# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import six

from collections import defaultdict
from paddle.fluid import core
from paddle.fluid import framework


class Tracer(core.Tracer):
    """
    :api_attr: imperative
    
    Tracer is used to execute and record the operators executed, to construct the 
    computation graph in dygraph model. Tracer has two mode, :code:`train_mode`
    and :code:`eval_mode`. In :code:`train_mode`, Tracer would add backward network 
    automatically and perform AutoGrad by method :code:`loss.backward()`. 
    In :code:`eval_mode`, Tracer would not add backward network.

    This is a low level API, users don't need to use it directly.
    """

    def __init__(self):
        super(Tracer, self).__init__()

        self._train_mode = True

    def trace_op(self,
                 type,
                 inputs,
                 outputs,
                 attrs,
                 stop_gradient=False,
                 inplace_map=None):
        self.trace(type, inputs, outputs, attrs,
                   framework._current_expected_place(), self._has_grad and
                   not stop_gradient, inplace_map if inplace_map else {})

    def train_mode(self):
        self._train_mode = True

    def eval_mode(self):
        self._train_mode = False