import math
def 							lowerCAmelCase(     __lowerCamelCase						):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5				,    int(math.sqrt(__lowerCamelCase						) + 1						)				,    6						):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True
def 							lowerCAmelCase(     __lowerCamelCase = 0.1						):
					__a		 =       3
					__a		 =       3

					while primes / (2 * j - 1) >= ratio:
										for i in range(j * j + j + 1				,    (j + 2) * (j + 2)				,    j + 1						):
															primes += is_prime(__lowerCamelCase						)
										j += 2
					return j


if __name__ == "__main__":
	import doctest

	doctest.testmod()






from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """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

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




