import inspect
import math
import re
from functools import partial
from typing import Any, Dict, Optional, Tuple

import mlx.core as mx
import mlx.nn as nn
from mlx_lm.models.switch_layers import SwitchGLU

from ..base import (
    LanguageModelOutput,
    create_attention_mask,
    scaled_dot_product_attention,
)
from ..cache import KVCache
from .config import TextConfig


def yarn_find_correction_dim(
    num_rotations, dim, base=10000, max_position_embeddings=2048
):
    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
        2 * math.log(base)
    )


def yarn_find_correction_range(
    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
):
    low = math.floor(
        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
    )
    high = math.ceil(
        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
    )
    return max(low, 0), min(high, dim - 1)


def yarn_get_mscale(scale=1, mscale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


def yarn_linear_ramp_mask(min_val, max_val, dim):
    if min_val == max_val:
        max_val += 0.001  # Prevent singularity

    linear_func = (mx.arange(dim, dtype=mx.float32) - min_val) / (max_val - min_val)
    return mx.clip(linear_func, 0, 1)


class DeepseekV3YarnRotaryEmbedding(nn.Module):
    def __init__(
        self,
        dim,
        max_position_embeddings=2048,
        base=10000,
        scaling_factor=1.0,
        original_max_position_embeddings=4096,
        beta_fast=32,
        beta_slow=1,
        mscale=1,
        mscale_all_dim=0,
    ):
        super().__init__()
        self.mscale = yarn_get_mscale(scaling_factor, mscale) / yarn_get_mscale(
            scaling_factor, mscale_all_dim
        )
        freq_extra = base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)
        freq_inter = scaling_factor * freq_extra
        low, high = yarn_find_correction_range(
            beta_fast,
            beta_slow,
            dim,
            base,
            original_max_position_embeddings,
        )
        freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2)
        self._freqs = (freq_inter * freq_extra) / (
            freq_inter * freq_mask + freq_extra * (1 - freq_mask)
        )

    def __call__(self, x, offset=0):
        if self.mscale != 1.0:
            x = self.mscale * x
        return mx.fast.rope(
            x,
            x.shape[-1],
            traditional=True,
            base=None,
            scale=1.0,
            offset=offset,
            freqs=self._freqs,
        )


# A clipped silu to prevent fp16 from overflowing
@partial(mx.compile, shapeless=True)
def clipped_silu(x, gate):
    return mx.clip(gate * mx.sigmoid(gate), a_min=-100, a_max=100) * x


class DeepseekV3Attention(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.kv_lora_rank = config.kv_lora_rank
        self.v_head_dim = config.v_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim

        self.scale = self.q_head_dim**-0.5

        if self.q_lora_rank is None:
            self.q_proj = nn.Linear(
                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
            )
        else:
            self.q_a_proj = nn.Linear(
                self.hidden_size, self.q_lora_rank, bias=config.attention_bias
            )
            self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank, eps=1e-6)
            self.q_b_proj = nn.Linear(
                self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
            )

        self.kv_a_proj_with_mqa = nn.Linear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=config.attention_bias,
        )
        self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank, eps=1e-6)
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads
            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
            bias=False,
        )

        self.o_proj = nn.Linear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=config.attention_bias,
        )

        if self.config.rope_scaling is not None:
            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
            scaling_factor = self.config.rope_scaling["factor"]
            if mscale_all_dim:
                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
                self.scale = self.scale * mscale * mscale

            rope_kwargs = {
                key: self.config.rope_scaling[key]
                for key in [
                    "original_max_position_embeddings",
                    "beta_fast",
                    "beta_slow",
                    "mscale",
                    "mscale_all_dim",
                ]
                if key in self.config.rope_scaling
            }
            self.rope = DeepseekV3YarnRotaryEmbedding(
                dim=self.qk_rope_head_dim,
                max_position_embeddings=self.max_position_embeddings,
                scaling_factor=scaling_factor,
                base=self.rope_theta,
                **rope_kwargs,
            )
        else:
            self.rope = nn.RoPE(
                dims=self.qk_rope_head_dim,
                base=self.rope_theta,
                traditional=True,
            )

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

        if self.q_lora_rank is None:
            q = self.q_proj(x)
        else:
            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(x)))

        q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3)
        q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1)
        compressed_kv = self.kv_a_proj_with_mqa(x)
        compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1)
        k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3)
        kv = self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
        kv = kv.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)

        k_nope, values = mx.split(kv, [self.qk_nope_head_dim], axis=-1)

        if cache is not None:
            q_pe = self.rope(q_pe, cache.offset)
            k_pe = self.rope(k_pe, cache.offset)
            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
            keys, values = cache.update_and_fetch(
                mx.concatenate([k_nope, k_pe], axis=-1), values
            )
        else:
            q_pe = self.rope(q_pe)
            k_pe = self.rope(k_pe)
            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
            keys = mx.concatenate([k_nope, k_pe], axis=-1)

        queries = mx.concatenate([q_nope, q_pe], axis=-1)

        output = scaled_dot_product_attention(
            queries, keys, values, cache, scale=self.scale, mask=mask
        )
        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        return self.o_proj(output)


class DeepseekV3MLP(nn.Module):
    def __init__(
        self, config: TextConfig, hidden_size: int = None, intermediate_size: int = None
    ):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
        self.intermediate_size = (
            config.intermediate_size if intermediate_size is None else intermediate_size
        )

