import os
import sys
from urllib.parse import urlparse

import mlflow
import mlflow.sklearn
import numpy as np
import pandas as pd
import pytest
from mlflow.models import infer_signature
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

from metadata.generated.schema.api.services.createMlModelService import (
    CreateMlModelServiceRequest,
)
from metadata.generated.schema.entity.data.mlmodel import MlModel
from metadata.generated.schema.entity.services.connections.mlmodel.mlflowConnection import (
    MlflowConnection,
)
from metadata.generated.schema.entity.services.mlmodelService import (
    MlModelConnection,
    MlModelService,
    MlModelServiceType,
)
from metadata.generated.schema.metadataIngestion.mlmodelServiceMetadataPipeline import (
    MlModelServiceMetadataPipeline,
)
from metadata.generated.schema.metadataIngestion.workflow import (
    OpenMetadataWorkflowConfig,
    Sink,
    Source,
    SourceConfig,
    WorkflowConfig,
)
from metadata.workflow.metadata import MetadataWorkflow

from ..integration_base import int_admin_ometa

MODEL_HYPERPARAMS = {
    "alpha": {"name": "alpha", "value": "0.5", "description": None},
    "l1_ratio": {"name": "l1_ratio", "value": "1.0", "description": None},
}

MODEL_NAME = "ElasticnetWineModel"

SERVICE_NAME = "docker_test_mlflow"


def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_squared_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2


@pytest.fixture(scope="module")
def create_data(mlflow_environment):
    mlflow_uri = f"http://localhost:{mlflow_environment.mlflow_configs.exposed_port}"
    mlflow.set_tracking_uri(mlflow_uri)

    os.environ["AWS_ACCESS_KEY_ID"] = "minio"
    os.environ["AWS_SECRET_ACCESS_KEY"] = "password"
    os.environ[
        "MLFLOW_S3_ENDPOINT_URL"
    ] = f"http://localhost:{mlflow_environment.minio_configs.exposed_port}"

    np.random.seed(40)

    # Read the wine-quality csv file from the URL
    csv_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
    data = pd.read_csv(csv_url, sep=";")

    # Split the data into training and test sets. (0.75, 0.25) split.
    train, test = train_test_split(data)

    # The predicted column is "quality" which is a scalar from [3, 9]
    train_x = train.drop(["quality"], axis=1)
    test_x = test.drop(["quality"], axis=1)
    train_y = train[["quality"]]
    test_y = test[["quality"]]

    alpha = float(MODEL_HYPERPARAMS["alpha"]["value"])
    l1_ratio = float(MODEL_HYPERPARAMS["l1_ratio"]["value"])

    with mlflow.start_run():
        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
        lr.fit(train_x, train_y)

        signature = infer_signature(train_x, lr.predict(train_x))

        predicted_qualities = lr.predict(test_x)

        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

        mlflow.log_param("alpha", alpha)
        mlflow.log_param("l1_ratio", l1_ratio)
        mlflow.log_metric("rmse", rmse)
        mlflow.log_metric("r2", r2)
        mlflow.log_metric("mae", mae)

        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme

        # Model registry does not work with file store
        if tracking_url_type_store != "file":

            # Register the model
            # There are other ways to use the Model Registry, which depends on the use case,
            # please refer to the doc for more information:
            # https://mlflow.org/docs/latest/model-registry.html#api-workflow
            mlflow.sklearn.log_model(
                lr,
                "model",
                registered_model_name=MODEL_NAME,
                signature=signature,
            )
        else:
            mlflow.sklearn.log_model(lr, "model")


@pytest.fixture(scope="module")
def metadata():
    return int_admin_ometa()


@pytest.fixture(scope="module")
def service(metadata, mlflow_environment):
    service = CreateMlModelServiceRequest(
        name=SERVICE_NAME,
        serviceType=MlModelServiceType.Mlflow,
        connection=MlModelConnection(
            config=MlflowConnection(
                type="Mlflow",
                trackingUri=f"http://localhost:{mlflow_environment.mlflow_configs.exposed_port}",
                registryUri=f"mysql+pymysql://mlflow:password@localhost:{mlflow_environment.mysql_configs.exposed_port}/experiments",
            )
        ),
    )

    service_entity = metadata.create_or_update(data=service)
    yield service_entity
    metadata.delete(MlModelService, service_entity.id, recursive=True, hard_delete=True)


@pytest.fixture(scope="module")
def ingest_mlflow(metadata, service, create_data):
    workflow_config = OpenMetadataWorkflowConfig(
        source=Source(
            type=service.connection.config.type.value.lower(),
            serviceName=service.fullyQualifiedName.__root__,
            serviceConnection=service.connection,
            sourceConfig=SourceConfig(config=MlModelServiceMetadataPipeline()),
        ),
        sink=Sink(type="metadata-rest", config={}),
        workflowConfig=WorkflowConfig(openMetadataServerConfig=metadata.config),
    )

    metadata_ingestion = MetadataWorkflow.create(workflow_config)
    metadata_ingestion.execute()
    return


@pytest.mark.skipif(
    sys.version_info < (3, 9),
    reason="testcontainers Network feature requires python3.9 or higher",
)
def test_mlflow(ingest_mlflow, metadata):
    ml_models = metadata.list_all_entities(entity=MlModel)

    # Check we only get the same amount of models we should have ingested
    filtered_ml_models = [
        ml_model for ml_model in ml_models if ml_model.service.name == SERVICE_NAME
    ]

    assert len(filtered_ml_models) == 1

    # Assert inner information about the model
    model = filtered_ml_models[0]

    # Assert name is as expected
    assert model.name.__root__ == MODEL_NAME

    # Assert HyperParameters are as expected
    assert len(model.mlHyperParameters) == 2

    for i, hp in enumerate(MODEL_HYPERPARAMS.values()):
        assert model.mlHyperParameters[i].name == hp["name"]
        assert model.mlHyperParameters[i].value == hp["value"]
        assert model.mlHyperParameters[i].description == hp["description"]

    # Assert MLStore is as expected
    assert "mlops.local.com" in model.mlStore.storage