

from typing import TYPE_CHECKING

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


snake_case_        =			{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''Wav2Vec2ForAudioFrameClassification''',
           '''Wav2Vec2ForCTC''',
           '''Wav2Vec2ForMaskedLM''',
           '''Wav2Vec2ForPreTraining''',
           '''Wav2Vec2ForSequenceClassification''',
           '''Wav2Vec2ForXVector''',
           '''Wav2Vec2Model''',
           '''Wav2Vec2PreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFWav2Vec2ForCTC''',
           '''TFWav2Vec2Model''',
           '''TFWav2Vec2PreTrainedModel''',
           '''TFWav2Vec2ForSequenceClassification''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			[
           '''FlaxWav2Vec2ForCTC''',
           '''FlaxWav2Vec2ForPreTraining''',
           '''FlaxWav2Vec2Model''',
           '''FlaxWav2Vec2PreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

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




import math
import sys

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     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
     lowercase__   :       Tuple				=	[-1] * (number + 1)
     lowercase__   :       Tuple				=	0
     for i in range(1     ,					number + 1      ):
          lowercase__   :       Tuple				=	sys.maxsize
          lowercase__   :       str				=	int(math.sqrt(SCREAMING_SNAKE_CASE_      )      )
          for j in range(1     ,					root + 1      ):
               lowercase__   :       List[Any]				=	1 + answers[i - (j**2)]
               lowercase__   :       str				=	min(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          lowercase__   :       List[str]				=	answer
     return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

