
def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			list[int]:







				"""simple docstring"""



				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"		)

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

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

				return [red, green, blue]


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			list[float]:







				"""simple docstring"""



				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"		)

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

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

				__A         =				(hue + 3_6_0) % 3_6_0

				return [hue, saturation, value]


def 		UpperCAmelCase    (			a_	,     a_		)	->			bool:







				"""simple docstring"""



				__A         =				abs(hsv_a[0] - hsv_a[0]		) < 0.2
				__A         =				abs(hsv_a[1] - hsv_a[1]		) < 0.002
				__A         =				abs(hsv_a[2] - hsv_a[2]		) < 0.002

				return check_hue and check_saturation and check_value


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
