# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/common.base_recurrent.ipynb.

# %% auto 0
__all__ = ['BaseRecurrent']

# %% ../../nbs/common.base_recurrent.ipynb 5
import random

import numpy as np
import torch
import torch.nn as nn
import pytorch_lightning as pl
from pytorch_lightning.callbacks import TQDMProgressBar

from ._scalers import TemporalNorm
from ..tsdataset import TimeSeriesDataModule

# %% ../../nbs/common.base_recurrent.ipynb 6
class BaseRecurrent(pl.LightningModule):
    def __init__(
        self,
        h,
        input_size,
        loss,
        learning_rate,
        batch_size=32,
        scaler_type="robust",
        futr_exog_list=None,
        hist_exog_list=None,
        stat_exog_list=None,
        num_workers_loader=0,
        drop_last_loader=False,
        random_seed=1,
        **trainer_kwargs,
    ):
        super(BaseRecurrent, self).__init__()

        self.save_hyperparameters()  # Allows instantiation from a checkpoint from class
        self.random_seed = random_seed
        pl.seed_everything(self.random_seed, workers=True)

        # Padder to complete train windows,
        # example y=[1,2,3,4,5] h=3 -> last y_output = [5,0,0]
        self.h = h
        self.input_size = input_size
        self.padder = nn.ConstantPad1d(padding=(0, self.h), value=0)

        # Loss
        self.loss = loss
        self.learning_rate = learning_rate

        # Scaler
        self.scaler = TemporalNorm(
            scaler_type=scaler_type, dim=-1
        )  # Time dimension is -1.

        # Variables
        self.futr_exog_list = futr_exog_list if futr_exog_list is not None else []
        self.hist_exog_list = hist_exog_list if hist_exog_list is not None else []
        self.stat_exog_list = stat_exog_list if stat_exog_list is not None else []

        # Fit arguments
        self.val_size = 0
        self.test_size = 0

        # Trainer
        # we need to instantiate the trainer each time we want to use it
        self.trainer_kwargs = {**trainer_kwargs}
        if self.trainer_kwargs.get("callbacks", None) is None:
            self.trainer_kwargs = {
                **{"callbacks": [TQDMProgressBar()], **trainer_kwargs}
            }
        else:
            self.trainer_kwargs = trainer_kwargs

        # Add GPU accelerator if available
        if self.trainer_kwargs.get("accelerator", None) is None:
            if torch.cuda.is_available():
                self.trainer_kwargs["accelerator"] = "gpu"
        if self.trainer_kwargs.get("devices", None) is None:
            if torch.cuda.is_available():
                self.trainer_kwargs["devices"] = -1

        # Avoid saturating local memory, disabled fit model checkpoints
        if self.trainer_kwargs.get("enable_checkpointing", None) is None:
            self.trainer_kwargs["enable_checkpointing"] = False

        # DataModule arguments
        self.batch_size = batch_size
        self.num_workers_loader = num_workers_loader
        self.drop_last_loader = drop_last_loader

    def on_fit_start(self):
        torch.manual_seed(self.random_seed)
        np.random.seed(self.random_seed)
        random.seed(self.random_seed)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=self.learning_rate)

    def _normalization(self, batch, val_size=0, test_size=0):

        temporal = batch["temporal"]  # B, C, T
        temporal_cols = batch["temporal_cols"].copy()

        # Separate data and mask
        temporal_data_cols = temporal_cols.drop("available_mask").tolist()
        temporal_data = temporal[:, temporal_cols.get_indexer(temporal_data_cols), :]
        temporal_mask = temporal[:, temporal_cols.get_loc("available_mask"), :].clone()

        # Remove validation and test set to prevent leakeage
        if val_size + test_size > 0:
            cutoff = val_size + test_size
            temporal_mask[:, -cutoff:] = 0

        # Normalize. self.scaler stores the shift and scale for inverse transform
        temporal_mask = temporal_mask.unsqueeze(
            1
        )  # Add channel dimension for scaler.transform.
        temporal_data = self.scaler.transform(x=temporal_data, mask=temporal_mask)

        # Replace values in windows dict
        temporal[:, temporal_cols.get_indexer(temporal_data_cols), :] = temporal_data
        batch["temporal"] = temporal

        return batch

    def _inv_normalization(self, y_hat, temporal_cols):
        # Receives window predictions [B, seq_len, H, output]
        # Broadcasts outputs and inverts normalization

