from typing import Any, Dict, List, Optional, Text

from tfx import v1 as tfx
import tensorflow_model_analysis as tfma

from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2

import absl
import tensorflow_model_analysis as tfma
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.proto import trainer_pb2
from tfx.types import Channel

from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
    LatestBlessedModelResolver,
)

def create_pipeline(
    pipeline_name: Text,
    pipeline_root: Text,
    data_path: Text,
    schema_path: Text,
    modules: Dict[Text, Text],
    train_args: trainer_pb2.TrainArgs,
    eval_args: trainer_pb2.EvalArgs,
    eval_configs: tfma.EvalConfig,
    serving_model_dir: Text,
    metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
    components = []

    input_config = example_gen_pb2.Input(
        splits=[
            example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
            example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
        ]
    )
    example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
    components.append(example_gen)

    statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
    components.append(statistics_gen)

    schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
    components.append(schema_gen)

    transform = Transform(
        examples=example_gen.outputs["examples"],
        schema=schema_gen.outputs["schema"],
        preprocessing_fn=modules["preprocessing_fn"],
    )
    components.append(transform)

    trainer = Trainer(
        run_fn=modules["training_fn"],
        transformed_examples=transform.outputs["transformed_examples"],
        transform_graph=transform.outputs["transform_graph"],
        schema=schema_gen.outputs["schema"],
        train_args=train_args,
        eval_args=eval_args,
    )
    components.append(trainer)

    model_resolver = resolver.Resolver(
        strategy_class=LatestBlessedModelResolver,
        model=Channel(type=Model),
        model_blessing=Channel(type=ModelBlessing),
    ).with_id("latest_blessed_model_resolver")
    components.append(model_resolver)

    evaluator = Evaluator(
        examples=example_gen.outputs["examples"],
        model=trainer.outputs["model"],
        baseline_model=model_resolver.outputs["model"],
        eval_config=eval_configs,
    )
    components.append(evaluator)

    pusher_args = {
        "model": trainer.outputs["model"],
        "model_blessing": evaluator.outputs['blessing'],
        "push_destination": tfx.proto.PushDestination(
            filesystem=tfx.proto.PushDestination.Filesystem(
                base_directory=serving_model_dir
            )
        ),
    }
    pusher = Pusher(**pusher_args)  # pylint: disable=unused-variable
    components.append(pusher)

    return pipeline.Pipeline(
        pipeline_name=pipeline_name,
        pipeline_root=pipeline_root,
        components=components,
        enable_cache=False,
        metadata_connection_config=metadata_connection_config,
    )
