



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Tuple									=		KandinskyVaaControlnetImgaImgPipeline
						UpperCAmelCase							:   Optional[Any]									=		["image_embeds", "negative_image_embeds", "image", "hint"]
						UpperCAmelCase							:   int									=		["image_embeds", "negative_image_embeds", "image", "hint"]
						UpperCAmelCase							:   Optional[int]									=		[
						    "generator",
						    "height",
						    "width",
						    "strength",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase							:   Optional[int]									=		False


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


						@property
						def     __snake_case   ( self				)     ->     List[str]:
							return 32


						@property
						def     __snake_case   ( self				)     ->     Any:
							return self.time_input_dim


						@property
						def     __snake_case   ( self				)     ->     Any:
							return self.time_input_dim * 4


						@property
						def     __snake_case   ( self				)     ->     Optional[int]:
							return 100


						@property
						def     __snake_case   ( self				)     ->     Optional[int]:
							torch.manual_seed(0				)

							lowerCAmelCase   =     {
							    """in_channels""": 8,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """image_hint""",
							    """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(**A_				)
							return model


						@property
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}


						@property
						def     __snake_case   ( self				)     ->     Dict:
							torch.manual_seed(0				)
							lowerCAmelCase   =     VQModel(**self.dummy_movq_kwargs				)
							return model


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.dummy_unet
							lowerCAmelCase   =     self.dummy_movq

							lowerCAmelCase   =     {
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.0_0_0_8_5,
							    """beta_end""": 0.0_1_2,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowerCAmelCase   =     DDIMScheduler(**A_				)

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

							return components


						def     __snake_case   ( self     ,	A_     ,	A_=0				)     ->     Tuple:
							lowerCAmelCase   =     floats_tensor((1, self.text_embedder_hidden_size)     ,	rng=random.Random(A_				)				).to(A_				)
							lowerCAmelCase   =     floats_tensor((1, self.text_embedder_hidden_size)     ,	rng=random.Random(seed + 1				)				).to(
							    A_				)
							# create init_image
							lowerCAmelCase   =     floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(A_				)				).to(A_				)
							lowerCAmelCase   =     image.cpu().permute(0     ,	2     ,	3     ,	1				)[0]
							lowerCAmelCase   =     Image.fromarray(np.uinta(A_				)				).convert("""RGB"""				).resize((256, 256)				)
							# create hint
							lowerCAmelCase   =     floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(A_				)				).to(A_				)

							if str(A_				).startswith("""mps"""				):
								lowerCAmelCase   =     torch.manual_seed(A_				)
							else:
								lowerCAmelCase   =     torch.Generator(device=A_				).manual_seed(A_				)
							lowerCAmelCase   =     {
							    """image""": init_image,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """hint""": hint,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 10,
							    """guidance_scale""": 7.0,
							    """strength""": 0.2,
							    """output_type""": """np""",
							}
							return inputs




						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     """cpu"""

							lowerCAmelCase   =     self.get_dummy_components()

							lowerCAmelCase   =     self.pipeline_class(**A_				)
							lowerCAmelCase   =     pipe.to(A_				)

							pipe.set_progress_bar_config(disable=A_				)

							lowerCAmelCase   =     pipe(**self.get_dummy_inputs(A_				)				)
							lowerCAmelCase   =     output.images

							lowerCAmelCase   =     pipe(
							    **self.get_dummy_inputs(A_				)     ,	return_dict=A_     ,	)[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(
							    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]				)
							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 __snake_case(				unittest.TestCase ):


						'''simple docstring'''



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




						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""				)

							lowerCAmelCase   =     load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""				)
							lowerCAmelCase   =     init_image.resize((512, 512)				)

							lowerCAmelCase   =     load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/hint_image_cat.png"""				)
							lowerCAmelCase   =     torch.from_numpy(np.array(A_				)				).float() / 2_5_5.0
							lowerCAmelCase   =     hint.permute(2     ,	0     ,	1				).unsqueeze(0				)

							lowerCAmelCase   =     """A robot, 4k photo"""

							lowerCAmelCase   =     KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-prior"""     ,	torch_dtype=torch.floataa				)
							pipe_prior.to(A_				)

							lowerCAmelCase   =     KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-controlnet-depth"""     ,	torch_dtype=torch.floataa				)
							lowerCAmelCase   =     pipeline.to(A_				)

							pipeline.set_progress_bar_config(disable=A_				)

							lowerCAmelCase   =     torch.Generator(device="""cpu"""				).manual_seed(0				)

							lowerCAmelCase,       lowerCAmelCase   =     pipe_prior(
							    A_     ,	image=A_     ,	strength=0.8_5     ,	generator=A_     ,	negative_prompt=""""""     ,	).to_tuple()

							lowerCAmelCase   =     pipeline(
							    image=A_     ,	image_embeds=A_     ,	negative_image_embeds=A_     ,	hint=A_     ,	generator=A_     ,	num_inference_steps=100     ,	height=512     ,	width=512     ,	strength=0.5     ,	output_type="""np"""     ,	)

							lowerCAmelCase   =     output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(A_     ,	A_				)




'''simple docstring'''

import cmath
import math






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							)      ->						complex:







	"""simple docstring"""
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)

	# Convert voltage and current to rectangular form
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()