






from typing import TYPE_CHECKING

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


lowercase     :							List[Any]	     =			{
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							List[Any]	     =			['''OpenAIGPTTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							Tuple	     =			[
							    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''OpenAIGPTDoubleHeadsModel''',
							    '''OpenAIGPTForSequenceClassification''',
							    '''OpenAIGPTLMHeadModel''',
							    '''OpenAIGPTModel''',
							    '''OpenAIGPTPreTrainedModel''',
							    '''load_tf_weights_in_openai_gpt''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							List[str]	     =			[
							    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFOpenAIGPTDoubleHeadsModel''',
							    '''TFOpenAIGPTForSequenceClassification''',
							    '''TFOpenAIGPTLMHeadModel''',
							    '''TFOpenAIGPTMainLayer''',
							    '''TFOpenAIGPTModel''',
							    '''TFOpenAIGPTPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
							from .tokenization_openai import OpenAIGPTTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_openai_fast import OpenAIGPTTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_openai import (
														    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OpenAIGPTDoubleHeadsModel,
														    OpenAIGPTForSequenceClassification,
														    OpenAIGPTLMHeadModel,
														    OpenAIGPTModel,
														    OpenAIGPTPreTrainedModel,
														    load_tf_weights_in_openai_gpt,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_openai import (
														    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFOpenAIGPTDoubleHeadsModel,
														    TFOpenAIGPTForSequenceClassification,
														    TFOpenAIGPTLMHeadModel,
														    TFOpenAIGPTMainLayer,
														    TFOpenAIGPTModel,
														    TFOpenAIGPTPreTrainedModel,
														)

else:
							import sys

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








import math

def    lowerCAmelCase__		(			_a				:	int			):

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

def    lowerCAmelCase__		(			_a				:	int = 60_08_51_47_51_43			):

		try:
				snake_case_ :							int         =				int(_a			)
		except (TypeError, ValueError):
				raise TypeError("Parameter n must be int or castable to int."			)
		if n <= 0:
				raise ValueError("Parameter n must be greater than or equal to one."			)
		snake_case_ :							Dict         =				0
		if is_prime(_a			):
				return n
		while n % 2 == 0:
				n //= 2
		if is_prime(_a			):
				return n
		for i in range(3     ,					int(math.sqrt(_a			)			) + 1     ,					2			):
				if n % i == 0:
						if is_prime(n // i			):
								snake_case_ :							List[Any]         =				n // i
								break
						elif is_prime(_a			):
								snake_case_ :							str         =				i
		return max_number


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