# Copyright (c) 2023, Tri Dao.

import json
import math
import os
import re
from collections import OrderedDict
from pathlib import Path
from typing import Union

import torch
import torch.nn.functional as F
from sentencepiece import SentencePieceProcessor
from transformers import GPT2Config, LlamaConfig


def remap_state_dict_meta_llama(
    state_dict: dict[str, torch.Tensor], config: GPT2Config
) -> dict[str, torch.Tensor]:
    """Convert the state_dict in Meta format to standard GPT format.

    This function modifies state_dict in place.
    """

    def key_mapping_layers(key):
        return f"transformer.{key}" if not key.startswith("output.") else key

    state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
    # Word embedding
    def key_mapping_emb(key):
        return re.sub(
            r"^transformer.tok_embeddings.", "transformer.embeddings.word_embeddings.", key
        )

    state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
    word_embeddings = state_dict.pop("transformer.embeddings.word_embeddings.weight")
    # It's possible that vocab_size is padded to be a multiple of 8, for example.
    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
    vocab_size = (
        math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple) * pad_vocab_size_multiple
    )
    state_dict["transformer.embeddings.word_embeddings.weight"] = F.pad(
        word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
    )
    if getattr(config, "tie_word_embeddings"):
        state_dict["lm_head.weight"] = state_dict["transformer.embeddings.word_embeddings.weight"]
    else:
        output_embeddings = state_dict.pop("output.weight")
        # Need to recompute vocab_size since LLaMa shards the word embeddings and output embeddings
        # differently.
        vocab_size = (
            math.ceil(output_embeddings.shape[0] / pad_vocab_size_multiple)
            * pad_vocab_size_multiple
        )
        # It's possible that vocab_size is padded to be a multiple of 8, for example.
        state_dict["lm_head.weight"] = F.pad(
            output_embeddings, (0, 0, 0, vocab_size - output_embeddings.shape[0])
        )

    # LayerNorm
    def key_mapping_ln(key):
        key = re.sub(r"^transformer.norm.", r"transformer.ln_f.", key)
        key = re.sub(
            r"^transformer.layers.(\d+).attention_norm.", r"transformer.layers.\1.norm1.", key
        )
        key = re.sub(r"^transformer.layers.(\d+).ffn_norm.", r"transformer.layers.\1.norm2.", key)
        return key

    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())

    # MLP
    for l in range(config.n_layer):
        w1 = state_dict.pop(f"transformer.layers.{l}.feed_forward.w1.weight")
        w3 = state_dict.pop(f"transformer.layers.{l}.feed_forward.w3.weight")
        # Our ordering is different
        state_dict[f"transformer.layers.{l}.mlp.fc1.weight"] = torch.cat([w3, w1], dim=0)

    def key_mapping_mlp(key):
        return re.sub(
            r"^transformer.layers.(\d+).feed_forward.w2.", r"transformer.layers.\1.mlp.fc2.", key
        )

    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())

    # Attention
    for l in range(config.n_layer):
        Wq = state_dict.pop(f"transformer.layers.{l}.attention.wq.weight")
        Wk = state_dict.pop(f"transformer.layers.{l}.attention.wk.weight")
        Wv = state_dict.pop(f"transformer.layers.{l}.attention.wv.weight")
        state_dict[f"transformer.layers.{l}.mixer.Wqkv.weight"] = torch.cat([Wq, Wk, Wv], dim=0)
        # We don't store these
        state_dict.pop(f"transformer.layers.{l}.attention.inner_attention.rope.freqs", None)

    def key_mapping_attn(key):
        return re.sub(
            r"^transformer.layers.(\d+).attention.wo.",
            r"transformer.layers.\1.mixer.out_proj.",
            key,
        )

    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())

    state_dict.pop("transformer.rope.freqs", None)

    return state_dict


def remap_state_dict_hf_llama(
    state_dict: dict[str, torch.Tensor], config: GPT2Config
) -> dict[str, torch.Tensor]:
    """Convert the state_dict in Hugging Face format to standard GPT format.

