






from manim import *



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    def _a							(				self      :     int      )      ->							Dict:
           __UpperCAmelCase     				=Rectangle(height=0.5 ,     width=0.5      )
           __UpperCAmelCase     				=Rectangle(height=0.46 ,     width=0.46      ).set_stroke(width=0      )

           __UpperCAmelCase     				=Rectangle(height=0.25 ,     width=0.25      )

           __UpperCAmelCase     				=[mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=[mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""CPU""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           cpu.move_to([-2.5, -0.5, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[mem.copy() for i in range(4      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""GPU""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           gpu.move_to([-1, -1, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )

           __UpperCAmelCase     				=Text("""Model""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           model.move_to([3, -1.0, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[]
           __UpperCAmelCase     				=[]

           for i, rect in enumerate(__SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				=fill.copy().set_fill(__SCREAMING_SNAKE_CASE ,     opacity=0.8      )
                  target.move_to(__SCREAMING_SNAKE_CASE      )
                  model_arr.append(__SCREAMING_SNAKE_CASE      )

                  __UpperCAmelCase     				=Rectangle(height=0.46 ,     width=0.46      ).set_stroke(width=0.0      ).set_fill(__SCREAMING_SNAKE_CASE ,     opacity=0.8      )
                  cpu_target.move_to(cpu_left_col_base[i]      )
                  model_cpu_arr.append(__SCREAMING_SNAKE_CASE      )

           self.add(*__SCREAMING_SNAKE_CASE ,     *__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[meta_mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=[meta_mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""Disk""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           disk.move_to([-4, -1.25, 0]      )
           self.add(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=Square(side_length=2.2      )
           key.move_to([-5, 2, 0]      )

           __UpperCAmelCase     				=MarkupText(
               f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' ,     font_size=18 ,     )

           key_text.move_to([-5, 2.4, 0]      )

           self.add(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=MarkupText(
               f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' ,     font_size=18 ,     )

           blue_text.next_to(__SCREAMING_SNAKE_CASE ,     DOWN * 2.4 ,     aligned_edge=key_text.get_left()      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=MarkupText(
               f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' ,     font_size=24 ,     )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=Square(0.3      )
           input.set_fill(__SCREAMING_SNAKE_CASE ,     opacity=1.0      )
           input.set_stroke(width=0.0      )
           input.next_to(model_base[0] ,     __SCREAMING_SNAKE_CASE ,     buff=0.5      )

           self.play(Write(__SCREAMING_SNAKE_CASE      )      )

           input.generate_target()
           input.target.next_to(model_arr[0] ,     direction=__SCREAMING_SNAKE_CASE ,     buff=0.02      )
           self.play(MoveToTarget(__SCREAMING_SNAKE_CASE      )      )

           self.play(FadeOut(__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=Arrow(start=__SCREAMING_SNAKE_CASE ,     end=__SCREAMING_SNAKE_CASE ,     color=__SCREAMING_SNAKE_CASE ,     buff=0.5      )
           a.next_to(model_arr[0].get_left() ,     __SCREAMING_SNAKE_CASE ,     buff=0.2      )

           model_cpu_arr[0].generate_target()
           model_cpu_arr[0].target.move_to(gpu_rect[0]      )

           __UpperCAmelCase     				=MarkupText(
               f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' ,     font_size=24 ,     )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE ,     run_time=3      )      )

           __UpperCAmelCase     				={"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.02}

           self.play(
               Write(__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_arr[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_cpu_arr[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )
           self.play(MoveToTarget(model_cpu_arr[0]      )      )

           __UpperCAmelCase     				=a.copy()
           for i in range(6      ):
                  a_c.next_to(model_arr[i].get_right() + 0.02 ,     __SCREAMING_SNAKE_CASE ,     buff=0.2      )

                  input.generate_target()
                  input.target.move_to(model_arr[i].get_right() + 0.02      )

                  __UpperCAmelCase     				=AnimationGroup(
                      FadeOut(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     MoveToTarget(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     FadeIn(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     lag_ratio=0.2      )

                  self.play(__SCREAMING_SNAKE_CASE      )

                  model_cpu_arr[i].generate_target()
                  model_cpu_arr[i].target.move_to(cpu_left_col_base[i]      )

                  if i < 5:
                         model_cpu_arr[i + 1].generate_target()
                         model_cpu_arr[i + 1].target.move_to(gpu_rect[0]      )
                         if i >= 1:
                                __UpperCAmelCase     				=0.7

                         self.play(
                             Circumscribe(model_arr[i] ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[i] ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[i + 1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_arr[i + 1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )
                         if i < 1:
                                self.play(
                                    MoveToTarget(model_cpu_arr[i]      ) ,     MoveToTarget(model_cpu_arr[i + 1]      ) ,     )
                         else:
                                self.play(
                                    MoveToTarget(model_cpu_arr[i] ,     run_time=0.7      ) ,     MoveToTarget(model_cpu_arr[i + 1] ,     run_time=0.7      ) ,     )
                  else:
                         model_cpu_arr[i].generate_target()
                         model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]      )
                         input.generate_target()
                         input.target.next_to(model_arr[-1].get_right() ,     RIGHT + 0.02 ,     buff=0.2      )

