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

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

from .base import BaseModelArgs


@dataclass
class ModelArgs(BaseModelArgs):
    hidden_size: int
    num_hidden_layers: int
    intermediate_size: int
    num_attention_heads: int
    rms_norm_eps: float
    vocab_size: int
    num_key_value_heads: int = None
    rope_theta: float = 10000
    rope_traditional: bool = False
    model_type: str = None
    rope_scaling: Optional[Dict[str, Union[float, str]]] = None

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

        if self.rope_scaling:
            required_keys = {"factor", "type"}
            if not all(key in self.rope_scaling for key in required_keys):
                raise ValueError(f"rope_scaling must contain keys {required_keys}")

            if self.rope_scaling["type"] != "linear":
                raise ValueError("rope_scaling 'type' currently only supports 'linear'")




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

        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 = n_heads // n_kv_heads

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

        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)

        # rope_scale = (
        #     1 / args.rope_scaling["factor"]
        #     if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
        #     else 1
        # )
        self.rope = nn.RoPE(
            head_dim,
            traditional=args.rope_traditional,
            base=args.rope_theta,
            # scale=rope_scale,
        )

    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.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)

    def __call__(self, x) -> mx.array:
        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))


class TransformerBlock(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.num_attention_heads = args.num_attention_heads
        self.hidden_size = args.hidden_size
        self.self_attn = Attention(args)
        self.mlp = MLP(args.hidden_size, args.intermediate_size)
        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.post_attention_layernorm = nn.RMSNorm(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 LlamaModel(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 = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        input_embeds: Optional[mx.array] = None,
        attention_mask: Optional[mx.array] = None,
        cache=None,
    ):
        if input_embeds is None:
            h = self.embed_tokens(inputs)
        else:
            h = input_embeds

        if attention_mask is not None:
            attention_mask = 1 - attention_mask
            attention_mask *= -1e9
            attention_mask = attention_mask.astype(h.dtype)

        if h.shape[1] > 1 and attention_mask is None:
            attention_mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
            attention_mask = attention_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, attention_mask, cache[e])

        return self.norm(h), cache


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.model_type = "llama"
        self.model = LlamaModel(args)
        self.n_kv_heads = args.num_attention_heads
        self.head_dim = args.intermediate_size
        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 = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
        self.value_head = nn.Linear(args.hidden_size, 1, bias=False)
        self.is_peft_model = False

    def __call__(
        self,
        input_ids: mx.array,
        input_embeds: Optional[mx.array] = None,
        attention_mask: Optional[mx.array] = None,
        cache=None,
    ):
        out, cache = self.model(input_ids, input_embeds, attention_mask, cache)
        return self.lm_head(out), cache, self.value_head(out).squeeze(-1)

    def generate(self, x, temp=1.0):
        def sample(logits):
            if temp == 0:
                return mx.argmax(logits, axis=-1)
            else:
                return mx.random.categorical(logits * (1 / temp))

        cache = []

        # Make an additive causal mask. We will need that to process the prompt.
        mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
        mask = mask.astype(self.model.embed_tokens.weight.dtype)

        # First we process the prompt x the same was as in __call__ but
        # save the caches in cache
        x = self.model.embed_tokens(x)
        for l in self.model.layers:
            x, c = l(x, mask=mask)
            # We store the per layer cache in a simple python list
            cache.append(c)
        x = self.model.norm(x)
        # We only care about the last logits that generate the next token
        y = self.lm_head(x[:, -1])
        y = sample(y)

        yield y

        # Now we parsed the prompt and generated the first token we
        # need to feed it back into the model and loop to generate the
        # rest.
        while True:
            # Unsqueezing the last dimension to add a sequence length
            # dimension of 1
            x = y[:, None]
            x = self.model.embed_tokens(x)
            for i in range(len(cache)):
                # We are overwriting the arrays in the cache list. When
                # the computation will happen, MLX will be discarding the
                # old cache the moment it is not needed anymore.
                x, cache[i] = self.model.layers[i](x, mask=None, cache=cache[i])
            x = self.model.norm(x)
            y = sample(self.lm_head(x[:, -1]))
            yield y
