





"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Any		    =  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




       @property
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :str		    =  VQModel(
                 block_out_channels=[32, 64]		,in_channels=3		,out_channels=3		,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,latent_channels=3		,)
             return model




       @property
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  CLIPTextConfig(
                 bos_token_id=0		,eos_token_id=2		,hidden_size=32		,intermediate_size=37		,layer_norm_eps=1E-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
             return CLIPTextModel(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.dummy_uncond_unet
             __SCREAMING_SNAKE_CASE       :List[str]		    =  DDIMScheduler()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.dummy_vq_model

             __SCREAMING_SNAKE_CASE       :Tuple		    =  LDMPipeline(unet=SCREAMING_SNAKE_CASE__		,vqvae=SCREAMING_SNAKE_CASE__		,scheduler=SCREAMING_SNAKE_CASE__							)
             ldm.to(SCREAMING_SNAKE_CASE__							)
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Any		    =  ldm(generator=SCREAMING_SNAKE_CASE__		,num_inference_steps=2		,output_type='''numpy'''							).images

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  ldm(generator=SCREAMING_SNAKE_CASE__		,num_inference_steps=2		,output_type='''numpy'''		,return_dict=SCREAMING_SNAKE_CASE__							)[0]

             __SCREAMING_SNAKE_CASE       :str		    =  image[0, -3:, -3:, -1]
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  1E-2 if torch_device != '''mps''' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance






@slow
@require_torch
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''							)
             ldm.to(SCREAMING_SNAKE_CASE__							)
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.manual_seed(0							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  ldm(generator=SCREAMING_SNAKE_CASE__		,num_inference_steps=5		,output_type='''numpy'''							).images

             __SCREAMING_SNAKE_CASE       :Tuple		    =  image[0, -3:, -3:, -1]

             assert image.shape == (1, 2_56, 2_56, 3)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  1E-2 if torch_device != '''mps''' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance






"""simple docstring"""
from __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

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

      if len(a_							) == 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			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

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



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()