# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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 List, Optional

import torch

from gluonts.dataset.field_names import FieldName
from gluonts.time_feature import TimeFeature
from gluonts.torch.model.predictor import PyTorchPredictor
from gluonts.torch.util import copy_parameters
from gluonts.model.predictor import Predictor
from gluonts.transform import (
    Transformation,
    Chain,
    InstanceSplitter,
    ExpectedNumInstanceSampler,
    ValidationSplitSampler,
    TestSplitSampler,
    RenameFields,
    AsNumpyArray,
    ExpandDimArray,
    AddObservedValuesIndicator,
    AddTimeFeatures,
    VstackFeatures,
    SetFieldIfNotPresent,
    TargetDimIndicator,
)

from pts import Trainer
from pts.feature import (
    fourier_time_features_from_frequency,
    lags_for_fourier_time_features_from_frequency,
)
from pts.model import PyTorchEstimator
from pts.model.utils import get_module_forward_input_names

from .time_grad_network import (
    TimeGradTrainingNetwork,
    TimeGradPredictionNetwork,
)


class TimeGradEstimator(PyTorchEstimator):
    def __init__(
        self,
        input_size: int,
        freq: str,
        prediction_length: int,
        target_dim: int,
        trainer: Trainer = Trainer(),
        context_length: Optional[int] = None,
        num_layers: int = 2,
        num_cells: int = 40,
        cell_type: str = "LSTM",
        num_parallel_samples: int = 100,
        dropout_rate: float = 0.1,
        cardinality: List[int] = [1],
        embedding_dimension: int = 5,
        conditioning_length: int = 100,
        diff_steps: int = 100,
        loss_type: str = "l2",
        beta_end=0.1,
        beta_schedule="linear",
        residual_layers=8,
        residual_channels=8,
        dilation_cycle_length=2,
        scaling: bool = True,
        pick_incomplete: bool = False,
        lags_seq: Optional[List[int]] = None,
        time_features: Optional[List[TimeFeature]] = None,
        **kwargs,
    ) -> None:
        super().__init__(trainer=trainer, **kwargs)

        self.freq = freq
        self.context_length = (
            context_length if context_length is not None else prediction_length
        )

        self.input_size = input_size
        self.prediction_length = prediction_length
        self.target_dim = target_dim
        self.num_layers = num_layers
        self.num_cells = num_cells
        self.cell_type = cell_type
        self.num_parallel_samples = num_parallel_samples
        self.dropout_rate = dropout_rate
        self.cardinality = cardinality
        self.embedding_dimension = embedding_dimension

        self.conditioning_length = conditioning_length
        self.diff_steps = diff_steps
        self.loss_type = loss_type
        self.beta_end = beta_end
        self.beta_schedule = beta_schedule
        self.residual_layers = residual_layers
        self.residual_channels = residual_channels
        self.dilation_cycle_length = dilation_cycle_length

        self.lags_seq = (
            lags_seq
            if lags_seq is not None
            else lags_for_fourier_time_features_from_frequency(freq_str=freq)
        )

        self.time_features = (
            time_features
            if time_features is not None
            else fourier_time_features_from_frequency(self.freq)
        )

        self.history_length = self.context_length + max(self.lags_seq)
        self.pick_incomplete = pick_incomplete
        self.scaling = scaling

        self.train_sampler = ExpectedNumInstanceSampler(
            num_instances=1.0,
            min_past=0 if pick_incomplete else self.history_length,
            min_future=prediction_length,
        )

        self.validation_sampler = ValidationSplitSampler(
            min_past=0 if pick_incomplete else self.history_length,
            min_future=prediction_length,
        )

