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'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    UnCLIPPipeline
  		__lowerCAmelCase 				=    TEXT_TO_IMAGE_PARAMS - {
  		    '''negative_prompt''',
  		    '''height''',
  		    '''width''',
  		    '''negative_prompt_embeds''',
  		    '''guidance_scale''',
  		    '''prompt_embeds''',
  		    '''cross_attention_kwargs''',
  		}
  		__lowerCAmelCase 				=    TEXT_TO_IMAGE_BATCH_PARAMS
  		__lowerCAmelCase 				=    [
  		    '''generator''',
  		    '''return_dict''',
  		    '''prior_num_inference_steps''',
  		    '''decoder_num_inference_steps''',
  		    '''super_res_num_inference_steps''',
  		]
  		__lowerCAmelCase 				=    False
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      str      ):
  			return 32
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      List[Any]      ):
  			return 32
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Any      ):
  			return self.time_input_dim
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Union[str, Any]      ):
  			return self.time_input_dim * 4
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Any      ):
  			return 100
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      int      ):
  			A							  =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )
  			return tokenizer
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      int      ):
  			torch.manual_seed(0      )
  			A							  =      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=1000      ,      )
  			return CLIPTextModelWithProjection(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      List[str]      ):
  			torch.manual_seed(0      )
  
  			A							  =      {
  			    """num_attention_heads""": 2,
  			    """attention_head_dim""": 12,
  			    """embedding_dim""": self.text_embedder_hidden_size,
  			    """num_layers""": 1,
  			}
  
  			A							  =      PriorTransformer(**_lowerCAmelCase      )
  			return model
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      List[Any]      ):
  			torch.manual_seed(0      )
  
  			A							  =      {
  			    """clip_embeddings_dim""": self.text_embedder_hidden_size,
  			    """time_embed_dim""": self.time_embed_dim,
  			    """cross_attention_dim""": self.cross_attention_dim,
  			}
  
  			A							  =      UnCLIPTextProjModel(**_lowerCAmelCase      )
  			return model
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      List[str]      ):
  			torch.manual_seed(0      )
  
  			A							  =      {
  			    """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""",
  			}
  
  			A							  =      UNetaDConditionModel(**_lowerCAmelCase      )
  			return model
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Union[str, 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        A     (self						:      Optional[int]      ):
  			torch.manual_seed(0      )
  
  			A							  =      UNetaDModel(**self.dummy_super_res_kwargs      )
  			return model
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      str      ):
  			# seeded differently to get different unet than `self.dummy_super_res_first`
  			torch.manual_seed(1      )
  
  			A							  =      UNetaDModel(**self.dummy_super_res_kwargs      )
  			return model
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			A							  =      self.dummy_prior
  			A							  =      self.dummy_decoder
  			A							  =      self.dummy_text_proj
  			A							  =      self.dummy_text_encoder
  			A							  =      self.dummy_tokenizer
  			A							  =      self.dummy_super_res_first
  			A							  =      self.dummy_super_res_last
  
  			A							  =      UnCLIPScheduler(
  			    variance_type="""fixed_small_log"""      ,      prediction_type="""sample"""      ,      num_train_timesteps=1000      ,      clip_sample_range=5.0      ,      )
  
  			A							  =      UnCLIPScheduler(
  			    variance_type="""learned_range"""      ,      prediction_type="""epsilon"""      ,      num_train_timesteps=1000      ,      )
  
  			A							  =      UnCLIPScheduler(
  			    variance_type="""fixed_small_log"""      ,      prediction_type="""epsilon"""      ,      num_train_timesteps=1000      ,      )
  
  			A							  =      {
  			    """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						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]=0      ):
  			if str(_lowerCAmelCase      ).startswith("""mps"""      ):
  				A							  =      torch.manual_seed(_lowerCAmelCase      )
  			else:
  				A							  =      torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
  			A							  =      {
  			    """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						:      Any      ):
  			A							  =      """cpu"""
  
  			A							  =      self.get_dummy_components()
  
  			A							  =      self.pipeline_class(**_lowerCAmelCase      )
  			A							  =      pipe.to(_lowerCAmelCase      )
  
  			pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
  
  			A							  =      pipe(**self.get_dummy_inputs(_lowerCAmelCase      )      )
  			A							  =      output.images
  
  			A							  =      pipe(
  			    **self.get_dummy_inputs(_lowerCAmelCase      )      ,      return_dict=_lowerCAmelCase      ,      )[0]
  
  			A							  =      image[0, -3:, -3:, -1]
  			A							  =      image_from_tuple[0, -3:, -3:, -1]
  
  			assert image.shape == (1, 64, 64, 3)
  
