





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):







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




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







      @property
      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Any										=1
        a__	:					Dict										=3
        a__	:					List[str]										=(3_2, 3_2)

        a__	:					Any										=floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0							)							).to(lowerCAmelCase__							)
        return image







      @property
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__	:					int										=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=lowerCAmelCase__		,      only_cross_attention=(True, True, False)		,      num_class_embeds=1_0_0		,      )
        return model







      @property
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__	:					Optional[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    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__	:					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(lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




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

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

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

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

        a__	:					Any										=output.images

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

        a__	:					str										=image[0, -3:, -3:, -1]
        a__	:					Tuple										=image_from_tuple[0, -3:, -3:, -1]

        a__	:					str										=low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        a__	:					Optional[int]										=np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]							)

        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    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




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

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

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

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

        a__	:					Tuple										=torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
        a__	:					int										=sd_pipe(
            [prompt]		,      image=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      num_images_per_prompt=2		,      guidance_scale=6.0		,      noise_level=2_0		,      num_inference_steps=2		,      output_type="np"		,      )
        a__	:					Optional[int]										=output.images
        assert image.shape[0] == 2







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



        '''simple docstring'''




        a__	:					str										=self.dummy_cond_unet_upscale
        a__	:					Tuple										=DDPMScheduler()
        a__	:					Dict										=DDIMScheduler(prediction_type="v_prediction"							)
        a__	:					Optional[int]										=self.dummy_vae
        a__	:					str										=self.dummy_text_encoder
        a__	:					Optional[int]										=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

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

        # put models in fp16, except vae as it overflows in fp16
        a__	:					Any										=unet.half()
        a__	:					int										=text_encoder.half()

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

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

        a__	:					List[str]										=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):







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




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

        a__	:					Optional[int]										="stabilityai/stable-diffusion-x4-upscaler"
        a__	:					str										=StableDiffusionUpscalePipeline.from_pretrained(lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
        pipe.enable_attention_slicing()

        a__	:					Union[str, Any]										="a cat sitting on a park bench"

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					List[Any]										=pipe(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="np"		,      )
        a__	:					int										=output.images[0]

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







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"							)
        a__	:					int										=load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat_fp16.npy"							)

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

        a__	:					Any										="a cat sitting on a park bench"

        a__	:					Any										=torch.manual_seed(0							)
        a__	:					Any										=pipe(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="np"		,      )
        a__	:					Dict										=output.images[0]

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







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




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

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

        a__	:					int										="stabilityai/stable-diffusion-x4-upscaler"
        a__	:					Union[str, Any]										=StableDiffusionUpscalePipeline.from_pretrained(
            lowerCAmelCase__		,      torch_dtype=torch.floataa		,      )
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
        pipe.enable_attention_slicing(1							)
        pipe.enable_sequential_cpu_offload()

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

        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Optional[int]										=pipe(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      num_inference_steps=5		,      output_type="np"		,      )

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













def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       input("""Enter numbers separated by a comma:\n""").strip()
     UpperCAmelCase	:	Optional[int]		     =       [int(item) for item in user_input.split(""",""")]
     print(pancake_sort(unsorted))