    This function modifies state_dict in place.
    """
    # Embedding
    def key_mapping_emb(key):
        return re.sub(r"^model.embed_tokens.", "transformer.embeddings.word_embeddings.", key)

    state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
    word_embeddings = state_dict.pop("transformer.embeddings.word_embeddings.weight")
    # It's possible that vocab_size is padded to be a multiple of 8, for example.
    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
    vocab_size = (
        math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple) * pad_vocab_size_multiple
    )
    state_dict["transformer.embeddings.word_embeddings.weight"] = F.pad(
        word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
    )

    # LM head
    if getattr(config, "tie_word_embeddings"):
        state_dict["lm_head.weight"] = state_dict["transformer.embeddings.word_embeddings.weight"]
    else:
        output_embeddings = state_dict.pop("lm_head.weight")
        # Need to recompute vocab_size since LLaMa shards the word embeddings and output embeddings
        # differently.
        vocab_size = (
            math.ceil(output_embeddings.shape[0] / pad_vocab_size_multiple)
            * pad_vocab_size_multiple
        )
        # It's possible that vocab_size is padded to be a multiple of 8, for example.
        state_dict["lm_head.weight"] = F.pad(
            output_embeddings, (0, 0, 0, vocab_size - output_embeddings.shape[0])
        )

    # MLP
    for l in range(config.n_layer):
        # Fusing weights this way based on difference in the following:
        # https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/modeling_llama.py#L220
        # https://github.com/Dao-AILab/flash-attention/blob/c60851a8253257eb970e06a022c82517a8033e8c/flash_attn/modules/mlp.py#L115
        w1 = state_dict.pop(f"model.layers.{l}.mlp.gate_proj.weight")
        w3 = state_dict.pop(f"model.layers.{l}.mlp.up_proj.weight")
        state_dict[f"transformer.layers.{l}.mlp.fc1.weight"] = torch.cat([w3, w1], dim=0)

    def key_mapping_mlp(key):
        return re.sub(r"^model.layers.(\d+).mlp.down_proj.", r"transformer.layers.\1.mlp.fc2.", key)

    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())

    # LayerNorm
    def key_mapping_ln(key):
        key = re.sub(r"^model.norm.", r"transformer.ln_f.", key)
        key = re.sub(r"^model.layers.(\d+).input_layernorm.", r"transformer.layers.\1.norm1.", key)
        key = re.sub(
            r"^model.layers.(\d+).post_attention_layernorm.", r"transformer.layers.\1.norm2.", key
        )
        return key

    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())

    def inv_permute(w):
        # Inverse of permute implemented in:
        # https://github.com/huggingface/transformers/blob/b42010bb1d3cbf262d27e0a328661885be46dfdb/src/transformers/models/llama/convert_llama_weights_to_hf.py#L114
        return (
            w.reshape(config.n_head, 2, config.n_embd // config.n_head // 2, config.n_embd)
            .transpose(1, 2)
            .reshape(config.n_embd, config.n_embd)
        )

    # Attention
    for l in range(config.n_layer):
        Wq = state_dict.pop(f"model.layers.{l}.self_attn.q_proj.weight")
        Wk = state_dict.pop(f"model.layers.{l}.self_attn.k_proj.weight")
        Wv = state_dict.pop(f"model.layers.{l}.self_attn.v_proj.weight")
        state_dict[f"transformer.layers.{l}.mixer.Wqkv.weight"] = torch.cat(
            [inv_permute(Wq), inv_permute(Wk), Wv], dim=0
        )
        # We don't store these
        state_dict.pop(f"model.layers.{l}.self_attn.rotary_emb.inv_freq", None)

    def key_mapping_attn(key):
        return re.sub(
            r"^model.layers.(\d+).self_attn.o_proj.", r"transformer.layers.\1.mixer.out_proj.", key
        )

    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
    return state_dict


def inv_remap_state_dict_hf_llama(
    state_dict: dict[str, torch.Tensor], config: GPT2Config
) -> dict[str, torch.Tensor]:
    """Convert the state_dict in standard GPT format to Hugging Face format.

