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 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				torch.FloatTensor
  
  
  
  
  
  
  
  class 					__lowercase					(       a_ ,       a_     ):
  
  
  
  		"""simple docstring"""
  
  		@register_to_config
  		def __init__(   self		,			A = None		,			A = 3		,			A = 3		,			A = False		,			A = "positional"		,			A = 0		,			A = True		,			A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,			A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,			A = (2_24, 4_48, 6_72, 8_96)		,			A = 2		,			A = 1		,			A = 1		,			A = "conv"		,			A = "conv"		,			A = "silu"		,			A = 8		,			A = 32		,			A = 1e-5		,			A = "default"		,			A = True		,			A = None		,			A = None		,			)     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					super().__init__()
  
  					lowerCamelCase								=     sample_size
  					lowerCamelCase								=     block_out_channels[0] * 4
  
  					# Check inputs
  					if len(A  ) != len(A  ):
  								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(A  ) != len(A  ):
  								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
  					lowerCamelCase								=     nn.Convad(A		,			block_out_channels[0]		,			kernel_size=3		,			padding=(1, 1)  )
  
  					# time
  					if time_embedding_type == "fourier":
  								lowerCamelCase								=     GaussianFourierProjection(embedding_size=block_out_channels[0]		,			scale=16  )
  								lowerCamelCase								=     2 * block_out_channels[0]
  					elif time_embedding_type == "positional":
  								lowerCamelCase								=     Timesteps(block_out_channels[0]		,			A		,			A  )
  								lowerCamelCase								=     block_out_channels[0]
  
  					lowerCamelCase								=     TimestepEmbedding(A		,			A  )
  
  					# class embedding
  					if class_embed_type is None and num_class_embeds is not None:
  								lowerCamelCase								=     nn.Embedding(A		,			A  )
  					elif class_embed_type == "timestep":
  								lowerCamelCase								=     TimestepEmbedding(A		,			A  )
  					elif class_embed_type == "identity":
  								lowerCamelCase								=     nn.Identity(A		,			A  )
  					else:
  								lowerCamelCase								=     None
  
  					lowerCamelCase								=     nn.ModuleList([]  )
  					lowerCamelCase								=     None
  					lowerCamelCase								=     nn.ModuleList([]  )
  
  					# down
  					lowerCamelCase								=     block_out_channels[0]
  					for i, down_block_type in enumerate(A  ):
  								lowerCamelCase								=     output_channel
  								lowerCamelCase								=     block_out_channels[i]
  								lowerCamelCase								=     i == len(A  ) - 1
  
  								lowerCamelCase								=     get_down_block(
  								    A		,			num_layers=A		,			in_channels=A		,			out_channels=A		,			temb_channels=A		,			add_downsample=not is_final_block		,			resnet_eps=A		,			resnet_act_fn=A		,			resnet_groups=A		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,			downsample_padding=A		,			resnet_time_scale_shift=A		,			downsample_type=A		,			)
  								self.down_blocks.append(A  )
  
  					# mid
  					lowerCamelCase								=     UNetMidBlockaD(
  					    in_channels=block_out_channels[-1]		,			temb_channels=A		,			resnet_eps=A		,			resnet_act_fn=A		,			output_scale_factor=A		,			resnet_time_scale_shift=A		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,			resnet_groups=A		,			add_attention=A		,			)
  
  					# up
  					lowerCamelCase								=     list(reversed(A  )  )
  					lowerCamelCase								=     reversed_block_out_channels[0]
  					for i, up_block_type in enumerate(A  ):
  								lowerCamelCase								=     output_channel
  								lowerCamelCase								=     reversed_block_out_channels[i]
  								lowerCamelCase								=     reversed_block_out_channels[min(i + 1		,			len(A  ) - 1  )]
  
  								lowerCamelCase								=     i == len(A  ) - 1
  
  								lowerCamelCase								=     get_up_block(
  								    A		,			num_layers=layers_per_block + 1		,			in_channels=A		,			out_channels=A		,			prev_output_channel=A		,			temb_channels=A		,			add_upsample=not is_final_block		,			resnet_eps=A		,			resnet_act_fn=A		,			resnet_groups=A		,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,			resnet_time_scale_shift=A		,			upsample_type=A		,			)
  								self.up_blocks.append(A  )
  								lowerCamelCase								=     output_channel
  
