





'''simple docstring'''


import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()


class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      @property
      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    Tuple			     =  1
        __lowerCAmelCase		:    str			     =  3
        __lowerCAmelCase		:    Optional[int]			     =  (32, 32)

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




      @property
      def 	snake_case  (     self )				->  List[Any]:
        torch.manual_seed(0 )
        __lowerCAmelCase		:    Union[str, Any]			     =  UNetaDModel(
            block_out_channels=(32, 64)     , layers_per_block=2     , sample_size=32     , in_channels=6     , out_channels=3     , down_block_types=('DownBlock2D', 'AttnDownBlock2D')     , up_block_types=('AttnUpBlock2D', 'UpBlock2D')     , )
        return model




      @property
      def 	snake_case  (     self )				->  List[str]:
        torch.manual_seed(0 )
        __lowerCAmelCase		:    Any			     =  VQModel(
            block_out_channels=[32, 64]     , in_channels=3     , out_channels=3     , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     , latent_channels=3     , )
        return model




      def 	snake_case  (     self )				->  Tuple:
        __lowerCAmelCase		:    List[str]			     =  'cpu'
        __lowerCAmelCase		:    Optional[int]			     =  self.dummy_uncond_unet
        __lowerCAmelCase		:    Optional[Any]			     =  DDIMScheduler()
        __lowerCAmelCase		:    Optional[int]			     =  self.dummy_vq_model

        __lowerCAmelCase		:    Dict			     =  LDMSuperResolutionPipeline(unet=UpperCAmelCase__     , vqvae=UpperCAmelCase__     , scheduler=UpperCAmelCase__ )
        ldm.to(UpperCAmelCase__ )
        ldm.set_progress_bar_config(disable=UpperCAmelCase__ )

        __lowerCAmelCase		:    Tuple			     =  self.dummy_image.to(UpperCAmelCase__ )

        __lowerCAmelCase		:    Optional[Any]			     =  torch.Generator(device=UpperCAmelCase__ ).manual_seed(0 )
        __lowerCAmelCase		:    int			     =  ldm(image=UpperCAmelCase__     , generator=UpperCAmelCase__     , num_inference_steps=2     , output_type='numpy' ).images

        __lowerCAmelCase		:    Union[str, Any]			     =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        __lowerCAmelCase		:    Dict			     =  np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0] )

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







      @unittest.skipIf(torch_device != 'cuda'     , 'This test requires a GPU' )
      def 	snake_case  (     self )				->  List[str]:
        __lowerCAmelCase		:    int			     =  self.dummy_uncond_unet
        __lowerCAmelCase		:    int			     =  DDIMScheduler()
        __lowerCAmelCase		:    Any			     =  self.dummy_vq_model

        # put models in fp16
        __lowerCAmelCase		:    Any			     =  unet.half()
        __lowerCAmelCase		:    Optional[Any]			     =  vqvae.half()

        __lowerCAmelCase		:    Optional[int]			     =  LDMSuperResolutionPipeline(unet=UpperCAmelCase__     , vqvae=UpperCAmelCase__     , scheduler=UpperCAmelCase__ )
        ldm.to(UpperCAmelCase__ )
        ldm.set_progress_bar_config(disable=UpperCAmelCase__ )

        __lowerCAmelCase		:    Tuple			     =  self.dummy_image.to(UpperCAmelCase__ )

        __lowerCAmelCase		:    Any			     =  ldm(UpperCAmelCase__     , num_inference_steps=2     , output_type='numpy' ).images

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



@slow
@require_torch
class 		UpperCamelCase__     (       unittest.TestCase							):
      '''simple docstring'''






      def 	snake_case  (     self )				->  Any:
        __lowerCAmelCase		:    Any			     =  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/vq_diffusion/teddy_bear_pool.png' )
        __lowerCAmelCase		:    Optional[int]			     =  init_image.resize((64, 64)     , resample=PIL_INTERPOLATION['lanczos'] )

        __lowerCAmelCase		:    Dict			     =  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'     , device_map='auto' )
        ldm.set_progress_bar_config(disable=UpperCAmelCase__ )

        __lowerCAmelCase		:    Union[str, Any]			     =  torch.manual_seed(0 )
        __lowerCAmelCase		:    int			     =  ldm(image=UpperCAmelCase__     , generator=UpperCAmelCase__     , num_inference_steps=20     , output_type='numpy' ).images

        __lowerCAmelCase		:    int			     =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        __lowerCAmelCase		:    List[Any]			     =  np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7] )

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








"""simple docstring"""





def     _A    (    UpperCamelCase_	:			list)      ->		list:
   '''simple docstring'''


   for i, _ in enumerate(UpperCamelCase_):
      if (i % 2 == 1) == (nums[i - 1] > nums[i]):
         __lowercase						,__lowercase								=		nums[i], nums[i - 1]

   return nums


if __name__ == "__main__":
   print('Enter the array elements:')
   _a					 =				list(map(int, input().split()))
   print('The unsorted array is:')
   print(array)
   print('Array after Wiggle sort:')
   print(wiggle_sort(array))




