



import math







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							bool:

		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(__SCREAMING_SNAKE_CASE ) + 1 )			,     6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True







def snake_case__  (			__SCREAMING_SNAKE_CASE = 1_0001 )		->							int:

		try:
				UpperCAmelCase_						=				int(__SCREAMING_SNAKE_CASE )
		except (TypeError, ValueError):
				raise TypeError("Parameter nth must be int or castable to int." ) from None
		if nth <= 0:
				raise ValueError("Parameter nth must be greater than or equal to one." )
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				2
		while len(__SCREAMING_SNAKE_CASE ) < nth:
				if is_prime(__SCREAMING_SNAKE_CASE ):
						primes.append(__SCREAMING_SNAKE_CASE )
						num += 1
				else:
						num += 1
		return primes[len(__SCREAMING_SNAKE_CASE ) - 1]


if __name__ == "__main__":
			print(f'''{solution() = }''')






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

