

from typing import TYPE_CHECKING

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


__UpperCAmelCase      = {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

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

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase      = [
       """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """CamembertForCausalLM""",
       """CamembertForMaskedLM""",
       """CamembertForMultipleChoice""",
       """CamembertForQuestionAnswering""",
       """CamembertForSequenceClassification""",
       """CamembertForTokenClassification""",
       """CamembertModel""",
       """CamembertPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase      = [
       """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFCamembertForCausalLM""",
       """TFCamembertForMaskedLM""",
       """TFCamembertForMultipleChoice""",
       """TFCamembertForQuestionAnswering""",
       """TFCamembertForSequenceClassification""",
       """TFCamembertForTokenClassification""",
       """TFCamembertModel""",
       """TFCamembertPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

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





from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


