"""Unlimited-OCR MLX Model Implementation.

High-precision OCR model fully implemented in MLX for Apple Silicon acceleration.
Architecture: Vision Encoder (SAM-ViT-B + CLIP-L) → DeepSeek-V2 MoE Language Model.
"""

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

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

from .config import UnlimitedOCRConfig, VisionConfig, LanguageConfig, ProjectorConfig


# =============================================================================
# Utility Functions
# =============================================================================

def _compute_default_rope_freqs(
    dim: int, max_position_embeddings: int = 32768, base: float = 10000.0
) -> mx.array:
    """Compute RoPE frequencies. Returns (max_pos, dim/2) for rotation."""
    theta = 1.0 / (base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim))
    t = mx.arange(max_position_embeddings, dtype=mx.float32)
    freqs = mx.outer(t, theta)
    return freqs


def _apply_rotary_pos_emb(q, k, cos, sin, position_ids=None):
    """Apply rotary position embeddings to query and key tensors.

    Args:
        q, k: [B, heads, seq_len, head_dim]
        cos, sin: [seq_len, half_dim] already sliced/indexed by caller
    """
    B, H, L, D = q.shape
    half_D = D // 2

    # cos/sin are already properly shaped by RotaryEmbedding
    # They should be [L, half_D] or [1, L, half_D]
    if cos.ndim == 3:
        cos = cos.reshape(-1, cos.shape[-1])
        sin = sin.reshape(-1, sin.shape[-1])

    # Ensure correct length
    cos = cos[:L]
    sin = sin[:L]

    # Reshape for broadcasting: [1, 1, L, half_D]
    cos = cos.reshape(1, 1, L, half_D)
    sin = sin.reshape(1, 1, L, half_D)

    def _rotate_half(x):
        x1 = x[..., :half_D]
        x2 = x[..., half_D:]
        return mx.concatenate([-x2, x1], axis=-1)

    # Duplicate cos/sin to full head_dim for element-wise multiply
    cos2 = mx.concatenate([cos, cos], axis=-1)
    sin2 = mx.concatenate([sin, sin], axis=-1)

    q_rot = q * cos2 + _rotate_half(q) * sin2
    k_rot = k * cos2 + _rotate_half(k) * sin2

    return q_rot, k_rot


def silu(x):
    """SiLU activation function."""
    return x * mx.sigmoid(x)


# =============================================================================
# RMSNorm
# =============================================================================

class RMSNorm(nn.Module):
    """Root Mean Square Layer Normalization."""

    def __init__(self, dims: int, eps: float = 1e-6):
        super().__init__()
        self.weight = mx.ones((dims,))
        self.eps = eps

    def __call__(self, x):
        return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)


# =============================================================================
# RoPE
# =============================================================================

class RotaryEmbedding:
    """Rotary Position Embedding."""

    def __init__(self, dim: int, max_position_embeddings: int = 32768, base: float = 10000.0):
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self._freqs_cos_sin = None

    def _ensure_freqs(self):
        if self._freqs_cos_sin is None:
            freqs = _compute_default_rope_freqs(self.dim, self.max_position_embeddings, self.base)
            self._freqs_cos_sin = (mx.cos(freqs), mx.sin(freqs))

    @property
    def cos_cached(self):
        self._ensure_freqs()
        return self._freqs_cos_sin[0]

    @property
    def sin_cached(self):
        self._ensure_freqs()
        return self._freqs_cos_sin[1]

    def __call__(self, x, position_ids=None, seq_len=None):
        self._ensure_freqs()
        cos, sin = self.cos_cached, self.sin_cached
        if seq_len is not None:
            cos, sin = cos[:seq_len], sin[:seq_len]
        if position_ids is not None:
            cos = cos[position_ids]
            sin = sin[position_ids]
        return cos, sin


# =============================================================================
# Standard Multi-Head Attention
# =============================================================================

class MultiHeadAttention(nn.Module):
    """Standard Multi-Head Attention with RoPE."""

    def __init__(self, config: LanguageConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_kv_heads = config.num_key_value_heads
        self.head_dim = config.head_dim
        self.layer_idx = layer_idx

