


snake_case_						:			Tuple													=							[
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



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  )