# Copyright 2020 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.
# ============================================================================
from typing import Dict, Any

import torch


class Scheduler:
    """ Parameter Scheduler Base Class
    A scheduler base class that can be used to schedule any optimizer parameter groups.

    Unlike the builtin PyTorch schedulers, this is intended to be consistently called
    * At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value
    * At the END of each optimizer update, after incrementing the update count, to calculate next update's value

    The schedulers built on this should try to remain as stateless as possible (for simplicity).

    This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch'
    and -1 values for special behaviour. All epoch and update counts must be tracked in the training
    code and explicitly passed in to the schedulers on the corresponding step or step_update call.

    Based on ideas from:
     * https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler
     * https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers
    """

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 param_group_field: str,
                 noise_range_t=None,
                 noise_type='normal',
                 noise_pct=0.67,
                 noise_std=1.0,
                 noise_seed=None,
                 initialize: bool = True) -> None:
        self.optimizer = optimizer
        self.param_group_field = param_group_field
        self._initial_param_group_field = f"initial_{param_group_field}"
        if initialize:
            for i, group in enumerate(self.optimizer.param_groups):
                if param_group_field not in group:
                    raise KeyError(f"{param_group_field} missing from param_groups[{i}]")
                group.setdefault(self._initial_param_group_field, group[param_group_field])
        else:
            for i, group in enumerate(self.optimizer.param_groups):
                if self._initial_param_group_field not in group:
                    raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]")
        self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups]
        self.metric = None  # any point to having this for all?
        self.noise_range_t = noise_range_t
        self.noise_pct = noise_pct
        self.noise_type = noise_type
        self.noise_std = noise_std
        self.noise_seed = noise_seed if noise_seed is not None else 42
        self.update_groups(self.base_values)

    def state_dict(self) -> Dict[str, Any]:
        return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}

    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
        self.__dict__.update(state_dict)

    def get_epoch_values(self, epoch: int):
        return None

    def get_update_values(self, num_updates: int):
        return None

    def step(self, epoch: int, metric: float = None) -> None:
        self.metric = metric
        values = self.get_epoch_values(epoch)
        if values is not None:
            values = self._add_noise(values, epoch)
            self.update_groups(values)

    def step_update(self, num_updates: int, metric: float = None):
        self.metric = metric
        values = self.get_update_values(num_updates)
        if values is not None:
            values = self._add_noise(values, num_updates)
            self.update_groups(values)

    def update_groups(self, values):
        if not isinstance(values, (list, tuple)):
            values = [values] * len(self.optimizer.param_groups)
        for param_group, value in zip(self.optimizer.param_groups, values):
            param_group[self.param_group_field] = value

    def _add_noise(self, lrs, t):
        if self.noise_range_t is not None:
            if isinstance(self.noise_range_t, (list, tuple)):
                apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1]
            else:
                apply_noise = t >= self.noise_range_t
            if apply_noise:
                g = torch.Generator()
                g.manual_seed(self.noise_seed + t)
                if self.noise_type == 'normal':
                    while True:
                        # resample if noise out of percent limit, brute force but shouldn't spin much
                        noise = torch.randn(1, generator=g).item()
                        if abs(noise) < self.noise_pct:
                            break
                else:
                    noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct
                lrs = [v + v * noise for v in lrs]
        return lrs