



'''simple docstring'''







from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class 						_SCREAMING_SNAKE_CASE      ( _a						):






			"""simple docstring"""







			SCREAMING_SNAKE_CASE_   			=				42





class 						_SCREAMING_SNAKE_CASE      ( nn.Module						):






			"""simple docstring"""







			def __init__(   self       , _snake_case=3       , _snake_case=3       , _snake_case=("DownEncoderBlock2D",)       , _snake_case=(64,)       , _snake_case=2       , _snake_case=32       , _snake_case="silu"       , _snake_case=True       , ):







				"""simple docstring"""



				super().__init__()
				__lowerCamelCase		       =		layers_per_block

				__lowerCamelCase		       =		torch.nn.Convad(
				    lowerCAmelCase_       , block_out_channels[0]       , kernel_size=3       , stride=1       , padding=1       , )

				__lowerCamelCase		       =		None
				__lowerCamelCase		       =		nn.ModuleList([]    )

				# down
				__lowerCamelCase		       =		block_out_channels[0]
				for i, down_block_type in enumerate(lowerCAmelCase_    ):
					__lowerCamelCase		       =		output_channel
					__lowerCamelCase		       =		block_out_channels[i]
					__lowerCamelCase		       =		i == len(lowerCAmelCase_    ) - 1

					__lowerCamelCase		       =		get_down_block(
					    lowerCAmelCase_       , num_layers=self.layers_per_block       , in_channels=lowerCAmelCase_       , out_channels=lowerCAmelCase_       , add_downsample=not is_final_block       , resnet_eps=1E-6       , downsample_padding=0       , resnet_act_fn=lowerCAmelCase_       , resnet_groups=lowerCAmelCase_       , attention_head_dim=lowerCAmelCase_       , temb_channels=lowerCAmelCase_       , )
					self.down_blocks.append(lowerCAmelCase_    )

				# mid
				__lowerCamelCase		       =		UNetMidBlockaD(
				    in_channels=block_out_channels[-1]       , resnet_eps=1E-6       , resnet_act_fn=lowerCAmelCase_       , output_scale_factor=1       , resnet_time_scale_shift='''default'''       , attention_head_dim=block_out_channels[-1]       , resnet_groups=lowerCAmelCase_       , temb_channels=lowerCAmelCase_       , )

				# out
				__lowerCamelCase		       =		nn.GroupNorm(num_channels=block_out_channels[-1]       , num_groups=lowerCAmelCase_       , eps=1E-6    )
				__lowerCamelCase		       =		nn.SiLU()

				__lowerCamelCase		       =		2 * out_channels if double_z else out_channels
				__lowerCamelCase		       =		nn.Convad(block_out_channels[-1]       , lowerCAmelCase_       , 3       , padding=1    )

				__lowerCamelCase		       =		False


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		x
				__lowerCamelCase		       =		self.conv_in(lowerCAmelCase_    )

				if self.training and self.gradient_checkpointing:

					def create_custom_forward(_snake_case    ):
						def custom_forward(*_snake_case    ):
							return module(*lowerCAmelCase_    )

						return custom_forward

					# down
					if is_torch_version('''>='''       , '''1.11.0'''    ):
						for down_block in self.down_blocks:
							__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(
							    create_custom_forward(lowerCAmelCase_    )       , lowerCAmelCase_       , use_reentrant=lowerCAmelCase_    )
						# middle
						__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block    )       , lowerCAmelCase_       , use_reentrant=lowerCAmelCase_    )
					else:
						for down_block in self.down_blocks:
							__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_    )       , lowerCAmelCase_    )
						# middle
						__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block    )       , lowerCAmelCase_    )

				else:
					# down
					for down_block in self.down_blocks:
						__lowerCamelCase		       =		down_block(lowerCAmelCase_    )

					# middle
					__lowerCamelCase		       =		self.mid_block(lowerCAmelCase_    )

				# post-process
				__lowerCamelCase		       =		self.conv_norm_out(lowerCAmelCase_    )
				__lowerCamelCase		       =		self.conv_act(lowerCAmelCase_    )
				__lowerCamelCase		       =		self.conv_out(lowerCAmelCase_    )

				return sample





class 						_SCREAMING_SNAKE_CASE      ( nn.Module						):






