"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> list[int]:
						if hue < 0 or hue > 360:
												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"    )

						lowerCamelCase__ =value * saturation
						lowerCamelCase__ =hue / 60
						lowerCamelCase__ =chroma * (1 - abs(hue_section % 2 - 1    ))
						lowerCamelCase__ =value - chroma

						if hue_section >= 0 and hue_section <= 1:
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )
												lowerCamelCase__ =round(255 * (match_value)    )
						elif hue_section > 1 and hue_section <= 2:
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
												lowerCamelCase__ =round(255 * (match_value)    )
						elif hue_section > 2 and hue_section <= 3:
												lowerCamelCase__ =round(255 * (match_value)    )
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )
						elif hue_section > 3 and hue_section <= 4:
												lowerCamelCase__ =round(255 * (match_value)    )
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
						elif hue_section > 4 and hue_section <= 5:
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )
												lowerCamelCase__ =round(255 * (match_value)    )
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
						else:
												lowerCamelCase__ =round(255 * (chroma + match_value)    )
												lowerCamelCase__ =round(255 * (match_value)    )
												lowerCamelCase__ =round(255 * (second_largest_component + match_value)    )

						return [red, green, blue]





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> list[float]:
						if red < 0 or red > 255:
												raise Exception("red should be between 0 and 255"    )

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

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

						lowerCamelCase__ =red / 255
						lowerCamelCase__ =green / 255
						lowerCamelCase__ =blue / 255
						lowerCamelCase__ =max(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_    )
						lowerCamelCase__ =value - min(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_    )
						lowerCamelCase__ =0 if value == 0 else chroma / value

						if chroma == 0:
												lowerCamelCase__ =0.0
						elif value == float_red:
												lowerCamelCase__ =60 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												lowerCamelCase__ =60 * (2 + (float_blue - float_red) / chroma)
						else:
												lowerCamelCase__ =60 * (4 + (float_red - float_green) / chroma)

						lowerCamelCase__ =(hue + 360) % 360

						return [hue, saturation, value]





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> bool:
						lowerCamelCase__ =abs(hsv_a[0] - hsv_a[0]    ) < 0.2
						lowerCamelCase__ =abs(hsv_a[1] - hsv_a[1]    ) < 0.002
						lowerCamelCase__ =abs(hsv_a[2] - hsv_a[2]    ) < 0.002

						return check_hue and check_saturation and check_value



"""simple docstring"""


from typing import TYPE_CHECKING

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


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

	a							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


