import os
from functools import partial
from itertools import chain, starmap

from funcy import rcompose, juxt, first, compose, second, chunks, curry

from image_prediction.config import CONFIG
from image_prediction.default_objects import get_formatter, get_mlflow_model_loader, get_image_classifier, get_extractor
from image_prediction.locations import MLRUNS_DIR
from image_prediction.utils.generic import lift, starlift

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"


def load_pipeline(**kwargs):
    model_loader = get_mlflow_model_loader(MLRUNS_DIR)
    model_identifier = CONFIG.service.run_id

    pipeline = Pipeline(model_loader, model_identifier, progress_message="Processing document", **kwargs)

    return pipeline


class Pipeline:
    def __init__(self, model_loader, model_identifier, batch_size=16, **kwargs):
        extractor = get_extractor(**kwargs)
        batcher = compose(lift(list), partial(chunks, batch_size))
        classifier = get_image_classifier(model_loader, model_identifier)

        left = compose(classifier, lift(first))
        right = lift(second)

        formatter = get_formatter()

        def join_prediction_and_metadata(prd, mdt):
            return {"classification": prd, **mdt}


        # --------
        # -- -- -- --
        # == == == ==
        # -- -- -- --
        # --------
        # --------

        def inspect(x):
            x = list(x)
            import IPython
            IPython.embed()
            return x

        self.pipe = rcompose(
            extractor,
            batcher,
            lift(juxt(left, right)),
            starlift(zip),
            lift(starlift(join_prediction_and_metadata)),
            chain.from_iterable,
            formatter,
        )

    def __call__(self, pdf: bytes, page_range: range = None):
        yield from self.pipe(pdf, page_range=page_range)