
'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    UpperCAmelCase_				(	__lowerCAmelCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__()

     # make sure scheduler can always be converted to DDIM
     snake_case_ :    Optional[int]     			=						DDIMScheduler.from_config(scheduler.config)

     self.register_modules(unet=lowerCAmelCase_ ,       scheduler=lowerCAmelCase_)





 @torch.no_grad()
 def __call__(    self ,       lowercase_ = 1 ,       lowercase_ = None ,       lowercase_ = 0.0 ,       lowercase_ = 50 ,       lowercase_ = None ,       lowercase_ = "pil" ,       lowercase_ = True ,       ):

     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size ,       lowerCAmelCase_):
         snake_case_ :    List[Any]     			=						(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         snake_case_ :    Optional[Any]     			=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if isinstance(lowerCAmelCase_ ,       lowerCAmelCase_) and len(lowerCAmelCase_) != batch_size:
         raise ValueError(
             F'You have passed a list of generators of length {len(lowerCAmelCase_)}, but requested an effective batch'
             F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

     snake_case_ :    List[Any]     			=						randn_tensor(lowerCAmelCase_ ,       generator=lowerCAmelCase_ ,       device=self.device ,       dtype=self.unet.dtype)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_)

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         snake_case_ :    List[str]     			=						self.unet(lowerCAmelCase_ ,       lowerCAmelCase_).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         snake_case_ :    Union[str, Any]     			=						self.scheduler.step(
             lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       eta=lowerCAmelCase_ ,       use_clipped_model_output=lowerCAmelCase_ ,       generator=lowerCAmelCase_).prev_sample

     snake_case_ :    Union[str, Any]     			=						(image / 2 + 0.5).clamp(0 ,       1)
     snake_case_ :    Tuple     			=						image.cpu().permute(0 ,       2 ,       3 ,       1).numpy()
     if output_type == "pil":
         snake_case_ :    Optional[Any]     			=						self.numpy_to_pil(lowerCAmelCase_)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_)






'''simple docstring'''
from __future__ import annotations

import math

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if depth < 0:
        raise ValueError("Depth cannot be less than 0"       )

    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise ValueError("Scores cannot be empty"       )

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

    return min(
        minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    snake_case_ :    Union[str, Any]     			=						math.log(len(__SCREAMING_SNAKE_CASE       ),		2       )
    print("Optimal value : ",		end=""       )
    print(minimax(0,		0,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




