





"""simple docstring"""


from typing import TYPE_CHECKING

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


a__   :       int   						=			{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       List[str]   						=			["""GPT2TokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Dict   						=			[
       """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GPT2DoubleHeadsModel""",
       """GPT2ForQuestionAnswering""",
       """GPT2ForSequenceClassification""",
       """GPT2ForTokenClassification""",
       """GPT2LMHeadModel""",
       """GPT2Model""",
       """GPT2PreTrainedModel""",
       """load_tf_weights_in_gpt2""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Union[str, Any]   						=			[
       """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFGPT2DoubleHeadsModel""",
       """TFGPT2ForSequenceClassification""",
       """TFGPT2LMHeadModel""",
       """TFGPT2MainLayer""",
       """TFGPT2Model""",
       """TFGPT2PreTrainedModel""",
   ]

try:
   if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       Optional[Any]   						=			["""TFGPT2Tokenizer"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__   :       List[str]   						=			["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
   from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
   from .tokenization_gpta import GPTaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_fast import GPTaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpta import (
          GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTaDoubleHeadsModel,
          GPTaForQuestionAnswering,
          GPTaForSequenceClassification,
          GPTaForTokenClassification,
          GPTaLMHeadModel,
          GPTaModel,
          GPTaPreTrainedModel,
          load_tf_weights_in_gpta,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_gpta import (
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFGPTaDoubleHeadsModel,
          TFGPTaForSequenceClassification,
          TFGPTaLMHeadModel,
          TFGPTaMainLayer,
          TFGPTaModel,
          TFGPTaPreTrainedModel,
      )

   try:
      if not is_keras_nlp_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_gpta_tf import TFGPTaTokenizer

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
   import sys

   a__   :       Tuple   						=			_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()

