






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder


@dataclass
class      lowercase__     (			_UpperCAmelCase	):
	a_         =42





class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =True

	@register_to_config
	def __init__(							self		,    __UpperCAmelCase = 3		,    __UpperCAmelCase = 3		,    __UpperCAmelCase = ("DownEncoderBlock2D",)		,    __UpperCAmelCase = ("UpDecoderBlock2D",)		,    __UpperCAmelCase = (64,)		,    __UpperCAmelCase = 1		,    __UpperCAmelCase = "silu"		,    __UpperCAmelCase = 4		,    __UpperCAmelCase = 32		,    __UpperCAmelCase = 32		,    __UpperCAmelCase = 0.18_215		,    )->					Tuple:







							'''simple docstring'''





							super().__init__()

							# pass init params to Encoder
							lowerCAmelCase__       =						Encoder(
							    in_channels=__UpperCAmelCase		,    out_channels=__UpperCAmelCase		,    down_block_types=__UpperCAmelCase		,    block_out_channels=__UpperCAmelCase		,    layers_per_block=__UpperCAmelCase		,    act_fn=__UpperCAmelCase		,    norm_num_groups=__UpperCAmelCase		,    double_z=__UpperCAmelCase		,    )

							# pass init params to Decoder
							lowerCAmelCase__       =						Decoder(
							    in_channels=__UpperCAmelCase		,    out_channels=__UpperCAmelCase		,    up_block_types=__UpperCAmelCase		,    block_out_channels=__UpperCAmelCase		,    layers_per_block=__UpperCAmelCase		,    norm_num_groups=__UpperCAmelCase		,    act_fn=__UpperCAmelCase		,    )

							lowerCAmelCase__       =						nn.Convad(2 * latent_channels		,    2 * latent_channels		,    1       )
							lowerCAmelCase__       =						nn.Convad(__UpperCAmelCase		,    __UpperCAmelCase		,    1       )

							lowerCAmelCase__       =						False
							lowerCAmelCase__       =						False

							# only relevant if vae tiling is enabled
							lowerCAmelCase__       =						self.config.sample_size
							lowerCAmelCase__       =						(
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size		,    (list, tuple)       )
							    else self.config.sample_size
							)
							lowerCAmelCase__       =						int(sample_size / (2 ** (len(self.config.block_out_channels       ) - 1))       )
							lowerCAmelCase__       =						0.25

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=False       )->					Optional[Any]:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase		,    (Encoder, Decoder)       ):
													lowerCAmelCase__       =						value

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase = True       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						use_tiling

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







							'''simple docstring'''





							self.enable_tiling(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						True

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						False

	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def 		UpperCAmelCase  (							self       )->					Dict[str, AttentionProcessor]:







							'''simple docstring'''





							lowerCAmelCase__       =						{}

							def fn_recursive_add_processors(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													if hasattr(__UpperCAmelCase		,    "set_processor"       ):
																			lowerCAmelCase__       =						module.processor

													for sub_name, child in module.named_children():
																			fn_recursive_add_processors(F"{name}.{sub_name}"		,    __UpperCAmelCase		,    __UpperCAmelCase       )

													return processors

							for name, module in self.named_children():
													fn_recursive_add_processors(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

							return processors

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						len(self.attn_processors.keys()       )

							if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) and len(__UpperCAmelCase       ) != count:
													raise ValueError(
													    F"A dict of processors was passed, but the number of processors {len(__UpperCAmelCase       )} does not match the"
													    F" number of attention layers: {count}. Please make sure to pass {count} processor classes."       )

							def fn_recursive_attn_processor(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													if hasattr(__UpperCAmelCase		,    "set_processor"       ):
																			if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									module.set_processor(__UpperCAmelCase       )
																			else:
																									module.set_processor(processor.pop(F"{name}.processor"       )       )

													for sub_name, child in module.named_children():
																			fn_recursive_attn_processor(F"{name}.{sub_name}"		,    __UpperCAmelCase		,    __UpperCAmelCase       )

							for name, module in self.named_children():
													fn_recursive_attn_processor(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							self.set_attn_processor(AttnProcessor()       )

