


import random







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    [], [], []
     for element in data:
          if element < pivot:
               less.append(SCREAMING_SNAKE_CASE				)
          elif element > pivot:
               greater.append(SCREAMING_SNAKE_CASE				)
          else:
               equal.append(SCREAMING_SNAKE_CASE				)
     return less, equal, greater







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







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

     _lowerCAmelCase   :					Any        =    items[random.randint(0		,							len(SCREAMING_SNAKE_CASE				) - 1				)]
     _lowerCAmelCase   :					List[str]        =    0
     _lowerCAmelCase   :					List[str]        =    _partition(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    len(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    len(SCREAMING_SNAKE_CASE				)

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









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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    1
      _lowerCAmelCase   :					List[str]        =    3
      _lowerCAmelCase   :					int        =    (3_2, 3_2)

      _lowerCAmelCase   :					Dict        =    floats_tensor((batch_size, num_channels) + sizes     ,						rng=random.Random(0       )       ).to(A_       )
      return image

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Dict        =    UNetaDConditionModel(
          block_out_channels=(3_2, 3_2, 6_4)     ,						layers_per_block=2     ,						sample_size=3_2     ,						in_channels=7     ,						out_channels=4     ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")     ,						up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")     ,						cross_attention_dim=3_2     ,						attention_head_dim=8     ,						use_linear_projection=A_     ,						only_cross_attention=(True, True, False)     ,						num_class_embeds=1_0_0     ,						)
      return model

 @property
 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    AutoencoderKL(
          block_out_channels=[3_2, 3_2, 6_4]     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]     ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]     ,						latent_channels=4     ,						)
      return model

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[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     ,						)
      return CLIPTextModel(A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase   :					Tuple        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					Optional[int]        =    DDPMScheduler()
      _lowerCAmelCase   :					Optional[Any]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					str        =    self.dummy_vae
      _lowerCAmelCase   :					Any        =    self.dummy_text_encoder
      _lowerCAmelCase   :					Dict        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					List[Any]        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					List[Any]        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Tuple        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Tuple        =    output.images

      _lowerCAmelCase   :					Optional[int]        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					Union[str, Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					List[Any]        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      _lowerCAmelCase   :					Optional[int]        =    low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      _lowerCAmelCase   :					str        =    np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]       )

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

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    "cpu"  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase   :					List[str]        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					str        =    DDPMScheduler()
      _lowerCAmelCase   :					Union[str, Any]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					Tuple        =    self.dummy_vae
      _lowerCAmelCase   :					Union[str, Any]        =    self.dummy_text_encoder
      _lowerCAmelCase   :					List[Any]        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					Dict        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					Optional[Any]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Dict        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Tuple        =    sd_pipe(
          2 * [prompt]     ,						image=2 * [low_res_image]     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					List[str]        =    output.images
      assert image.shape[0] == 2

      _lowerCAmelCase   :					str        =    torch.Generator(device=A_       ).manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						num_images_per_prompt=2     ,						guidance_scale=6.0     ,						noise_level=2_0     ,						num_inference_steps=2     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images
      assert image.shape[0] == 2


 @unittest.skipIf(torch_device != "cuda"     ,						"This test requires a GPU"       )
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.dummy_cond_unet_upscale
      _lowerCAmelCase   :					List[str]        =    DDPMScheduler()
      _lowerCAmelCase   :					Optional[int]        =    DDIMScheduler(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					Optional[Any]        =    self.dummy_vae
      _lowerCAmelCase   :					List[str]        =    self.dummy_text_encoder
      _lowerCAmelCase   :					Union[str, Any]        =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

      _lowerCAmelCase   :					Any        =    self.dummy_image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Any        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((6_4, 6_4)       )

      # put models in fp16, except vae as it overflows in fp16
      _lowerCAmelCase   :					Tuple        =    unet.half()
      _lowerCAmelCase   :					Optional[Any]        =    text_encoder.half()

      # make sure here that pndm scheduler skips prk
      _lowerCAmelCase   :					List[str]        =    StableDiffusionUpscalePipeline(
          unet=A_     ,						low_res_scheduler=A_     ,						scheduler=A_     ,						vae=A_     ,						text_encoder=A_     ,						tokenizer=A_     ,						max_noise_level=3_5_0     ,						)
      _lowerCAmelCase   :					Optional[Any]        =    sd_pipe.to(A_       )
      sd_pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					str        =    "A painting of a squirrel eating a burger"
      _lowerCAmelCase   :					Any        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[Any]        =    sd_pipe(
          [prompt]     ,						image=A_     ,						generator=A_     ,						num_inference_steps=2     ,						output_type="np"     ,						).images

      _lowerCAmelCase   :					List[str]        =    low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)





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



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )
      _lowerCAmelCase   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat.npy"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Optional[int]        =    StableDiffusionUpscalePipeline.from_pretrained(A_       )
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

      _lowerCAmelCase   :					str        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					str        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					Optional[int]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					int        =    output.images[0]

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

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )
      _lowerCAmelCase   :					Dict        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
          "/upsampled_cat_fp16.npy"       )

      _lowerCAmelCase   :					Union[str, Any]        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing()

      _lowerCAmelCase   :					Optional[Any]        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					Any        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					int        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						output_type="np"     ,						)
      _lowerCAmelCase   :					Union[str, Any]        =    output.images[0]

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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					int        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/sd2-upscale/low_res_cat.png"       )

      _lowerCAmelCase   :					Tuple        =    "stabilityai/stable-diffusion-x4-upscaler"
      _lowerCAmelCase   :					Any        =    StableDiffusionUpscalePipeline.from_pretrained(
          A_     ,						torch_dtype=torch.floataa     ,						)
      pipe.to(A_       )
      pipe.set_progress_bar_config(disable=A_       )
      pipe.enable_attention_slicing(1       )
      pipe.enable_sequential_cpu_offload()

      _lowerCAmelCase   :					List[Any]        =    "a cat sitting on a park bench"

      _lowerCAmelCase   :					Union[str, Any]        =    torch.manual_seed(0       )
      _lowerCAmelCase   :					List[Any]        =    pipe(
          prompt=A_     ,						image=A_     ,						generator=A_     ,						num_inference_steps=5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					Optional[Any]        =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.9 GB is allocated
      assert mem_bytes < 2.9 * 1_0**9