        # Get 'y' scale and shift, and add W dimension
        temporal_data_cols = temporal_cols.drop("available_mask")
        y_shift = self.scaler.x_shift[
            :, temporal_data_cols.get_indexer(["y"]), 0
        ].flatten()  # [B,C,T] -> [B]
        y_scale = self.scaler.x_scale[
            :, temporal_data_cols.get_indexer(["y"]), 0
        ].flatten()  # [B,C,T] -> [B]

        # Expand scale and shift to y_hat dimensions
        y_shift = y_shift.view(
            *y_shift.shape, *(1,) * (y_hat.ndim - 1)
        )  # .expand(y_hat)
        y_scale = y_scale.view(
            *y_scale.shape, *(1,) * (y_hat.ndim - 1)
        )  # .expand(y_hat)

        y_hat = self.scaler.inverse_transform(z=y_hat, x_scale=y_scale, x_shift=y_shift)

        return y_hat, y_shift, y_scale

    def _create_windows(self, batch, step):
        temporal = batch["temporal"]
        temporal_cols = batch["temporal_cols"]

        if step == "train":
            if self.val_size + self.test_size > 0:
                cutoff = -self.val_size - self.test_size
                temporal = temporal[:, :, :cutoff]
            temporal = self.padder(temporal)

            # Truncate batch to shorter time-series
            av_condition = torch.nonzero(
                torch.min(
                    temporal[:, temporal_cols.get_loc("available_mask")], axis=0
                ).values
            )
            min_time_stamp = int(av_condition.min())

            available_ts = (
                temporal.shape[-1] - min_time_stamp + 1
            )  # +1, inclusive counting
            if available_ts < 1 + self.h:
                raise Exception(
                    "Time series too short for given input and output size. \n"
                    f"Available timestamps: {available_ts}"
                )

            temporal = temporal[:, :, min_time_stamp:]

        if step == "val":
            if self.test_size > 0:
                temporal = temporal[:, :, : -self.test_size]
            temporal = self.padder(temporal)

        if step == "predict":
            if (self.test_size == 0) and (len(self.futr_exog_list) == 0):
                temporal = self.padder(temporal)

        # Parse batch
        window_size = 1 + self.h  # 1 for current t and h for future
        windows = temporal.unfold(dimension=-1, size=window_size, step=1)

        # Truncated backprogatation during training (shorten sequence where RNNs unroll)
        n_windows = windows.shape[2]
        if (
            (step == "train")
            and (self.input_size > 0)
            and (n_windows > self.input_size)
        ):
            max_sampleable_time = n_windows - self.input_size + 1
            start = np.random.choice(max_sampleable_time)
            windows = windows[:, :, start : (start + self.input_size), :]

        # [B, C, input_size, 1+H]
        windows_batch = dict(
            temporal=windows,
            temporal_cols=temporal_cols,
            static=batch.get("static", None),
            static_cols=batch.get("static_cols", None),
        )

        return windows_batch

    def _parse_windows(self, batch, windows):
        # [B, C, seq_len, 1+H]
        # Filter insample lags from outsample horizon
        y_idx = batch["temporal_cols"].get_loc("y")
        mask_idx = batch["temporal_cols"].get_loc("available_mask")
        insample_y = windows["temporal"][:, y_idx, :, : -self.h]
        insample_mask = windows["temporal"][:, mask_idx, :, : -self.h]
        outsample_y = windows["temporal"][:, y_idx, :, -self.h :].contiguous()
        outsample_mask = windows["temporal"][:, mask_idx, :, -self.h :].contiguous()

        # Filter historic exogenous variables
        if len(self.hist_exog_list):
            hist_exog_idx = windows["temporal_cols"].get_indexer(self.hist_exog_list)
            hist_exog = windows["temporal"][:, hist_exog_idx, :, : -self.h]
        else:
            hist_exog = None

        # Filter future exogenous variables
        if len(self.futr_exog_list):
            futr_exog_idx = windows["temporal_cols"].get_indexer(self.futr_exog_list)
            futr_exog = windows["temporal"][:, futr_exog_idx, :, :]
        else:
            futr_exog = None
        # Filter static variables
        if len(self.stat_exog_list):
            static_idx = windows["static_cols"].get_indexer(self.stat_exog_list)
            stat_exog = windows["static"][:, static_idx]
        else:
            stat_exog = None

        return (
            insample_y,
            insample_mask,
            outsample_y,
            outsample_mask,
            hist_exog,
            futr_exog,
            stat_exog,
        )

    def training_step(self, batch, batch_idx):
        # Create and normalize windows [Ws, L+H, C]
        batch = self._normalization(
            batch, val_size=self.val_size, test_size=self.test_size
        )
        windows = self._create_windows(batch, step="train")

