


"""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, 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 						snake_case_(				a__							,    a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    StableDiffusionInpaintPipeline
     __UpperCamelCase     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCamelCase     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase     =    frozenset(
         []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __UpperCamelCase     =    frozenset([]	)






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           torch.manual_seed(0 )
           lowerCAmelCase    :		Dict				=      UNetaDConditionModel(
               block_out_channels=(3_2, 6_4)    , layers_per_block=2    , sample_size=3_2    , in_channels=9    , out_channels=4    , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    , cross_attention_dim=3_2    , attention_head_dim=(2, 4)    , use_linear_projection=UpperCamelCase_    , )
           lowerCAmelCase    :		List[Any]				=      PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
           torch.manual_seed(0 )
           lowerCAmelCase    :		Union[str, Any]				=      AutoencoderKL(
               block_out_channels=[3_2, 6_4]    , in_channels=3    , out_channels=3    , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    , latent_channels=4    , sample_size=1_2_8    , )
           torch.manual_seed(0 )
           lowerCAmelCase    :		Union[str, Any]				=      CLIPTextConfig(
               bos_token_id=0    , eos_token_id=2    , hidden_size=3_2    , intermediate_size=3_7    , layer_norm_eps=1E-05    , num_attention_heads=4    , num_hidden_layers=5    , pad_token_id=1    , vocab_size=1_0_0_0    , hidden_act='''gelu'''    , projection_dim=5_1_2    , )
           lowerCAmelCase    :		Any				=      CLIPTextModel(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

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






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Dict=0 ):
           # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
           lowerCAmelCase    :		Any				=      floats_tensor((1, 3, 3_2, 3_2)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      image.cpu().permute(0    , 2    , 3    , 1 )[0]
           lowerCAmelCase    :		Union[str, Any]				=      Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((6_4, 6_4) )
           lowerCAmelCase    :		Any				=      Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((6_4, 6_4) )
           if str(UpperCamelCase_ ).startswith('''mps''' ):
                 lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		Dict				=      torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      {
               '''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     :    Dict ):
           lowerCAmelCase    :		Union[str, Any]				=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           lowerCAmelCase    :		Dict				=      self.get_dummy_components()
           lowerCAmelCase    :		Any				=      StableDiffusionInpaintPipeline(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      sd_pipe.to(UpperCamelCase_ )
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

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

           assert image.shape == (1, 6_4, 6_4, 3)
           lowerCAmelCase    :		Optional[Any]				=      np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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






     def    lowerCamelCase__   (		self     :    str ):
           super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



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






     def    lowerCamelCase__   (		self     :    List[Any] ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
           lowerCAmelCase    :		List[str]				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench.npy''' )

           lowerCAmelCase    :		Union[str, Any]				=      '''stabilityai/stable-diffusion-2-inpainting'''
           lowerCAmelCase    :		Tuple				=      StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    , safety_checker=UpperCamelCase_ )
           pipe.to(UpperCamelCase_ )
           pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           pipe.enable_attention_slicing()

           lowerCAmelCase    :		int				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

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

           assert image.shape == (5_1_2, 5_1_2, 3)
           assert np.abs(expected_image - image ).max() < 9E-3






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Optional[Any]				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		str				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
           lowerCAmelCase    :		int				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

           lowerCAmelCase    :		int				=      '''stabilityai/stable-diffusion-2-inpainting'''
           lowerCAmelCase    :		Any				=      StableDiffusionInpaintPipeline.from_pretrained(
               UpperCamelCase_    , torch_dtype=torch.floataa    , safety_checker=UpperCamelCase_    , )
           pipe.to(UpperCamelCase_ )
           pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           pipe.enable_attention_slicing()

           lowerCAmelCase    :		str				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

           lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(0 )
           lowerCAmelCase    :		Tuple				=      pipe(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , generator=UpperCamelCase_    , output_type='''np'''    , )
           lowerCAmelCase    :		Tuple				=      output.images[0]

           assert image.shape == (5_1_2, 5_1_2, 3)
           assert np.abs(expected_image - image ).max() < 5E-1






     def    lowerCamelCase__   (		self     :    Any ):
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		Any				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

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

           lowerCAmelCase    :		int				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

           lowerCAmelCase    :		Tuple				=      torch.manual_seed(0 )
           lowerCAmelCase    :		Dict				=      pipe(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=2    , output_type='''np'''    , )

           lowerCAmelCase    :		List[str]				=      torch.cuda.max_memory_allocated()
           # make sure that less than 2.65 GB is allocated
           assert mem_bytes < 2.65 * 1_0**9









'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
