#   Copyright (c) 2020 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

import paddle.static.amp as mixed_precision

from .meta_optimizer_base import MetaOptimizerBase

__all__ = []


class AMPOptimizer(MetaOptimizerBase):
    def __init__(self, optimizer):
        super().__init__(optimizer)
        self.inner_opt = optimizer
        self.wrapped_opt = None
        # we do not allow meta optimizer to be inner optimizer currently
        self.meta_optimizers_white_list = [
            "LarsOptimizer",
            "LambOptimizer",
            "RecomputeOptimizer",
            "GraphExecutionOptimizer",
        ]
        self.meta_optimizers_black_list = ["DGCOptimizer"]

    def _set_basic_info(
        self, loss, role_maker, user_defined_optimizer, user_defined_strategy
    ):
        super()._set_basic_info(
            loss, role_maker, user_defined_optimizer, user_defined_strategy
        )

    def _init_wrapped_opt(self):
        if self.wrapped_opt is not None:
            return

        config = self.user_defined_strategy.amp_configs

        custom_white_list = set(config['custom_white_list'])
        custom_black_list = set(config['custom_black_list'])
        custom_black_varnames = set(config['custom_black_varnames'])
        amp_lists = mixed_precision.AutoMixedPrecisionLists(
            custom_white_list, custom_black_list, custom_black_varnames
        )

        self.wrapped_opt = mixed_precision.decorate(
            self.inner_opt,
            amp_lists,
            config['init_loss_scaling'],
            config['incr_every_n_steps'],
            config['decr_every_n_nan_or_inf'],
            config['incr_ratio'],
            config['decr_ratio'],
            config['use_dynamic_loss_scaling'],
            config['use_pure_fp16'],
            config['use_fp16_guard'],
        )

        # if worker_num > 1, all cards will communication with each other,
        # add is_distributed to optimize amp, overlap communication and
        # computation by split the check_finite_and_unscale op.
        is_distributed = self.role_maker._worker_num() > 1
        if self.user_defined_strategy.sharding:
            # FIXME(wangxi). sharding failed when split check_finite_and_unscale
            # FIXME(JZ-LIANG). To support Sharding-Megatron-AMP, Megatron should follow Sharding's behavior that to disable is_distributed.
            is_distributed = False
        self.wrapped_opt._set_distributed(is_distributed)

    def _can_apply(self):
        if not self.role_maker._is_collective:
            return False

        if self.user_defined_strategy.amp:
            return True
        return False

    def _disable_strategy(self, dist_strategy):
        dist_strategy.amp = False
        dist_strategy.amp_configs = {}

    def _enable_strategy(self, dist_strategy, context):
        dist_strategy.amp = True
        dist_strategy.amp_configs = {
            "init_loss_scaling": 32768.0,
            "incr_every_n_steps": 1000,
            "decr_every_n_nan_or_inf": 2,
            "incr_ratio": 2.0,
            "decr_ratio": 0.8,
            "use_dynamic_loss_scaling": True,
        }

    def backward(
        self,
        loss,
        startup_program=None,
        parameter_list=None,
        no_grad_set=None,
        callbacks=None,
    ):
        # maybe inner_opt of other meta optimizer
        self._init_wrapped_opt()
        return self.wrapped_opt.backward(
            loss, startup_program, parameter_list, no_grad_set, callbacks
        )

    def apply_gradients(self, params_grads):
        return self.wrapped_opt.apply_gradients(params_grads=params_grads)

    def apply_optimize(self, loss, startup_program, params_grads):
        return self.wrapped_opt.apply_optimize(
            loss, startup_program=startup_program, params_grads=params_grads
        )

    def minimize_impl(
        self, loss, startup_program=None, parameter_list=None, no_grad_set=None
    ):
        self._init_wrapped_opt()
        optimize_ops, params_grads = self.wrapped_opt.minimize(
            loss, startup_program, parameter_list, no_grad_set
        )
        return optimize_ops, params_grads

    def amp_init(
        self, place, scope=None, test_program=None, use_fp16_test=False
    ):
        return self.wrapped_opt.amp_init(
            place, scope, test_program, use_fp16_test
        )

    def get_loss_scaling(self):
        return self.wrapped_opt.get_loss_scaling()