mlx

-module(mlx_nn).

%% Advanced neural network layers and architectures for SOTA ML
-export([
    %% Core layer types
    linear/3, linear/4,
    embedding/3, embedding/4,
    
    %% Convolutional layers
    conv1d/4, conv1d/5, conv1d/6,
    conv2d/4, conv2d/5, conv2d/6,
    conv3d/4, conv3d/5, conv3d/6,
    conv_transpose1d/4, conv_transpose2d/4, conv_transpose3d/4,
    
    %% Pooling layers
    max_pool1d/2, max_pool1d/3,
    max_pool2d/2, max_pool2d/3,
    avg_pool1d/2, avg_pool1d/3,
    avg_pool2d/2, avg_pool2d/3,
    adaptive_avg_pool1d/2, adaptive_avg_pool2d/2,
    global_avg_pool/1, global_max_pool/1,
    
    %% Attention mechanisms
    scaled_dot_product_attention/3, scaled_dot_product_attention/4,
    multi_head_attention/6, multi_head_attention/7,
    self_attention/3, self_attention/4,
    cross_attention/5, cross_attention/6,
    
    %% Transformer components
    transformer_encoder_layer/6, transformer_encoder_layer/7,
    transformer_decoder_layer/7, transformer_decoder_layer/8,
    positional_encoding/3, positional_encoding/4,
    
    %% Normalization layers
    layer_norm/2, layer_norm/3, layer_norm/4,
    batch_norm/2, batch_norm/3, batch_norm/4,
    group_norm/3, group_norm/4,
    instance_norm/2, instance_norm/3,
    rms_norm/2, rms_norm/3,
    
    %% Dropout and regularization
    dropout/2, dropout/3,
    dropout2d/2, dropout3d/2,
    alpha_dropout/2, feature_alpha_dropout/2,
    
    %% Advanced architectures
    residual_block/3, residual_block/4,
    dense_block/3, dense_block/4,
    squeeze_excitation/2, squeeze_excitation/3,
    
    %% Recurrent layers
    lstm_cell/4, lstm_cell/5,
    gru_cell/3, gru_cell/4,
    rnn_cell/3, rnn_cell/4,
    
    %% Modern architectures
    vision_transformer_block/4, vision_transformer_block/5,
    swin_transformer_block/5, swin_transformer_block/6,
    efficientnet_block/5, efficientnet_block/6,
    
    %% Initialization functions
    xavier_uniform/1, xavier_normal/1,
    kaiming_uniform/1, kaiming_normal/1,
    orthogonal_init/1, identity_init/1
]).

%% Core layer implementations
linear(Input, Weight, Bias) ->
    linear(Input, Weight, Bias, #{}).

linear(Input, Weight, Bias, Options) ->
    % Linear transformation: y = xW^T + b
    {ok, Output} = mlx:matmul(Input, mlx:transpose(Weight)),
    case Bias of
        undefined -> {ok, Output};
        _ -> mlx:add(Output, Bias)
    end.

embedding(Input, EmbeddingMatrix, VocabSize) ->
    embedding(Input, EmbeddingMatrix, VocabSize, #{}).

embedding(Input, EmbeddingMatrix, VocabSize, Options) ->
    % Embedding layer: lookup embeddings by index
    mlx_nif:embedding(Input, EmbeddingMatrix, VocabSize, Options).

%% Convolutional layers
conv1d(Input, Weight, Bias, Stride) ->
    conv1d(Input, Weight, Bias, Stride, 0, 1).

conv1d(Input, Weight, Bias, Stride, Padding) ->
    conv1d(Input, Weight, Bias, Stride, Padding, 1).

conv1d(Input, Weight, Bias, Stride, Padding, Dilation) ->
    {ok, Output} = mlx_nif:conv1d(Input, Weight, #{
        stride => Stride,
        padding => Padding,
        dilation => Dilation
    }),
    case Bias of
        undefined -> {ok, Output};
        _ -> mlx:add(Output, Bias)
    end.

