#!/usr/bin/python
from itertools import product
import qa
from latencyParse import getLatencyTable
import os, stat, subprocess
import pandas as pd
from extra import log_args_decorator
from autotestConfig import AutotestConfig
import logging

# Configure logging to show informational messages
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

config = None
def get_config():
    global config
    if config is None:
        config = AutotestConfig()
    return config

# Step-by-step:
# 1. Generate all combinations for each encoder
# 2. For each combination, create a GStreamer pipeline string
# 3. Start each pipeline with latency tracing enabled
# 3.1 Monitor CPU, GPU and memory usage during each pipeline run (nah, later, maybe)
# 4. Start latency parsing script after each pipeline and store results in a pandas dataframe:
#   - two key columns: encoder name, parameters string
# 5. Run PSNR check after each pipeline and add results in the dataframe
# 6. Save dataframe to CSV file
class Pipeline:
    def __init__(self):
        self.pipeline = "gst-launch-1.0 -e "
        self.options = ""

    def add_tracing(self):
        self.pipeline = (
            "GST_DEBUG_COLOR_MODE=off " +
            "GST_TRACERS=\"latency(flags=pipeline+element)\" " +
            "GST_DEBUG=GST_TRACER:7 GST_DEBUG_FILE=" + get_config().latency_filename + " " +
            self.pipeline
        )
        return self

    def add_source(self, source):
        self.pipeline += source + " ! clocksync sync-to-first=true ! "
        return self

    def __add_tee(self, encoder):
        pass
        #self.pipeline += "tee name=t t. ! queue max-size-time=5000000000 max-size-bytes=100485760 max-size-buffers=1000 ! filesink location=\"base-autotest.yuv\" "

    def add_encoder(self, encoder, params):
        self.pipeline += get_config().videoconvert[encoder] + " ! "
        self.pipeline += "capsfilter caps=video/x-raw,format=" + get_config().formats[encoder] + " ! "
        #self.__add_tee(encoder)
        self.options += " ".join(params) + " "
        #self.pipeline += "t. ! queue max-size-time=5000000000 max-size-bytes=100485760 max-size-buffers=1000 ! "
        self.pipeline += encoder + " "
        self.pipeline += " ".join(params) + " "
        return self
    
    def add_profile(self, profile):
        self.pipeline += "! capsfilter caps=\"video/x-h264,profile=" + profile + "\" ! "
        self.options += "profile=" + profile + " "
        return self
    
    def to_file(self, filename):
        self.pipeline += "h264parse ! mpegtsmux ! filesink location=\"" + filename + "\""
        return self


def makeVideoSrc(videoName):
    return (
        get_config().videosrc["raw"][0] + 
        get_config().videos[videoName] + 
        get_config().videosrc["raw"][1] + 
        get_config().gst_video_info[videoName]
        )


def generateEncoderStrings():
    options = get_config().options
    result = dict()
    for encoder, value in options.items():
        result[encoder] = generate_combinations(value)
    return result


def generate_combinations(config_dict):
    """
    Generate all combinations of values from a configuration dictionary.

    Args:
        config_dict (dict): Dictionary with parameter names as keys and lists of values as values

    Returns:
        list: List of strings containing all parameter combinations
    """
    combinations = []

    keys = list(config_dict.keys())
    value_lists = [config_dict[key] for key in keys]

    for combo in product(*value_lists):
        param_strings = []
        for key, value in zip(keys, combo):
            param_strings.append(f"{key}={value}")

        combinations.append(" ".join(param_strings))

    return combinations

qualityDataframe = pd.DataFrame()
latencyDataframe = pd.DataFrame()

def execPermissions(scriptFile = "to_exec.sh"):
    current_permissions = os.stat(scriptFile).st_mode
    new_permissions = current_permissions | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH
    os.chmod(scriptFile, new_permissions)

def writeToExecFile(contents, file):
    with open(file, "w") as f:
        f.write(str(contents))
        execPermissions(file)

def is_docker(func):
    def wrapper(pipeline):
        script_name = "to_exec.sh"
        for encoder in get_config().with_docker:
            if encoder in pipeline:
                writeToExecFile(pipeline, script_name)
                pipeline = get_config().docker_run_string + f" {script_name}"
            
        func(pipeline)
    return wrapper

def is_sudo(pipeline):
    if pipeline.startswith("sudo"):
            return True
    return False

def passwordAuth(proc):
    password = os.getenv("UAUTH")
    if password is not None:
        proc.communicate(password)

def printLog(file):
    with open(file, "r") as f:
        out = f.read()
        print(out)

@is_docker
@log_args_decorator
def run_pipeline(pipeline):
    logfile = "pipeline-log.txt"
    with open(logfile, "w") as f:
        proc = subprocess.Popen(pipeline, shell=True, 
                              stdin=subprocess.PIPE, stdout=f, 
                              stderr=subprocess.STDOUT, text=True)
        if is_sudo(pipeline):
            passwordAuth(proc)   
        code = proc.wait() 
    printLog(logfile)
    if proc.returncode != 0:
        raise Exception("Pipeline failed, see log for details")

def time_trace(func):
    def wrapper():
        import time
        start_time = time.time()
        func()
        end_time = time.time()
        elapsed_time = end_time - start_time
        print(f"Total execution time: {elapsed_time} seconds")  
    return wrapper

@time_trace
def run_autotest():
    encoders = generateEncoderStrings()
    for encoder, combinations in encoders.items():
        qualityDataframe = pd.DataFrame()
        latencyDataframe = pd.DataFrame()
        for params in combinations:
            for profile in get_config().profiles:
                for videoName, videoPath in get_config().videos.items():
                    for _ in range(get_config().repeats):
                        filename = "autotest-" + encoder + "-" + profile + "-test-" + videoName + ".mp4"
                        pipeline = Pipeline()
                        pipeline = ( 
                            pipeline.add_tracing()
                            .add_source(makeVideoSrc(videoName))
                            .add_encoder(encoder, params.split(" "))
                            .add_profile(profile)
                            .to_file(filename)
                        )
                        print(pipeline.pipeline)
                        try:
                            run_pipeline(pipeline.pipeline)
                        except Exception as e:
                            print(f"Error occurred: {e}")
                            continue
                        psnr_metrics, ssim_metrics = qa.run_quality_check(
                            videoPath, 
                            filename, 
                            get_config().video_info[videoName] + " " + get_config().psnr_check[encoder]
                        ) 
                        dfPsnr = qa.parse_quality_report(psnr_metrics, ssim_metrics)
                        print("-----")
                        dfLatency = getLatencyTable(get_config().latency_filename)
                        columnsQ = pd.MultiIndex.from_tuples(
                            [(encoder, profile, videoName, params, col) for col in dfPsnr.columns]
                        )
                        columnsLatency = pd.MultiIndex.from_tuples(
                            [(encoder, profile, videoName, params, col) for col in dfLatency.columns]
                        )
                        dfPsnr.columns = columnsQ
                        dfLatency.columns = columnsLatency
                        qualityDataframe = pd.concat([qualityDataframe, dfPsnr], axis=1)
                        latencyDataframe = pd.concat([latencyDataframe, dfLatency], axis=1)
                        print("=====")
                        print("Current results:")
                        print(dfPsnr)
                        print(dfLatency)
        qualityDataframe.to_csv(f"qualityResults{encoder}.csv")
        latencyDataframe.to_csv(f"latencyDataframe{encoder}.csv")

if __name__ == "__main__":
    run_autotest()