                         self.play(
                             Circumscribe(model_arr[-1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[-1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )

                         self.play(MoveToTarget(model_cpu_arr[i]      )      )

                  __UpperCAmelCase     				=a_c
                  __UpperCAmelCase     				=a_c.copy()

           input.generate_target()
           input.target.next_to(model_base[-1] ,     RIGHT + 0.02 ,     buff=0.5      )
           self.play(
               FadeOut(__SCREAMING_SNAKE_CASE      ) ,     FadeOut(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     )

           __UpperCAmelCase     				=MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.''' ,     font_size=24      )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE ,     run_time=3      ) ,     MoveToTarget(__SCREAMING_SNAKE_CASE      )      )

           self.wait()








'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
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_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							StableDiffusionInstructPixaPixPipeline
		_a       					=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		_a       					=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Optional[Any]				   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=8       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=32       ,  )
							UpperCamelCase__  :List[str]				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							torch.manual_seed(0				)
							UpperCamelCase__  :Dict				   =					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				)
							UpperCamelCase__  :int				   =					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       ,  )
							UpperCamelCase__  :Tuple				   =					CLIPTextModel(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

							UpperCamelCase__  :Dict				   =					{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
							UpperCamelCase__  :List[Any]				   =					Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				)
							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :Tuple				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :List[Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''image_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :str				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :List[str]				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_components()
							UpperCamelCase__  :Any				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					'''french fries'''
							UpperCamelCase__  :Any				   =					sd_pipe(**UpperCamelCase_       ,  negative_prompt=UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					output.images
							UpperCamelCase__  :Any				   =					image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[str]				   =					np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Dict				   =					self.get_dummy_components()
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Dict				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					[inputs['''prompt''']] * 2

							UpperCamelCase__  :str				   =					np.array(inputs['''image''']				).astype(np.floataa				) / 255.0
							UpperCamelCase__  :int				   =					torch.from_numpy(UpperCamelCase_				).unsqueeze(0				).to(UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					image / 2 + 0.5
							UpperCamelCase__  :Union[str, Any]				   =					image.permute(0       ,  3       ,  1       ,  2				)
							UpperCamelCase__  :Union[str, Any]				   =					image.repeat(2       ,  1       ,  1       ,  1				)

							UpperCamelCase__  :Optional[Any]				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Any				   =					image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__  :List[Any]				   =					np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Tuple				   =					self.get_dummy_components()
							UpperCamelCase__  :Union[str, Any]				   =					EulerAncestralDiscreteScheduler(
							    beta_start=0.00085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''				)
							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :str				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Tuple				   =					image[0, -3:, -3:, -1]

							UpperCamelCase__  :Dict				   =					[round(UpperCamelCase_       ,  4				) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_				) for x in slice]				)				)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					VaeImageProcessor(do_resize=UpperCamelCase_       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)				)[0]

							UpperCamelCase__  :List[Any]				   =					components['''vae''']
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)

							for image_param in self.image_latents_params:
												if image_param in inputs.keys():
																	UpperCamelCase__  :List[str]				   =					vae.encode(inputs[image_param]				).latent_dist.mode()

							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				)[0]

							UpperCamelCase__  :Optional[int]				   =					np.abs(out - out_latents_inputs				).max()
							self.assertLess(UpperCamelCase_       ,  1e-4       ,  '''passing latents as image input generate different result from passing image'''				)



@slow
@require_torch_gpu
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					torch.manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
							UpperCamelCase__  :Dict				   =					{
							    '''prompt''': '''turn him into a cyborg''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''image_guidance_scale''': 1.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[Any]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :str				   =					np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :int				   =					self.get_inputs()
							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Optional[int]				   =					np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :str				   =					DDIMScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Union[str, Any]				   =					self.get_inputs()
							UpperCamelCase__  :Dict				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[int]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Tuple				   =					np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					0

							def callback_fn(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				) -> None:
												UpperCamelCase__  :Optional[Any]				   =					True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Optional[Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[str]				   =					np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]				)

																	assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2
												elif step == 2:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Union[str, Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[Any]				   =					np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]				)

																	assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2

							UpperCamelCase__  :Union[str, Any]				   =					False

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :Dict				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Dict				   =					self.get_inputs()
							pipe(**UpperCamelCase_       ,  callback=UpperCamelCase_       ,  callback_steps=1				)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :List[str]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing(1				)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__  :Any				   =					self.get_inputs()
							UpperCamelCase__  :Tuple				   =					pipe(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__  :int				   =					inputs['''image'''].resize((504, 504)				)

							UpperCamelCase__  :Union[str, Any]				   =					'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :str				   =					output.images[0]

							UpperCamelCase__  :str				   =					image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__  :Dict				   =					np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3