


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 			a       (	_A			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Any							     =     KandinskyVaaControlnetImgaImgPipeline
     SCREAMING_SNAKE_CASE :				Dict							     =     ["image_embeds", "negative_image_embeds", "image", "hint"]
     SCREAMING_SNAKE_CASE :				int							     =     ["image_embeds", "negative_image_embeds", "image", "hint"]
     SCREAMING_SNAKE_CASE :				Any							     =     [
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     False

     @property
     def 				lowerCamelCase__				(       self  :	int    )		->							List[Any]:
          return 32

     @property
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return 32

     @property
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Union[str, Any]:
          return self.time_input_dim

     @property
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Any:
          return self.time_input_dim * 4

     @property
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Union[str, Any]:
          return 100

     @property
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          torch.manual_seed(0    )

          __UpperCAmelCase			:				str													=    {
              "in_channels": 8,
              # Out channels is double in channels because predicts mean and variance
              "out_channels": 8,
              "addition_embed_type": "image_hint",
              "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
              "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
              "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
              "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
              "layers_per_block": 1,
              "encoder_hid_dim": self.text_embedder_hidden_size,
              "encoder_hid_dim_type": "image_proj",
              "cross_attention_dim": self.cross_attention_dim,
              "attention_head_dim": 4,
              "resnet_time_scale_shift": "scale_shift",
              "class_embed_type": None,
          }

          __UpperCAmelCase			:				Optional[int]													=    UNetaDConditionModel(**__lowerCamelCase    )
          return model

     @property
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							List[str]:
          return {
              "block_out_channels": [32, 32, 64, 64],
              "down_block_types": [
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "DownEncoderBlock2D",
                  "AttnDownEncoderBlock2D",
              ],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }

     @property
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Union[str, Any]:
          torch.manual_seed(0    )
          __UpperCAmelCase			:				List[str]													=    VQModel(**self.dummy_movq_kwargs    )
          return model

     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          __UpperCAmelCase			:				Optional[Any]													=    self.dummy_unet
          __UpperCAmelCase			:				Optional[Any]													=    self.dummy_movq

          __UpperCAmelCase			:				Optional[Any]													=    {
              "num_train_timesteps": 1000,
              "beta_schedule": "linear",
              "beta_start": 0.00_085,
              "beta_end": 0.012,
              "clip_sample": False,
              "set_alpha_to_one": False,
              "steps_offset": 0,
              "prediction_type": "epsilon",
              "thresholding": False,
          }

          __UpperCAmelCase			:				str													=    DDIMScheduler(**__lowerCamelCase    )

          __UpperCAmelCase			:				Tuple													=    {
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	List[str]=0    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[Any]													=    floats_tensor((1, self.text_embedder_hidden_size)							,				rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
          __UpperCAmelCase			:				Optional[int]													=    floats_tensor((1, self.text_embedder_hidden_size)							,				rng=random.Random(seed + 1    )    ).to(
              __lowerCamelCase    )
          # create init_image
          __UpperCAmelCase			:				Any													=    floats_tensor((1, 3, 64, 64)							,				rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )
          __UpperCAmelCase			:				Union[str, Any]													=    image.cpu().permute(0							,				2							,				3							,				1    )[0]
          __UpperCAmelCase			:				List[Any]													=    Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
          # create hint
          __UpperCAmelCase			:				List[Any]													=    floats_tensor((1, 3, 64, 64)							,				rng=random.Random(__lowerCamelCase    )    ).to(__lowerCamelCase    )

          if str(__lowerCamelCase    ).startswith('''mps'''    ):
               __UpperCAmelCase			:				List[str]													=    torch.manual_seed(__lowerCamelCase    )
          else:
               __UpperCAmelCase			:				str													=    torch.Generator(device=__lowerCamelCase    ).manual_seed(__lowerCamelCase    )
          __UpperCAmelCase			:				List[str]													=    {
              "image": init_image,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "hint": hint,
              "generator": generator,
              "height": 64,
              "width": 64,
              "num_inference_steps": 10,
              "guidance_scale": 7.0,
              "strength": 0.2,
              "output_type": "np",
          }
          return inputs






     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          __UpperCAmelCase			:				int													=    "cpu"

          __UpperCAmelCase			:				Union[str, Any]													=    self.get_dummy_components()

          __UpperCAmelCase			:				List[str]													=    self.pipeline_class(**__lowerCamelCase    )
          __UpperCAmelCase			:				List[Any]													=    pipe.to(__lowerCamelCase    )

          pipe.set_progress_bar_config(disable=__lowerCamelCase    )

          __UpperCAmelCase			:				str													=    pipe(**self.get_dummy_inputs(__lowerCamelCase    )    )
          __UpperCAmelCase			:				List[Any]													=    output.images

          __UpperCAmelCase			:				Dict													=    pipe(
              **self.get_dummy_inputs(__lowerCamelCase    )							,				return_dict=__lowerCamelCase							,				)[0]

          __UpperCAmelCase			:				int													=    image[0, -3:, -3:, -1]
          __UpperCAmelCase			:				Any													=    image_from_tuple[0, -3:, -3:, -1]

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

          __UpperCAmelCase			:				List[str]													=    np.array(
              [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]    )
          assert (
              np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
          ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



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


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	str    )		->							Tuple:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Optional[int]													=    load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''    )

          __UpperCAmelCase			:				Tuple													=    load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
          __UpperCAmelCase			:				List[str]													=    init_image.resize((512, 512)    )

          __UpperCAmelCase			:				int													=    load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/hint_image_cat.png'''    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.from_numpy(np.array(__lowerCamelCase    )    ).float() / 255.0
          __UpperCAmelCase			:				Optional[Any]													=    hint.permute(2							,				0							,				1    ).unsqueeze(0    )

          __UpperCAmelCase			:				Tuple													=    "A robot, 4k photo"

          __UpperCAmelCase			:				Union[str, Any]													=    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-prior'''							,				torch_dtype=torch.floataa    )
          pipe_prior.to(__lowerCamelCase    )

          __UpperCAmelCase			:				int													=    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-controlnet-depth'''							,				torch_dtype=torch.floataa    )
          __UpperCAmelCase			:				Union[str, Any]													=    pipeline.to(__lowerCamelCase    )

          pipeline.set_progress_bar_config(disable=__lowerCamelCase    )

          __UpperCAmelCase			:				Tuple													=    torch.Generator(device='''cpu'''    ).manual_seed(0    )

          __UpperCAmelCase			:				Any													=    pipe_prior(
              __lowerCamelCase							,				image=__lowerCamelCase							,				strength=0.85							,				generator=__lowerCamelCase							,				negative_prompt=''''''							,				).to_tuple()

          __UpperCAmelCase			:				Any													=    pipeline(
              image=__lowerCamelCase							,				image_embeds=__lowerCamelCase							,				negative_image_embeds=__lowerCamelCase							,				hint=__lowerCamelCase							,				generator=__lowerCamelCase							,				num_inference_steps=100							,				height=512							,				width=512							,				strength=0.5							,				output_type='''np'''							,				)

          __UpperCAmelCase			:				int													=    output.images[0]

          assert image.shape == (512, 512, 3)

          assert_mean_pixel_difference(__lowerCamelCase							,				__lowerCamelCase    )



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






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


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