
"""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"""



				__UpperCamelCase      :						int					       =   KandinskyVaaInpaintPipeline
				__UpperCamelCase      :						List[str]					       =   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
				__UpperCamelCase      :						List[Any]					       =   [
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				    '''mask_image''',
				]
				__UpperCamelCase      :						List[Any]					       =   [
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				__UpperCamelCase      :						Any					       =   False



				@property
				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											return 32



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return 32



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



											"""simple docstring"""




											return self.time_input_dim



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



											"""simple docstring"""




											return self.time_input_dim * 4



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



											"""simple docstring"""




											return 1_00



				@property
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											torch.manual_seed(0    )

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   {
											    """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,
											}

											SCREAMING_SNAKE_CASE__						:       Any           =   UNetaDConditionModel(**SCREAMING_SNAKE_CASE__    )
											return model



				@property
				def 	__magic_name__    (self    )					->		Tuple:



											"""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 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											torch.manual_seed(0    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   VQModel(**self.dummy_movq_kwargs    )
											return model



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.dummy_unet
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.dummy_movq

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   DDIMScheduler(
											    num_train_timesteps=10_00   ,       beta_schedule="""linear"""   ,       beta_start=0.00085   ,       beta_end=0.012   ,       clip_sample=SCREAMING_SNAKE_CASE__   ,       set_alpha_to_one=SCREAMING_SNAKE_CASE__   ,       steps_offset=1   ,       prediction_type="""epsilon"""   ,       thresholding=SCREAMING_SNAKE_CASE__   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}

											return components



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(SCREAMING_SNAKE_CASE__    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   floats_tensor((1, self.text_embedder_hidden_size)   ,       rng=random.Random(seed + 1    )    ).to(
											    SCREAMING_SNAKE_CASE__    )
											# create init_image
											SCREAMING_SNAKE_CASE__						:       Tuple           =   floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(SCREAMING_SNAKE_CASE__    )    ).to(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   image.cpu().permute(0   ,       2   ,       3   ,       1    )[0]
											SCREAMING_SNAKE_CASE__						:       Any           =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__    )    ).convert("""RGB"""    ).resize((2_56, 2_56)    )
											# create mask
											SCREAMING_SNAKE_CASE__						:       str           =   np.ones((64, 64)   ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   0

											if str(SCREAMING_SNAKE_CASE__    ).startswith("""mps"""    ):
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.manual_seed(SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       str           =   torch.Generator(device=SCREAMING_SNAKE_CASE__    ).manual_seed(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   {
											    """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 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   """cpu"""

											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_dummy_components()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.pipeline_class(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe.to(SCREAMING_SNAKE_CASE__    )

											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__    )    )
											SCREAMING_SNAKE_CASE__						:       Any           =   output.images

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   pipe(
											    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__    )   ,       return_dict=SCREAMING_SNAKE_CASE__   ,       )[0]

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

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

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

											SCREAMING_SNAKE_CASE__						:       List[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 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






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


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   np.ones((7_68, 7_68)   ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   0

											SCREAMING_SNAKE_CASE__						:       str           =   """a hat"""

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

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   KandinskyVaaInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""   ,       torch_dtype=torch.floataa    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   pipeline.to(SCREAMING_SNAKE_CASE__    )
											pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__    )

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

											SCREAMING_SNAKE_CASE__						:       Tuple           =   pipeline(
											    image=SCREAMING_SNAKE_CASE__   ,       mask_image=SCREAMING_SNAKE_CASE__   ,       image_embeds=SCREAMING_SNAKE_CASE__   ,       negative_image_embeds=SCREAMING_SNAKE_CASE__   ,       generator=SCREAMING_SNAKE_CASE__   ,       num_inference_steps=1_00   ,       height=7_68   ,       width=7_68   ,       output_type="""np"""   ,       )

											SCREAMING_SNAKE_CASE__						:       Dict           =   output.images[0]

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

											assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )






"""simple docstring"""




def lowercase_  (   _snake_case						):
							if a < 0:
														raise ValueError("""Input value must be a positive integer"""						)
							elif isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""Input value must be a 'int' type"""						)
							return bin(_snake_case						).count("""1"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




