






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

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


@unique
class 				__a				(	__A    ):





		'''simple docstring'''



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






@unique
class 				__a				(	__A    ):





		'''simple docstring'''



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


		@classmethod
		def 	__snake_case      (    cls							):
						return {unit.name: unit.value for unit in cls if unit.value > 0}






		@classmethod
		def 	__snake_case      (    cls							):
						return {unit.name: unit.value for unit in cls if unit.value < 0}






def        _lowerCamelCase(      lowerCAmelCase__					:		float   ):





				'''simple docstring'''







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



def        _lowerCamelCase(      lowerCAmelCase__					:		float   ):





				'''simple docstring'''







				for prefix in BinaryUnit:
								SCREAMING_SNAKE_CASE_ :						List[Any]    				= value / (2**prefix.value)
								if numerical_part > 1:
												return F'''{numerical_part!s} {prefix.name}'''
				return str(lowerCAmelCase__   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()







from typing import TYPE_CHECKING

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


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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    '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

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