mlx

-module(mlx_io).

-include_lib("kernel/include/file.hrl").

%% Model serialization, checkpointing, and format conversion
-export([
    %% Model saving and loading
    save_model/2, save_model/3,
    load_model/1, load_model/2,
    
    %% Checkpointing
    save_checkpoint/2, save_checkpoint/3,
    load_checkpoint/1, load_checkpoint/2,
    list_checkpoints/1,
    cleanup_checkpoints/2,
    
    %% Format conversion
    convert_format/3, convert_format/4,
    supported_formats/0,
    
    %% Safetensors support
    save_safetensors/2, load_safetensors/1,
    verify_safetensors/1,
    
    %% ONNX support
    export_onnx/2, export_onnx/3,
    import_onnx/1, import_onnx/2,
    
    %% PyTorch/HuggingFace compatibility
    save_pytorch_state_dict/2,
    load_pytorch_state_dict/1,
    convert_from_huggingface/2,
    
    %% TensorFlow compatibility
    export_tensorflow/2, export_tensorflow/3,
    import_tensorflow/1, import_tensorflow/2,
    
    %% Quantized model I/O
    save_quantized_model/3,
    load_quantized_model/1,
    
    %% Model metadata
    save_metadata/2, load_metadata/1,
    extract_model_info/1,
    
    %% Distributed checkpointing
    save_distributed_checkpoint/3,
    load_distributed_checkpoint/2,
    merge_distributed_checkpoints/2,
    
    %% Model compression for storage
    compress_for_storage/2, compress_for_storage/3,
    decompress_from_storage/1,
    
    %% Streaming I/O for large models
    create_model_stream/2,
    stream_model_weights/2,
    close_model_stream/1
]).

%% Model saving and loading
save_model(Model, FilePath) ->
    save_model(Model, FilePath, #{}).

save_model(Model, FilePath, Options) ->
    % Save model in MLX native format
    Format = maps:get(format, Options, mlx_native),
    Compression = maps:get(compression, Options, none),
    IncludeMetadata = maps:get(include_metadata, Options, true),
    
    % Extract model components
    Weights = extract_weights(Model),
    Architecture = extract_architecture(Model),
    Metadata = case IncludeMetadata of
        true -> extract_model_metadata(Model);
        false -> #{}
    end,
    
    % Prepare data for serialization
    ModelData = #{
        format_version => "1.0",
        architecture => Architecture,
        weights => Weights,
        metadata => Metadata,
        creation_time => erlang:system_time(second)
    },
    
    % Serialize and compress if needed
    SerializedData = serialize_model_data(ModelData, Format),
    FinalData = case Compression of
        none -> SerializedData;
        gzip -> zlib:gzip(SerializedData);
        lz4 -> compress_lz4(SerializedData)
    end,
    
    % Write to file
    case file:write_file(FilePath, FinalData) of
        ok -> 
            FileSize = byte_size(FinalData),
            {ok, #{file_size => FileSize, format => Format, compression => Compression}};
        {error, Reason} -> 
            {error, {file_write_failed, Reason}}
    end.

load_model(FilePath) ->
    load_model(FilePath, #{}).

load_model(FilePath, Options) ->
    % Load model from file
    case file:read_file(FilePath) of
        {ok, FileData} ->
            % Detect compression and decompress if needed
            DecompressedData = detect_and_decompress(FileData),
            
            % Deserialize model data
            case deserialize_model_data(DecompressedData) of
                {ok, ModelData} ->
                    % Reconstruct model
                    Architecture = maps:get(architecture, ModelData),
                    Weights = maps:get(weights, ModelData),
                    reconstruct_model(Architecture, Weights, Options);
                {error, Reason} ->
                    {error, {deserialization_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {file_read_failed, Reason}}
    end.

