



"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__      =    {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__      =    [
       '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''XLMForMultipleChoice''',
       '''XLMForQuestionAnswering''',
       '''XLMForQuestionAnsweringSimple''',
       '''XLMForSequenceClassification''',
       '''XLMForTokenClassification''',
       '''XLMModel''',
       '''XLMPreTrainedModel''',
       '''XLMWithLMHeadModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__      =    [
       '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFXLMForMultipleChoice''',
       '''TFXLMForQuestionAnsweringSimple''',
       '''TFXLMForSequenceClassification''',
       '''TFXLMForTokenClassification''',
       '''TFXLMMainLayer''',
       '''TFXLMModel''',
       '''TFXLMPreTrainedModel''',
       '''TFXLMWithLMHeadModel''',
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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







"""simple docstring"""






from maths.prime_factors import prime_factors






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       if not isinstance(A_,    A_						):
              _lowerCamelCase						:    str         =			F'''Input value of [number={number}] must be an integer'''
              raise TypeError(A_						)
       if number < 1:
              raise ValueError('''Input must be a positive integer'''						)
       return -1 if len(prime_factors(A_						)						) % 2 else 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()


