






'''simple docstring'''





def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	Optional[int] ):
							_validate_point(a )
							_validate_point(a )
							if len(a ) != len(a ):
														raise ValueError("""Both points must be in the same n-dimensional space""" )

							return float(sum(abs(a - b ) for a, b in zip(a  ,					a ) ) )

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							if point:
														if isinstance(a  ,					a ):
																					for item in point:
																												if not isinstance(a  ,					(int, float) ):
																																			snake_case__	  =       (
																																			    """Expected a list of numbers as input, found """
																																			    F'''{type(a ).__name__}'''
																																			)
																																			raise TypeError(a )
														else:
																					snake_case__	  =       F'''Expected a list of numbers as input, found {type(a ).__name__}'''
																					raise TypeError(a )
							else:
														raise ValueError("""Missing an input""" )

def _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Tuple ):
							_validate_point(a )
							_validate_point(a )
							if len(a ) != len(a ):
														raise ValueError("""Both points must be in the same n-dimensional space""" )

							return float(sum(abs(x - y ) for x, y in zip(a  ,					a ) ) )


if __name__ == "__main__":
	import doctest

	doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

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