import logging
from clearml import Task
from clearml.automation import HyperParameterOptimizer
from clearml.automation.optuna import OptimizerOptuna
from clearml.automation import (
    DiscreteParameterRange, HyperParameterOptimizer, RandomSearch,
    UniformIntegerParameterRange)

# trying to load Bayesian optimizer package
try:
    from clearml.automation.optuna import OptimizerOptuna  # noqa
    aSearchStrategy = OptimizerOptuna
except ImportError as ex:
    try:
        from clearml.automation.hpbandster import OptimizerBOHB  # noqa
        aSearchStrategy = OptimizerBOHB
    except ImportError as ex:
        logging.getLogger().warning(
            'Apologies, it seems you do not have \'optuna\' or \'hpbandster\' installed, '
            'we will be using RandomSearch strategy instead')
        aSearchStrategy = RandomSearch

# input task id to optimize using argparse
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--task_id", help="Task ID to optimize", type=str)
args = parser.parse_args()
input_task_id = args.task_id

# check if task id is valid
if not Task.get_task(task_id=input_task_id):
    raise ValueError("Invalid task id")

Task.add_requirements("requirements.txt")
Task.ignore_requirements("torch")
Task.ignore_requirements("torchvision")
Task.ignore_requirements("tensorboard")
task = Task.init(project_name='Thesis/NrvForecast',
                 task_name='Autoregressive Quantile Regression Hyper-Parameter Optimization',
                 task_type=Task.TaskTypes.optimizer,
                 reuse_last_task_id=False)
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
task.set_packages("requirements.txt")


execution_queue = "default"


### HYPER PARAMETERS ###
#### Quantiles ####
quantile_lists = [
    [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],  # Deciles
    [0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95],  # 10% Increments, Excluding Extremes
    [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99],  # Combining Deciles with Extremes
    [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],  # Including 0 and 1
    [0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99],  # Mixed Small and Large Increments
    [0.125, 0.375, 0.625, 0.875],  # Eighths
    [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95],  # 10% Increments
]
quantiles_range = DiscreteParameterRange("general/quantiles", values=quantile_lists)

#### Data Config ####
quarter_range = DiscreteParameterRange("data_features/quarter", values=[True])
day_of_week_range = DiscreteParameterRange("data_features/day_of_week", values=[True])

load_forecast_range = DiscreteParameterRange("data_features/load_forecast", values=[True])

learning_rate = DiscreteParameterRange("model_parameters/learning_rate", values=[0.00001, 0.00005, 0.0001, 0.0005, 0.001])
hidden_size = DiscreteParameterRange("model_parameters/hidden_size", values=[64, 128, 256, 512, 1024, 2048])
num_layers = DiscreteParameterRange("model_parameters/num_layers", values=[1, 2, 3, 4, 5, 6])
dropout = DiscreteParameterRange("model_parameters/dropout", values=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
time_feature_embedding = DiscreteParameterRange("model_parameters/time_feature_embedding", values=[1,2,3,4,5,6])

### OPTIMIZER OBJECT ###
optimizer = HyperParameterOptimizer(
    base_task_id=input_task_id,
    objective_metric_title="Summary",
    objective_metric_series="test_CRPSLoss",
    objective_metric_sign="min",
    execution_queue=execution_queue,
    max_number_of_concurrent_tasks=1,
    optimizer_class=aSearchStrategy,
    max_iteration_per_job=300,
    # save_top_k_tasks_only=3,
    pool_period_min=1,
    total_max_jobs=40,

    hyper_parameters=[
        quarter_range,
        day_of_week_range,
        load_forecast_range,
        learning_rate,
        hidden_size,
        num_layers,
        dropout,
        time_feature_embedding
    ]

)
        
task.execute_remotely(queue_name="hypertuning", exit_process=True)

optimizer.set_report_period(1)

def job_complete_callback(
    job_id,                 # type: str
    objective_value,        # type: float
    objective_iteration,    # type: int
    job_parameters,         # type: dict
    top_performance_job_id  # type: str
):
    print('Job completed!', job_id, objective_value, objective_iteration, job_parameters)
    if job_id == top_performance_job_id:
        print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value))

optimizer.start(job_complete_callback=job_complete_callback)
optimizer.set_time_limit(in_minutes=120.0*8)
optimizer.wait()
top_exp = optimizer.get_top_experiments(top_k=5)
print([t.id for t in top_exp])
# make sure background optimization stopped
optimizer.stop()

print('We are done, good bye')