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)

        self.rotary_emb = RotaryEmbedding(
            self.head_dim,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
        )
        self.scale = self.head_dim ** -0.5

    def __call__(
        self,
        hidden_states: mx.array,
        attention_mask: Optional[mx.array] = None,
        position_ids: Optional[mx.array] = None,
        past_key_value: Optional[Tuple[mx.array, mx.array]] = None,
        use_cache: bool = False,
    ) -> Tuple[mx.array, Optional[Tuple[mx.array, mx.array]]]:
        B, L, _ = hidden_states.shape

        q = self.q_proj(hidden_states).reshape(B, L, self.num_heads, self.head_dim).transpose(0, 2, 1, 3)
        k = self.k_proj(hidden_states).reshape(B, L, self.num_kv_heads, self.head_dim).transpose(0, 2, 1, 3)
        v = self.v_proj(hidden_states).reshape(B, L, self.num_kv_heads, self.head_dim).transpose(0, 2, 1, 3)

        cos, sin = self.rotary_emb(q, position_ids=position_ids, seq_len=L)
        q, k = _apply_rotary_pos_emb(q, k, cos, sin, position_ids)

        if past_key_value is not None:
            pk, pv = past_key_value
            k = mx.concatenate([pk, k], axis=2)
            v = mx.concatenate([pv, v], axis=2)

        past_kv = (k, v) if use_cache else None

        # GQA: repeat k/v heads
        n_rep = self.num_heads // self.num_kv_heads
        if n_rep > 1:
            k = mx.repeat(k, n_rep, axis=1)
            v = mx.repeat(v, n_rep, axis=1)

        # Scaled dot-product attention
        scores = (q @ k.transpose(0, 1, 3, 2)) * self.scale
        if attention_mask is not None:
            scores = scores + attention_mask

        attn_weights = mx.softmax(scores.astype(mx.float32), axis=-1).astype(q.dtype)
        attn_output = attn_weights @ v

        attn_output = attn_output.transpose(0, 2, 1, 3).reshape(B, L, -1)
        output = self.o_proj(attn_output)
        return output, past_kv


# =============================================================================
# MLP (SwiGLU)
# =============================================================================

class SwiGLUMLP(nn.Module):
    """SwiGLU MLP used in dense layers and experts."""

    def __init__(self, hidden_size: int, intermediate_size: int):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)

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


# =============================================================================
# MoE (Mixture of Experts)
# =============================================================================

class MoEGate(nn.Module):
    """Top-k gating for MoE."""

    def __init__(self, config: LanguageConfig):
        super().__init__()
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.scoring_func = config.scoring_func
        self.topk_method = config.topk_method
        self.norm_topk_prob = config.norm_topk_prob

        # Gate weight: [n_experts, hidden_size]
        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))

    def __call__(self, hidden_states: mx.array) -> Tuple[mx.array, mx.array]:
        # hidden_states: [B*L, hidden_size]
        logits = hidden_states.astype(mx.float32) @ self.weight.astype(mx.float32).T

        if self.scoring_func == "softmax":
            scores = mx.softmax(logits, axis=-1)
        else:
            scores = mx.sigmoid(logits)

        # Top-k selection (MLX topk returns indices, then we gather weights)
        topk_indices = mx.argpartition(-scores, kth=self.top_k - 1, axis=-1)[:, :self.top_k]
        # Gather the actual scores for these indices
        topk_weights = mx.take_along_axis(scores, topk_indices, axis=-1)

        if self.norm_topk_prob:
            denom = topk_weights.sum(axis=-1, keepdims=True) + 1e-20
            topk_weights = topk_weights / denom

        return topk_indices, topk_weights


class DeepSeekMoE(nn.Module):
    """DeepSeek-V2 MoE block with shared experts."""

    def __init__(self, config: LanguageConfig):
        super().__init__()
        self.num_experts_per_tok = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.moe_intermediate_size = config.moe_intermediate_size

        # Create routed experts
        self.experts = [
            SwiGLUMLP(config.hidden_size, self.moe_intermediate_size)
            for _ in range(self.n_routed_experts)
        ]

        self.gate = MoEGate(config)

        # Shared experts (2 experts with combined intermediate size)
        if config.n_shared_experts is not None:
            shared_dim = self.moe_intermediate_size * config.n_shared_experts
            self.shared_experts = SwiGLUMLP(config.hidden_size, shared_dim)

    def _moe_infer(self, x: mx.array, topk_ids: mx.array, topk_weights: mx.array) -> mx.array:
        """Inference-time MoE computation."""
        B, L, D = x.shape
        x_flat = x.reshape(-1, D)  # [B*L, D]
        tk_flat = topk_ids.reshape(-1)  # [B*L*K]
        tw_flat = topk_weights.reshape(-1)  # [B*L*K]

        # Count tokens per expert
        import numpy as np
        tk_np = np.array(tk_flat, dtype=np.int32)
        token_counts = np.bincount(tk_np, minlength=self.n_routed_experts)

        # Sort tokens by expert
        sort_indices = mx.argsort(tk_flat)
        repeated_x = mx.repeat(x_flat, self.num_experts_per_tok, axis=0)
        sorted_tokens = repeated_x[sort_indices]
        sorted_weights = tw_flat[sort_indices]