			"""simple docstring"""







			def __init__(   self       , _snake_case=3       , _snake_case=3       , _snake_case=("UpDecoderBlock2D",)       , _snake_case=(64,)       , _snake_case=2       , _snake_case=32       , _snake_case="silu"       , _snake_case="group"       , ):







				"""simple docstring"""



				super().__init__()
				__lowerCamelCase		       =		layers_per_block

				__lowerCamelCase		       =		nn.Convad(
				    lowerCAmelCase_       , block_out_channels[-1]       , kernel_size=3       , stride=1       , padding=1       , )

				__lowerCamelCase		       =		None
				__lowerCamelCase		       =		nn.ModuleList([]    )

				__lowerCamelCase		       =		in_channels if norm_type == '''spatial''' else None

				# mid
				__lowerCamelCase		       =		UNetMidBlockaD(
				    in_channels=block_out_channels[-1]       , resnet_eps=1E-6       , resnet_act_fn=lowerCAmelCase_       , output_scale_factor=1       , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type       , attention_head_dim=block_out_channels[-1]       , resnet_groups=lowerCAmelCase_       , temb_channels=lowerCAmelCase_       , )

				# up
				__lowerCamelCase		       =		list(reversed(lowerCAmelCase_    )    )
				__lowerCamelCase		       =		reversed_block_out_channels[0]
				for i, up_block_type in enumerate(lowerCAmelCase_    ):
					__lowerCamelCase		       =		output_channel
					__lowerCamelCase		       =		reversed_block_out_channels[i]

					__lowerCamelCase		       =		i == len(lowerCAmelCase_    ) - 1

					__lowerCamelCase		       =		get_up_block(
					    lowerCAmelCase_       , num_layers=self.layers_per_block + 1       , in_channels=lowerCAmelCase_       , out_channels=lowerCAmelCase_       , prev_output_channel=lowerCAmelCase_       , add_upsample=not is_final_block       , resnet_eps=1E-6       , resnet_act_fn=lowerCAmelCase_       , resnet_groups=lowerCAmelCase_       , attention_head_dim=lowerCAmelCase_       , temb_channels=lowerCAmelCase_       , resnet_time_scale_shift=lowerCAmelCase_       , )
					self.up_blocks.append(lowerCAmelCase_    )
					__lowerCamelCase		       =		output_channel

				# out
				if norm_type == "spatial":
					__lowerCamelCase		       =		SpatialNorm(block_out_channels[0]       , lowerCAmelCase_    )
				else:
					__lowerCamelCase		       =		nn.GroupNorm(num_channels=block_out_channels[0]       , num_groups=lowerCAmelCase_       , eps=1E-6    )
				__lowerCamelCase		       =		nn.SiLU()
				__lowerCamelCase		       =		nn.Convad(block_out_channels[0]       , lowerCAmelCase_       , 3       , padding=1    )

				__lowerCamelCase		       =		False


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case=None    ):







				"""simple docstring"""



				__lowerCamelCase		       =		z
				__lowerCamelCase		       =		self.conv_in(lowerCAmelCase_    )

				__lowerCamelCase		       =		next(iter(self.up_blocks.parameters()    )    ).dtype
				if self.training and self.gradient_checkpointing:

					def create_custom_forward(_snake_case    ):
						def custom_forward(*_snake_case    ):
							return module(*lowerCAmelCase_    )

						return custom_forward

					if is_torch_version('''>='''       , '''1.11.0'''    ):
						# middle
						__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block    )       , lowerCAmelCase_       , lowerCAmelCase_       , use_reentrant=lowerCAmelCase_    )
						__lowerCamelCase		       =		sample.to(lowerCAmelCase_    )

						# up
						for up_block in self.up_blocks:
							__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(
							    create_custom_forward(lowerCAmelCase_    )       , lowerCAmelCase_       , lowerCAmelCase_       , use_reentrant=lowerCAmelCase_    )
					else:
						# middle
						__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(
						    create_custom_forward(self.mid_block    )       , lowerCAmelCase_       , lowerCAmelCase_    )
						__lowerCamelCase		       =		sample.to(lowerCAmelCase_    )

