
'''simple docstring'''







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE		:  List[str]        =      {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

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

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Union[str, Any]        =      [
					    'TFGPTJForCausalLM',
					    'TFGPTJForQuestionAnswering',
					    'TFGPTJForSequenceClassification',
					    'TFGPTJModel',
					    'TFGPTJPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  int        =      [
					    '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

					__SCREAMING_SNAKE_CASE		:  List[str]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring'''







def 				_snake_case (		lowercase						)       ->       int:
				if not isinstance(lowercase			,    lowercase						):
								__a       :     Union[str, Any]								=	F"""Input value of [number={number}] must be an integer"""
								raise TypeError(lowercase						)
				if number < 1:
								raise ValueError("""Input must be a positive integer"""						)
				return number * (2 * number - 1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()