        # Process each expert's tokens
        outputs = []
        start = 0
        for i in range(self.n_routed_experts):
            count = int(token_counts[i])
            if count == 0:
                continue
            end = start + count
            expert_out = self.experts[i](sorted_tokens[start:end].astype(mx.float16))
            expert_out = expert_out * sorted_weights[start:end][:, None]
            outputs.append((sort_indices[start:end], expert_out))
            start = end

        if not outputs:
            return mx.zeros_like(x)

        # Scatter back
        all_indices = mx.concatenate([o[0] for o in outputs], axis=0)
        all_outputs = mx.concatenate([o[1] for o in outputs], axis=0)

        # Restore original order via argsort of indices
        restore = mx.argsort(all_indices)
        final = all_outputs[restore]

        # Sum across top-k experts for each token: (B*L, K, D) → (B*L, D)
        final = final.reshape(B * L, self.num_experts_per_tok, D).sum(axis=1)
        return final.reshape(B, L, D)

    def __call__(self, hidden_states: mx.array) -> mx.array:
        identity = hidden_states
        B, L, D = hidden_states.shape
        x_flat = hidden_states.reshape(-1, D)

        topk_idx, topk_weight = self.gate(x_flat)

        # Reshape routing back
        topk_idx = topk_idx.reshape(B * L, self.num_experts_per_tok)
        topk_weight = topk_weight.reshape(B * L, self.num_experts_per_tok)

        moe_out = self._moe_infer(hidden_states, topk_idx.reshape(B, L, -1), topk_weight.reshape(B, L, -1))

        if hasattr(self, 'shared_experts'):
            moe_out = moe_out + self.shared_experts(identity)

        return moe_out


# =============================================================================
# DeepSeek-V2 Decoder Layer
# =============================================================================

class DeepSeekDecoderLayer(nn.Module):
    """Single decoder layer with attention + MLP/MoE."""

    def __init__(self, config: LanguageConfig, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size

        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.self_attn = MultiHeadAttention(config, layer_idx)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # Layer 0 is dense MLP, rest are MoE
        is_dense = layer_idx < config.first_k_dense_replace
        if is_dense:
            self.mlp = SwiGLUMLP(config.hidden_size, config.intermediate_size)
            self.is_moe = False
        else:
            self.mlp = DeepSeekMoE(config)
            self.is_moe = True

    def __call__(
        self,
        hidden_states: mx.array,
        attention_mask: Optional[mx.array] = None,
        position_ids: Optional[mx.array] = None,
        past_key_value: Optional[Tuple[mx.array, mx.array]] = None,
        use_cache: bool = False,
    ) -> Tuple[mx.array, Optional[Tuple[mx.array, mx.array]]]:
        # Self-attention
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, present_kv = self.self_attn(
            hidden_states, attention_mask, position_ids, past_key_value, use_cache
        )
        hidden_states = residual + hidden_states

        # MLP / MoE
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states, present_kv


# =============================================================================
# DeepSeek-V2 Language Model
# =============================================================================

class DeepSeekModel(nn.Module):
    """DeepSeek-V2 Language Model (12 layers, MoE)."""

    def __init__(self, config: LanguageConfig):
        super().__init__()
        self.config = config

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = [
            DeepSeekDecoderLayer(config, i)
            for i in range(config.num_hidden_layers)
        ]
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def __call__(
        self,
        input_ids: Optional[mx.array] = None,
        inputs_embeds: Optional[mx.array] = None,
        attention_mask: Optional[mx.array] = None,
        position_ids: Optional[mx.array] = None,
        past_key_values: Optional[List[Tuple[mx.array, mx.array]]] = None,
        use_cache: bool = False,
    ) -> Tuple[mx.array, Optional[List[Tuple[mx.array, mx.array]]]]:

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        B, L, _ = inputs_embeds.shape

        # Create causal mask
        if attention_mask is None:
            attention_mask = mx.tril(mx.ones((L, L), dtype=mx.bool_))
            attention_mask = mx.where(attention_mask, 0.0, float('-inf'))
            attention_mask = attention_mask[None, None, :, :]  # [1, 1, L, L]

        # Create position IDs
        if position_ids is None:
            if past_key_values is not None and past_key_values[0] is not None:
                cache_len = past_key_values[0][0].shape[2]
                position_ids = mx.arange(cache_len, cache_len + L, dtype=mx.int32)[None, :]
            else:
                position_ids = mx.arange(0, L, dtype=mx.int32)[None, :]

        hidden_states = inputs_embeds
        new_kv_cache = [] if use_cache else None

        for i, layer in enumerate(self.layers):
            pkv = past_key_values[i] if past_key_values else None
            hidden_states, nkv = layer(
                hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=pkv,
                use_cache=use_cache,
            )
            if use_cache:
                new_kv_cache.append(nkv)

        hidden_states = self.norm(hidden_states)
        return hidden_states, new_kv_cache


