



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


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return 32


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


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


	@property
	def    _a     (self							):
				return 100


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

				A_     :						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,
				}

				A_     :						str     =					UNetaDConditionModel(**lowercase							)
				return model


	@property
	def    _a     (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    _a     (self							):
				torch.manual_seed(0							)
				A_     :						int     =					VQModel(**self.dummy_movq_kwargs							)
				return model


	def    _a     (self							):
				A_     :						Optional[Any]     =					self.dummy_unet
				A_     :						Dict     =					self.dummy_movq

				A_     :						List[Any]     =					DDIMScheduler(
				    num_train_timesteps=1000	,				beta_schedule="""linear"""	,				beta_start=0.0_00_85	,				beta_end=0.0_12	,				clip_sample=lowercase	,				set_alpha_to_one=lowercase	,				steps_offset=1	,				prediction_type="""epsilon"""	,				thresholding=lowercase	,				)

				A_     :						Any     =					{
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components


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

				if str(lowercase							).startswith("""mps"""							):
							A_     :						int     =					torch.manual_seed(lowercase							)
				else:
							A_     :						Dict     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						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    _a     (self							):
				A_     :						List[str]     =					"""cpu"""

				A_     :						str     =					self.get_dummy_components()

				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						List[Any]     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Union[str, Any]     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						List[str]     =					output.images

				A_     :						Union[str, Any]     =					pipe(
				    **self.get_dummy_inputs(lowercase							)	,				return_dict=lowercase	,				)[0]

				A_     :						List[str]     =					image[0, -3:, -3:, -1]
				A_     :						Any     =					image_from_tuple[0, -3:, -3:, -1]

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

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

				A_     :						Optional[int]     =					np.array(
				    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]							)

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







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


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


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

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

				A_     :						str     =					"""a hat"""

				A_     :						Union[str, Any]     =					KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""	,				torch_dtype=torch.floataa							)
				pipe_prior.to(lowercase							)

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

				A_     :						int     =					torch.Generator(device="""cpu"""							).manual_seed(0							)
				A_,   A_     :						Dict     =					pipe_prior(
				    lowercase	,				generator=lowercase	,				num_inference_steps=5	,				negative_prompt=""""""	,				).to_tuple()

				A_     :						List[Any]     =					pipeline(
				    image=lowercase	,				mask_image=lowercase	,				image_embeds=lowercase	,				negative_image_embeds=lowercase	,				generator=lowercase	,				num_inference_steps=100	,				height=768	,				width=768	,				output_type="""np"""	,				)

				A_     :						Tuple     =					output.images[0]

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

				assert_mean_pixel_difference(lowercase	,				lowercase							)




'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable

lowerCamelCase							:List[Any]       = list[list[float | int]]



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					len(lowerCamelCase__   )
			A_     :						Matrix     =					[[0 for _ in range(size + 1   )] for _ in range(lowerCamelCase__   )]
			A_     :						int
			A_     :						int
			A_     :						int
			A_     :						int
			A_     :						int
			A_     :						float

			for row in range(lowerCamelCase__   ):
						for col in range(lowerCamelCase__   ):
									A_     :						List[str]     =					matrix[row][col]

						A_     :						Optional[int]     =					vector[row][0]

			A_     :						Tuple     =					0
			A_     :						Tuple     =					0
			while row < size and col < size:
						# pivoting
						A_     :						Dict     =					max((abs(augmented[rowa][col]   ), rowa) for rowa in range(lowerCamelCase__						,				lowerCamelCase__   )   )[
						    1
						]
						if augmented[pivot_row][col] == 0:
									col += 1
									continue
						else:
									A_,   A_     :						Optional[Any]     =					augmented[pivot_row], augmented[row]

						for rowa in range(row + 1						,				lowerCamelCase__   ):
									A_     :						int     =					augmented[rowa][col] / augmented[row][col]
									A_     :						Union[str, Any]     =					0
									for cola in range(col + 1						,				size + 1   ):
												augmented[rowa][cola] -= augmented[row][cola] * ratio

						row += 1
						col += 1

			# back substitution
			for col in range(1						,				lowerCamelCase__   ):
						for row in range(lowerCamelCase__   ):
									A_     :						Tuple     =					augmented[row][col] / augmented[col][col]
									for cola in range(lowerCamelCase__						,				size + 1   ):
												augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
			return [
			    [round(augmented[row][size] / augmented[row][row]						,				10   )] for row in range(lowerCamelCase__   )
			]



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					len(lowerCamelCase__   )
			A_     :						Matrix     =					[[0 for _ in range(lowerCamelCase__   )] for _ in range(lowerCamelCase__   )]
			A_     :						Matrix     =					[[0] for _ in range(lowerCamelCase__   )]
			A_     :						Matrix
			A_     :						int
			A_     :						int
			A_     :						int

			for x_val, y_val in enumerate(lowerCamelCase__   ):
						for col in range(lowerCamelCase__   ):
									A_     :						Dict     =					(x_val + 1) ** (size - col - 1)
						A_     :						Any     =					y_val

			A_     :						Dict     =					solve(lowerCamelCase__						,				lowerCamelCase__   )

			def interpolated_func(lowerCamelCase__   ) -> int:
						return sum(
						    round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
						    for x_val in range(lowerCamelCase__   )   )

			return interpolated_func



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return (
			    1
			    - variable
			    + variable**2
			    - variable**3
			    + variable**4
			    - variable**5
			    + variable**6
			    - variable**7
			    + variable**8
			    - variable**9
			    + variable**10
			)



def      a      (			lowerCamelCase__ = question_function						,				lowerCamelCase__ = 10   ):
			'''simple docstring'''





			A_     :						list[int]     =					[func(lowerCamelCase__   ) for x_val in range(1						,				order + 1   )]

			A_     :						list[Callable[[int], int]]     =					[
			    interpolate(data_points[:max_coeff]   ) for max_coeff in range(1						,				order + 1   )
			]

			A_     :						int     =					0
			A_     :						Callable[[int], int]
			A_     :						int

			for poly in polynomials:
						A_     :						int     =					1
						while func(lowerCamelCase__   ) == poly(lowerCamelCase__   ):
									x_val += 1

						ret += poly(lowerCamelCase__   )

			return ret


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