




"""simple docstring"""







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 					SCREAMING_SNAKE_CASE__    (    UpperCamelCase__  , unittest.TestCase    ):
						_a			  =  KandinskyVaaPipeline
						_a			  =  [
						    'image_embeds',
						    'negative_image_embeds',
						]
						_a			  =  ['image_embeds', 'negative_image_embeds']
						_a			  =  [
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_a			  =  False


						@property
						def      __lowercase       (							self :	Optional[Any]    ):
										return 32


						@property
						def      __lowercase       (							self :	int    ):
										return 32


						@property
						def      __lowercase       (							self :	Optional[Any]    ):
										return self.time_input_dim


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


						@property
						def      __lowercase       (							self :	Any    ):
										return 100


						@property
						def      __lowercase       (							self :	Union[str, Any]    ):
										torch.manual_seed(0    )

										lowerCAmelCase              = {
										    """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,
										}

										lowerCAmelCase              = UNetaDConditionModel(**lowercase_    )
										return model


						@property
						def      __lowercase       (							self :	Any    ):
										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      __lowercase       (							self :	Dict    ):
										torch.manual_seed(0    )
										lowerCAmelCase              = VQModel(**self.dummy_movq_kwargs    )
										return model


						def      __lowercase       (							self :	Dict    ):
										lowerCAmelCase              = self.dummy_unet
										lowerCAmelCase              = self.dummy_movq

										lowerCAmelCase              = DDIMScheduler(
										    num_train_timesteps=1000  ,  beta_schedule="""linear"""  ,  beta_start=0.0_0085  ,  beta_end=0.012  ,  clip_sample=lowercase_  ,  set_alpha_to_one=lowercase_  ,  steps_offset=1  ,  prediction_type="""epsilon"""  ,  thresholding=lowercase_  ,  )

										lowerCAmelCase              = {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}
										return components


						def      __lowercase       (							self :	Dict  ,  lowerCAmelCase :	int  ,  lowerCAmelCase :	Union[str, Any]=0    ):
										lowerCAmelCase              = floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(lowercase_    )    ).to(lowercase_    )
										lowerCAmelCase              = floats_tensor((1, self.text_embedder_hidden_size)  ,  rng=random.Random(seed + 1    )    ).to(
										    lowercase_    )
										if str(lowercase_    ).startswith("""mps"""    ):
														lowerCAmelCase              = torch.manual_seed(lowercase_    )
										else:
														lowerCAmelCase              = torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
										lowerCAmelCase              = {
										    """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      __lowercase       (							self :	Optional[Any]    ):
										lowerCAmelCase              = """cpu"""

										lowerCAmelCase              = self.get_dummy_components()

										lowerCAmelCase              = self.pipeline_class(**lowercase_    )
										lowerCAmelCase              = pipe.to(lowercase_    )

										pipe.set_progress_bar_config(disable=lowercase_    )

										lowerCAmelCase              = pipe(**self.get_dummy_inputs(lowercase_    )    )
										lowerCAmelCase              = output.images

										lowerCAmelCase              = pipe(
										    **self.get_dummy_inputs(lowercase_    )  ,  return_dict=lowercase_  ,  )[0]

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

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

										lowerCAmelCase              = np.array(
										    [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]    )

										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 					SCREAMING_SNAKE_CASE__    (    unittest.TestCase    ):


						def      __lowercase       (							self :	Dict    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def      __lowercase       (							self :	Any    ):
										lowerCAmelCase              = load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""    )

										lowerCAmelCase              = KandinskyVaaPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-prior"""  ,  torch_dtype=torch.floataa    )
										pipe_prior.to(lowercase_    )

										lowerCAmelCase              = KandinskyVaaPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-2-decoder"""  ,  torch_dtype=torch.floataa    )
										lowerCAmelCase              = pipeline.to(lowercase_    )
										pipeline.set_progress_bar_config(disable=lowercase_    )

										lowerCAmelCase              = """red cat, 4k photo"""

										lowerCAmelCase              = torch.Generator(device="""cuda"""    ).manual_seed(0    )
										lowerCAmelCase       ,  lowerCAmelCase              = pipe_prior(
										    lowercase_  ,  generator=lowercase_  ,  num_inference_steps=5  ,  negative_prompt=""""""  ,  ).to_tuple()

										lowerCAmelCase              = torch.Generator(device="""cuda"""    ).manual_seed(0    )
										lowerCAmelCase              = pipeline(
										    image_embeds=lowercase_  ,  negative_image_embeds=lowercase_  ,  generator=lowercase_  ,  num_inference_steps=100  ,  output_type="""np"""  ,  )

										lowerCAmelCase              = output.images[0]

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

										assert_mean_pixel_difference(lowercase_  ,  lowercase_    )












'''simple docstring'''

from ..utils import DummyObject, requires_backends






class    UpperCAmelCase	(       metaclass=UpperCamelCase__				):
 __lowercase   		=				["""note_seq"""]
 def __init__(				self      :Optional[Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :List[str]							)->							int:
     requires_backends(self						,       ["note_seq"]							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :str						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							Optional[int]:
     requires_backends(cls						,       ["note_seq"]							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       *lowercase_      :Tuple						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     requires_backends(cls						,       ["note_seq"]							)