# =============================================================================
# SAM-ViT-B Vision Encoder
# =============================================================================

class SAMAttention(nn.Module):
    """SAM attention block with relative position bias."""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        window_size: int = 0,
        use_rel_pos: bool = True,
        input_size: Tuple[int, int] = (64, 64),
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.window_size = window_size
        self.scale = self.head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=True)
        self.proj = nn.Linear(dim, dim, bias=True)

        self.use_rel_pos = use_rel_pos
        if use_rel_pos:
            self.rel_pos_h = mx.zeros((2 * input_size[0] - 1, self.head_dim))
            self.rel_pos_w = mx.zeros((2 * input_size[1] - 1, self.head_dim))

    def _get_rel_pos(self, H: int, W: int) -> mx.array:
        """Compute relative position bias."""
        if not self.use_rel_pos or self.window_size > 0:
            return 0.0

        # Height relative positions
        h_coords = mx.arange(H)
        h_rel = h_coords[:, None] - h_coords[None, :] + (H - 1)
        rh = self.rel_pos_h[h_rel]  # [H, H, head_dim]

        # Weight relative positions
        w_coords = mx.arange(W)
        w_rel = w_coords[:, None] - w_coords[None, :] + (W - 1)
        rw = self.rel_pos_w[w_rel]  # [W, W, head_dim]

        # Combine: for each head, compute Q @ R.T for all positions
        # Simplified: compute rel_pos as additive bias
        # rel_pos: [H*W, H*W]
        Rh = rh.reshape(H, 1, H, 1, self.head_dim).transpose(0, 3, 1, 2, 4)
        Rw = rw.reshape(1, W, 1, W, self.head_dim).transpose(0, 3, 1, 2, 4)

        return 0.0  # Simplified - full rel pos computation omitted for brevity

    def __call__(self, x: mx.array) -> mx.array:
        B, N, C = x.shape
        H = W = int(N ** 0.5)

        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
        q = q.transpose(0, 2, 1, 3)  # [B, heads, N, head_dim]
        k = k.transpose(0, 2, 1, 3)
        v = v.transpose(0, 2, 1, 3)

        # Window attention
        if self.window_size > 0:
            attn = self._window_attention(q, k, v, H, W)
        else:
            attn = (q @ k.transpose(0, 1, 3, 2)) * self.scale
            attn = mx.softmax(attn.astype(mx.float32), axis=-1).astype(q.dtype)
            attn = attn @ v

        attn = attn.transpose(0, 2, 1, 3).reshape(B, N, C)
        return self.proj(attn)

    def _window_attention(self, q, k, v, H, W):
        """Window-based attention for SAM blocks with padding support."""
        B, heads, N, d = q.shape
        ws = self.window_size

        # Pad if needed
        pad_h = (ws - H % ws) % ws
        pad_w = (ws - W % ws) % ws
        Hp, Wp = H + pad_h, W + pad_w

        def pad_tensor(x, H, W, pad_h, pad_w):
            # x: [B, heads, H*W, d]
            x = x.reshape(B, heads, H, W, d)
            if pad_h > 0 or pad_w > 0:
                x = mx.pad(x, [(0, 0), (0, 0), (0, pad_h), (0, pad_w), (0, 0)])
            return x

        q_p = pad_tensor(q, H, W, pad_h, pad_w)
        k_p = pad_tensor(k, H, W, pad_h, pad_w)
        v_p = pad_tensor(v, H, W, pad_h, pad_w)

        # Now partition into windows
        nw_h, nw_w = Hp // ws, Wp // ws

        def window_partition(x):
            # x: [B, heads, Hp, Wp, d]
            x = x.reshape(B, heads, nw_h, ws, nw_w, ws, d)
            x = x.transpose(0, 1, 2, 4, 3, 5, 6)  # [B, heads, nw_h, nw_w, ws, ws, d]
            x = x.reshape(B * nw_h * nw_w, heads, ws * ws, d)
            return x

        def window_reverse(x):
            x = x.reshape(B, heads, nw_h, nw_w, ws, ws, d)
            x = x.transpose(0, 1, 2, 4, 3, 5, 6)  # [B, heads, nw_h, ws, nw_w, ws, d]
            x = x.reshape(B, heads, Hp, Wp, d)
            return x

        q_w = window_partition(q_p)
        k_w = window_partition(k_p)
        v_w = window_partition(v_p)

        attn = (q_w @ k_w.transpose(0, 1, 3, 2)) * self.scale
        attn = mx.softmax(attn.astype(mx.float32), axis=-1).astype(q.dtype)
        out_w = attn @ v_w

        out = window_reverse(out_w)

        # Crop back to original size
        if pad_h > 0:
            out = out[:, :, :H, :, :]
        if pad_w > 0:
            out = out[:, :, :, :W, :]

        out = out.reshape(B, heads, H * W, d)
        return out


class SAMMLP(nn.Module):
    """SAM MLP block."""

    def __init__(self, dim: int, mlp_dim: int):
        super().__init__()
        self.lin1 = nn.Linear(dim, mlp_dim)
        self.lin2 = nn.Linear(mlp_dim, dim)

    def __call__(self, x):
        return self.lin2(nn.gelu(self.lin1(x)))


class SAMBlock(nn.Module):
    """SAM ViT block."""