conv2d(Input, Weight, Bias, Stride) ->
    conv2d(Input, Weight, Bias, Stride, 0, 1).

conv2d(Input, Weight, Bias, Stride, Padding) ->
    conv2d(Input, Weight, Bias, Stride, Padding, 1).

conv2d(Input, Weight, Bias, Stride, Padding, Dilation) ->
    {ok, Output} = mlx_nif:conv2d(Input, Weight, #{
        stride => Stride,
        padding => Padding,
        dilation => Dilation
    }),
    case Bias of
        undefined -> {ok, Output};
        _ -> mlx:add(Output, Bias)
    end.

conv3d(Input, Weight, Bias, Stride) ->
    conv3d(Input, Weight, Bias, Stride, 0, 1).

conv3d(Input, Weight, Bias, Stride, Padding) ->
    conv3d(Input, Weight, Bias, Stride, Padding, 1).

conv3d(Input, Weight, Bias, Stride, Padding, Dilation) ->
    {ok, Output} = mlx_nif:conv3d(Input, Weight, #{
        stride => Stride,
        padding => Padding,
        dilation => Dilation
    }),
    case Bias of
        undefined -> {ok, Output};
        _ -> mlx:add(Output, Bias)
    end.

conv_transpose1d(Input, Weight, Bias, Stride) ->
    mlx_nif:conv_transpose1d(Input, Weight, Bias, Stride).

conv_transpose2d(Input, Weight, Bias, Stride) ->
    mlx_nif:conv_transpose2d(Input, Weight, Bias, Stride).

conv_transpose3d(Input, Weight, Bias, Stride) ->
    mlx_nif:conv_transpose3d(Input, Weight, Bias, Stride).

%% Pooling layers
max_pool1d(Input, KernelSize) ->
    max_pool1d(Input, KernelSize, KernelSize).

max_pool1d(Input, KernelSize, Stride) ->
    mlx_nif:max_pool1d(Input, KernelSize, Stride).

max_pool2d(Input, KernelSize) ->
    max_pool2d(Input, KernelSize, KernelSize).

max_pool2d(Input, KernelSize, Stride) ->
    mlx_nif:max_pool2d(Input, KernelSize, Stride).

avg_pool1d(Input, KernelSize) ->
    avg_pool1d(Input, KernelSize, KernelSize).

avg_pool1d(Input, KernelSize, Stride) ->
    mlx_nif:avg_pool1d(Input, KernelSize, Stride).

avg_pool2d(Input, KernelSize) ->
    avg_pool2d(Input, KernelSize, KernelSize).

avg_pool2d(Input, KernelSize, Stride) ->
    mlx_nif:avg_pool2d(Input, KernelSize, Stride).

adaptive_avg_pool1d(Input, OutputSize) ->
    mlx_nif:adaptive_avg_pool1d(Input, OutputSize).

adaptive_avg_pool2d(Input, OutputSize) ->
    mlx_nif:adaptive_avg_pool2d(Input, OutputSize).

global_avg_pool(Input) ->
    % Global average pooling
    {ok, Shape} = mlx:shape(Input),
    SpatialDims = lists:seq(2, length(Shape) - 1),
    mlx:mean(Input, SpatialDims, true).

global_max_pool(Input) ->
    % Global max pooling
    {ok, Shape} = mlx:shape(Input),
    SpatialDims = lists:seq(2, length(Shape) - 1),
    mlx:max(Input, SpatialDims, true).

