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"""Example workflow pipeline script for abalone pipeline.

                                               . -RegisterModel
                                              .
    Process-> Train -> Evaluate -> Condition .
                                              .
                                               . -(stop)

Implements a get_pipeline(**kwargs) method.
"""
import os

import boto3
import logging
import sagemaker
import sagemaker.session

from sagemaker.estimator import Estimator
from sagemaker.inputs import TrainingInput
from sagemaker.model_metrics import (
    MetricsSource,
    ModelMetrics,
)
from sagemaker.processing import (
    ProcessingInput,
    ProcessingOutput,
    ScriptProcessor,
)
from sagemaker.sklearn.processing import SKLearnProcessor
from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo
from sagemaker.workflow.condition_step import (
    ConditionStep,
)
from sagemaker.workflow.functions import (
    JsonGet,
)
from sagemaker.workflow.parameters import (
    ParameterInteger,
    ParameterString,
)
from sagemaker.workflow.pipeline import Pipeline
from sagemaker.workflow.properties import PropertyFile
from sagemaker.workflow.steps import (
    ProcessingStep,
    TrainingStep,
)
from sagemaker.workflow.step_collections import RegisterModel

from botocore.exceptions import ClientError
from sagemaker.network import NetworkConfig


# BASE_DIR = os.path.dirname(os.path.realpath(__file__))

logger = logging.getLogger(__name__)


def get_session(region, default_bucket):
    """Gets the sagemaker session based on the region.

    Args:
        region: the aws region to start the session
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        `sagemaker.session.Session instance
    """

    boto_session = boto3.Session(region_name=region)

    sagemaker_client = boto_session.client("sagemaker")
    runtime_client = boto_session.client("sagemaker-runtime")
    session = sagemaker.session.Session(
        boto_session=boto_session,
        sagemaker_client=sagemaker_client,
        sagemaker_runtime_client=runtime_client,
        default_bucket=default_bucket,
    )

    return session


def get_pipeline(
    region,
    role=None,
    default_bucket=None,
    bucket_kms_id=None,
    model_package_group_name="AbalonePackageGroup",
    pipeline_name="AbalonePipeline",
    base_job_prefix="Abalone",
    project_id="SageMakerProjectId",
    git_hash="",
    ecr_repo_uri="",
    default_input_data="",
):
    """Gets a SageMaker ML Pipeline instance working with on abalone data.

    Args:
        region: AWS region to create and run the pipeline.
        role: IAM role to create and run steps and pipeline.
        default_bucket: the bucket to use for storing the artifacts
        git_hash: the hash id of the current commit. Used to determine which docker image version to use
        ecr_repo_uri: uri of the ECR repository used by this project
        default_input_data: s3 location with data to be used by pipeline

    Returns:
        an instance of a pipeline
    """

    sagemaker_session = get_session(region, default_bucket)
    if role is None:
        role = sagemaker.session.get_execution_role(sagemaker_session)

    # parameters for pipeline execution
    processing_instance_count = ParameterInteger(name="ProcessingInstanceCount", default_value=1)
    processing_instance_type = ParameterString(name="ProcessingInstanceType", default_value="ml.m5.xlarge")
    training_instance_type = ParameterString(name="TrainingInstanceType", default_value="ml.m5.xlarge")
    inference_instance_type = ParameterString(name="InferenceInstanceType", default_value="ml.m5.xlarge")
    model_approval_status = ParameterString(name="ModelApprovalStatus", default_value="PendingManualApproval")
    input_data = ParameterString(
        name="InputDataUrl",
        default_value=default_input_data,
    )
    processing_image_uri = f"{ecr_repo_uri}:processing-{git_hash}"
    training_image_uri = f"{ecr_repo_uri}:training-{git_hash}"
    inference_image_uri = f"{ecr_repo_uri}:training-{git_hash}"

    # network_config = NetworkConfig(
    #     enable_network_isolation=True,
    #     security_group_ids=security_group_ids,
    #     subnets=subnets,
    #     encrypt_inter_container_traffic=True,
    # )

    script_processor = ScriptProcessor(
        image_uri=processing_image_uri,
        instance_type=processing_instance_type,
        instance_count=processing_instance_count,
        base_job_name=f"{base_job_prefix}/byoc-abalone-preprocess",
        command=["Rscript"],
        sagemaker_session=sagemaker_session,
        role=role,
        output_kms_key=bucket_kms_id,
    )
    step_process = ProcessingStep(
        name="PreprocessAbaloneData",
        processor=script_processor,
        inputs=[ProcessingInput(source=input_data, destination="/opt/ml/processing/input")],
        outputs=[
            ProcessingOutput(output_name="train", source="/opt/ml/processing/output/train"),
            ProcessingOutput(output_name="validation", source="/opt/ml/processing/output/validation"),
            ProcessingOutput(output_name="test", source="/opt/ml/processing/output/test"),
        ],
        code="source_scripts/preprocessing/prepare_abalone_data/preprocessing.R",  # we must figure out this path to get it from step_source directory
    )

