import logging
from typing import Any, Dict

import numpy as np
import pandas as pd
import torch

from llm_studio.src.datasets.text_causal_language_modeling_ds import (
    CustomDataset as TextCausalLanguageModelingCustomDataset,
)
from llm_studio.src.utils.exceptions import LLMDataException

logger = logging.getLogger(__name__)


class CustomDataset(TextCausalLanguageModelingCustomDataset):
    def __init__(self, df: pd.DataFrame, cfg: Any, mode: str = "train"):
        super().__init__(df=df, cfg=cfg, mode=mode)
        check_for_non_int_answers(cfg, df)
        self.answers_int = df[cfg.dataset.answer_column].astype(int).values
        max_value = np.max(self.answers_int)
        min_value = np.min(self.answers_int)

        if 1 < cfg.dataset.num_classes <= max_value:
            raise LLMDataException(
                "Number of classes is smaller than max label "
                f"{max_value}. Please increase the setting accordingly."
            )
        elif cfg.dataset.num_classes == 1 and max_value > 1:
            raise LLMDataException(
                "For binary classification, max label should be 1 but is "
                f"{max_value}."
            )
        if min_value < 0:
            raise LLMDataException(
                "Labels should be non-negative but min label is " f"{min_value}."
            )
        if min_value != 0 or max_value != np.unique(self.answers_int).size - 1:
            logger.warning(
                "Labels should start at 0 and be continuous but are "
                f"{sorted(np.unique(self.answers_int))}."
            )

        if cfg.dataset.parent_id_column != "None":
            raise LLMDataException(
                "Parent ID column is not supported for classification datasets."
            )

    def __getitem__(self, idx: int) -> Dict:
        sample = super().__getitem__(idx)
        sample["class_label"] = self.answers_int[idx]
        return sample

    def postprocess_output(self, cfg, df: pd.DataFrame, output: Dict) -> Dict:
        output["logits"] = output["logits"].float()

        if cfg.training.loss_function == "CrossEntropyLoss":
            output["probabilities"] = torch.softmax(output["logits"], dim=-1)
        else:
            output["probabilities"] = torch.sigmoid(output["logits"])

        if len(cfg.dataset.answer_column) == 1:
            if cfg.dataset.num_classes == 1:
                output["predictions"] = (output["probabilities"] > 0.5).long()
            else:
                output["predictions"] = output["probabilities"].argmax(
                    dim=-1, keepdim=True
                )
        else:
            output["predictions"] = (output["probabilities"] > 0.5).long()

        preds = []
        for col in np.arange(output["probabilities"].shape[1]):
            preds.append(
                np.round(output["probabilities"][:, col].cpu().numpy(), 3).astype(str)
            )
        preds = [",".join(pred) for pred in zip(*preds)]
        output["predicted_text"] = preds
        return super().postprocess_output(cfg, df, output)

    def clean_output(self, output, cfg):
        return output

    @classmethod
    def sanity_check(cls, df: pd.DataFrame, cfg: Any, mode: str = "train"):

        for answer_col in cfg.dataset.answer_column:
            assert answer_col in df.columns, (
                f"Answer column {answer_col} not found in the " f"{mode} DataFrame."
            )
            assert df.shape[0] == df[answer_col].dropna().shape[0], (
                f"The {mode} DataFrame"
                f" column {answer_col}"
                " contains missing values."
            )

        check_for_non_int_answers(cfg, df)


def check_for_non_int_answers(cfg, df):
    answers_non_int: list = []
    for column in cfg.dataset.answer_column:
        answers_non_int.extend(
            x for x in df[column].values if not is_castable_to_int(x)
        )
    if len(answers_non_int) > 0:
        raise LLMDataException(
            f"Column {cfg.dataset.answer_column} contains non int items. "
            f"Sample values: {answers_non_int[:5]}."
        )


def is_castable_to_int(s):
    try:
        int(s)
        return True
    except ValueError:
        return False