






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     _SCREAMING_SNAKE_CASE       (			SCREAMING_SNAKE_CASE__		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  KandinskyVaaControlnetPipeline
	snake_case__							:     Dict     	=  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
	snake_case__							:     str     	=  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
	snake_case__							:     Dict     	=  [
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''latents''',
	    '''guidance_scale''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	snake_case__							:     str     	=  False





	@property
	def 						_A (		self				:					Optional[int]	):
		return 32





	@property
	def 						_A (		self				:					Dict	):
		return 32





	@property
	def 						_A (		self				:					int	):
		return self.time_input_dim





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





	@property
	def 						_A (		self				:					Tuple	):
		return 100





	@property
	def 						_A (		self				:					Any	):
		torch.manual_seed(0	)

		UpperCamelCase     :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     :Optional[Any]          =	UNetaDConditionModel(**A__	)
		return model





	@property
	def 						_A (		self				:					Optional[Any]	):
		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 						_A (		self				:					Optional[Any]	):
		torch.manual_seed(0	)
		UpperCamelCase     :Tuple          =	VQModel(**self.dummy_movq_kwargs	)
		return model





	def 						_A (		self				:					Any	):
		UpperCamelCase     :Optional[Any]          =	self.dummy_unet
		UpperCamelCase     :Optional[int]          =	self.dummy_movq

		UpperCamelCase     :List[Any]          =	DDIMScheduler(
		    num_train_timesteps=1_000 ,  beta_schedule="""linear""" ,  beta_start=0.00085 ,  beta_end=0.012 ,  clip_sample=A__ ,  set_alpha_to_one=A__ ,  steps_offset=1 ,  prediction_type="""epsilon""" ,  thresholding=A__ ,  )

		UpperCamelCase     :Dict          =	{
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """movq""": movq,
		}
		return components





	def 						_A (		self				:					int ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[Any]=0	):
		UpperCamelCase     :List[str]          =	floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(A__	)	).to(A__	)
		UpperCamelCase     :Any          =	floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(seed + 1	)	).to(
		    A__	)

		# create hint
		UpperCamelCase     :Any          =	floats_tensor((1, 3, 64, 64) ,  rng=random.Random(A__	)	).to(A__	)

		if str(A__	).startswith("""mps"""	):
			UpperCamelCase     :str          =	torch.manual_seed(A__	)
		else:
			UpperCamelCase     :Optional[Any]          =	torch.Generator(device=A__	).manual_seed(A__	)
		UpperCamelCase     :Optional[int]          =	{
		    """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 						_A (		self				:					Union[str, Any]	):
		UpperCamelCase     :Optional[int]          =	"""cpu"""

		UpperCamelCase     :int          =	self.get_dummy_components()

		UpperCamelCase     :Any          =	self.pipeline_class(**A__	)
		UpperCamelCase     :Tuple          =	pipe.to(A__	)

		pipe.set_progress_bar_config(disable=A__	)

		UpperCamelCase     :Tuple          =	pipe(**self.get_dummy_inputs(A__	)	)
		UpperCamelCase     :Any          =	output.images

		UpperCamelCase     :Optional[int]          =	pipe(
		    **self.get_dummy_inputs(A__	) ,  return_dict=A__ ,  )[0]

		UpperCamelCase     :Optional[Any]          =	image[0, -3:, -3:, -1]
		UpperCamelCase     :List[Any]          =	image_from_tuple[0, -3:, -3:, -1]

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

		UpperCamelCase     :List[Any]          =	np.array(
		    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]	)

		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 						_A (		self				:					List[str]	):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :str          =	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""	)

		UpperCamelCase     :Dict          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/kandinskyv22/hint_image_cat.png"""	)
		UpperCamelCase     :Dict          =	torch.from_numpy(np.array(A__	)	).float() / 255.0
		UpperCamelCase     :Any          =	hint.permute(2 ,  0 ,  1	).unsqueeze(0	)

		UpperCamelCase     :Tuple          =	KandinskyVaaPriorPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-prior""" ,  torch_dtype=torch.floataa	)
		pipe_prior.to(A__	)

		UpperCamelCase     :int          =	KandinskyVaaControlnetPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-controlnet-depth""" ,  torch_dtype=torch.floataa	)
		UpperCamelCase     :Any          =	pipeline.to(A__	)
		pipeline.set_progress_bar_config(disable=A__	)

		UpperCamelCase     :Tuple          =	"""A robot, 4k photo"""

		UpperCamelCase     :Dict          =	torch.Generator(device="""cuda"""	).manual_seed(0	)
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	pipe_prior(
		    A__ ,  generator=A__ ,  num_inference_steps=5 ,  negative_prompt="""""" ,  ).to_tuple()

		UpperCamelCase     :List[str]          =	torch.Generator(device="""cuda"""	).manual_seed(0	)
		UpperCamelCase     :str          =	pipeline(
		    image_embeds=A__ ,  negative_image_embeds=A__ ,  hint=A__ ,  generator=A__ ,  num_inference_steps=100 ,  output_type="""np""" ,  )

		UpperCamelCase     :Dict          =	output.images[0]

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

		assert_mean_pixel_difference(A__ ,  A__	)


def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




