code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import floats_tensor, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     List[Any]     						=KandinskyVaaPriorEmbaEmbPipeline
  		a     :     str     						=["""prompt""", """image"""]
  		a     :     List[str]     						=["""prompt""", """image"""]
  		a     :     Any     						=[
  		    """num_images_per_prompt""",
  		    """strength""",
  		    """generator""",
  		    """num_inference_steps""",
  		    """latents""",
  		    """negative_prompt""",
  		    """guidance_scale""",
  		    """output_type""",
  		    """return_dict""",
  		]
  		a     :     Dict     						=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    )
  					# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
  					__lowerCAmelCase      		=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       CLIPVisionConfig(
  					    hidden_size=self.text_embedder_hidden_size,image_size=2_24,projection_dim=self.text_embedder_hidden_size,intermediate_size=37,num_attention_heads=4,num_channels=3,num_hidden_layers=5,patch_size=14,)
  
  					__lowerCAmelCase      		=       CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE    )
  					return model
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       CLIPImageProcessor(
  					    crop_size=2_24,do_center_crop=__SCREAMING_SNAKE_CASE,do_normalize=__SCREAMING_SNAKE_CASE,do_resize=__SCREAMING_SNAKE_CASE,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073],image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711],resample=3,size=2_24,)
  
  					return image_processor
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.dummy_prior
  					__lowerCAmelCase      		=       self.dummy_image_encoder
  					__lowerCAmelCase      		=       self.dummy_text_encoder
  					__lowerCAmelCase      		=       self.dummy_tokenizer
  					__lowerCAmelCase      		=       self.dummy_image_processor
  
  					__lowerCAmelCase      		=       UnCLIPScheduler(
  					    variance_type="""fixed_small_log""",prediction_type="""sample""",num_train_timesteps=10_00,clip_sample=__SCREAMING_SNAKE_CASE,clip_sample_range=10.0,)
  
  					__lowerCAmelCase      		=       {
  					    """prior""": prior,
  					    """image_encoder""": image_encoder,
  					    """text_encoder""": text_encoder,
  					    """tokenizer""": tokenizer,
  					    """scheduler""": scheduler,
  					    """image_processor""": image_processor,
  					}
  
  					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      		=       floats_tensor((1, 3, 64, 64),rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((2_56, 2_56)    )
  
  					__lowerCAmelCase      		=       {
  					    """prompt""": """horse""",
  					    """image""": init_image,
  					    """strength""": 0.5,
  					    """generator""": generator,
  					    """guidance_scale""": 4.0,
  					    """num_inference_steps""": 2,
  					    """output_type""": """np""",
  					}
  					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.image_embeds
  
  					__lowerCAmelCase      		=       pipe(
  					    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    ),return_dict=__SCREAMING_SNAKE_CASE,)[0]
  
  					__lowerCAmelCase      		=       image[0, -10:]
  					__lowerCAmelCase      		=       image_from_tuple[0, -10:]
  
  					assert image.shape == (1, 32)
  
  					__lowerCAmelCase      		=       np.array(
  					    [
  					        0.107_1284,
  					        1.333_0271,
  					        0.6126_0223,
  					        -0.669_1065,
  					        -0.384_6852,
  					        -1.030_3661,
  					        0.2271_6111,
  					        0.0334_8901,
  					        0.3004_0675,
  					        -0.2480_5029,
  					    ]    )
  
  					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
  					assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       torch_device == """cpu"""
  					__lowerCAmelCase      		=       True
  					__lowerCAmelCase      		=       False
  
  					self._test_inference_batch_single_identical(
  					    test_max_difference=__SCREAMING_SNAKE_CASE,relax_max_difference=__SCREAMING_SNAKE_CASE,test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,)
  
  
  
  
  
  
  
  		@skip_mps
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       torch_device == """cpu"""
  					__lowerCAmelCase      		=       False
  
  					self._test_attention_slicing_forward_pass(
  					    test_max_difference=__SCREAMING_SNAKE_CASE,test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _a      :				Dict											=					TypeVar("""T""", bound=bool)
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       value
  					__lowerCAmelCase      		=       None
  					__lowerCAmelCase      		=       None
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._value
  
  
  
  
  
  
  
  		@staticmethod
  		def 						lowerCamelCase__    (						__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if not roota:
  								return roota
  
  					if not roota:
  								return roota
  
  					if roota.value > roota.value:
  								__lowerCAmelCase		, __lowerCAmelCase      		=       roota, roota
  
  					__lowerCAmelCase      		=       roota
  					__lowerCAmelCase      		=       roota.right
  					__lowerCAmelCase      		=       roota.left
  					__lowerCAmelCase      		=       SkewNode.merge(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  					return result
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE = ()    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  					if data:
  								for item in data:
  											self.insert(__SCREAMING_SNAKE_CASE    )
  		def __bool__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._root is not None
  		def __iter__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					while self:
  								result.append(self.pop()    )
  
  					# Pushing items back to the heap not to clear it.
  					for item in result:
  								self.insert(__SCREAMING_SNAKE_CASE    )
  
  					return iter(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       SkewNode.merge(self._root,SkewNode(__SCREAMING_SNAKE_CASE    )    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.top()
  					__lowerCAmelCase      		=       (
  					    SkewNode.merge(self._root.left,self._root.right    ) if self._root else None
  					)
  
  					return result
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if not self._root:
  								raise IndexError("""Can't get top element for the empty heap."""    )
  					return self._root.value
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  