

def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list )     -> bool:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ):
								raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]" )
				if len(SCREAMING_SNAKE_CASE_ ) == 0:
								raise ValueError("Input list must be a non empty list" )
				if len(SCREAMING_SNAKE_CASE_ ) == 1 and series[0] != 0:
								return True
				A__            =     []
				A__            =     len(SCREAMING_SNAKE_CASE_ )
				for i in range(0		,    SCREAMING_SNAKE_CASE_ ):
								if series[i] == 0:
												raise ValueError("Input series cannot have 0 as an element" )
								rec_series.append(1 / series[i] )
				A__            =     rec_series[1] - rec_series[0]
				for index in range(2		,    SCREAMING_SNAKE_CASE_ ):
								if rec_series[index] - rec_series[index - 1] != common_diff:
												return False
				return True



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list )     -> float:




				'''simple docstring'''





				if not isinstance(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ ):
								raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
				if len(SCREAMING_SNAKE_CASE_ ) == 0:
								raise ValueError("Input list must be a non empty list" )
				A__            =     0
				for val in series:
								answer += 1 / val
				return len(SCREAMING_SNAKE_CASE_ ) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__						=	{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """NatForImageClassification""",
	    """NatModel""",
	    """NatPreTrainedModel""",
	    """NatBackbone""",
	]

if TYPE_CHECKING:
	from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nat import (
		    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NatBackbone,
		    NatForImageClassification,
		    NatModel,
		    NatPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase__						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

