code: 
  
  
  '''simple docstring'''
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  
  from diffusers import (
      AutoencoderKL,
      DPMSolverMultistepScheduler,
      PNDMScheduler,
      StableDiffusionImageVariationPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    StableDiffusionImageVariationPipeline
      lowerCamelCase						:      Optional[Any]							  =    IMAGE_VARIATION_PARAMS
      lowerCamelCase						:      List[Any]							  =    IMAGE_VARIATION_BATCH_PARAMS
      lowerCamelCase						:      Optional[int]							  =    frozenset([]						)
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCamelCase						:      Optional[Any]							  =    frozenset([]						)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           torch.manual_seed(0			)
           __lowerCamelCase     :			List[str]              =	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')    ,  cross_attention_dim=32    ,  )
           __lowerCamelCase     :			Optional[int]              =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_			)
           torch.manual_seed(0			)
           __lowerCamelCase     :			Optional[int]              =	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     :			Optional[int]              =	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     :			List[str]              =	CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	CLIPImageProcessor(crop_size=32    ,  size=32			)
  
           __lowerCamelCase     :			List[str]              =	{
               'unet': unet,
               'scheduler': scheduler,
               'vae': vae,
               'image_encoder': image_encoder,
               'feature_extractor': feature_extractor,
               'safety_checker': None,
           }
           return components
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			)       ->  str:
           __lowerCamelCase     :			Any              =	floats_tensor((1, 3, 32, 32)    ,  rng=random.Random(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Union[str, Any]              =	image.cpu().permute(0    ,  2    ,  3    ,  1			)[0]
           __lowerCamelCase     :			Dict              =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_			)			).convert('RGB'			).resize((32, 32)			)
           if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
                __lowerCamelCase     :			Dict              =	torch.manual_seed(SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			List[str]              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	{
               'image': image,
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			str              =	'cpu'  # ensure determinism for the device-dependent torch.Generator
           __lowerCamelCase     :			Dict              =	self.get_dummy_components()
           __lowerCamelCase     :			List[str]              =	StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			List[str]              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
           __lowerCamelCase     :			int              =	image[0, -3:, -3:, -1]
  
           assert image.shape == (1, 64, 64, 3)
           __lowerCamelCase     :			List[Any]              =	np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Any              =	'cpu'  # ensure determinism for the device-dependent torch.Generator
           __lowerCamelCase     :			Optional[Any]              =	self.get_dummy_components()
           __lowerCamelCase     :			List[str]              =	StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Tuple              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Dict              =	2 * [inputs['image']]
           __lowerCamelCase     :			int              =	sd_pipe(**SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	output.images
  
           __lowerCamelCase     :			Dict              =	image[-1, -3:, -3:, -1]
  
           assert image.shape == (2, 64, 64, 3)
           __lowerCamelCase     :			int              =	np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]			)
  
           assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3			)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=torch.floataa    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Dict:
           __lowerCamelCase     :			Optional[Any]              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	load_image(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/input_image_vermeer.png'			)
           __lowerCamelCase     :			Union[str, Any]              =	np.random.RandomState(SCREAMING_SNAKE_CASE_			).standard_normal((1, 4, 64, 64)			)
           __lowerCamelCase     :			List[str]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_    ,  dtype=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	{
               'image': init_image,
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained(
               'lambdalabs/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
           __lowerCamelCase     :			str              =	image[0, -3:, -3:, -1].flatten()
  
           assert image.shape == (1, 5_12, 5_12, 3)
           __lowerCamelCase     :			Dict              =	np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]			)
           assert np.abs(image_slice - expected_slice			).max() < 6E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Optional[Any]              =	0
  
           def callback_fn(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) -> None:
                __lowerCamelCase     :			List[Any]              =	True
                nonlocal number_of_steps
                number_of_steps += 1
                if step == 1:
                     __lowerCamelCase     :			List[Any]              =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     __lowerCamelCase     :			Tuple              =	latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :			Any              =	np.array(
                         [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]			)
  
                     assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
                elif step == 2:
                     __lowerCamelCase     :			int              =	latents.detach().cpu().numpy()
                     assert latents.shape == (1, 4, 64, 64)
                     __lowerCamelCase     :			Union[str, Any]              =	latents[0, -3:, -3:, -1]
                     __lowerCamelCase     :			Tuple              =	np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]			)
  
                     assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
  
           __lowerCamelCase     :			Tuple              =	False
  
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
               'fusing/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa    ,  )
           pipe.to(SCREAMING_SNAKE_CASE_			)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
           pipe.enable_attention_slicing()
  
           __lowerCamelCase     :			Tuple              =	self.get_inputs(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           pipe(**SCREAMING_SNAKE_CASE_    ,  callback=SCREAMING_SNAKE_CASE_    ,  callback_steps=1			)
           assert callback_fn.has_been_called
           assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()
  
           __lowerCamelCase     :			Any              =	'fusing/sd-image-variations-diffusers'
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
               SCREAMING_SNAKE_CASE_    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa			)
           __lowerCamelCase     :			int              =	pipe.to(SCREAMING_SNAKE_CASE_			)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
           pipe.enable_attention_slicing(1			)
           pipe.enable_sequential_cpu_offload()
  
           __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           __lowerCamelCase     :			Optional[Any]              =	pipe(**SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[Any]              =	torch.cuda.max_memory_allocated()
           # make sure that less than 2.6 GB is allocated
           assert mem_bytes < 2.6 * 10**9
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=torch.floataa    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Optional[Any]:
           __lowerCamelCase     :			Tuple              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[Any]              =	load_image(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/input_image_vermeer.png'			)
           __lowerCamelCase     :			Union[str, Any]              =	np.random.RandomState(SCREAMING_SNAKE_CASE_			).standard_normal((1, 4, 64, 64)			)
           __lowerCamelCase     :			Dict              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_    ,  dtype=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	{
               'image': init_image,
               'latents': latents,
               'generator': generator,
               'num_inference_steps': 50,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
           sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]
  
           __lowerCamelCase     :			List[str]              =	load_numpy(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'			)
           __lowerCamelCase     :			Dict              =	np.abs(expected_image - image			).max()
           assert max_diff < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
           __lowerCamelCase     :			Optional[Any]              =	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
           sd_pipe.to(SCREAMING_SNAKE_CASE_			)
           sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Tuple              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	25
           __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]
  
           __lowerCamelCase     :			Tuple              =	load_numpy(
               'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
               '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'			)
           __lowerCamelCase     :			int              =	np.abs(expected_image - image			).max()
           assert max_diff < 1E-3
  
  
  
  
  
  
  
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()
  
  
  
  
  
  
  