





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class 				_UpperCAmelCase    :

  """simple docstring"""


  def 				lowercase		(				self :		List[str]     )							->						Any:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	UNetaDConditionModel(
         sample_size=3_2					,  layers_per_block=1					,  block_out_channels=[3_2, 6_4]					,  down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]					,  mid_block_type='UNetMidBlock2DSimpleCrossAttn'					,  up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']					,  in_channels=3					,  out_channels=6					,  cross_attention_dim=3_2					,  encoder_hid_dim=3_2					,  attention_head_dim=8					,  addition_embed_type='text'					,  addition_embed_type_num_heads=2					,  cross_attention_norm='group_norm'					,  resnet_time_scale_shift='scale_shift'					,  act_fn='gelu'					,  )
     unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     torch.manual_seed(0     )
     __lowerCAmelCase								=	DDPMScheduler(
         num_train_timesteps=1_0_0_0					,  beta_schedule='squaredcos_cap_v2'					,  beta_start=0.00_01					,  beta_end=0.02					,  thresholding=lowerCAmelCase_					,  dynamic_thresholding_ratio=0.95					,  sample_max_value=1.0					,  prediction_type='epsilon'					,  variance_type='learned_range'					,  )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }






  def 				lowercase		(				self :		Optional[int]     )							->						List[Any]:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'     )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	UNetaDConditionModel(
         sample_size=3_2					,  layers_per_block=[1, 2]					,  block_out_channels=[3_2, 6_4]					,  down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]					,  mid_block_type='UNetMidBlock2DSimpleCrossAttn'					,  up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']					,  in_channels=6					,  out_channels=6					,  cross_attention_dim=3_2					,  encoder_hid_dim=3_2					,  attention_head_dim=8					,  addition_embed_type='text'					,  addition_embed_type_num_heads=2					,  cross_attention_norm='group_norm'					,  resnet_time_scale_shift='scale_shift'					,  act_fn='gelu'					,  class_embed_type='timestep'					,  mid_block_scale_factor=1.4_14					,  time_embedding_act_fn='gelu'					,  time_embedding_dim=3_2					,  )
     unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     torch.manual_seed(0     )
     __lowerCAmelCase								=	DDPMScheduler(
         num_train_timesteps=1_0_0_0					,  beta_schedule='squaredcos_cap_v2'					,  beta_start=0.00_01					,  beta_end=0.02					,  thresholding=lowerCAmelCase_					,  dynamic_thresholding_ratio=0.95					,  sample_max_value=1.0					,  prediction_type='epsilon'					,  variance_type='learned_range'					,  )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	DDPMScheduler(
         num_train_timesteps=1_0_0_0					,  beta_schedule='squaredcos_cap_v2'					,  beta_start=0.00_01					,  beta_end=0.02					,  )

     torch.manual_seed(0     )
     __lowerCAmelCase								=	IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }






  def 				lowercase		(				self :		List[Any]     )							->						Dict:
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	self.pipeline_class(**lowerCAmelCase_     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )

     __lowerCAmelCase								=	inputs['prompt']
     __lowerCAmelCase								=	inputs['generator']
     __lowerCAmelCase								=	inputs['num_inference_steps']
     __lowerCAmelCase								=	inputs['output_type']

     if "image" in inputs:
        __lowerCAmelCase								=	inputs['image']
     else:
        __lowerCAmelCase								=	None

     if "mask_image" in inputs:
        __lowerCAmelCase								=	inputs['mask_image']
     else:
        __lowerCAmelCase								=	None

     if "original_image" in inputs:
        __lowerCAmelCase								=	inputs['original_image']
     else:
        __lowerCAmelCase								=	None

     __lowerCAmelCase	,	__lowerCAmelCase								=	pipe.encode_prompt(lowerCAmelCase_     )

     # inputs with prompt converted to embeddings
     __lowerCAmelCase								=	{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
        __lowerCAmelCase								=	image

     if mask_image is not None:
        __lowerCAmelCase								=	mask_image

     if original_image is not None:
        __lowerCAmelCase								=	original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
        setattr(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase_     )
        __lowerCAmelCase								=	self.pipeline_class.from_pretrained(lowerCAmelCase_     )
        pipe_loaded.to(lowerCAmelCase_     )
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_     )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(lowerCAmelCase_					,  lowerCAmelCase_     ) is None					,  f"""`{optional_component}` did not stay set to None after loading."""					,  )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )

     __lowerCAmelCase								=	inputs['generator']
     __lowerCAmelCase								=	inputs['num_inference_steps']
     __lowerCAmelCase								=	inputs['output_type']

     # inputs with prompt converted to embeddings
     __lowerCAmelCase								=	{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
        __lowerCAmelCase								=	image

     if mask_image is not None:
        __lowerCAmelCase								=	mask_image

     if original_image is not None:
        __lowerCAmelCase								=	original_image

     __lowerCAmelCase								=	pipe_loaded(**lowerCAmelCase_     )[0]

     __lowerCAmelCase								=	np.abs(to_np(lowerCAmelCase_     ) - to_np(lowerCAmelCase_     )     ).max()
     self.assertLess(lowerCAmelCase_					,  1e-4     )






  def 				lowercase		(				self :		List[str]     )							->						Optional[int]:
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	self.pipeline_class(**lowerCAmelCase_     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase_     )
        __lowerCAmelCase								=	self.pipeline_class.from_pretrained(lowerCAmelCase_     )
        pipe_loaded.to(lowerCAmelCase_     )
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_     )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()     )  # For reproducibility tests

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	pipe_loaded(**lowerCAmelCase_     )[0]

     __lowerCAmelCase								=	np.abs(to_np(lowerCAmelCase_     ) - to_np(lowerCAmelCase_     )     ).max()
     self.assertLess(lowerCAmelCase_					,  1e-4     )








from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

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

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

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

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

