
'''simple docstring'''







import math

def 				_snake_case (		lowercase						)       ->       list:
				if n <= 0:
								raise ValueError("""Only positive integers have prime factors"""						)
				__a       :     str								=	[]
				while n % 2 == 0:
								pf.append(2						)
								__a       :     int								=	int(n / 2						)
				for i in range(3			,    int(math.sqrt(lowercase						)						) + 1			,    2						):
								while n % i == 0:
												pf.append(lowercase						)
												__a       :     Any								=	int(n / i						)
				if n > 2:
								pf.append(lowercase						)
				return pf

def 				_snake_case (		lowercase						)       ->       int:
				if n <= 0:
								raise ValueError("""Only positive numbers are accepted"""						)
				__a       :     List[str]								=	1
				__a       :     Optional[int]								=	1
				while n % 2 == 0:
								temp += 1
								__a       :     str								=	int(n / 2						)
				div *= temp
				for i in range(3			,    int(math.sqrt(lowercase						)						) + 1			,    2						):
								__a       :     List[str]								=	1
								while n % i == 0:
												temp += 1
												__a       :     Dict								=	int(n / i						)
								div *= temp
				if n > 1:
								div *= 2
				return div

def 				_snake_case (		lowercase						)       ->       int:
				if n <= 0:
								raise ValueError("""Only positive numbers are accepted"""						)
				__a       :     int								=	1
				__a       :     Any								=	1
				while n % 2 == 0:
								temp += 1
								__a       :     Optional[Any]								=	int(n / 2						)
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3			,    int(math.sqrt(lowercase						)						) + 1			,    2						):
								__a       :     List[str]								=	1
								while n % i == 0:
												temp += 1
												__a       :     str								=	int(n / i						)
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowercase						)

def 				_snake_case (		lowercase						)       ->       int:
				__a       :     Optional[int]								=	n
				for x in set(prime_factors(lowercase						)						):
								s *= (x - 1) / x
				return int(lowercase						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

'''simple docstring'''







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

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