code: 
  
  
  
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import torch
  import torch.nn as nn
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
  from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
  
  
  
  @dataclass
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   42
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = 3			,    SCREAMING_SNAKE_CASE = 3			,    SCREAMING_SNAKE_CASE = False			,    SCREAMING_SNAKE_CASE = "positional"			,    SCREAMING_SNAKE_CASE = 0			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,    SCREAMING_SNAKE_CASE = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,    SCREAMING_SNAKE_CASE = (224, 448, 672, 896)			,    SCREAMING_SNAKE_CASE = 2			,    SCREAMING_SNAKE_CASE = 1			,    SCREAMING_SNAKE_CASE = 1			,    SCREAMING_SNAKE_CASE = "conv"			,    SCREAMING_SNAKE_CASE = "conv"			,    SCREAMING_SNAKE_CASE = "silu"			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = 32			,    SCREAMING_SNAKE_CASE = 1e-5			,    SCREAMING_SNAKE_CASE = "default"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE_			=	sample_size
  				SCREAMING_SNAKE_CASE_			=	block_out_channels[0] * 4
  
  				# Check inputs
  				if len(SCREAMING_SNAKE_CASE			) != len(SCREAMING_SNAKE_CASE			):
  							raise ValueError(
  							    f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'			)
  
  				if len(SCREAMING_SNAKE_CASE			) != len(SCREAMING_SNAKE_CASE			):
  							raise ValueError(
  							    f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'			)
  
  				# input
  				SCREAMING_SNAKE_CASE_			=	nn.Convad(SCREAMING_SNAKE_CASE			,    block_out_channels[0]			,    kernel_size=3			,    padding=(1, 1)			)
  
  				# time
  				if time_embedding_type == "fourier":
  							SCREAMING_SNAKE_CASE_			=	GaussianFourierProjection(embedding_size=block_out_channels[0]			,    scale=16			)
  							SCREAMING_SNAKE_CASE_			=	2 * block_out_channels[0]
  				elif time_embedding_type == "positional":
  							SCREAMING_SNAKE_CASE_			=	Timesteps(block_out_channels[0]			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	block_out_channels[0]
  
  				SCREAMING_SNAKE_CASE_			=	TimestepEmbedding(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				# class embedding
  				if class_embed_type is None and num_class_embeds is not None:
  							SCREAMING_SNAKE_CASE_			=	nn.Embedding(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				elif class_embed_type == "timestep":
  							SCREAMING_SNAKE_CASE_			=	TimestepEmbedding(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				elif class_embed_type == "identity":
  							SCREAMING_SNAKE_CASE_			=	nn.Identity(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	None
  
  				SCREAMING_SNAKE_CASE_			=	nn.ModuleList([]			)
  				SCREAMING_SNAKE_CASE_			=	None
  				SCREAMING_SNAKE_CASE_			=	nn.ModuleList([]			)
  
  				# down
  				SCREAMING_SNAKE_CASE_			=	block_out_channels[0]
  				for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	output_channel
  							SCREAMING_SNAKE_CASE_			=	block_out_channels[i]
  							SCREAMING_SNAKE_CASE_			=	i == len(SCREAMING_SNAKE_CASE			) - 1
  
  							SCREAMING_SNAKE_CASE_			=	get_down_block(
  							    SCREAMING_SNAKE_CASE			,    num_layers=SCREAMING_SNAKE_CASE			,    in_channels=SCREAMING_SNAKE_CASE			,    out_channels=SCREAMING_SNAKE_CASE			,    temb_channels=SCREAMING_SNAKE_CASE			,    add_downsample=not is_final_block			,    resnet_eps=SCREAMING_SNAKE_CASE			,    resnet_act_fn=SCREAMING_SNAKE_CASE			,    resnet_groups=SCREAMING_SNAKE_CASE			,    attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,    downsample_padding=SCREAMING_SNAKE_CASE			,    resnet_time_scale_shift=SCREAMING_SNAKE_CASE			,    downsample_type=SCREAMING_SNAKE_CASE			,    )
  							self.down_blocks.append(SCREAMING_SNAKE_CASE			)
  
  				# mid
  				SCREAMING_SNAKE_CASE_			=	UNetMidBlockaD(
  				    in_channels=block_out_channels[-1]			,    temb_channels=SCREAMING_SNAKE_CASE			,    resnet_eps=SCREAMING_SNAKE_CASE			,    resnet_act_fn=SCREAMING_SNAKE_CASE			,    output_scale_factor=SCREAMING_SNAKE_CASE			,    resnet_time_scale_shift=SCREAMING_SNAKE_CASE			,    attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,    resnet_groups=SCREAMING_SNAKE_CASE			,    add_attention=SCREAMING_SNAKE_CASE			,    )
  
  				# up
  				SCREAMING_SNAKE_CASE_			=	list(reversed(SCREAMING_SNAKE_CASE			)			)
  				SCREAMING_SNAKE_CASE_			=	reversed_block_out_channels[0]
  				for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	output_channel
  							SCREAMING_SNAKE_CASE_			=	reversed_block_out_channels[i]
  							SCREAMING_SNAKE_CASE_			=	reversed_block_out_channels[min(i + 1			,    len(SCREAMING_SNAKE_CASE			) - 1			)]
  
