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_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       42
  
  
  
  
  
  
  class 					A_   (      _a ,     _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      @register_to_config
      def __init__(self     ,      lowercase__ = None     ,      lowercase__ = 3     ,      lowercase__ = 3     ,      lowercase__ = False     ,      lowercase__ = "positional"     ,      lowercase__ = 0     ,      lowercase__ = True     ,      lowercase__ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")     ,      lowercase__ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")     ,      lowercase__ = (224, 448, 672, 896)     ,      lowercase__ = 2     ,      lowercase__ = 1     ,      lowercase__ = 1     ,      lowercase__ = "conv"     ,      lowercase__ = "conv"     ,      lowercase__ = "silu"     ,      lowercase__ = 8     ,      lowercase__ = 32     ,      lowercase__ = 1E-5     ,      lowercase__ = "default"     ,      lowercase__ = True     ,      lowercase__ = None     ,      lowercase__ = None     ,      )     ->      Tuple:
          super().__init__()
  
          __UpperCAmelCase									=			sample_size
          __UpperCAmelCase									=			block_out_channels[0] * 4
  
          # Check inputs
          if len(lowercase__  ) != len(lowercase__  ):
              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(lowercase__  ) != len(lowercase__  ):
              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
          __UpperCAmelCase									=			nn.Convad(lowercase__     ,      block_out_channels[0]     ,      kernel_size=3     ,      padding=(1, 1)  )
  
          # time
          if time_embedding_type == "fourier":
              __UpperCAmelCase									=			GaussianFourierProjection(embedding_size=block_out_channels[0]     ,      scale=16  )
              __UpperCAmelCase									=			2 * block_out_channels[0]
          elif time_embedding_type == "positional":
              __UpperCAmelCase									=			Timesteps(block_out_channels[0]     ,      lowercase__     ,      lowercase__  )
              __UpperCAmelCase									=			block_out_channels[0]
  
          __UpperCAmelCase									=			TimestepEmbedding(lowercase__     ,      lowercase__  )
  
          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
              __UpperCAmelCase									=			nn.Embedding(lowercase__     ,      lowercase__  )
          elif class_embed_type == "timestep":
              __UpperCAmelCase									=			TimestepEmbedding(lowercase__     ,      lowercase__  )
          elif class_embed_type == "identity":
              __UpperCAmelCase									=			nn.Identity(lowercase__     ,      lowercase__  )
          else:
              __UpperCAmelCase									=			None
  
          __UpperCAmelCase									=			nn.ModuleList([]  )
          __UpperCAmelCase									=			None
          __UpperCAmelCase									=			nn.ModuleList([]  )
  
          # down
          __UpperCAmelCase									=			block_out_channels[0]
          for i, down_block_type in enumerate(lowercase__  ):
              __UpperCAmelCase									=			output_channel
              __UpperCAmelCase									=			block_out_channels[i]
              __UpperCAmelCase									=			i == len(lowercase__  ) - 1
  
              __UpperCAmelCase									=			get_down_block(
                  lowercase__     ,      num_layers=lowercase__     ,      in_channels=lowercase__     ,      out_channels=lowercase__     ,      temb_channels=lowercase__     ,      add_downsample=not is_final_block     ,      resnet_eps=lowercase__     ,      resnet_act_fn=lowercase__     ,      resnet_groups=lowercase__     ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,      downsample_padding=lowercase__     ,      resnet_time_scale_shift=lowercase__     ,      downsample_type=lowercase__     ,      )
              self.down_blocks.append(lowercase__  )
  
          # mid
          __UpperCAmelCase									=			UNetMidBlockaD(
              in_channels=block_out_channels[-1]     ,      temb_channels=lowercase__     ,      resnet_eps=lowercase__     ,      resnet_act_fn=lowercase__     ,      output_scale_factor=lowercase__     ,      resnet_time_scale_shift=lowercase__     ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]     ,      resnet_groups=lowercase__     ,      add_attention=lowercase__     ,      )
  
          # up
          __UpperCAmelCase									=			list(reversed(lowercase__  )  )
          __UpperCAmelCase									=			reversed_block_out_channels[0]
          for i, up_block_type in enumerate(lowercase__  ):
              __UpperCAmelCase									=			output_channel
              __UpperCAmelCase									=			reversed_block_out_channels[i]
              __UpperCAmelCase									=			reversed_block_out_channels[min(i + 1     ,      len(lowercase__  ) - 1  )]
  
