



from typing import TYPE_CHECKING

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


_lowercase	:   Optional[int]    	={"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase	:   Tuple    	=[
		    "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

		_lowercase	:   Any    	=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









from __future__ import annotations

def 			lowerCAmelCase_  (						_lowercase       :     list[int]		,     _lowercase       :     int)							-> list[int]:







			"""simple docstring"""
			a__     :		dict[int, int]		 =						{}
			for index, val in enumerate(_lowercase):
						a__     :		str		 =						target - val
						if compl in chk_map:
									return [chk_map[compl], index]
						a__     :		str		 =						index
			return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'{two_sum([2, 7, 11, 15], 9) = }')