%% Checkpointing
save_checkpoint(Model, CheckpointDir) ->
    save_checkpoint(Model, CheckpointDir, #{}).

save_checkpoint(Model, CheckpointDir, Options) ->
    % Save training checkpoint
    Epoch = maps:get(epoch, Options, 0),
    Step = maps:get(step, Options, 0),
    OptimizerState = maps:get(optimizer_state, Options, #{}),
    TrainingState = maps:get(training_state, Options, #{}),
    
    % Create checkpoint filename
    Timestamp = erlang:system_time(second),
    CheckpointName = io_lib:format("checkpoint_epoch_~w_step_~w_~w.ckpt", 
                                   [Epoch, Step, Timestamp]),
    CheckpointPath = filename:join(CheckpointDir, CheckpointName),
    
    % Prepare checkpoint data
    CheckpointData = #{
        model => extract_weights(Model),
        optimizer_state => OptimizerState,
        training_state => TrainingState,
        epoch => Epoch,
        step => Step,
        timestamp => Timestamp,
        mlx_version => get_mlx_version()
    },
    
    % Ensure checkpoint directory exists
    ok = filelib:ensure_dir(CheckpointPath),
    
    % Save checkpoint
    case save_model_data(CheckpointData, CheckpointPath) of
        ok ->
            % Update latest checkpoint symlink
            LatestPath = filename:join(CheckpointDir, "latest.ckpt"),
            case file:make_symlink(CheckpointName, LatestPath) of
                ok -> {ok, CheckpointPath};
                {error, eexist} ->
                    file:delete(LatestPath),
                    file:make_symlink(CheckpointName, LatestPath),
                    {ok, CheckpointPath};
                {error, Reason} ->
                    {error, {symlink_failed, Reason}}
            end;
        {error, Reason} ->
            {error, Reason}
    end.

load_checkpoint(CheckpointPath) ->
    load_checkpoint(CheckpointPath, #{}).

load_checkpoint(CheckpointPath, Options) ->
    % Load training checkpoint
    LoadLatest = maps:get(load_latest, Options, false),
    
    ActualPath = case LoadLatest of
        true ->
            CheckpointDir = filename:dirname(CheckpointPath),
            LatestPath = filename:join(CheckpointDir, "latest.ckpt"),
            case file:read_link(LatestPath) of
                {ok, LatestFile} -> filename:join(CheckpointDir, LatestFile);
                {error, _} -> CheckpointPath
            end;
        false ->
            CheckpointPath
    end,
    
    case load_model_data(ActualPath) of
        {ok, CheckpointData} ->
            Model = maps:get(model, CheckpointData),
            OptimizerState = maps:get(optimizer_state, CheckpointData, #{}),
            TrainingState = maps:get(training_state, CheckpointData, #{}),
            
            {ok, #{
                model => Model,
                optimizer_state => OptimizerState,
                training_state => TrainingState,
                epoch => maps:get(epoch, CheckpointData, 0),
                step => maps:get(step, CheckpointData, 0)
            }};
        {error, Reason} ->
            {error, Reason}
    end.

list_checkpoints(CheckpointDir) ->
    % List all checkpoints in directory
    Pattern = filename:join(CheckpointDir, "checkpoint_*.ckpt"),
    case filelib:wildcard(Pattern) of
        [] -> {ok, []};
        Files ->
            % Sort by modification time
            FilesWithTime = lists:map(fun(File) ->
                case file:read_file_info(File) of
                    {ok, FileInfo} -> {File, FileInfo#file_info.mtime};
                    {error, _} -> {File, 0}
                end
            end, Files),
            
            SortedFiles = lists:reverse(lists:keysort(2, FilesWithTime)),
            {ok, [File || {File, _} <- SortedFiles]}
    end.

cleanup_checkpoints(CheckpointDir, KeepLast) ->
    % Keep only the last N checkpoints
    case list_checkpoints(CheckpointDir) of
        {ok, Checkpoints} when length(Checkpoints) > KeepLast ->
            ToDelete = lists:nthtail(KeepLast, Checkpoints),
            DeleteResults = lists:map(fun(File) ->
                case file:delete(File) of
                    ok -> {ok, File};
                    {error, Reason} -> {error, {File, Reason}}
                end
            end, ToDelete),
            {ok, DeleteResults};
        {ok, _} ->
            {ok, []};
        {error, Reason} ->
            {error, Reason}
    end.

