




'''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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				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 :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE	=				KarrasVeScheduler()

      _SCREAMING_SNAKE_CASE	=				KarrasVePipeline(unet=__lowerCamelCase  ,				scheduler=__lowerCamelCase   )
      pipe.to(__lowerCamelCase   )
      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				pipe(num_inference_steps=2  ,				generator=__lowerCamelCase  ,				output_type="numpy"   ).images

      _SCREAMING_SNAKE_CASE	=				torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				pipe(num_inference_steps=2  ,				generator=__lowerCamelCase  ,				output_type="numpy"  ,				return_dict=__lowerCamelCase   )[0]

      _SCREAMING_SNAKE_CASE	=				image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE	=				image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _SCREAMING_SNAKE_CASE	=				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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"google/ncsnpp-celebahq-256"
      _SCREAMING_SNAKE_CASE	=				UNetaDModel.from_pretrained(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				KarrasVeScheduler()

      _SCREAMING_SNAKE_CASE	=				KarrasVePipeline(unet=__lowerCamelCase  ,				scheduler=__lowerCamelCase   )
      pipe.to(__lowerCamelCase   )
      pipe.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				pipe(num_inference_steps=2_0  ,				generator=__lowerCamelCase  ,				output_type="numpy"   ).images

      _SCREAMING_SNAKE_CASE	=				image[0, -3:, -3:, -1]
      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      _SCREAMING_SNAKE_CASE	=				np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

