code: 
  
  
  '''simple docstring'''
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  class      UpperCAmelCase_						(_SCREAMING_SNAKE_CASE      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Tuple							  =    PaintByExamplePipeline
      lowerCamelCase						:      Optional[int]							  =    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCamelCase						:      List[str]							  =    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowerCamelCase						:      Optional[Any]							  =    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           torch.manual_seed(0			)
           __lowerCamelCase     :			Any              =	UNetaDConditionModel(
               block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=9    ,  out_channels=4    ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,  cross_attention_dim=32    ,  )
           __lowerCamelCase     :			Optional[int]              =	PNDMScheduler(skip_prk_steps=__A			)
           torch.manual_seed(0			)
           __lowerCamelCase     :			Optional[Any]              =	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    ,  )
           torch.manual_seed(0			)
           __lowerCamelCase     :			Any              =	CLIPVisionConfig(
               hidden_size=32    ,  projection_dim=32    ,  intermediate_size=37    ,  layer_norm_eps=1E-05    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  image_size=32    ,  patch_size=4    ,  )
           __lowerCamelCase     :			str              =	PaintByExampleImageEncoder(__A    ,  proj_size=32			)
           __lowerCamelCase     :			List[str]              =	CLIPImageProcessor(crop_size=32    ,  size=32			)
  
           __lowerCamelCase     :			Dict              =	{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'image_encoder': image_encoder,
               'safety_checker': None,
               'feature_extractor': feature_extractor,
           }
           return components
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Dict              =	np.array(image.convert('RGB'			)			)
           __lowerCamelCase     :			int              =	image[None].transpose(0    ,  3    ,  1    ,  2			)
           __lowerCamelCase     :			int              =	torch.from_numpy(__A			).to(dtype=torch.floataa			) / 1_2_7.5 - 1.0
           return image
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Any:
           # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
           __lowerCamelCase     :			int              =	floats_tensor((1, 3, 32, 32)    ,  rng=random.Random(__A			)			).to(__A			)
           __lowerCamelCase     :			Tuple              =	image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
           __lowerCamelCase     :			List[Any]              =	Image.fromarray(np.uinta(__A			)			).convert('RGB'			).resize((64, 64)			)
           __lowerCamelCase     :			List[Any]              =	Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
           __lowerCamelCase     :			Optional[Any]              =	Image.fromarray(np.uinta(__A			)			).convert('RGB'			).resize((32, 32)			)
  
           if str(__A			).startswith('mps'			):
                __lowerCamelCase     :			List[Any]              =	torch.manual_seed(__A			)
           else:
                __lowerCamelCase     :			Dict              =	torch.Generator(device=__A			).manual_seed(__A			)
           __lowerCamelCase     :			Any              =	{
               'example_image': example_image,
               'image': init_image,
               'mask_image': mask_image,
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Optional[int]              =	self.get_dummy_components()
  
           # make sure here that pndm scheduler skips prk
           __lowerCamelCase     :			str              =	PaintByExamplePipeline(**__A			)
           __lowerCamelCase     :			List[str]              =	pipe.to('cpu'			)
           pipe.set_progress_bar_config(disable=__A			)
  
           __lowerCamelCase     :			Any              =	self.get_dummy_inputs()
           __lowerCamelCase     :			str              =	pipe(**__A			)
           __lowerCamelCase     :			int              =	output.images
  
           __lowerCamelCase     :			Optional[int]              =	image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 64, 64, 3)
           __lowerCamelCase     :			Tuple              =	np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			List[Any]              =	'cpu'
           __lowerCamelCase     :			str              =	self.get_dummy_inputs()
           inputs.pop('mask_image'			)
           __lowerCamelCase     :			Any              =	self.convert_to_pt(inputs.pop('image'			)			)
           __lowerCamelCase     :			Optional[int]              =	image.clamp(0    ,  1			) / 2
  
           # make sure here that pndm scheduler skips prk
           __lowerCamelCase     :			str              =	PaintByExamplePipeline(**self.get_dummy_components()			)
           __lowerCamelCase     :			Dict              =	pipe.to(__A			)
           pipe.set_progress_bar_config(disable=__A			)
  
           __lowerCamelCase     :			Optional[Any]              =	pipe(image=__A    ,  mask_image=mask_image[:, 0]    ,  **__A			)
           __lowerCamelCase     :			Optional[Any]              =	output.images
  
           __lowerCamelCase     :			int              =	image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
           __lowerCamelCase     :			str              =	mask_image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
  
           __lowerCamelCase     :			Tuple              =	Image.fromarray(np.uinta(__A			)			).convert('RGB'			)
           __lowerCamelCase     :			List[Any]              =	Image.fromarray(np.uinta(__A			)			).convert('RGB'			)
  
           __lowerCamelCase     :			List[str]              =	pipe(**self.get_dummy_inputs()			)
           __lowerCamelCase     :			Dict              =	output.images
  
           assert out_a.shape == (1, 64, 64, 3)
           assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5E-2
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           # make sure here that pndm scheduler skips prk
           __lowerCamelCase     :			Tuple              =	load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/paint_by_example/dog_in_bucket.png'			)
           __lowerCamelCase     :			Union[str, Any]              =	load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/paint_by_example/mask.png'			)
           __lowerCamelCase     :			List[str]              =	load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/paint_by_example/panda.jpg'			)
  
           __lowerCamelCase     :			Union[str, Any]              =	PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'			)
           __lowerCamelCase     :			List[str]              =	pipe.to(__A			)
           pipe.set_progress_bar_config(disable=__A			)
  
           __lowerCamelCase     :			Union[str, Any]              =	torch.manual_seed(3_21			)
           __lowerCamelCase     :			Dict              =	pipe(
               image=__A    ,  mask_image=__A    ,  example_image=__A    ,  generator=__A    ,  guidance_scale=5.0    ,  num_inference_steps=50    ,  output_type='np'    ,  )
  
           __lowerCamelCase     :			Union[str, Any]              =	output.images
  
           __lowerCamelCase     :			Any              =	image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 5_12, 5_12, 3)
           __lowerCamelCase     :			Dict              =	np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  