import numpy as np

import pandas as pd

import yaml

from joblib import Parallel

from tqdm.auto import tqdm

def encode_biopsy_site(df_rna):

    d = {}

    for site in df_rna["Biopsy site"].unique():

        if pd.isnull(site) | (site == "Non disponible"):

            d[site] = np.nan

        elif site in ["PRIMITIF", "META_PULM", "META_PULM_HL", "META_PULM_CL"]:

            d[site] = 0

        elif site in ["META_PLEVRE", "META_PLEVRE_HL", "META_PLEVRE_CL"]:

            d[site] = 1

        elif site.split("_")[0] == "ADP":

            d[site] = 2

        elif site == "META_OS":

            d[site] = 3

        elif site == "META_FOIE":

            d[site] = 4

        elif site == "META_SURRENALE":

            d[site] = 5

        elif site == "META_BRAIN":

            d[site] = 6

        else:

            d[site] = 7

    return df_rna.replace({"Biopsy site": d})

def process_radiomics(df_rad, transformed_features):

    df_rad[transformed_features] = np.log(df_rad[transformed_features] + 1)

    return df_rad

def write_yaml(content, fname):

    content = _clean_nested_dict(content)

    with open(fname, "w") as yaml_file:

        yaml.safe_dump(

            content, yaml_file, default_flow_style=None

        )  # , default_flow_style=False)

def read_yaml(fname):

    with open(fname) as yaml_file:

        return yaml.safe_load(yaml_file)

class ProgressParallel(Parallel):

    def __init__(self, use_tqdm=True, total=None, *args, **kwargs):

        self._use_tqdm = use_tqdm

        self._total = total

        super().__init__(*args, **kwargs)

    def __call__(self, *args, **kwargs):

        with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar:

            return Parallel.__call__(self, *args, **kwargs)

    def print_progress(self):

        if self._total is None:

            self._pbar.total = self.n_dispatched_tasks

        self._pbar.n = self.n_completed_tasks

        self._pbar.refresh()

def _clean_nested_dict(d):

    if isinstance(d, (np.int8, np.int16, np.int32, np.int64)):

        return int(d)

    if isinstance(d, (np.float16, np.float32, np.float64)):

        return float(d)

    if isinstance(d, list):

        return [_clean_nested_dict(x) for x in d]

    if isinstance(d, dict):

        for key, value in d.items():

            d.update({key: _clean_nested_dict(value)})

    return d