import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()




@skip_mps
class  __magic_name__  (		lowerCAmelCase__      ,	lowerCAmelCase__      ,	unittest.TestCase    ):





  """simple docstring"""




  lowerCAmelCase      :    Tuple  			=   StableDiffusionPanoramaPipeline
  lowerCAmelCase      :    List[Any]  			=   TEXT_TO_IMAGE_PARAMS
  lowerCAmelCase      :    Optional[int]  			=   TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCAmelCase      :    Any  			=   TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCAmelCase      :    Dict  			=   TEXT_TO_IMAGE_IMAGE_PARAMS



  def   lowerCAmelCase						(	self					:  List[Any]							):



   """simple docstring"""



   torch.manual_seed(0							)
   _UpperCamelCase:     int							= UNetaDConditionModel(
       block_out_channels=(32, 64)		,  layers_per_block=1		,  sample_size=32		,  in_channels=4		,  out_channels=4		,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,  cross_attention_dim=32		,  )
   _UpperCamelCase:     Dict							= DDIMScheduler()
   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:     str							= 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=1_000		,  )
   _UpperCamelCase:     str							= CLIPTextModel(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Any							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

   _UpperCamelCase:     Optional[Any]							= {
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''vae''': vae,
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''safety_checker''': None,
       '''feature_extractor''': None,
   }
   return components



  def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Any		,  _lowercase					:  List[Any]=0							):



   """simple docstring"""



   _UpperCamelCase:     Optional[int]							= torch.manual_seed(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Tuple							= {
       '''prompt''': '''a photo of the dolomites''',
       '''generator''': generator,
       # Setting height and width to None to prevent OOMs on CPU.
       '''height''': None,
       '''width''': None,
       '''num_inference_steps''': 1,
       '''guidance_scale''': 6.0,
       '''output_type''': '''numpy''',
   }
   return inputs



  def   lowerCAmelCase						(	self					:  Any							):



   """simple docstring"""



   _UpperCamelCase:     str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase:     Dict							= self.get_dummy_components()
   _UpperCamelCase:     int							= StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Union[str, Any]							= sd_pipe.to(_SCREAMING_SNAKE_CASE							)
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

   _UpperCamelCase:     Dict							= self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     List[str]							= sd_pipe(**_SCREAMING_SNAKE_CASE							).images
   _UpperCamelCase:     Optional[int]							= image[0, -3:, -3:, -1]
   assert image.shape == (1, 64, 64, 3)

   _UpperCamelCase:     Optional[Any]							= np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



  def   lowerCAmelCase						(	self					:  Union[str, Any]							):



   """simple docstring"""



   super().test_inference_batch_consistent(batch_sizes=[1, 2]							)



  def   lowerCAmelCase						(	self					:  Optional[int]							):



   """simple docstring"""



   super().test_inference_batch_single_identical(batch_size=2		,  expected_max_diff=3.25E-3							)



  def   lowerCAmelCase						(	self					:  List[str]							):



   """simple docstring"""



   _UpperCamelCase:     Tuple							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase:     List[Any]							= self.get_dummy_components()
   _UpperCamelCase:     Dict							= StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Dict							= sd_pipe.to(_SCREAMING_SNAKE_CASE							)
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

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

   assert image.shape == (1, 64, 64, 3)

   _UpperCamelCase:     Any							= np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



  def   lowerCAmelCase						(	self					:  Union[str, Any]							):



   """simple docstring"""



   _UpperCamelCase:     str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase:     Tuple							= self.get_dummy_components()
   _UpperCamelCase:     Optional[int]							= StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Optional[Any]							= sd_pipe.to(_SCREAMING_SNAKE_CASE							)
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

   _UpperCamelCase:     str							= self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     List[Any]							= sd_pipe(**_SCREAMING_SNAKE_CASE		,  view_batch_size=2							)
   _UpperCamelCase:     Tuple							= output.images
   _UpperCamelCase:     str							= image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   _UpperCamelCase:     List[str]							= np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



  def   lowerCAmelCase						(	self					:  int							):



   """simple docstring"""



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

   _UpperCamelCase:     int							= self.get_dummy_inputs(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Optional[int]							= sd_pipe(**_SCREAMING_SNAKE_CASE							).images
   _UpperCamelCase:     Optional[int]							= image[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)

