






"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase				:    List[Any]     =						{'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    str     =						[
       '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

   _UpperCamelCase				:    Optional[Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure)





'''simple docstring'''




from scipy.constants import g





def      _UpperCAmelCase    (							_UpperCamelCase    :	float,   _UpperCamelCase    :	float   )	->      float:
    if mass < 0:
        # handling of negative values of mass
        raise ValueError('''The mass of a body cannot be negative'''   )
    if height < 0:
        # handling of negative values of height
        raise ValueError('''The height above the ground cannot be negative'''   )
    return mass * g * height


if __name__ == "__main__":
  from doctest import testmod

  testmod(name='potential_energy')






