



"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				lowerCamelCase_( A__,					unittest.TestCase     ):





					'''simple docstring'''



					lowercase__ :							int              =			KandinskyInpaintPipeline
					lowercase__ :							Optional[Any]              =			['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
					lowercase__ :							str              =			[
					    'prompt',
					    'negative_prompt',
					    'image_embeds',
					    'negative_image_embeds',
					    'image',
					    'mask_image',
					]
					lowercase__ :							Optional[Any]              =			[
					    'generator',
					    'height',
					    'width',
					    'latents',
					    'guidance_scale',
					    'negative_prompt',
					    'num_inference_steps',
					    'return_dict',
					    'guidance_scale',
					    'num_images_per_prompt',
					    'output_type',
					    'return_dict',
					]
					lowercase__ :							List[str]              =			False

					@property
					def       snake_case__   (     self ):
									return 3_2

					@property
					def       snake_case__   (     self ):
									return 3_2

					@property
					def       snake_case__   (     self ):
									return self.time_input_dim

					@property
					def       snake_case__   (     self ):
									return self.time_input_dim * 4

					@property
					def       snake_case__   (     self ):
									return 1_0_0

					@property
					def       snake_case__   (     self ):
									_lowerCamelCase   	=					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
									return tokenizer

					@property
					def       snake_case__   (     self ):
									torch.manual_seed(0 )
									_lowerCamelCase   	=					MCLIPConfig(
									    numDims=self.cross_attention_dim					,							transformerDimensions=self.text_embedder_hidden_size					,							hidden_size=self.text_embedder_hidden_size					,							intermediate_size=3_7					,							num_attention_heads=4					,							num_hidden_layers=5					,							vocab_size=1_0_0_5					,							)

									_lowerCamelCase   	=					MultilingualCLIP(UpperCamelCase_ )
									_lowerCamelCase   	=					text_encoder.eval()

									return text_encoder

					@property
					def       snake_case__   (     self ):
									torch.manual_seed(0 )

									_lowerCamelCase   	=					{
									    '''in_channels''': 9,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''text_image''',
									    '''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''': '''text_image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									_lowerCamelCase   	=					UNetaDConditionModel(**UpperCamelCase_ )
									return model

					@property
					def       snake_case__   (     self ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}

					@property
					def       snake_case__   (     self ):
									torch.manual_seed(0 )
									_lowerCamelCase   	=					VQModel(**self.dummy_movq_kwargs )
									return model

					def       snake_case__   (     self ):
									_lowerCamelCase   	=					self.dummy_text_encoder
									_lowerCamelCase   	=					self.dummy_tokenizer
									_lowerCamelCase   	=					self.dummy_unet
									_lowerCamelCase   	=					self.dummy_movq

									_lowerCamelCase   	=					DDIMScheduler(
									    num_train_timesteps=1_0_0_0					,							beta_schedule='''linear'''					,							beta_start=0.0_0_0_8_5					,							beta_end=0.0_1_2					,							clip_sample=UpperCamelCase_					,							set_alpha_to_one=UpperCamelCase_					,							steps_offset=1					,							prediction_type='''epsilon'''					,							thresholding=UpperCamelCase_					,							)

									_lowerCamelCase   	=					{
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components

					def       snake_case__   (     self					,							lowerCamelCase__					,							lowerCamelCase__=0 ):
									_lowerCamelCase   	=					floats_tensor((1, self.cross_attention_dim)					,							rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
									_lowerCamelCase   	=					floats_tensor((1, self.cross_attention_dim)					,							rng=random.Random(seed + 1 ) ).to(UpperCamelCase_ )
									# create init_image
									_lowerCamelCase   	=					floats_tensor((1, 3, 6_4, 6_4)					,							rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
									_lowerCamelCase   	=					image.cpu().permute(0					,							2					,							3					,							1 )[0]
									_lowerCamelCase   	=					Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
									# create mask
									_lowerCamelCase   	=					np.ones((6_4, 6_4)					,							dtype=np.floataa )
									_lowerCamelCase   	=					0

									if str(UpperCamelCase_ ).startswith('''mps''' ):
													_lowerCamelCase   	=					torch.manual_seed(UpperCamelCase_ )
									else:
													_lowerCamelCase   	=					torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
									_lowerCamelCase   	=					{
									    '''prompt''': '''horse''',
									    '''image''': init_image,
									    '''mask_image''': mask,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 6_4,
									    '''width''': 6_4,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 4.0,
									    '''output_type''': '''np''',
									}
									return inputs

					def       snake_case__   (     self ):
									_lowerCamelCase   	=					'''cpu'''

									_lowerCamelCase   	=					self.get_dummy_components()

									_lowerCamelCase   	=					self.pipeline_class(**UpperCamelCase_ )
									_lowerCamelCase   	=					pipe.to(UpperCamelCase_ )

									pipe.set_progress_bar_config(disable=UpperCamelCase_ )

									_lowerCamelCase   	=					pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
									_lowerCamelCase   	=					output.images

									_lowerCamelCase   	=					pipe(
									    **self.get_dummy_inputs(UpperCamelCase_ )					,							return_dict=UpperCamelCase_					,							)[0]

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

									print(F"""image.shape {image.shape}""" )

									assert image.shape == (1, 6_4, 6_4, 3)

									_lowerCamelCase   	=					np.array(
									    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6] )

									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()}"""





					def       snake_case__   (     self ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3 )


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





					'''simple docstring'''



					def       snake_case__   (     self ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





					def       snake_case__   (     self ):
									_lowerCamelCase   	=					load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )

									_lowerCamelCase   	=					load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
									_lowerCamelCase   	=					np.ones((7_6_8, 7_6_8)					,							dtype=np.floataa )
									_lowerCamelCase   	=					0

									_lowerCamelCase   	=					'''a hat'''

									_lowerCamelCase   	=					KandinskyPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-prior'''					,							torch_dtype=torch.floataa )
									pipe_prior.to(UpperCamelCase_ )

									_lowerCamelCase   	=					KandinskyInpaintPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-inpaint'''					,							torch_dtype=torch.floataa )
									_lowerCamelCase   	=					pipeline.to(UpperCamelCase_ )
									pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

									_lowerCamelCase   	=					torch.Generator(device='''cpu''' ).manual_seed(0 )
									_lowerCamelCase   	=					pipe_prior(
									    UpperCamelCase_					,							generator=UpperCamelCase_					,							num_inference_steps=5					,							negative_prompt=''''''					,							).to_tuple()

									_lowerCamelCase   	=					pipeline(
									    UpperCamelCase_					,							image=UpperCamelCase_					,							mask_image=UpperCamelCase_					,							image_embeds=UpperCamelCase_					,							negative_image_embeds=UpperCamelCase_					,							generator=UpperCamelCase_					,							num_inference_steps=1_0_0					,							height=7_6_8					,							width=7_6_8					,							output_type='''np'''					,							)

									_lowerCamelCase   	=					output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(UpperCamelCase_					,							UpperCamelCase_ )




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

import math


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      int:



					'''simple docstring'''






					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      a (     )    ->      None:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__  :List[Any]				   =					math.log(len(__a  )					,   2  )
					print('''Optimal value : '''					,   end=''''''  )
					print(minimax(0					,   0					,   __a					,   __a					,   __a  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()