


'''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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




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





def      __snake_case       (       lowerCamelCase_ :				List[str]	,  lowerCamelCase_ :				Any=0.999	,  lowerCamelCase_ :				Tuple="cosine"	,  ):
				'''simple docstring'''




				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_ :				Union[str, Any]						):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_ :				List[Any]						):
												return math.exp(t * -12.0						)

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

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






class 		UpperCamelCase_		(					A   ,		A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     [e.name for e in KarrasDiffusionSchedulers]
						UpperCAmelCase__	:       Optional[int]         =     1
						@register_to_config
						def __init__( self				:					Tuple	,				_lowerCamelCase				:					int = 10_00	,				_lowerCamelCase				:					float = 0.0_001	,				_lowerCamelCase				:					float = 0.02	,				_lowerCamelCase				:					str = "linear"	,				_lowerCamelCase				:					Optional[Union[np.ndarray, List[float]]] = None	,				_lowerCamelCase				:					str = "epsilon"	,				_lowerCamelCase				:					str = "linear"	,				_lowerCamelCase				:					Optional[bool] = False	,				_lowerCamelCase				:					str = "linspace"	,				_lowerCamelCase				:					int = 0	,				)							->					Dict:
										if trained_betas is not None:
														__magic_name__	  = torch.tensor(_lowerCamelCase	,				dtype=torch.floataa  )
										elif beta_schedule == "linear":
														__magic_name__	  = torch.linspace(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				dtype=torch.floataa  )
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														__magic_name__	  = (
														    torch.linspace(beta_start**0.5	,				beta_end**0.5	,				_lowerCamelCase	,				dtype=torch.floataa  ) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														__magic_name__	  = betas_for_alpha_bar(_lowerCamelCase  )
										else:
														raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

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

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

										# setable values
										__magic_name__	  = None
										__magic_name__	  = np.linspace(0	,				num_train_timesteps - 1	,				_lowerCamelCase	,				dtype=_lowerCamelCase  )[::-1].copy()
										__magic_name__	  = torch.from_numpy(_lowerCamelCase  )
										__magic_name__	  = False
										__magic_name__	  = use_karras_sigmas
						@property
						def 					__A					( self				:					Union[str, Any]  )							->					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 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					Union[float, torch.FloatTensor]  )							->					torch.FloatTensor:
										if isinstance(_lowerCamelCase	,				torch.Tensor  ):
														__magic_name__	  = timestep.to(self.timesteps.device  )
										__magic_name__	  = (self.timesteps == timestep).nonzero().item()
										__magic_name__	  = self.sigmas[step_index]

										__magic_name__	  = sample / ((sigma**2 + 1) ** 0.5)

										__magic_name__	  = True
										return sample
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Union[str, torch.device] = None  )							->					Dict:
										__magic_name__	  = 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":
														__magic_name__	  = np.linspace(0	,				self.config.num_train_timesteps - 1	,				_lowerCamelCase	,				dtype=_lowerCamelCase  )[
														    ::-1
														].copy()
										elif self.config.timestep_spacing == "leading":
														__magic_name__	  = 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
														__magic_name__	  = (np.arange(0	,				_lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase  )
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														__magic_name__	  = 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
														__magic_name__	  = (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\'.'  )

										__magic_name__	  = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
										__magic_name__	  = np.log(_lowerCamelCase  )

										if self.config.interpolation_type == "linear":
														__magic_name__	  = np.interp(_lowerCamelCase	,				np.arange(0	,				len(_lowerCamelCase  )  )	,				_lowerCamelCase  )
										elif self.config.interpolation_type == "log_linear":
														__magic_name__	  = torch.linspace(np.log(sigmas[-1]  )	,				np.log(sigmas[0]  )	,				num_inference_steps + 1  ).exp()
										else:
														raise ValueError(
														    f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
														    " 'linear' or 'log_linear'"  )

										if self.use_karras_sigmas:
														__magic_name__	  = self._convert_to_karras(in_sigmas=_lowerCamelCase	,				num_inference_steps=self.num_inference_steps  )
														__magic_name__	  = np.array([self._sigma_to_t(_lowerCamelCase	,				_lowerCamelCase  ) for sigma in sigmas]  )

