import os
from functools import partial
import math

import torch
from torch import nn
import numpy as np

from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from torch.nn import BCEWithLogitsLoss, MSELoss
from torch.optim.lr_scheduler import OneCycleLR
from torch.optim import AdamW

from data_analysis.models.ov_detection.config import model_config
from utils.feature_functions import *
from utils.loss_functions import weighted_bce_loss_fn
from utils.training_utils import collate

from vsm_datascience_common import constants

feature_config = {
    "feature_set_name": "full_feature_set",
    "filter_criteria": {
        "measurements.length": {
            "$gt": constants.MEASUREMENTS_PER_DAY * 10,
            "$lt": constants.MEASUREMENTS_PER_DAY * 150
        },
        "duration_in_days": {
            "$gt": 10,
            "$lt": 150,
        },
        "ends_at": {
            "$exists": True,
            "$ne": None
        },
        "$and": [
            {"measurements.values": {"$not": {"$elemMatch": {"$lt": 35}}}},
            {"measurements.values": {"$not": {"$elemMatch": {"$gt": 43}}}}
        ]
    },
    "augmentation": {
        "use_augmentation": True,
        "max_lookback": 4,
    },
    "feature_sets": [
        "static_categorical_features",
        "static_continuous_features",
        "known_categorical_features",
        "known_continuous_features",
        "observed_categorical_features",
        "observed_continuous_features",
        "target_features"
    ],
    "static_categorical_features": [
    ],
    "static_continuous_features": [
        {
            "name": "user_age",
            "fn": get_user_age,
            "scaler": RobustScaler
        },
        {
            "name": "user_age",
            "fn": get_user_height,
            "scaler": RobustScaler
        },
        {
            "name": "user_weight",
            "fn": get_user_weight,
            "scaler": RobustScaler
        },
        {
            "name": "average_cycle_length",
            "fn": get_cycle_length_stats,
            "scaler": RobustScaler
        },
        {
            "name": "average_ovulation_day",
            "fn": get_average_ovulation_day,
            "scaler": RobustScaler
        },
        {
            "name": "ovulation_std",
            "fn": get_ovulation_std,
            "scaler": StandardScaler
        },
        {
            "name": "biphasic_fraction",
            "fn": get_biphasic_fraction,
            "scaler": MinMaxScaler
        },
        {
            "name": "num_cycles",
            "fn": get_num_cycles,
            "scaler": RobustScaler
        },
        {
            "name": "temperature_averages",
            "fn": get_average_temperatures,
            "scaler": StandardScaler
        }
    ],
    "known_categorical_features": [
    ],
    "known_continuous_features": [
        {
            "name": "hours_from_start",
            "fn": partial(get_hours_from_start, shift=0),
            "scaler": MinMaxScaler,
            "accumulation_fn": np.max
        },
        {
            "name": "hour_of_day",
            "fn": partial(get_hour_of_day_encoded, shift=0),
            "scaler": None,
            "accumulation_fn": np.max
        },
        {
            "name": "day_of_week",
            "fn": partial(get_day_of_week_encoded, shift=0),
            "scaler": None,
            "accumulation_fn": np.max
        },
        {
            "name": "month_of_year",
            "fn": partial(get_month_of_year_encoded, shift=0),
            "scaler": None,
            "accumulation_fn": np.max
        }
    ],
    "observed_categorical_features": [
    ],
    "observed_continuous_features": [
        {
            "name": "temperature",
            "fn": partial(get_temperature, shift=0),
            "scaler": StandardScaler
        },
        {
            "name": "rolling_average_temperature",
            "fn": partial(get_rolling_average_with_padding, shift=0),
            "scaler": StandardScaler
        },
        {
            "name": "rolling_window_temperature_min",
            "fn": partial(get_window_fn, window_size=constants.MEASUREMENTS_PER_DAY, fn=partial(np.min, axis=1)),
            "scaler": StandardScaler
        },
        {
            "name": "rolling_window_temperature_max",
            "fn": partial(get_window_fn, window_size=constants.MEASUREMENTS_PER_DAY, fn=partial(np.max, axis=1)),
            "scaler": StandardScaler
        },
    ],
    "target_features": [
        {
            "name": "fertility_probability",
            "fn": partial(get_fertility_probability, shift=0),
            "scaler": MinMaxScaler,
            "accumulation_fn": np.max,
            "loss_fn": MSELoss,
        },
        {
            "name": "ov_over_probability",
            "fn": partial(get_ov_over_probability, shift=0),
            "scaler": MinMaxScaler,
            "accumulation_fn": np.max,
            "loss_fn": BCEWithLogitsLoss,
        },
        {
            "name": "days_relative_to_ov",
            "fn": get_days_relative_to_ov,
            "scaler": RobustScaler,
            "accumulation_fn": np.max,
            "loss_fn": MSELoss,
        },
        {
            "name": "ov_day",
            "fn": get_ov_day,
            "scaler": RobustScaler,
            "accumulation_fn": np.max,
            "loss_fn": MSELoss,
        },
        {
            "name": "is_biphasic",
            "fn": get_is_biphasic,
            "scaler": MinMaxScaler,
            "accumulation_fn": np.max,
            "loss_fn": BCEWithLogitsLoss
        }
    ]
}