






def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	float:
    SCREAMING_SNAKE_CASE__       							= float()
    try:
        SCREAMING_SNAKE_CASE__       							= mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # demo
  _A				:							Dict							= 12.5
  _A				:							Any							= 10
  _A				:							Dict							= newtons_second_law_of_motion(mass, acceleration)
  print("""The force is """, force, """N""")







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True  )							->	Tuple:
    if not is_fpa_available():
        raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''  )
    for name, module in model.named_children():
        if isinstance(lowerCAmelCase_   ,       nn.Linear  ) and to_transformer_engine and _convert_linear:
            # Return early if the linear layer weights are not multiples of 16
            if any(p % 1_6 != 0 for p in module.weight.shape  ):
                return
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= te.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       nn.LayerNorm  ) and to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= te.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.Linear  ) and not to_transformer_engine and _convert_linear:
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= nn.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.LayerNorm  ) and not to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= nn.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        else:
            convert_model(
                lowerCAmelCase_   ,       to_transformer_engine=lowerCAmelCase_   ,       _convert_linear=_convert_linear   ,       _convert_ln=_convert_ln   ,       )



def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    if not is_fpa_available():
        raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''  )
    for m in model.modules():
        if isinstance(lowerCAmelCase_   ,       (te.LayerNorm, te.Linear)  ):
            return True
    return False