        # Parse windows
        (
            insample_y,
            insample_mask,
            outsample_y,
            outsample_mask,
            hist_exog,
            futr_exog,
            stat_exog,
        ) = self._parse_windows(batch, windows)

        windows_batch = dict(
            insample_y=insample_y,  # [B, seq_len, 1]
            insample_mask=insample_mask,  # [B, seq_len, 1]
            futr_exog=futr_exog,  # [B, F, seq_len, 1+H]
            hist_exog=hist_exog,  # [B, C, seq_len]
            stat_exog=stat_exog,
        )  # [B, S]

        # Model predictions
        output = self(windows_batch)  # tuple([B, seq_len, H, output])
        if self.loss.is_distribution_output:
            outsample_y, y_shift, y_scale = self._inv_normalization(
                y_hat=outsample_y, temporal_cols=batch["temporal_cols"]
            )
            B = output[0].size()[0]
            T = output[0].size()[1]
            H = output[0].size()[2]
            output = [arg.view(-1, *(arg.size()[2:])) for arg in output]
            outsample_y = outsample_y.view(B * T, H)
            outsample_mask = outsample_mask.view(B * T, H)
            y_shift = y_shift.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            y_scale = y_scale.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            loss = self.loss(
                y=outsample_y,
                distr_args=output,
                loc=y_shift,
                scale=y_scale,
                mask=outsample_mask,
            )
        else:
            loss = self.loss(y=outsample_y, y_hat=output, mask=outsample_mask)

        self.log(
            "train_loss", loss, batch_size=self.batch_size, prog_bar=True, on_epoch=True
        )
        return loss

    def validation_step(self, batch, batch_idx):
        if self.val_size == 0:
            return np.nan

        # Create and normalize windows [Ws, L+H, C]
        batch = self._normalization(
            batch, val_size=self.val_size, test_size=self.test_size
        )
        windows = self._create_windows(batch, step="val")

        # Parse windows
        (
            insample_y,
            insample_mask,
            outsample_y,
            outsample_mask,
            hist_exog,
            futr_exog,
            stat_exog,
        ) = self._parse_windows(batch, windows)

        windows_batch = dict(
            insample_y=insample_y,  # [B, seq_len, 1]
            insample_mask=insample_mask,  # [B, seq_len, 1]
            futr_exog=futr_exog,  # [B, F, seq_len, 1+H]
            hist_exog=hist_exog,  # [B, C, seq_len]
            stat_exog=stat_exog,
        )  # [B, S]

        # Remove train y_hat (+1 and -1 for padded last window with zeros)
        # tuple([B, seq_len, H, output]) -> tuple([B, validation_size, H, output])
        val_windows = (self.val_size) + 1
        outsample_y = outsample_y[:, -val_windows:-1, :]
        outsample_mask = outsample_mask[:, -val_windows:-1, :]

        # Model predictions
        output = self(windows_batch)  # tuple([B, seq_len, H, output])
        if self.loss.is_distribution_output:
            output = [arg[:, -val_windows:-1] for arg in output]
            outsample_y, y_shift, y_scale = self._inv_normalization(
                y_hat=outsample_y, temporal_cols=batch["temporal_cols"]
            )
            B = output[0].size()[0]
            T = output[0].size()[1]
            H = output[0].size()[2]
            output = [arg.view(-1, *(arg.size()[2:])) for arg in output]
            outsample_y = outsample_y.view(B * T, H)
            outsample_mask = outsample_mask.view(B * T, H)
            y_shift = y_shift.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            y_scale = y_scale.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            loss = self.loss(
                y=outsample_y,
                distr_args=output,
                loc=y_shift,
                scale=y_scale,
                mask=outsample_mask,
            )
        else:
            y_hat = output[:, -val_windows:-1, :]
            loss = self.loss(y=outsample_y, y_hat=y_hat, mask=outsample_mask)

        self.log(
            "val_loss", loss, batch_size=self.batch_size, prog_bar=True, on_epoch=True
        )
        return loss

    def validation_epoch_end(self, outputs):
        if self.val_size == 0:
            return
        avg_loss = torch.stack(outputs).mean()
        self.log("ptl/val_loss", avg_loss, batch_size=self.batch_size)

    def predict_step(self, batch, batch_idx):
        # Create and normalize windows [Ws, L+H, C]
        batch = self._normalization(batch, val_size=0, test_size=self.test_size)
        windows = self._create_windows(batch, step="predict")

