





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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   A      (SCREAMING_SNAKE_CASE       ,					unittest.TestCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   KandinskyVaaImgaImgPipeline
			__lowerCamelCase     :	int          =   ['''image_embeds''', '''negative_image_embeds''', '''image''']
			__lowerCamelCase     :	int          =   [
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			__lowerCamelCase     :	List[Any]          =   [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			__lowerCamelCase     :	List[str]          =   False






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







					"""simple docstring"""






					return 32






			@property
			def        a_	(      self       :   str       )     ->     List[Any]:







					"""simple docstring"""






					return 32






			@property
			def        a_	(      self       :   Any       )     ->     Any:







					"""simple docstring"""






					return self.time_input_dim






			@property
			def        a_	(      self       :   List[str]       )     ->     List[Any]:







					"""simple docstring"""






					return self.time_input_dim * 4






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







					"""simple docstring"""






					return 1_00






			@property
			def        a_	(      self       :   str       )     ->     List[Any]:







					"""simple docstring"""






					torch.manual_seed(0       )

					A__  				=       {
					    """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,
					}

					A__  				=       UNetaDConditionModel(**__lowerCAmelCase       )
					return model






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







					"""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        a_	(      self       :   List[Any]       )     ->     Optional[Any]:







					"""simple docstring"""






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






			def        a_	(      self       :   int       )     ->     int:







					"""simple docstring"""






					A__  				=       self.dummy_unet
					A__  				=       self.dummy_movq

					A__  				=       {
					    """num_train_timesteps""": 10_00,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.0_0_0_8_5,
					    """beta_end""": 0.0_1_2,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					A__  				=       DDIMScheduler(**__lowerCAmelCase       )

					A__  				=       {
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components






			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Dict=0       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       floats_tensor((1, self.text_embedder_hidden_size)							,     rng=random.Random(__lowerCAmelCase       )       ).to(__lowerCAmelCase       )
					A__  				=       floats_tensor((1, self.text_embedder_hidden_size)							,     rng=random.Random(seed + 1       )       ).to(
					    __lowerCAmelCase       )
					# create init_image
					A__  				=       floats_tensor((1, 3, 64, 64)							,     rng=random.Random(__lowerCAmelCase       )       ).to(__lowerCAmelCase       )
					A__  				=       image.cpu().permute(0							,     2							,     3							,     1       )[0]
					A__  				=       Image.fromarray(np.uinta(__lowerCAmelCase       )       ).convert("""RGB"""       ).resize((2_56, 2_56)       )

					if str(__lowerCAmelCase       ).startswith("""mps"""       ):
							A__  				=       torch.manual_seed(__lowerCAmelCase       )
					else:
							A__  				=       torch.Generator(device=__lowerCAmelCase       ).manual_seed(__lowerCAmelCase       )
					A__  				=       {
					    """image""": init_image,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 10,
					    """guidance_scale""": 7.0,
					    """strength""": 0.2,
					    """output_type""": """np""",
					}
					return inputs







			def        a_	(      self       :   Tuple       )     ->     Any:







					"""simple docstring"""






					A__  				=       """cpu"""

					A__  				=       self.get_dummy_components()

					A__  				=       self.pipeline_class(**__lowerCAmelCase       )
					A__  				=       pipe.to(__lowerCAmelCase       )

					pipe.set_progress_bar_config(disable=__lowerCAmelCase       )

					A__  				=       pipe(**self.get_dummy_inputs(__lowerCAmelCase       )       )
					A__  				=       output.images

					A__  				=       pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase       )							,     return_dict=__lowerCAmelCase							,     )[0]

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

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

					A__  				=       np.array(
					    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]       )
					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   ):
			'''simple docstring'''




			def        a_	(      self       :   Any       )     ->     str:







					"""simple docstring"""






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







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







					"""simple docstring"""






					A__  				=       load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""       )

					A__  				=       load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
					A__  				=       """A red cartoon frog, 4k"""

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

					A__  				=       KandinskyVaaImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-decoder"""							,     torch_dtype=torch.floataa       )
					A__  				=       pipeline.to(__lowerCAmelCase       )

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase       )

					A__  				=       torch.Generator(device="""cpu"""       ).manual_seed(0       )
					A__				,      A__  				=       pipe_prior(
					    __lowerCAmelCase							,     generator=__lowerCAmelCase							,     num_inference_steps=5							,     negative_prompt=""""""							,     ).to_tuple()

					A__  				=       pipeline(
					    image=__lowerCAmelCase							,     image_embeds=__lowerCAmelCase							,     negative_image_embeds=__lowerCAmelCase							,     generator=__lowerCAmelCase							,     num_inference_steps=1_00							,     height=7_68							,     width=7_68							,     strength=0.2							,     output_type="""np"""							,     )

					A__  				=       output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(__lowerCAmelCase							,     __lowerCAmelCase       )










import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


if __name__ == "__main__":
					print(F'''{solution() = }''')