  			A							  =      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						:      str      ):
  			A							  =      torch.device("""cpu"""      )
  
  
  
  			class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					__lowerCAmelCase 				=    1
  
  
  
  
  
  
  			A							  =      self.get_dummy_components()
  
  			A							  =      self.pipeline_class(**_lowerCAmelCase      )
  			A							  =      pipe.to(_lowerCAmelCase      )
  
  			A							  =      components["""prior"""]
  			A							  =      components["""decoder"""]
  			A							  =      components["""super_res_first"""]
  			A							  =      components["""tokenizer"""]
  			A							  =      components["""text_encoder"""]
  
  			A							  =      torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )
  			A							  =      prior.dtype
  			A							  =      1
  
  			A							  =      (batch_size, prior.config.embedding_dim)
  			A							  =      pipe.prepare_latents(
  			    _lowerCAmelCase      ,      dtype=_lowerCAmelCase      ,      device=_lowerCAmelCase      ,      generator=_lowerCAmelCase      ,      latents=_lowerCAmelCase      ,      scheduler=DummyScheduler()      )
  			A							  =      (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  			A							  =      pipe.prepare_latents(
  			    _lowerCAmelCase      ,      dtype=_lowerCAmelCase      ,      device=_lowerCAmelCase      ,      generator=_lowerCAmelCase      ,      latents=_lowerCAmelCase      ,      scheduler=DummyScheduler()      )
  
  			A							  =      (
  			    batch_size,
  			    super_res_first.config.in_channels // 2,
  			    super_res_first.config.sample_size,
  			    super_res_first.config.sample_size,
  			)
  			A							  =      pipe.prepare_latents(
  			    _lowerCAmelCase      ,      dtype=_lowerCAmelCase      ,      device=_lowerCAmelCase      ,      generator=_lowerCAmelCase      ,      latents=_lowerCAmelCase      ,      scheduler=DummyScheduler()      )
  
  			pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
  
  			A							  =      """this is a prompt example"""
  
  			A							  =      torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )
  			A							  =      pipe(
  			    [prompt]      ,      generator=_lowerCAmelCase      ,      prior_num_inference_steps=2      ,      decoder_num_inference_steps=2      ,      super_res_num_inference_steps=2      ,      prior_latents=_lowerCAmelCase      ,      decoder_latents=_lowerCAmelCase      ,      super_res_latents=_lowerCAmelCase      ,      output_type="""np"""      ,      )
  			A							  =      output.images
  
  			A							  =      tokenizer(
  			    _lowerCAmelCase      ,      padding="""max_length"""      ,      max_length=tokenizer.model_max_length      ,      return_tensors="""pt"""      ,      )
  			A							  =      text_encoder(text_inputs.input_ids      )
  			A							  =      text_inputs.attention_mask
  
  			A							  =      torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )
  			A							  =      pipe(
  			    generator=_lowerCAmelCase      ,      prior_num_inference_steps=2      ,      decoder_num_inference_steps=2      ,      super_res_num_inference_steps=2      ,      prior_latents=_lowerCAmelCase      ,      decoder_latents=_lowerCAmelCase      ,      super_res_latents=_lowerCAmelCase      ,      text_model_output=_lowerCAmelCase      ,      text_attention_mask=_lowerCAmelCase      ,      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						:      Any      ):
  			A							  =      torch_device == """cpu"""
  
  			self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase      ,      expected_max_diff=0.01      )
  
  
  
  
  
  
  
  		@skip_mps
  		def        A     (self						:      Tuple      ):
  			A							  =      torch_device == """cpu"""
  			A							  =      True
  			A							  =      [
  			    """prior_num_inference_steps""",
  			    """decoder_num_inference_steps""",
  			    """super_res_num_inference_steps""",
  			]
  
  			self._test_inference_batch_single_identical(
  			    test_max_difference=_lowerCAmelCase      ,      relax_max_difference=_lowerCAmelCase      ,      additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			A							  =      [
  			    """prior_num_inference_steps""",
  			    """decoder_num_inference_steps""",
  			    """super_res_num_inference_steps""",
  			]
  
  			if torch_device == "mps":
  				# TODO: MPS errors with larger batch sizes
  				A							  =      [2, 3]
  				self._test_inference_batch_consistent(
  				    batch_sizes=_lowerCAmelCase      ,      additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,      )
  			else:
  				self._test_inference_batch_consistent(
  				    additional_params_copy_to_batched_inputs=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@skip_mps
  		def        A     (self						:      List[str]      ):
  			return super().test_dict_tuple_outputs_equivalent()
  