										__magic_name__	  = np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
										__magic_name__	  = torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
										if str(_lowerCamelCase  ).startswith("mps"  ):
														# mps does not support float64
														__magic_name__	  = torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase	,				dtype=torch.floataa  )
										else:
														__magic_name__	  = torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Optional[Any]  )							->					List[Any]:
										# get log sigma
										__magic_name__	  = np.log(_lowerCamelCase  )

										# get distribution
										__magic_name__	  = log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										__magic_name__	  = np.cumsum((dists >= 0)	,				axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
										__magic_name__	  = low_idx + 1

										__magic_name__	  = log_sigmas[low_idx]
										__magic_name__	  = log_sigmas[high_idx]

										# interpolate sigmas
										__magic_name__	  = (low - log_sigma) / (low - high)
										__magic_name__	  = np.clip(_lowerCamelCase	,				0	,				1  )

										# transform interpolation to time range
										__magic_name__	  = (1 - w) * low_idx + w * high_idx
										__magic_name__	  = t.reshape(sigma.shape  )
										return t
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					int  )							->					torch.FloatTensor:
										__magic_name__	  = in_sigmas[-1].item()
										__magic_name__	  = in_sigmas[0].item()

										__magic_name__	  = 7.0  # 7.0 is the value used in the paper
										__magic_name__	  = np.linspace(0	,				1	,				_lowerCamelCase  )
										__magic_name__	  = sigma_min ** (1 / rho)
										__magic_name__	  = sigma_max ** (1 / rho)
										__magic_name__	  = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					Union[float, torch.FloatTensor]	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					float = 0.0	,				_lowerCamelCase				:					float = 0.0	,				_lowerCamelCase				:					float = float("inf"  )	,				_lowerCamelCase				:					float = 1.0	,				_lowerCamelCase				:					Optional[torch.Generator] = None	,				_lowerCamelCase				:					bool = True	,				)							->					Union[EulerDiscreteSchedulerOutput, Tuple]:

										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  ):
														__magic_name__	  = timestep.to(self.timesteps.device  )

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

										__magic_name__	  = min(s_churn / (len(self.sigmas  ) - 1)	,				2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

										__magic_name__	  = randn_tensor(
										    model_output.shape	,				dtype=model_output.dtype	,				device=model_output.device	,				generator=_lowerCamelCase  )

										__magic_name__	  = noise * s_noise
										__magic_name__	  = sigma * (gamma + 1)

										if gamma > 0:
														__magic_name__	  = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
														__magic_name__	  = model_output
										elif self.config.prediction_type == "epsilon":
														__magic_name__	  = sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
														# * c_out + input * c_skip
														__magic_name__	  = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
														raise ValueError(
														    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

										# 2. Convert to an ODE derivative
										__magic_name__	  = (sample - pred_original_sample) / sigma_hat

										__magic_name__	  = self.sigmas[step_index + 1] - sigma_hat

										__magic_name__	  = sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=_lowerCamelCase	,				pred_original_sample=_lowerCamelCase  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					torch.FloatTensor	,				_lowerCamelCase				:					torch.FloatTensor	,				)							->					torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__magic_name__	  = 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
														__magic_name__	  = self.timesteps.to(original_samples.device	,				dtype=torch.floataa  )
														__magic_name__	  = timesteps.to(original_samples.device	,				dtype=torch.floataa  )
										else:
														__magic_name__	  = self.timesteps.to(original_samples.device  )
														__magic_name__	  = timesteps.to(original_samples.device  )

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

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

										__magic_name__	  = original_samples + noise * sigma
										return noisy_samples

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







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



