# Copyright The PyTorch Lightning team.
#
# 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 os
import torch
from typing import Optional, Sequence, List, Union
from torch.utils.data import DataLoader
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.loggers.base import DummyLogger
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from torch.optim.lr_scheduler import _LRScheduler
import importlib
from pytorch_lightning import _logger as log
import numpy as np
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr


# check if ipywidgets is installed before importing tqdm.auto
# to ensure it won't fail and a progress bar is displayed
if importlib.util.find_spec('ipywidgets') is not None:
    from tqdm.auto import tqdm
else:
    from tqdm import tqdm


def _run_lr_finder_internally(trainer, model: LightningModule):
    """ Call lr finder internally during Trainer.fit() """
    lr_finder = lr_find(trainer, model)
    lr = lr_finder.suggestion()

    # TODO: log lr.results to self.logger
    if isinstance(trainer.auto_lr_find, str):
        # Try to find requested field, may be nested
        if lightning_hasattr(model, trainer.auto_lr_find):
            lightning_setattr(model, trainer.auto_lr_find, lr)
        else:
            raise MisconfigurationException(
                f'`auto_lr_find` was set to {trainer.auto_lr_find}, however'
                ' could not find this as a field in `model` or `model.hparams`.')
    else:
        if lightning_hasattr(model, 'lr'):
            lightning_setattr(model, 'lr', lr)
        elif lightning_hasattr(model, 'learning_rate'):
            lightning_setattr(model, 'learning_rate', lr)
        else:
            raise MisconfigurationException(
                'When auto_lr_find is set to True, expects that `model` or'
                ' `model.hparams` either has field `lr` or `learning_rate`'
                ' that can overridden')
    log.info(f'Learning rate set to {lr}')


def lr_find(
        trainer,
        model: LightningModule,
        train_dataloader: Optional[DataLoader] = None,
        val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
        min_lr: float = 1e-8,
        max_lr: float = 1,
        num_training: int = 100,
        mode: str = 'exponential',
        early_stop_threshold: float = 4.0,
):
    r"""
    lr_find enables the user to do a range test of good initial learning rates,
    to reduce the amount of guesswork in picking a good starting learning rate.

    Args:
        model: Model to do range testing for

        train_dataloader: A PyTorch
            DataLoader with training samples. If the model has
            a predefined train_dataloader method this will be skipped.

        min_lr: minimum learning rate to investigate

        max_lr: maximum learning rate to investigate

        num_training: number of learning rates to test

        mode: search strategy, either 'linear' or 'exponential'. If set to
            'linear' the learning rate will be searched by linearly increasing
            after each batch. If set to 'exponential', will increase learning
            rate exponentially.

        early_stop_threshold: threshold for stopping the search. If the
            loss at any point is larger than early_stop_threshold*best_loss
            then the search is stopped. To disable, set to None.

    Example::

        # Setup model and trainer
        model = MyModelClass(hparams)
        trainer = pl.Trainer()

        # Run lr finder
        lr_finder = trainer.lr_find(model, ...)

        # Inspect results
        fig = lr_finder.plot(); fig.show()
        suggested_lr = lr_finder.suggestion()

        # Overwrite lr and create new model
        hparams.lr = suggested_lr
        model = MyModelClass(hparams)

        # Ready to train with new learning rate
        trainer.fit(model)

    """
    save_path = os.path.join(trainer.default_root_dir, 'lr_find_temp.ckpt')

    __lr_finder_dump_params(trainer, model)

    # Prevent going into infinite loop
    trainer.auto_lr_find = False

    # Initialize lr finder object (stores results)
    lr_finder = _LRFinder(mode, min_lr, max_lr, num_training)

    # Use special lr logger callback
    trainer.callbacks = [_LRCallback(num_training,
                                     early_stop_threshold,
                                     progress_bar_refresh_rate=1)]

    # No logging
    trainer.logger = DummyLogger()

    # Max step set to number of iterations
    trainer.max_steps = num_training

    # Disable standard progress bar for fit
    if trainer.progress_bar_callback:
        trainer.progress_bar_callback.disable()

    # Disable standard checkpoint & early stopping
    trainer.checkpoint_callback = False
    trainer.early_stop_callback = None

    # Required for saving the model
    trainer.optimizers, trainer.schedulers = [], [],
    trainer.model = model

    # Dump model checkpoint
    trainer.save_checkpoint(str(save_path))

    # Configure optimizer and scheduler
    optimizers, _, _ = trainer.init_optimizers(model)

    if len(optimizers) != 1:
        raise MisconfigurationException(
            f'`model.configure_optimizers()` returned {len(optimizers)}, but'
            ' learning rate finder only works with single optimizer')
    model.configure_optimizers = lr_finder._get_new_optimizer(optimizers[0])

    # Fit, lr & loss logged in callback
    trainer.fit(model,
                train_dataloader=train_dataloader,
                val_dataloaders=val_dataloaders)

    # Prompt if we stopped early
    if trainer.global_step != num_training:
        log.info('LR finder stopped early due to diverging loss.')

