





"""simple docstring"""




import math





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   str      =       0
						if n % 2 == 0:
												count += 1
												while n % 2 == 0:
																		_UpperCamelCase     :   str      =       int(n / 2  )
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

						_UpperCamelCase     :   Dict      =       3

						while i <= int(math.sqrt(_lowerCAmelCase  )  ):
												if n % i == 0:
																		count += 1
																		while n % i == 0:
																								_UpperCamelCase     :   Tuple      =       int(n / i  )
												_UpperCamelCase     :   Any      =       i + 2

						# this condition checks the prime
						# number n is greater than 2

						if n > 2:
												count += 1
						return count


if __name__ == "__main__":
						UpperCamelCase_	 =51_242_183
						print(F"The number of distinct prime factors is/are {exact_prime_factor_count(n)}")
						print(F"The value of log(log(n)) is {math.log(math.log(n)):.4f}")








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

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