	@apply_forward_hook
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = True       )->					AutoencoderKLOutput:







							'''simple docstring'''





							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
													return self.tiled_encode(__UpperCAmelCase		,    return_dict=__UpperCAmelCase       )

							if self.use_slicing and x.shape[0] > 1:
													lowerCAmelCase__       =						[self.encoder(__UpperCAmelCase       ) for x_slice in x.split(1       )]
													lowerCAmelCase__       =						torch.cat(__UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						self.encoder(__UpperCAmelCase       )

							lowerCAmelCase__       =						self.quant_conv(__UpperCAmelCase       )
							lowerCAmelCase__       =						DiagonalGaussianDistribution(__UpperCAmelCase       )

							if not return_dict:
													return (posterior,)

							return AutoencoderKLOutput(latent_dist=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = True       )->					Union[DecoderOutput, torch.FloatTensor]:







							'''simple docstring'''





							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
													return self.tiled_decode(__UpperCAmelCase		,    return_dict=__UpperCAmelCase       )

							lowerCAmelCase__       =						self.post_quant_conv(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.decoder(__UpperCAmelCase       )

							if not return_dict:
													return (dec,)

							return DecoderOutput(sample=__UpperCAmelCase       )

	@apply_forward_hook
	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = True       )->					Union[DecoderOutput, torch.FloatTensor]:







							'''simple docstring'''





							if self.use_slicing and z.shape[0] > 1:
													lowerCAmelCase__       =						[self._decode(__UpperCAmelCase       ).sample for z_slice in z.split(1       )]
													lowerCAmelCase__       =						torch.cat(__UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						self._decode(__UpperCAmelCase       ).sample

							if not return_dict:
													return (decoded,)

							return DecoderOutput(sample=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						min(a.shape[2]		,    b.shape[2]		,    __UpperCAmelCase       )
							for y in range(__UpperCAmelCase       ):
													lowerCAmelCase__       =						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						min(a.shape[3]		,    b.shape[3]		,    __UpperCAmelCase       )
							for x in range(__UpperCAmelCase       ):
													lowerCAmelCase__       =						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = True       )->					AutoencoderKLOutput:







							'''simple docstring'''





							lowerCAmelCase__       =						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)       )
							lowerCAmelCase__       =						int(self.tile_latent_min_size * self.tile_overlap_factor       )
							lowerCAmelCase__       =						self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							lowerCAmelCase__       =						[]
							for i in range(0		,    x.shape[2]		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						[]
													for j in range(0		,    x.shape[3]		,    __UpperCAmelCase       ):
																			lowerCAmelCase__       =						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																			lowerCAmelCase__       =						self.encoder(__UpperCAmelCase       )
																			lowerCAmelCase__       =						self.quant_conv(__UpperCAmelCase       )
																			row.append(__UpperCAmelCase       )
													rows.append(__UpperCAmelCase       )
							lowerCAmelCase__       =						[]
							for i, row in enumerate(__UpperCAmelCase       ):
													lowerCAmelCase__       =						[]
													for j, tile in enumerate(__UpperCAmelCase       ):
																			# blend the above tile and the left tile
																			# to the current tile and add the current tile to the result row
																			if i > 0:
																									lowerCAmelCase__       =						self.blend_v(rows[i - 1][j]		,    __UpperCAmelCase		,    __UpperCAmelCase       )
																			if j > 0:
																									lowerCAmelCase__       =						self.blend_h(row[j - 1]		,    __UpperCAmelCase		,    __UpperCAmelCase       )
																			result_row.append(tile[:, :, :row_limit, :row_limit]       )
													result_rows.append(torch.cat(__UpperCAmelCase		,    dim=3       )       )

							lowerCAmelCase__       =						torch.cat(__UpperCAmelCase		,    dim=2       )
							lowerCAmelCase__       =						DiagonalGaussianDistribution(__UpperCAmelCase       )

							if not return_dict:
													return (posterior,)

							return AutoencoderKLOutput(latent_dist=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = True       )->					Union[DecoderOutput, torch.FloatTensor]:







