# Copyright 2020 Google LLC
#
# 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
#
#     https://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.

import os
from uuid import uuid4

import cancel_training_pipeline_sample
import create_training_pipeline_tabular_forecasting_sample
import delete_training_pipeline_sample
from google.cloud import aiplatform
import pytest

import helpers

PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
DATASET_ID = "3003302817130610688"  # COVID Dataset
DISPLAY_NAME = f"temp_create_training_pipeline_test_{uuid4()}"
TARGET_COLUMN = "deaths"
PREDICTION_TYPE = "forecasting"


@pytest.fixture
def shared_state():
    state = {}
    yield state


@pytest.fixture(scope="function", autouse=True)
def teardown(shared_state):
    yield

    training_pipeline_id = shared_state["training_pipeline_name"].split("/")[-1]

    # Stop the training pipeline
    cancel_training_pipeline_sample.cancel_training_pipeline_sample(
        project=PROJECT_ID, training_pipeline_id=training_pipeline_id
    )

    client_options = {"api_endpoint": "us-central1-aiplatform.googleapis.com"}
    pipeline_client = aiplatform.gapic.PipelineServiceClient(
        client_options=client_options
    )

    # Waiting for training pipeline to be in CANCELLED state
    helpers.wait_for_job_state(
        get_job_method=pipeline_client.get_training_pipeline,
        name=shared_state["training_pipeline_name"],
    )

    # Delete the training pipeline
    delete_training_pipeline_sample.delete_training_pipeline_sample(
        project=PROJECT_ID, training_pipeline_id=training_pipeline_id
    )


@pytest.mark.skip(reason="https://github.com/googleapis/java-aiplatform/issues/420")
def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state):

    shared_state["cancel_batch_prediction_job_timeout"] = 300

    create_training_pipeline_tabular_forecasting_sample.create_training_pipeline_tabular_forecasting_sample(
        project=PROJECT_ID,
        display_name=DISPLAY_NAME,
        dataset_id=DATASET_ID,
        model_display_name="permanent_tabular_forecasting_model",
        target_column=TARGET_COLUMN,
        time_series_identifier_column="county",
        time_column="date",
        static_columns=["state_name"],
        time_variant_past_only_columns=["deaths"],
        time_variant_past_and_future_columns=["date"],
        forecast_window_end=10,
    )

    out, _ = capsys.readouterr()
    assert "response:" in out

    # Save resource name of the newly created training pipeline
    shared_state["training_pipeline_name"] = helpers.get_name(out)
