from __future__ import annotations

import json
import struct
import sys
from pathlib import Path

import gguf
import numpy as np
from sentencepiece import SentencePieceProcessor
from transformers import AutoModelForCausalLM, AutoTokenizer


if not 2 <= len(sys.argv) < 4:
    print("Usage: {} dir-model [ftype]\n".format(Path(__file__).name))
    print("  ftype == 0 -> float32")
    print("  ftype == 1 -> float16")
    sys.exit(1)

# output in the same directory as the model
dir_model = Path(sys.argv[1])

# possible data types
#   ftype == 0 -> float32
#   ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]

ftype = 1
if len(sys.argv) > 2:
    ftype = int(sys.argv[2])
    if ftype < 0 or ftype > 1:
        print("Invalid ftype: " + str(ftype))
        sys.exit(1)

fname_out = dir_model / ("ggml-replit-code-v1-3b-" + ftype_str[ftype] + ".gguf")


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

print("gguf: get model metadata")

model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True)
config = model.config
#print(model)

block_count = config.n_layers
gguf_writer.add_name("Replit")
gguf_writer.add_context_length(config.max_seq_len)
gguf_writer.add_embedding_length(config.d_model)
gguf_writer.add_block_count(block_count)
gguf_writer.add_head_count(config.n_heads)
gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max)
gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon)
gguf_writer.add_file_type(ftype)

clip_qkv = config.attn_config.clip_qkv
if clip_qkv is not None:
    gguf_writer.add_clamp_kqv(clip_qkv)

print("gguf: get sentencepiece tokenizer vocab")

tokenizer = SentencePieceProcessor(str(dir_model / "spiece.model"))
#print(tokenizer.encode('I believe the meaning of life is'))

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

for i in range(tokenizer.vocab_size()):
    tokens.append(tokenizer.id_to_piece(i).encode('utf-8'))
    scores.append(tokenizer.get_score(i))

    toktype = gguf.TokenType.NORMAL
    if tokenizer.is_unknown(i):
        toktype = gguf.TokenType.UNKNOWN
    elif tokenizer.is_control(i):
        toktype = gguf.TokenType.CONTROL
    elif tokenizer.is_unused(i):
        toktype = gguf.TokenType.UNUSED
    elif tokenizer.is_byte(i):
        toktype = gguf.TokenType.BYTE

    toktypes.append(toktype)

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

special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
special_vocab.add_to_gguf(gguf_writer)

print("gguf: get tensor metadata")

tensor_map = gguf.get_tensor_name_map(ARCH, block_count)

list_vars = model.state_dict()
for name in list_vars.keys():
    print(name, list_vars[name].shape, list_vars[name].dtype)

print(config)

for name in list_vars.keys():
    data = list_vars[name].squeeze().numpy()
    print("Processing variable:", name, "with shape:", data.shape)

    n_dims = len(data.shape)

    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype_cur = 0
    if ftype == 1 and name[-7:] == ".weight" and n_dims == 2:
        print("  Converting to float16")
        data = data.astype(np.float16)
        ftype_cur = 1
    elif ftype == 1 or data.dtype != np.float32:
        print("  Converting to float32")
        data = data.astype(np.float32)
        ftype_cur = 0

    # 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()

    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()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()

gguf_writer.close()

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