





from typing import TYPE_CHECKING

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


A_        =  {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =  [
						    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTJForCausalLM",
						    "GPTJForQuestionAnswering",
						    "GPTJForSequenceClassification",
						    "GPTJModel",
						    "GPTJPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =  [
						    "TFGPTJForCausalLM",
						    "TFGPTJForQuestionAnswering",
						    "TFGPTJForSequenceClassification",
						    "TFGPTJModel",
						    "TFGPTJPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =  [
						    "FlaxGPTJForCausalLM",
						    "FlaxGPTJModel",
						    "FlaxGPTJPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gptj import (
												    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTJForCausalLM,
												    GPTJForQuestionAnswering,
												    GPTJForSequenceClassification,
												    GPTJModel,
												    GPTJPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_gptj import (
												    TFGPTJForCausalLM,
												    TFGPTJForQuestionAnswering,
												    TFGPTJForSequenceClassification,
												    TFGPTJModel,
												    TFGPTJPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
						import sys

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








def  __UpperCamelCase   (     a = 998001)			->int:

					# fetches the next number
					for number in range(n - 1,  9999,  -1):
										lowerCamelCase__      =							str(a)

										# checks whether 'str_number' is a palindrome.
										if str_number == str_number[::-1]:
															lowerCamelCase__      =							999

															# if 'number' is a product of two 3-digit numbers
															# then number is the answer otherwise fetch next number.
															while divisor != 99:
																				if (number % divisor == 0) and (len(str(number // divisor)) == 3.0):
																									return number
																				divisor -= 1
					raise ValueError("That number is larger than our acceptable range.")


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

