
'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase	=    TypeVar('''T''', bound='''Enum''')

@unique
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  int								=		80
	_UpperCamelCase							:  Optional[int]								=		70
	_UpperCamelCase							:  Any								=		60
	_UpperCamelCase							:  str								=		50
	_UpperCamelCase							:  Optional[int]								=		40
	_UpperCamelCase							:  int								=		30
	_UpperCamelCase							:  List[Any]								=		20
	_UpperCamelCase							:  List[str]								=		10







@unique
class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Any								=		24
	_UpperCamelCase							:  Dict								=		21
	_UpperCamelCase							:  Optional[int]								=		18
	_UpperCamelCase							:  Any								=		15
	_UpperCamelCase							:  Optional[int]								=		12
	_UpperCamelCase							:  Any								=		9
	_UpperCamelCase							:  Dict								=		6
	_UpperCamelCase							:  Any								=		3
	_UpperCamelCase							:  List[Any]								=		2
	_UpperCamelCase							:  Optional[Any]								=		1
	_UpperCamelCase							:  Union[str, Any]								=		-1
	_UpperCamelCase							:  Optional[Any]								=		-2
	_UpperCamelCase							:  Tuple								=		-3
	_UpperCamelCase							:  str								=		-6
	_UpperCamelCase							:  Optional[Any]								=		-9
	_UpperCamelCase							:  Optional[Any]								=		-12
	_UpperCamelCase							:  List[str]								=		-15
	_UpperCamelCase							:  Any								=		-18
	_UpperCamelCase							:  Union[str, Any]								=		-21
	_UpperCamelCase							:  int								=		-24




	@classmethod
	def 	__A      (						cls				: type[T]      ):
			return {unit.name: unit.value for unit in cls if unit.value > 0}





	@classmethod
	def 	__A      (						cls				: type[T]      ):
			return {unit.name: unit.value for unit in cls if unit.value < 0}







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       str    							=SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
		for name_prefix, value_prefix in prefixes.items():
				lowerCAmelCase_   :       List[Any]    							=value / (10**value_prefix)
				if numerical_part > 1:
						return f'{numerical_part!s} {name_prefix}'
		return str(_SCREAMING_SNAKE_CASE  )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		for prefix in BinaryUnit:
				lowerCAmelCase_   :       Dict    							=value / (2**prefix.value)
				if numerical_part > 1:
						return f'{numerical_part!s} {prefix.name}'
		return str(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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






