

"""simple docstring"""




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class      __UpperCamelCase					(  unittest.TestCase		):


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


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   1
							A     =   3
							A     =   (3_2, 3_2)

							A     =   floats_tensor((batch_size, num_channels) + sizes	,	rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
							return image


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							torch.manual_seed(0)
							A     =   UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)	,	layers_per_block=2	,	sample_size=3_2	,	in_channels=4	,	out_channels=4	,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,	cross_attention_dim=3_2	,	)
							return model


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							torch.manual_seed(0)
							A     =   AutoencoderKL(
							    block_out_channels=[3_2, 6_4]	,	in_channels=3	,	out_channels=3	,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,	latent_channels=4	,	)
							return model


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							torch.manual_seed(0)
							A     =   CLIPTextConfig(
							    bos_token_id=0	,	eos_token_id=2	,	hidden_size=3_2	,	intermediate_size=3_7	,	layer_norm_eps=1E-05	,	num_attention_heads=4	,	num_hidden_layers=5	,	pad_token_id=1	,	vocab_size=1_0_0_0	,	)
							return CLIPTextModel(__SCREAMING_SNAKE_CASE)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							def extract(*__SCREAMING_SNAKE_CASE					:						List[Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]):





												class      __UpperCamelCase					:


														def __init__(self					:						Any):
																			A     =   torch.ones([0])


														def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						Tuple):
																			self.pixel_values.to(__SCREAMING_SNAKE_CASE)
																			return self

												return Out()

							return extract


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
							A     =   self.dummy_cond_unet
							A     =   DDIMScheduler(
							    beta_start=0.0_0_0_8_5	,	beta_end=0.0_1_2	,	beta_schedule="scaled_linear"	,	clip_sample=__SCREAMING_SNAKE_CASE	,	set_alpha_to_one=__SCREAMING_SNAKE_CASE	,	)

							A     =   self.dummy_vae
							A     =   self.dummy_text_encoder
							A     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "A painting of a squirrel eating a burger"

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe([prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np")
							A     =   output.images

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np"	,	return_dict=__SCREAMING_SNAKE_CASE	,	)[0]

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

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
							A     =   self.dummy_cond_unet
							A     =   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
							A     =   self.dummy_vae
							A     =   self.dummy_text_encoder
							A     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "A painting of a squirrel eating a burger"
							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe([prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np")

							A     =   output.images

							A     =   torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
							A     =   sd_pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=6.0	,	num_inference_steps=2	,	output_type="np"	,	return_dict=__SCREAMING_SNAKE_CASE	,	)[0]

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

							assert image.shape == (1, 6_4, 6_4, 3)
							A     =   np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   StableDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"	,	safety_checker=__SCREAMING_SNAKE_CASE)
							assert isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							assert isinstance(pipe.scheduler	,	__SCREAMING_SNAKE_CASE)
							assert pipe.safety_checker is None

							A     =   pipe("example prompt"	,	num_inference_steps=2).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__SCREAMING_SNAKE_CASE)
												A     =   StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							A     =   pipe("example prompt"	,	num_inference_steps=2).images[0]
							assert image is not None


		@unittest.skipIf(torch_device != "cuda"	,	"This test requires a GPU")
		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   self.dummy_cond_unet
							A     =   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
							A     =   self.dummy_vae
							A     =   self.dummy_text_encoder
							A     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

							# put models in fp16
							A     =   unet.half()
							A     =   vae.half()
							A     =   bert.half()

							# make sure here that pndm scheduler skips prk
							A     =   StableDiffusionPipeline(
							    unet=__SCREAMING_SNAKE_CASE	,	scheduler=__SCREAMING_SNAKE_CASE	,	vae=__SCREAMING_SNAKE_CASE	,	text_encoder=__SCREAMING_SNAKE_CASE	,	tokenizer=__SCREAMING_SNAKE_CASE	,	safety_checker=__SCREAMING_SNAKE_CASE	,	feature_extractor=self.dummy_extractor	,	)
							A     =   sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "A painting of a squirrel eating a burger"
							A     =   sd_pipe([prompt]	,	num_inference_steps=2	,	output_type="np").images

							assert image.shape == (1, 6_4, 6_4, 3)





