from jinja2 import Environment, BaseLoader
import glob
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import re
import statistics
import sys

def aggregated_metrics(metrics):
    all_encoding_times              = []
    all_encoding_time_percentages   = []
    all_filesize_percentages        = []
    all_vmaf_scores                 = []

    for k, v in metrics["samples"].items():
        all_encoding_times.append(v["encoding_time"])
        all_filesize_percentages.append(v["filesize_percentage"])
        all_vmaf_scores.append(v["vmaf_score"])
        if "encoding_time_percentage" in v.keys():
            all_encoding_time_percentages.append(v["encoding_time_percentage"])

    metrics["encoding_time"] = {
        "max": max(all_encoding_times),
        "mean": int(sum(all_encoding_times) / len(all_encoding_times)),
        "median": int(statistics.median(all_encoding_times)),
        "min": min(all_encoding_times)
    }
    metrics["filesize_percentage"] = {
        "max": max(all_filesize_percentages),
        "mean": round(sum(all_filesize_percentages) / len(all_filesize_percentages), 2),
        "median": round(statistics.median(all_filesize_percentages), 2),
        "min": min(all_filesize_percentages)
    }
    metrics["vmaf_score"] = {
        "max": max(all_vmaf_scores),
        "mean": round(sum(all_vmaf_scores) / len(all_vmaf_scores), 2),
        "median": round(statistics.median(all_vmaf_scores), 2),
        "min": min(all_vmaf_scores)
    }

    if len(all_encoding_time_percentages) > 0:
        metrics["encoding_time_percentage"] = {
            "max": max(all_encoding_time_percentages),
            "mean": round(sum(all_encoding_time_percentages) / len(all_encoding_time_percentages), 2),
            "median": round(statistics.median(all_encoding_time_percentages), 2),
            "min": min(all_encoding_time_percentages)
        }

    return metrics

def bootstrap_folder_structure():
    folders = [
        get_path_data(),
        get_path_data_encodes(),
        get_path_data_encodes_av1(),
        get_path_data_encodes_hevc(),
        get_path_data_samples(),
        get_path_results(),
        get_path_results_aggregations(),
        get_path_results_candidates(),
        get_path_results_diagrams(),
        get_path_results_encoding_time(),
        get_path_results_metrics(),
        get_path_templates()
    ]

    for f in folders:
       os.makedirs(f, exist_ok=True)

def generate_diagram_bars(data, title, ylabel):
    fig, ax = plt.subplots()
    fig.patch.set_facecolor(get_background_color_for_diagrams())
    ax.set_facecolor(get_background_color_for_diagrams())

    presets = list(data.keys())
    crfs = list(data[presets[0]].keys())

    bar_width = 0.35
    index = np.arange(len(presets) * len(crfs))

    for i, preset in enumerate(presets):
        for j, crf in enumerate(crfs):
            mean_value = data[preset][crf]['mean']
            median_value = data[preset][crf]['median']

            mean_bar = ax.bar(index[i * len(crfs) + j] + 1 * bar_width, mean_value, bar_width, label=f'Mean ({preset} - {crf})', color='#106daa')
            median_bar = ax.bar(index[i * len(crfs) + j] + (1 + 1) * bar_width, median_value, bar_width, label=f'Median ({preset} - {crf})', color='#3B758C')

    # styling
    ax.set_title(title, color='white')
    ax.set_xlabel("Preset - CRF", color='white')
    ax.set_ylabel(ylabel, color='white')
    ax.spines['bottom'].set_color('white')
    ax.spines['top'].set_color('white')
    ax.spines['right'].set_color('white')
    ax.spines['left'].set_color('white')
    ax.xaxis.label.set_color('white')
    ax.yaxis.label.set_color('white')
    ax.tick_params(axis='x', colors='white')
    ax.tick_params(axis='y', colors='white')
    ax.set_xticks(index + bar_width * len(presets) / 2)
    ax.set_xticklabels([f"{preset} - {crf}" for preset in presets for crf in crfs], rotation=45, ha='right')

