# 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 utils
from kfp import dsl


# keep identical to create_custom_training_job_from_component
@dsl.container_component
def custom_training_job(
    project: str,
    display_name: str,
    gcp_resources: dsl.OutputPath(str),
    location: str = 'us-central1',
    worker_pool_specs: List[Dict[str, str]] = [],
    timeout: str = '604800s',
    restart_job_on_worker_restart: bool = False,
    service_account: str = '',
    tensorboard: str = '',
    enable_web_access: bool = False,
    network: str = '',
    reserved_ip_ranges: List[str] = [],
    base_output_directory: str = '',
    labels: Dict[str, str] = {},
    encryption_spec_key_name: str = '',
):
  # fmt: off
  """Launch a Vertex AI `custom training job <https://cloud.google.com/vertex-ai/docs/training/create-custom-job>`_ using the `CustomJob <https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs>`_ API.

  See `Create custom training jobs
  <https://cloud.google.com/vertex-ai/docs/training/create-custom-job>`_ for
  more information.

  Args:
    project: Project to create the custom training job in.
    location: Location for creating the custom training job.
      If not set, default to us-central1.
    display_name: The name of the CustomJob.
    worker_pool_specs: Serialized json spec of the
      worker pools including machine type and Docker image. All worker pools
      except the first one are optional and can be skipped by providing an
      empty value. See `more information <https://cloud.google.com/vertex-ai/docs/reference/rest/v1/CustomJobSpec#WorkerPoolSpec>`_.
    timeout: The maximum job running time. The default is 7 days. A duration in
      seconds with up to nine fractional digits, terminated by 's', for example:
      "3.5s".
    restart_job_on_worker_restart: Restarts the entire CustomJob if a worker
      gets restarted. This feature can be used by distributed training jobs that
      are not resilient to workers leaving and joining a job.
    service_account: Sets the default service account for workload run-as
      account. The `service account
      <https://cloud.google.com/vertex-ai/docs/pipelines/configure-project#service-account>`_
      running the pipeline submitting jobs must have act-as permission on this
      run-as account. If unspecified, the Vertex AI Custom Code `Service Agent
      <https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents>`_
      for the CustomJob's project.
    tensorboard: The name of a Vertex AI Tensorboard resource to which this
      CustomJob will upload Tensorboard logs.
    enable_web_access: Whether you want Vertex AI to enable `interactive shell
      access
      <https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell>`_
      to training containers. If ``True``, you can access interactive shells at
      the URIs given by [CustomJob.web_access_uris][].
    network: The full name of the Compute Engine network to which the job should
      be peered. For example, ``projects/12345/global/networks/myVPC``. Format
      is of the form ``projects/{project}/global/networks/{network}``. Where
      ``{project}`` is a project number, as in ``12345``, and ``{network}`` is a
      network name. Private services access must already be configured for the
      network. If left unspecified, the job is not peered with any network.
    reserved_ip_ranges: A list of names for the reserved IP ranges under the VPC
      network that can be used for this job. If set, we will deploy the job
      within the provided IP ranges. Otherwise, the job will be deployed to any
      IP ranges under the provided VPC network.
    base_output_directory: The Cloud Storage location to store the output of
      this CustomJob or HyperparameterTuningJob. See `more information
      <https://cloud.google.com/vertex-ai/docs/reference/rest/v1/GcsDestination>`_.
    labels: The labels with user-defined metadata to organize the CustomJob. See
      `more information <https://goo.gl/xmQnxf>`_.
    encryption_spec_key_name: Customer-managed encryption key options for the
      CustomJob. If this is set, then all resources created by the CustomJob
      will be encrypted with the provided encryption key.

  Returns:
    gcp_resources: Serialized JSON of ``gcp_resources`` `proto <https://github.com/kubeflow/pipelines/tree/master/components/google-cloud/google_cloud_pipeline_components/proto>`_ which tracks the CustomJob.
  """
  # fmt: on
  return utils.build_serverless_customjob_container_spec(
      project=project,
      location=location,
      custom_job_payload={
          'display_name': display_name,
          'job_spec': {
              'worker_pool_specs': worker_pool_specs,
              'scheduling': {
                  'timeout': timeout,
                  'restart_job_on_worker_restart': (
                      restart_job_on_worker_restart
                  ),
              },
              'service_account': service_account,
              'tensorboard': tensorboard,
              'enable_web_access': enable_web_access,
              'network': network,
              'reserved_ip_ranges': reserved_ip_ranges,
              'base_output_directory': {
                  'output_uri_prefix': base_output_directory
              },
          },
          'labels': labels,
          'encryption_spec_key_name': {
              'kms_key_name': encryption_spec_key_name
          },
      },
      gcp_resources=gcp_resources,
  )