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        window_size: int = 0,
        use_rel_pos: bool = True,
        input_size: Tuple[int, int] = (64, 64),
    ):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim, eps=1e-6)
        self.attn = SAMAttention(
            dim, num_heads,
            window_size=window_size,
            use_rel_pos=use_rel_pos,
            input_size=input_size,
        )
        self.norm2 = nn.LayerNorm(dim, eps=1e-6)
        self.mlp = SAMMLP(dim, int(dim * mlp_ratio))

    def __call__(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.mlp(self.norm2(x))
        return x


class PatchEmbed(nn.Module):
    """Patch embedding for SAM. Uses NHWC format for MLX."""

    def __init__(self, kernel_size=16, stride=16, in_chans=3, embed_dim=768):
        super().__init__()
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size, stride=stride, bias=True)

    def __call__(self, x):
        # x: [B, H, W, C] (NHWC)
        return self.proj(x)


class SAMVisionEncoder(nn.Module):
    """SAM-ViT-B vision encoder."""

    def __init__(self, config: VisionConfig):
        super().__init__()
        self.img_size = config.sam_img_size
        self.patch_size = config.sam_patch_size
        grid_size = self.img_size // self.patch_size  # 64

        self.patch_embed = PatchEmbed(
            kernel_size=config.sam_patch_size,
            stride=config.sam_patch_size,
            in_chans=3,
            embed_dim=config.sam_embed_dim,
        )
        self.pos_embed = mx.zeros((1, grid_size, grid_size, config.sam_embed_dim))

        input_size = (grid_size, grid_size)
        self.blocks = []
        for i in range(config.sam_depth):
            use_global = i in config.sam_global_attn_indexes
            window_size = 0 if use_global else config.sam_window_size
            self.blocks.append(SAMBlock(
                dim=config.sam_embed_dim,
                num_heads=config.sam_num_heads,
                mlp_ratio=config.sam_mlp_ratio,
                window_size=window_size,
                input_size=input_size,
            ))

        # Neck
        self.neck = nn.Sequential(
            nn.Conv2d(config.sam_embed_dim, config.sam_out_chans, 1, bias=False),
            nn.LayerNorm(config.sam_out_chans, eps=1e-6),
            nn.Conv2d(config.sam_out_chans, config.sam_out_chans, 3, padding=1, bias=False),
            nn.LayerNorm(config.sam_out_chans, eps=1e-6),
        )

        # Downsampling convolutions
        self.net_2 = nn.Conv2d(256, 512, 3, stride=2, padding=1, bias=False)
        self.net_3 = nn.Conv2d(512, 1024, 3, stride=2, padding=1, bias=False)

    def __call__(self, x: mx.array) -> mx.array:
        # x: [B, H, W, C] (NHWC format for MLX)
        B, H_in, W_in, C_in = x.shape

        x = self.patch_embed(x)  # [B, H_p, W_p, 768]
        H_p, W_p = x.shape[1], x.shape[2]

        # Add positional embedding (flatten to sequence)
        x = x.reshape(B, H_p * W_p, -1)  # [B, N, 768]

        if self.pos_embed.shape[1] != H_p:
            pos = _interpolate_pos_embed(self.pos_embed, H_p)
        else:
            pos = self.pos_embed
        pos = pos.reshape(1, H_p * W_p, -1)
        x = x + pos

        for blk in self.blocks:
            x = blk(x)

        # Back to NHWC for convolution
        x = x.reshape(B, H_p, W_p, -1)  # [B, 64, 64, 768]

        # Neck (Conv2d with NHWC)
        x = self.neck(x)  # [B, 64, 64, 256]

        # Downsampling (NHWC)
        x = self.net_2(x)  # [B, 32, 32, 512]
        x = self.net_3(x)  # [B, 16, 16, 1024]

        # Return in NHWC then convert to NCHW for CLIP compatibility
        return x


def _interpolate_pos_embed(pos_embed, target_size):
    """Interpolate position embeddings to target grid size."""
    # pos_embed: [1, src, src, dim]
    B = pos_embed.shape[0]
    src = pos_embed.shape[1]
    dim = pos_embed.shape[-1]

    # Reshape to [B, dim, src, src]
    x = pos_embed.transpose(0, 3, 1, 2)
    # Simple interpolation using reshape
    # MLX doesn't have native interpolate, use simple scaling
    x = x.reshape(B, dim, src * src)
    x = x.reshape(B, dim, target_size, target_size)
    x = x.transpose(0, 2, 3, 1)
    return x


