code: 
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DiffusionPipeline,
      EulerDiscreteScheduler,
      StableDiffusionXLPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  class 			a_					(    A__     ,						A__     ,						unittest.TestCase    ):
   A						       =   StableDiffusionXLPipeline
   A						       =   TEXT_TO_IMAGE_PARAMS
   A						       =   TEXT_TO_IMAGE_BATCH_PARAMS
   A						       =   TEXT_TO_IMAGE_IMAGE_PARAMS
   A						       =   TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
   def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
      """simple docstring"""
  
  
  
      torch.manual_seed(0			)
      SCREAMING_SNAKE_CASE_			=	UNetaDConditionModel(
          block_out_channels=(32, 64)			,    layers_per_block=2			,    sample_size=32			,    in_channels=4			,    out_channels=4			,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,    attention_head_dim=(2, 4)			,    use_linear_projection=_lowerCAmelCase			,    addition_embed_type='text_time'			,    addition_time_embed_dim=8			,    transformer_layers_per_block=(1, 2)			,    projection_class_embeddings_input_dim=80			,    cross_attention_dim=64			,    )
      SCREAMING_SNAKE_CASE_			=	EulerDiscreteScheduler(
          beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    steps_offset=1			,    beta_schedule='scaled_linear'			,    timestep_spacing='leading'			,    )
      torch.manual_seed(0			)
      SCREAMING_SNAKE_CASE_			=	AutoencoderKL(
          block_out_channels=[32, 64]			,    in_channels=3			,    out_channels=3			,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,    latent_channels=4			,    sample_size=128			,    )
      torch.manual_seed(0			)
      SCREAMING_SNAKE_CASE_			=	CLIPTextConfig(
          bos_token_id=0			,    eos_token_id=2			,    hidden_size=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    hidden_act='gelu'			,    projection_dim=32			,    )
      SCREAMING_SNAKE_CASE_			=	CLIPTextModel(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,    local_files_only=_lowerCAmelCase			)
  
      SCREAMING_SNAKE_CASE_			=	CLIPTextModelWithProjection(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,    local_files_only=_lowerCAmelCase			)
  
      SCREAMING_SNAKE_CASE_			=	{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'text_encoder_2': text_encoder_a,
          'tokenizer_2': tokenizer_a,
          # "safety_checker": None,
          # "feature_extractor": None,
      }
      return components
  
  
  
  
  
   def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=0			)     ->				List[Any]:
  
  
  
  
      """simple docstring"""
  
  
  
      if str(_lowerCAmelCase			).startswith('mps'			):
         SCREAMING_SNAKE_CASE_			=	torch.manual_seed(_lowerCAmelCase			)
      else:
         SCREAMING_SNAKE_CASE_			=	torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	{
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 5.0,
          'output_type': 'numpy',
      }
      return inputs
  
  
  
  
  
   def 	A_(       self			)     ->				int:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	'cpu'  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
      SCREAMING_SNAKE_CASE_			=	StableDiffusionXLPipeline(**_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	sd_pipe.to(_lowerCAmelCase			)
      sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
  
      SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	sd_pipe(**_lowerCAmelCase			).images
      SCREAMING_SNAKE_CASE_			=	image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 64, 64, 3)
      SCREAMING_SNAKE_CASE_			=	np.array([0.5_8_7_3, 0.6_1_2_8, 0.4_7_9_7, 0.5_1_2_2, 0.5_6_7_4, 0.4_6_3_9, 0.5_2_2_7, 0.5_1_4_9, 0.4_7_4_7]			)
  
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
  
  
  
  
  
   def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
      SCREAMING_SNAKE_CASE_			=	StableDiffusionXLPipeline(**_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	sd_pipe.to(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	sd_pipe.to(_lowerCAmelCase			)
      sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
  
      # forward without prompt embeds
      SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	3 * ['this is a negative prompt']
      SCREAMING_SNAKE_CASE_			=	negative_prompt
      SCREAMING_SNAKE_CASE_			=	3 * [inputs['prompt']]
  
      SCREAMING_SNAKE_CASE_			=	sd_pipe(**_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	output.images[0, -3:, -3:, -1]
  
      # forward with prompt embeds
      SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	3 * ['this is a negative prompt']
      SCREAMING_SNAKE_CASE_			=	3 * [inputs.pop('prompt'			)]
  
      (
          (
          SCREAMING_SNAKE_CASE_    
      )	,		(
          SCREAMING_SNAKE_CASE_    
      )	,		(
          SCREAMING_SNAKE_CASE_    
      )	,		(
          SCREAMING_SNAKE_CASE_    
      )	,		    
      )			=	sd_pipe.encode_prompt(_lowerCAmelCase			,    negative_prompt=_lowerCAmelCase			)
  
      SCREAMING_SNAKE_CASE_			=	sd_pipe(
          **_lowerCAmelCase			,    prompt_embeds=_lowerCAmelCase			,    negative_prompt_embeds=_lowerCAmelCase			,    pooled_prompt_embeds=_lowerCAmelCase			,    negative_pooled_prompt_embeds=_lowerCAmelCase			,    )
      SCREAMING_SNAKE_CASE_			=	output.images[0, -3:, -3:, -1]
  
      # make sure that it's equal
      assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4
  
  
  
  
  
   def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
      """simple docstring"""
  
  
  
      super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)
  
  
  
  
  
   def 	A_(       self			)     ->				Tuple:
  
  
  
  
      """simple docstring"""
  
  
  
      super().test_inference_batch_single_identical(expected_max_diff=3e-3			)
  
  
  
  
  @slow
  @require_torch_gpu
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
   def 	A_(       self			)     ->				List[Any]:
  
  
  
  
      """simple docstring"""
  
  
  
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
  
  
  
  
  
   def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE="cpu"			,    SCREAMING_SNAKE_CASE=torch.floataa			,    SCREAMING_SNAKE_CASE=0			)     ->				List[str]:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	np.random.RandomState(_lowerCAmelCase			).standard_normal((1, 4, 64, 64)			)
      SCREAMING_SNAKE_CASE_			=	torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			,    dtype=_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	{
          'prompt': 'a photograph of an astronaut riding a horse',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs
  
  
  
  
  
   def 	A_(       self			)     ->				Any:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'			)
      pipe.to(_lowerCAmelCase			)
      pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
  
      SCREAMING_SNAKE_CASE_			=	self.get_inputs(_lowerCAmelCase			)
      SCREAMING_SNAKE_CASE_			=	pipe(**_lowerCAmelCase			).images
      SCREAMING_SNAKE_CASE_			=	image[0, -3:, -3:, -1].flatten()
  
      assert image.shape == (1, 512, 512, 3)
      SCREAMING_SNAKE_CASE_			=	np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]			)
      assert np.abs(image_slice - expected_slice			).max() < 7e-3
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  