						# up
						for up_block in self.up_blocks:
							__lowerCamelCase		       =		torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase_    )       , lowerCAmelCase_       , lowerCAmelCase_    )
				else:
					# middle
					__lowerCamelCase		       =		self.mid_block(lowerCAmelCase_       , lowerCAmelCase_    )
					__lowerCamelCase		       =		sample.to(lowerCAmelCase_    )

					# up
					for up_block in self.up_blocks:
						__lowerCamelCase		       =		up_block(lowerCAmelCase_       , lowerCAmelCase_    )

        # post-process
				if latent_embeds is None:
					__lowerCamelCase		       =		self.conv_norm_out(lowerCAmelCase_    )
				else:
					__lowerCamelCase		       =		self.conv_norm_out(lowerCAmelCase_       , lowerCAmelCase_    )
				__lowerCamelCase		       =		self.conv_act(lowerCAmelCase_    )
				__lowerCamelCase		       =		self.conv_out(lowerCAmelCase_    )

				return sample





class 						_SCREAMING_SNAKE_CASE      ( nn.Module						):






			"""simple docstring"""







			def __init__(   self       , _snake_case       , _snake_case       , _snake_case       , _snake_case=None       , _snake_case="random"       , _snake_case=False       , _snake_case=True    ):







				"""simple docstring"""



				super().__init__()
				__lowerCamelCase		       =		n_e
				__lowerCamelCase		       =		vq_embed_dim
				__lowerCamelCase		       =		beta
				__lowerCamelCase		       =		legacy

				__lowerCamelCase		       =		nn.Embedding(self.n_e       , self.vq_embed_dim    )
				self.embedding.weight.data.uniform_(-1.0 / self.n_e       , 1.0 / self.n_e    )

				__lowerCamelCase		       =		remap
				if self.remap is not None:
					self.register_buffer('''used'''       , torch.tensor(np.load(self.remap    )    )    )
					__lowerCamelCase		       =		self.used.shape[0]
					__lowerCamelCase		       =		unknown_index  # "random" or "extra" or integer
					if self.unknown_index == "extra":
						__lowerCamelCase		       =		self.re_embed
						__lowerCamelCase		       =		self.re_embed + 1
					print(
					    F'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
					    F'''Using {self.unknown_index} for unknown indices.'''    )
				else:
					__lowerCamelCase		       =		n_e

				__lowerCamelCase		       =		sane_index_shape


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		inds.shape
				assert len(lowerCAmelCase_    ) > 1
				__lowerCamelCase		       =		inds.reshape(ishape[0]       , -1    )
				__lowerCamelCase		       =		self.used.to(lowerCAmelCase_    )
				__lowerCamelCase		       =		(inds[:, :, None] == used[None, None, ...]).long()
				__lowerCamelCase		       =		match.argmax(-1    )
				__lowerCamelCase		       =		match.sum(2    ) < 1
				if self.unknown_index == "random":
					__lowerCamelCase		       =		torch.randint(0       , self.re_embed       , size=new[unknown].shape    ).to(device=new.device    )
				else:
					__lowerCamelCase		       =		self.unknown_index
				return new.reshape(lowerCAmelCase_    )


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		inds.shape
				assert len(lowerCAmelCase_    ) > 1
				__lowerCamelCase		       =		inds.reshape(ishape[0]       , -1    )
				__lowerCamelCase		       =		self.used.to(lowerCAmelCase_    )
				if self.re_embed > self.used.shape[0]:  # extra token
					__lowerCamelCase		       =		0  # simply set to zero
				__lowerCamelCase		       =		torch.gather(used[None, :][inds.shape[0] * [0], :]       , 1       , lowerCAmelCase_    )
				return back.reshape(lowerCAmelCase_    )


			def 						_lowerCamelCase       (   self       , _snake_case    ):







				"""simple docstring"""



				__lowerCamelCase		       =		z.permute(0       , 2       , 3       , 1    ).contiguous()
				__lowerCamelCase		       =		z.view(-1       , self.vq_embed_dim    )

				# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
				__lowerCamelCase		       =		torch.argmin(torch.cdist(lowerCAmelCase_       , self.embedding.weight    )       , dim=1    )

				__lowerCamelCase		       =		self.embedding(lowerCAmelCase_    ).view(z.shape    )
				__lowerCamelCase		       =		None
				__lowerCamelCase		       =		None

