

'''simple docstring'''





def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  str:
						lowercase :							Tuple    		=int(__magic_name__						)
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(__magic_name__						)
						lowercase		,	lowercase :							Optional[Any]    		=divmod(__magic_name__   , 2						)
						return binary_recursive(__magic_name__						) + str(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  str:
						lowercase :							List[Any]    		=str(__magic_name__						).strip()
						if not number:
												raise ValueError('''No input value was provided'''						)
						lowercase :							str    		='''-''' if number.startswith('''-'''						) else ''''''
						lowercase :							List[str]    		=number.lstrip('''-'''						)
						if not number.isnumeric():
												raise ValueError('''Input value is not an integer'''						)
						return f'''{negative}0b{binary_recursive(int(__magic_name__						)						)}'''


if __name__ == "__main__":
							from doctest import testmod

							testmod()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_							=    {
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

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





