import pandas as pd
from training.types import (
    ModelOverTime,
    TransformationsOverTime,
    PredictionsSeries,
    ProbabilitiesDataFrame,
)
from utils.helpers import get_first_valid_return_index
from tqdm import tqdm
from typing import Optional
from data_loader.types import XDataFrame
from tqdm import tqdm


def walk_forward_inference_batched(
    model_name: str,
    model_over_time: ModelOverTime,
    transformations_over_time: TransformationsOverTime,
    X: XDataFrame,
    expanding_window: bool,
    window_size: int,
    retrain_every: int,
    from_index: Optional[pd.Timestamp],
) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
    predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
    probabilities = pd.DataFrame(index=X.index, columns=["0", "1"])

    inference_from = (
        max(
            get_first_valid_return_index(model_over_time),
            get_first_valid_return_index(X.iloc[:, 0]),
        )
        if from_index is None
        else X.index.to_list().index(from_index)
    )
    inference_till = X.shape[0]
    first_model = model_over_time[inference_from]

    if first_model.only_column is not None:
        X = X[[column for column in X.columns if first_model.only_column in column]]

    if first_model.data_transformation == "original":
        transformations_over_time = []

    batch_indices = (
        range(inference_from, inference_till, retrain_every)
        if inference_till - inference_from > retrain_every
        else [inference_from]
    )
    batched_results = [
        __inference_from_window(
            index,
            index + retrain_every,
            X,
            model_over_time,
            transformations_over_time,
            expanding_window,
            window_size,
        )
        for index in tqdm(batch_indices)
    ]
    for batch in batched_results:
        for index, prediction, probs in batch:
            predictions[X.index[index]] = prediction
            probabilities.loc[X.index[index]] = probs

    return predictions, probabilities


def __inference_from_window(
    index_start: int,
    index_end: int,
    X: XDataFrame,
    model_over_time: ModelOverTime,
    transformations_over_time: TransformationsOverTime,
    expanding_window: bool,
    window_size: int,
) -> list[tuple[int, float, pd.Series]]:
    current_model = model_over_time[X.index[index_start]]
    current_transformations = [
        transformation_over_time[X.index[index_start]]
        for transformation_over_time in transformations_over_time
    ]

    input_data = X.iloc[index_start:index_end]

    for transformation in current_transformations:
        input_data = transformation.transform(input_data)

    input_data = input_data.to_numpy()

    predictions = current_model.predict(input_data)
    probs = current_model.predict_proba(input_data)
    results = [
        (index_start + index, predictions[index], probs[index])
        for index in range(len(predictions))
    ]

    return results