# =============================================================================
# CLIP-L Vision Encoder
# =============================================================================

class CLIPAttention(nn.Module):
    """CLIP multi-head self-attention."""

    def __init__(self, hidden_size: int, num_heads: int):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads
        self.qkv_proj = nn.Linear(hidden_size, hidden_size * 3, bias=True)
        self.out_proj = nn.Linear(hidden_size, hidden_size, bias=True)
        self.scale = self.head_dim ** -0.5

    def __call__(self, x):
        B, N, C = x.shape
        qkv = self.qkv_proj(x).reshape(B, N, 3, self.num_heads, self.head_dim)
        q, k, v = qkv[:, :, 0].transpose(0, 2, 1, 3), qkv[:, :, 1].transpose(0, 2, 1, 3), qkv[:, :, 2].transpose(0, 2, 1, 3)

        attn = (q @ k.transpose(0, 1, 3, 2)) * self.scale
        attn = mx.softmax(attn.astype(mx.float32), axis=-1).astype(q.dtype)
        out = attn @ v
        out = out.transpose(0, 2, 1, 3).reshape(B, N, C)
        return self.out_proj(out)


class CLIPMLP(nn.Module):
    """CLIP MLP with QuickGELU."""

    def __init__(self, hidden_size: int, ffn_hidden_size: int):
        super().__init__()
        self.fc1 = nn.Linear(hidden_size, ffn_hidden_size, bias=True)
        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size, bias=True)

    def __call__(self, x):
        # QuickGELU: fc1 → QuickGELU → fc2
        h = self.fc1(x)
        h = h * mx.sigmoid(1.702 * h)
        return self.fc2(h)


class CLIPTransformerLayer(nn.Module):
    """CLIP transformer layer."""

    def __init__(self, hidden_size: int, num_heads: int, ffn_hidden_size: int, eps: float = 1e-5):
        super().__init__()
        self.layer_norm1 = nn.LayerNorm(hidden_size, eps=eps)
        self.self_attn = CLIPAttention(hidden_size, num_heads)
        self.layer_norm2 = nn.LayerNorm(hidden_size, eps=eps)
        self.mlp = CLIPMLP(hidden_size, ffn_hidden_size)

    def __call__(self, x):
        x = x + self.self_attn(self.layer_norm1(x))
        x = x + self.mlp(self.layer_norm2(x))
        return x


class CLIPVisionEmbeddings(nn.Module):
    """CLIP vision embeddings that takes SAM features as input."""

    def __init__(self, hidden_size: int = 1024, image_size: int = 224, patch_size: int = 14):
        super().__init__()
        self.embed_dim = hidden_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = (image_size // patch_size) ** 2
        self.num_positions = self.num_patches + 1

        self.class_embedding = mx.zeros((hidden_size,))

        # Patch embedding (projects SAM features) - NHWC conv
        self.patch_embedding = nn.Conv2d(3, hidden_size, patch_size, stride=patch_size, bias=False)

        # Position embedding
        self.position_embedding = nn.Embedding(self.num_positions, hidden_size)
        self.position_ids = mx.arange(self.num_positions)[None, :]

    def __call__(self, pixel_values, patch_embeds=None):
        batch_size = pixel_values.shape[0]

        if patch_embeds is not None:
            # Use pre-computed SAM features
            # patch_embeds: [B, H, W, C] (NHWC from SAM)
            B, H, W, C = patch_embeds.shape
            patch_embeds = patch_embeds.reshape(B, H * W, C)
        else:
            # Use raw conv on NHWC input
            patch_embeds = self.patch_embedding(pixel_values)
            B, H, W, C = patch_embeds.shape
            patch_embeds = patch_embeds.reshape(B, H * W, C)

        class_embeds = mx.tile(self.class_embedding.reshape(1, 1, -1), (batch_size, 1, 1))
        embeddings = mx.concatenate([class_embeds, patch_embeds], axis=1)

        # Add position embeddings with interpolation
        pos_ids = self.position_ids[:, :embeddings.shape[1]]
        pos_embeds = self.position_embedding(pos_ids)
        embeddings = embeddings + pos_embeds

        return embeddings


class CLIPVisionTransformer(nn.Module):
    """CLIP-L vision transformer."""

    def __init__(self, config: VisionConfig):
        super().__init__()
        self.embeddings = CLIPVisionEmbeddings(
            hidden_size=config.clip_hidden_size,
            image_size=config.clip_image_size,
            patch_size=config.clip_patch_size,
        )
        self.pre_layrnorm = nn.LayerNorm(config.clip_hidden_size, eps=config.clip_layernorm_epsilon)
        self.transformer = nn.Sequential(*[
            CLIPTransformerLayer(
                config.clip_hidden_size,
                config.clip_num_heads,
                config.clip_ffn_hidden_size,
                eps=config.clip_layernorm_epsilon,
            )
            for _ in range(config.clip_num_layers)
        ])

    def __call__(self, pixel_values, patch_embeds=None):
        x = self.embeddings(pixel_values, patch_embeds)
        x = self.pre_layrnorm(x)
        x = self.transformer(x)
        return x


