
'''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	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Dict			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.dummy_uncond_unet
     _a				:	Optional[int]			=   KarrasVeScheduler()

     _a				:	Optional[Any]			=   KarrasVePipeline(unet=_a							,scheduler=_a       )
     pipe.to(_a       )
     pipe.set_progress_bar_config(disable=_a       )

     _a				:	Tuple			=   torch.manual_seed(0       )
     _a				:	Tuple			=   pipe(num_inference_steps=2							,generator=_a							,output_type='numpy'       ).images

     _a				:	Any			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pipe(num_inference_steps=2							,generator=_a							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Dict			=   image[0, -3:, -3:, -1]
     _a				:	int			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	int			=   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	):





  """simple docstring"""

  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Any			=   'google/ncsnpp-celebahq-256'
     _a				:	List[Any]			=   UNetaDModel.from_pretrained(_a       )
     _a				:	List[Any]			=   KarrasVeScheduler()

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

     _a				:	Dict			=   torch.manual_seed(0       )
     _a				:	List[str]			=   pipe(num_inference_steps=20							,generator=_a							,output_type='numpy'       ).images

     _a				:	Optional[int]			=   image[0, -3:, -3:, -1]
     assert image.shape == (1, 256, 256, 3)
     _a				:	List[str]			=   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'''







from __future__ import annotations

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



