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							(		lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     Union[str, Any]     						=UnCLIPPipeline
  		a     :     Optional[int]     						=TEXT_TO_IMAGE_PARAMS - {
  		    """negative_prompt""",
  		    """height""",
  		    """width""",
  		    """negative_prompt_embeds""",
  		    """guidance_scale""",
  		    """prompt_embeds""",
  		    """cross_attention_kwargs""",
  		}
  		a     :     int     						=TEXT_TO_IMAGE_BATCH_PARAMS
  		a     :     Optional[int]     						=[
  		    """generator""",
  		    """return_dict""",
  		    """prior_num_inference_steps""",
  		    """decoder_num_inference_steps""",
  		    """super_res_num_inference_steps""",
  		]
  		a     :     Optional[int]     						=False
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return 32
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return 32
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self.time_input_dim
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self.time_input_dim * 4
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return 1_00
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )
  					return tokenizer
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       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=10_00,)
  					return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE    )
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  
  					__lowerCAmelCase      		=       {
  					    """num_attention_heads""": 2,
  					    """attention_head_dim""": 12,
  					    """embedding_dim""": self.text_embedder_hidden_size,
  					    """num_layers""": 1,
  					}
  
  					__lowerCAmelCase      		=       PriorTransformer(**__SCREAMING_SNAKE_CASE    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  
  					__lowerCAmelCase      		=       {
  					    """clip_embeddings_dim""": self.text_embedder_hidden_size,
  					    """time_embed_dim""": self.time_embed_dim,
  					    """cross_attention_dim""": self.cross_attention_dim,
  					}
  
  					__lowerCAmelCase      		=       UnCLIPTextProjModel(**__SCREAMING_SNAKE_CASE    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  
  					__lowerCAmelCase      		=       {
  					    """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""",
  					}
  
  					__lowerCAmelCase      		=       UNetaDConditionModel(**__SCREAMING_SNAKE_CASE    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					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    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  
  					__lowerCAmelCase      		=       UNetaDModel(**self.dummy_super_res_kwargs    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(1    )
  
  					__lowerCAmelCase      		=       UNetaDModel(**self.dummy_super_res_kwargs    )
  					return model
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.dummy_prior
  					__lowerCAmelCase      		=       self.dummy_decoder
  					__lowerCAmelCase      		=       self.dummy_text_proj
  					__lowerCAmelCase      		=       self.dummy_text_encoder
  					__lowerCAmelCase      		=       self.dummy_tokenizer
  					__lowerCAmelCase      		=       self.dummy_super_res_first
  					__lowerCAmelCase      		=       self.dummy_super_res_last
  
  					__lowerCAmelCase      		=       UnCLIPScheduler(
  					    variance_type="""fixed_small_log""",prediction_type="""sample""",num_train_timesteps=10_00,clip_sample_range=5.0,)
  
  					__lowerCAmelCase      		=       UnCLIPScheduler(
  					    variance_type="""learned_range""",prediction_type="""epsilon""",num_train_timesteps=10_00,)
  
  					__lowerCAmelCase      		=       UnCLIPScheduler(
  					    variance_type="""fixed_small_log""",prediction_type="""epsilon""",num_train_timesteps=10_00,)
  
  					__lowerCAmelCase      		=       {
  					    """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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0    ):
  
  
  
  					'''simple docstring'''
  
  					if str(__SCREAMING_SNAKE_CASE    ).startswith("""mps"""    ):
  								__lowerCAmelCase      		=       torch.manual_seed(__SCREAMING_SNAKE_CASE    )
  					else:
  								__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {
  					    """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    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       """cpu"""
  
  					__lowerCAmelCase      		=       self.get_dummy_components()
  
  					__lowerCAmelCase      		=       self.pipeline_class(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    )
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       pipe(
  					    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    ),return_dict=__SCREAMING_SNAKE_CASE,)[0]
  
  					__lowerCAmelCase      		=       image[0, -3:, -3:, -1]
  					__lowerCAmelCase      		=       image_from_tuple[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 64, 64, 3)
  
  					__lowerCAmelCase      		=       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    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       torch.device("""cpu"""    )
  
  
  
  
  
  					class 					_UpperCAmelCase							:
  							a     :     Optional[Any]     						=1
  
  
  
  
  
  					__lowerCAmelCase      		=       self.get_dummy_components()
  
  					__lowerCAmelCase      		=       self.pipeline_class(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       components["""prior"""]
  					__lowerCAmelCase      		=       components["""decoder"""]
  					__lowerCAmelCase      		=       components["""super_res_first"""]
  					__lowerCAmelCase      		=       components["""tokenizer"""]
  					__lowerCAmelCase      		=       components["""text_encoder"""]
  
  					__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(0    )
  					__lowerCAmelCase      		=       prior.dtype
  					__lowerCAmelCase      		=       1
  
  					__lowerCAmelCase      		=       (batch_size, prior.config.embedding_dim)
  					__lowerCAmelCase      		=       pipe.prepare_latents(
  					    __SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE,device=__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,latents=__SCREAMING_SNAKE_CASE,scheduler=DummyScheduler()    )
  					__lowerCAmelCase      		=       (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  					__lowerCAmelCase      		=       pipe.prepare_latents(
  					    __SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE,device=__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,latents=__SCREAMING_SNAKE_CASE,scheduler=DummyScheduler()    )
  
