



import math



def  _lowerCAmelCase     (_lowerCAmelCase):
					if not isinstance(_lowerCAmelCase ,						_lowerCAmelCase):
										UpperCamelCase_			= f"""Input value of [number={number}] must be an integer"""
										raise TypeError(_lowerCAmelCase)
					if number < 1:
										UpperCamelCase_			= f"""Input value of [number={number}] must be a positive integer"""
										raise ValueError(_lowerCAmelCase)
					UpperCamelCase_			= [number]
					while number != 1:
										if number % 2 == 0:
															UpperCamelCase_			= math.floor(math.sqrt(_lowerCAmelCase))
										else:
															UpperCamelCase_			= math.floor(
															    math.sqrt(_lowerCAmelCase) * math.sqrt(_lowerCAmelCase) * math.sqrt(_lowerCAmelCase))
										sequence.append(_lowerCAmelCase)
					return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()





from typing import TYPE_CHECKING

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


UpperCAmelCase :				List[Any]							={
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""RetriBertTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				str							=[
	    """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RetriBertModel""",
	    """RetriBertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
	from .tokenization_retribert import RetriBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_retribert_fast import RetriBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_retribert import (
		    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RetriBertModel,
		    RetriBertPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase :				List[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
