import sys
import time
from pathlib import Path
from typing import Literal, Optional

import lightning as L
import torch
from lightning.fabric.plugins import BitsandbytesPrecision
from lightning.fabric.strategies import FSDPStrategy

# support running without installing as a package
wd = Path(__file__).parent.parent.resolve()
sys.path.append(str(wd))

#from lit_gpt import GPT, Config, Tokenizer
from .config import Config
from .tokenizer import Tokenizer
from .model import GPT, Block
from .utils import (
    check_valid_checkpoint_dir,
    get_default_supported_precision,
    gptq_quantization,
    load_checkpoint,
)


def sample(logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None) -> torch.Tensor:
    logits = logits[0, -1]
    # optionally crop the logits to only the top k options
    if top_k is not None:
        v, i = torch.topk(logits, min(top_k, logits.size(-1)))
        # do not use `torch.where` as in nanogpt because it will repeat top-k collisions
        logits = torch.full_like(logits, float("-inf")).scatter_(-1, i, v)
    # optionally scale the logits and sample from a probability distribution
    if temperature > 0.0:
        probs = torch.nn.functional.softmax(logits / temperature, dim=-1)
        return torch.multinomial(probs, num_samples=1)
    return torch.argmax(logits, dim=-1, keepdim=True)


@torch.inference_mode()
def generate(
    model: GPT,
    idx: torch.Tensor,
    max_returned_tokens: int,
    *,
    temperature: float = 1.0,
    top_k: Optional[int] = None,
    eos_id: Optional[int] = None,
) -> torch.Tensor:
    """Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.

    The implementation of this function is modified from A. Karpathy's nanoGPT.

    Args:
        model: The model to use.
        idx: Tensor of shape (T) with indices of the prompt sequence.
        max_returned_tokens: The maximum number of tokens to return (given plus generated).
        temperature: Scales the predicted logits by 1 / temperature.
        top_k: If specified, only sample among the tokens with the k highest probabilities.
        eos_id: If specified, stop generating any more token once the <eos> token is triggered.
    """
    T = idx.size(0)
    assert max_returned_tokens > T
    if model.max_seq_length < max_returned_tokens - 1:
        # rolling the kv cache based on the `input_pos` value would be necessary. However, doing so would introduce a
        # data dependency on the `input_pos` tensor and impact model compilation. Since this setting is uncommon, we do
        # not support it to avoid negatively impacting the overall speed
        raise NotImplementedError(f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}")

    device, dtype = idx.device, idx.dtype
    # create an empty tensor of the expected final shape and fill in the current tokens
    empty = torch.empty(max_returned_tokens, dtype=dtype, device=device)
    empty[:T] = idx
    idx = empty
    input_pos = torch.arange(0, T, device=device)

    # generate up to a fixed number of tokens
    for _ in range(max_returned_tokens - T):
        x = idx.index_select(0, input_pos).view(1, -1)

        # forward
        logits = model(x, input_pos)
        idx_next = sample(logits, temperature, top_k).to(dtype=dtype)

        # advance
        input_pos = input_pos[-1:] + 1

        # concatenate the new generation
        idx = idx.index_copy(0, input_pos, idx_next)

        # if <eos> token is triggered, return the output (stop generation)
        if idx_next == eos_id:
            return idx[:input_pos]  # include the EOS token

    return idx


def generate_for_app(
    prompt: str = "Hello, my name is",
    *,
    num_samples: int = 1,
    max_new_tokens: int = 50,
    top_k: Optional[int] = 200,
    temperature: float = 0.8,
    checkpoint_dir: Path = Path("checkpoints/stabilityai/stablelm-base-alpha-3b"),
    quantize: Optional[Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8", "gptq.int4"]] = None,
    strategy: str = "auto",
    devices: int = 1,
    precision: Optional[str] = None,
) -> str:
    """Generates text samples based on a pre-trained model and tokenizer.

    Args:
        prompt: The prompt string to use for generating the samples.
        num_samples: The number of text samples to generate.
        max_new_tokens: The number of generation steps to take.
        top_k: The number of top most probable tokens to consider in the sampling process.
        temperature: A value controlling the randomness of the sampling process. Higher values result in more random
            samples.
        checkpoint_dir: The checkpoint directory to load.
        quantize: Whether to quantize the model and using which method:
            - bnb.nf4, bnb.nf4-dq, bnb.fp4, bnb.fp4-dq: 4-bit quantization from bitsandbytes
            - bnb.int8: 8-bit quantization from bitsandbytes
            - gptq.int4: 4-bit quantization from GPTQ
            for more details, see https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials/quantize.md
        strategy: Indicates the Fabric strategy setting to use.
        devices: How many devices to use.
        precision: Indicates the Fabric precision setting to use.
    """
    precision = precision or get_default_supported_precision(training=False)

    plugins = None
    if quantize is not None:
        if devices > 1:
            raise NotImplementedError(
                "Quantization is currently not supported for multi-GPU training. Please set devices=1 when using the"
                " --quantize flag."
            )
        if quantize.startswith("bnb."):
            if "mixed" in precision:
                raise ValueError("Quantization and mixed precision is not supported.")
            dtype = {"16-true": torch.float16, "bf16-true": torch.bfloat16, "32-true": torch.float32}[precision]
            plugins = BitsandbytesPrecision(quantize[4:], dtype)
            precision = None


    fabric = L.Fabric(devices=devices, precision=precision, strategy=strategy, plugins=plugins)
    fabric.launch()

    check_valid_checkpoint_dir(checkpoint_dir)

    config = Config.from_json(checkpoint_dir / "lit_config.json")

    if quantize == "gptq.int4":
        model_file = "lit_model_gptq.4bit.pth"
        if not (checkpoint_dir / model_file).is_file():
            raise ValueError("Please run `python quantize/gptq.py` first")
    else:
        model_file = "lit_model.pth" #"lit_model.pth"
    checkpoint_path = checkpoint_dir / model_file

    fabric.print(f"Loading model {str(checkpoint_path)!r} with {config.__dict__}", file=sys.stderr)
    with fabric.init_module(empty_init=True), gptq_quantization(quantize == "gptq.int4"):
        model = GPT(config)

    model.eval()
    model = fabric.setup_module(model)

    tokenizer = Tokenizer(checkpoint_dir)
    encoded = tokenizer.encode(prompt, device=fabric.device)
    prompt_length = encoded.size(0)
    max_returned_tokens = prompt_length + max_new_tokens

    with fabric.init_tensor():
        # set the max_seq_length to limit the memory usage to what we need
        model.max_seq_length = max_returned_tokens

    L.seed_everything(1234)
    generated_text = []
    for i in range(num_samples):
        with fabric.init_tensor():
            # enable the kv cache
            model.set_kv_cache(batch_size=1)
        y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k)

        generated_text.append(tokenizer.decode(y))
        tokens_generated = y.size(0) - prompt_length


    if fabric.device.type == "cuda":
        fabric.print(f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", file=sys.stderr)

    fabric.print(' '.join(generated_text))

    return ' '.join(generated_text)



if __name__ == "__main__":
    from jsonargparse import CLI

    torch.set_float32_matmul_precision("high")
    CLI(generate_for_app)