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

    def __call__(self, x):
        down_proj = self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


@mx.compile
def group_expert_select(
    gates,
    e_score_correction_bias,
    top_k,
    n_group,
    topk_group,
    routed_scaling_factor,
    norm_topk_prob,
):

    k = top_k
    scores = mx.sigmoid(gates.astype(mx.float32))
    orig_scores = scores
    scores = scores + e_score_correction_bias
    scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1))
    group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True)
    k = n_group - topk_group
    group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
    scores = mx.put_along_axis(scores, group_idx, mx.array(0.0), axis=-2)
    scores = mx.flatten(scores, -2, -1)

    k = top_k
    inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
    scores = mx.take_along_axis(orig_scores, inds, axis=-1)
    if top_k > 1 and norm_topk_prob:
        denominator = scores.sum(axis=-1, keepdims=True)
        scores = scores / denominator
    scores = scores * routed_scaling_factor

    return inds, scores


class MoEGate(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.norm_topk_prob = config.norm_topk_prob
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
        self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))
        assert config.topk_method == "noaux_tc", "Unsupported topk method."

    def __call__(self, x):
        return group_expert_select(
            x @ self.weight.T,
            self.e_score_correction_bias,
            self.top_k,
            self.n_group,
            self.topk_group,
            self.routed_scaling_factor,
            self.norm_topk_prob,
        )


class DeepseekV3MoE(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.config = config
        self.num_experts_per_tok = config.num_experts_per_tok
        self.switch_mlp = SwitchGLU(
            config.hidden_size,
            config.moe_intermediate_size,
            config.n_routed_experts,
            activation=clipped_silu,
        )

        self.gate = MoEGate(config)
        if config.n_shared_experts is not None:
            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
            self.shared_experts = DeepseekV3MLP(
                config=config, intermediate_size=intermediate_size
            )

    def __call__(self, x):
        inds, scores = self.gate(x)
        y = self.switch_mlp(x, inds)
        y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype)
        if self.config.n_shared_experts is not None:
            y = y + self.shared_experts(x)

        return y


class DeepseekV3DecoderLayer(nn.Module):
    def __init__(self, config: TextConfig, layer_idx: int):
        super().__init__()
        self.self_attn = DeepseekV3Attention(config)
        self.mlp = (
            DeepseekV3MoE(config)
            if (
                config.n_routed_experts is not None
                and layer_idx >= config.first_k_dense_replace
                and layer_idx % config.moe_layer_freq == 0
            )
            else DeepseekV3MLP(config)
        )
        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[Any] = 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 DeepseekV3Model(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = [
            DeepseekV3DecoderLayer(config, idx)
            for idx in range(config.num_hidden_layers)
        ]
        self.start_idx = 0
        self.end_idx = len(self.layers)
        self.num_layers = self.end_idx

        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def __call__(
        self,
        x: mx.array,
        inputs_embeds: Optional[mx.array] = None,
        cache: Optional[Any] = None,
        mask: Optional[mx.array] = None,
    ) -> mx.array:

        if inputs_embeds is None:
            h = self.embed_tokens(x)
        else:
            h = inputs_embeds

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

        if cache is None:
            cache = [None] * self.num_layers

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

        return self.norm(h)


class LanguageModel(nn.Module):
    def __init__(self, config: TextConfig):
        super().__init__()
        self.config = config
        self.model_type = config.model_type
        self.model = DeepseekV3Model(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

    def __call__(
        self,
        inputs: mx.array,
        inputs_embeds: Optional[mx.array] = None,
        cache: Optional[Any] = None,
        mask: Optional[mx.array] = None,
    ):
        out = self.model(inputs, inputs_embeds=inputs_embeds, cache=cache, mask=mask)
        out = self.lm_head(out)
        return LanguageModelOutput(logits=out)

    def sanitize(self, weights):
        def keep(key):
            return "rotary_emb" not in key

        weights = {k: v for k, v in weights.items() if keep(k)}
        # Stack experts
        for l in range(self.config.num_hidden_layers):
            prefix = f"language_model.model.layers.{l}"
            for m in [("gate_proj"), ("down_proj"), ("up_proj")]:
                for k in ["weight", "scales", "biases"]:
                    if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
                        to_join = [
                            weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
                            for e in range(self.config.n_routed_experts)
                        ]
                        weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)

        return weights

    def embed_tokens(self, x):
        return self.model.embed_tokens(x)

    @property
    def layers(self):
        return self.model.layers[self.model.start_idx : self.model.end_idx]

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

    def cast_predicate(self):
        def predicate(k):
            return "e_score_correction_bias" not in k

        return predicate