code: 
  
  
  
  
  
  
  """simple docstring"""
  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 					UpperCamelCase__   (							a_       ,  unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   UnCLIPPipeline
      __UpperCAmelCase											=   TEXT_TO_IMAGE_PARAMS - {
          """negative_prompt""",
          """height""",
          """width""",
          """negative_prompt_embeds""",
          """guidance_scale""",
          """prompt_embeds""",
          """cross_attention_kwargs""",
      }
      __UpperCAmelCase											=   TEXT_TO_IMAGE_BATCH_PARAMS
      __UpperCAmelCase											=   [
          """generator""",
          """return_dict""",
          """prior_num_inference_steps""",
          """decoder_num_inference_steps""",
          """super_res_num_inference_steps""",
      ]
      __UpperCAmelCase											=   False
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return 3_2
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return 3_2
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.time_input_dim
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.time_input_dim * 4
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             return 1_0_0
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
             return tokenizer
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(0   )
             __magic_name__										=						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=3_7							,						layer_norm_eps=1e-0_5							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=1_0_0_0							,						)
             return CLIPTextModelWithProjection(UpperCamelCase_   )
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(0   )
  
             __magic_name__										=						{
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }
  
             __magic_name__										=						PriorTransformer(**UpperCamelCase_   )
             return model
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(0   )
  
             __magic_name__										=						{
                 'clip_embeddings_dim': self.text_embedder_hidden_size,
                 'time_embed_dim': self.time_embed_dim,
                 'cross_attention_dim': self.cross_attention_dim,
             }
  
             __magic_name__										=						UnCLIPTextProjModel(**UpperCamelCase_   )
             return model
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(0   )
  
             __magic_name__										=						{
                 'sample_size': 3_2,
                 # 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',
             }
  
             __magic_name__										=						UNetaDConditionModel(**UpperCamelCase_   )
             return model
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             return {
                 "sample_size": 6_4,
                 "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 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(0   )
  
             __magic_name__										=						UNetaDModel(**self.dummy_super_res_kwargs   )
             return model
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.manual_seed(1   )
  
             __magic_name__										=						UNetaDModel(**self.dummy_super_res_kwargs   )
             return model
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.dummy_prior
             __magic_name__										=						self.dummy_decoder
             __magic_name__										=						self.dummy_text_proj
             __magic_name__										=						self.dummy_text_encoder
             __magic_name__										=						self.dummy_tokenizer
             __magic_name__										=						self.dummy_super_res_first
             __magic_name__										=						self.dummy_super_res_last
  
             __magic_name__										=						UnCLIPScheduler(
                 variance_type='fixed_small_log'							,						prediction_type='sample'							,						num_train_timesteps=1_0_0_0							,						clip_sample_range=5.0							,						)
  
             __magic_name__										=						UnCLIPScheduler(
                 variance_type='learned_range'							,						prediction_type='epsilon'							,						num_train_timesteps=1_0_0_0							,						)
  
             __magic_name__										=						UnCLIPScheduler(
                 variance_type='fixed_small_log'							,						prediction_type='epsilon'							,						num_train_timesteps=1_0_0_0							,						)
  
             __magic_name__										=						{
                 '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 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Tuple=0   ):
  
  
  
  
  
             '''simple docstring'''
  
             if str(UpperCamelCase_   ).startswith('mps'   ):
                    __magic_name__										=						torch.manual_seed(UpperCamelCase_   )
             else:
                    __magic_name__										=						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __magic_name__										=						{
                 '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 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						'cpu'
  
             __magic_name__										=						self.get_dummy_components()
  
             __magic_name__										=						self.pipeline_class(**UpperCamelCase_   )
             __magic_name__										=						pipe.to(UpperCamelCase_   )
  
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
  
             __magic_name__										=						pipe(**self.get_dummy_inputs(UpperCamelCase_   )   )
             __magic_name__										=						output.images
  
             __magic_name__										=						pipe(
                 **self.get_dummy_inputs(UpperCamelCase_   )							,						return_dict=UpperCamelCase_							,						)[0]
  
             __magic_name__										=						image[0, -3:, -3:, -1]
             __magic_name__										=						image_from_tuple[0, -3:, -3:, -1]
  
             assert image.shape == (1, 6_4, 6_4, 3)
  
             __magic_name__										=						np.array(
                 [
                     0.9_997,
                     0.9_988,
                     0.0_028,
                     0.9_997,
                     0.9_984,
                     0.9_965,
                     0.0_029,
                     0.9_986,
                     0.0_025,
                 ]   )
  
             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 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						torch.device('cpu'   )
  
  
  
             class 					UpperCamelCase__   :
  
  
  
  
  
  
  
                 """simple docstring"""
                 __UpperCAmelCase											=   1
             __magic_name__										=						self.get_dummy_components()
  
             __magic_name__										=						self.pipeline_class(**UpperCamelCase_   )
             __magic_name__										=						pipe.to(UpperCamelCase_   )
  
             __magic_name__										=						components['prior']
             __magic_name__										=						components['decoder']
             __magic_name__										=						components['super_res_first']
             __magic_name__										=						components['tokenizer']
             __magic_name__										=						components['text_encoder']
  
             __magic_name__										=						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __magic_name__										=						prior.dtype
             __magic_name__										=						1
  