   _UpperCamelCase:     Optional[Any]							= np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



  def   lowerCAmelCase						(	self					:  Optional[int]							):



   """simple docstring"""



   _UpperCamelCase:     Optional[int]							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _UpperCamelCase:     Optional[Any]							= self.get_dummy_components()
   _UpperCamelCase:     Optional[int]							= PNDMScheduler(
       beta_start=0.00085		,  beta_end=0.012		,  beta_schedule='''scaled_linear'''		,  skip_prk_steps=_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Dict							= StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Tuple							= sd_pipe.to(_SCREAMING_SNAKE_CASE							)
   sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)

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

   assert image.shape == (1, 64, 64, 3)

   _UpperCamelCase:     List[str]							= np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2




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





  """simple docstring"""




  def   lowerCAmelCase						(	self					:  str							):



   """simple docstring"""



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



  def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Tuple=0							):



   """simple docstring"""



   _UpperCamelCase:     Any							= torch.manual_seed(_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Dict							= {
       '''prompt''': '''a photo of the dolomites''',
       '''generator''': generator,
       '''num_inference_steps''': 3,
       '''guidance_scale''': 7.5,
       '''output_type''': '''numpy''',
   }
   return inputs



  def   lowerCAmelCase						(	self					:  Any							):



   """simple docstring"""



   _UpperCamelCase:     Any							= '''stabilityai/stable-diffusion-2-base'''
   _UpperCamelCase:     str							= DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		,  subfolder='''scheduler'''							)
   _UpperCamelCase:     Dict							= StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		,  scheduler=_SCREAMING_SNAKE_CASE		,  safety_checker=_SCREAMING_SNAKE_CASE							)
   pipe.to(_SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
   pipe.enable_attention_slicing()

   _UpperCamelCase:     str							= self.get_inputs()
   _UpperCamelCase:     Union[str, Any]							= pipe(**_SCREAMING_SNAKE_CASE							).images
   _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 2_048, 3)

   _UpperCamelCase:     Optional[Any]							= np.array(
       [
           0.36968392,
           0.27025372,
           0.32446766,
           0.28379387,
           0.36363274,
           0.30733347,
           0.27100027,
           0.27054125,
           0.25536096,
       ]							)

   assert np.abs(expected_slice - image_slice							).max() < 1E-2



  def   lowerCAmelCase						(	self					:  Dict							):



   """simple docstring"""



   _UpperCamelCase:     str							= StableDiffusionPanoramaPipeline.from_pretrained(
       '''stabilityai/stable-diffusion-2-base'''		,  safety_checker=_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     str							= LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
   pipe.to(_SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
   pipe.enable_attention_slicing()

   _UpperCamelCase:     Optional[int]							= self.get_inputs()
   _UpperCamelCase:     Any							= pipe(**_SCREAMING_SNAKE_CASE							).images
   _UpperCamelCase:     Dict							= image[0, -3:, -3:, -1].flatten()

   assert image.shape == (1, 512, 2_048, 3)

   _UpperCamelCase:     Dict							= np.array(
       [
           [
               0.0,
               0.0,
               0.0,
               0.0,
               0.0,
               0.0,
               0.0,
               0.0,
               0.0,
           ]
       ]							)

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



  def   lowerCAmelCase						(	self					:  str							):



   """simple docstring"""



   _UpperCamelCase:     int							= 0

   def callback_fn(_lowercase					:  Dict		,  _lowercase					:  Dict		,  _lowercase					:  Optional[int]							) -> None:
    _UpperCamelCase:     str							= True
    nonlocal number_of_steps
    number_of_steps += 1
    if step == 1:
     _UpperCamelCase:     Optional[Any]							= latents.detach().cpu().numpy()
     assert latents.shape == (1, 4, 64, 256)
     _UpperCamelCase:     List[Any]							= latents[0, -3:, -3:, -1]

     _UpperCamelCase:     List[Any]							= np.array(
         [
             0.18681869,
             0.33907816,
             0.5361276,
             0.14432865,
             -0.02856611,
             -0.73941123,
             0.23397987,
             0.47322682,
             -0.37823164,
         ]							)
     assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
    elif step == 2:
     _UpperCamelCase:     Optional[Any]							= latents.detach().cpu().numpy()
     assert latents.shape == (1, 4, 64, 256)
     _UpperCamelCase:     List[Any]							= latents[0, -3:, -3:, -1]

