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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_											=						logging.get_logger(__name__)  # pylint: disable=invalid-name

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






    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead",		__SCREAMING_SNAKE_CASE,		)
    if isinstance(__SCREAMING_SNAKE_CASE,		torch.Tensor       ):
        return image
    elif isinstance(__SCREAMING_SNAKE_CASE,		PIL.Image.Image       ):
        snake_case_ :    Tuple     			=						[image]

    if isinstance(image[0],		PIL.Image.Image       ):
        snake_case_			, snake_case_ :    List[str]     			=						image[0].size
        snake_case_			, snake_case_ :    Optional[int]     			=						(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        snake_case_ :    Tuple     			=						[np.array(i.resize((w, h),		resample=PIL_INTERPOLATION["lanczos"]       )       )[None, :] for i in image]
        snake_case_ :    Dict     			=						np.concatenate(__SCREAMING_SNAKE_CASE,		axis=0       )
        snake_case_ :    Union[str, Any]     			=						np.array(__SCREAMING_SNAKE_CASE       ).astype(np.floataa       ) / 2_5_5.0
        snake_case_ :    Tuple     			=						image.transpose(0,		3,		1,		2       )
        snake_case_ :    Dict     			=						2.0 * image - 1.0
        snake_case_ :    Tuple     			=						torch.from_numpy(__SCREAMING_SNAKE_CASE       )
    elif isinstance(image[0],		torch.Tensor       ):
        snake_case_ :    str     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    return image

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






    if isinstance(__SCREAMING_SNAKE_CASE,		torch.Tensor       ):
        return mask
    elif isinstance(__SCREAMING_SNAKE_CASE,		PIL.Image.Image       ):
        snake_case_ :    Union[str, Any]     			=						[mask]

    if isinstance(mask[0],		PIL.Image.Image       ):
        snake_case_			, snake_case_ :    str     			=						mask[0].size
        snake_case_			, snake_case_ :    Dict     			=						(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
        snake_case_ :    Optional[Any]     			=						[np.array(m.convert("L"       ).resize((w, h),		resample=PIL_INTERPOLATION["nearest"]       )       )[None, :] for m in mask]
        snake_case_ :    Optional[Any]     			=						np.concatenate(__SCREAMING_SNAKE_CASE,		axis=0       )
        snake_case_ :    List[str]     			=						mask.astype(np.floataa       ) / 2_5_5.0
        snake_case_ :    int     			=						0
        snake_case_ :    List[str]     			=						1
        snake_case_ :    Optional[int]     			=						torch.from_numpy(__SCREAMING_SNAKE_CASE       )
    elif isinstance(mask[0],		torch.Tensor       ):
        snake_case_ :    str     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    return mask






class    UpperCAmelCase_				(	UpperCamelCase__					):
 UpperCAmelCase_		    =						42
 UpperCAmelCase_		    =						42





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__()
     self.register_modules(unet=lowercase_ ,       scheduler=lowercase_)





 @torch.no_grad()
 def __call__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ = 2_50 ,       lowercase_ = 0.0 ,       lowercase_ = 10 ,       lowercase_ = 10 ,       lowercase_ = None ,       lowercase_ = "pil" ,       lowercase_ = True ,       ):
     snake_case_ :    Tuple     			=						image

     snake_case_ :    Dict     			=						_preprocess_image(lowercase_)
     snake_case_ :    int     			=						original_image.to(device=self.device ,       dtype=self.unet.dtype)
     snake_case_ :    Tuple     			=						_preprocess_mask(lowercase_)
     snake_case_ :    Tuple     			=						mask_image.to(device=self.device ,       dtype=self.unet.dtype)

     snake_case_ :    Tuple     			=						original_image.shape[0]

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

     snake_case_ :    Tuple     			=						original_image.shape
     snake_case_ :    int     			=						randn_tensor(lowercase_ ,       generator=lowercase_ ,       device=self.device ,       dtype=self.unet.dtype)

     # set step values
     self.scheduler.set_timesteps(lowercase_ ,       lowercase_ ,       lowercase_ ,       self.device)
     snake_case_ :    Any     			=						eta

     snake_case_ :    Any     			=						self.scheduler.timesteps[0] + 1
     snake_case_ :    int     			=						generator[0] if isinstance(lowercase_ ,       lowercase_) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
         if t < t_last:
             # predict the noise residual
             snake_case_ :    str     			=						self.unet(lowercase_ ,       lowercase_).sample
             # compute previous image: x_t -> x_t-1
             snake_case_ :    List[str]     			=						self.scheduler.step(lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_).prev_sample

         else:
             # compute the reverse: x_t-1 -> x_t
             snake_case_ :    int     			=						self.scheduler.undo_step(lowercase_ ,       lowercase_ ,       lowercase_)
         snake_case_ :    List[str]     			=						t

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

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_)






'''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()