@nightly
@require_torch_gpu
class      __UpperCamelCase					(  unittest.TestCase		):


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_6_7_3_1_1_3,
							    0.3_8_4_9_2_7_3_3,
							    0.3_7_5_9_7_3_5_2,
							    0.3_4_0_8_6_3_3_5,
							    0.3_5_6_5_0_7_4_8,
							    0.3_5_5_7_9_2_0_5,
							    0.3_3_8_4_7_6_3,
							    0.3_4_3_4_0_2_3_6,
							    0.3_5_7_3_2_7_1,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_1_8_8_7_8_2_6,
							    0.3_7_7_2_8_7_6_6,
							    0.3_0_1_3_8_2_7_2,
							    0.4_1_4_1_6_3_3_5,
							    0.4_1_6_6_4_9_8_5,
							    0.3_6_2_8_3_3_9_2,
							    0.3_6_1_9_1_2_4_6,
							    0.4_3_3_6_4_4_6_5,
							    0.4_3_0_0_1_7_3_2,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "an image of a crowded boulevard, realistic, 4k"
							A     =   {
							    "editing_prompt": "crowd, crowded, people",
							    "reverse_editing_direction": True,
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 8.3,
							    "edit_threshold": 0.9,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   9
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_3_4_9_7_9_9_8,
							    0.9_1_8_1_4_0_6_5,
							    0.7_5_4_0_7_3_9,
							    0.5_5_5_8_0_2_0_5,
							    0.8_4_6_7_2_6_5,
							    0.5_3_8_9_6_9_1,
							    0.6_2_5_7_4_5_0_6,
							    0.5_8_8_9_7_7_6_3,
							    0.5_0_9_2_6_7_5_7,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_0_8_9_7_1_9,
							    0.3_0_5_0_0_1_4_4,
							    0.2_9_0_1_6_0_4_2,
							    0.3_0_6_3_0_9_6_4,
							    0.3_2_5_6_8_7,
							    0.2_9_4_1_9_2_2_5,
							    0.2_9_0_8_0_9_1,
							    0.2_8_7_2_3_5_9_8,
							    0.2_7_6_9_6_2_9_4,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a castle next to a river"
							A     =   {
							    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
							    "reverse_editing_direction": False,
							    "edit_warmup_steps": [1_5, 1_8],
							    "edit_guidance_scale": 6,
							    "edit_threshold": [0.9, 0.8],
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   4_8
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_5_1_6_3_5_5_5,
							    0.7_6_0_3_7_1_4_5,
							    0.6_1_7_8_5,
							    0.9_1_8_9_6_7_3,
							    0.8_6_2_7_7_0_1,
							    0.8_5_1_8_9_6_9_4,
							    0.8_5_1_2_8_1_3,
							    0.8_7_0_1_2_0_7_6,
							    0.8_3_1_2_8_5_7,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.7_3_5_5_3_3_6_5,
							    0.7_5_3_7_2_7_1,
							    0.7_4_3_4_1_9_0_5,
							    0.6_6_4_8_0_3_5_6,
							    0.6_4_7_2_9_2_5,
							    0.6_3_0_3_9_4_1_6,
							    0.6_4_8_1_2_9_0_5,
							    0.6_7_4_9_7_1_7,
							    0.6_5_1_7_1_0_2,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "cuda"
							A     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	,	torch_dtype=torch.floataa)
							A     =   pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A     =   "a photo of a cat"
							A     =   {
							    "editing_prompt": ["sunglasses"],
							    "reverse_editing_direction": [False],
							    "edit_warmup_steps": 1_0,
							    "edit_guidance_scale": 6,
							    "edit_threshold": 0.9_5,
							    "edit_momentum_scale": 0.5,
							    "edit_mom_beta": 0.6,
							}

							A     =   3
							A     =   7

							# no sega enabled
							A     =   torch.Generator(__SCREAMING_SNAKE_CASE)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.3_4_8_8_7_6_9_5,
							    0.3_8_7_6_9_5_3,
							    0.3_7_5,
							    0.3_4_4_2_3_8_2_8,
							    0.3_5_8_1_5_4_3,
							    0.3_5_7_1_7_7_7_3,
							    0.3_3_8_3_7_8_9,
							    0.3_4_5_7_0_3_1_2,
							    0.3_5_9_3_7_5,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

