code: 
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils import load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  
  
  enable_full_determinism()
  class 					A_   (      _a ,     unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       UnCLIPPipeline
      a__     							=       TEXT_TO_IMAGE_PARAMS - {
          "negative_prompt",
          "height",
          "width",
          "negative_prompt_embeds",
          "guidance_scale",
          "prompt_embeds",
          "cross_attention_kwargs",
      }
      a__     							=       TEXT_TO_IMAGE_BATCH_PARAMS
      a__     							=       [
          "generator",
          "return_dict",
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]
      a__     							=       False
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Dict:
          return 32
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          return 32
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          return self.time_input_dim
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          return self.time_input_dim * 4
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          return 100
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          __UpperCAmelCase									=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
          return tokenizer
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Dict:
          torch.manual_seed(0  )
          __UpperCAmelCase									=			CLIPTextConfig(
              bos_token_id=0     ,      eos_token_id=2     ,      hidden_size=self.text_embedder_hidden_size     ,      projection_dim=self.text_embedder_hidden_size     ,      intermediate_size=37     ,      layer_norm_eps=1E-05     ,      num_attention_heads=4     ,      num_hidden_layers=5     ,      pad_token_id=1     ,      vocab_size=1_000     ,      )
          return CLIPTextModelWithProjection(lowercase__  )
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      int:
          torch.manual_seed(0  )
  
          __UpperCAmelCase									=			{
              '''num_attention_heads''': 2,
              '''attention_head_dim''': 12,
              '''embedding_dim''': self.text_embedder_hidden_size,
              '''num_layers''': 1,
          }
  
          __UpperCAmelCase									=			PriorTransformer(**lowercase__  )
          return model
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          torch.manual_seed(0  )
  
          __UpperCAmelCase									=			{
              '''clip_embeddings_dim''': self.text_embedder_hidden_size,
              '''time_embed_dim''': self.time_embed_dim,
              '''cross_attention_dim''': self.cross_attention_dim,
          }
  
          __UpperCAmelCase									=			UnCLIPTextProjModel(**lowercase__  )
          return model
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          torch.manual_seed(0  )
  
          __UpperCAmelCase									=			{
              '''sample_size''': 32,
              # RGB in channels
              '''in_channels''': 3,
              # Out channels is double in channels because predicts mean and variance
              '''out_channels''': 6,
              '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
              '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
              '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
              '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
              '''layers_per_block''': 1,
              '''cross_attention_dim''': self.cross_attention_dim,
              '''attention_head_dim''': 4,
              '''resnet_time_scale_shift''': '''scale_shift''',
              '''class_embed_type''': '''identity''',
          }
  
          __UpperCAmelCase									=			UNetaDConditionModel(**lowercase__  )
          return model
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          return {
              "sample_size": 64,
              "layers_per_block": 1,
              "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
              "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "in_channels": 6,
              "out_channels": 3,
          }
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          torch.manual_seed(0  )
  
          __UpperCAmelCase									=			UNetaDModel(**self.dummy_super_res_kwargs  )
          return model
  
  
      @property
      def      lowerCAmelCase_						(self  )     ->      Any:
          # seeded differently to get different unet than `self.dummy_super_res_first`
          torch.manual_seed(1  )
  
          __UpperCAmelCase									=			UNetaDModel(**self.dummy_super_res_kwargs  )
          return model
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			self.dummy_prior
          __UpperCAmelCase									=			self.dummy_decoder
          __UpperCAmelCase									=			self.dummy_text_proj
          __UpperCAmelCase									=			self.dummy_text_encoder
          __UpperCAmelCase									=			self.dummy_tokenizer
          __UpperCAmelCase									=			self.dummy_super_res_first
          __UpperCAmelCase									=			self.dummy_super_res_last
  
          __UpperCAmelCase									=			UnCLIPScheduler(
              variance_type='''fixed_small_log'''     ,      prediction_type='''sample'''     ,      num_train_timesteps=1_000     ,      clip_sample_range=5.0     ,      )
  
          __UpperCAmelCase									=			UnCLIPScheduler(
              variance_type='''learned_range'''     ,      prediction_type='''epsilon'''     ,      num_train_timesteps=1_000     ,      )
  
          __UpperCAmelCase									=			UnCLIPScheduler(
              variance_type='''fixed_small_log'''     ,      prediction_type='''epsilon'''     ,      num_train_timesteps=1_000     ,      )
  
