


"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case             =   {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["RemBertTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["RemBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RemBertForCausalLM",
      "RemBertForMaskedLM",
      "RemBertForMultipleChoice",
      "RemBertForQuestionAnswering",
      "RemBertForSequenceClassification",
      "RemBertForTokenClassification",
      "RemBertLayer",
      "RemBertModel",
      "RemBertPreTrainedModel",
      "load_tf_weights_in_rembert",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRemBertForCausalLM",
      "TFRemBertForMaskedLM",
      "TFRemBertForMultipleChoice",
      "TFRemBertForQuestionAnswering",
      "TFRemBertForSequenceClassification",
      "TFRemBertForTokenClassification",
      "TFRemBertLayer",
      "TFRemBertModel",
      "TFRemBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

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









"""simple docstring"""



import math
import sys







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





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


if __name__ == "__main__":
  import doctest

  doctest.testmod()