%% Attention mechanisms
scaled_dot_product_attention(Query, Key, Value) ->
    scaled_dot_product_attention(Query, Key, Value, undefined).

scaled_dot_product_attention(Query, Key, Value, Mask) ->
    % Scaled dot-product attention
    {ok, [_B, _N, DK]} = mlx:shape(Key),
    {ok, Scale} = mlx:array(1.0 / math:sqrt(DK)),
    
    % Compute attention scores
    {ok, Scores} = mlx:matmul(Query, mlx:transpose(Key, [0, 2, 1])),
    {ok, ScaledScores} = mlx:multiply(Scores, Scale),
    
    % Apply mask if provided
    ScoresWithMask = case Mask of
        undefined -> ScaledScores;
        _ ->
            {ok, MaskedScores} = mlx:where(Mask, ScaledScores, mlx:array(-1.0e9)),
            MaskedScores
    end,
    
    % Compute attention weights
    {ok, AttentionWeights} = mlx:softmax(ScoresWithMask, -1),
    
    % Apply attention to values
    {ok, Output} = mlx:matmul(AttentionWeights, Value),
    {ok, {Output, AttentionWeights}}.

multi_head_attention(Query, Key, Value, WQ, WK, WV) ->
    multi_head_attention(Query, Key, Value, WQ, WK, WV, #{}).

multi_head_attention(Query, Key, Value, WQ, WK, WV, Options) ->
    NumHeads = maps:get(num_heads, Options, 8),
    Dropout = maps:get(dropout, Options, 0.0),
    
    % Linear projections
    {ok, Q} = linear(Query, WQ, undefined),
    {ok, K} = linear(Key, WK, undefined),
    {ok, V} = linear(Value, WV, undefined),
    
    % Reshape for multi-head attention
    {ok, [BatchSize, SeqLen, DModel]} = mlx:shape(Q),
    DHead = DModel div NumHeads,
    
    {ok, QReshaped} = mlx:reshape(Q, [BatchSize, SeqLen, NumHeads, DHead]),
    {ok, KReshaped} = mlx:reshape(K, [BatchSize, SeqLen, NumHeads, DHead]),
    {ok, VReshaped} = mlx:reshape(V, [BatchSize, SeqLen, NumHeads, DHead]),
    
    % Transpose to [BatchSize, NumHeads, SeqLen, DHead]
    {ok, QTransposed} = mlx:transpose(QReshaped, [0, 2, 1, 3]),
    {ok, KTransposed} = mlx:transpose(KReshaped, [0, 2, 1, 3]),
    {ok, VTransposed} = mlx:transpose(VReshaped, [0, 2, 1, 3]),
    
    % Scaled dot-product attention
    {ok, {AttentionOutput, Weights}} = scaled_dot_product_attention(
        QTransposed, KTransposed, VTransposed),
    
    % Transpose back and reshape
    {ok, OutputTransposed} = mlx:transpose(AttentionOutput, [0, 2, 1, 3]),
    {ok, Output} = mlx:reshape(OutputTransposed, [BatchSize, SeqLen, DModel]),
    
    % Apply dropout if training
    case Dropout > 0.0 of
        true -> dropout(Output, Dropout);
        false -> {ok, Output}
    end.

self_attention(Input, WQ, WK) ->
    self_attention(Input, WQ, WK, #{}).

self_attention(Input, WQ, WK, Options) ->
    WV = maps:get(wv, Options, WK),
    multi_head_attention(Input, Input, Input, WQ, WK, WV, Options).

cross_attention(Query, Key, Value, WQ, WK) ->
    cross_attention(Query, Key, Value, WQ, WK, #{}).

cross_attention(Query, Key, Value, WQ, WK, Options) ->
    WV = maps:get(wv, Options, WK),
    multi_head_attention(Query, Key, Value, WQ, WK, WV, Options).

%% Transformer components
transformer_encoder_layer(Input, SelfAttnW, FFNWeights, LayerNorm1, LayerNorm2, Dropout) ->
    transformer_encoder_layer(Input, SelfAttnW, FFNWeights, LayerNorm1, LayerNorm2, Dropout, #{}).

transformer_encoder_layer(Input, SelfAttnW, FFNWeights, LayerNorm1, LayerNorm2, Dropout, Options) ->
    % Self-attention block
    {ok, AttnOutput} = self_attention(Input, SelfAttnW, SelfAttnW, Options),
    {ok, AttnDropped} = dropout(AttnOutput, Dropout),
    {ok, AttnResidual} = mlx:add(Input, AttnDropped),
    {ok, AttnNormed} = layer_norm(AttnResidual, LayerNorm1),
    