    # Transfer results from callback to lr finder object
    lr_finder.results.update({'lr': trainer.callbacks[0].lrs,
                              'loss': trainer.callbacks[0].losses})
    lr_finder._total_batch_idx = trainer.total_batch_idx  # for debug purpose

    # Reset model state
    trainer.checkpoint_connector.restore(str(save_path), on_gpu=trainer.on_gpu)
    os.remove(save_path)

    # Finish by resetting variables so trainer is ready to fit model
    __lr_finder_restore_params(trainer, model)
    if trainer.progress_bar_callback:
        trainer.progress_bar_callback.enable()

    return lr_finder


def __lr_finder_dump_params(trainer, model):
    # Prevent going into infinite loop
    trainer.__dumped_params = {
        'auto_lr_find': trainer.auto_lr_find,
        'callbacks': trainer.callbacks,
        'logger': trainer.logger,
        'max_steps': trainer.max_steps,
        'checkpoint_callback': trainer.checkpoint_callback,
        'early_stop_callback': trainer.early_stop_callback,
        'configure_optimizers': model.configure_optimizers,
    }


def __lr_finder_restore_params(trainer, model):
    trainer.auto_lr_find = trainer.__dumped_params['auto_lr_find']
    trainer.logger = trainer.__dumped_params['logger']
    trainer.callbacks = trainer.__dumped_params['callbacks']
    trainer.max_steps = trainer.__dumped_params['max_steps']
    trainer.checkpoint_callback = trainer.__dumped_params['checkpoint_callback']
    trainer.early_stop_callback = trainer.__dumped_params['early_stop_callback']
    model.configure_optimizers = trainer.__dumped_params['configure_optimizers']
    del trainer.__dumped_params


class _LRFinder(object):
    """ LR finder object. This object stores the results of Trainer.lr_find().

    Args:
        mode: either `linear` or `exponential`, how to increase lr after each step

        lr_min: lr to start search from

        lr_max: lr to stop search

        num_training: number of steps to take between lr_min and lr_max

    Example::
        # Run lr finder
        lr_finder = trainer.lr_find(model)

        # Results stored in
        lr_finder.results

        # Plot using
        lr_finder.plot()

        # Get suggestion
        lr = lr_finder.suggestion()
    """
    def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int):
        assert mode in ('linear', 'exponential'), \
            'mode should be either `linear` or `exponential`'

        self.mode = mode
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.num_training = num_training

        self.results = {}
        self._total_batch_idx = 0  # for debug purpose

    def _get_new_optimizer(self, optimizer: torch.optim.Optimizer):
        """ Construct a new `configure_optimizers()` method, that has a optimizer
            with initial lr set to lr_min and a scheduler that will either
            linearly or exponentially increase the lr to lr_max in num_training steps.

        Args:
            optimizer: instance of `torch.optim.Optimizer`

        """
        new_lrs = [self.lr_min] * len(optimizer.param_groups)
        for param_group, new_lr in zip(optimizer.param_groups, new_lrs):
            param_group["lr"] = new_lr
            param_group["initial_lr"] = new_lr

        args = (optimizer, self.lr_max, self.num_training)
        scheduler = _LinearLR(*args) if self.mode == 'linear' else _ExponentialLR(*args)

        def configure_optimizers():
            return [optimizer], [{'scheduler': scheduler,
                                  'interval': 'step'}]

        return configure_optimizers

    def plot(self, suggest: bool = False, show: bool = False):
        """ Plot results from lr_find run
        Args:
            suggest: if True, will mark suggested lr to use with a red point

            show: if True, will show figure
        """
        import matplotlib.pyplot as plt

        lrs = self.results["lr"]
        losses = self.results["loss"]

        fig, ax = plt.subplots()

        # Plot loss as a function of the learning rate
        ax.plot(lrs, losses)
        if self.mode == 'exponential':
            ax.set_xscale("log")
        ax.set_xlabel("Learning rate")
        ax.set_ylabel("Loss")

        if suggest:
            _ = self.suggestion()
            if self._optimal_idx:
                ax.plot(lrs[self._optimal_idx], losses[self._optimal_idx],
                        markersize=10, marker='o', color='red')

        if show:
            plt.show()

        return fig

    def suggestion(self, skip_begin: int = 10, skip_end: int = 1):
        """ This will propose a suggestion for choice of initial learning rate
        as the point with the steepest negative gradient.

