





'''simple docstring'''




import math
import sys
def       UpperCAmelCase_				(   __lowercase				: Tuple			)						->			Union[str, Any]:


     '''simple docstring'''



     if number != int(_a			):
          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
     _UpperCAmelCase					=  [-1] * (number + 1)
     _UpperCAmelCase					=  0
     for i in range(1     ,	number + 1			):
          _UpperCAmelCase					=  sys.maxsize
          _UpperCAmelCase					=  int(math.sqrt(_a			)			)
          for j in range(1     ,	root + 1			):
               _UpperCAmelCase					=  1 + answers[i - (j**2)]
               _UpperCAmelCase					=  min(_a     ,	_a			)
          _UpperCAmelCase					=  answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()







from typing import TYPE_CHECKING

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


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['PLBartTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)