


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 						snake_case			(       lowercase__	):
			'''simple docstring'''

			UpperCAmelCase       :       torch.FloatTensor

class 						snake_case			(       lowercase__	, lowercase__	):
			'''simple docstring'''

			UpperCAmelCase       :       Tuple       = 1

			@register_to_config
			def __init__(	self     :     int							,							lowerCAmelCase_     :     int = 40							,							lowerCAmelCase_     :     float = 0.002							,							lowerCAmelCase_     :     float = 80.0							,							lowerCAmelCase_     :     float = 0.5							,							lowerCAmelCase_     :     float = 1.0							,							lowerCAmelCase_     :     float = 7.0							,							lowerCAmelCase_     :     bool = True							,							)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       sigma_max

								SCREAMING_SNAKE_CASE_	      =       np.linspace(0							,							1							,							_A					)
								SCREAMING_SNAKE_CASE_	      =       self._convert_to_karras(_A					)
								SCREAMING_SNAKE_CASE_	      =       self.sigma_to_t(_A					)

								# setable values
								SCREAMING_SNAKE_CASE_	      =       None
								SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(_A					)
								SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(_A					)
								SCREAMING_SNAKE_CASE_	      =       False
								SCREAMING_SNAKE_CASE_	      =       False

			def _lowercase (	self     :     Optional[Any]							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     Tuple=None					)					->					List[Any]:




								"""simple docstring"""






								if schedule_timesteps is None:
													SCREAMING_SNAKE_CASE_	      =       self.timesteps

								SCREAMING_SNAKE_CASE_	      =       (schedule_timesteps == timestep).nonzero()
								return indices.item()

			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     torch.FloatTensor							,							lowerCAmelCase_     :     Union[float, torch.FloatTensor]					)					->					torch.FloatTensor:




								"""simple docstring"""






								if isinstance(_A							,							torch.Tensor					):
													SCREAMING_SNAKE_CASE_	      =       timestep.to(self.timesteps.device					)
								SCREAMING_SNAKE_CASE_	      =       self.index_for_timestep(_A					)
								SCREAMING_SNAKE_CASE_	      =       self.sigmas[step_idx]

								SCREAMING_SNAKE_CASE_	      =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

								SCREAMING_SNAKE_CASE_	      =       True
								return sample

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




								"""simple docstring"""






								if not isinstance(_A							,							np.ndarray					):
													SCREAMING_SNAKE_CASE_	      =       np.array(_A							,							dtype=np.floataa					)

								SCREAMING_SNAKE_CASE_	      =       1_000 * 0.25 * np.log(sigmas + 1e-44					)

								return timesteps

			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Optional[int] = None							,							lowerCAmelCase_     :     Union[str, torch.device] = None							,							lowerCAmelCase_     :     Optional[List[int]] = None							,							)					->					List[Any]:




								"""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(_A					)					):
																		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}.'''					)

													SCREAMING_SNAKE_CASE_	      =       np.array(_A							,							dtype=np.intaa					)
													SCREAMING_SNAKE_CASE_	      =       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.'''					)

													SCREAMING_SNAKE_CASE_	      =       num_inference_steps

													SCREAMING_SNAKE_CASE_	      =       self.config.num_train_timesteps // self.num_inference_steps
													SCREAMING_SNAKE_CASE_	      =       (np.arange(0							,							_A					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
													SCREAMING_SNAKE_CASE_	      =       False

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

								SCREAMING_SNAKE_CASE_	      =       np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
								SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(_A					).to(device=_A					)

								if str(_A					).startswith('''mps'''					):
													# mps does not support float64
													SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(_A					).to(_A							,							dtype=torch.floataa					)
								else:
													SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(_A					).to(device=_A					)

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Optional[int]					)					->					Any:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_min
								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_max