  					__lowerCAmelCase      		=       (
  					    batch_size,
  					    super_res_first.config.in_channels // 2,
  					    super_res_first.config.sample_size,
  					    super_res_first.config.sample_size,
  					)
  					__lowerCAmelCase      		=       pipe.prepare_latents(
  					    __SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE,device=__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,latents=__SCREAMING_SNAKE_CASE,scheduler=DummyScheduler()    )
  
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       """this is a prompt example"""
  
  					__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(0    )
  					__lowerCAmelCase      		=       pipe(
  					    [prompt],generator=__SCREAMING_SNAKE_CASE,prior_num_inference_steps=2,decoder_num_inference_steps=2,super_res_num_inference_steps=2,prior_latents=__SCREAMING_SNAKE_CASE,decoder_latents=__SCREAMING_SNAKE_CASE,super_res_latents=__SCREAMING_SNAKE_CASE,output_type="""np""",)
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       tokenizer(
  					    __SCREAMING_SNAKE_CASE,padding="""max_length""",max_length=tokenizer.model_max_length,return_tensors="""pt""",)
  					__lowerCAmelCase      		=       text_encoder(text_inputs.input_ids    )
  					__lowerCAmelCase      		=       text_inputs.attention_mask
  
  					__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(0    )
  					__lowerCAmelCase      		=       pipe(
  					    generator=__SCREAMING_SNAKE_CASE,prior_num_inference_steps=2,decoder_num_inference_steps=2,super_res_num_inference_steps=2,prior_latents=__SCREAMING_SNAKE_CASE,decoder_latents=__SCREAMING_SNAKE_CASE,super_res_latents=__SCREAMING_SNAKE_CASE,text_model_output=__SCREAMING_SNAKE_CASE,text_attention_mask=__SCREAMING_SNAKE_CASE,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    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       torch_device == """cpu"""
  
  					self._test_attention_slicing_forward_pass(test_max_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=0.01    )
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       torch_device == """cpu"""
  					__lowerCAmelCase      		=       True
  					__lowerCAmelCase      		=       [
  					    """prior_num_inference_steps""",
  					    """decoder_num_inference_steps""",
  					    """super_res_num_inference_steps""",
  					]
  
  					self._test_inference_batch_single_identical(
  					    test_max_difference=__SCREAMING_SNAKE_CASE,relax_max_difference=__SCREAMING_SNAKE_CASE,additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE,)
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       [
  					    """prior_num_inference_steps""",
  					    """decoder_num_inference_steps""",
  					    """super_res_num_inference_steps""",
  					]
  
  					if torch_device == "mps":
  								# TODO: MPS errors with larger batch sizes
  								__lowerCAmelCase      		=       [2, 3]
  								self._test_inference_batch_consistent(
  								    batch_sizes=__SCREAMING_SNAKE_CASE,additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE,)
  					else:
  								self._test_inference_batch_consistent(
  								    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE    )
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return super().test_dict_tuple_outputs_equivalent()
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return super().test_save_load_local()
  
  
  
  
  
  
  
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return super().test_save_load_optional_components()
  
  
  
  
  
  @nightly
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       load_numpy(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/unclip/karlo_v1_alpha_horse_cpu.npy"""    )
  
  					__lowerCAmelCase      		=       UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    )
  					pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.manual_seed(0    )
  					__lowerCAmelCase      		=       pipeline(
  					    """horse""",num_images_per_prompt=1,generator=__SCREAMING_SNAKE_CASE,output_type="""np""",)
  
  					__lowerCAmelCase      		=       output.images[0]
  
  					assert image.shape == (2_56, 2_56, 3)
  					assert np.abs(expected_image - image    ).max() < 1e-1
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       load_numpy(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/unclip/karlo_v1_alpha_horse_fp16.npy"""    )
  
  					__lowerCAmelCase      		=       UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha""",torch_dtype=torch.floataa    )
  					__lowerCAmelCase      		=       pipeline.to(__SCREAMING_SNAKE_CASE    )
  					pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.Generator(device="""cpu"""    ).manual_seed(0    )
  					__lowerCAmelCase      		=       pipeline(
  					    """horse""",generator=__SCREAMING_SNAKE_CASE,output_type="""np""",)
  
  					__lowerCAmelCase      		=       output.images[0]
  
  					assert image.shape == (2_56, 2_56, 3)
  
  					assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.cuda.empty_cache()
  					torch.cuda.reset_max_memory_allocated()
  					torch.cuda.reset_peak_memory_stats()
  
  					__lowerCAmelCase      		=       UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha""",torch_dtype=torch.floataa    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  					pipe.enable_attention_slicing()
  					pipe.enable_sequential_cpu_offload()
  
  					__lowerCAmelCase      		=       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""",)
  
  					__lowerCAmelCase      		=       torch.cuda.max_memory_allocated()
  					# make sure that less than 7 GB is allocated
  					assert mem_bytes < 7 * 10**9
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       3
  			__lowerCAmelCase      		=       5
  			__lowerCAmelCase      		=       1
  			while True:
  						__lowerCAmelCase      		=       z * temp
  						if result < n:
  									zmulti.append(lowercase			)
  									temp += 1
  						else:
  									__lowerCAmelCase      		=       1
  									break
  			while True:
  						__lowerCAmelCase      		=       x * temp
  						if result < n:
  									xmulti.append(lowercase			)
  									temp += 1
  						else:
  									break
  			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
  			return sum(lowercase			)
  
  
  if __name__ == "__main__":
  	print(f'{solution() = }')
  
  