# =============================================================================
# Projector
# =============================================================================

class MlpProjector(nn.Module):
    """Linear projector from vision to language space."""

    def __init__(self, config: ProjectorConfig):
        super().__init__()
        self.layers = nn.Linear(config.input_dim, config.n_embed, bias=True)

    def __call__(self, x):
        return self.layers(x)


# =============================================================================
# Unlimited OCR Model
# =============================================================================

@dataclass
class ModelOutput:
    logits: mx.array
    past_key_values: Optional[List[Tuple[mx.array, mx.array]]] = None


class UnlimitedOCRModel(nn.Module):
    """Complete Unlimited-OCR model with vision + language.

    Architecture:
    Image → SAM-ViT-B → CLIP-L → Projector → DeepSeek-V2 MoE → Text
    """

    def __init__(self, config: UnlimitedOCRConfig):
        super().__init__()
        self.config = config

        # Vision
        self.sam_model = SAMVisionEncoder(config.vision)
        self.vision_model = CLIPVisionTransformer(config.vision)

        # Projector: 2048 → 1280
        self.projector = MlpProjector(config.projector)

        # Language
        self.language_model = DeepSeekModel(config.language)
        self.lm_head = nn.Linear(config.language.hidden_size, config.language.vocab_size, bias=False)

        # Image special tokens
        embed_std = 1.0 / math.sqrt(config.language.hidden_size)
        self.image_newline = mx.random.normal((config.language.hidden_size,)) * embed_std
        self.view_seperator = mx.random.normal((config.language.hidden_size,)) * embed_std

    def encode_images(self, images: mx.array, images_spatial_crop=None) -> List[mx.array]:
        """Encode images through vision encoder.

        Args:
            images: List of [patches, original] image tensors (in NCHW from preprocessing)
            images_spatial_crop: List of (width_crops, height_crops) tuples

        Returns:
            List of image feature tensors [N, hidden_size]
        """
        all_features = []

        for idx, image_pair in enumerate(images):
            patches = image_pair[0]  # [N, 3, 640, 640] NCHW
            image_ori = image_pair[1]  # [1, 3, 1024, 1024] NCHW

            has_patches = patches is not None and patches.shape[0] > 0

            # Convert to NHWC for MLX conv
            def to_nhwc(t):
                if t is None:
                    return None
                ndim = len(t.shape)
                if ndim == 4:
                    return t.transpose(0, 2, 3, 1)  # NCHW → NHWC
                return t

            patches_nhwc = to_nhwc(patches)
            image_ori_nhwc = to_nhwc(image_ori)

            if has_patches and images_spatial_crop is not None:
                crop_shape = images_spatial_crop[idx]
                width_crop_num, height_crop_num = crop_shape

                # Process patches (local features)
                sam_local = self.sam_model(patches_nhwc)  # [P, 16, 16, 1024]
                clip_local = self.vision_model(patches_nhwc, sam_local)  # [P, 257, 1024]

                # Combine: CLIP[:, 1:] + SAM flatten
                # SAM: [P, 16, 16, 1024] → [P, 256, 1024]
                sam_flat = sam_local.reshape(patches.shape[0], -1, 1024)
                local_feats = mx.concatenate([
                    clip_local[:, 1:, :],  # [P, 256, 1024]
                    sam_flat,  # [P, 256, 1024]
                ], axis=-1)  # [P, 256, 2048]
                local_feats = self.projector(local_feats)  # [P, 256, 1280]

                # Process original (global features)
                sam_global = self.sam_model(image_ori_nhwc)  # [1, 16, 16, 1024]
                clip_global = self.vision_model(image_ori_nhwc, sam_global)  # [1, 257, 1024]

                sam_gflat = sam_global.reshape(1, -1, 1024)
                global_feats = mx.concatenate([
                    clip_global[:, 1:, :],  # [1, 256, 1024]
                    sam_gflat,  # [1, 256, 1024]
                ], axis=-1)  # [1, 256, 2048]
                global_feats = self.projector(global_feats)  # [1, 256, 1280]

                # Reshape and organize
                _, hw_g, nd = global_feats.shape
                h_g = w_g = int(hw_g ** 0.5)

                _, hw_l, nd2 = local_feats.shape
                h_l = w_l = int(hw_l ** 0.5)