				# compute loss for embedding
				if not self.legacy:
					__lowerCamelCase		       =		self.beta * torch.mean((z_q.detach() - z) ** 2    ) + torch.mean((z_q - z.detach()) ** 2    )
				else:
					__lowerCamelCase		       =		torch.mean((z_q.detach() - z) ** 2    ) + self.beta * torch.mean((z_q - z.detach()) ** 2    )

				# preserve gradients
				__lowerCamelCase		       =		z + (z_q - z).detach()

				# reshape back to match original input shape
				__lowerCamelCase		       =		z_q.permute(0       , 3       , 1       , 2    ).contiguous()

				if self.remap is not None:
					__lowerCamelCase		       =		min_encoding_indices.reshape(z.shape[0]       , -1    )  # add batch axis
					__lowerCamelCase		       =		self.remap_to_used(lowerCAmelCase_    )
					__lowerCamelCase		       =		min_encoding_indices.reshape(-1       , 1    )  # flatten

				if self.sane_index_shape:
					__lowerCamelCase		       =		min_encoding_indices.reshape(z_q.shape[0]       , z_q.shape[2]       , z_q.shape[3]    )

				return z_q, loss, (perplexity, min_encodings, min_encoding_indices)


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case    ):







				"""simple docstring"""



				if self.remap is not None:
					__lowerCamelCase		       =		indices.reshape(shape[0]       , -1    )  # add batch axis
					__lowerCamelCase		       =		self.unmap_to_all(lowerCAmelCase_    )
					__lowerCamelCase		       =		indices.reshape(-1    )  # flatten again

				# get quantized latent vectors
				__lowerCamelCase		       =		self.embedding(lowerCAmelCase_    )

				if shape is not None:
					__lowerCamelCase		       =		z_q.view(lowerCAmelCase_    )
					# reshape back to match original input shape
					__lowerCamelCase		       =		z_q.permute(0       , 3       , 1       , 2    ).contiguous()

				return z_q





class 						_SCREAMING_SNAKE_CASE      ( _a						):






			"""simple docstring"""







			def __init__(   self       , _snake_case       , _snake_case=False    ):







				"""simple docstring"""



				__lowerCamelCase		       =		parameters
				__lowerCamelCase       ,	__lowerCamelCase		       =		torch.chunk(lowerCAmelCase_       , 2       , dim=1    )
				__lowerCamelCase		       =		torch.clamp(self.logvar       , -30.0       , 20.0    )
				__lowerCamelCase		       =		deterministic
				__lowerCamelCase		       =		torch.exp(0.5 * self.logvar    )
				__lowerCamelCase		       =		torch.exp(self.logvar    )
				if self.deterministic:
					__lowerCamelCase		       =		__lowerCamelCase		       =		torch.zeros_like(
					    self.mean       , device=self.parameters.device       , dtype=self.parameters.dtype    )


			def 						_lowerCamelCase       (   self       , _snake_case = None    ):







				"""simple docstring"""



				__lowerCamelCase		       =		randn_tensor(
				    self.mean.shape       , generator=lowerCAmelCase_       , device=self.parameters.device       , dtype=self.parameters.dtype    )
				__lowerCamelCase		       =		self.mean + self.std * sample
				return x


			def 						_lowerCamelCase       (   self       , _snake_case=None    ):







				"""simple docstring"""



				if self.deterministic:
					return torch.Tensor([0.0]    )
				else:
					if other is None:
						return 0.5 * torch.sum(torch.pow(self.mean       , 2    ) + self.var - 1.0 - self.logvar       , dim=[1, 2, 3]    )
					else:
						return 0.5 * torch.sum(
						    torch.pow(self.mean - other.mean       , 2    ) / other.var
						    + self.var / other.var
						    - 1.0
						    - self.logvar
						    + other.logvar       , dim=[1, 2, 3]       , )


			def 						_lowerCamelCase       (   self       , _snake_case       , _snake_case=[1, 2, 3]    ):







				"""simple docstring"""



				if self.deterministic:
					return torch.Tensor([0.0]    )
				__lowerCamelCase		       =		np.log(2.0 * np.pi    )
				return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       , 2    ) / self.var       , dim=lowerCAmelCase_    )


			def 						_lowerCamelCase       (   self    ):







				"""simple docstring"""



				return self.mean







'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







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


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







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


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

