# Copyright 2023 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

import uuid

from google.cloud import aiplatform

from google.cloud import storage

from google.cloud.aiplatform.compat.types import pipeline_state

import pytest

from vertexai.preview.language_models import TextGenerationModel

import tuning

_PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")

_LOCATION = "us-central1"

_BUCKET = os.environ["CLOUD_STORAGE_BUCKET"]

def get_model_display_name(tuned_model: TextGenerationModel) -> str:

language_model_tuning_job = tuned_model._job

pipeline_job = language_model_tuning_job._job

return dict(pipeline_job._gca_resource.runtime_config.parameter_values)[

"model_display_name"

]

def upload_to_gcs(bucket: str, name: str, data: str) -> None:

client = storage.Client()

bucket = client.get_bucket(bucket)

blob = bucket.blob(name)

blob.upload_from_string(data)

def download_from_gcs(bucket: str, name: str) -> str:

client = storage.Client()

bucket = client.get_bucket(bucket)

blob = bucket.blob(name)

data = blob.download_as_bytes()

return "\n".join(data.decode().splitlines()[:10])

def delete_from_gcs(bucket: str, name: str) -> None:

client = storage.Client()

bucket = client.get_bucket(bucket)

blob = bucket.blob(name)

blob.delete()

@pytest.fixture(scope="function")

def training_data_filename() -> str:

temp_filename = f"{uuid.uuid4()}.jsonl"

data = download_from_gcs(

"cloud-samples-data", "ai-platform/generative_ai/headline_classification.jsonl"

)

upload_to_gcs(_BUCKET, temp_filename, data)

try:

yield f"gs://{_BUCKET}/{temp_filename}"

finally:

delete_from_gcs(_BUCKET, temp_filename)

def teardown_model(

tuned_model: TextGenerationModel, training_data_filename: str

) -> None:

for tuned_model_name in tuned_model.list_tuned_model_names():

model_registry = aiplatform.models.ModelRegistry(model=tuned_model_name)

if (

training_data_filename

in model_registry.get_version_info("1").model_display_name

):

display_name = model_registry.get_version_info("1").model_display_name

for endpoint in aiplatform.Endpoint.list():

for _ in endpoint.list_models():

if endpoint.display_name == display_name:

endpoint.undeploy_all()

endpoint.delete()

aiplatform.Model(model_registry.model_resource_name).delete()

@pytest.mark.skip("Blocked on b/277959219")

def test_tuning(training_data_filename: str) -> None:

"""Takes approx. 20 minutes."""

tuned_model = tuning.tuning(

training_data=training_data_filename,

project_id=_PROJECT_ID,

location=_LOCATION,

model_display_name="YOUR_TUNED_MODEL",

train_steps=1,

evaluation_dataset=training_data_filename,

tensorboard_instance_name="python-docs-samples-test",

)

try:

assert (

tuned_model._job.status

== pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED

)

finally:

teardown_model(tuned_model, training_data_filename)