    # legend
    legend = ax.legend(handles=ax.containers[:2], loc='upper right')
    frame = legend.get_frame()
    frame.set_facecolor(get_background_color_for_diagrams())
    frame.set_edgecolor(get_background_color_for_diagrams())
    legend_texts = ['Mean', 'Median']
    for i, text in enumerate(legend.get_texts()):
        text.set_text(legend_texts[i])
        text.set_color('white')
    
    return fig

def generate_diagram_normal_distribution(values, title, legend_unit, xlabel):
    fig, ax = plt.subplots()

    # add histogram
    hist = ax.hist(values, bins=20, density=True, alpha=0.6, color='#106daa', edgecolor='black')

    # add normal distribution
    mu, sigma = np.mean(values), np.std(values)
    xmin, xmax = plt.xlim()
    x = np.linspace(xmin, xmax, 100)
    p = np.exp(-(x - mu)**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))
    ax.plot(x, p, linewidth=2, color='white')

    # add mean
    ax.axvline(x=mu, linestyle='--', label=f'Mean: {int(round(mu, 0))} {legend_unit}', color='red')

    # add median
    median_value = np.median(values)
    ax.axvline(x=median_value, linestyle='--', label=f'Median: {int(round(median_value, 0))} {legend_unit}', color='blue')

    # add min and max value
    min_value = min(values)
    max_value = max(values)
    ax.axvline(x=min_value, linestyle='--', label=f'Min: {int(round(min_value, 0))} {legend_unit}', color='green')
    ax.axvline(x=max_value, linestyle='--', label=f'Max: {int(round(max_value, 0))} {legend_unit}', color='green')

    # title and labels
    ax.set_title(title, color='white')
    ax.set_xlabel(xlabel, color='white')
    ax.set_ylabel("Frequency", color='white')

    # legend
    legend = ax.legend()
    frame = legend.get_frame()
    frame.set_facecolor(get_background_color_for_diagrams())
    frame.set_edgecolor(get_background_color_for_diagrams())
    for text in legend.get_texts():
        text.set_color('white')

    # styling
    fig.patch.set_facecolor(get_background_color_for_diagrams())
    ax.set_facecolor(get_background_color_for_diagrams())
    ax.spines['bottom'].set_color('white')
    ax.spines['top'].set_color('white')
    ax.spines['right'].set_color('white')
    ax.spines['left'].set_color('white')
    ax.xaxis.label.set_color('white')
    ax.yaxis.label.set_color('white')
    ax.tick_params(axis='x', colors='white')
    ax.tick_params(axis='y', colors='white')

    return fig

def get_all_diagrams():
    samples = glob.glob(os.path.join(get_path_results_diagrams(), "*.png"))
    samples.sort()
    return samples

def get_all_encoded_files_av1():
    encodes = glob.glob(os.path.join(get_path_data_encodes_av1(), "*.mkv"))
    encodes.sort()
    return encodes

def get_all_encoded_files_hevc():
    encodes = glob.glob(os.path.join(get_path_data_encodes_hevc(), "*.mkv"))
    encodes.sort()
    return encodes

def get_all_sample_files():
    samples = glob.glob(os.path.join(get_path_data_samples(), "*.mkv"))
    samples.sort()
    return samples

def get_background_color_for_diagrams():
    return "#11171f"

def get_benchmark_command(f):
    filenameEncode = os.path.splitext(os.path.basename(f))[0]
    
    cmd_parts = [
        'ffmpeg',
        f'-i "{f}"',
        f'-i "{get_sample_file_of_encode(filenameEncode)}"',
        '-lavfi',
        f'libvmaf="n_threads={os.cpu_count()}:log_fmt=json:log_path={get_filepath_metric_log(f)}"',
        '-f',
        'null',
        '-'
    ]
    
    return " ".join(cmd_parts)

def get_diagrams_hevc():
    diagrams = {}

    for path_to_diagram in get_all_diagrams():
        filename = os.path.splitext(os.path.basename(path_to_diagram))[0]
        if not "hevc" in filename:
            continue
        if "encoding_time" in filename:
            diagrams["encoding_time"] = f"{filename}.png"
            continue
        if "filesize_percentage" in filename:
            diagrams["filesize_percentage"] = f"{filename}.png"
            continue
        if "vmaf_score" in filename:
            diagrams["vmaf_score"] = f"{filename}.png"
            continue