    # training step for generating model artifacts
    model_path = f"s3://{default_bucket}/{base_job_prefix}/AbaloneTrain"

    train_estimator = Estimator(
        image_uri=training_image_uri,
        instance_type=training_instance_type,
        instance_count=1,
        output_path=model_path,
        base_job_name=f"{base_job_prefix}/abalone-train",
        sagemaker_session=sagemaker_session,
        role=role,
        output_kms_key=bucket_kms_id,
        source_dir="source_scripts/training/",
        entry_point="train.R",
        metric_definitions=[{"Name": "rmse-validation", "Regex": "Calculated validation RMSE: ([0-9.]+);.*$"}],
    )

    step_train = TrainingStep(
        name="TrainAbaloneModel",
        estimator=train_estimator,
        inputs={
            "train": TrainingInput(
                s3_data=step_process.properties.ProcessingOutputConfig.Outputs["train"].S3Output.S3Uri,
                content_type="text/csv",
            ),
            "validation": TrainingInput(
                s3_data=step_process.properties.ProcessingOutputConfig.Outputs["validation"].S3Output.S3Uri,
                content_type="text/csv",
            ),
        },
    )

    # processing step for evaluation
    script_eval = ScriptProcessor(
        image_uri=training_image_uri,
        command=["Rscript"],
        instance_type=processing_instance_type,
        instance_count=1,
        base_job_name=f"{base_job_prefix}/script-abalone-eval",
        sagemaker_session=sagemaker_session,
        role=role,
        output_kms_key=bucket_kms_id,
    )
    evaluation_report = PropertyFile(
        name="AbaloneEvaluationReport",
        output_name="evaluation",
        path="evaluation.json",
    )
    step_eval = ProcessingStep(
        name="EvaluateAbaloneModel",
        processor=script_eval,
        inputs=[
            ProcessingInput(
                source=step_train.properties.ModelArtifacts.S3ModelArtifacts,
                destination="/opt/ml/processing/model",
            ),
            ProcessingInput(
                source=step_process.properties.ProcessingOutputConfig.Outputs["test"].S3Output.S3Uri,
                destination="/opt/ml/processing/test",
            ),
        ],
        outputs=[
            ProcessingOutput(output_name="evaluation", source="/opt/ml/processing/evaluation"),
        ],
        code="source_scripts/evaluate/evaluation.R",
        property_files=[evaluation_report],
    )

    # register model step that will be conditionally executed
    model_metrics = ModelMetrics(
        model_statistics=MetricsSource(
            s3_uri="{}/evaluation.json".format(
                step_eval.arguments["ProcessingOutputConfig"]["Outputs"][0]["S3Output"]["S3Uri"]
            ),
            content_type="application/json",
        )
    )

    step_register = RegisterModel(
        name="RegisterAbaloneModel",
        estimator=train_estimator,
        image_uri=inference_image_uri,
        model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
        content_types=["application/json"],
        response_types=["application/json"],
        inference_instances=["ml.t2.medium", "ml.m5.large"],
        transform_instances=["ml.m5.large"],
        model_package_group_name=model_package_group_name,
        approval_status=model_approval_status,
        model_metrics=model_metrics,
    )

    # condition step for evaluating model quality and branching execution
    cond_lte = ConditionLessThanOrEqualTo(
        left=JsonGet(
            step_name=step_eval.name, property_file=evaluation_report, json_path="regression_metrics.rmse.value"
        ),
        right=6.0,
    )
    step_cond = ConditionStep(
        name="CheckMSEAbaloneEvaluation",
        conditions=[cond_lte],
        if_steps=[step_register],
        else_steps=[],
    )

    # pipeline instance
    pipeline = Pipeline(
        name=pipeline_name,
        parameters=[
            processing_instance_type,
            processing_instance_count,
            training_instance_type,
            model_approval_status,
            input_data,
        ],
        steps=[step_process, step_train, step_eval, step_cond],
        sagemaker_session=sagemaker_session,
    )
    return pipeline