     _UpperCamelCase:     Optional[Any]							= np.array(
         [
             0.18539645,
             0.33987248,
             0.5378559,
             0.14437142,
             -0.02455261,
             -0.7338317,
             0.23990755,
             0.47356272,
             -0.3786505,
         ]							)

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

   _UpperCamelCase:     Optional[int]							= False

   _UpperCamelCase:     List[Any]							= '''stabilityai/stable-diffusion-2-base'''
   _UpperCamelCase:     str							= DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		,  subfolder='''scheduler'''							)
   _UpperCamelCase:     int							= StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		,  scheduler=_SCREAMING_SNAKE_CASE		,  safety_checker=_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     Tuple							= pipe.to(_SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
   pipe.enable_attention_slicing()

   _UpperCamelCase:     str							= self.get_inputs()
   pipe(**_SCREAMING_SNAKE_CASE		,  callback=_SCREAMING_SNAKE_CASE		,  callback_steps=1							)
   assert callback_fn.has_been_called
   assert number_of_steps == 3



  def   lowerCAmelCase						(	self					:  List[str]							):



   """simple docstring"""



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

   _UpperCamelCase:     Any							= '''stabilityai/stable-diffusion-2-base'''
   _UpperCamelCase:     Optional[Any]							= DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		,  subfolder='''scheduler'''							)
   _UpperCamelCase:     Optional[int]							= StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		,  scheduler=_SCREAMING_SNAKE_CASE		,  safety_checker=_SCREAMING_SNAKE_CASE							)
   _UpperCamelCase:     List[Any]							= pipe.to(_SCREAMING_SNAKE_CASE							)
   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE							)
   pipe.enable_attention_slicing(1							)
   pipe.enable_sequential_cpu_offload()

   _UpperCamelCase:     List[str]							= self.get_inputs()
   _UpperCamelCase:     List[Any]							= pipe(**_SCREAMING_SNAKE_CASE							)

   _UpperCamelCase:     int							= torch.cuda.max_memory_allocated()
   # make sure that less than 5.2 GB is allocated
   assert mem_bytes < 5.5 * 10**9
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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   StableDiffusionInpaintPipeline
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase      :    str  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    str  			=   frozenset(
       []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCAmelCase      :    Tuple  			=   frozenset([]    )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= 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		,  attention_head_dim=(2, 4)		,  use_linear_projection=_lowercase		,  )
    _UpperCamelCase:     List[str]							= PNDMScheduler(skip_prk_steps=_lowercase							)
    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= 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							)
    _UpperCamelCase:     Optional[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=1_000		,  hidden_act='''gelu'''		,  projection_dim=512		,  )
    _UpperCamelCase:     Optional[int]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     Optional[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  int=0							):



    """simple docstring"""



    _UpperCamelCase:     Any							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							)[0]
    _UpperCamelCase:     Optional[int]							= Image.fromarray(np.uinta(_lowercase							)							).convert('''RGB'''							).resize((64, 64)							)
    _UpperCamelCase:     Optional[Any]							= Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Dict							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Union[str, Any]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



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

    _UpperCamelCase:     Tuple							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     List[Any]							= sd_pipe(**_lowercase							).images
    _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    _UpperCamelCase:     List[str]							= np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

    assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



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




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





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Tuple							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     Dict							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Optional[Any]							= StableDiffusionInpaintPipeline.from_pretrained(_lowercase		,  safety_checker=_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     List[str]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     str							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 9E-3



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     str							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     int							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  torch_dtype=torch.floataa		,  safety_checker=_lowercase		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     Optional[Any]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)
    _UpperCamelCase:     int							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 5E-1



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



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

    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

    _UpperCamelCase:     Optional[int]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Union[str, Any]							= PNDMScheduler.from_pretrained(_lowercase		,  subfolder='''scheduler'''							)
    _UpperCamelCase:     Optional[int]							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  safety_checker=_lowercase		,  scheduler=_lowercase		,  torch_dtype=torch.floataa		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    _UpperCamelCase:     Optional[int]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Union[str, Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     Union[str, Any]							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  num_inference_steps=2		,  output_type='''np'''		,  )

    _UpperCamelCase:     Dict							= torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9