


'''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      :      List[str]       				=      {
    "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      :      int       				=      ["GPT2TokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _a      :      Optional[Any]       				=      [
           "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      :      str       				=      [
           "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      :      int       				=      ["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		(lowercase__ :      int	) -> int:






     """simple docstring"""
     return 1 if input_a == 0 else 0





def      _a		() -> None:






     """simple docstring"""
     assert not_gate(0	) == 1
     assert not_gate(1	) == 0


if __name__ == "__main__":
       print(not_gate(0))
       print(not_gate(1))