    This function is meant to be the inverse of remap_state_dict_hf_llama, up to a
    multiplier pad in the embedding and lm_head. That is if the original embedding
    isn't a multiple of pad_vocab_size_multiple, then
    inv_remap_state_dict_hf_llama(remap_state_dict_hf_llama(state_dict)) != state_dict.

    This function modifies state_dict in place.
    """

    # Embedding
    def key_mapping_emb(key):
        return re.sub(r"^transformer.embeddings.word_embeddings.", "model.embed_tokens.", key)

    state_dict = OrderedDict((key_mapping_emb(k), v) for k, v in state_dict.items())
    word_embeddings = state_dict.pop("model.embed_tokens.weight")
    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
    vocab_size = (
        math.ceil(word_embeddings.shape[0] / pad_vocab_size_multiple) * pad_vocab_size_multiple
    )
    state_dict["model.embed_tokens.weight"] = F.pad(
        word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
    )

    # LM head
    if getattr(config, "tie_word_embeddings"):
        state_dict["lm_head.weight"] = state_dict["model.embed_tokens.weight"]
    else:
        output_embeddings = state_dict.pop("lm_head.weight")
        vocab_size = (
            math.ceil(output_embeddings.shape[0] / pad_vocab_size_multiple)
            * pad_vocab_size_multiple
        )
        state_dict["lm_head.weight"] = F.pad(
            output_embeddings, (0, 0, 0, vocab_size - output_embeddings.shape[0])
        )

    # MLP
    for l in range(config.n_layer):
        w3, w1 = torch.chunk(
            state_dict.pop(f"transformer.layers.{l}.mlp.fc1.weight"), chunks=2, dim=0
        )
        state_dict[f"model.layers.{l}.mlp.gate_proj.weight"] = w1
        state_dict[f"model.layers.{l}.mlp.up_proj.weight"] = w3

    def key_mapping_mlp(key):
        return re.sub(r"^transformer.layers.(\d+).mlp.fc2.", r"model.layers.\1.mlp.down_proj.", key)

    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())

    # LayerNorm
    def key_mapping_ln(key):
        key = re.sub(r"^transformer.ln_f.", r"model.norm.", key)
        key = re.sub(r"^transformer.layers.(\d+).norm1.", r"model.layers.\1.input_layernorm.", key)
        key = re.sub(
            r"^transformer.layers.(\d+).norm2.", r"model.layers.\1.post_attention_layernorm.", key
        )
        return key

    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())

    def permute(w):
        return (
            w.view(config.n_head, config.n_embd // config.n_head // 2, 2, config.n_embd)
            .transpose(1, 2)
            .reshape(config.n_embd, config.n_embd)
        )

    n_head = config.n_head
    n_head_kv = getattr(config, "n_head_kv", n_head)

    embed_dim = config.hidden_size
    head_dim = embed_dim // n_head

    q_dim = n_head * head_dim
    k_dim = v_dim = n_head_kv * head_dim

    # Attention
    for l in range(config.n_layer):
        Wqkv = state_dict.pop(f"transformer.layers.{l}.mixer.Wqkv.weight")
        Wq = Wqkv[:q_dim]
        Wk = Wqkv[q_dim : q_dim + k_dim]
        Wv = Wqkv[q_dim + k_dim : q_dim + k_dim + v_dim]
        state_dict[f"model.layers.{l}.self_attn.q_proj.weight"] = permute(Wq)
        state_dict[f"model.layers.{l}.self_attn.k_proj.weight"] = permute(Wk)
        state_dict[f"model.layers.{l}.self_attn.v_proj.weight"] = Wv
        state_dict.pop(f"transformer.layers.{l}.attention.inner_attention.rope.freqs", None)

    def key_mapping_attn(key):
        return re.sub(
            r"^transformer.layers.(\d+).mixer.out_proj.", r"model.layers.\1.self_attn.o_proj.", key
        )

