






"""simple docstring"""




from maths.prime_factors import prime_factors






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							int:







		'''simple docstring'''







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


if __name__ == "__main__":
					import doctest

					doctest.testmod()










"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCamelCase							       =    {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FNetForMaskedLM""",
         """FNetForMultipleChoice""",
         """FNetForNextSentencePrediction""",
         """FNetForPreTraining""",
         """FNetForQuestionAnswering""",
         """FNetForSequenceClassification""",
         """FNetForTokenClassification""",
         """FNetLayer""",
         """FNetModel""",
         """FNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

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


