

import math

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     if n <= 0:
          raise ValueError('Only positive integers have prime factors'    )
     lowercase__					:     Optional[Any]   = []
     while n % 2 == 0:
          pf.append(2    )
          lowercase__					:     List[Any]   = int(n / 2    )
     for i in range(3			,  int(math.sqrt(SCREAMING_SNAKE_CASE_    )    ) + 1			,  2    ):
          while n % i == 0:
               pf.append(SCREAMING_SNAKE_CASE_    )
               lowercase__					:     Dict   = int(n / i    )
     if n > 2:
          pf.append(SCREAMING_SNAKE_CASE_    )
     return pf

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     if n <= 0:
          raise ValueError('Only positive numbers are accepted'    )
     lowercase__					:     Optional[int]   = 1
     lowercase__					:     Any   = 1
     while n % 2 == 0:
          temp += 1
          lowercase__					:     List[Any]   = int(n / 2    )
     div *= temp
     for i in range(3			,  int(math.sqrt(SCREAMING_SNAKE_CASE_    )    ) + 1			,  2    ):
          lowercase__					:     Dict   = 1
          while n % i == 0:
               temp += 1
               lowercase__					:     Dict   = int(n / i    )
          div *= temp
     if n > 1:
          div *= 2
     return div

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     if n <= 0:
          raise ValueError('Only positive numbers are accepted'    )
     lowercase__					:     Tuple   = 1
     lowercase__					:     Dict   = 1
     while n % 2 == 0:
          temp += 1
          lowercase__					:     Optional[int]   = int(n / 2    )
     if temp > 1:
          s *= (2**temp - 1) / (2 - 1)
     for i in range(3			,  int(math.sqrt(SCREAMING_SNAKE_CASE_    )    ) + 1			,  2    ):
          lowercase__					:     List[Any]   = 1
          while n % i == 0:
               temp += 1
               lowercase__					:     Any   = int(n / i    )
          if temp > 1:
               s *= (i**temp - 1) / (i - 1)
     return int(SCREAMING_SNAKE_CASE_    )

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__					:     Union[str, Any]   = n
     for x in set(prime_factors(SCREAMING_SNAKE_CASE_    )    ):
          s *= (x - 1) / x
     return int(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()




from typing import TYPE_CHECKING

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


snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''GPTJForCausalLM''',
           '''GPTJForQuestionAnswering''',
           '''GPTJForSequenceClassification''',
           '''GPTJModel''',
           '''GPTJPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_    						=				[
           '''FlaxGPTJForCausalLM''',
           '''FlaxGPTJModel''',
           '''FlaxGPTJPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

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

