# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
#
# 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.
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from typing import Dict, List

from google_cloud_pipeline_components import _image
from google_cloud_pipeline_components.types.artifact_types import BQMLModel
from google_cloud_pipeline_components.types.artifact_types import BQTable
from kfp.dsl import ConcatPlaceholder
from kfp.dsl import container_component
from kfp.dsl import ContainerSpec
from kfp.dsl import Input
from kfp.dsl import Output
from kfp.dsl import OutputPath


@container_component
def bigquery_forecast_model_job(
    project: str,
    model: Input[BQMLModel],
    destination_table: Output[BQTable],
    gcp_resources: OutputPath(str),
    location: str = 'us-central1',
    horizon: int = 3,
    confidence_level: float = 0.95,
    query_parameters: List[str] = [],
    job_configuration_query: Dict[str, str] = {},
    labels: Dict[str, str] = {},
    encryption_spec_key_name: str = '',
):
  # fmt: off
  """Launch a BigQuery ML.FORECAST job and let you forecast an ARIMA_PLUS or
  ARIMA model.

  This function only applies to the time-series ARIMA_PLUS and ARIMA models.

  Args:
      project: Project to run the BigQuery job.
      location: Location to run the BigQuery job. If not set,
        default to `US` multi-region. For more details, see
        https://cloud.google.com/bigquery/docs/locations#specifying_your_location
      model: BigQuery ML model for ML.FORECAST.
        For more details, see
        https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-forecast
      horizon: Horizon is the number of time points to
        forecast. For more details, see
        https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-forecast#horizon
      confidence_level: The percentage of the future values
        that fall in the prediction interval. For more details, see
          https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-forecast#confidence_level
      query_parameters: jobs.query parameters for
        standard SQL queries. If query_parameters are both specified in here
        and in job_configuration_query, the value in here will override the
        other one.
      job_configuration_query: A json formatted string
        describing the rest of the job configuration. For more details, see
        https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationQuery
      labels: The labels associated with this job. You can
        use these to organize and group your jobs. Label keys and values can
        be no longer than 63 characters, can only containlowercase letters,
        numeric characters, underscores and dashes. International characters
        are allowed. Label values are optional. Label keys must start with a
        letter and each label in the list must have a different key.
          Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
      encryption_spec_key_name: Describes the Cloud KMS
        encryption key that will be used to protect destination BigQuery
        table. The BigQuery Service Account associated with your project
        requires access to this encryption key. If encryption_spec_key_name
        are both specified in here and in job_configuration_query, the value
        in here will override the other one.

  Returns:
      destination_table: Describes the table where the model forecast results should be
          stored.
          For more details, see
          https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-forecast#mlforecast_output
      gcp_resources: Serialized gcp_resources proto tracking the BigQuery job.
          For more details, see
          https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
  """
  # fmt: on
  return ContainerSpec(
      image=_image.GCPC_IMAGE_TAG,
      command=[
          'python3',
          '-u',
          '-m',
          'google_cloud_pipeline_components.container.v1.bigquery.forecast_model.launcher',
      ],
      args=[
          '--type',
          'BigqueryMLForecastJob',
          '--project',
          project,
          '--location',
          location,
          '--model_name',
          ConcatPlaceholder([
              model.metadata['projectId'],
              '.',
              model.metadata['datasetId'],
              '.',
              model.metadata['modelId'],
          ]),
          '--horizon',
          horizon,
          '--confidence_level',
          confidence_level,
          '--payload',
          ConcatPlaceholder([
              '{',
              '"configuration": {',
              '"query": ',
              job_configuration_query,
              '}',
              '}',
          ]),
          '--job_configuration_query_override',
          ConcatPlaceholder([
              '{',
              '"query_parameters": ',
              query_parameters,
              ', "destination_encryption_configuration": {',
              '"kmsKeyName": "',
              encryption_spec_key_name,
              '"}',
              '}',
          ]),
          '--gcp_resources',
          gcp_resources,
          '--executor_input',
          '{{$}}',
      ],
  )