    return diagrams

def get_filepath_metric_log(f):
    filenameEncode = os.path.splitext(os.path.basename(f))[0]
    return os.path.join(
        get_path_results_metrics(),
        f"{filenameEncode}.json"
    )

def get_filesize_percentage(f):
    filesize_encode = os.path.getsize(f)
    filesize_sample = os.path.getsize(
        get_sample_file_of_encode(
            os.path.splitext(os.path.basename(f))[0]
        )
    )

    return round((filesize_encode / filesize_sample) * 100, 2)

def get_encoding_time_av1(filenameEncode):
    path_to_file_results_encoding_time_av1 = os.path.join(
        get_path_results_encoding_time(),
        get_filename_results_encoding_time_av1()
    )

    encoding_times_av1 = read_dict_from_json_file(
        path_to_file_results_encoding_time_av1
    )

    preset = get_preset_from_encode_filename(filenameEncode)
    if not preset in encoding_times_av1.keys():
        raise ValueError(
            f'Missing preset "{preset}" in file: {path_to_file_results_encoding_time_av1}'
        )

    crf = get_crf_from_encode_filename(filenameEncode)
    if not crf in encoding_times_av1[preset].keys():
        raise ValueError(
            f'Missing crf "{crf}" for preset "{preset}" in file: {path_to_file_results_encoding_time_av1}'
        )

    filename_sample = filenameEncode.split('.')[0]
    if not filename_sample in encoding_times_av1[preset][crf].keys():
        raise ValueError(
            f'Missing sample filename "{filename_sample}" for preset "{preset}" and crf "{crf}" in file: {path_to_file_results_encoding_time_av1}'
        )

    return encoding_times_av1[preset][crf][filename_sample]

def get_encoding_time_hevc(filenameEncode):
    path_to_file_results_encoding_time_hevc = os.path.join(
        get_path_results_encoding_time(),
        get_filename_results_encoding_time_hevc()
    )

    encoding_times_hevc = read_dict_from_json_file(
        path_to_file_results_encoding_time_hevc
    )

    filename_sample = filenameEncode.split('.')[0]
    if not filename_sample in encoding_times_hevc.keys():
        raise ValueError(
            f'Missing key "{filename_sample}" in file: {path_to_file_results_encoding_time_hevc}'
        )
    
    return encoding_times_hevc[filename_sample]

def get_filename_results_aggregations_av1():
    return "av1.json"

def get_filename_results_aggregations_hevc():
    return "hevc.json"

def get_filename_results_candidates_viable():
    return "viable.json"

def get_filename_results_candidates_viable_with_tolerance():
    return "viable_with_tolerance.json"

def get_filename_results_encoding_time_av1():
    return "av1.json"

def get_filename_results_encoding_time_hevc():
    return "hevc.json"

def get_all_values_encoding_time(aggregated_metrics):
    values = []

    for k, v in aggregated_metrics["samples"].items():
        values.append(v["encoding_time"])

    return values

def get_all_values_filesize_percentage(aggregated_metrics):
    values = []

    for k, v in aggregated_metrics["samples"].items():
        values.append(v["filesize_percentage"])

    return values

def get_all_values_vmaf_score(aggregated_metrics):
    values = []

    for k, v in aggregated_metrics["samples"].items():
        values.append(v["vmaf_score"])

    return values

def get_path_data():
    return os.path.join(get_path_project(), "data")

def get_path_data_encodes():
    return os.path.join(get_path_data(), "encodes")

def get_path_data_encodes_av1():
    return os.path.join(get_path_data_encodes(), "av1")

def get_path_data_encodes_hevc():
    return os.path.join(get_path_data_encodes(), "hevc")

def get_path_data_samples():
    return os.path.join(get_path_data(), "samples")

def get_path_project():
    return os.path.dirname(get_path_script())

