
"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		KandinskyVaaInpaintPipeline
							_snake_case						      =		['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							_snake_case						      =		[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							_snake_case						      =		[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_snake_case						      =		False




							@property
							def    A__						( self				)       ->  Any:
													return 32




							@property
							def    A__						( self				)       ->  int:
													return 32




							@property
							def    A__						( self				)       ->  List[str]:
													return self.time_input_dim




							@property
							def    A__						( self				)       ->  Dict:
													return self.time_input_dim * 4




							@property
							def    A__						( self				)       ->  str:
													return 100




							@property
							def    A__						( self				)       ->  Dict:
													torch.manual_seed(0				)

													__lowerCAmelCase     =  {
													    """in_channels""": 9,
													    # 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(**snake_case_				)
													return model




							@property
							def    A__						( self				)       ->  int:
													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				)       ->  Union[str, Any]:
													torch.manual_seed(0				)
													__lowerCAmelCase     =  VQModel(**self.dummy_movq_kwargs				)
													return model




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.dummy_unet
													__lowerCAmelCase     =  self.dummy_movq

													__lowerCAmelCase     =  DDIMScheduler(
													    num_train_timesteps=1_000    ,				beta_schedule="""linear"""    ,				beta_start=0.00_085    ,				beta_end=0.012    ,				clip_sample=snake_case_    ,				set_alpha_to_one=snake_case_    ,				steps_offset=1    ,				prediction_type="""epsilon"""    ,				thresholding=snake_case_    ,				)

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

													return components




							def    A__						( self    ,				snake_case_    ,				snake_case_=0				)       ->  Dict:
													__lowerCAmelCase     =  floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(snake_case_				)				).to(snake_case_				)
													__lowerCAmelCase     =  floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1				)				).to(
													    snake_case_				)
													# create init_image
													__lowerCAmelCase     =  floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(snake_case_				)				).to(snake_case_				)
													__lowerCAmelCase     =  image.cpu().permute(0    ,				2    ,				3    ,				1				)[0]
													__lowerCAmelCase     =  Image.fromarray(np.uinta(snake_case_				)				).convert("""RGB"""				).resize((256, 256)				)
													# create mask
													__lowerCAmelCase     =  np.ones((64, 64)    ,				dtype=np.floataa				)
													__lowerCAmelCase     =  0

													if str(snake_case_				).startswith("""mps"""				):
																			__lowerCAmelCase     =  torch.manual_seed(snake_case_				)
													else:
																			__lowerCAmelCase     =  torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
													__lowerCAmelCase     =  {
													    """image""": init_image,
													    """mask_image""": mask,
													    """image_embeds""": image_embeds,
													    """negative_image_embeds""": negative_image_embeds,
													    """generator""": generator,
													    """height""": 64,
													    """width""": 64,
													    """num_inference_steps""": 2,
													    """guidance_scale""": 4.0,
													    """output_type""": """np""",
													}
													return inputs




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  """cpu"""

													__lowerCAmelCase     =  self.get_dummy_components()

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

													pipe.set_progress_bar_config(disable=snake_case_				)

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

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

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

													print(f"""image.shape {image.shape}"""				)

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

													__lowerCAmelCase     =  np.array(
													    [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]				)

													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()}"""




							def    A__						( self				)       ->  Union[str, Any]:
													super().test_inference_batch_single_identical(expected_max_diff=3e-3				)
@slow
@require_torch_gpu
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





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




							def    A__						( self				)       ->  List[Any]:
													__lowerCAmelCase     =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""				)

													__lowerCAmelCase     =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""				)
													__lowerCAmelCase     =  np.ones((768, 768)    ,				dtype=np.floataa				)
													__lowerCAmelCase     =  0

													__lowerCAmelCase     =  """a hat"""

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

													__lowerCAmelCase     =  KandinskyVaaInpaintPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""    ,				torch_dtype=torch.floataa				)
													__lowerCAmelCase     =  pipeline.to(snake_case_				)
													pipeline.set_progress_bar_config(disable=snake_case_				)

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

													__lowerCAmelCase     =  pipeline(
													    image=snake_case_    ,				mask_image=snake_case_    ,				image_embeds=snake_case_    ,				negative_image_embeds=snake_case_    ,				generator=snake_case_    ,				num_inference_steps=100    ,				height=768    ,				width=768    ,				output_type="""np"""    ,				)

													__lowerCAmelCase     =  output.images[0]

													assert image.shape == (768, 768, 3)

													assert_mean_pixel_difference(snake_case_    ,				snake_case_				)


"""simple docstring"""
from __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
