



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class __A    (						_UpperCAmelCase     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: List[str]    = KandinskyVaaControlnetImgaImgPipeline
 __lowerCamelCase					: List[str]    = ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 __lowerCamelCase					: Optional[Any]    = ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 __lowerCamelCase					: Tuple    = [
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 __lowerCamelCase					: Dict    = False



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''in_channels''': 8,
          # Out channels is double in channels because predicts mean and variance
          '''out_channels''': 8,
          '''addition_embed_type''': '''image_hint''',
          '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
          '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
          '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
          '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
          '''layers_per_block''': 1,
          '''encoder_hid_dim''': self.text_embedder_hidden_size,
          '''encoder_hid_dim_type''': '''image_proj''',
          '''cross_attention_dim''': self.cross_attention_dim,
          '''attention_head_dim''': 4,
          '''resnet_time_scale_shift''': '''scale_shift''',
          '''class_embed_type''': None,
      }

      _a											=						UNetaDConditionModel(**A_ )
      return model



 @property
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						VQModel(**self.dummy_movq_kwargs )
      return model



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.dummy_unet
      _a											=						self.dummy_movq

      _a											=						{
          '''num_train_timesteps''': 1_000,
          '''beta_schedule''': '''linear''',
          '''beta_start''': 0.00085,
          '''beta_end''': 0.012,
          '''clip_sample''': False,
          '''set_alpha_to_one''': False,
          '''steps_offset''': 0,
          '''prediction_type''': '''epsilon''',
          '''thresholding''': False,
      }

      _a											=						DDIMScheduler(**A_ )

      _a											=						{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }

      return components



 def       a__						(self				,     A				,     A=0 )					->  str:


      """simple docstring"""



      _a											=						floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(A_ ) ).to(A_ )
      _a											=						floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(seed + 1 ) ).to(
          A_ )
      # create init_image
      _a											=						floats_tensor((1, 3, 64, 64)				,     rng=random.Random(A_ ) ).to(A_ )
      _a											=						image.cpu().permute(0				,     2				,     3				,     1 )[0]
      _a											=						Image.fromarray(np.uinta(A_ ) ).convert('''RGB''' ).resize((256, 256) )
      # create hint
      _a											=						floats_tensor((1, 3, 64, 64)				,     rng=random.Random(A_ ) ).to(A_ )

      if str(A_ ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A_ )
      else:
           _a											=						torch.Generator(device=A_ ).manual_seed(A_ )
      _a											=						{
          '''image''': init_image,
          '''image_embeds''': image_embeds,
          '''negative_image_embeds''': negative_image_embeds,
          '''hint''': hint,
          '''generator''': generator,
          '''height''': 64,
          '''width''': 64,
          '''num_inference_steps''': 10,
          '''guidance_scale''': 7.0,
          '''strength''': 0.2,
          '''output_type''': '''np''',
      }
      return inputs



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A_ )
      _a											=						pipe.to(A_ )

      pipe.set_progress_bar_config(disable=A_ )

      _a											=						pipe(**self.get_dummy_inputs(A_ ) )
      _a											=						output.images

      _a											=						pipe(
          **self.get_dummy_inputs(A_ )				,     return_dict=A_				,     )[0]

      _a											=						image[0, -3:, -3:, -1]
      _a											=						image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      _a											=						np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
      assert (
          np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
      ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
      ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  int:


      """simple docstring"""



      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )

      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
      _a											=						init_image.resize((512, 512) )

      _a											=						load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/hint_image_cat.png''' )
      _a											=						torch.from_numpy(np.array(A_ ) ).float() / 255.0
      _a											=						hint.permute(2				,     0				,     1 ).unsqueeze(0 )

      _a											=						'''A robot, 4k photo'''

      _a											=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-prior'''				,     torch_dtype=torch.floataa )
      pipe_prior.to(A_ )

      _a											=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-controlnet-depth'''				,     torch_dtype=torch.floataa )
      _a											=						pipeline.to(A_ )

      pipeline.set_progress_bar_config(disable=A_ )

      _a											=						torch.Generator(device='''cpu''' ).manual_seed(0 )

      _a						,					_a											=						pipe_prior(
          A_				,     image=A_				,     strength=0.85				,     generator=A_				,     negative_prompt=''''''				,     ).to_tuple()

      _a											=						pipeline(
          image=A_				,     image_embeds=A_				,     negative_image_embeds=A_				,     hint=A_				,     generator=A_				,     num_inference_steps=100				,     height=512				,     width=512				,     strength=0.5				,     output_type='''np'''				,     )

      _a											=						output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(A_				,     A_ )








'''simple docstring'''





from __future__ import annotations

import math




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



     """simple docstring"""



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

     if not scores:
          raise ValueError('''Scores cannot be empty''')

     if depth == height:
          return scores[node_index]

     return (
         max(
             minimax(depth + 1						,			node_index * 2						,			__A						,			__A						,			__A)						,			minimax(depth + 1						,			node_index * 2 + 1						,			__A						,			__A						,			__A)						,			)
         if is_max
         else min(
             minimax(depth + 1						,			node_index * 2						,			__A						,			__A						,			__A)						,			minimax(depth + 1						,			node_index * 2 + 1						,			__A						,			__A						,			__A)						,			)
     )




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						[90, 23, 6, 33, 21, 65, 123, 34_423]
     _a											=						math.log(len(__A)						,			2)
     print(F'''Optimal value : {minimax(0						,			0						,			__A						,			__A						,			__A)}''')


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



