp(min=lowerCAmelCase__,				max=lowerCAmelCase__							)

										# 3. Return noisy sample
										# tau = sigma_hat, eps = sigma_min
										_UpperCamelCase    :      Optional[int]						     =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

										if not return_dict:
																return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCAmelCase__							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					torch.FloatTensor,				)			->   torch.FloatTensor:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					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
																_UpperCamelCase    :      List[str]						     =					self.timesteps.to(original_samples.device,				dtype=torch.floataa							)
																_UpperCamelCase    :      List[Any]						     =					timesteps.to(original_samples.device,				dtype=torch.floataa							)
										else:
																_UpperCamelCase    :      Dict						     =					self.timesteps.to(original_samples.device							)
																_UpperCamelCase    :      List[str]						     =					timesteps.to(original_samples.device							)

										_UpperCamelCase    :      Any						     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

										_UpperCamelCase    :      str						     =					original_samples + noise * sigma
										return noisy_samples






				def __len__(				self	:					List[Any]							)			->   Any:




										'''simple docstring'''



										return self.config.num_train_timesteps










"""simple docstring"""




def  a_       (							_lowercase     ):
						return 1 if input_a == 0 else 0





def  a_       (							):
						assert not_gate(0     ) == 1
						assert not_gate(1     ) == 0


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



