from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import mlx.core as mx
import mlx.nn as nn
import numpy as np

from .base import BaseModelArgs, create_additive_causal_mask


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    n_ctx: int
    n_embd: int
    n_head: int
    n_layer: int
    n_positions: int
    layer_norm_epsilon: float
    vocab_size: int
    num_key_value_heads: int = None

    def __post_init__(self):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.n_head


class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        assert args.n_embd % args.n_head == 0, "n_embd must be divisible by n_head"

        self.n_embd = args.n_embd
        self.n_head = args.n_head
        self.head_dim = self.n_embd // self.n_head

        self.scale = self.head_dim**-0.5

        self.c_attn = nn.Linear(self.n_embd, 3 * self.n_embd, bias=True)
        self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=True)

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Tuple[mx.array, mx.array]] = None,
    ) -> mx.array:
        B, L, D = x.shape

        qkv = self.c_attn(x)
        queries, keys, values = mx.split(qkv, 3, axis=-1)

        # Prepare the queries, keys and values for the attention computation
        queries = queries.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
        keys = keys.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)
        values = values.reshape(B, L, self.n_head, -1).transpose(0, 2, 1, 3)

        if cache is not None:
            keys, values = cache.update_and_fetch(keys, values)

        output = mx.fast.scaled_dot_product_attention(
            queries, keys, values, scale=self.scale, mask=mask
        )

        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.c_proj(output)


class MLP(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.n_embd = args.n_embd
        self.c_fc = nn.Linear(self.n_embd, 4 * self.n_embd)
        self.c_proj = nn.Linear(4 * self.n_embd, self.n_embd)

    def __call__(self, x) -> mx.array:
        return self.c_proj(nn.gelu_approx(self.c_fc(x)))


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()

        self.n_head = args.n_head
        self.n_embd = args.n_embd
        self.layer_norm_epsilon = args.layer_norm_epsilon
        self.attn = Attention(args)
        self.mlp = MLP(args)
        self.ln_1 = nn.LayerNorm(
            self.n_embd,
            eps=self.layer_norm_epsilon,
        )
        self.ln_2 = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)

    def __call__(
        self,
        x: mx.array,
        mask: Optional[mx.array] = None,
        cache: Optional[Tuple[mx.array, mx.array]] = None,
    ) -> mx.array:
        r = self.attn(self.ln_1(x), mask, cache)
        h = x + r
        r = self.mlp(self.ln_2(h))
        out = h + r
        return out


class GPT2Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_embd = args.n_embd
        self.n_positions = args.n_positions
        self.vocab_size = args.vocab_size
        self.n_layer = args.n_layer
        self.layer_norm_epsilon = args.layer_norm_epsilon
        assert self.vocab_size > 0
        self.wte = nn.Embedding(self.vocab_size, self.n_embd)
        self.wpe = nn.Embedding(self.n_positions, self.n_embd)
        self.h = [TransformerBlock(args=args) for _ in range(self.n_layer)]
        self.ln_f = nn.LayerNorm(self.n_embd, eps=self.layer_norm_epsilon)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        _, L = inputs.shape

        hidden_states = self.wte(inputs)

        mask = None
        if hidden_states.shape[1] > 1:

            position_ids = mx.array(np.arange(L))
            hidden_states += self.wpe(position_ids)

            mask = create_additive_causal_mask(
                hidden_states.shape[1], cache[0].offset if cache is not None else 0
            )
            mask = mask.astype(hidden_states.dtype)

        if cache is None:
            cache = [None] * len(self.h)

        for layer, c in zip(self.h, cache):
            hidden_states = layer(hidden_states, mask, cache=c)

        return self.ln_f(hidden_states)


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.model_type = args.model_type
        self.model = GPT2Model(args)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out = self.model(inputs, cache)
        out = self.model.wte.as_linear(out)
        return out

    def sanitize(self, weights):
        new_weights = {}
        for i in range(self.args.n_layer):
            if f"h.{i}.attn.bias" in weights:
                del weights[f"h.{i}.attn.bias"]
            if f"h.{i}.attn.c_attn.weight" in weights:
                weights[f"h.{i}.attn.c_attn.weight"] = weights[
                    f"h.{i}.attn.c_attn.weight"
                ].transpose(1, 0)
            if f"h.{i}.attn.c_proj.weight" in weights:
                weights[f"h.{i}.attn.c_proj.weight"] = weights[
                    f"h.{i}.attn.c_proj.weight"
                ].transpose(1, 0)
            if f"h.{i}.mlp.c_fc.weight" in weights:
                weights[f"h.{i}.mlp.c_fc.weight"] = weights[
                    f"h.{i}.mlp.c_fc.weight"
                ].transpose(1, 0)
            if f"h.{i}.mlp.c_proj.weight" in weights:
                weights[f"h.{i}.mlp.c_proj.weight"] = weights[
                    f"h.{i}.mlp.c_proj.weight"
                ].transpose(1, 0)
        for weight in weights:
            if not weight.startswith("model."):
                new_weights[f"model.{weight}"] = weights[weight]
            else:
                new_weights[weight] = weights[weight]
        return new_weights

    @property
    def layers(self):
        return self.model.h

    @property
    def head_dim(self):
        return self.args.n_embd // self.args.n_head

    @property
    def n_kv_heads(self):
        return self.args.num_key_value_heads