
'''simple docstring'''






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  UpperCAmelCase__						(       unittest.TestCase ):



    @property
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Any								=   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   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Tuple								=   self.dummy_uncond_unet
          _lowerCamelCase				:   List[str]								=   KarrasVeScheduler()

          _lowerCamelCase				:   Union[str, Any]								=   KarrasVePipeline(unet=__A,scheduler=__A     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

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

          _lowerCamelCase				:   List[Any]								=   torch.manual_seed(0     )
          _lowerCamelCase				:   str								=   pipe(num_inference_steps=2,generator=__A,output_type="numpy",return_dict=__A     )[0]

          _lowerCamelCase				:   List[Any]								=   image[0, -3:, -3:, -1]
          _lowerCamelCase				:   Optional[int]								=   image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          _lowerCamelCase				:   Any								=   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  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   int								=   "google/ncsnpp-celebahq-256"
          _lowerCamelCase				:   Optional[Any]								=   UNetaDModel.from_pretrained(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   KarrasVeScheduler()

          _lowerCamelCase				:   Dict								=   KarrasVePipeline(unet=__A,scheduler=__A     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Optional[Any]								=   torch.manual_seed(0     )
          _lowerCamelCase				:   Any								=   pipe(num_inference_steps=2_0,generator=__A,output_type="numpy"     ).images

          _lowerCamelCase				:   List[Any]								=   image[0, -3:, -3:, -1]
          assert image.shape == (1, 2_5_6, 2_5_6, 3)
          _lowerCamelCase				:   Optional[int]								=   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

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

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()