

from timeit import timeit

def 						UpperCamelCase (lowercase_:			int )					->				int:
	if number < 0:
		raise ValueError("""the value of input must not be negative""" )
	A__							:   List[str]					       =		0
	while number:
		number &= number - 1
		result += 1
	return result

def 						UpperCamelCase (lowercase_:			int )					->				int:
	if number < 0:
		raise ValueError("""the value of input must not be negative""" )
	A__							:   int					       =		0
	while number:
		if number % 2 == 1:
			result += 1
		number >>= 1
	return result

def 						UpperCamelCase ()					->				None:

	def do_benchmark(lowercase_:			int ) -> None:
		A__							:   List[Any]					       =		"""import __main__ as z"""
		print(f"""Benchmark when {number = }:""" )
		print(f"""{get_set_bits_count_using_modulo_operator(lowercase_ ) = }""" )
		A__							:   Union[str, Any]					       =		timeit("""z.get_set_bits_count_using_modulo_operator(25)"""   ,				setup=lowercase_ )
		print(f"""timeit() runs in {timing} seconds""" )
		print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(lowercase_ ) = }""" )
		A__							:   List[Any]					       =		timeit(
		    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""   ,				setup=lowercase_   ,				)
		print(f"""timeit() runs in {timing} seconds""" )

	for number in (25, 37, 58, 0):
		do_benchmark(lowercase_ )
		print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()






from typing import TYPE_CHECKING

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


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



