# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.

from typing import Tuple
import os
import sys
import torch
import fire
import time
import json

from pathlib import Path

from fairscale.nn.model_parallel.initialize import initialize_model_parallel

from repositories.llama_int8.llama import ModelArgs, Transformer, Tokenizer, LLaMA


def setup_model_parallel() -> Tuple[int, int]:
    local_rank = int(os.environ.get("LOCAL_RANK", -1))
    world_size = int(os.environ.get("WORLD_SIZE", -1))

    torch.distributed.init_process_group("nccl")
    initialize_model_parallel(world_size)
    torch.cuda.set_device(local_rank)

    # seed must be the same in all processes
    torch.manual_seed(1)
    return local_rank, world_size


def load(
    ckpt_dir: str,
    tokenizer_path: str,
    max_seq_len: int,
    max_batch_size: int,
) -> LLaMA:
    start_time = time.time()
    checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))

    with open(Path(ckpt_dir) / "params.json", "r") as f:
        params = json.loads(f.read())

    model_args: ModelArgs = ModelArgs(
        max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
    )
    tokenizer = Tokenizer(model_path=tokenizer_path)
    model_args.vocab_size = tokenizer.n_words
    # torch.set_default_tensor_type(torch.cuda.HalfTensor)
    torch.set_default_tensor_type(torch.HalfTensor)
    print("Creating transformer")
    model = Transformer(model_args)
    print("Transformer created")

    key_to_dim = {
        "w1": 0,
        "w2": -1,
        "w3": 0,
        "wo": -1,
        "wq": 0,
        "wk": 0,
        "wv": 0,
        "output": 0,
        "tok_embeddings": -1,
        "ffn_norm": None,
        "attention_norm": None,
        "norm": None,
        "rope": None,
    }

    # ?
    torch.set_default_tensor_type(torch.FloatTensor)

    # load the state dict incrementally, to avoid memory problems
    for i, ckpt in enumerate(checkpoints):
        print(f"Loading checkpoint {i}")
        checkpoint = torch.load(ckpt, map_location="cpu")
        for parameter_name, parameter in model.named_parameters():
            short_name = parameter_name.split(".")[-2]
            if key_to_dim[short_name] is None and i == 0:
                parameter.data = checkpoint[parameter_name]
            elif key_to_dim[short_name] == 0:
                size = checkpoint[parameter_name].size(0)
                parameter.data[size * i : size * (i + 1), :] = checkpoint[
                    parameter_name
                ]
            elif key_to_dim[short_name] == -1:
                size = checkpoint[parameter_name].size(-1)
                parameter.data[:, size * i : size * (i + 1)] = checkpoint[
                    parameter_name
                ]
        del checkpoint

    # model.load_state_dict(checkpoint, strict=False)
    model.quantize()

    generator = LLaMA(model, tokenizer)
    print(f"Loaded in {time.time() - start_time:.2f} seconds")
    return generator


class LLaMAModel_8bit:
    def __init__(self):
        pass

    @classmethod
    def from_pretrained(self, path, max_seq_len=2048, max_batch_size=1):
        tokenizer_path = path / "tokenizer.model"
        path = os.path.abspath(path)
        tokenizer_path = os.path.abspath(tokenizer_path)
        
        generator = load(path, tokenizer_path, max_seq_len, max_batch_size)

        result = self()
        result.pipeline = generator
        return result

    def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):

        results = self.pipeline.generate(
            [prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
        )

        return results[0]