
from typing import TYPE_CHECKING

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


a_       :Any				  =				{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

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

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

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :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

 a_       :Optional[int]				  =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











def 	lowercase_							(A			:			int		, A			:			int				):
       return (input_a, input_a).count(0				) % 2
def 	lowercase_							():
       assert xor_gate(0		, 0				) == 0
       assert xor_gate(0		, 1				) == 1
       assert xor_gate(1		, 0				) == 1
       assert xor_gate(1		, 1				) == 0


if __name__ == "__main__":
 print(xor_gate(0, 0))
 print(xor_gate(0, 1))






