

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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					(		__lowercase							,	unittest.TestCase			):
						_lowercase        =     KandinskyVaaControlnetPipeline
						_lowercase        =     ["image_embeds", "negative_image_embeds", "hint"]
						_lowercase        =     ["image_embeds", "negative_image_embeds", "hint"]
						_lowercase        =     [
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						_lowercase        =     False


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return 32


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return 32


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.time_input_dim


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.time_input_dim * 4


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return 100


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										torch.manual_seed(0		)

										_UpperCAmelCase    :						Optional[int]							=      {
										    '''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,
										}

										_UpperCAmelCase    :						str							=      UNetaDConditionModel(**snake_case_		)
										return model


						@property
						def     _UpperCAmelCase			(    self		):







										'''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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										_UpperCAmelCase    :						Optional[Any]							=      VQModel(**self.dummy_movq_kwargs		)
										return model


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.dummy_unet
										_UpperCAmelCase    :						str							=      self.dummy_movq

										_UpperCAmelCase    :						List[Any]							=      DDIMScheduler(
										    num_train_timesteps=1000  ,   beta_schedule="linear"  ,   beta_start=0.0_00_85  ,   beta_end=0.0_12  ,   clip_sample=snake_case_  ,   set_alpha_to_one=snake_case_  ,   steps_offset=1  ,   prediction_type="epsilon"  ,   thresholding=snake_case_  ,   )

										_UpperCAmelCase    :						Dict							=      {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}
										return components


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=0		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      floats_tensor((1, self.text_embedder_hidden_size)  ,   rng=random.Random(snake_case_		)		).to(snake_case_		)
										_UpperCAmelCase    :						Union[str, Any]							=      floats_tensor((1, self.text_embedder_hidden_size)  ,   rng=random.Random(seed + 1		)		).to(
										    snake_case_		)

										# create hint
										_UpperCAmelCase    :						Union[str, Any]							=      floats_tensor((1, 3, 64, 64)  ,   rng=random.Random(snake_case_		)		).to(snake_case_		)

										if str(snake_case_		).startswith("mps"		):
														_UpperCAmelCase    :						str							=      torch.manual_seed(snake_case_		)
										else:
														_UpperCAmelCase    :						Dict							=      torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
										_UpperCAmelCase    :						List[str]							=      {
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''hint''': hint,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      '''cpu'''

										_UpperCAmelCase    :						int							=      self.get_dummy_components()

										_UpperCAmelCase    :						Any							=      self.pipeline_class(**snake_case_		)
										_UpperCAmelCase    :						Dict							=      pipe.to(snake_case_		)

										pipe.set_progress_bar_config(disable=snake_case_		)

										_UpperCAmelCase    :						Optional[Any]							=      pipe(**self.get_dummy_inputs(snake_case_		)		)
										_UpperCAmelCase    :						Union[str, Any]							=      output.images

										_UpperCAmelCase    :						Optional[Any]							=      pipe(
										    **self.get_dummy_inputs(snake_case_		)  ,   return_dict=snake_case_  ,   )[0]

										_UpperCAmelCase    :						Tuple							=      image[0, -3:, -3:, -1]
										_UpperCAmelCase    :						Dict							=      image_from_tuple[0, -3:, -3:, -1]

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

										_UpperCAmelCase    :						Optional[int]							=      np.array(
										    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]		)

										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			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"		)

										_UpperCAmelCase    :						Tuple							=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/hint_image_cat.png"		)
										_UpperCAmelCase    :						Any							=      torch.from_numpy(np.array(snake_case_		)		).float() / 2_55.0
										_UpperCAmelCase    :						Tuple							=      hint.permute(2  ,   0  ,   1		).unsqueeze(0		)

										_UpperCAmelCase    :						Union[str, Any]							=      KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"  ,   torch_dtype=torch.floataa		)
										pipe_prior.to(snake_case_		)

										_UpperCAmelCase    :						Tuple							=      KandinskyVaaControlnetPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-controlnet-depth"  ,   torch_dtype=torch.floataa		)
										_UpperCAmelCase    :						str							=      pipeline.to(snake_case_		)
										pipeline.set_progress_bar_config(disable=snake_case_		)

										_UpperCAmelCase    :						str							=      '''A robot, 4k photo'''

										_UpperCAmelCase    :						Optional[Any]							=      torch.Generator(device="cuda"		).manual_seed(0		)
										_UpperCAmelCase    :						int							=      pipe_prior(
										    snake_case_  ,   generator=snake_case_  ,   num_inference_steps=5  ,   negative_prompt=""  ,   ).to_tuple()

										_UpperCAmelCase    :						Tuple							=      torch.Generator(device="cuda"		).manual_seed(0		)
										_UpperCAmelCase    :						Optional[Any]							=      pipeline(
										    image_embeds=snake_case_  ,   negative_image_embeds=snake_case_  ,   hint=snake_case_  ,   generator=snake_case_  ,   num_inference_steps=100  ,   output_type="np"  ,   )

										_UpperCAmelCase    :						Any							=      output.images[0]

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

										assert_mean_pixel_difference(snake_case_  ,   snake_case_		)





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