							# with sega enabled
							# generator = torch.manual_seed(seed)
							generator.manual_seed(__SCREAMING_SNAKE_CASE)
							A     =   pipe(
							    [prompt]	,	generator=__SCREAMING_SNAKE_CASE	,	guidance_scale=__SCREAMING_SNAKE_CASE	,	num_inference_steps=5_0	,	output_type="np"	,	width=5_1_2	,	height=5_1_2	,	**__SCREAMING_SNAKE_CASE	,	)

							A     =   output.images
							A     =   image[0, -3:, -3:, -1]
							A     =   [
							    0.4_2_2_8_5_1_5_6,
							    0.3_6_9_1_4_0_6_2,
							    0.2_9_0_7_7_1_4_8,
							    0.4_2_0_4_1_0_1_6,
							    0.4_1_9_1_8_9_4_5,
							    0.3_5_4_9_8_0_4_7,
							    0.3_6_1_8_1_6_4,
							    0.4_4_2_3_8_2_8,
							    0.4_3_1_1_5_2_3_4,
							]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2











'''simple docstring'''







def 		snake_case_				(_a      :				float       ,  _a      :				float       ,  _a      :				float	):
    if hue < 0 or hue > 3_6_0:
        raise Exception('''hue should be between 0 and 360'''	)

    if saturation < 0 or saturation > 1:
        raise Exception('''saturation should be between 0 and 1'''	)

    if value < 0 or value > 1:
        raise Exception('''value should be between 0 and 1'''	)

    UpperCAmelCase     			=						value * saturation
    UpperCAmelCase     			=						hue / 6_0
    UpperCAmelCase     			=						chroma * (1 - abs(hue_section % 2 - 1	))
    UpperCAmelCase     			=						value - chroma

    if hue_section >= 0 and hue_section <= 1:
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
    elif hue_section > 1 and hue_section <= 2:
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
    elif hue_section > 2 and hue_section <= 3:
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
    elif hue_section > 3 and hue_section <= 4:
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
    elif hue_section > 4 and hue_section <= 5:
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
    else:
        UpperCAmelCase     			=						round(2_5_5 * (chroma + match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (match_value)	)
        UpperCAmelCase     			=						round(2_5_5 * (second_largest_component + match_value)	)

    return [red, green, blue]





def 		snake_case_				(_a      :				int       ,  _a      :				int       ,  _a      :				int	):
    if red < 0 or red > 2_5_5:
        raise Exception('''red should be between 0 and 255'''	)

    if green < 0 or green > 2_5_5:
        raise Exception('''green should be between 0 and 255'''	)

    if blue < 0 or blue > 2_5_5:
        raise Exception('''blue should be between 0 and 255'''	)

    UpperCAmelCase     			=						red / 2_5_5
    UpperCAmelCase     			=						green / 2_5_5
    UpperCAmelCase     			=						blue / 2_5_5
    UpperCAmelCase     			=						max(_a       ,  _a       ,  _a	)
    UpperCAmelCase     			=						value - min(_a       ,  _a       ,  _a	)
    UpperCAmelCase     			=						0 if value == 0 else chroma / value

    if chroma == 0:
        UpperCAmelCase     			=						0.0
    elif value == float_red:
        UpperCAmelCase     			=						6_0 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        UpperCAmelCase     			=						6_0 * (2 + (float_blue - float_red) / chroma)
    else:
        UpperCAmelCase     			=						6_0 * (4 + (float_red - float_green) / chroma)

    UpperCAmelCase     			=						(hue + 3_6_0) % 3_6_0

    return [hue, saturation, value]







def 		snake_case_				(_a      :				list[float]       ,  _a      :				list[float]	):
    UpperCAmelCase     			=						abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCAmelCase     			=						abs(hsv_a[1] - hsv_a[1]	) < 0.002
    UpperCAmelCase     			=						abs(hsv_a[2] - hsv_a[2]	) < 0.002

    return check_hue and check_saturation and check_value