%% Format conversion
convert_format(InputPath, OutputPath, TargetFormat) ->
    convert_format(InputPath, OutputPath, TargetFormat, #{}).

convert_format(InputPath, OutputPath, TargetFormat, Options) ->
    % Convert between different model formats
    case load_model(InputPath) of
        {ok, Model} ->
            case TargetFormat of
                onnx -> export_onnx(Model, OutputPath, Options);
                tensorflow -> export_tensorflow(Model, OutputPath, Options);
                pytorch -> save_pytorch_state_dict(Model, OutputPath);
                safetensors -> save_safetensors(Model, OutputPath);
                mlx_native -> save_model(Model, OutputPath, Options);
                _ -> {error, {unsupported_format, TargetFormat}}
            end;
        {error, Reason} ->
            {error, Reason}
    end.

supported_formats() ->
    [mlx_native, onnx, tensorflow, pytorch, safetensors].

%% Safetensors support
save_safetensors(Model, FilePath) ->
    % Save model in Safetensors format
    Weights = extract_weights(Model),
    
    % Convert weights to safetensors format
    SafetensorsData = convert_to_safetensors(Weights),
    
    % Write safetensors file
    mlx_nif:save_safetensors(SafetensorsData, FilePath).

load_safetensors(FilePath) ->
    % Load model from Safetensors format
    case mlx_nif:load_safetensors(FilePath) of
        {ok, WeightsData} ->
            Weights = convert_from_safetensors(WeightsData),
            {ok, Weights};
        {error, Reason} ->
            {error, Reason}
    end.

verify_safetensors(FilePath) ->
    % Verify safetensors file integrity
    mlx_nif:verify_safetensors(FilePath).

%% ONNX support
export_onnx(Model, FilePath) ->
    export_onnx(Model, FilePath, #{}).

export_onnx(Model, FilePath, Options) ->
    % Export model to ONNX format
    OpsetVersion = maps:get(opset_version, Options, 17),
    InputShape = maps:get(input_shape, Options, undefined),
    InputNames = maps:get(input_names, Options, ["input"]),
    OutputNames = maps:get(output_names, Options, ["output"]),
    
    % Convert MLX model to ONNX graph
    case convert_to_onnx_graph(Model, InputShape, OpsetVersion) of
        {ok, ONNXGraph} ->
            % Create ONNX model proto
            ONNXModel = create_onnx_model(ONNXGraph, InputNames, OutputNames, OpsetVersion),
            
            % Serialize and save
            case serialize_onnx_model(ONNXModel) of
                {ok, SerializedData} ->
                    file:write_file(FilePath, SerializedData);
                {error, Reason} ->
                    {error, {onnx_serialization_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {onnx_conversion_failed, Reason}}
    end.

import_onnx(FilePath) ->
    import_onnx(FilePath, #{}).

import_onnx(FilePath, Options) ->
    % Import ONNX model to MLX
    case file:read_file(FilePath) of
        {ok, ONNXData} ->
            case deserialize_onnx_model(ONNXData) of
                {ok, ONNXModel} ->
                    convert_from_onnx_model(ONNXModel, Options);
                {error, Reason} ->
                    {error, {onnx_deserialization_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {file_read_failed, Reason}}
    end.

%% PyTorch/HuggingFace compatibility
save_pytorch_state_dict(Model, FilePath) ->
    % Save model in PyTorch state_dict format
    Weights = extract_weights(Model),
    
    % Convert to PyTorch tensor format
    PytorchStateDict = convert_to_pytorch_format(Weights),
    
    % Save as pickle file (simplified - would need proper Python pickle format)
    SerializedData = serialize_pytorch_state_dict(PytorchStateDict),
    file:write_file(FilePath, SerializedData).

load_pytorch_state_dict(FilePath) ->
    % Load PyTorch state_dict
    case file:read_file(FilePath) of
        {ok, PickleData} ->
            case deserialize_pytorch_state_dict(PickleData) of
                {ok, StateDict} ->
                    Weights = convert_from_pytorch_format(StateDict),
                    {ok, Weights};
                {error, Reason} ->
                    {error, Reason}
            end;
        {error, Reason} ->
            {error, {file_read_failed, Reason}}
    end.

convert_from_huggingface(ModelName, CacheDir) ->
    % Convert HuggingFace model to MLX format
    % This would typically involve downloading from HuggingFace Hub
    % and converting the model weights and configuration
    