    % Feed-forward block
    [W1, W2] = FFNWeights,
    {ok, FFNIntermediate} = linear(AttnNormed, W1, undefined),
    {ok, FFNActivated} = mlx:relu(FFNIntermediate),
    {ok, FFNOutput} = linear(FFNActivated, W2, undefined),
    {ok, FFNDropped} = dropout(FFNOutput, Dropout),
    {ok, FFNResidual} = mlx:add(AttnNormed, FFNDropped),
    layer_norm(FFNResidual, LayerNorm2).

transformer_decoder_layer(Input, Memory, SelfAttnW, CrossAttnW, FFNWeights, LayerNorms, Dropout) ->
    transformer_decoder_layer(Input, Memory, SelfAttnW, CrossAttnW, FFNWeights, LayerNorms, Dropout, #{}).

transformer_decoder_layer(Input, Memory, SelfAttnW, CrossAttnW, FFNWeights, LayerNorms, Dropout, Options) ->
    [LayerNorm1, LayerNorm2, LayerNorm3] = LayerNorms,
    
    % Self-attention block
    {ok, SelfAttnOutput} = self_attention(Input, SelfAttnW, SelfAttnW, Options),
    {ok, SelfAttnDropped} = dropout(SelfAttnOutput, Dropout),
    {ok, SelfAttnResidual} = mlx:add(Input, SelfAttnDropped),
    {ok, SelfAttnNormed} = layer_norm(SelfAttnResidual, LayerNorm1),
    
    % Cross-attention block
    {ok, CrossAttnOutput} = cross_attention(SelfAttnNormed, Memory, Memory, CrossAttnW, CrossAttnW, Options),
    {ok, CrossAttnDropped} = dropout(CrossAttnOutput, Dropout),
    {ok, CrossAttnResidual} = mlx:add(SelfAttnNormed, CrossAttnDropped),
    {ok, CrossAttnNormed} = layer_norm(CrossAttnResidual, LayerNorm2),
    
    % Feed-forward block
    [W1, W2] = FFNWeights,
    {ok, FFNIntermediate} = linear(CrossAttnNormed, W1, undefined),
    {ok, FFNActivated} = mlx:relu(FFNIntermediate),
    {ok, FFNOutput} = linear(FFNActivated, W2, undefined),
    {ok, FFNDropped} = dropout(FFNOutput, Dropout),
    {ok, FFNResidual} = mlx:add(CrossAttnNormed, FFNDropped),
    layer_norm(FFNResidual, LayerNorm3).

positional_encoding(MaxLen, DModel, Base) ->
    positional_encoding(MaxLen, DModel, Base, float32).

positional_encoding(MaxLen, DModel, Base, Dtype) ->
    % Sinusoidal positional encoding
    {ok, Position} = mlx:arange(0, MaxLen, 1),
    {ok, PosReshaped} = mlx:reshape(Position, [MaxLen, 1]),
    
    {ok, DimIndices} = mlx:arange(0, DModel div 2, 1),
    {ok, DivTerm} = mlx:power(mlx:array(Base), 
                             mlx:divide(mlx:multiply(DimIndices, mlx:array(2.0)), 
                                       mlx:array(DModel))),
    
    {ok, Args} = mlx:divide(PosReshaped, DivTerm),
    {ok, SinEncoding} = mlx:sin(Args),
    {ok, CosEncoding} = mlx:cos(Args),
    
    % Interleave sin and cos
    {ok, Encoding} = mlx:stack([SinEncoding, CosEncoding], -1),
    {ok, EncodingFlat} = mlx:reshape(Encoding, [MaxLen, DModel]),
    
    case Dtype of
        float32 -> {ok, EncodingFlat};
        _ -> mlx:cast(EncodingFlat, Dtype)
    end.

