





from __future__ import annotations





def lowerCAmelCase__(__snake_case							)    ->				bool:







	'''simple docstring'''







	lowerCamelCase__        =		42
	lowerCamelCase__        =		list(__snake_case							)
	lowerCamelCase__    ,     lowerCamelCase__        =		_vector[:-1], int(_vector[-1]							)
	lowerCamelCase__        =		[int(__snake_case							) for digit in __vector]

	vector.reverse()
	for i, digit in enumerate(__snake_case							):
		if i & 1 == 0:
			lowerCamelCase__        =		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")










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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						None





def lowerCAmelCase__(__snake_case	,__snake_case=0.9_9_9	,__snake_case="cosine"	,)    ->				Tuple:







	'''simple docstring'''







	if alpha_transform_type == "cosine":

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

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

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






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						[e.name for e in KarrasDiffusionSchedulers]
			lowerCAmelCase_												=						1





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 1_0_0_0	,  __lowerCAmelCase = 0.0001	,  __lowerCAmelCase = 0.02	,  __lowerCAmelCase = "linear"	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "epsilon"	,  __lowerCAmelCase = "linspace"	,  __lowerCAmelCase = 0	,  ):



				'''simple docstring'''







				if trained_betas is not None:
					lowerCamelCase__        =		torch.tensor(__lowerCAmelCase	,  dtype=torch.floataa							)
				elif beta_schedule == "linear":
					lowerCamelCase__        =		torch.linspace(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  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	,  __lowerCAmelCase	,  dtype=torch.floataa							) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					lowerCamelCase__        =		betas_for_alpha_bar(__lowerCAmelCase							)
				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(__lowerCAmelCase							)

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





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

				return (self.sigmas.max() ** 2 + 1) ** 0.5





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase	,  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 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = None							):



				'''simple docstring'''







				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	,  __lowerCAmelCase	,  dtype=__lowerCAmelCase							)[
					    ::-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	,  __lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase							)
					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(__lowerCAmelCase							)
					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(__lowerCAmelCase	,  np.arange(0	,  len(__lowerCAmelCase							)							)	,  __lowerCAmelCase							)
				lowerCamelCase__        =		np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)
				if str(__lowerCAmelCase							).startswith('''mps'''							):
					# mps does not support float64
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(__lowerCAmelCase	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''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'''
					        ''' `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(__lowerCAmelCase	,  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=__lowerCAmelCase	,  generator=__lowerCAmelCase							)

				lowerCamelCase__        =		prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=__lowerCAmelCase	,  pred_original_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		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
					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							):



				'''simple docstring'''







				return self.config.num_train_timesteps



