





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


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class       __lowerCAmelCase (    _a  ):
			lowerCamelCase_  :  torch.FloatTensor
			lowerCamelCase_  :  Optional[torch.FloatTensor]     =   None






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase=0.999				,       _UpperCamelCase="cosine"				,       )	->		Optional[int]:






				"""simple docstring"""


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(_UpperCamelCase				):
												return math.cos((t + 0.008) / 1.008 * 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}'''				)

				snake_case_		:					Tuple			    =						[]
				for i in range(_UpperCamelCase				):
								snake_case_		:					Optional[Any]			    =						i / num_diffusion_timesteps
								snake_case_		:					Union[str, Any]			    =						(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       __lowerCAmelCase (    _a,  _a  ):
			lowerCamelCase_  :  Tuple     =   [e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase_  :  Any     =   1






			@register_to_config
			def __init__(self      ,							__magic_name__ = 1000      ,							__magic_name__ = 0.0_001      ,							__magic_name__ = 0.02      ,							__magic_name__ = "linear"      ,							__magic_name__ = None      ,							__magic_name__ = "epsilon"      ,							__magic_name__ = "linspace"      ,							__magic_name__ = 0      ,							)  ->     Optional[int]:


							'''simple docstring'''



							if trained_betas is not None:
											snake_case_		:					Optional[Any]			    =						torch.tensor(__magic_name__      ,							dtype=torch.floataa					)
							elif beta_schedule == "linear":
											snake_case_		:					Optional[Any]			    =						torch.linspace(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											snake_case_		:					List[Any]			    =						(
											    torch.linspace(beta_start**0.5      ,							beta_end**0.5      ,							__magic_name__      ,							dtype=torch.floataa					) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											snake_case_		:					Any			    =						betas_for_alpha_bar(__magic_name__					)
							else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

							snake_case_		:					str			    =						1.0 - self.betas
							snake_case_		:					List[Any]			    =						torch.cumprod(self.alphas      ,							dim=0					)

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

							# setable values
							snake_case_		:					List[Any]			    =						None
							snake_case_		:					Optional[Any]			    =						np.linspace(0      ,							num_train_timesteps - 1      ,							__magic_name__      ,							dtype=__magic_name__					)[::-1].copy()
							snake_case_		:					int			    =						torch.from_numpy(__magic_name__					)
							snake_case_		:					Optional[Any]			    =						False






			@property
			def     lowerCamelCase						(self					)  ->     Tuple:


							'''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      ,							__magic_name__      ,							__magic_name__					)  ->     torch.FloatTensor:


							'''simple docstring'''



							if isinstance(__magic_name__      ,							torch.Tensor					):
											snake_case_		:					List[str]			    =						timestep.to(self.timesteps.device					)
							snake_case_		:					Optional[Any]			    =						(self.timesteps == timestep).nonzero().item()
							snake_case_		:					int			    =						self.sigmas[step_index]
							snake_case_		:					Optional[Any]			    =						sample / ((sigma**2 + 1) ** 0.5)
							snake_case_		:					List[str]			    =						True
							return sample






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__ = None					)  ->     Optional[int]:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						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":
											snake_case_		:					Any			    =						np.linspace(0      ,							self.config.num_train_timesteps - 1      ,							__magic_name__      ,							dtype=__magic_name__					)[
											    ::-1
											].copy()
							elif self.config.timestep_spacing == "leading":
											snake_case_		:					Optional[Any]			    =						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
											snake_case_		:					int			    =						(np.arange(0      ,							__magic_name__					) * step_ratio).round()[::-1].copy().astype(__magic_name__					)
											timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
											snake_case_		:					Optional[Any]			    =						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
											snake_case_		:					Dict			    =						(np.arange(self.config.num_train_timesteps      ,							0      ,							-step_ratio					)).round().copy().astype(__magic_name__					)
											timesteps -= 1
							else:
											raise ValueError(
											    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

							snake_case_		:					int			    =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							snake_case_		:					Optional[int]			    =						np.interp(__magic_name__      ,							np.arange(0      ,							len(__magic_name__					)					)      ,							__magic_name__					)
							snake_case_		:					Optional[Any]			    =						np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
							snake_case_		:					str			    =						torch.from_numpy(__magic_name__					).to(device=__magic_name__					)
							if str(__magic_name__					).startswith('''mps'''					):
											# mps does not support float64
											snake_case_		:					Optional[Any]			    =						torch.from_numpy(__magic_name__					).to(__magic_name__      ,							dtype=torch.floataa					)
							else:
											snake_case_		:					Optional[Any]			    =						torch.from_numpy(__magic_name__					).to(device=__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__ = None      ,							__magic_name__ = True      ,							)  ->     Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:


							'''simple docstring'''



							if (
							    isinstance(__magic_name__      ,							__magic_name__					)
							    or isinstance(__magic_name__      ,							torch.IntTensor					)
							    or isinstance(__magic_name__      ,							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(__magic_name__      ,							torch.Tensor					):
											snake_case_		:					Any			    =						timestep.to(self.timesteps.device					)

							snake_case_		:					Any			    =						(self.timesteps == timestep).nonzero().item()
							snake_case_		:					Any			    =						self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
											snake_case_		:					str			    =						sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											snake_case_		:					Any			    =						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`'''					)

							snake_case_		:					str			    =						self.sigmas[step_index]
							snake_case_		:					List[Any]			    =						self.sigmas[step_index + 1]
							snake_case_		:					Union[str, Any]			    =						(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							snake_case_		:					List[Any]			    =						(sigma_to**2 - sigma_up**2) ** 0.5

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

							snake_case_		:					Dict			    =						sigma_down - sigma

							snake_case_		:					Tuple			    =						sample + derivative * dt

							snake_case_		:					Optional[int]			    =						model_output.device
							snake_case_		:					Optional[int]			    =						randn_tensor(model_output.shape      ,							dtype=model_output.dtype      ,							device=__magic_name__      ,							generator=__magic_name__					)

							snake_case_		:					str			    =						prev_sample + noise * sigma_up

							if not return_dict:
											return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=__magic_name__      ,							pred_original_sample=__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							)  ->     torch.FloatTensor:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.sigmas.to(device=original_samples.device      ,							dtype=original_samples.dtype					)
							if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__					):
											# mps does not support float64
											snake_case_		:					List[str]			    =						self.timesteps.to(original_samples.device      ,							dtype=torch.floataa					)
											snake_case_		:					Optional[int]			    =						timesteps.to(original_samples.device      ,							dtype=torch.floataa					)
							else:
											snake_case_		:					Dict			    =						self.timesteps.to(original_samples.device					)
											snake_case_		:					Optional[int]			    =						timesteps.to(original_samples.device					)

							snake_case_		:					Optional[int]			    =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							snake_case_		:					Tuple			    =						sigmas[step_indices].flatten()
							while len(sigma.shape					) < len(original_samples.shape					):
											snake_case_		:					Union[str, Any]			    =						sigma.unsqueeze(-1					)

							snake_case_		:					Dict			    =						original_samples + noise * sigma
							return noisy_samples






			def __len__(self					)  ->     str:


							'''simple docstring'''



							return self.config.num_train_timesteps







class       __lowerCAmelCase :






			def __init__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						[]
							snake_case_		:					Any			    =						0
							snake_case_		:					Optional[int]			    =						0






			def __str__(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					int			    =						'''<''' + str(self.entries					)[1:-1] + '''>'''
							return printed






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Dict:


							'''simple docstring'''



							self.entries.append(__magic_name__					)
							snake_case_		:					List[str]			    =						self.length + 1






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.length - 1
							snake_case_		:					Dict			    =						self.entries[self.front]
							# self.front-=1
							# self.entries = self.entries[self.front:]
							snake_case_		:					List[str]			    =						self.entries[1:]
							return dequeued






			def     lowerCamelCase						(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							for _ in range(__magic_name__					):
											self.put(self.get()					)






			def     lowerCamelCase						(self					)  ->     Optional[Any]:


							'''simple docstring'''



							return self.entries[0]






			def     lowerCamelCase						(self					)  ->     Union[str, Any]:


							'''simple docstring'''



							return self.length