							'''simple docstring'''





							lowerCAmelCase__       =						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)       )
							lowerCAmelCase__       =						int(self.tile_sample_min_size * self.tile_overlap_factor       )
							lowerCAmelCase__       =						self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							lowerCAmelCase__       =						[]
							for i in range(0		,    z.shape[2]		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						[]
													for j in range(0		,    z.shape[3]		,    __UpperCAmelCase       ):
																			lowerCAmelCase__       =						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																			lowerCAmelCase__       =						self.post_quant_conv(__UpperCAmelCase       )
																			lowerCAmelCase__       =						self.decoder(__UpperCAmelCase       )
																			row.append(__UpperCAmelCase       )
													rows.append(__UpperCAmelCase       )
							lowerCAmelCase__       =						[]
							for i, row in enumerate(__UpperCAmelCase       ):
													lowerCAmelCase__       =						[]
													for j, tile in enumerate(__UpperCAmelCase       ):
																			# blend the above tile and the left tile
																			# to the current tile and add the current tile to the result row
																			if i > 0:
																									lowerCAmelCase__       =						self.blend_v(rows[i - 1][j]		,    __UpperCAmelCase		,    __UpperCAmelCase       )
																			if j > 0:
																									lowerCAmelCase__       =						self.blend_h(row[j - 1]		,    __UpperCAmelCase		,    __UpperCAmelCase       )
																			result_row.append(tile[:, :, :row_limit, :row_limit]       )
													result_rows.append(torch.cat(__UpperCAmelCase		,    dim=3       )       )

							lowerCAmelCase__       =						torch.cat(__UpperCAmelCase		,    dim=2       )
							if not return_dict:
													return (dec,)

							return DecoderOutput(sample=__UpperCAmelCase       )


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = False		,    __UpperCAmelCase = True		,    __UpperCAmelCase = None		,    )->					Union[DecoderOutput, torch.FloatTensor]:







							'''simple docstring'''





							lowerCAmelCase__       =						sample
							lowerCAmelCase__       =						self.encode(__UpperCAmelCase       ).latent_dist
							if sample_posterior:
													lowerCAmelCase__       =						posterior.sample(generator=__UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						posterior.mode()
							lowerCAmelCase__       =						self.decode(__UpperCAmelCase       ).sample

							if not return_dict:
													return (dec,)

							return DecoderOutput(sample=__UpperCAmelCase       )














import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Tuple=0.999    ,   UpperCamelCase_					:				Union[str, Any]="cosine"    ,   ) ->   str:
						"""simple docstring"""







						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCamelCase_					:				List[Any]   ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

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

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

						lowerCAmelCase__       =						[]
						for i in range(UpperCamelCase_   ):
												lowerCAmelCase__       =						i / num_diffusion_timesteps
												lowerCAmelCase__       =						(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      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase	):
	a_         =[e.name for e in KarrasDiffusionSchedulers]
	a_         =2

	@register_to_config
	def __init__(							self		,    __UpperCAmelCase = 1000		,    __UpperCAmelCase = 0.00_085		,    __UpperCAmelCase = 0.012		,    __UpperCAmelCase = "linear"		,    __UpperCAmelCase = None		,    __UpperCAmelCase = "epsilon"		,    __UpperCAmelCase = "linspace"		,    __UpperCAmelCase = 0		,    )->					int:







							'''simple docstring'''





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

							#  set all values
							self.set_timesteps(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					str:







							'''simple docstring'''





							if schedule_timesteps is None:
													lowerCAmelCase__       =						self.timesteps

							lowerCAmelCase__       =						(schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter       ) == 0:
													lowerCAmelCase__       =						1 if len(__UpperCAmelCase       ) > 1 else 0
							else:
													lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
													lowerCAmelCase__       =						self._index_counter[timestep_int]

							return indices[pos].item()

