import paddle

# 以下两种调用方法得到同一个对象，因为ProgramTranslator是个单例
paddle.jit.ProgramTranslator()
paddle.jit.ProgramTranslator.get_instance()

import paddle


@paddle.jit.to_static
def func(x):
    if paddle.mean(x) > 0:
        x_v = x - 1
    else:
        x_v = x + 1
    return x_v


prog_trans = paddle.jit.ProgramTranslator()
prog_trans.enable(False)

x = paddle.ones([1, 2])
# ProgramTranslator被关闭所以func会以动态图模式运行
print(func(x))  # [[0. 0.]]

import paddle


def func(x):
    if paddle.mean(x) > 0:
        x_v = x - 1
    else:
        x_v = x + 1
    return x_v


prog_trans = paddle.jit.ProgramTranslator()

x = paddle.ones([1, 2])
x_v = prog_trans.get_output(func, x)
print(x_v)  # [[0. 0.]]

import paddle


def func(x):
    if paddle.mean(x) > 0:
        x_v = x - 1
    else:
        x_v = x + 1
    return x_v


prog_trans = paddle.jit.ProgramTranslator()
static_func = prog_trans.get_func(func)
print(callable(static_func)) # True

import paddle


def func(x):
    if paddle.mean(x) > 0:
        x_v = x - 1
    else:
        x_v = x + 1
    return x_v


prog_trans = paddle.jit.ProgramTranslator()

x = paddle.ones([1, 2])
main_prog, start_prog, inputs, outputs = prog_trans.get_program(func, x)
print([i.name for i in inputs])
# [u'generated_tensor_0'] 需要被feed的输入Tensor名字，对应x
print([o.name for o in outputs])
# [u'_generated_var_4'] 需要被fetch的输出Tensor名字，对应x_v

import paddle


def func(x):
    if paddle.mean(x) > 0:
        x_v = x - 1
    else:
        x_v = x + 1
    return x_v


prog_trans = paddle.jit.ProgramTranslator()

code = prog_trans.get_code(func)
print(type(code)) # <class 'str'>

import paddle

prog_trans = paddle.jit.ProgramTranslator()
prog_cache = prog_trans.get_program_cache()