




from typing import TYPE_CHECKING

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


__lowerCAmelCase						:							int			     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase						:							Optional[int]			     =						['OpenAIGPTTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase						:							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:
		__lowerCAmelCase						:							Any			     =						[
		    '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

		__lowerCAmelCase						:							Dict			     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  return (input_a, input_a).count(0				) % 2


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  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))