								SCREAMING_SNAKE_CASE_	      =       self.config.rho
								SCREAMING_SNAKE_CASE_	      =       sigma_min ** (1 / rho)
								SCREAMING_SNAKE_CASE_	      =       sigma_max ** (1 / rho)
								SCREAMING_SNAKE_CASE_	      =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas

			def _lowercase (	self     :     Union[str, Any]							,							lowerCAmelCase_     :     Dict					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_data

								SCREAMING_SNAKE_CASE_	      =       sigma_data**2 / (sigma**2 + sigma_data**2)
								SCREAMING_SNAKE_CASE_	      =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out

			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     str					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_min
								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_data

								SCREAMING_SNAKE_CASE_	      =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
								SCREAMING_SNAKE_CASE_	      =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
								return c_skip, c_out

			def _lowercase (	self     :     Optional[Any]							,							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(_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'''
													        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(_A							,							torch.Tensor					):
													SCREAMING_SNAKE_CASE_	      =       timestep.to(self.timesteps.device					)

								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_min
								SCREAMING_SNAKE_CASE_	      =       self.config.sigma_max

								SCREAMING_SNAKE_CASE_	      =       self.index_for_timestep(_A					)

								# sigma_next corresponds to next_t in original implementation
								SCREAMING_SNAKE_CASE_	      =       self.sigmas[step_index]
								if step_index + 1 < self.config.num_train_timesteps:
													SCREAMING_SNAKE_CASE_	      =       self.sigmas[step_index + 1]
								else:
													# Set sigma_next to sigma_min
													SCREAMING_SNAKE_CASE_	      =       self.sigmas[-1]

								# Get scalings for boundary conditions
								SCREAMING_SNAKE_CASE_	      =       self.get_scalings_for_boundary_condition(_A					)

								# 1. Denoise model output using boundary conditions
								SCREAMING_SNAKE_CASE_	      =       c_out * model_output + c_skip * sample
								if self.config.clip_denoised:
													SCREAMING_SNAKE_CASE_	      =       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:
													SCREAMING_SNAKE_CASE_	      =       randn_tensor(
													    model_output.shape							,							dtype=model_output.dtype							,							device=model_output.device							,							generator=_A					)
								else:
													SCREAMING_SNAKE_CASE_	      =       torch.zeros_like(_A					)
								SCREAMING_SNAKE_CASE_	      =       noise * self.config.s_noise

								SCREAMING_SNAKE_CASE_	      =       sigma_next.clamp(min=_A							,							max=_A					)

								# 3. Return noisy sample
								# tau = sigma_hat, eps = sigma_min
								SCREAMING_SNAKE_CASE_	      =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

								if not return_dict:
													return (prev_sample,)

								return CMStochasticIterativeSchedulerOutput(prev_sample=_A					)

			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     torch.FloatTensor							,							lowerCAmelCase_     :     torch.FloatTensor							,							lowerCAmelCase_     :     torch.FloatTensor							,							)					->					torch.FloatTensor:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       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
													SCREAMING_SNAKE_CASE_	      =       self.timesteps.to(original_samples.device							,							dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE_	      =       timesteps.to(original_samples.device							,							dtype=torch.floataa					)
								else:
													SCREAMING_SNAKE_CASE_	      =       self.timesteps.to(original_samples.device					)
													SCREAMING_SNAKE_CASE_	      =       timesteps.to(original_samples.device					)

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

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

								SCREAMING_SNAKE_CASE_	      =       original_samples + noise * sigma
								return noisy_samples




			def __len__(	self     :     str					)					->					Optional[Any]:




								"""simple docstring"""






								return self.config.num_train_timesteps






def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		int:







					'''simple docstring'''



					return (input_a, input_a).count(0      ) % 2







def 							UpperCAmelCase				(     )->		None:







					'''simple docstring'''



					assert xor_gate(0				,0      ) == 0
					assert xor_gate(0				,1      ) == 1
					assert xor_gate(1				,0      ) == 1
					assert xor_gate(1				,1      ) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))