    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
    return state_dict


def config_from_meta_checkpoint(
    checkpoint_path: Union[str, os.PathLike], model_name: str
) -> LlamaConfig:
    """Load a LlamaConfig from a checkpoint path."""
    with open(Path(checkpoint_path) / model_name / "params.json") as f:
        params = json.load(f)
    config = LlamaConfig(
        hidden_size=params["dim"],
        intermediate_size=None,
        num_attention_heads=params["n_heads"],
        num_hidden_layers=params["n_layers"],
        rms_norm_eps=params["norm_eps"],
        num_key_value_heads=params.get("n_kv_heads", None),
    )
    multiple_of = params.get("multiple_of", 1)
    ffn_dim_multiplier = params.get("ffn_dim_multiplier", None)

    # Compute the hidden dimension of the MLP
    # https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/model.py#L224
    intermediate_size = 4 * config.hidden_size
    # https://github.com/facebookresearch/llama/blob/1a240688810f8036049e8da36b073f63d2ac552c/llama/model.py#L195-L199
    intermediate_size = int(2 * intermediate_size / 3)
    # custom dim factor multiplier
    if ffn_dim_multiplier is not None:
        intermediate_size = int(ffn_dim_multiplier * intermediate_size)
    intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)

    config.intermediate_size = intermediate_size
    if "rope_theta" in params:
        config.rotary_emb_base = params["rope_theta"]
    config.vocab_size = 32000
    # some CodeLLaMa have vocab_size 32000, some 32016
    # Sadly it's not specified in the `params.json` file :(
    tokenizer = Path(checkpoint_path) / model_name / "tokenizer.model"
    if tokenizer.is_file():
        config.vocab_size = SentencePieceProcessor(str(tokenizer)).vocab_size()
    return config


def config_from_hf_checkpoint(
    checkpoint_path: Union[str, os.PathLike], model_name: str
) -> LlamaConfig:
    return LlamaConfig.from_pretrained(Path(checkpoint_path) / f"{model_name}-hf" / "config.json")


def config_from_checkpoint(
    checkpoint_path: Union[str, os.PathLike], model_name: str, checkpoint_format="meta"
) -> LlamaConfig:
    if checkpoint_format == "meta":
        return config_from_meta_checkpoint(checkpoint_path, model_name)
    else:
        return config_from_hf_checkpoint(checkpoint_path, model_name)


def state_dicts_from_checkpoint(
    checkpoint_path: Union[str, os.PathLike], model_name: str
) -> list[dict]:
    # Need to sort, otherwise we mess up the ordering and the weights are wrong
    return [
        torch.load(path, map_location="cpu")
        for path in sorted((Path(checkpoint_path) / model_name).glob("consolidated.*.pth"))
    ]


def llama_config_to_gpt2_config(llama_config: LlamaConfig) -> GPT2Config:
    return GPT2Config(
        vocab_size=llama_config.vocab_size,
        n_positions=0,  # No absolute position embedding
        n_embd=llama_config.hidden_size,
        n_layer=llama_config.num_hidden_layers,
        n_head=llama_config.num_attention_heads,
        n_inner=llama_config.intermediate_size,
        activation_function="swiglu",  # Hardcode since HF calls it 'silu'
        # Llama doesn't have dropout, idk if it's because they only release the inference code
        resid_pdrop=0.0,
        embd_pdrop=0.0,
        attn_pdrop=0.0,
        layer_norm_epsilon=llama_config.rms_norm_eps,
        initializer_range=llama_config.initializer_range,
        bos_token_id=llama_config.bos_token_id,
        eos_token_id=llama_config.eos_token_id,
        # These are new arguments not in the original GPT2Config
        pad_token_id=llama_config.pad_token_id,  # Idk if this does anything
        rms_norm=True,
        rotary_emb_fraction=1.0,
        rotary_emb_interleaved=True,
        tie_word_embeddings=False,
        qkv_proj_bias=False,
        out_proj_bias=False,
        mlp_fc1_bias=False,
        mlp_fc2_bias=False,
        rotary_emb_base=getattr(llama_config, "rotary_emb_base", 10000.0),
        n_head_kv=llama_config.num_key_value_heads,
    )