    ConfigPath = filename:join([CacheDir, ModelName, "config.json"]),
    WeightsPath = filename:join([CacheDir, ModelName, "pytorch_model.bin"]),
    
    case {file:read_file(ConfigPath), load_pytorch_state_dict(WeightsPath)} of
        {{ok, ConfigData}, {ok, Weights}} ->
            Config = jsx:decode(ConfigData, [return_maps]),
            Architecture = convert_hf_config_to_mlx(Config),
            Model = reconstruct_model(Architecture, Weights, #{}),
            {ok, Model};
        {{error, Reason}, _} ->
            {error, {config_load_failed, Reason}};
        {_, {error, Reason}} ->
            {error, {weights_load_failed, Reason}}
    end.

%% TensorFlow compatibility
export_tensorflow(Model, FilePath) ->
    export_tensorflow(Model, FilePath, #{}).

export_tensorflow(Model, FilePath, Options) ->
    % Export model to TensorFlow SavedModel format
    SignatureName = maps:get(signature_name, Options, "serving_default"),
    
    % Convert MLX model to TensorFlow graph
    case convert_to_tf_graph(Model) of
        {ok, TFGraph} ->
            % Create SavedModel
            SavedModel = create_tf_saved_model(TFGraph, SignatureName),
            
            % Save to directory
            case file:make_dir(FilePath) of
                ok -> save_tf_saved_model(SavedModel, FilePath);
                {error, eexist} -> save_tf_saved_model(SavedModel, FilePath);
                {error, Reason} -> {error, {dir_creation_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {tf_conversion_failed, Reason}}
    end.

import_tensorflow(SavedModelPath) ->
    import_tensorflow(SavedModelPath, #{}).

import_tensorflow(SavedModelPath, Options) ->
    % Import TensorFlow SavedModel
    case load_tf_saved_model(SavedModelPath) of
        {ok, SavedModel} ->
            convert_from_tf_saved_model(SavedModel, Options);
        {error, Reason} ->
            {error, Reason}
    end.

%% Quantized model I/O
save_quantized_model(Model, QuantParams, FilePath) ->
    % Save quantized model with quantization parameters
    ModelWeights = extract_weights(Model),
    
    QuantizedData = #{
        quantized_weights => ModelWeights,
        quantization_params => QuantParams,
        quantization_version => "1.0",
        creation_time => erlang:system_time(second)
    },
    
    save_model_data(QuantizedData, FilePath).

load_quantized_model(FilePath) ->
    % Load quantized model
    case load_model_data(FilePath) of
        {ok, QuantizedData} ->
            Weights = maps:get(quantized_weights, QuantizedData),
            QuantParams = maps:get(quantization_params, QuantizedData),
            {ok, {Weights, QuantParams}};
        {error, Reason} ->
            {error, Reason}
    end.

%% Model metadata
save_metadata(Metadata, FilePath) ->
    % Save model metadata to JSON
    MetadataJson = jsx:encode(Metadata),
    file:write_file(FilePath, MetadataJson).

load_metadata(FilePath) ->
    % Load model metadata from JSON
    case file:read_file(FilePath) of
        {ok, MetadataData} ->
            try
                Metadata = jsx:decode(MetadataData, [return_maps]),
                {ok, Metadata}
            catch
                error:Reason -> {error, {json_decode_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {file_read_failed, Reason}}
    end.

extract_model_info(Model) ->
    % Extract comprehensive model information
    Weights = extract_weights(Model),
    
    % Calculate model statistics
    TotalParams = maps:fold(fun(_Name, Weight, Acc) ->
        {ok, Size} = mlx:size(Weight),
        Acc + Size
    end, 0, Weights),
    
    ModelSize = maps:fold(fun(_Name, Weight, Acc) ->
        {ok, Size} = mlx:size(Weight),
        {ok, Dtype} = mlx:dtype(Weight),
        BytesPerElement = case Dtype of
            float32 -> 4; float16 -> 2; int8 -> 1; int4 -> 0.5
        end,
        Acc + Size * BytesPerElement
    end, 0, Weights),
    
    LayerInfo = maps:map(fun(_Name, Weight) ->
        {ok, Shape} = mlx:shape(Weight),
        {ok, Dtype} = mlx:dtype(Weight),
        {ok, Size} = mlx:size(Weight),
        #{shape => Shape, dtype => Dtype, size => Size}
    end, Weights),
    
    #{
        total_parameters => TotalParams,
        model_size_bytes => ModelSize,
        num_layers => maps:size(Weights),
        layer_info => LayerInfo,
        creation_time => erlang:system_time(second)
    }.

