




'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_	 =		{'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_	 =		[
     'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RegNetForImageClassification',
     'RegNetModel',
     'RegNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_	 =		[
     'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRegNetForImageClassification',
     'TFRegNetModel',
     'TFRegNetPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_	 =		[
     'FlaxRegNetForImageClassification',
     'FlaxRegNetModel',
     'FlaxRegNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_regnet import (
      REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      RegNetForImageClassification,
      RegNetModel,
      RegNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_regnet import (
      TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRegNetForImageClassification,
      TFRegNetModel,
      TFRegNetPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_regnet import (
      FlaxRegNetForImageClassification,
      FlaxRegNetModel,
      FlaxRegNetPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_	 =		_LazyModule(__name__, globals()['__file__'], _import_structure)







'''simple docstring'''



import math
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   0
   if n % 2 == 0:
      count += 1
      while n % 2 == 0:
         _SCREAMING_SNAKE_CASE =   int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

   _SCREAMING_SNAKE_CASE =   3

   while i <= int(math.sqrt(__A						)						):
      if n % i == 0:
         count += 1
         while n % i == 0:
            _SCREAMING_SNAKE_CASE =   int(n / i						)
      _SCREAMING_SNAKE_CASE =   i + 2

   # this condition checks the prime
   # number n is greater than 2

   if n > 2:
      count += 1
   return count


if __name__ == "__main__":
 lowerCamelCase_	 =		51_24_21_83
 print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
 print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

