


'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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__       (							a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Tuple					=   KandinskyVaaPipeline
							lowercase__    :     str					=   [
							    "image_embeds",
							    "negative_image_embeds",
							]
							lowercase__    :     Tuple					=   ["image_embeds", "negative_image_embeds"]
							lowercase__    :     str					=   [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							lowercase__    :     Union[str, Any]					=   False







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														return 32







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







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







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														return self.time_input_dim * 4







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														return 1_00







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														torch.manual_seed(0     )

														lowerCAmelCase__      =			{
														    '''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,
														}

														lowerCAmelCase__      =			UNetaDConditionModel(**lowerCamelCase_     )
														return model







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														torch.manual_seed(0     )
														lowerCAmelCase__      =			VQModel(**self.dummy_movq_kwargs     )
														return model







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			self.dummy_unet
														lowerCAmelCase__      =			self.dummy_movq

														lowerCAmelCase__      =			DDIMScheduler(
														    num_train_timesteps=10_00      ,					beta_schedule='''linear'''      ,					beta_start=0.00_085      ,					beta_end=0.012      ,					clip_sample=lowerCamelCase_      ,					set_alpha_to_one=lowerCamelCase_      ,					steps_offset=1      ,					prediction_type='''epsilon'''      ,					thresholding=lowerCamelCase_      ,					)

														lowerCAmelCase__      =			{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}
														return components







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=0     ) ->							Dict:
														lowerCAmelCase__      =			floats_tensor((1, self.text_embedder_hidden_size)      ,					rng=random.Random(lowerCamelCase_     )     ).to(lowerCamelCase_     )
														lowerCAmelCase__      =			floats_tensor((1, self.text_embedder_hidden_size)      ,					rng=random.Random(seed + 1     )     ).to(
														    lowerCamelCase_     )
														if str(lowerCamelCase_     ).startswith('''mps'''     ):
																					lowerCAmelCase__      =			torch.manual_seed(lowerCamelCase_     )
														else:
																					lowerCAmelCase__      =			torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
														lowerCAmelCase__      =			{
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''guidance_scale''': 4.0,
														    '''num_inference_steps''': 2,
														    '''output_type''': '''np''',
														}
														return inputs







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			'''cpu'''

														lowerCAmelCase__      =			self.get_dummy_components()

														lowerCAmelCase__      =			self.pipeline_class(**lowerCamelCase_     )
														lowerCAmelCase__      =			pipe.to(lowerCamelCase_     )

														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			pipe(**self.get_dummy_inputs(lowerCamelCase_     )     )
														lowerCAmelCase__      =			output.images

														lowerCAmelCase__      =			pipe(
														    **self.get_dummy_inputs(lowerCamelCase_     )      ,					return_dict=lowerCamelCase_      ,					)[0]

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

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

														lowerCAmelCase__      =			np.array(
														    [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

														lowerCAmelCase__      =			KandinskyVaaPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''      ,					torch_dtype=torch.floataa     )
														pipe_prior.to(lowerCamelCase_     )

														lowerCAmelCase__      =			KandinskyVaaPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-decoder'''      ,					torch_dtype=torch.floataa     )
														lowerCAmelCase__      =			pipeline.to(lowerCamelCase_     )
														pipeline.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			'''red cat, 4k photo'''

														lowerCAmelCase__      =			torch.Generator(device='''cuda'''     ).manual_seed(0     )
														lowerCAmelCase__     ,      lowerCAmelCase__      =			pipe_prior(
														    lowerCamelCase_      ,					generator=lowerCamelCase_      ,					num_inference_steps=5      ,					negative_prompt=''''''      ,					).to_tuple()

														lowerCAmelCase__      =			torch.Generator(device='''cuda'''     ).manual_seed(0     )
														lowerCAmelCase__      =			pipeline(
														    image_embeds=lowerCamelCase_      ,					negative_image_embeds=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					num_inference_steps=1_00      ,					output_type='''np'''      ,					)

														lowerCAmelCase__      =			output.images[0]

														assert image.shape == (5_12, 5_12, 3)

														assert_mean_pixel_difference(lowerCamelCase_      ,					lowerCamelCase_     )



'''simple docstring'''






from math import pi, sqrt, tan


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''surface_area_cube() only accepts non-negative values''' )
							return 6 * side_length**2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if length < 0 or breadth < 0 or height < 0:
														raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
							return 2 * ((length * breadth) + (breadth * height) + (length * height))


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
							return 4 * pi * radius**2


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
							return 3 * pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cone() only accepts non-negative values''' )
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def       _snake_case     (  A				,					A				,					A )		->					float:
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    '''surface_area_conical_frustum() only accepts non-negative values''' )
							lowerCAmelCase__      =			(height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
							return 2 * pi * radius * (height + radius)


def       _snake_case     (  A				,					A )		->					float:
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError('''surface_area_torus() only accepts non-negative values''' )
							if torus_radius < tube_radius:
														raise ValueError(
														    '''surface_area_torus() does not support spindle or self intersecting tori''' )
							return 4 * pow(A				,					2 ) * torus_radius * tube_radius


def       _snake_case     (  A				,					A )		->					float:
							if length < 0 or width < 0:
														raise ValueError('''area_rectangle() only accepts non-negative values''' )
							return length * width


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''area_square() only accepts non-negative values''' )
							return side_length**2


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_triangle() only accepts non-negative values''' )
							return (base * height) / 2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError('''Given three sides do not form a triangle''' )
							lowerCAmelCase__      =			(sidea + sidea + sidea) / 2
							lowerCAmelCase__      =			sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea) )
							return area


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_parallelogram() only accepts non-negative values''' )
							return base * height


def       _snake_case     (  A				,					A				,					A )		->					float:
							if basea < 0 or basea < 0 or height < 0:
														raise ValueError('''area_trapezium() only accepts non-negative values''' )
							return 1 / 2 * (basea + basea) * height


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''area_circle() only accepts non-negative values''' )
							return pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius_x < 0 or radius_y < 0:
														raise ValueError('''area_ellipse() only accepts non-negative values''' )
							return pi * radius_x * radius_y


def       _snake_case     (  A				,					A )		->					float:
							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError('''area_rhombus() only accepts non-negative values''' )
							return 1 / 2 * diagonal_a * diagonal_a


def       _snake_case     (  A				,					A )		->					float:
							if not isinstance(A				,					A ) or sides < 3:
														raise ValueError(
														    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
							elif length < 0:
														raise ValueError(
														    '''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
							return (sides * length**2) / (4 * tan(pi / sides ))
							return (sides * length**2) / (4 * tan(pi / sides ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('''[DEMO] Areas of various geometric shapes: \n''')
		print(f"""Rectangle: {area_rectangle(10, 20) = }""")
		print(f"""Square: {area_square(10) = }""")
		print(f"""Triangle: {area_triangle(10, 10) = }""")
		print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(f"""Rhombus: {area_rhombus(10, 20) = }""")
		print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(f"""Circle: {area_circle(20) = }""")
		print(f"""Ellipse: {area_ellipse(10, 20) = }""")
		print('''\nSurface Areas of various geometric shapes: \n''')
		print(f"""Cube: {surface_area_cube(20) = }""")
		print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(f"""Sphere: {surface_area_sphere(20) = }""")
		print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(f"""Cone: {surface_area_cone(10, 20) = }""")
		print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(f"""Torus: {surface_area_torus(20, 10) = }""")
		print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(f"""Square: {area_reg_polygon(4, 10) = }""")
		print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")