%% Distributed checkpointing
save_distributed_checkpoint(Model, CheckpointDir, NodeId) ->
    % Save checkpoint for distributed training
    NodeCheckpointDir = filename:join(CheckpointDir, io_lib:format("node_~w", [NodeId])),
    ok = filelib:ensure_dir(filename:join(NodeCheckpointDir, "dummy")),
    
    % Save local model shard
    LocalWeights = extract_local_weights(Model, NodeId),
    CheckpointPath = filename:join(NodeCheckpointDir, "model_shard.ckpt"),
    
    case save_model_data(LocalWeights, CheckpointPath) of
        ok ->
            % Save metadata about this shard
            ShardMetadata = #{
                node_id => NodeId,
                shard_keys => maps:keys(LocalWeights),
                timestamp => erlang:system_time(second)
            },
            MetadataPath = filename:join(NodeCheckpointDir, "shard_metadata.json"),
            save_metadata(ShardMetadata, MetadataPath);
        {error, Reason} ->
            {error, Reason}
    end.

load_distributed_checkpoint(CheckpointDir, NodeId) ->
    % Load checkpoint for specific node
    NodeCheckpointDir = filename:join(CheckpointDir, io_lib:format("node_~w", [NodeId])),
    CheckpointPath = filename:join(NodeCheckpointDir, "model_shard.ckpt"),
    
    load_model_data(CheckpointPath).

merge_distributed_checkpoints(CheckpointDir, OutputPath) ->
    % Merge distributed checkpoints into single model
    % Find all node directories
    Pattern = filename:join(CheckpointDir, "node_*"),
    NodeDirs = filelib:wildcard(Pattern),
    
    % Load all shards
    AllShards = lists:foldl(fun(NodeDir, Acc) ->
        CheckpointPath = filename:join(NodeDir, "model_shard.ckpt"),
        case load_model_data(CheckpointPath) of
            {ok, Shard} -> maps:merge(Acc, Shard);
            {error, _} -> Acc
        end
    end, #{}, NodeDirs),
    
    % Save merged model
    save_model_data(AllShards, OutputPath).

%% Model compression for storage
compress_for_storage(Model, CompressionLevel) ->
    compress_for_storage(Model, CompressionLevel, #{}).

compress_for_storage(Model, CompressionLevel, Options) ->
    % Compress model for efficient storage
    Method = maps:get(method, Options, adaptive),
    
    Weights = extract_weights(Model),
    
    case Method of
        lossless ->
            % Use lossless compression (gzip, lz4, etc.)
            SerializedWeights = serialize_weights(Weights),
            CompressedData = compress_data(SerializedWeights, CompressionLevel),
            {ok, CompressedData};
        
        lossy ->
            % Use lossy compression (quantization + compression)
            QuantizedWeights = apply_storage_quantization(Weights, CompressionLevel),
            SerializedWeights = serialize_weights(QuantizedWeights),
            CompressedData = compress_data(SerializedWeights, CompressionLevel),
            {ok, CompressedData};
        
        adaptive ->
            % Choose compression method per layer
            CompressedLayers = maps:map(fun(LayerName, Weight) ->
                choose_optimal_compression(LayerName, Weight, CompressionLevel)
            end, Weights),
            {ok, CompressedLayers}
    end.

decompress_from_storage(CompressedModel) ->
    % Decompress model from storage format
    case detect_compression_format(CompressedModel) of
        {ok, Format} ->
            decompress_with_format(CompressedModel, Format);
        {error, Reason} ->
            {error, Reason}
    end.

