


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    UpperCAmelCase					(       __snake_case      , 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   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						return self.time_input_dim




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						return self.time_input_dim * 4




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return 1_0_0




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						torch.manual_seed(0						)

						UpperCamelCase         =					{
						    """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         =					UNetaDConditionModel(**__magic_name__						)
						return model




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return {
						    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}




			@property
			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_unet
						UpperCamelCase         =					self.dummy_movq

						UpperCamelCase         =					DDIMScheduler(
						    num_train_timesteps=1_0_0_0       ,       beta_schedule="""linear"""       ,       beta_start=0.00_085       ,       beta_end=0.012       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       steps_offset=1       ,       prediction_type="""epsilon"""       ,       thresholding=__magic_name__       ,       )

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




			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		int       ,       __magic_name__							:		List[str]=0						):







						"""simple docstring"""







						UpperCamelCase         =					floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(seed + 1						)						).to(
						    __magic_name__						)

						# create hint
						UpperCamelCase         =					floats_tensor((1, 3, 6_4, 6_4)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)

						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """hint""": hint,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """guidance_scale""": 4.0,
						    """num_inference_steps""": 2,
						    """output_type""": """np""",
						}
						return inputs





			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""

						UpperCamelCase         =					self.get_dummy_components()

						UpperCamelCase         =					self.pipeline_class(**__magic_name__						)
						UpperCamelCase         =					pipe.to(__magic_name__						)

						pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					pipe(**self.get_dummy_inputs(__magic_name__						)						)
						UpperCamelCase         =					output.images

						UpperCamelCase         =					pipe(
						    **self.get_dummy_inputs(__magic_name__						)       ,       return_dict=__magic_name__       ,       )[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)

						UpperCamelCase         =					np.array(
						    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]						)

						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    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







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

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

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

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

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

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

						UpperCamelCase         =					torch.Generator(device="""cuda"""						).manual_seed(0						)
						UpperCamelCase         =					pipeline(
						    image_embeds=__magic_name__       ,       negative_image_embeds=__magic_name__       ,       hint=__magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=1_0_0       ,       output_type="""np"""       ,       )

						UpperCamelCase         =					output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						assert_mean_pixel_difference(__magic_name__       ,       __magic_name__						)








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					int(SCREAMING_SNAKE_CASE_	)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase  ,  UpperCamelCase         =					divmod(SCREAMING_SNAKE_CASE_       ,  2	)
			return binary_recursive(SCREAMING_SNAKE_CASE_	) + str(SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			UpperCamelCase         =					str(SCREAMING_SNAKE_CASE_	).strip()
			if not number:
						raise ValueError("""No input value was provided"""	)
			UpperCamelCase         =					"""-""" if number.startswith("""-"""	) else """"""
			UpperCamelCase         =					number.lstrip("""-"""	)
			if not number.isnumeric():
						raise ValueError("""Input value is not an integer"""	)
			return f'{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_	)	)}'


if __name__ == "__main__":
			from doctest import testmod

			testmod()




