
'''simple docstring'''







import math
import sys



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						if number != int(_SCREAMING_SNAKE_CASE  ):
												raise ValueError("the value of input must be a natural number"  )
						if number < 0:
												raise ValueError("the value of input must not be a negative number"  )
						if number == 0:
												return 1
						_SCREAMING_SNAKE_CASE				= [-1] * (number + 1)
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(1	,			number + 1  ):
												_SCREAMING_SNAKE_CASE				= sys.maxsize
												_SCREAMING_SNAKE_CASE				= int(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
												for j in range(1	,			root + 1  ):
																		_SCREAMING_SNAKE_CASE				= 1 + answers[i - (j**2)]
																		_SCREAMING_SNAKE_CASE				= min(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= answer
						return answers[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

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