                # Global: reshape to 2D and add newlines
                gf = global_feats.reshape(h_g, w_g, nd)
                gf = mx.concatenate([gf, mx.tile(self.image_newline[None, None, :], (h_g, 1, 1))], axis=1)
                gf = gf.reshape(-1, nd)

                # Local: reshape grid
                lf = local_feats.reshape(height_crop_num, width_crop_num, h_l, w_l, nd2)
                lf = lf.transpose(0, 2, 1, 3, 4).reshape(height_crop_num * h_l, width_crop_num * w_l, nd2)
                lf = mx.concatenate([lf, mx.tile(self.image_newline[None, None, :], (height_crop_num * h_l, 1, 1))], axis=1)
                lf = lf.reshape(-1, nd2)

                # Concat: local + global + separator
                full_feats = mx.concatenate([lf, gf, self.view_seperator[None, :]], axis=0)
                all_features.append(full_feats)

            else:
                # Multiple images or single image without crop
                if len(image_ori_nhwc.shape) == 3:
                    image_ori_nhwc = image_ori_nhwc[None, :, :, :]

                num_imgs = image_ori_nhwc.shape[0]
                for i in range(num_imgs):
                    img = image_ori_nhwc[i:i+1]
                    sam_out = self.sam_model(img)
                    clip_out = self.vision_model(img, sam_out)

                    sam_flat = sam_out.reshape(1, -1, 1024)
                    gf = mx.concatenate([
                        clip_out[:, 1:, :],
                        sam_flat,
                    ], axis=-1)
                    gf = self.projector(gf)

                    _, hw, nd = gf.shape
                    h = w = int(hw ** 0.5)

                    gf_2d = gf.reshape(h, w, nd)
                    gf_2d = mx.concatenate([gf_2d, mx.tile(self.image_newline[None, None, :], (h, 1, 1))], axis=1)
                    gf_2d = gf_2d.reshape(-1, nd)

                    full_feats = mx.concatenate([gf_2d, self.view_seperator[None, :]], axis=0)
                    all_features.append(full_feats)

        return all_features

    def __call__(
        self,
        input_ids: Optional[mx.array] = None,
        attention_mask: Optional[mx.array] = None,
        position_ids: Optional[mx.array] = None,
        past_key_values: Optional[List[Tuple[mx.array, mx.array]]] = None,
        inputs_embeds: Optional[mx.array] = None,
        images: Optional[List[mx.array]] = None,
        images_seq_mask: Optional[mx.array] = None,
        images_spatial_crop: Optional[List[Tuple[int, int]]] = None,
        use_cache: bool = False,
    ) -> ModelOutput:
        B = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]

        if inputs_embeds is None:
            inputs_embeds = self.language_model.embed_tokens(input_ids)

        # Inject image features into embeddings
        if images is not None and images_seq_mask is not None:
            image_features = self.encode_images(images, images_spatial_crop)

            for idx, img_feats in enumerate(image_features):
                if img_feats is not None and img_feats.shape[0] > 0:
                    mask = images_seq_mask[idx].reshape(-1, 1)
                    # Scatter image features into positions where mask is True
                    inputs_embeds = inputs_embeds.at[idx].set(
                        mx.where(mask, img_feats, inputs_embeds[idx])
                    )

        hidden_states, new_kv = self.language_model(
            input_ids=None,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
        )

        logits = self.lm_head(hidden_states)
        return ModelOutput(logits=logits, past_key_values=new_kv)

    def generate(
        self,
        input_ids: mx.array,
        images: Optional[List] = None,
        images_seq_mask: Optional[mx.array] = None,
        images_spatial_crop: Optional[List] = None,
        max_length: int = 32768,
        temperature: float = 0.0,
        eos_token_id: int = 1,
    ) -> mx.array:
        """Autoregressive text generation."""
        generated = [input_ids]
        past_kv = None
        use_images = (images is not None)

        for step in range(max_length):
            if step == 0:
                # Prefill: process full sequence with images
                output = self(
                    input_ids=input_ids,
                    images=images if use_images else None,
                    images_seq_mask=images_seq_mask if use_images else None,
                    images_spatial_crop=images_spatial_crop if use_images else None,
                    use_cache=True,
                )
            else:
                # Decode: process only the last token
                output = self(
                    input_ids=input_ids[:, -1:],
                    past_key_values=past_kv,
                    use_cache=True,
                )

            past_kv = output.past_key_values
            logits = output.logits[:, -1, :]

            if temperature > 0:
                logits = logits / temperature
                probs = mx.softmax(logits.astype(mx.float32), axis=-1)
                next_token = mx.random.categorical(probs, axis=-1).reshape(1, 1)
            else:
                next_token = mx.argmax(logits, axis=-1, keepdims=True)

            generated.append(next_token)
            input_ids = next_token

            if next_token.item() == eos_token_id:
                break

        return mx.concatenate(generated, axis=1)