             __magic_name__										=						(batch_size, prior.config.embedding_dim)
             __magic_name__										=						pipe.prepare_latents(
                 UpperCamelCase_							,						dtype=UpperCamelCase_							,						device=UpperCamelCase_							,						generator=UpperCamelCase_							,						latents=UpperCamelCase_							,						scheduler=DummyScheduler()   )
             __magic_name__										=						(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
             __magic_name__										=						pipe.prepare_latents(
                 UpperCamelCase_							,						dtype=UpperCamelCase_							,						device=UpperCamelCase_							,						generator=UpperCamelCase_							,						latents=UpperCamelCase_							,						scheduler=DummyScheduler()   )
  
             __magic_name__										=						(
                 batch_size,
                 super_res_first.config.in_channels // 2,
                 super_res_first.config.sample_size,
                 super_res_first.config.sample_size,
             )
             __magic_name__										=						pipe.prepare_latents(
                 UpperCamelCase_							,						dtype=UpperCamelCase_							,						device=UpperCamelCase_							,						generator=UpperCamelCase_							,						latents=UpperCamelCase_							,						scheduler=DummyScheduler()   )
  
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
  
             __magic_name__										=						'this is a prompt example'
  
             __magic_name__										=						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __magic_name__										=						pipe(
                 [prompt]							,						generator=UpperCamelCase_							,						prior_num_inference_steps=2							,						decoder_num_inference_steps=2							,						super_res_num_inference_steps=2							,						prior_latents=UpperCamelCase_							,						decoder_latents=UpperCamelCase_							,						super_res_latents=UpperCamelCase_							,						output_type='np'							,						)
             __magic_name__										=						output.images
  
             __magic_name__										=						tokenizer(
                 UpperCamelCase_							,						padding='max_length'							,						max_length=tokenizer.model_max_length							,						return_tensors='pt'							,						)
             __magic_name__										=						text_encoder(text_inputs.input_ids   )
             __magic_name__										=						text_inputs.attention_mask
  
             __magic_name__										=						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __magic_name__										=						pipe(
                 generator=UpperCamelCase_							,						prior_num_inference_steps=2							,						decoder_num_inference_steps=2							,						super_res_num_inference_steps=2							,						prior_latents=UpperCamelCase_							,						decoder_latents=UpperCamelCase_							,						super_res_latents=UpperCamelCase_							,						text_model_output=UpperCamelCase_							,						text_attention_mask=UpperCamelCase_							,						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 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						torch_device == 'cpu'
  
             self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_							,						expected_max_diff=0.01   )
  
  
  
  
  
  
  
      @skip_mps
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						torch_device == 'cpu'
             __magic_name__										=						True
             __magic_name__										=						[
                 'prior_num_inference_steps',
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]
  
             self._test_inference_batch_single_identical(
                 test_max_difference=UpperCamelCase_							,						relax_max_difference=UpperCamelCase_							,						additional_params_copy_to_batched_inputs=UpperCamelCase_							,						)
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[
                 'prior_num_inference_steps',
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]
  
             if torch_device == "mps":
                    # TODO: MPS errors with larger batch sizes
                    __magic_name__										=						[2, 3]
                    self._test_inference_batch_consistent(
                        batch_sizes=UpperCamelCase_							,						additional_params_copy_to_batched_inputs=UpperCamelCase_							,						)
             else:
                    self._test_inference_batch_consistent(
                        additional_params_copy_to_batched_inputs=UpperCamelCase_   )
  
  
  
  
  
  
  
      @skip_mps
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return super().test_dict_tuple_outputs_equivalent()
  
  
  
  
  
  
  
      @skip_mps
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             return super().test_save_load_local()
  
  
  
  
  
  
  
      @skip_mps
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return super().test_save_load_optional_components()
  
  
  
  @nightly
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/unclip/karlo_v1_alpha_horse_cpu.npy'   )
  
             __magic_name__										=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'   )
             pipeline.set_progress_bar_config(disable=UpperCamelCase_   )
  
             __magic_name__										=						torch.manual_seed(0   )
             __magic_name__										=						pipeline(
                 'horse'							,						num_images_per_prompt=1							,						generator=UpperCamelCase_							,						output_type='np'							,						)
  
             __magic_name__										=						output.images[0]
  
             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image   ).max() < 1e-1
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/unclip/karlo_v1_alpha_horse_fp16.npy'   )
  
             __magic_name__										=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,						torch_dtype=torch.floataa   )
             __magic_name__										=						pipeline.to(UpperCamelCase_   )
             pipeline.set_progress_bar_config(disable=UpperCamelCase_   )
  
             __magic_name__										=						torch.Generator(device='cpu'   ).manual_seed(0   )
             __magic_name__										=						pipeline(
                 'horse'							,						generator=UpperCamelCase_							,						output_type='np'							,						)
  
             __magic_name__										=						output.images[0]
  
             assert image.shape == (2_5_6, 2_5_6, 3)
  
             assert_mean_pixel_difference(UpperCamelCase_							,						UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()
  
             __magic_name__										=						UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,						torch_dtype=torch.floataa   )
             __magic_name__										=						pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()
  
             __magic_name__										=						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'							,						)
  
             __magic_name__										=						torch.cuda.max_memory_allocated()
             # make sure that less than 7 GB is allocated
             assert mem_bytes < 7 * 1_0**9
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         sequence.append(__snake_case     )
  
         __magic_name__										=						0
         while sequence[index] != target:
                index += 1
  
         sequence.pop()
  
         if index == len(__snake_case     ):
                return None
  
         return index
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
   snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]
  
   snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
   snake_case   :   str    		=		int(target_input)
   snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
   if result is not None:
    print(f"""{target} found at positions: {result}""")
   else:
    print("""Not found""")