          __UpperCAmelCase									=			{
              '''prior''': prior,
              '''decoder''': decoder,
              '''text_proj''': text_proj,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''super_res_first''': super_res_first,
              '''super_res_last''': super_res_last,
              '''prior_scheduler''': prior_scheduler,
              '''decoder_scheduler''': decoder_scheduler,
              '''super_res_scheduler''': super_res_scheduler,
          }
  
          return components
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__=0  )     ->      List[Any]:
          if str(lowercase__  ).startswith('''mps'''  ):
              __UpperCAmelCase									=			torch.manual_seed(lowercase__  )
          else:
              __UpperCAmelCase									=			torch.Generator(device=lowercase__  ).manual_seed(lowercase__  )
          __UpperCAmelCase									=			{
              '''prompt''': '''horse''',
              '''generator''': generator,
              '''prior_num_inference_steps''': 2,
              '''decoder_num_inference_steps''': 2,
              '''super_res_num_inference_steps''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs
  
  
      def      lowerCAmelCase_						(self  )     ->      int:
          __UpperCAmelCase									=			'''cpu'''
  
          __UpperCAmelCase									=			self.get_dummy_components()
  
          __UpperCAmelCase									=			self.pipeline_class(**lowercase__  )
          __UpperCAmelCase									=			pipe.to(lowercase__  )
  
          pipe.set_progress_bar_config(disable=lowercase__  )
  
          __UpperCAmelCase									=			pipe(**self.get_dummy_inputs(lowercase__  )  )
          __UpperCAmelCase									=			output.images
  
          __UpperCAmelCase									=			pipe(
              **self.get_dummy_inputs(lowercase__  )     ,      return_dict=lowercase__     ,      )[0]
  
          __UpperCAmelCase									=			image[0, -3:, -3:, -1]
          __UpperCAmelCase									=			image_from_tuple[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
  
          __UpperCAmelCase									=			np.array(
              [
                  0.9997,
                  0.9988,
                  0.0028,
                  0.9997,
                  0.9984,
                  0.9965,
                  0.0029,
                  0.9986,
                  0.0025,
              ]  )
  
          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			torch.device('''cpu'''  )
          class 					A_   :
  
  
              '''simple docstring'''
  
  
  
  
  
  
              a__     							=       1
  
  
  
  
  
  
          __UpperCAmelCase									=			self.get_dummy_components()
  
          __UpperCAmelCase									=			self.pipeline_class(**lowercase__  )
          __UpperCAmelCase									=			pipe.to(lowercase__  )
  
          __UpperCAmelCase									=			components['''prior''']
          __UpperCAmelCase									=			components['''decoder''']
          __UpperCAmelCase									=			components['''super_res_first''']
          __UpperCAmelCase									=			components['''tokenizer''']
          __UpperCAmelCase									=			components['''text_encoder''']
  
          __UpperCAmelCase									=			torch.Generator(device=lowercase__  ).manual_seed(0  )
          __UpperCAmelCase									=			prior.dtype
          __UpperCAmelCase									=			1
  
          __UpperCAmelCase									=			(batch_size, prior.config.embedding_dim)
          __UpperCAmelCase									=			pipe.prepare_latents(
              lowercase__     ,      dtype=lowercase__     ,      device=lowercase__     ,      generator=lowercase__     ,      latents=lowercase__     ,      scheduler=DummyScheduler()  )
          __UpperCAmelCase									=			(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
          __UpperCAmelCase									=			pipe.prepare_latents(
              lowercase__     ,      dtype=lowercase__     ,      device=lowercase__     ,      generator=lowercase__     ,      latents=lowercase__     ,      scheduler=DummyScheduler()  )
  
          __UpperCAmelCase									=			(
              batch_size,
              super_res_first.config.in_channels // 2,
              super_res_first.config.sample_size,
              super_res_first.config.sample_size,
          )
          __UpperCAmelCase									=			pipe.prepare_latents(
              lowercase__     ,      dtype=lowercase__     ,      device=lowercase__     ,      generator=lowercase__     ,      latents=lowercase__     ,      scheduler=DummyScheduler()  )
  
          pipe.set_progress_bar_config(disable=lowercase__  )
  
          __UpperCAmelCase									=			'''this is a prompt example'''
  
          __UpperCAmelCase									=			torch.Generator(device=lowercase__  ).manual_seed(0  )
          __UpperCAmelCase									=			pipe(
              [prompt]     ,      generator=lowercase__     ,      prior_num_inference_steps=2     ,      decoder_num_inference_steps=2     ,      super_res_num_inference_steps=2     ,      prior_latents=lowercase__     ,      decoder_latents=lowercase__     ,      super_res_latents=lowercase__     ,      output_type='''np'''     ,      )
          __UpperCAmelCase									=			output.images
  