        Returns:
            lr: suggested initial learning rate to use
            skip_begin: how many samples to skip in the beginning. Prevent too naive estimates
            skip_end: how many samples to skip in the end. Prevent too optimistic estimates

        """
        try:
            loss = np.array(self.results["loss"][skip_begin:-skip_end])
            loss = loss[np.isfinite(loss)]
            min_grad = np.gradient(loss).argmin()
            self._optimal_idx = min_grad + skip_begin
            return self.results["lr"][self._optimal_idx]
        except Exception:
            log.exception('Failed to compute suggesting for `lr`. There might not be enough points.')
            self._optimal_idx = None


class _LRCallback(Callback):
    """ Special callback used by the learning rate finder. This callbacks log
    the learning rate before each batch and log the corresponding loss after
    each batch.

    Args:
        num_training: number of iterations done by the learning rate finder
        early_stop_threshold: threshold for stopping the search. If the
            loss at any point is larger than ``early_stop_threshold*best_loss``
            then the search is stopped. To disable, set to ``None``.
        progress_bar_refresh_rate: rate to refresh the progress bar for
            the learning rate finder
        beta: smoothing value, the loss being logged is a running average of
            loss values logged until now. ``beta`` controls the forget rate i.e.
            if ``beta=0`` all past information is ignored.

    """
    def __init__(self, num_training: int,
                 early_stop_threshold: float = 4.0,
                 progress_bar_refresh_rate: int = 0,
                 beta: float = 0.98):
        self.num_training = num_training
        self.early_stop_threshold = early_stop_threshold
        self.beta = beta
        self.losses = []
        self.lrs = []
        self.avg_loss = 0.0
        self.best_loss = 0.0
        self.progress_bar_refresh_rate = progress_bar_refresh_rate
        self.progress_bar = None

    def on_batch_start(self, trainer, pl_module):
        """ Called before each training batch, logs the lr that will be used """
        if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
            return

        if self.progress_bar_refresh_rate and self.progress_bar is None:
            self.progress_bar = tqdm(desc='Finding best initial lr', total=self.num_training)

        self.lrs.append(trainer.lr_schedulers[0]['scheduler'].lr[0])

    def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
        """ Called when the training batch ends, logs the calculated loss """
        if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:
            return

        if self.progress_bar:
            self.progress_bar.update()

        current_loss = trainer.train_loop.running_loss.last().item()
        current_step = trainer.global_step + 1  # remove the +1 in 1.0

        # Avg loss (loss with momentum) + smoothing
        self.avg_loss = self.beta * self.avg_loss + (1 - self.beta) * current_loss
        smoothed_loss = self.avg_loss / (1 - self.beta**current_step)

        # Check if we diverging
        if self.early_stop_threshold is not None:
            if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:
                trainer.max_steps = current_step  # stop signal
                if self.progress_bar:
                    self.progress_bar.close()

        # Save best loss for diverging checking
        if smoothed_loss < self.best_loss or current_step == 1:
            self.best_loss = smoothed_loss

        self.losses.append(smoothed_loss)


class _LinearLR(_LRScheduler):
    """Linearly increases the learning rate between two boundaries
    over a number of iterations.
    Arguments:

        optimizer: wrapped optimizer.

        end_lr: the final learning rate.

        num_iter: the number of iterations over which the test occurs.

        last_epoch: the index of last epoch. Default: -1.
    """
    last_epoch: int
    base_lrs: Sequence

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 end_lr: float,
                 num_iter: int,
                 last_epoch: int = -1):
        self.end_lr = end_lr
        self.num_iter = num_iter
        super(_LinearLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        curr_iter = self.last_epoch + 1
        r = curr_iter / self.num_iter

        if self.last_epoch > 0:
            val = [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]
        else:
            val = [base_lr for base_lr in self.base_lrs]
        self._lr = val
        return val

    @property
    def lr(self):
        return self._lr


class _ExponentialLR(_LRScheduler):
    """Exponentially increases the learning rate between two boundaries
    over a number of iterations.

    Arguments:

        optimizer: wrapped optimizer.

        end_lr: the final learning rate.

        num_iter: the number of iterations over which the test occurs.

        last_epoch: the index of last epoch. Default: -1.
    """
    last_epoch: int
    base_lrs: Sequence

    def __init__(self,
                 optimizer: torch.optim.Optimizer,
                 end_lr: float,
                 num_iter: int,
                 last_epoch: int = -1):
        self.end_lr = end_lr
        self.num_iter = num_iter
        super(_ExponentialLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        curr_iter = self.last_epoch + 1
        r = curr_iter / self.num_iter

        if self.last_epoch > 0:
            val = [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]
        else:
            val = [base_lr for base_lr in self.base_lrs]
        self._lr = val
        return val

    @property
    def lr(self):
        return self._lr