import gzip
import io
import json
import logging
import sys

from image_prediction.config import Config
from image_prediction.locations import CONFIG_FILE
from image_prediction.pipeline import load_pipeline
from image_prediction.utils.banner import load_banner
from image_prediction.utils.process_wrapping import wrap_in_process
from pyinfra import config
from pyinfra.queue.queue_manager import QueueManager
from pyinfra.storage.storage import get_storage

PYINFRA_CONFIG = config.get_config()
IMAGE_CONFIG = Config(CONFIG_FILE)

# logging.getLogger().addHandler(logging.StreamHandler())
# logger = logging.getLogger("main")
# logger.setLevel(PYINFRA_CONFIG.logging_level_root)

LOG_FORMAT = "%(asctime)s [%(levelname)s] - [%(filename)s -> %(funcName)s() -> %(lineno)s] : %(message)s"
DATE_FORMAT = "%Y-%m-%d %H:%M:%S"

logger = logging.getLogger("main")
logger.setLevel("DEBUG")

stream_handler = logging.StreamHandler(sys.stdout)
stream_handler_format = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
stream_handler.setFormatter(stream_handler_format)

logger.addHandler(stream_handler)


# A component of the callback (probably tensorflow) does not release allocated memory (see RED-4206).
# See: https://stackoverflow.com/questions/39758094/clearing-tensorflow-gpu-memory-after-model-execution
# Workaround: Manage Memory with the operating system, by wrapping the callback in a sub-process.
# FIXME: Find more fine-grained solution or if the problem occurs persistently for python services,
# FIXME: move the process wrapper to a general module (see RED-4929).
@wrap_in_process
def process_request(request_message):
    dossier_id = request_message["dossierId"]
    file_id = request_message["fileId"]
    target_file_name = f"{dossier_id}/{file_id}.{request_message['targetFileExtension']}"
    response_file_name = f"{dossier_id}/{file_id}.{request_message['responseFileExtension']}"
    figure_data_file_name = f"{dossier_id}/{file_id}.FIGURE.json.gz"
    logger.info("Processing file %s w/ file_id=%s, and daossier_id=%s", target_file_name, file_id, dossier_id)

    bucket = PYINFRA_CONFIG.storage_bucket
    storage = get_storage(PYINFRA_CONFIG)

    logger.debug("loading model pipeline")
    pipeline = load_pipeline(verbose=IMAGE_CONFIG.service.verbose, batch_size=IMAGE_CONFIG.service.batch_size)

    if storage.exists(bucket, target_file_name):
        # should_publish_result = True
        object_bytes = storage.get_object(bucket, target_file_name)
        object_bytes = gzip.decompress(object_bytes)
        classifications = list(pipeline(pdf=object_bytes))

        if storage.exists(bucket, figure_data_file_name):
            metadata_bytes = storage.get_object(bucket, figure_data_file_name)
            metadata_bytes = gzip.decompress(metadata_bytes)
            metadata_per_image = json.load(io.BytesIO(metadata_bytes))["data"]
            classifications_cv = list(pipeline(pdf=object_bytes, metadata_per_image=metadata_per_image))
        else:
            classifications_cv = []

        result = {**request_message, "data": classifications, "dataCV": classifications_cv}
        logger.debug("predictions ready")
        storage_bytes = gzip.compress(json.dumps(result).encode("utf-8"))
        storage.put_object(bucket, response_file_name, storage_bytes)

        return {"dossierId": dossier_id, "fileId": file_id}
    else:
        return None


def main():
    logger.info(load_banner())

    queue_manager = QueueManager(PYINFRA_CONFIG)
    queue_manager.start_consuming(process_request)


if __name__ == "__main__":
    main()