          __UpperCAmelCase									=			tokenizer(
              lowercase__     ,      padding='''max_length'''     ,      max_length=tokenizer.model_max_length     ,      return_tensors='''pt'''     ,      )
          __UpperCAmelCase									=			text_encoder(text_inputs.input_ids  )
          __UpperCAmelCase									=			text_inputs.attention_mask
  
          __UpperCAmelCase									=			torch.Generator(device=lowercase__  ).manual_seed(0  )
          __UpperCAmelCase									=			pipe(
              generator=lowercase__     ,      prior_num_inference_steps=2     ,      decoder_num_inference_steps=2     ,      super_res_num_inference_steps=2     ,      prior_latents=lowercase__     ,      decoder_latents=lowercase__     ,      super_res_latents=lowercase__     ,      text_model_output=lowercase__     ,      text_attention_mask=lowercase__     ,      output_type='''np'''     ,      )[0]
  
          # make sure passing text embeddings manually is identical
          assert np.abs(image - image_from_text  ).max() < 1E-4
  
  
      @skip_mps
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			torch_device == '''cpu'''
  
          self._test_attention_slicing_forward_pass(test_max_difference=lowercase__     ,      expected_max_diff=0.01  )
  
  
      @skip_mps
      def      lowerCAmelCase_						(self  )     ->      str:
          __UpperCAmelCase									=			torch_device == '''cpu'''
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			[
              '''prior_num_inference_steps''',
              '''decoder_num_inference_steps''',
              '''super_res_num_inference_steps''',
          ]
  
          self._test_inference_batch_single_identical(
              test_max_difference=lowercase__     ,      relax_max_difference=lowercase__     ,      additional_params_copy_to_batched_inputs=lowercase__     ,      )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			[
              '''prior_num_inference_steps''',
              '''decoder_num_inference_steps''',
              '''super_res_num_inference_steps''',
          ]
  
          if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              __UpperCAmelCase									=			[2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=lowercase__     ,      additional_params_copy_to_batched_inputs=lowercase__     ,      )
          else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=lowercase__  )
  
  
      @skip_mps
      def      lowerCAmelCase_						(self  )     ->      Any:
          return super().test_dict_tuple_outputs_equivalent()
  
  
      @skip_mps
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          return super().test_save_load_local()
  
  
  
  
  
  
      @skip_mps
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          return super().test_save_load_optional_components()
  
  
  
  
  
  
  @nightly
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Any:
          __UpperCAmelCase									=			load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/unclip/karlo_v1_alpha_horse_cpu.npy'''  )
  
          __UpperCAmelCase									=			UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''  )
          pipeline.set_progress_bar_config(disable=lowercase__  )
  
          __UpperCAmelCase									=			torch.manual_seed(0  )
          __UpperCAmelCase									=			pipeline(
              '''horse'''     ,      num_images_per_prompt=1     ,      generator=lowercase__     ,      output_type='''np'''     ,      )
  
          __UpperCAmelCase									=			output.images[0]
  
          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image  ).max() < 1E-1
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					A_   (      unittest.TestCase						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      List[Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          __UpperCAmelCase									=			load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/unclip/karlo_v1_alpha_horse_fp16.npy'''  )
  
          __UpperCAmelCase									=			UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,      torch_dtype=torch.floataa  )
          __UpperCAmelCase									=			pipeline.to(lowercase__  )
          pipeline.set_progress_bar_config(disable=lowercase__  )
  
          __UpperCAmelCase									=			torch.Generator(device='''cpu'''  ).manual_seed(0  )
          __UpperCAmelCase									=			pipeline(
              '''horse'''     ,      generator=lowercase__     ,      output_type='''np'''     ,      )
  
          __UpperCAmelCase									=			output.images[0]
  
          assert image.shape == (256, 256, 3)
  
          assert_mean_pixel_difference(lowercase__     ,      lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          __UpperCAmelCase									=			UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     ,      torch_dtype=torch.floataa  )
          __UpperCAmelCase									=			pipe.to(lowercase__  )
          pipe.set_progress_bar_config(disable=lowercase__  )
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()
  
          __UpperCAmelCase									=			pipe(
              '''horse'''     ,      num_images_per_prompt=1     ,      prior_num_inference_steps=2     ,      decoder_num_inference_steps=2     ,      super_res_num_inference_steps=2     ,      output_type='''np'''     ,      )
  
          __UpperCAmelCase									=			torch.cuda.max_memory_allocated()
          # make sure that less than 7 GB is allocated
          assert mem_bytes < 7 * 10**9
  
  
  
  
  
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"],
                      }
  
  
  
  
  