import uuid
from contextlib import contextmanager
from pathlib import Path
from typing import Callable

import pytest
from devtools_testutils import AzureRecordedTestCase
from test_utilities.utils import wait_until_done

from azure.ai.ml import MLClient, load_batch_deployment, load_batch_endpoint, load_environment, load_model
from azure.ai.ml._utils._arm_id_utils import AMLVersionedArmId
from azure.ai.ml.constants._common import AssetTypes
from azure.ai.ml.entities import BatchDeployment, BatchEndpoint
from azure.ai.ml.entities._inputs_outputs import Input
from azure.ai.ml.operations._run_history_constants import JobStatus


@contextmanager
def deployEndpointAndDeployment(client: MLClient, endpoint: BatchEndpoint, deployment: BatchDeployment):
    """Deploys endpoint and deployment, then automatically deletes the endpoint

    :param MLClient client: _description_
    :param BatchEndpoint endpoint: _description_
    :param BatchDeployment deployment: _description_
    :yield _type_: _description_
    """
    endpoint_res = client.batch_endpoints.begin_create_or_update(endpoint)
    endpoint_res = endpoint_res.result()
    deployment_res = client.batch_deployments.begin_create_or_update(deployment)
    deployment_res = deployment_res.result()

    yield (endpoint, deployment)

    client.batch_endpoints.begin_delete(name=endpoint.name)


@pytest.mark.e2etest
@pytest.mark.usefixtures("recorded_test")
@pytest.mark.production_experiences_test
class TestBatchDeployment(AzureRecordedTestCase):
    @pytest.mark.skip(reason="TODO (1546262): Test failing constantly, so disabling it")
    def test_batch_deployment(self, client: MLClient, data_with_2_versions: str) -> None:
        endpoint_yaml = "tests/test_configs/endpoints/batch/batch_endpoint_mlflow_new.yaml"
        deployment_yaml = "tests/test_configs/deployments/batch/batch_deployment_mlflow_new.yaml"
        name = "batch-ept-" + uuid.uuid4().hex[:15]
        endpoint = load_batch_endpoint(endpoint_yaml)
        endpoint.name = name

        deployment = load_batch_deployment(deployment_yaml)
        deployment.endpoint_name = name
        deployment.name = "batch-dpm-" + uuid.uuid4().hex[:15]

        # create an endpoint
        client.batch_endpoints.begin_create_or_update(endpoint)
        # create a deployment
        client.batch_deployments.begin_create_or_update(deployment)

        dep = client.batch_deployments.get(name=deployment.name, endpoint_name=endpoint.name)
        assert dep.name == deployment.name

        deps = client.batch_deployments.list(endpoint_name=endpoint.name)
        assert len(list(deps)) > 0

        endpoint.traffic = {deployment.name: 100}
        client.batch_endpoints.begin_create_or_update(endpoint)

        # Invoke with output configs
        client.batch_endpoints.invoke(
            endpoint_name=endpoint.name,
            deployment_name=deployment.name,
            input=":".join((data_with_2_versions, "1")),
        )
        client.batch_endpoints.begin_delete(name=endpoint.name)

    def test_batch_deployment_dependency_label_resolution(self, client: MLClient, randstr: Callable[[], str], rand_batch_name: Callable[[], str], rand_batch_deployment_name: Callable[[], str]) -> None:
        endpoint_yaml = "./tests/test_configs/endpoints/batch/batch_endpoint_mlflow_new.yaml"
        name = rand_batch_name("name")
        deployment_yaml = "./tests/test_configs/deployments/batch/batch_deployment_mlflow_new.yaml"
        deployment_name = rand_batch_deployment_name("deployment_name")

        environment_name = randstr("environment_name")
        environment_versions = ["foo", "bar"]

        for version in environment_versions:
            client.environments.create_or_update(
                load_environment(
                    "./tests/test_configs/environment/environment_conda_inline.yml",
                    params_override=[{"name": environment_name}, {"version": version}],
                )
            )

        model_name = randstr("model_name")
        model_versions = ["1", "2"]

        for version in model_versions:
            client.models.create_or_update(
                load_model(
                    "./tests/test_configs/model/model_minimal.yml",
                    params_override=[{"name": model_name}, {"version": version}],
                )
            )

        endpoint = load_batch_endpoint(endpoint_yaml, params_override=[{"name": name}])
        deployment = load_batch_deployment(
            deployment_yaml,
            params_override=[
                {"endpoint_name": name},
                {"name": deployment_name},
                {"environment": f"azureml:{environment_name}@latest"},
                {"model": f"azureml:{model_name}@latest"},
            ],
        )

        # create an endpoint
        endpoint_res = client.batch_endpoints.begin_create_or_update(endpoint)
        endpoint_res = endpoint_res.result()
        # create a deployment
        deployment_res = client.batch_deployments.begin_create_or_update(deployment)
        deployment_res = deployment_res.result()
        dep = client.batch_deployments.get(name=deployment.name, endpoint_name=endpoint.name)
        client.batch_endpoints.begin_delete(name=endpoint.name)

        resolved_environment = AMLVersionedArmId(dep.environment)
        resolved_model = AMLVersionedArmId(dep.model)
        assert dep.name == deployment.name
        assert (
            resolved_environment.asset_name == environment_name
            and resolved_environment.asset_version == environment_versions[-1]
        )
        assert resolved_model.asset_name == model_name and resolved_model.asset_version == model_versions[-1]

    def test_batch_job_download(self, client: MLClient, tmp_path: Path, rand_batch_name: Callable[[], str], rand_batch_deployment_name: Callable[[], str]) -> str:
        endpoint_name = rand_batch_name("name")
        endpoint = load_batch_endpoint(
            "./tests/test_configs/endpoints/batch/batch_endpoint_mlflow_new.yaml",
            params_override=[{"name": endpoint_name}],
        )
        deployment_name = rand_batch_deployment_name("deployment_name")
        deployment = load_batch_deployment(
            "./tests/test_configs/deployments/batch/batch_deployment_quick.yaml",
            params_override=[{"endpoint_name": endpoint.name}, {"name": deployment_name}],
        )
        endpoint.traffic = {deployment.name: 100}

        with deployEndpointAndDeployment(client, endpoint, deployment):
            batchjobresource = client.batch_endpoints.invoke(
                endpoint_name=endpoint.name,
                deployment_name=deployment.name,
                input=Input(
                    path="https://azuremlexamples.blob.core.windows.net/datasets/iris.csv", type=AssetTypes.URI_FILE
                ),
            )

            batchjob = client.jobs.get(batchjobresource.name)

            # Adding a timeout value of 5 minutes to avoid future scenarios where wait
            # long periods for the test to finish. Instead if the job takes longer
            # than 5 minutes to finish we will make the test fail
            job_status = wait_until_done(job=batchjob, client=client, timeout=300)

            # Check if the job was canceled due to a timeout.
            # If the job's status is CANCELED, we will make the test fail
            if job_status == JobStatus.CANCELED:
                pytest.fail(
                    "Job was canceled because it was taking longer than 5 minutes to finish, not execute downloaded artifacts assertion."
                )
            else:
                client.jobs.download(batchjob.name, download_path=tmp_path)

                assert (tmp_path / "predictions.csv").exists(), "Scoring output was not downloaded"