  							SCREAMING_SNAKE_CASE_			=	i == len(SCREAMING_SNAKE_CASE			) - 1
  
  							SCREAMING_SNAKE_CASE_			=	get_up_block(
  							    SCREAMING_SNAKE_CASE			,    num_layers=layers_per_block + 1			,    in_channels=SCREAMING_SNAKE_CASE			,    out_channels=SCREAMING_SNAKE_CASE			,    prev_output_channel=SCREAMING_SNAKE_CASE			,    temb_channels=SCREAMING_SNAKE_CASE			,    add_upsample=not is_final_block			,    resnet_eps=SCREAMING_SNAKE_CASE			,    resnet_act_fn=SCREAMING_SNAKE_CASE			,    resnet_groups=SCREAMING_SNAKE_CASE			,    attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,    resnet_time_scale_shift=SCREAMING_SNAKE_CASE			,    upsample_type=SCREAMING_SNAKE_CASE			,    )
  							self.up_blocks.append(SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	output_channel
  
  				# out
  				SCREAMING_SNAKE_CASE_			=	norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,    32			)
  				SCREAMING_SNAKE_CASE_			=	nn.GroupNorm(num_channels=block_out_channels[0]			,    num_groups=SCREAMING_SNAKE_CASE			,    eps=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	nn.SiLU()
  				SCREAMING_SNAKE_CASE_			=	nn.Convad(block_out_channels[0]			,    SCREAMING_SNAKE_CASE			,    kernel_size=3			,    padding=1			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = True			,    )     ->				Union[UNetaDOutput, Tuple]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.config.center_input_sample:
  							SCREAMING_SNAKE_CASE_			=	2 * sample - 1.0
  
  				# 1. time
  				SCREAMING_SNAKE_CASE_			=	timestep
  				if not torch.is_tensor(SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	torch.tensor([timesteps]			,    dtype=torch.long			,    device=sample.device			)
  				elif torch.is_tensor(SCREAMING_SNAKE_CASE			) and len(timesteps.shape			) == 0:
  							SCREAMING_SNAKE_CASE_			=	timesteps[None].to(sample.device			)
  
  				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  				SCREAMING_SNAKE_CASE_			=	timesteps * torch.ones(sample.shape[0]			,    dtype=timesteps.dtype			,    device=timesteps.device			)
  
  				SCREAMING_SNAKE_CASE_			=	self.time_proj(SCREAMING_SNAKE_CASE			)
  
  				# timesteps does not contain any weights and will always return f32 tensors
  				# but time_embedding might actually be running in fp16. so we need to cast here.
  				# there might be better ways to encapsulate this.
  				SCREAMING_SNAKE_CASE_			=	t_emb.to(dtype=self.dtype			)
  				SCREAMING_SNAKE_CASE_			=	self.time_embedding(SCREAMING_SNAKE_CASE			)
  
  				if self.class_embedding is not None:
  							if class_labels is None:
  										raise ValueError('class_labels should be provided when doing class conditioning'			)
  
  							if self.config.class_embed_type == "timestep":
  										SCREAMING_SNAKE_CASE_			=	self.time_proj(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_			=	self.class_embedding(SCREAMING_SNAKE_CASE			).to(dtype=self.dtype			)
  							SCREAMING_SNAKE_CASE_			=	emb + class_emb
  
  				# 2. pre-process
  				SCREAMING_SNAKE_CASE_			=	sample
  				SCREAMING_SNAKE_CASE_			=	self.conv_in(SCREAMING_SNAKE_CASE			)
  
  				# 3. down
  				SCREAMING_SNAKE_CASE_			=	(sample,)
  				for downsample_block in self.down_blocks:
  							if hasattr(SCREAMING_SNAKE_CASE			,    'skip_conv'			):
  										SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	downsample_block(
  										    hidden_states=SCREAMING_SNAKE_CASE			,    temb=SCREAMING_SNAKE_CASE			,    skip_sample=SCREAMING_SNAKE_CASE			)
  							else:
  										SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	downsample_block(hidden_states=SCREAMING_SNAKE_CASE			,    temb=SCREAMING_SNAKE_CASE			)
  
  							down_block_res_samples += res_samples
  
  				# 4. mid
  				SCREAMING_SNAKE_CASE_			=	self.mid_block(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				# 5. up
  				SCREAMING_SNAKE_CASE_			=	None
  				for upsample_block in self.up_blocks:
  							SCREAMING_SNAKE_CASE_			=	down_block_res_samples[-len(upsample_block.resnets			) :]
  							SCREAMING_SNAKE_CASE_			=	down_block_res_samples[: -len(upsample_block.resnets			)]
  
  							if hasattr(SCREAMING_SNAKE_CASE			,    'skip_conv'			):
  										SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	upsample_block(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  							else:
  										SCREAMING_SNAKE_CASE_			=	upsample_block(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
          # 6. post-process
  				SCREAMING_SNAKE_CASE_			=	self.conv_norm_out(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.conv_act(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.conv_out(SCREAMING_SNAKE_CASE			)
  
  				if skip_sample is not None:
  							sample += skip_sample
  
  				if self.config.time_embedding_type == "fourier":
  							SCREAMING_SNAKE_CASE_			=	timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]			)))			)
  							SCREAMING_SNAKE_CASE_			=	sample / timesteps
  
  				if not return_dict:
  							return (sample,)
  
  				return UNetaDOutput(sample=SCREAMING_SNAKE_CASE			)
  
style_context: 
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
      "{processor_class}": "FakeProcessorClass",
      "{model_class}": "FakeModelClass",
      "{object_class}": "FakeObjectClass",
  }
  