    def create_transformation(self) -> Transformation:
        return Chain(
            [
                AsNumpyArray(
                    field=FieldName.TARGET,
                    expected_ndim=2,
                ),
                # maps the target to (1, T)
                # if the target data is uni dimensional
                ExpandDimArray(
                    field=FieldName.TARGET,
                    axis=None,
                ),
                AddObservedValuesIndicator(
                    target_field=FieldName.TARGET,
                    output_field=FieldName.OBSERVED_VALUES,
                ),
                AddTimeFeatures(
                    start_field=FieldName.START,
                    target_field=FieldName.TARGET,
                    output_field=FieldName.FEAT_TIME,
                    time_features=self.time_features,
                    pred_length=self.prediction_length,
                ),
                VstackFeatures(
                    output_field=FieldName.FEAT_TIME,
                    input_fields=[FieldName.FEAT_TIME],
                ),
                SetFieldIfNotPresent(
                    field=FieldName.FEAT_STATIC_CAT, value=[0]
                ),
                TargetDimIndicator(
                    field_name="target_dimension_indicator",
                    target_field=FieldName.TARGET,
                ),
                AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
            ]
        )

    def create_instance_splitter(self, mode: str):
        assert mode in ["training", "validation", "test"]

        instance_sampler = {
            "training": self.train_sampler,
            "validation": self.validation_sampler,
            "test": TestSplitSampler(),
        }[mode]

        return InstanceSplitter(
            target_field=FieldName.TARGET,
            is_pad_field=FieldName.IS_PAD,
            start_field=FieldName.START,
            forecast_start_field=FieldName.FORECAST_START,
            instance_sampler=instance_sampler,
            past_length=self.history_length,
            future_length=self.prediction_length,
            time_series_fields=[
                FieldName.FEAT_TIME,
                FieldName.OBSERVED_VALUES,
            ],
        ) + (
            RenameFields(
                {
                    f"past_{FieldName.TARGET}": f"past_{FieldName.TARGET}_cdf",
                    f"future_{FieldName.TARGET}": f"future_{FieldName.TARGET}_cdf",
                }
            )
        )

    def create_training_network(
        self, device: torch.device
    ) -> TimeGradTrainingNetwork:
        return TimeGradTrainingNetwork(
            input_size=self.input_size,
            target_dim=self.target_dim,
            num_layers=self.num_layers,
            num_cells=self.num_cells,
            cell_type=self.cell_type,
            history_length=self.history_length,
            context_length=self.context_length,
            prediction_length=self.prediction_length,
            dropout_rate=self.dropout_rate,
            cardinality=self.cardinality,
            embedding_dimension=self.embedding_dimension,
            diff_steps=self.diff_steps,
            loss_type=self.loss_type,
            beta_end=self.beta_end,
            beta_schedule=self.beta_schedule,
            residual_layers=self.residual_layers,
            residual_channels=self.residual_channels,
            dilation_cycle_length=self.dilation_cycle_length,
            lags_seq=self.lags_seq,
            scaling=self.scaling,
            conditioning_length=self.conditioning_length,
        ).to(device)

    def create_predictor(
        self,
        transformation: Transformation,
        trained_network: TimeGradTrainingNetwork,
        device: torch.device,
    ) -> Predictor:
        prediction_network = TimeGradPredictionNetwork(
            input_size=self.input_size,
            target_dim=self.target_dim,
            num_layers=self.num_layers,
            num_cells=self.num_cells,
            cell_type=self.cell_type,
            history_length=self.history_length,
            context_length=self.context_length,
            prediction_length=self.prediction_length,
            dropout_rate=self.dropout_rate,
            cardinality=self.cardinality,
            embedding_dimension=self.embedding_dimension,
            diff_steps=self.diff_steps,
            loss_type=self.loss_type,
            beta_end=self.beta_end,
            beta_schedule=self.beta_schedule,
            residual_layers=self.residual_layers,
            residual_channels=self.residual_channels,
            dilation_cycle_length=self.dilation_cycle_length,
            lags_seq=self.lags_seq,
            scaling=self.scaling,
            conditioning_length=self.conditioning_length,
            num_parallel_samples=self.num_parallel_samples,
        ).to(device)

        copy_parameters(trained_network, prediction_network)
        input_names = get_module_forward_input_names(prediction_network)
        prediction_splitter = self.create_instance_splitter("test")

        return PyTorchPredictor(
            input_transform=transformation + prediction_splitter,
            input_names=input_names,
            prediction_net=prediction_network,
            batch_size=self.trainer.batch_size,
            freq=self.freq,
            prediction_length=self.prediction_length,
            device=device,
        )
