


"""simple docstring"""







import math






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

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

						UpperCAmelCase__												=     3

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

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

						if n > 2:
												count += 1
						return count


if __name__ == "__main__":
						__a      			= 51_24_21_83
						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}''')



"""simple docstring"""







from typing import TYPE_CHECKING

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


__a      			= {"configuration_bert_generation": ["BertGenerationConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BertGenerationTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= [
						    "BertGenerationDecoder",
						    "BertGenerationEncoder",
						    "BertGenerationPreTrainedModel",
						    "load_tf_weights_in_bert_generation",
						]


if TYPE_CHECKING:
						from .configuration_bert_generation import BertGenerationConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_generation import BertGenerationTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert_generation import (
												    BertGenerationDecoder,
												    BertGenerationEncoder,
												    BertGenerationPreTrainedModel,
												    load_tf_weights_in_bert_generation,
												)

else:
						import sys

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