





"""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


_snake_case	:						List[Any]					=					logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				42
					UpperCamelCase						=				None







def    A__							(     UpperCamelCase   ,    UpperCamelCase=0.9_99   ,    UpperCamelCase="cosine"   ,    ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase			):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

							elif alpha_transform_type == "exp":

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

							else:
														raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

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






class    _UpperCAmelCase  (      lowercase_	,				lowercase_					):
					UpperCamelCase						=				[e.name for e in KarrasDiffusionSchedulers]
					UpperCamelCase						=				1


					@register_to_config
					def __init__(   self     :int	,			__UpperCamelCase     :int = 10_00	,			__UpperCamelCase     :float = 0.0_001	,			__UpperCamelCase     :float = 0.02	,			__UpperCamelCase     :str = "linear"	,			__UpperCamelCase     :Optional[Union[np.ndarray, List[float]]] = None	,			__UpperCamelCase     :str = "epsilon"	,			__UpperCamelCase     :str = "linspace"	,			__UpperCamelCase     :int = 0	,			):
												if trained_betas is not None:
																			A						=    torch.tensor(__UpperCamelCase	,			dtype=torch.floataa						)
												elif beta_schedule == "linear":
																			A						=    torch.linspace(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			dtype=torch.floataa						)
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			A						=    (
																			    torch.linspace(beta_start**0.5	,			beta_end**0.5	,			__UpperCamelCase	,			dtype=torch.floataa						) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			A						=    betas_for_alpha_bar(__UpperCamelCase						)
												else:
																			raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

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

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

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


					@property
					def       lowerCamelCase			(   self     :Dict						):
												# 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       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :Union[float, torch.FloatTensor]						):
												if isinstance(__UpperCamelCase	,			torch.Tensor						):
																			A						=    timestep.to(self.timesteps.device						)
												A						=    (self.timesteps == timestep).nonzero().item()
												A						=    self.sigmas[step_index]
												A						=    sample / ((sigma**2 + 1) ** 0.5)
												A						=    True
												return sample


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


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :Union[float, torch.FloatTensor]	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :Optional[torch.Generator] = None	,			__UpperCamelCase     :bool = True	,			):

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

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

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																			A						=    sample - sigma * model_output
												elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			A						=    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						=    self.sigmas[step_index]
												A						=    self.sigmas[step_index + 1]
												A						=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
												A						=    (sigma_to**2 - sigma_up**2) ** 0.5

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

												A						=    sigma_down - sigma

												A						=    sample + derivative * dt

												A						=    model_output.device
												A						=    randn_tensor(model_output.shape	,			dtype=model_output.dtype	,			device=__UpperCamelCase	,			generator=__UpperCamelCase						)

												A						=    prev_sample + noise * sigma_up

												if not return_dict:
																			return (prev_sample,)

												return EulerAncestralDiscreteSchedulerOutput(
												    prev_sample=__UpperCamelCase	,			pred_original_sample=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :torch.FloatTensor	,			):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A						=    self.sigmas.to(device=original_samples.device	,			dtype=original_samples.dtype						)
												if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase						):
																			# mps does not support float64
																			A						=    self.timesteps.to(original_samples.device	,			dtype=torch.floataa						)
																			A						=    timesteps.to(original_samples.device	,			dtype=torch.floataa						)
												else:
																			A						=    self.timesteps.to(original_samples.device						)
																			A						=    timesteps.to(original_samples.device						)

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

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

												A						=    original_samples + noise * sigma
												return noisy_samples


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









"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


