import os
import subprocess

import yaml

YAML_PATH = "./config.yaml"
PIPE_PATH = "./tmp/pipe"


class Dumper(yaml.Dumper):
    def increase_indent(self, flow=False, *args, **kwargs):
        return super().increase_indent(flow=flow, indentless=False)


# read scanners from yaml file
# return a list of scanners
def read_scanners(path):
    scanners = []
    with open(path, "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        scanners = config.get("detectors", [])
    return scanners


# convert a list of scanners to yaml file
def write_scanners(scanners):
    print(scanners)
    with open(YAML_PATH, "r+") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        if config:
            config["detectors"] = scanners
            # save scanners to detectors in yaml
            yaml.dump(config, f, Dumper=Dumper)


# read model_type from yaml file
def read_inference_type(path):
    inference_type = ""
    with open(path, "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        inference_type = config.get("inference_type", "")
    return inference_type


# write model_type to yaml file
def write_inference_type(use_inference):
    with open(YAML_PATH, "r+") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
    if use_inference:
        config["inference_type"] = "hf_inference_api"
    else:
        config["inference_type"] = "hf_pipeline"
    # save inference_type to inference_type in yaml
    yaml.dump(config, f, Dumper=Dumper)


# read column mapping from yaml file
def read_column_mapping(path):
    column_mapping = {}
    with open(path, "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
        if config:
            column_mapping = config.get("column_mapping", dict())
    return column_mapping


# write column mapping to yaml file
def write_column_mapping(mapping):
    with open(YAML_PATH, "r") as f:
        config = yaml.load(f, Loader=yaml.FullLoader)
    if config is None:
        return
    if mapping is None and "column_mapping" in config.keys():
        del config["column_mapping"]
    else:
        config["column_mapping"] = mapping
    with open(YAML_PATH, "w") as f:
        # save column_mapping to column_mapping in yaml
        yaml.dump(config, f, Dumper=Dumper)


# convert column mapping dataframe to json
def convert_column_mapping_to_json(df, label=""):
    column_mapping = {}
    column_mapping[label] = []
    for _, row in df.iterrows():
        column_mapping[label].append(row.tolist())
    return column_mapping


def get_logs_file(uid):
    try:
        file = open(f"./tmp/{uid}_log", "r")
        return file.read()
    except Exception:
        return "Log file does not exist"


def write_log_to_user_file(id, log):
    with open(f"./tmp/{id}_log", "a") as f:
        f.write(log)


def save_job_to_pipe(id, job, lock):
    if not os.path.exists("./tmp"):
        os.makedirs("./tmp")
    job = [str(i) for i in job]
    job = ",".join(job)
    print(job)
    with lock:
        with open(PIPE_PATH, "a") as f:
            # write each element in job
            f.write(f"{id}@{job}\n")


def pop_job_from_pipe():
    if not os.path.exists(PIPE_PATH):
        return
    with open(PIPE_PATH, "r") as f:
        job = f.readline().strip()
        remaining = f.readlines()
        f.close()
    print(job, remaining, ">>>>")
    with open(PIPE_PATH, "w") as f:
        f.write("\n".join(remaining))
        f.close()
    if len(job) == 0:
        return
    job_info = job.split("\n")[0].split("@")
    if len(job_info) != 2:
        raise ValueError("Invalid job info: ", job_info)

    write_log_to_user_file(job_info[0], f"Running job {job_info}")
    command = job_info[1].split(",")

    write_log_to_user_file(job_info[0], f"Running command {command}")
    log_file = open(f"./tmp/{job_info[0]}_log", "a")
    subprocess.Popen(
        command,
        cwd=os.path.join(os.path.dirname(os.path.realpath(__file__)), "cicd"),
        stdout=log_file,
        stderr=log_file,
    )