



"""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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						torch.FloatTensor
		lowerCAmelCase_							:						Optional[torch.FloatTensor]							=  None



def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				Any:
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

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

							A    =       []
							for i in range(lowerCamelCase__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (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__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						Dict							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						str							=  1



		@register_to_config
		def __init__(			self	:     str   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.0001   ,				snake_case	:     float = 0.02   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[bool] = False   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    List[Any]:






									'''simple docstring'''

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

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



		@property
		def        A_		(			self	:     str					)      ->    str:






									'''simple docstring'''

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

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



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]					)      ->    torch.FloatTensor:






									'''simple docstring'''

									if isinstance(snake_case   ,				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        A_		(			self	:     str   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None					)      ->    Union[str, Any]:






									'''simple docstring'''

									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   ,				snake_case   ,				dtype=snake_case					)[
																    ::-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   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																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(snake_case					)
																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.log(snake_case					)

									if self.config.interpolation_type == "linear":
																A    =       np.interp(snake_case   ,				np.arange(0   ,				len(snake_case					)					)   ,				snake_case					)
									elif self.config.interpolation_type == "log_linear":
																A    =       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:
																A    =       self._convert_to_karras(in_sigmas=snake_case   ,				num_inference_steps=self.num_inference_steps					)
																A    =       np.array([self._sigma_to_t(snake_case   ,				snake_case					) for sigma in sigmas]					)

									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(snake_case					).to(device=snake_case					)
									if str(snake_case					).startswith('mps'					):
																# mps does not support float64
																A    =       torch.from_numpy(snake_case					).to(snake_case   ,				dtype=torch.floataa					)
									else:
																A    =       torch.from_numpy(snake_case					).to(device=snake_case					)



		def        A_		(			self	:     Any   ,				snake_case	:     Optional[int]   ,				snake_case	:     Tuple					)      ->    Any:






									'''simple docstring'''

									A    =       np.log(snake_case					)

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

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

									A    =       log_sigmas[low_idx]
									A    =       log_sigmas[high_idx]

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

									# transform interpolation to time range
									A    =       (1 - w) * low_idx + w * high_idx
									A    =       t.reshape(sigma.shape					)
									return t



		def        A_		(			self	:     List[Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Tuple					)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       in_sigmas[-1].item()
									A    =       in_sigmas[0].item()

									A    =       7.0  # 7.0 is the value used in the paper
									A    =       np.linspace(0   ,				1   ,				snake_case					)
									A    =       sigma_min ** (1 / rho)
									A    =       sigma_max ** (1 / rho)
									A    =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas



		def        A_		(			self	:     int   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     float = 0.0   ,				snake_case	:     float = 0.0   ,				snake_case	:     float = float('inf'					)   ,				snake_case	:     float = 1.0   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[EulerDiscreteSchedulerOutput, Tuple]:






									'''simple docstring'''

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

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

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

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

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

									if gamma > 0:
																A    =       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":
																A    =       model_output
									elif self.config.prediction_type == "epsilon":
																A    =       sample - sigma_hat * 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))
									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
									A    =       (sample - pred_original_sample) / sigma_hat

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

									A    =       sample + derivative * dt

									if not return_dict:
																return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=snake_case   ,				pred_original_sample=snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.sigmas.to(device=original_samples.device   ,				dtype=original_samples.dtype					)
									if original_samples.device.type == "mps" and torch.is_floating_point(snake_case					):
																# 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	:     Optional[int]					)      ->    List[str]:






									'''simple docstring'''

									return self.config.num_train_timesteps








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				list:
							for i, _ in enumerate(lowerCamelCase__      ):
														if (i % 2 == 1) == (nums[i - 1] > nums[i]):
																					A   ,	A    =       nums[i], nums[i - 1]

							return nums


if __name__ == "__main__":
							print('Enter the array elements:')
							A     =      list(map(int, input().split()))
							print('The unsorted array is:')
							print(array)
							print('Array after Wiggle sort:')
							print(wiggle_sort(array))



