from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
import argparse, os, shutil
import sys
import json
from conversion.tokenize import tokenize
from conversion.measure import embeddings, measure_quant
from conversion.quantize import quant
from conversion.optimize import optimize
from conversion.compile import compile_model
from conversion.qparams import qparams_headoptions
import torch

parser = argparse.ArgumentParser(description = "Convert model to ExLlamaV2")
parser.add_argument("-i", "--in_dir", type = str, help = "Input directory", default = "")
parser.add_argument("-o", "--out_dir", type = str, help = "Output (working) directory")
parser.add_argument("-nr", "--no_resume", action = "store_true", help = "Do not resume an interrupted job (deletes all files in the output directory)")
parser.add_argument("-cf", "--compile_full", type = str, help = "Output folder for compiled model with all config/tokenizer files")
parser.add_argument("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)")
parser.add_argument("-b", "--bits", type = float, default = 4.125, help = "Target bits per weight")
parser.add_argument("-ss", "--shard_size", type = float, help = "Max shard size in MB (default: 8192)", default = 8192)
parser.add_argument("-rs", "--rope_scale", type = float, help = "RoPE scaling factor")
parser.add_argument("-ra", "--rope_alpha", type = float, help = "RoPE alpha value (NTK)")
parser.add_argument("-hb", "--head_bits", type = int, default = 6, help = "Target bits per weight (head layer)")
parser.add_argument("-om", "--output_measurement", type = str, help = "Only perform measurement pass, then save measurement to the specified file")
parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
parser.add_argument("-r", "--dataset_rows", type = int, default = 100, help = "Number of rows to apply from dataset")
parser.add_argument("-mr", "--measurement_rows", type = int, default = 16, help = "Number of rows to apply from dataset when measuring")
parser.add_argument("-l", "--length", type = int, default = 2048, help = "Max no. tokens per sample")
parser.add_argument("-ml", "--measurement_length", type = int, default = 2048, help = "Max no. tokens per sample when measuring")

args = parser.parse_args()

torch.set_printoptions(precision = 7, sci_mode = False, linewidth = 200)

# Check some args

if not args.in_dir:
    print(" ## Please specify input model directory (-i, --in_dir)")
    sys.exit()

if not args.out_dir:
    print(" ## Please specify output/working directory (-o, --out_dir)")
    sys.exit()

if args.length > 2048 or args.measurement_length > 2048:
    print(" !! Warning: calibration rows > 2048 tokens may result in excessive VRAM use")

if not args.head_bits in qparams_headoptions:
    print(f" ## Error: {args.head_bits} is not a supported option for head layer bitrate")
    sys.exit()

if args.output_measurement is not None and args.compile_full is not None:
    print(" ## Conflicting options: --output_measurement and --compile_full")
    sys.exit()

if args.bits < 2 or args.bits > 8:
    print(f" !! Warning: target bitrate {args.bits} will likely not be attainable")

if not os.path.exists(args.out_dir):
    print(f" ## Error: Directory not found: {args.out_dir}")
    sys.exit()

# Create config

config = ExLlamaV2Config()
config.model_dir = args.in_dir
config.qkv_embed = False
config.prepare()

# Tokenizer

tokenizer = ExLlamaV2Tokenizer(config)

# Create job

def save_job():
    global job_file, job
    with open(job_file, "w", encoding = "utf8") as f:
        f.write(json.dumps(job, indent = 4))

job_file = os.path.join(args.out_dir, "job_new.json")

if args.no_resume or not os.path.exists(job_file):

    print(f" -- Beginning new job")
    if len(os.listdir(args.out_dir)) != 0:
        print(f" !! Warning: Output directory is not empty: {args.out_dir}")

        if args.no_resume:
            print(f" !! Cleaning output directory: {args.out_dir}")
            for filename in os.listdir(args.out_dir):
                file_path = os.path.join(args.out_dir, filename)
                if os.path.isfile(file_path):
                    os.unlink(file_path)
                elif os.path.isdir(file_path):
                    shutil.rmtree(file_path)

output_measurement = args.output_measurement
if output_measurement is not None:
    if os.path.isdir(output_measurement):
        output_measurement = os.path.join(output_measurement, "measurement.json")

job = {"in_dir": args.in_dir,
       "out_dir": args.out_dir,
       "cal_dataset": args.cal_dataset,
       "bits": args.bits,
       "dataset_rows": args.dataset_rows,
       "measurement_rows": args.measurement_rows,
       "length": args.length,
       "measurement_length": args.measurement_length,
       "head_bits": args.head_bits,
       "shard_size": args.shard_size if args.shard_size > 0 else 1024 ** 3,  # 1 PB = unlimited,
       "compile_full": args.compile_full,
       "rope_scale": args.rope_scale,
       "rope_alpha": args.rope_alpha,
       "output_measurement": output_measurement,
       "progress": "begin"}

if args.measurement is not None:
    with open(args.measurement, "r", encoding = "utf8") as f:
        imp_measurement = json.load(f)
        job["measurement"] = imp_measurement["measurement"]
        job["last_module_idx"] = imp_measurement["last_module_idx"]
        job["reuse_measurement"] = args.measurement

