from src.policies.PolicyEvaluator import PolicyEvaluator
from src.policies.simple_baseline import BaselinePolicy, Battery
from src.models.lstm_model import GRUModel
from src.data import DataProcessor, DataConfig
from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer
from src.trainers.trainer import Trainer
from src.utils.clearml import ClearMLHelper
from src.models import *
from src.losses import *
import torch
from torch.nn import MSELoss, L1Loss
import torch.nn as nn
from src.models.time_embedding_layer import TimeEmbedding


#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Non Linear")


#### Data Processor ####
data_config = DataConfig()

data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True

data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True

data_config.QUARTER = True
data_config.DAY_OF_WEEK = True

data_config.NOMINAL_NET_POSITION = True



data_config = task.connect(data_config, name="data_features")

data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)


#### Hyperparameters ####
data_processor.set_output_size(1)
inputDim = data_processor.get_input_size()
epochs = 300

# add parameters to clearml
quantiles = task.get_parameter("general/quantiles", cast=True)
# make sure it is a list
if quantiles is None:
    quantiles = [0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]
    task.set_parameter("general/quantiles", quantiles)
else:
    # if string, convert to list "[0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]""
    if isinstance(quantiles, str):
        quantiles = eval(quantiles)

model_parameters = {
    "learning_rate": 0.001,
    "hidden_size": 512,
    "num_layers": 4,
    "dropout": 0.2,
    "time_feature_embedding": 8,
}

model_parameters = task.connect(model_parameters, name="model_parameters")

time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"])
# lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
non_linear_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters["hidden_size"], numLayers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
# linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))

model = nn.Sequential(time_embedding, non_linear_model)
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])

### Policy Evaluator ###
battery = Battery(2, 1)
baseline_policy = BaselinePolicy(battery, data_path="")
policy_evaluator = PolicyEvaluator(baseline_policy, task)

#### Trainer ####
trainer = AutoRegressiveQuantileTrainer(
    model,
    inputDim,
    optimizer,
    data_processor,
    quantiles,
    "cuda",
    policy_evaluator=policy_evaluator,
    debug=False,
)

trainer.add_metrics_to_track(
    [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
)
trainer.early_stopping(patience=15)
trainer.plot_every(5)
trainer.train(task=task, epochs=epochs, remotely=True)

### Policy Evaluation ###
idx_samples = trainer.test_set_samples
_, test_loader = trainer.data_processor.get_dataloaders(
            predict_sequence_length=trainer.model.output_size)

policy_evaluator.evaluate_test_set(idx_samples, test_loader)
policy_evaluator.plot_profits_table()
policy_evaluator.plot_thresholds_per_day()

task.close()