        # Parse windows
        (
            insample_y,
            insample_mask,
            _,
            _,
            hist_exog,
            futr_exog,
            stat_exog,
        ) = self._parse_windows(batch, windows)

        windows_batch = dict(
            insample_y=insample_y,  # [B, seq_len, 1]
            insample_mask=insample_mask,  # [B, seq_len, 1]
            futr_exog=futr_exog,  # [B, F, seq_len, 1+H]
            hist_exog=hist_exog,  # [B, C, seq_len]
            stat_exog=stat_exog,
        )  # [B, S]

        # Model Predictions
        output = self(windows_batch)  # tuple([B, seq_len, H], ...)
        if self.loss.is_distribution_output:
            _, y_shift, y_scale = self._inv_normalization(
                y_hat=output[0], temporal_cols=batch["temporal_cols"]
            )
            B = output[0].size()[0]
            T = output[0].size()[1]
            H = output[0].size()[2]
            output = [arg.view(-1, *(arg.size()[2:])) for arg in output]
            y_shift = y_shift.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            y_scale = y_scale.repeat_interleave(repeats=T, dim=0).squeeze(-1)
            _, quants = self.loss.sample(
                distr_args=output, loc=y_shift, scale=y_scale, num_samples=500
            )
            y_hat = quants.view(B, T, H, -1)

            if self.loss.return_params:
                params_hat = torch.stack(output, dim=-1)
                params_hat = torch.reshape(
                    params_hat, (len(windows["temporal"]), self.h, -1)
                )
                y_hat = torch.concat((y_hat, params_hat), axis=2)
        else:
            y_hat, _, _ = self._inv_normalization(
                y_hat=output, temporal_cols=batch["temporal_cols"]
            )

        return y_hat

    def fit(self, dataset, val_size=0, test_size=0):
        """Fit.

        The `fit` method, optimizes the neural network's weights using the
        initialization parameters (`learning_rate`, `batch_size`, ...)
        and the `loss` function as defined during the initialization.
        Within `fit` we use a PyTorch Lightning `Trainer` that
        inherits the initialization's `self.trainer_kwargs`, to customize
        its inputs, see [PL's trainer arguments](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).

        The method is designed to be compatible with SKLearn-like classes
        and in particular to be compatible with the StatsForecast library.

        By default the `model` is not saving training checkpoints to protect
        disk memory, to get them change `enable_checkpointing=True` in `__init__`.

        **Parameters:**<br>
        `dataset`: NeuralForecast's `TimeSeriesDataset`, see [documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).<br>
        `val_size`: int, validation size for temporal cross-validation.<br>
        `test_size`: int, test size for temporal cross-validation.<br>
        """
        self.val_size = val_size
        self.test_size = test_size
        datamodule = TimeSeriesDataModule(
            dataset,
            batch_size=self.batch_size,
            num_workers=self.num_workers_loader,
            drop_last=self.drop_last_loader,
        )
        trainer = pl.Trainer(**self.trainer_kwargs)
        trainer.fit(self, datamodule=datamodule)

    def predict(self, dataset, step_size=1, **data_module_kwargs):
        """Predict.

        Neural network prediction with PL's `Trainer` execution of `predict_step`.

        **Parameters:**<br>
        `dataset`: NeuralForecast's `TimeSeriesDataset`, see [documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).<br>
        `step_size`: int=1, Step size between each window.<br>
        `**data_module_kwargs`: PL's TimeSeriesDataModule args, see [documentation](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule).
        """
        if step_size > 1:
            raise Exception("Recurrent models do not support step_size > 1")

        # fcsts (window, batch, h)
        trainer = pl.Trainer(**self.trainer_kwargs)
        datamodule = TimeSeriesDataModule(
            dataset, num_workers=self.num_workers_loader, **data_module_kwargs
        )
        fcsts = trainer.predict(self, datamodule=datamodule)
        if self.test_size > 0:
            # Remove warmup windows (from train and validation)
            # [N,T,H,output], avoid indexing last dim for univariate output compatibility
            fcsts = torch.vstack(
                [fcst[:, -(1 + self.test_size - self.h) :, :] for fcst in fcsts]
            )
            fcsts = fcsts.numpy().flatten()
            fcsts = fcsts.reshape(-1, len(self.loss.output_names))
        else:
            fcsts = torch.vstack([fcst[:, -1:, :] for fcst in fcsts]).numpy().flatten()
            fcsts = fcsts.reshape(-1, len(self.loss.output_names))
        return fcsts

    def set_test_size(self, test_size):
        self.test_size = test_size

    def save(self, path):
        """BaseRecurrent.save

        Save the fitted model to disk.

        **Parameters:**<br>
        `path`: str, path to save the model.<br>
        """
        self.trainer.save_checkpoint(path)