# Resume existing job

if args.no_resume or not os.path.exists(job_file):
    pass

else:
    print(f" -- Resuming job")
    print(f" !! Note: Overriding options with settings from existing job")

    with open(job_file, "r", encoding = "utf8") as f:
        resume_job = json.load(f)

    # Override keys in existing job
    del resume_job["out_dir"]

    job.update(resume_job)
    if "invalid" in job:
        print(" ** Error: Corrupted job")
        sys.exit()

# Feedback

print(f" -- Input: {job['in_dir']}")
print(f" -- Output: {job['out_dir']}")
if job.get("cal_dataset"):
    print(f" -- Calibration dataset: {job['cal_dataset']}, {job['dataset_rows']} / {job['measurement_rows']} rows, {job['length']} tokens per sample")
else:
    print(f" -- Using default calibration dataset")
if job["output_measurement"] is None:
    print(f" -- Target bits per weight: {job['bits']} (decoder), {job['head_bits']} (head)")
    print(f" -- Max shard size: {job['shard_size']} MB")
else:
    print(f" -- Measurement will be saved to {job['output_measurement']}")
    print(f" !! Conversion script will end after measurement pass")


if job['rope_scale']: print(f" -- RoPE scale: {job['rope_scale']:.2f}")
if job['rope_alpha']: print(f" -- RoPE alpha: {job['rope_alpha']:.2f}")

# Make sure subfolders exist

if job.get("compile_full"):
    print(f" -- Full model will be compiled to: {job['compile_full']}")
    if os.path.exists(job["compile_full"]):
        if not os.path.isdir(job["compile_full"]):
            print(f" ## Error: Output path {job['compile_full']} exists but is not a directory")
            sys.exit()
        if len(os.listdir(job["compile_full"])) > 0:
            print(f" !! Warning: Output path {job['compile_full']} exists but is not empty")

out_tensor_dir = os.path.join(job["out_dir"], "out_tensor")
if not os.path.exists(out_tensor_dir):
    os.makedirs(out_tensor_dir)

# Set scaling for input model

if job["rope_scale"] is not None: config.scale_pos_emb = job["rope_scale"]
if job["rope_alpha"] is not None: config.scale_alpha_value = job["rope_alpha"]

# Create model without loading weights

model = ExLlamaV2(config)
model.load(lazy = True)

# Do the things

save_job()

while True:

    progress = job["progress"]

    if progress == "begin":

        if "reuse_measurement" in job:

            print(f" -- Reusing measurement: {job['reuse_measurement']}")
            job["progress"] = "optimize"
            save_job()

        else:

            print(f" -- Tokenizing samples (measurement)...")
            tokenize(job, save_job, tokenizer, measure = True)
            job["progress"] = "initial_embeddings"
            save_job()

    if progress == "initial_embeddings":

        print(f" -- Token embeddings (measurement)...")
        embeddings(job, save_job, model)
        job["progress"] = "measure_quant"
        save_job()

    if progress == "measure_quant":
        print(f" -- Measuring quantization impact...")

        model.unload()
        config.max_output_len = 16
        model = ExLlamaV2(config)
        model.load(lazy = True)

        status = measure_quant(job, save_job, model)  # capturing the graceful exits
        if status == "interrupted":
            print("Process interrupted. Exiting gracefully.")
            save_job()
            sys.exit(1)  
        if job["output_measurement"] is None:
            job["progress"] = "optimize"
        else:
            job["progress"] = "finished"
        save_job()

        model.unload()
        config.max_output_len = None
        model = ExLlamaV2(config)
        model.load(lazy = True)


    if progress == "optimize":

        print(f" -- Optimizing...")
        optimize(job, save_job, model)
        job["progress"] = "tokens_cal"
        save_job()

    if progress == "tokens_cal":

        print(f" -- Tokenizing samples...")
        tokenize(job, save_job, tokenizer)
        job["progress"] = "embeddings"
        save_job()

    if progress == "embeddings":
        print(f" -- Token embeddings again...")
        embeddings(job, save_job, model)
        job["progress"] = "quant"
        save_job()

    if progress == "quant":

        print(f" -- Quantizing...")
        quant(job, save_job, model)
        job["progress"] = "compile"
        save_job()

    if progress == "compile":

        print(f" -- Compiling output file...")
        compile_model(job, save_job, model)
        job["progress"] = "finished"
        save_job()

    if progress == "finished": break

print(f" -- Finished")