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





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

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
							else:
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]

							lowerCAmelCase__       =						sample / ((sigma**2 + 1) ** 0.5)
							return sample

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						num_inference_steps

							lowerCAmelCase__       =						num_train_timesteps or self.config.num_train_timesteps

							# "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		,    num_train_timesteps - 1		,    __UpperCAmelCase		,    dtype=__UpperCAmelCase       )[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													lowerCAmelCase__       =						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		,    __UpperCAmelCase       ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase       )
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowerCAmelCase__       =						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(__UpperCAmelCase		,    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'."       )

							lowerCAmelCase__       =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
							lowerCAmelCase__       =						torch.from_numpy(np.log(__UpperCAmelCase       )       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						np.interp(__UpperCAmelCase		,    np.arange(0		,    len(__UpperCAmelCase       )       )		,    __UpperCAmelCase       )
							lowerCAmelCase__       =						np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
							lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(device=__UpperCAmelCase       )

							# interpolate sigmas
							lowerCAmelCase__       =						sigmas.log().lerp(sigmas.roll(1       ).log()		,    0.5       ).exp()

							lowerCAmelCase__       =						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
							lowerCAmelCase__       =						torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

							if str(__UpperCAmelCase       ).startswith("mps"       ):
													# mps does not support float64
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=torch.floataa       )
							else:
													lowerCAmelCase__       =						torch.from_numpy(__UpperCAmelCase       ).to(__UpperCAmelCase       )

							# interpolate timesteps
							lowerCAmelCase__       =						self.sigma_to_t(__UpperCAmelCase       ).to(__UpperCAmelCase		,    dtype=timesteps.dtype       )
							lowerCAmelCase__       =						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,    dim=-1       ).flatten()

							lowerCAmelCase__       =						torch.cat([timesteps[:1], interleaved_timesteps]       )

							lowerCAmelCase__       =						None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							lowerCAmelCase__       =						defaultdict(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						sigma.log()

							# get distribution
							lowerCAmelCase__       =						log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							lowerCAmelCase__       =						dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
							lowerCAmelCase__       =						low_idx + 1

							lowerCAmelCase__       =						self.log_sigmas[low_idx]
							lowerCAmelCase__       =						self.log_sigmas[high_idx]

							# interpolate sigmas
							lowerCAmelCase__       =						(low - log_sigma) / (low - high)
							lowerCAmelCase__       =						w.clamp(0		,    1       )

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

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return self.sample is None

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = True		,    )->					Union[SchedulerOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.index_for_timestep(__UpperCAmelCase       )

							# advance index counter by 1
							lowerCAmelCase__       =						timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase       ) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													lowerCAmelCase__       =						self.sigmas[step_index]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index + 1]
													lowerCAmelCase__       =						self.sigmas[step_index + 1]
							else:
													# 2nd order / KDPM2's method
													lowerCAmelCase__       =						self.sigmas[step_index - 1]
													lowerCAmelCase__       =						self.sigmas_interpol[step_index]
													lowerCAmelCase__       =						self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							lowerCAmelCase__       =						0
							lowerCAmelCase__       =						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													lowerCAmelCase__       =						sigma_hat if self.state_in_first_order else sigma_interpol
													lowerCAmelCase__       =						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**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`"       )

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													lowerCAmelCase__       =						sigma_interpol - sigma_hat

													# store for 2nd order step
													lowerCAmelCase__       =						sample
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													lowerCAmelCase__       =						(sample - pred_original_sample) / sigma_interpol

													# 3. delta timestep
													lowerCAmelCase__       =						sigma_next - sigma_hat

													lowerCAmelCase__       =						self.sample
													lowerCAmelCase__       =						None

							lowerCAmelCase__       =						sample + derivative * dt

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    )->					torch.FloatTensor:







							'''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(__UpperCAmelCase       ):
													# 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__       =						[self.index_for_timestep(__UpperCAmelCase		,    __UpperCAmelCase       ) 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       )->					List[Any]:







							'''simple docstring'''





							return self.config.num_train_timesteps