%% Normalization layers
layer_norm(Input, Weight) ->
    layer_norm(Input, Weight, undefined, 1.0e-5).

layer_norm(Input, Weight, Bias) ->
    layer_norm(Input, Weight, Bias, 1.0e-5).

layer_norm(Input, Weight, Bias, Eps) ->
    % Layer normalization
    {ok, Mean} = mlx:mean(Input, -1, true),
    {ok, Variance} = mlx:var(Input, -1, true),
    {ok, EpsArray} = mlx:array(Eps),
    {ok, VarPlusEps} = mlx:add(Variance, EpsArray),
    {ok, Std} = mlx:sqrt(VarPlusEps),
    {ok, Normalized} = mlx:divide(mlx:subtract(Input, Mean), Std),
    
    {ok, Scaled} = mlx:multiply(Normalized, Weight),
    case Bias of
        undefined -> {ok, Scaled};
        _ -> mlx:add(Scaled, Bias)
    end.

batch_norm(Input, Weight) ->
    batch_norm(Input, Weight, undefined, 1.0e-5).

batch_norm(Input, Weight, Bias) ->
    batch_norm(Input, Weight, Bias, 1.0e-5).

batch_norm(Input, Weight, Bias, Eps) ->
    % Batch normalization
    {ok, Mean} = mlx:mean(Input, 0, true),
    {ok, Variance} = mlx:var(Input, 0, true),
    {ok, EpsArray} = mlx:array(Eps),
    {ok, VarPlusEps} = mlx:add(Variance, EpsArray),
    {ok, Std} = mlx:sqrt(VarPlusEps),
    {ok, Normalized} = mlx:divide(mlx:subtract(Input, Mean), Std),
    
    {ok, Scaled} = mlx:multiply(Normalized, Weight),
    case Bias of
        undefined -> {ok, Scaled};
        _ -> mlx:add(Scaled, Bias)
    end.

group_norm(Input, NumGroups, Weight) ->
    group_norm(Input, NumGroups, Weight, undefined).

group_norm(Input, NumGroups, Weight, Bias) ->
    % Group normalization
    mlx_nif:group_norm(Input, NumGroups, Weight, Bias).

instance_norm(Input, Weight) ->
    instance_norm(Input, Weight, undefined).

instance_norm(Input, Weight, Bias) ->
    % Instance normalization
    mlx_nif:instance_norm(Input, Weight, Bias).

rms_norm(Input, Weight) ->
    rms_norm(Input, Weight, 1.0e-8).

rms_norm(Input, Weight, Eps) ->
    % Root Mean Square normalization
    {ok, SquaredInput} = mlx:square(Input),
    {ok, MeanSquared} = mlx:mean(SquaredInput, -1, true),
    {ok, EpsArray} = mlx:array(Eps),
    {ok, MeanSquaredPlusEps} = mlx:add(MeanSquared, EpsArray),
    {ok, Rms} = mlx:sqrt(MeanSquaredPlusEps),
    {ok, Normalized} = mlx:divide(Input, Rms),
    mlx:multiply(Normalized, Weight).

%% Dropout and regularization
dropout(Input, Rate) ->
    dropout(Input, Rate, true).

dropout(Input, Rate, Training) ->
    case Training andalso Rate > 0.0 of
        true ->
            {ok, Shape} = mlx:shape(Input),
            {ok, Mask} = random_bernoulli(Shape, 1.0 - Rate),
            {ok, Scale} = mlx:array(1.0 / (1.0 - Rate)),
            {ok, Dropped} = mlx:multiply(Input, Mask),
            mlx:multiply(Dropped, Scale);
        false ->
            {ok, Input}
    end.

dropout2d(Input, Rate) ->
    mlx_nif:dropout2d(Input, Rate).

dropout3d(Input, Rate) ->
    mlx_nif:dropout3d(Input, Rate).

alpha_dropout(Input, Rate) ->
    mlx_nif:alpha_dropout(Input, Rate).

feature_alpha_dropout(Input, Rate) ->
    mlx_nif:feature_alpha_dropout(Input, Rate).

