

"""simple docstring"""




from typing import TYPE_CHECKING

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


A_							:     Optional[Any]     =	{
    "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     Dict     =	["GPT2TokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     int     =	[
						    "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPT2DoubleHeadsModel",
						    "GPT2ForQuestionAnswering",
						    "GPT2ForSequenceClassification",
						    "GPT2ForTokenClassification",
						    "GPT2LMHeadModel",
						    "GPT2Model",
						    "GPT2PreTrainedModel",
						    "load_tf_weights_in_gpt2",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     Dict     =	[
						    "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFGPT2DoubleHeadsModel",
						    "TFGPT2ForSequenceClassification",
						    "TFGPT2LMHeadModel",
						    "TFGPT2MainLayer",
						    "TFGPT2Model",
						    "TFGPT2PreTrainedModel",
						]

try:
						if not is_keras_nlp_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     List[str]     =	["TFGPT2Tokenizer"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_							:     Optional[int]     =	["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
						from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
						from .tokenization_gpta import GPTaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_gpta_fast import GPTaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpta import (
												    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTaDoubleHeadsModel,
												    GPTaForQuestionAnswering,
												    GPTaForSequenceClassification,
												    GPTaForTokenClassification,
												    GPTaLMHeadModel,
												    GPTaModel,
												    GPTaPreTrainedModel,
												    load_tf_weights_in_gpta,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_gpta import (
												    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFGPTaDoubleHeadsModel,
												    TFGPTaForSequenceClassification,
												    TFGPTaLMHeadModel,
												    TFGPTaMainLayer,
												    TFGPTaModel,
												    TFGPTaPreTrainedModel,
												)

						try:
												if not is_keras_nlp_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_gpta_tf import TFGPTaTokenizer

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
						import sys

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





"""simple docstring"""




def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			return a + b


if __name__ == "__main__":
						A_							:     List[str]     =	5
						A_							:     Optional[int]     =	6
						print(F'The sum of {a} + {b} is {add(a, b)}')


