
"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			UNetaDModel
						_lowerCamelCase:			KarrasVeScheduler


						def __init__(       self   :					List[Any]     ,A_   :					UNetaDModel     ,A_   :					KarrasVeScheduler					)   ->      Dict:
							super().__init__()
							self.register_modules(unet=A_     ,scheduler=A_					)




						@torch.no_grad()
						def __call__(       self   :					Optional[Any]     ,A_   :					int = 1     ,A_   :					int = 50     ,A_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,A_   :					Optional[str] = "pil"     ,A_   :					bool = True     ,**A_   :					Optional[Any]     ,)   ->      Union[Tuple, ImagePipelineOutput]:
							A        =      self.unet.config.sample_size
							A        =      (batch_size, 3, img_size, img_size)

							A        =      self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							A        =      randn_tensor(A_     ,generator=A_     ,device=self.device					) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(A_					)

							for t in self.progress_bar(self.scheduler.timesteps					):
								# here sigma_t == t_i from the paper
								A        =      self.scheduler.schedule[t]
								A        =      self.scheduler.schedule[t - 1] if t > 0 else 0

								# 1. Select temporarily increased noise level sigma_hat
								# 2. Add new noise to move from sample_i to sample_hat
								A      ,					A        =      self.scheduler.add_noise_to_input(A_     ,A_     ,generator=A_					)

								# 3. Predict the noise residual given the noise magnitude `sigma_hat`
								# The model inputs and output are adjusted by following eq. (213) in [1].
								A        =      (sigma_hat / 2) * model((sample_hat + 1) / 2     ,sigma_hat / 2					).sample

								# 4. Evaluate dx/dt at sigma_hat
								# 5. Take Euler step from sigma to sigma_prev
								A        =      self.scheduler.step(A_     ,A_     ,A_     ,A_					)

								if sigma_prev != 0:
									# 6. Apply 2nd order correction
									# The model inputs and output are adjusted by following eq. (213) in [1].
									A        =      (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2     ,sigma_prev / 2					).sample
									A        =      self.scheduler.step_correct(
									    A_     ,A_     ,A_     ,A_     ,step_output.prev_sample     ,step_output['derivative']     ,)
								A        =      step_output.prev_sample

							A        =      (sample / 2 + 0.5).clamp(0     ,1					)
							A        =      sample.cpu().permute(0     ,2     ,3     ,1					).numpy()
							if output_type == "pil":
								A        =      self.numpy_to_pil(A_					)

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_					)

'''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),
  )



