


def 						lowerCamelCase(   a__	,a__):
				while a != 0:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =b % a, a
				return b







def 						lowerCamelCase(   a__	,a__):
				if gcd(a__	,a__) != 1:
								_SCREAMING_SNAKE_CASE			  =f"mod inverse of {a!r} and {m!r} does not exist"
								raise ValueError(a__)
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =1, 0, a
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =0, 1, m
				while va != 0:
								_SCREAMING_SNAKE_CASE			  =ua // va
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
				return ua % m



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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  ):
						UpperCAmelCase             =						KandinskyVaaPipeline
						UpperCAmelCase             =						[
						    "image_embeds",
						    "negative_image_embeds",
						]
						UpperCAmelCase             =						["image_embeds", "negative_image_embeds"]
						UpperCAmelCase             =						[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase             =						False




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     str:


										"""simple docstring"""





										return 32




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


										"""simple docstring"""





										return 32




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


										"""simple docstring"""





										return self.time_input_dim




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


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Dict:


										"""simple docstring"""





										return 100




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


										"""simple docstring"""





										torch.manual_seed(0  )

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

										_SCREAMING_SNAKE_CASE			  =UNetaDConditionModel(**_a  )
										return model




						@property
						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Tuple:


										"""simple docstring"""





										return {
										    "block_out_channels": [32, 64],
										    "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": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}




						@property
						def 							__UpperCamelCase				(       self					:      Any  )							->     Tuple:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.dummy_unet
										_SCREAMING_SNAKE_CASE			  =self.dummy_movq

										_SCREAMING_SNAKE_CASE			  =DDIMScheduler(
										    num_train_timesteps=1000				,    beta_schedule='''linear'''				,    beta_start=0.0_00_85				,    beta_end=0.0_12				,    clip_sample=_a				,    set_alpha_to_one=_a				,    steps_offset=1				,    prediction_type='''epsilon'''				,    thresholding=_a				,    )

										_SCREAMING_SNAKE_CASE			  ={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}
										return components




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      Tuple				,    _a					:      Tuple=0  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =floats_tensor((1, self.text_embedder_hidden_size)				,    rng=random.Random(_a  )  ).to(_a  )
										_SCREAMING_SNAKE_CASE			  =floats_tensor((1, self.text_embedder_hidden_size)				,    rng=random.Random(seed + 1  )  ).to(
										    _a  )
										if str(_a  ).startswith('''mps'''  ):
														_SCREAMING_SNAKE_CASE			  =torch.manual_seed(_a  )
										else:
														_SCREAMING_SNAKE_CASE			  =torch.Generator(device=_a  ).manual_seed(_a  )
										_SCREAMING_SNAKE_CASE			  ={
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''cpu'''

										_SCREAMING_SNAKE_CASE			  =self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=_a  )

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

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

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

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

										_SCREAMING_SNAKE_CASE			  =np.array(
										    [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]  )

										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					:      List[Any]  )							->     Dict:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''  )

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

										_SCREAMING_SNAKE_CASE			  =KandinskyVaaPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-decoder'''				,    torch_dtype=torch.floataa  )
										_SCREAMING_SNAKE_CASE			  =pipeline.to(_a  )
										pipeline.set_progress_bar_config(disable=_a  )

										_SCREAMING_SNAKE_CASE			  ='''red cat, 4k photo'''

										_SCREAMING_SNAKE_CASE			  =torch.Generator(device='''cuda'''  ).manual_seed(0  )
										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =pipe_prior(
										    _a				,    generator=_a				,    num_inference_steps=5				,    negative_prompt=''''''				,    ).to_tuple()

										_SCREAMING_SNAKE_CASE			  =torch.Generator(device='''cuda'''  ).manual_seed(0  )
										_SCREAMING_SNAKE_CASE			  =pipeline(
										    image_embeds=_a				,    negative_image_embeds=_a				,    generator=_a				,    num_inference_steps=100				,    output_type='''np'''				,    )

										_SCREAMING_SNAKE_CASE			  =output.images[0]

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

										assert_mean_pixel_difference(_a				,    _a  )