# Copyright 2024 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================

import time
import stat
import os
from mindspore.nn import Cell
from mindspore.ops.composite import GradOperation
from mindspore.common import ParameterTuple


class _Grad(Cell):
    def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
        super().__init__()
        self.network = network
        self.grad = grad
        self.sens_param = self.grad.sens_param
        self.wrt_params = wrt_params
        self.real_inputs_count = real_inputs_count
        if self.wrt_params:
            self.params = ParameterTuple(self.network.trainable_params())

    def __call__(self, *inputs):
        if self.sens_param and self._dynamic_shape_inputs is not None:
            # not support dynamic shape sens
            if self.real_inputs_count is None:
                dyn_inputs = self._dynamic_shape_inputs[:-1]
                real_sens = inputs[-1:]
            else:
                idx = self.real_inputs_count
                dyn_inputs = self._dynamic_shape_inputs[:idx]
                real_sens = inputs[idx:]
            static_sens = list(dyn_inputs) + list(real_sens)
            super().set_inputs(*static_sens)

        a = time.perf_counter()
        out = super().__call__(*inputs)
        b = time.perf_counter()
        if os.environ.get("perf") == '1':
            phase = os.environ.get("PHASE")
            flags = os.O_WRONLY | os.O_CREAT
            modes = stat.S_IWUSR | stat.S_IRUSR
            with os.fdopen(os.open(phase, flags, modes), 'w') as f:
                f.write(str(b - a))
        return out

    def construct(self, *inputs):
        if self.wrt_params:
            if self.real_inputs_count is None or self.sens_param is False:
                return self.grad(self.network, self.params)(*inputs)
            real_inputs = inputs[:self.real_inputs_count]
            sense_param_inputs = inputs[self.real_inputs_count:]
            return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
        if self.real_inputs_count is None or self.sens_param is False:
            return self.grad(self.network)(*inputs)
        real_inputs = inputs[:self.real_inputs_count]
        sense_param_inputs = inputs[self.real_inputs_count:]
        return self.grad(self.network)(*real_inputs, sense_param_inputs)


class GradOfAllInputsAndParams(_Grad):
    """
    get grads of all inputs and params
    """
    def __init__(self, network, sens_param=True, real_inputs_count=None):
        super().__init__(grad=GradOperation(get_all=True, get_by_list=True,
                                            sens_param=sens_param),
                         network=network, wrt_params=True, real_inputs_count=real_inputs_count)

class GradOfFirstInput(_Grad):
    """
    get grad of first input
    """

    def __init__(self, network, sens_param=True, real_inputs_count=None):
        super().__init__(grad=GradOperation(sens_param=sens_param),
                         network=network, real_inputs_count=real_inputs_count)