%% Advanced architectures
residual_block(Input, Layers, Activation) ->
    residual_block(Input, Layers, Activation, #{}).

residual_block(Input, Layers, Activation, Options) ->
    % Residual connection block
    Output = lists:foldl(fun(Layer, Acc) ->
        case Layer(Acc) of
            {ok, Result} -> Result;
            Result -> Result
        end
    end, Input, Layers),
    
    % Apply activation before residual connection
    {ok, Activated} = case Activation of
        relu -> mlx:relu(Output);
        gelu -> mlx_advanced:gelu(Output);
        swish -> mlx_advanced:swish(Output);
        identity -> {ok, Output}
    end,
    
    mlx:add(Input, Activated).

dense_block(Input, Layers, GrowthRate) ->
    dense_block(Input, Layers, GrowthRate, #{}).

dense_block(Input, Layers, GrowthRate, Options) ->
    % Dense connection block (DenseNet style)
    lists:foldl(fun(Layer, {Acc, Features}) ->
        {ok, NewFeature} = Layer(Acc),
        {ok, ConcatFeatures} = mlx:concatenate([Features, NewFeature], -1),
        {ConcatFeatures, ConcatFeatures}
    end, {Input, Input}, Layers).

squeeze_excitation(Input, ReductionRatio) ->
    squeeze_excitation(Input, ReductionRatio, sigmoid).

squeeze_excitation(Input, ReductionRatio, Activation) ->
    % Squeeze-and-Excitation block
    {ok, [B, C, H, W]} = mlx:shape(Input),
    
    % Squeeze: Global average pooling
    {ok, Squeezed} = global_avg_pool(Input),
    {ok, SqueezedFlat} = mlx:reshape(Squeezed, [B, C]),
    
    % Excitation: FC layers
    ReducedChannels = erlang:max(1, C div ReductionRatio),
    {ok, FC1Weight} = xavier_uniform([C, ReducedChannels]),
    {ok, FC2Weight} = xavier_uniform([ReducedChannels, C]),
    
    {ok, Excited1} = linear(SqueezedFlat, FC1Weight, undefined),
    {ok, Activated} = mlx:relu(Excited1),
    {ok, Excited2} = linear(Activated, FC2Weight, undefined),
    
    {ok, Scale} = case Activation of
        sigmoid -> mlx:sigmoid(Excited2);
        tanh -> mlx:tanh(Excited2)
    end,
    
    {ok, ScaleReshaped} = mlx:reshape(Scale, [B, C, 1, 1]),
    mlx:multiply(Input, ScaleReshaped).

%% Recurrent layers
lstm_cell(Input, Hidden, Cell, Weights) ->
    lstm_cell(Input, Hidden, Cell, Weights, #{}).

lstm_cell(Input, Hidden, Cell, Weights, Options) ->
    % LSTM cell implementation
    [WIH, WHH, BIH, BHH] = Weights,
    
    % Compute gates
    {ok, GateInput} = linear(Input, WIH, BIH),
    {ok, GateHidden} = linear(Hidden, WHH, BHH),
    {ok, Gates} = mlx:add(GateInput, GateHidden),
    
    % Split gates
    {ok, [InputGate, ForgetGate, CellGate, OutputGate]} = mlx:split(Gates, 4, -1),
    
    % Apply activations
    {ok, InputActivated} = mlx:sigmoid(InputGate),
    {ok, ForgetActivated} = mlx:sigmoid(ForgetGate),
    {ok, CellActivated} = mlx:tanh(CellGate),
    {ok, OutputActivated} = mlx:sigmoid(OutputGate),
    
    % Update cell state
    {ok, ForgetComponent} = mlx:multiply(ForgetActivated, Cell),
    {ok, InputComponent} = mlx:multiply(InputActivated, CellActivated),
    {ok, NewCell} = mlx:add(ForgetComponent, InputComponent),
    
