# 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.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Dict, List

from google_cloud_pipeline_components import _image
from google_cloud_pipeline_components import _placeholders
from google_cloud_pipeline_components.types.artifact_types import BQMLModel
from kfp.dsl import Artifact
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_evaluate_model_job(
    model: Input[BQMLModel],
    evaluation_metrics: Output[Artifact],
    gcp_resources: OutputPath(str),
    location: str = 'us-central1',
    table_name: str = '',
    query_statement: str = '',
    threshold: float = -1.0,
    query_parameters: List[str] = [],
    job_configuration_query: Dict[str, str] = {},
    labels: Dict[str, str] = {},
    encryption_spec_key_name: str = '',
    project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
):
  # fmt: off
  """Launch a BigQuery evaluate model job and waits for it to finish.

  Args:
      location: Location to run the BigQuery model evaluation 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 evaluation. For more details, see https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#eval_model_name
      table_name: BigQuery table id of the input table that contains the evaluation data, as in ML.EVALUATE(MODEL model_name[, {TABLE table_name | (query_statement)}] For more details, see https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#eval_table_name
      query_statement: Query statement string used to generate the evaluation data, as in ML.EVALUATE(MODEL model_name[, {TABLE table_name | (query_statement)}] For more details, see https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#eval_query_statement
      threshold: A custom threshold for the binary-class classification model to be used for evaluation. The default value is 0.5. The threshold value that is supplied must be of type STRUCT. https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#eval_threshold
      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.
      project: Project to run BigQuery model evaluation job. Defaults to the project in which the PipelineJob is run.

  Returns:
      destination_table: Describes the table where the model prediction results should be stored. This property must be set for large results that exceed the maximum response size. For queries that produce anonymous (cached) results, this field will be populated by BigQuery.
      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.evaluate_model.launcher',
      ],
      args=[
          '--type',
          'BigqueryEvaluateModelJob',
          '--project',
          project,
          '--location',
          location,
          '--model_name',
          ConcatPlaceholder([
              model.metadata['projectId'],
              '.',
              model.metadata['datasetId'],
              '.',
              model.metadata['modelId'],
          ]),
          '--table_name',
          table_name,
          '--query_statement',
          query_statement,
          '--threshold',
          threshold,
          '--payload',
          ConcatPlaceholder([
              '{',
              '"configuration": {',
              '"query": ',
              job_configuration_query,
              ', "labels": ',
              labels,
              '}',
              '}',
          ]),
          '--job_configuration_query_override',
          ConcatPlaceholder([
              '{',
              '"query_parameters": ',
              query_parameters,
              ', "destination_encryption_configuration": {',
              '"kmsKeyName": "',
              encryption_spec_key_name,
              '"}',
              '}',
          ]),
          '--gcp_resources',
          gcp_resources,
          '--executor_input',
          '{{$}}',
      ],
  )
