





"""simple docstring"""


from typing import TYPE_CHECKING

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


a__   :       Optional[int]   						=			{
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Union[str, Any]   						=			["""OpenAIGPTTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Optional[Any]   						=			[
       """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """OpenAIGPTDoubleHeadsModel""",
       """OpenAIGPTForSequenceClassification""",
       """OpenAIGPTLMHeadModel""",
       """OpenAIGPTModel""",
       """OpenAIGPTPreTrainedModel""",
       """load_tf_weights_in_openai_gpt""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Any   						=			[
       """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFOpenAIGPTDoubleHeadsModel""",
       """TFOpenAIGPTForSequenceClassification""",
       """TFOpenAIGPTLMHeadModel""",
       """TFOpenAIGPTMainLayer""",
       """TFOpenAIGPTModel""",
       """TFOpenAIGPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   a__   :       Union[str, Any]   						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

