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

import mlx.core as mx
import mlx.nn as nn

from .base import BaseModelArgs
from .layers import LayerNorm


@dataclass
class ModelArgs(BaseModelArgs):
    model_type: str
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    num_key_value_heads: int = None
    max_position_embeddings: int = 16384
    norm_eps: float = None
    rms_norm_eps: float = 1e-5
    norm_type: str = "layer_norm"
    vocab_size: int = 49152
    rope_theta: float = 100000
    tie_word_embeddings: bool = True

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

        if self.norm_eps is None:
            self.norm_eps = self.rms_norm_eps


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

        dim = args.hidden_size
        self.n_heads = n_heads = args.num_attention_heads
        self.n_kv_heads = n_kv_heads = args.num_key_value_heads

        self.repeats = self.n_heads // self.n_kv_heads

        head_dim = args.hidden_size // args.num_attention_heads
        self.scale = head_dim**-0.5

        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=True)
        self.rope = nn.RoPE(head_dim, traditional=False, base=args.rope_theta)

    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

        queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)

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

        def repeat(a):
            a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
            return a.reshape([B, self.n_heads, L, -1])

        if self.repeats > 1:
            keys, values = map(repeat, (keys, values))

        if cache is not None:
            key_cache, value_cache = cache
            queries = self.rope(queries, offset=key_cache.shape[2])
            keys = self.rope(keys, offset=key_cache.shape[2])
            keys = mx.concatenate([key_cache, keys], axis=2)
            values = mx.concatenate([value_cache, values], axis=2)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
        if mask is not None:
            scores += mask
        scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
        output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output), (keys, values)


class MLP(nn.Module):
    def __init__(self, dim, hidden_dim):
        super().__init__()
        self.c_fc = nn.Linear(dim, hidden_dim, bias=True)
        self.c_proj = nn.Linear(hidden_dim, dim, bias=True)

    def __call__(self, x):
        return self.c_proj(nn.gelu(self.c_fc(x)))


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.hidden_size = args.hidden_size
        self.n_heads = args.num_attention_heads

        self.self_attn = Attention(args)
        self.mlp = MLP(args.hidden_size, args.intermediate_size)
        self.input_layernorm = LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.post_attention_layernorm = LayerNorm(
            args.hidden_size, eps=args.rms_norm_eps
        )
        self.args = args

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


class Starcoder2Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.vocab_size = args.vocab_size
        self.num_hidden_layers = args.num_hidden_layers
        assert self.vocab_size > 0
        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
        self.layers = [
            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
        ]
        self.norm = LayerNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        h = self.embed_tokens(inputs)

        mask = None
        if h.shape[1] > 1:
            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
            mask = mask.astype(h.dtype)

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

        for e, layer in enumerate(self.layers):
            h, cache[e] = layer(h, mask, cache[e])

        return self.norm(h), cache


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.model = Starcoder2Model(args)
        # This is for 15B starcoder2 since it doesn't tie word embeddings
        if not args.tie_word_embeddings:
            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out, cache = self.model(inputs, cache)
        if not self.model.args.tie_word_embeddings:
            return self.lm_head(out), cache
        else:
            out = out @ self.model.embed_tokens.weight.T
            return out, cache

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