
'''simple docstring'''





from typing import TYPE_CHECKING

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


a__		:   Union[str, Any]            =       {
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Union[str, Any]            =       ["OpenAIGPTTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Dict            =       [
     "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "OpenAIGPTDoubleHeadsModel",
     "OpenAIGPTForSequenceClassification",
     "OpenAIGPTLMHeadModel",
     "OpenAIGPTModel",
     "OpenAIGPTPreTrainedModel",
     "load_tf_weights_in_openai_gpt",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   int            =       [
     "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFOpenAIGPTDoubleHeadsModel",
     "TFOpenAIGPTForSequenceClassification",
     "TFOpenAIGPTLMHeadModel",
     "TFOpenAIGPTMainLayer",
     "TFOpenAIGPTModel",
     "TFOpenAIGPTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .tokenization_openai import OpenAIGPTTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_openai_fast import OpenAIGPTTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_openai import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
      OpenAIGPTPreTrainedModel,
      load_tf_weights_in_openai_gpt,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTMainLayer,
      TFOpenAIGPTModel,
      TFOpenAIGPTPreTrainedModel,
  )

else:
 import sys

 a__		:   str            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''





def        snake_case							(		UpperCAmelCase	)->			int:







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





def        snake_case							(		)->			None:







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


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






