





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2





def 	lowerCAmelCase_					(			__a      ,       __a=0						)	->						Union[str, Any]:







  """simple docstring"""





  return sorted(__a      ,       key=lambda __a						: x[column]						)





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=float("inf"						)						)	->						Dict:







  """simple docstring"""





  for i in range(points_counts - 1						):
    for j in range(i + 1      ,       __a						):
      lowerCamelCase__: List[str]         =euclidean_distance_sqr(points[i]      ,       points[j]						)
      if current_dis < min_dis:
        lowerCamelCase__: List[Any]         =current_dis
  return min_dis





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a=float("inf"						)						)	->						List[str]:







  """simple docstring"""





  for i in range(min(6      ,       points_counts - 1						)      ,       __a						):
    for j in range(max(0      ,       i - 6						)      ,       __a						):
      lowerCamelCase__: Union[str, Any]         =euclidean_distance_sqr(points[i]      ,       points[j]						)
      if current_dis < min_dis:
        lowerCamelCase__: List[Any]         =current_dis
  return min_dis





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a						)	->						Optional[Any]:







  """simple docstring"""





  if points_counts <= 3:
    return dis_between_closest_pair(__a      ,       __a						)

  # recursion
  lowerCamelCase__: Tuple         =points_counts // 2
  lowerCamelCase__: Optional[int]         =closest_pair_of_points_sqr(
      __a      ,       points_sorted_on_y[:mid]      ,       __a						)
  lowerCamelCase__: Optional[int]         =closest_pair_of_points_sqr(
      __a      ,       points_sorted_on_y[mid:]      ,       points_counts - mid						)
  lowerCamelCase__: str         =min(__a      ,       __a						)
  lowerCamelCase__: Any         =[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
      cross_strip.append(__a						)

  lowerCamelCase__: int         =dis_between_closest_in_strip(
      __a      ,       len(__a						)      ,       __a						)
  return min(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						List[str]:







  """simple docstring"""





  lowerCamelCase__: Any         =column_based_sort(__a      ,       column=0						)
  lowerCamelCase__: List[Any]         =column_based_sort(__a      ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          __a      ,       __a      ,       __a						)
  ) ** 0.5


if __name__ == "__main__":
     __A							   =							[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
     print("Distance:", closest_pair_of_points(points, len(points)))






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  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		1
           lowerCamelCase             =		3
           lowerCamelCase             =		(32, 32)

           lowerCamelCase             =		floats_tensor((batch_size, num_channels) + sizes					,      rng=random.Random(0     )     ).to(__a     )
           return image







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		UNetaDConditionModel(
               block_out_channels=(32, 32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=7					,      out_channels=4					,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")					,      up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")					,      cross_attention_dim=32					,      attention_head_dim=8					,      use_linear_projection=__a					,      only_cross_attention=(True, True, False)					,      num_class_embeds=1_00					,      )
           return model







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		AutoencoderKL(
               block_out_channels=[32, 32, 64]					,      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     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		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=10_00					,      hidden_act="gelu"					,      projection_dim=5_12					,      )
           return CLIPTextModel(__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )

           lowerCamelCase             =		output.images

           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      return_dict=__a					,      )[0]

           lowerCamelCase             =		image[0, -3:, -3:, -1]
           lowerCamelCase             =		image_from_tuple[0, -3:, -3:, -1]

           lowerCamelCase             =		low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           lowerCamelCase             =		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     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		sd_pipe(
               2 * [prompt]					,      image=2 * [low_res_image]					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )
           lowerCamelCase             =		output.images
           assert image.shape[0] == 2

           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      num_images_per_prompt=2					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )
           lowerCamelCase             =		output.images
           assert image.shape[0] == 2







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






           '''simple docstring'''



           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # put models in fp16, except vae as it overflows in fp16
           lowerCamelCase             =		unet.half()
           lowerCamelCase             =		text_encoder.half()

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      num_inference_steps=2					,      output_type="np"					,      ).images

           lowerCamelCase             =		low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)





@slow
@require_torch_gpu
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		StableDiffusionUpscalePipeline.from_pretrained(__a     )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           assert image.shape == (5_12, 5_12, 3)
           assert np.abs(expected_image - image     ).max() < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-upscale/low_res_cat.png"     )
           lowerCamelCase             =		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
               "/upsampled_cat_fp16.npy"     )

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		StableDiffusionUpscalePipeline.from_pretrained(
               __a					,      torch_dtype=torch.floataa					,      )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           assert image.shape == (5_12, 5_12, 3)
           assert np.abs(expected_image - image     ).max() < 5E-1







     def     _a   (self     ):






           '''simple docstring'''



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

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

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		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             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      num_inference_steps=5					,      output_type="np"					,      )

           lowerCamelCase             =		torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9