def get_path_results():
    return os.path.join(get_path_project(), "results")

def get_path_results_aggregations():
    return os.path.join(get_path_results(), "aggregations")

def get_path_results_candidates():
    return os.path.join(get_path_results(), "candidates")

def get_path_results_diagrams():
    return os.path.join(get_path_results(), "diagrams")

def get_path_results_encoding_time():
    return os.path.join(get_path_results(), "encoding_time")

def get_path_results_metrics():
    return os.path.join(get_path_results(), "metrics")

def get_path_script():
    return os.path.realpath(sys.argv[0])

def get_path_templates():
    return os.path.join(get_path_project(), "templates")

def get_crf_from_encode_filename(filename):
    match = re.compile(
        r'.+\.Preset\.\w+\.CRF\.(\d+)'
    ).search(filename)
    
    if match is None:
        raise ValueError('Could not determine crf from filename')
        
    return ''.join(match.groups())

def get_preset_from_encode_filename(filename):
    match = re.compile(
        r'.+\.Preset\.(\w+)\.CRF\..+'
    ).search(filename)
    
    if match is None:
        raise ValueError('Could not determine preset from filename')
        
    return ''.join(match.groups())

def get_sample_file_of_encode(filename):
    match = re.compile(
        r'(sample\d\d)'
    ).search(filename)
    
    if match is None:
        raise ValueError('Could not determine sample from filename')
        
    return os.path.join(
        get_path_data_samples(),
        f"{''.join(match.groups())}.mkv"
    )

def get_sample_number_from_filename(filename):
    match = re.compile(
        r'sample(\d+)\.'
    ).search(filename)
    
    if match is None:
        raise ValueError('Could not determine sample number from filename')
        
    return ''.join(match.groups())

def get_vmaf_score_of_encode(filename):
    metric_file = os.path.join(
        get_path_results_metrics(),
        f"{filename}.json"
    )

    metrics = read_dict_from_json_file(metric_file)
   
    if "frames" in metrics.keys():
        del metrics['frames']
        write_dict_to_json_file(metric_file, metrics)

    if not "pooled_metrics" in metrics.keys():
        return 0.0

    if not "vmaf" in metrics["pooled_metrics"].keys():
        return 0.0

    if not "mean" in metrics["pooled_metrics"]["vmaf"].keys():
        return 0.0

    return round(metrics["pooled_metrics"]["vmaf"]["mean"], 2)

def read_dict_from_json_file(path_to_file):
    if not os.path.exists(path_to_file):
        write_dict_to_json_file(path_to_file, {})
        return {}

    with open(path_to_file, 'r', encoding='UTF-8') as f:
        data_as_dict = json.load(f)
        f.close()
    
    return data_as_dict

def render_template(template_file_path, output_file_path, data):
    with open(template_file_path, "r") as template_file:
        template_content = template_file.read()

    template_env = Environment(loader=BaseLoader())
    template = template_env.from_string(template_content)

    rendered_template = template.render(data)

    with open(output_file_path, "w") as output_file:
        output_file.write(rendered_template)
        
def save_diagram_bars(diagram, path_to_file):
    diagram.set_size_inches(24, 18)
    diagram.savefig(
        path_to_file,
        bbox_inches="tight",
        facecolor=get_background_color_for_diagrams(),
        dpi=300
    )

def save_diagram_normal_distribution(diagram, path_to_file):
    diagram.set_size_inches(12, 9)
    diagram.savefig(
        path_to_file,
        bbox_inches="tight",
        facecolor=get_background_color_for_diagrams(),
        dpi=300
    )

def sort_dict(input_dict):
    sorted_dict = {}
    for key, value in sorted(input_dict.items()):
        if isinstance(value, dict):
            sorted_dict[key] = sort_dict(value)
        else:
            sorted_dict[key] = value
    return sorted_dict
    
def write_dict_to_json_file(path_to_file, data_as_dict):
    with open(path_to_file, 'w', encoding='UTF-8') as f:
        f.write(
            json.dumps(
                sort_dict(data_as_dict),
                indent=4
            )
        )
        f.close()