    % Compute new hidden state
    {ok, CellTanh} = mlx:tanh(NewCell),
    {ok, NewHidden} = mlx:multiply(OutputActivated, CellTanh),
    
    {ok, {NewHidden, NewCell}}.

gru_cell(Input, Hidden, Weights) ->
    gru_cell(Input, Hidden, Weights, #{}).

gru_cell(Input, Hidden, Weights, Options) ->
    % GRU cell implementation
    [WIH, WHH, BIH, BHH] = Weights,
    
    % Reset and update gates
    {ok, ResetUpdateInput} = linear(Input, WIH, BIH),
    {ok, ResetUpdateHidden} = linear(Hidden, WHH, BHH),
    {ok, ResetUpdate} = mlx:add(ResetUpdateInput, ResetUpdateHidden),
    
    {ok, [ResetGate, UpdateGate]} = mlx:split(ResetUpdate, 2, -1),
    {ok, ResetActivated} = mlx:sigmoid(ResetGate),
    {ok, UpdateActivated} = mlx:sigmoid(UpdateGate),
    
    % New gate
    {ok, ResetHidden} = mlx:multiply(ResetActivated, Hidden),
    {ok, NewInput} = linear(Input, WIH, BIH),
    {ok, NewHidden} = linear(ResetHidden, WHH, BHH),
    {ok, NewGate} = mlx:add(NewInput, NewHidden),
    {ok, NewActivated} = mlx:tanh(NewGate),
    
    % Final hidden state
    {ok, OneMinusUpdate} = mlx:subtract(mlx:array(1.0), UpdateActivated),
    {ok, HiddenComponent} = mlx:multiply(OneMinusUpdate, Hidden),
    {ok, NewComponent} = mlx:multiply(UpdateActivated, NewActivated),
    {ok, FinalHidden} = mlx:add(HiddenComponent, NewComponent),
    
    {ok, FinalHidden}.

rnn_cell(Input, Hidden, Weights) ->
    rnn_cell(Input, Hidden, Weights, #{}).

rnn_cell(Input, Hidden, Weights, Options) ->
    % Simple RNN cell
    [WIH, WHH, BIH, BHH] = Weights,
    
    {ok, InputComponent} = linear(Input, WIH, BIH),
    {ok, HiddenComponent} = linear(Hidden, WHH, BHH),
    {ok, Combined} = mlx:add(InputComponent, HiddenComponent),
    
    Activation = maps:get(activation, Options, tanh),
    case Activation of
        tanh -> mlx:tanh(Combined);
        relu -> mlx:relu(Combined);
        sigmoid -> mlx:sigmoid(Combined)
    end.

%% Modern architectures
vision_transformer_block(Input, Attention, MLP, LayerNorm1) ->
    vision_transformer_block(Input, Attention, MLP, LayerNorm1, undefined).

vision_transformer_block(Input, Attention, MLP, LayerNorm1, LayerNorm2) ->
    % Vision Transformer block
    {ok, Normed1} = layer_norm(Input, LayerNorm1),
    {ok, AttnOutput} = Attention(Normed1),
    {ok, Residual1} = mlx:add(Input, AttnOutput),
    
    LayerNorm2Final = case LayerNorm2 of
        undefined -> LayerNorm1;
        _ -> LayerNorm2
    end,
    
    {ok, Normed2} = layer_norm(Residual1, LayerNorm2Final),
    {ok, MLPOutput} = MLP(Normed2),
    mlx:add(Residual1, MLPOutput).

swin_transformer_block(Input, WindowSize, Attention, MLP, LayerNorm1) ->
    swin_transformer_block(Input, WindowSize, Attention, MLP, LayerNorm1, undefined).

swin_transformer_block(Input, WindowSize, Attention, MLP, LayerNorm1, LayerNorm2) ->
    % Swin Transformer block with windowed attention
    {ok, Windows} = window_partition(Input, WindowSize),
    {ok, AttnWindows} = Attention(Windows),
    {ok, Merged} = window_reverse(AttnWindows, WindowSize),
    vision_transformer_block(Input, fun(_) -> {ok, Merged} end, MLP, LayerNorm1, LayerNorm2).

