




from typing import TYPE_CHECKING

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


a_      						=   {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

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

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

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_      						=   [
     "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

 a_      						=   _LazyModule(__name__, globals()["__file__"], _import_structure)










'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