  
  
  
  
  
  
  		@skip_mps
  		def        A     (self						:      List[Any]      ):
  			return super().test_save_load_local()
  
  
  
  
  
  
  
  		@skip_mps
  		def        A     (self						:      Union[str, Any]      ):
  			return super().test_save_load_optional_components()
  
  
  
  
  
  
  @nightly
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			# clean up the VRAM after each test
  			super().tearDown()
  			gc.collect()
  			torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			A							  =      load_numpy(
  			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  			    """/unclip/karlo_v1_alpha_horse_cpu.npy"""      )
  
  			A							  =      UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""      )
  			pipeline.set_progress_bar_config(disable=_lowerCAmelCase      )
  
  			A							  =      torch.manual_seed(0      )
  			A							  =      pipeline(
  			    """horse"""      ,      num_images_per_prompt=1      ,      generator=_lowerCAmelCase      ,      output_type="""np"""      ,      )
  
  			A							  =      output.images[0]
  
  			assert image.shape == (256, 256, 3)
  			assert np.abs(expected_image - image      ).max() < 1e-1
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			# clean up the VRAM after each test
  			super().tearDown()
  			gc.collect()
  			torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			A							  =      load_numpy(
  			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  			    """/unclip/karlo_v1_alpha_horse_fp16.npy"""      )
  
  			A							  =      UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""      ,      torch_dtype=torch.floataa      )
  			A							  =      pipeline.to(_lowerCAmelCase      )
  			pipeline.set_progress_bar_config(disable=_lowerCAmelCase      )
  
  			A							  =      torch.Generator(device="""cpu"""      ).manual_seed(0      )
  			A							  =      pipeline(
  			    """horse"""      ,      generator=_lowerCAmelCase      ,      output_type="""np"""      ,      )
  
  			A							  =      output.images[0]
  
  			assert image.shape == (256, 256, 3)
  
  			assert_mean_pixel_difference(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			torch.cuda.empty_cache()
  			torch.cuda.reset_max_memory_allocated()
  			torch.cuda.reset_peak_memory_stats()
  
  			A							  =      UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""      ,      torch_dtype=torch.floataa      )
  			A							  =      pipe.to(_lowerCAmelCase      )
  			pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
  			pipe.enable_attention_slicing()
  			pipe.enable_sequential_cpu_offload()
  
  			A							  =      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"""      ,      )
  
  			A							  =      torch.cuda.max_memory_allocated()
  			# make sure that less than 7 GB is allocated
  			assert mem_bytes < 7 * 10**9
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			# numbermap keys are the number and its values are lists of indexes sorted
  			# in ascending order
  			A							  =      {}
  			# indexmap keys are an index and it's values are the number at that index
  			A							  =      {}
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      list | str | range      ,      _lowerCAmelCase						:      int      ):
  			if isinstance(_lowerCAmelCase      ,      (range, str)      ):
  				A							  =      list(_lowerCAmelCase      )
  			elif not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				raise TypeError(
  				    """binary_search_delete() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(_lowerCAmelCase      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == item:
  					array.pop(_lowerCAmelCase      )
  					return array
  				elif array[mid] < item:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  			raise ValueError(
  			    """Either the item is not in the array or the array was unsorted"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      list | str | range      ,      _lowerCAmelCase						:      int      ):
  			if isinstance(_lowerCAmelCase      ,      (range, str)      ):
  				A							  =      list(_lowerCAmelCase      )
  			elif not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				raise TypeError(
  				    """binary_search_insert() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(_lowerCAmelCase      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == index:
  					# If the item already exists in the array,
  					# insert it after the existing item
  					array.insert(mid + 1      ,      _lowerCAmelCase      )
  					return array
  				elif array[mid] < index:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  
          # If the item doesn't exist in the array, insert it at the appropriate position
  			array.insert(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			return array
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ):
  			# Remove previous index
  			if index in self.indexmap:
  				A							  =      self.indexmap[index]
  				if len(self.numbermap[n]      ) == 1:
  					del self.numbermap[n]
  				else:
  					A							  =      self.binary_search_delete(self.numbermap[n]      ,      _lowerCAmelCase      )
  
          # Set new index
  			A							  =      number
  
  			# Number not seen before or empty so insert number value
  			if number not in self.numbermap:
  				A							  =      [index]
  
  			# Here we need to perform a binary search insertion in order to insert
  			# The item in the correct place
  			else:
  				A							  =      self.binary_search_insert(
  				    self.numbermap[number]      ,      _lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      int      ):
  			return self.numbermap.get(_lowerCAmelCase      ,      [-1]      )[0]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  