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import argparse
import logging
from typing import Callable
from typing import Iterable
from typing import List
from typing import Tuple
from typing import Union

import numpy
import pandas
import scipy
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

import apache_beam as beam
import datatable
import xgboost
from apache_beam.ml.inference.base import KeyedModelHandler
from apache_beam.ml.inference.base import PredictionResult
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerDatatable
from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerNumpy
from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas
from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerSciPy
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.runners.runner import PipelineResult


class PostProcessor(beam.DoFn):
  """Process the PredictionResult to get the predicted label.
  Returns a comma separated string with true label and predicted label.
  """
  def process(self, element: Tuple[int, PredictionResult]) -> Iterable[str]:
    label, prediction_result = element
    prediction = prediction_result.inference
    yield '{},{}'.format(label, prediction)


def parse_known_args(argv):
  """Parses args for the workflow."""
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--input_type',
      dest='input_type',
      required=True,
      choices=['numpy', 'pandas', 'scipy', 'datatable'],
      help='Datatype of the input data.')
  parser.add_argument(
      '--output',
      dest='output',
      required=True,
      help='Path to save output predictions.')
  parser.add_argument(
      '--model_state',
      dest='model_state',
      required=True,
      help='Path to the state of the XGBoost model loaded for Inference.')
  group = parser.add_mutually_exclusive_group(required=True)
  group.add_argument('--split', action='store_true', dest='split')
  group.add_argument('--no_split', action='store_false', dest='split')
  return parser.parse_known_args(argv)


def load_sklearn_iris_test_data(
    data_type: Callable,
    split: bool = True,
    seed: int = 999) -> List[Union[numpy.array, pandas.DataFrame]]:
  """
    Loads test data from the sklearn Iris dataset in a given format,
    either in a single or multiple batches.
    Args:
      data_type: Datatype of the iris test dataset.
      split: Split the dataset in different batches or return single batch.
      seed: Random state for splitting the train and test set.
  """
  dataset = load_iris()
  _, x_test, _, _ = train_test_split(
      dataset['data'], dataset['target'], test_size=.2, random_state=seed)

  if split:
    return [(index, data_type(sample.reshape(1, -1))) for index,
            sample in enumerate(x_test)]
  return [(0, data_type(x_test))]


def run(
    argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult:
  """
    Args:
      argv: Command line arguments defined for this example.
      save_main_session: Used for internal testing.
      test_pipeline: Used for internal testing.
  """
  known_args, pipeline_args = parse_known_args(argv)
  pipeline_options = PipelineOptions(pipeline_args)
  pipeline_options.view_as(SetupOptions).save_main_session = save_main_session

  data_types = {
      'numpy': (numpy.array, XGBoostModelHandlerNumpy),
      'pandas': (pandas.DataFrame, XGBoostModelHandlerPandas),
      'scipy': (scipy.sparse.csr_matrix, XGBoostModelHandlerSciPy),
      'datatable': (datatable.Frame, XGBoostModelHandlerDatatable),
  }

  input_data_type, model_handler = data_types[known_args.input_type]

  xgboost_model_handler = KeyedModelHandler(
      model_handler(
          model_class=xgboost.XGBClassifier,
          model_state=known_args.model_state))

  input_data = load_sklearn_iris_test_data(
      data_type=input_data_type, split=known_args.split)

  pipeline = test_pipeline
  if not test_pipeline:
    pipeline = beam.Pipeline(options=pipeline_options)

  predictions = (
      pipeline
      | "ReadInputData" >> beam.Create(input_data)
      | "RunInference" >> RunInference(xgboost_model_handler)
      | "PostProcessOutputs" >> beam.ParDo(PostProcessor()))

  _ = predictions | "WriteOutput" >> beam.io.WriteToText(
      known_args.output, shard_name_template='', append_trailing_newlines=True)

  result = pipeline.run()
  result.wait_until_finish()
  return result


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()