              __UpperCAmelCase									=			i == len(lowercase__  ) - 1
  
              __UpperCAmelCase									=			get_up_block(
                  lowercase__     ,      num_layers=layers_per_block + 1     ,      in_channels=lowercase__     ,      out_channels=lowercase__     ,      prev_output_channel=lowercase__     ,      temb_channels=lowercase__     ,      add_upsample=not is_final_block     ,      resnet_eps=lowercase__     ,      resnet_act_fn=lowercase__     ,      resnet_groups=lowercase__     ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel     ,      resnet_time_scale_shift=lowercase__     ,      upsample_type=lowercase__     ,      )
              self.up_blocks.append(lowercase__  )
              __UpperCAmelCase									=			output_channel
  
          # out
          __UpperCAmelCase									=			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,      32  )
          __UpperCAmelCase									=			nn.GroupNorm(num_channels=block_out_channels[0]     ,      num_groups=lowercase__     ,      eps=lowercase__  )
          __UpperCAmelCase									=			nn.SiLU()
          __UpperCAmelCase									=			nn.Convad(block_out_channels[0]     ,      lowercase__     ,      kernel_size=3     ,      padding=1  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      lowercase__ = True     ,      )     ->      Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
              __UpperCAmelCase									=			2 * sample - 1.0
  
          # 1. time
          __UpperCAmelCase									=			timestep
          if not torch.is_tensor(lowercase__  ):
              __UpperCAmelCase									=			torch.tensor([timesteps]     ,      dtype=torch.long     ,      device=sample.device  )
          elif torch.is_tensor(lowercase__  ) and len(timesteps.shape  ) == 0:
              __UpperCAmelCase									=			timesteps[None].to(sample.device  )
  
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          __UpperCAmelCase									=			timesteps * torch.ones(sample.shape[0]     ,      dtype=timesteps.dtype     ,      device=timesteps.device  )
  
          __UpperCAmelCase									=			self.time_proj(lowercase__  )
  
          # 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.
          __UpperCAmelCase									=			t_emb.to(dtype=self.dtype  )
          __UpperCAmelCase									=			self.time_embedding(lowercase__  )
  
          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":
                  __UpperCAmelCase									=			self.time_proj(lowercase__  )
  
              __UpperCAmelCase									=			self.class_embedding(lowercase__  ).to(dtype=self.dtype  )
              __UpperCAmelCase									=			emb + class_emb
  
          # 2. pre-process
          __UpperCAmelCase									=			sample
          __UpperCAmelCase									=			self.conv_in(lowercase__  )
  
          # 3. down
          __UpperCAmelCase									=			(sample,)
          for downsample_block in self.down_blocks:
              if hasattr(lowercase__     ,      '''skip_conv'''  ):
                  __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			downsample_block(
                      hidden_states=lowercase__     ,      temb=lowercase__     ,      skip_sample=lowercase__  )
              else:
                  __UpperCAmelCase					,     __UpperCAmelCase									=			downsample_block(hidden_states=lowercase__     ,      temb=lowercase__  )
  
              down_block_res_samples += res_samples
  
          # 4. mid
          __UpperCAmelCase									=			self.mid_block(lowercase__     ,      lowercase__  )
  
          # 5. up
          __UpperCAmelCase									=			None
          for upsample_block in self.up_blocks:
              __UpperCAmelCase									=			down_block_res_samples[-len(upsample_block.resnets  ) :]
              __UpperCAmelCase									=			down_block_res_samples[: -len(upsample_block.resnets  )]
  
              if hasattr(lowercase__     ,      '''skip_conv'''  ):
                  __UpperCAmelCase					,     __UpperCAmelCase									=			upsample_block(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )
              else:
                  __UpperCAmelCase									=			upsample_block(lowercase__     ,      lowercase__     ,      lowercase__  )
  
          # 6. post-process
          __UpperCAmelCase									=			self.conv_norm_out(lowercase__  )
          __UpperCAmelCase									=			self.conv_act(lowercase__  )
          __UpperCAmelCase									=			self.conv_out(lowercase__  )
  
          if skip_sample is not None:
              sample += skip_sample
  
          if self.config.time_embedding_type == "fourier":
              __UpperCAmelCase									=			timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]  )))  )
              __UpperCAmelCase									=			sample / timesteps
  
          if not return_dict:
              return (sample,)
  
          return UNetaDOutput(sample=lowercase__  )
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  