


'''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 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Any						=   KandinskyVaaInpaintPipeline
					lowerCAmelCase :Optional[Any]						=   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					lowerCAmelCase :List[Any]						=   [
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					lowerCAmelCase :Any						=   [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase :str						=   False




					@property
					def 			snake_case__  ( self):
								return 32




					@property
					def 			snake_case__  ( self):
								return 32




					@property
					def 			snake_case__  ( self):
								return self.time_input_dim




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




					@property
					def 			snake_case__  ( self):
								return 100




					@property
					def 			snake_case__  ( self):
								torch.manual_seed(0)

								UpperCAmelCase__					:   List[str]       				=       {
								    """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,
								}

								UpperCAmelCase__					:   List[str]       				=       UNetaDConditionModel(**_lowerCamelCase)
								return model




					@property
					def 			snake_case__  ( self):
								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 			snake_case__  ( self):
								torch.manual_seed(0)
								UpperCAmelCase__					:   Tuple       				=       VQModel(**self.dummy_movq_kwargs)
								return model




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       self.dummy_unet
								UpperCAmelCase__					:   Optional[Any]       				=       self.dummy_movq

								UpperCAmelCase__					:   Tuple       				=       DDIMScheduler(
								    num_train_timesteps=1000							,							beta_schedule="""linear"""							,							beta_start=0.00085							,							beta_end=0.012							,							clip_sample=_lowerCamelCase							,							set_alpha_to_one=_lowerCamelCase							,							steps_offset=1							,							prediction_type="""epsilon"""							,							thresholding=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Union[str, Any]       				=       {
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=0):
								UpperCAmelCase__					:   str       				=       floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(seed + 1)).to(
								    _lowerCamelCase)
								# create init_image
								UpperCAmelCase__					:   Union[str, Any]       				=       floats_tensor((1, 3, 64, 64)							,							rng=random.Random(_lowerCamelCase)).to(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       image.cpu().permute(0							,							2							,							3							,							1)[0]
								UpperCAmelCase__					:   Tuple       				=       Image.fromarray(np.uinta(_lowerCamelCase)).convert("""RGB""").resize((256, 256))
								# create mask
								UpperCAmelCase__					:   Any       				=       np.ones((64, 64)							,							dtype=np.floataa)
								UpperCAmelCase__					:   Any       				=       0

								if str(_lowerCamelCase).startswith("""mps"""):
											UpperCAmelCase__					:   Tuple       				=       torch.manual_seed(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Dict       				=       torch.Generator(device=_lowerCamelCase).manual_seed(_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       {
								    """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 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       """cpu"""

								UpperCAmelCase__					:   Optional[Any]       				=       self.get_dummy_components()

								UpperCAmelCase__					:   List[Any]       				=       self.pipeline_class(**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       pipe.to(_lowerCamelCase)

								pipe.set_progress_bar_config(disable=_lowerCamelCase)

								UpperCAmelCase__					:   Tuple       				=       pipe(**self.get_dummy_inputs(_lowerCamelCase))
								UpperCAmelCase__					:   Union[str, Any]       				=       output.images

								UpperCAmelCase__					:   List[Any]       				=       pipe(
								    **self.get_dummy_inputs(_lowerCamelCase)							,							return_dict=_lowerCamelCase							,							)[0]

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

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

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

								UpperCAmelCase__					:   Optional[Any]       				=       np.array(
								    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848])

								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 			snake_case__  ( self):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3)


@slow
@require_torch_gpu
class 						_snake_case   (       unittest.TestCase		):




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




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Union[str, Any]       				=       load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy""")

								UpperCAmelCase__					:   Union[str, Any]       				=       load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
								UpperCAmelCase__					:   Tuple       				=       np.ones((768, 768)							,							dtype=np.floataa)
								UpperCAmelCase__					:   List[Any]       				=       0

								UpperCAmelCase__					:   Union[str, Any]       				=       """a hat"""

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

								UpperCAmelCase__					:   int       				=       KandinskyVaaInpaintPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""							,							torch_dtype=torch.floataa)
								UpperCAmelCase__					:   Dict       				=       pipeline.to(_lowerCamelCase)
								pipeline.set_progress_bar_config(disable=_lowerCamelCase)

								UpperCAmelCase__					:   List[Any]       				=       torch.Generator(device="""cpu""").manual_seed(0)
								UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       pipe_prior(
								    _lowerCamelCase							,							generator=_lowerCamelCase							,							num_inference_steps=5							,							negative_prompt=""""""							,							).to_tuple()

								UpperCAmelCase__					:   Tuple       				=       pipeline(
								    image=_lowerCamelCase							,							mask_image=_lowerCamelCase							,							image_embeds=_lowerCamelCase							,							negative_image_embeds=_lowerCamelCase							,							generator=_lowerCamelCase							,							num_inference_steps=100							,							height=768							,							width=768							,							output_type="""np"""							,							)

								UpperCAmelCase__					:   Any       				=       output.images[0]

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

								assert_mean_pixel_difference(_lowerCamelCase							,							_lowerCamelCase)



'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__ = 1_0	, UpperCamelCase__ = 2_2   ):
			UpperCAmelCase__					:   List[str]       				=       range(1	, UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       range(1	, UpperCamelCase__   )
			return sum(
			    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
		print(f"""{solution(10, 22) = }""")