from kfp.components import InputPath, OutputPath


def StatisticsGen(
    examples_path: InputPath('Examples'),
    #examples_path: 'ExamplesUri',

    statistics_path: OutputPath('ExampleStatistics'),
    #statistics_path: 'ExampleStatisticsUri',

    stats_options:  {'JsonObject': {'data_type': 'proto:tensorflow_data_validation.StatsOptions'}} = None,

    schema_path: InputPath('Schema') = None,
    #schema_path: 'SchemaUri' = None,
):
#) -> NamedTuple('Outputs', [
#    ('statistics', 'ExampleStatisticsUri'),
#]):
    """Construct a StatisticsGen component.

    Args:
      examples: A Channel of `ExamplesPath` type, likely generated by the
        [ExampleGen component](https://www.tensorflow.org/tfx/guide/examplegen).
        This needs to contain two splits labeled `train` and `eval`. _required_
      schema: A `Schema` channel to use for automatically configuring the value
        of stats options passed to TFDV.

    Returns:
      statistics: `ExampleStatistics` channel for statistics of each split
        provided in the input examples.
    """
    from tfx.components.statistics_gen.component import StatisticsGen as component_class

    #Generated code
    import json
    import os
    import tensorflow
    from google.protobuf import json_format, message
    from tfx.types import Artifact, channel_utils, artifact_utils

    arguments = locals().copy()

    component_class_args = {}

    for name, execution_parameter in component_class.SPEC_CLASS.PARAMETERS.items():
        argument_value_obj = argument_value = arguments.get(name, None)
        if argument_value is None:
            continue
        parameter_type = execution_parameter.type
        if isinstance(parameter_type, type) and issubclass(parameter_type, message.Message): # Maybe FIX: execution_parameter.type can also be a tuple
            argument_value_obj = parameter_type()
            json_format.Parse(argument_value, argument_value_obj)
        component_class_args[name] = argument_value_obj

    for name, channel_parameter in component_class.SPEC_CLASS.INPUTS.items():
        artifact_path = arguments[name + '_path']
        if artifact_path:
            artifact = channel_parameter.type()
            artifact.uri = artifact_path + '/' # ?
            if channel_parameter.type.PROPERTIES and 'split_names' in channel_parameter.type.PROPERTIES:
                # Recovering splits
                subdirs = tensorflow.io.gfile.listdir(artifact_path)
                artifact.split_names = artifact_utils.encode_split_names(sorted(subdirs))
            component_class_args[name] = channel_utils.as_channel([artifact])

    component_class_instance = component_class(**component_class_args)

    input_dict = {name: channel.get() for name, channel in component_class_instance.inputs.get_all().items()}
    output_dict = {name: channel.get() for name, channel in component_class_instance.outputs.get_all().items()}
    exec_properties = component_class_instance.exec_properties

    # Generating paths for output artifacts
    for name, artifacts in output_dict.items():
        base_artifact_path = arguments[name + '_path']
        # Are there still cases where output channel has multiple artifacts?
        for idx, artifact in enumerate(artifacts):
            subdir = str(idx + 1) if idx > 0 else ''
            artifact.uri = os.path.join(base_artifact_path, subdir)  # Ends with '/'

    print('component instance: ' + str(component_class_instance))

    #executor = component_class.EXECUTOR_SPEC.executor_class() # Same
    executor = component_class_instance.executor_spec.executor_class()
    executor.Do(
        input_dict=input_dict,
        output_dict=output_dict,
        exec_properties=exec_properties,
    )
    #return (statistics_path,)


if __name__ == '__main__':
    import kfp
    kfp.components.func_to_container_op(
        StatisticsGen,
        base_image='tensorflow/tfx:0.21.4',
        output_component_file='component.yaml'
    )