text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import Dict, List, Tuple
  
  import numpy as np
  
  from flash.core.utilities.imports import _PANDAS_AVAILABLE
  
  if _PANDAS_AVAILABLE:
      import pandas as pd
      from pandas import Series
      from pandas.core.frame import DataFrame
  else:
      DataFrame = None
  
  
  def _impute(df: DataFrame, num_cols: List) -> DataFrame:
      for col in num_cols:
          df[col] = df[col].fillna(df[col].median())
      return df
  
  
  def _compute_normalization(df: DataFrame, num_cols: List) -> Tuple:
      return df[num_cols].mean().to_dict(), df[num_cols].std().to_dict()
  
  
  def _normalize(df: DataFrame, num_cols: List, mean: Dict, std: Dict) -> DataFrame:
      mean = Series(mean)
      std = Series(std)
      df[num_cols] = (df[num_cols] - mean) / std
      return df
  
  
  def _generate_codes(df: DataFrame, cat_cols: List) -> dict:
      tmp = df.copy()
      for col in cat_cols:
          tmp[col] = tmp[col].astype("category").cat.as_ordered()
  
      # list of categories for each column (always a column for None)
      codes = {col: list(tmp[col].cat.categories) for col in cat_cols}
  
      return codes
  
  
  def _categorize(df: DataFrame, cat_cols: List, codes) -> DataFrame:
      # apply codes to each column
      for col in cat_cols:
          df[col] = pd.Categorical(df[col], categories=codes[col], ordered=True)
      df[cat_cols] = df[cat_cols].apply(lambda x: x.cat.codes)
  
      # we add one here as Nones are -1, so they turn into 0's
      df[cat_cols] = df[cat_cols] + 1
      return df
  
  
  def _pre_transform(
      df: DataFrame,
      num_cols: List[str],
      cat_cols: List[str],
      codes: Dict,
      mean: Dict,
      std: Dict,
  ) -> DataFrame:
      df = _impute(df, num_cols)
      df = _normalize(df, num_cols, mean=mean, std=std)
      df = _categorize(df, cat_cols, codes=codes)
      return df
  
  
  def _to_cat_vars_numpy(df: DataFrame, cat_cols: List[str]) -> list:
      return [c.to_numpy().astype(np.int64) for n, c in df[cat_cols].items()]
  
  
  def _to_num_vars_numpy(df: DataFrame, num_cols: List[str]) -> list:
      return [c.to_numpy().astype(np.float32) for n, c in df[num_cols].items()]
  