



'''simple docstring'''
import math
import sys






def A__   (					UpperCAmelCase_							):
						if number != int(UpperCAmelCase_							):
												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 :		str			    =       [-1] * (number + 1)
						_UpperCamelCase :		List[Any]			    =       0
						for i in range(1  ,  number + 1							):
												_UpperCamelCase :		Dict			    =       sys.maxsize
												_UpperCamelCase :		List[str]			    =       int(math.sqrt(UpperCAmelCase_							)							)
												for j in range(1  ,  root + 1							):
																		_UpperCamelCase :		Optional[int]			    =       1 + answers[i - (j**2)]
																		_UpperCamelCase :		List[Any]			    =       min(UpperCAmelCase_  ,  UpperCAmelCase_							)
												_UpperCamelCase :		Union[str, Any]			    =       answer
						return answers[number]


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					snake_case_			:     Optional[Any]  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


