from dataclasses import dataclass, field

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

from mlx_lm.models.base import create_attention_mask
from mlx_lm.models.phi3 import TransformerBlock, ModelArgs

from ...shard import Shard
from .base import IdentityBlock


@dataclass
class ModelArgs(ModelArgs):
    shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0))

    def __post_init__(self):
        super().__post_init__()

        if isinstance(self.shard, Shard):
            return
        if not isinstance(self.shard, dict):
            raise TypeError(
                f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead"
            )

        self.shard = Shard(**self.shard)


class Phi3Model(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

        if self.args.shard.is_first_layer():
            self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)

        self.layers = []
        for i in range(self.num_hidden_layers):
            if self.args.shard.start_layer <= i <= self.args.shard.end_layer:
                self.layers.append(TransformerBlock(args=args))
            else:
                self.layers.append(IdentityBlock())

        if self.args.shard.is_last_layer():
            self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        if self.args.shard.is_first_layer():
            h = self.embed_tokens(inputs)
        else:
            h = inputs

        mask = None
        if h.shape[1] > 1:
            mask = create_attention_mask(h, cache)

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

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

        if self.args.shard.is_last_layer():
            h = self.norm(h)
        return h


class Model(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.args = args
        self.model_type = args.model_type
        self.model = Phi3Model(args)
        if self.args.shard.is_last_layer():
            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        cache=None,
    ):
        out = self.model(inputs, cache)
        if self.args.shard.is_last_layer():
            out = self.lm_head(out)
        return out

    def sanitize(self, weights):
        shard_state_dict = {}

        for key, value in weights.items():
            if "self_attn.rope.inv_freq" in key:
                continue
            if key.startswith("model.layers."):
                layer_num = int(key.split(".")[2])
                if (
                    self.args.shard.start_layer
                    <= layer_num
                    <= self.args.shard.end_layer
                ):
                    shard_state_dict[key] = value
            elif self.args.shard.is_first_layer() and key.startswith(
                "model.embed_tokens"
            ):
                shard_state_dict[key] = value
            elif self.args.shard.is_last_layer() and (
                key.startswith("lm_head") or key.startswith("model.norm")
            ):
                shard_state_dict[key] = value

        return shard_state_dict

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

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

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