





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class     __lowerCAmelCase	(   unittest.TestCase):







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__     : List[str]  =UNetaDModel(
            block_out_channels=(3_2, 6_4)		,				layers_per_block=2		,				sample_size=3_2		,				in_channels=3		,				out_channels=3		,				down_block_types=("DownBlock2D", "AttnDownBlock2D")		,				up_block_types=("AttnUpBlock2D", "UpBlock2D")		,				)
        return model







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Any  =self.dummy_uncond_unet
        a__     : str  =KarrasVeScheduler()

        a__     : List[str]  =KarrasVePipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Optional[Any]  =torch.manual_seed(0							)
        a__     : Optional[Any]  =pipe(num_inference_steps=2		,				generator=lowerCAmelCase__		,				output_type="numpy"							).images

        a__     : Tuple  =torch.manual_seed(0							)
        a__     : List[Any]  =pipe(num_inference_steps=2		,				generator=lowerCAmelCase__		,				output_type="numpy"		,				return_dict=lowerCAmelCase__							)[0]

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

        assert image.shape == (1, 3_2, 3_2, 3)
        a__     : Tuple  =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class     __lowerCAmelCase	(   unittest.TestCase):







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



        '''simple docstring'''




        a__     : Optional[Any]  ="""google/ncsnpp-celebahq-256"""
        a__     : str  =UNetaDModel.from_pretrained(lowerCAmelCase__							)
        a__     : List[Any]  =KarrasVeScheduler()

        a__     : Tuple  =KarrasVePipeline(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__     : Tuple  =torch.manual_seed(0							)
        a__     : List[Any]  =pipe(num_inference_steps=2_0		,				generator=lowerCAmelCase__		,				output_type="numpy"							).images

        a__     : Optional[int]  =image[0, -3:, -3:, -1]
        assert image.shape == (1, 2_5_6, 2_5_6, 3)
        a__     : Union[str, Any]  =np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]							)

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







import random
from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       list						):





  """simple docstring"""



  for _ in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : Tuple  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__     : Any  =random.randint(0	,	len(SCREAMING_SNAKE_CASE						) - 1						)
    a__ ,							a__     : Tuple  =data[b], data[a]
  return data


if __name__ == "__main__":
     UpperCAmelCase     :     Dict							=	[0, 1, 2, 3, 4, 5, 6, 7]
     UpperCAmelCase     :     Optional[Any]							=	["""python""", """says""", """hello""", """!"""]
     print("""Fisher-Yates Shuffle:""")
     print("""List""", integers, strings)
     print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






