#!/usr/bin/env python3
# 7b pth llama --> gguf conversion
# Only models with a single datafile are supported, like 7B
# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model

from __future__ import annotations

import argparse
import json
import os
import struct
import sys
from pathlib import Path
from typing import TYPE_CHECKING, Any

import gguf
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor  # type: ignore[import]

if TYPE_CHECKING:
    from typing import TypeAlias

NDArray: TypeAlias = 'np.ndarray[Any, Any]'


def count_model_parts(dir_model: Path) -> int:
    num_parts = 0
    for filename in os.listdir(dir_model):
        if filename.startswith("consolidated."):
            num_parts += 1

    if num_parts > 0:
        print("gguf: found " + str(num_parts) + " model parts")
    return num_parts


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file")
    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
    return parser.parse_args()

args = parse_args()

dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
    print(f'Error: {args.model} is not a directory', file = sys.stderr)
    sys.exit(1)

# possible tensor data types
#   ftype == 0 -> float32
#   ftype == 1 -> float16

# map from ftype to string
ftype_str = ["f32", "f16"]

if args.outfile is not None:
    fname_out = args.outfile
else:
    # output in the same directory as the model by default
    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'

print("gguf: loading model "+dir_model.name)

with open(dir_model / "config.json", "r", encoding="utf-8") as f:
    hparams = json.load(f)

if hparams["architectures"][0] != "LlamaForCausalLM":
    print("Model architecture not supported: " + hparams["architectures"][0])
    sys.exit()

# get number of model parts
num_parts = count_model_parts(dir_model)

if num_parts > 1:
    print("gguf: Only models with a single datafile are supported.")

    sys.exit()

ARCH=gguf.MODEL_ARCH.LLAMA
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])


print("gguf: get model metadata")

block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]

if "num_key_value_heads" in hparams:
    head_count_kv = hparams["num_key_value_heads"]
else:
    head_count_kv = head_count

if "_name_or_path" in hparams:
    hf_repo = hparams["_name_or_path"]
else:
    hf_repo = ""

if "max_sequence_length" in hparams:
    ctx_length = hparams["max_sequence_length"]
elif "max_position_embeddings" in hparams:
    ctx_length = hparams["max_position_embeddings"]
else:
    print("gguf: can not find ctx length parameter.")

    sys.exit()


gguf_writer.add_name(dir_model.name)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])

if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
    if "type" in hparams["rope_scaling"]:
        if hparams["rope_scaling"]["type"] == "linear":
            gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])


# TOKENIZATION

print("gguf: get tokenizer metadata")

tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []

tokenizer_model_file = dir_model / 'tokenizer.model'
if not tokenizer_model_file.is_file():
    print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
    sys.exit(1)

# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab and scores")

tokenizer = SentencePieceProcessor(str(tokenizer_model_file))

for i in range(tokenizer.vocab_size()):
    text: bytes
    score: float

    piece = tokenizer.id_to_piece(i)
    text = piece.encode("utf-8")
    score = tokenizer.get_score(i)

    toktype = 1  # defualt to normal token type
    if tokenizer.is_unknown(i):
        toktype = 2
    if tokenizer.is_control(i):
        toktype = 3

    # toktype = 4 is user-defined = tokens from added_tokens.json

    if tokenizer.is_unused(i):
        toktype = 5
    if tokenizer.is_byte(i):
        toktype = 6

    tokens.append(text)
    scores.append(score)
    toktypes.append(toktype)

added_tokens_file = dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
    with open(added_tokens_file, "r", encoding="utf-8") as f:
        addtokens_json = json.load(f)

        print("gguf: get added tokens")

        for key in addtokens_json:
            tokens.append( key.encode("utf-8") )
            scores.append(-1000.0)
            toktypes.append(4) # user-defined token type

gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)

special_vocab = gguf.SpecialVocab(dir_model)
special_vocab.add_to_gguf(gguf_writer)

# TENSORS

tensor_map = gguf.get_tensor_name_map(ARCH,block_count)

# tensor info
print("gguf: get tensor metadata")

part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))

for part_name in part_names:
    if args.vocab_only:
        break
    print("gguf: loading model part '" + part_name + "'")
    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")

    for name in model_part.keys():
        data = model_part[name]

        # we don't need these
        if name == "rope.freqs":
            continue

        old_dtype = data.dtype

        # convert any unsupported data types to float32
        if data.dtype != torch.float16 and data.dtype != torch.float32:
            data = data.to(torch.float32)

        data = data.squeeze().numpy()

        # map tensor names
        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
        if new_name is None:
            print("Can not map tensor '" + name + "'")
            sys.exit()

        n_dims = len(data.shape)
        data_dtype = data.dtype

        # if f32 desired, convert any float16 to float32
        if ftype == 0 and data_dtype == np.float16:
            data = data.astype(np.float32)

        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
        if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
            data = data.astype(np.float32)

        # if f16 desired, convert any float32 2-dim weight tensors to float16
        if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
            data = data.astype(np.float16)

        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))

        gguf_writer.add_tensor(new_name, data)


print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
if not args.vocab_only:
    print("gguf: write tensors")
    gguf_writer.write_tensors_to_file()

gguf_writer.close()

print(f"gguf: model successfully exported to '{fname_out}'")
print("")