from typing import Optional

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

from ..base import LanguageModelOutput, create_attention_mask
from ..cache import KVCache
from .config import ModelConfig


class Attention(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()

        dim = config.hidden_size
        self.n_heads = n_heads = config.num_attention_heads
        self.n_kv_heads = n_kv_heads = config.num_key_value_heads
        self.head_dim = head_dim = config.hidden_size // n_heads
        self.scale = head_dim**-0.5

        op_size = n_heads * head_dim + 2 * (n_kv_heads * head_dim)
        self.qkv_proj = nn.Linear(dim, op_size, bias=False)
        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)

        rope_dim = int(head_dim * config.partial_rotary_factor)
        self.rope = nn.RoPE(
            rope_dim,
            traditional=config.rope_traditional,
            base=config.rope_theta,
        )

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

        qkv = self.qkv_proj(x)
        query_pos = self.n_heads * self.head_dim
        queries, keys, values = mx.split(
            qkv, [query_pos, query_pos + self.n_kv_heads * self.head_dim], axis=-1
        )

        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)

        if cache is not None:
            queries = self.rope(queries, offset=cache.offset)
            keys = self.rope(keys, offset=cache.offset)
            keys, values = cache.update_and_fetch(keys, values)
        else:
            queries = self.rope(queries)
            keys = self.rope(keys)

        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.o_proj(output)


class MLP(nn.Module):
    def __init__(self, dim, hidden_dim):
        super().__init__()
        self.gate_up_proj = nn.Linear(dim, 2 * hidden_dim, bias=False)
        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)

    def __call__(self, x) -> mx.array:
        x = self.gate_up_proj(x)
        gate, x = mx.split(x, 2, axis=-1)
        return self.down_proj(nn.silu(gate) * x)


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

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


class TextModel(nn.Module):
    """Phi-4 transformer backbone (embed + layers + norm)."""

    def __init__(self, config: ModelConfig):
        super().__init__()
        self.config = config
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = [
            TransformerBlock(config=config) for _ in range(config.num_hidden_layers)
        ]
        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

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

        if mask is None:
            mask = create_attention_mask(h, cache[0])

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

        return self.norm(h)


class LanguageModel(nn.Module):
    """Phi-4 language model (backbone + output head)."""

    def __init__(self, config: ModelConfig):
        super().__init__()
        self.model = TextModel(config)
        if not getattr(config, "tie_word_embeddings", True):
            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    @property
    def head_dim(self):
        return self.model.config.hidden_size // self.model.config.num_attention_heads

    @property
    def n_kv_heads(self):
        return self.model.config.num_key_value_heads

    def __call__(
        self,
        inputs: mx.array,
        inputs_embeds: Optional[mx.array] = None,
        mask: Optional[mx.array] = None,
        cache=None,
        **kwargs,
    ):
        out = self.model(inputs, inputs_embeds, mask=mask, cache=cache)

        if hasattr(self, "lm_head"):
            logits = self.lm_head(out)
        else:
            logits = self.model.embed_tokens.as_linear(out)

        return LanguageModelOutput(logits=logits)