  					# out
  					lowerCamelCase								=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,			32  )
  					lowerCamelCase								=     nn.GroupNorm(num_channels=block_out_channels[0]		,			num_groups=A		,			eps=A  )
  					lowerCamelCase								=     nn.SiLU()
  					lowerCamelCase								=     nn.Convad(block_out_channels[0]		,			A		,			kernel_size=3		,			padding=1  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A = None		,			A = True		,			)     ->   Union[UNetaDOutput, Tuple]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.config.center_input_sample:
  								lowerCamelCase								=     2 * sample - 1.0
  
  					# 1. time
  					lowerCamelCase								=     timestep
  					if not torch.is_tensor(A  ):
  								lowerCamelCase								=     torch.tensor([timesteps]		,			dtype=torch.long		,			device=sample.device  )
  					elif torch.is_tensor(A  ) and len(timesteps.shape  ) == 0:
  								lowerCamelCase								=     timesteps[None].to(sample.device  )
  
  					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  					lowerCamelCase								=     timesteps * torch.ones(sample.shape[0]		,			dtype=timesteps.dtype		,			device=timesteps.device  )
  
  					lowerCamelCase								=     self.time_proj(A  )
  
  					# 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.
  					lowerCamelCase								=     t_emb.to(dtype=self.dtype  )
  					lowerCamelCase								=     self.time_embedding(A  )
  
  					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":
  											lowerCamelCase								=     self.time_proj(A  )
  
  								lowerCamelCase								=     self.class_embedding(A  ).to(dtype=self.dtype  )
  								lowerCamelCase								=     emb + class_emb
  
  					# 2. pre-process
  					lowerCamelCase								=     sample
  					lowerCamelCase								=     self.conv_in(A  )
  
  					# 3. down
  					lowerCamelCase								=     (sample,)
  					for downsample_block in self.down_blocks:
  								if hasattr(A		,			"""skip_conv"""  ):
  											lowerCamelCase								=     downsample_block(
  											    hidden_states=A		,			temb=A		,			skip_sample=A  )
  								else:
  											lowerCamelCase								=     downsample_block(hidden_states=A		,			temb=A  )
  
  								down_block_res_samples += res_samples
  
  					# 4. mid
  					lowerCamelCase								=     self.mid_block(A		,			A  )
  
  					# 5. up
  					lowerCamelCase								=     None
  					for upsample_block in self.up_blocks:
  								lowerCamelCase								=     down_block_res_samples[-len(upsample_block.resnets  ) :]
  								lowerCamelCase								=     down_block_res_samples[: -len(upsample_block.resnets  )]
  
  								if hasattr(A		,			"""skip_conv"""  ):
  											lowerCamelCase								=     upsample_block(A		,			A		,			A		,			A  )
  								else:
  											lowerCamelCase								=     upsample_block(A		,			A		,			A  )
  
          # 6. post-process
  					lowerCamelCase								=     self.conv_norm_out(A  )
  					lowerCamelCase								=     self.conv_act(A  )
  					lowerCamelCase								=     self.conv_out(A  )
  
  					if skip_sample is not None:
  								sample += skip_sample
  
  					if self.config.time_embedding_type == "fourier":
  								lowerCamelCase								=     timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]  )))  )
  								lowerCamelCase								=     sample / timesteps
  
  					if not return_dict:
  								return (sample,)
  
  					return UNetaDOutput(sample=A  )
  
  
style_context: 
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  UpperCAmelCase      :						List[Any]    =		TypeVar("T", bound="TaskTemplate")
  
  
  
  
  
  
  
  @dataclass(frozen=a_     )
  class 					__lowercase					(       abc.ABC     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				str
  		UpperCamelCase    :				ClassVar[Features]
  		UpperCamelCase    :				ClassVar[Features]
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   T:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return copy.deepcopy(self  )
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   Features:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return Features(**self.input_schema		,			**self.label_schema  )
  
  
  
  
  
  
  
  		@property
  		@abc.abstractmethod
  		def 				__A      (   self  )     ->   Dict[str, str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					raise NotImplementedError
  
  
  
  
  
  
  
  		@classmethod
  		def 				__A      (   cls		,			A  )     ->   T:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     {f.name for f in dataclasses.fields(cls  )}
  					return cls(**{k: v for k, v in template_dict.items() if k in field_names}  )
  
  