efficientnet_block(Input, ExpandRatio, KernelSize, Stride, SEReduction) ->
    efficientnet_block(Input, ExpandRatio, KernelSize, Stride, SEReduction, #{}).

efficientnet_block(Input, ExpandRatio, KernelSize, Stride, SEReduction, Options) ->
    % EfficientNet MBConv block
    {ok, [_B, C, _H, _W]} = mlx:shape(Input),
    ExpandedChannels = C * ExpandRatio,
    
    % Expansion phase
    {ok, Expanded} = case ExpandRatio > 1 of
        true ->
            {ok, ExpWeight} = xavier_uniform([C, ExpandedChannels]),
            conv2d(Input, ExpWeight, undefined, 1);
        false ->
            {ok, Input}
    end,
    
    % Depthwise convolution
    {ok, DWWeight} = xavier_uniform([ExpandedChannels, 1, KernelSize, KernelSize]),
    {ok, DepthwiseOutput} = conv2d(Expanded, DWWeight, undefined, Stride),
    
    % Squeeze-and-Excitation
    {ok, SEOutput} = squeeze_excitation(DepthwiseOutput, SEReduction),
    
    % Projection phase
    {ok, ProjWeight} = xavier_uniform([ExpandedChannels, C]),
    {ok, ProjectionOutput} = conv2d(SEOutput, ProjWeight, undefined, 1),
    
    % Residual connection (if stride = 1 and same channels)
    case Stride =:= 1 andalso element(2, mlx:shape(Input)) =:= C of
        true -> mlx:add(Input, ProjectionOutput);
        false -> {ok, ProjectionOutput}
    end.

%% Initialization functions
xavier_uniform(Shape) ->
    % Xavier/Glorot uniform initialization
    [Fan_in, Fan_out] = case length(Shape) of
        2 -> Shape;
        _ -> [lists:nth(1, Shape), lists:nth(2, Shape)]
    end,
    Limit = math:sqrt(6.0 / (Fan_in + Fan_out)),
    {ok, Random} = random_uniform(Shape, -Limit, Limit),
    {ok, Random}.

xavier_normal(Shape) ->
    % Xavier/Glorot normal initialization
    [Fan_in, Fan_out] = case length(Shape) of
        2 -> Shape;
        _ -> [lists:nth(1, Shape), lists:nth(2, Shape)]
    end,
    Std = math:sqrt(2.0 / (Fan_in + Fan_out)),
    random_normal(Shape, 0.0, Std).

kaiming_uniform(Shape) ->
    % He/Kaiming uniform initialization
    Fan_in = lists:nth(1, Shape),
    Limit = math:sqrt(6.0 / Fan_in),
    random_uniform(Shape, -Limit, Limit).

kaiming_normal(Shape) ->
    % He/Kaiming normal initialization
    Fan_in = lists:nth(1, Shape),
    Std = math:sqrt(2.0 / Fan_in),
    random_normal(Shape, 0.0, Std).

orthogonal_init(Shape) ->
    % Orthogonal initialization
    mlx_nif:orthogonal_init(Shape).

identity_init(Shape) ->
    % Identity initialization
    mlx:eye(lists:nth(1, Shape)).

%% Helper functions
random_uniform(Shape, Low, High) ->
    mlx_nif:random_uniform(Shape, Low, High).

random_normal(Shape, Mean, Std) ->
    mlx_nif:random_normal(Shape, Mean, Std).

random_bernoulli(Shape, Prob) ->
    mlx_nif:random_bernoulli(Shape, Prob).

window_partition(Input, WindowSize) ->
    mlx_nif:window_partition(Input, WindowSize).

window_reverse(Windows, WindowSize) ->
    mlx_nif:window_reverse(Windows, WindowSize).