%% Streaming I/O for large models
create_model_stream(FilePath, Mode) ->
    % Create streaming interface for large models
    case Mode of
        read ->
            case file:open(FilePath, [read, binary]) of
                {ok, FileHandle} ->
                    StreamState = #{
                        file_handle => FileHandle,
                        mode => read,
                        position => 0,
                        buffer_size => 1024 * 1024 % 1MB buffer
                    },
                    {ok, StreamState};
                {error, Reason} ->
                    {error, Reason}
            end;
        write ->
            case file:open(FilePath, [write, binary]) of
                {ok, FileHandle} ->
                    StreamState = #{
                        file_handle => FileHandle,
                        mode => write,
                        position => 0,
                        buffer => <<>>
                    },
                    {ok, StreamState};
                {error, Reason} ->
                    {error, Reason}
            end
    end.

stream_model_weights(StreamState, LayerName) ->
    % Stream individual layer weights
    case maps:get(mode, StreamState) of
        read ->
            FileHandle = maps:get(file_handle, StreamState),
            BufferSize = maps:get(buffer_size, StreamState),
            
            case read_layer_from_stream(FileHandle, LayerName, BufferSize) of
                {ok, WeightData, NewPosition} ->
                    NewState = maps:put(position, NewPosition, StreamState),
                    {ok, WeightData, NewState};
                {error, Reason} ->
                    {error, Reason}
            end;
        write ->
            % Implementation for writing weights to stream
            {error, not_implemented}
    end.

close_model_stream(StreamState) ->
    % Close model stream
    FileHandle = maps:get(file_handle, StreamState),
    file:close(FileHandle).

%% Helper functions
extract_weights(Model) ->
    % Extract weights from model (simplified)
    Model.

extract_architecture(Model) ->
    % Extract architecture description (simplified)
    #{type => unknown}.

extract_model_metadata(Model) ->
    % Extract model metadata (simplified)
    #{}.

serialize_model_data(ModelData, Format) ->
    % Serialize model data in specified format
    case Format of
        mlx_native -> term_to_binary(ModelData);
        json -> jsx:encode(ModelData);
        _ -> term_to_binary(ModelData)
    end.

deserialize_model_data(SerializedData) ->
    % Deserialize model data
    try
        ModelData = binary_to_term(SerializedData),
        {ok, ModelData}
    catch
        error:Reason -> {error, Reason}
    end.

reconstruct_model(Architecture, Weights, Options) ->
    % Reconstruct model from architecture and weights
    {ok, Weights}. % Simplified

detect_and_decompress(FileData) ->
    % Detect compression and decompress
    case FileData of
        <<31, 139, _/binary>> -> % gzip magic number
            zlib:gunzip(FileData);
        _ ->
            FileData
    end.

compress_lz4(Data) ->
    % LZ4 compression (would use proper LZ4 library)
    Data.

save_model_data(Data, FilePath) ->
    SerializedData = term_to_binary(Data),
    file:write_file(FilePath, SerializedData).

load_model_data(FilePath) ->
    case file:read_file(FilePath) of
        {ok, SerializedData} ->
            try
                Data = binary_to_term(SerializedData),
                {ok, Data}
            catch
                error:Reason -> {error, {deserialization_failed, Reason}}
            end;
        {error, Reason} ->
            {error, {file_read_failed, Reason}}
    end.

get_mlx_version() ->
    "1.0.0".

convert_to_safetensors(Weights) ->
    % Convert to safetensors format
    Weights.

convert_from_safetensors(WeightsData) ->
    % Convert from safetensors format
    WeightsData.

convert_to_onnx_graph(Model, InputShape, OpsetVersion) ->
    % Convert MLX model to ONNX graph
    {ok, #{}}.

create_onnx_model(Graph, InputNames, OutputNames, OpsetVersion) ->
    % Create ONNX model proto
    #{}.

serialize_onnx_model(ONNXModel) ->
    % Serialize ONNX model
    {ok, <<>>}.

deserialize_onnx_model(ONNXData) ->
    % Deserialize ONNX model
    {ok, #{}}.

convert_from_onnx_model(ONNXModel, Options) ->
    % Convert ONNX model to MLX
    {ok, #{}}.

convert_to_pytorch_format(Weights) ->
    % Convert to PyTorch format
    Weights.

serialize_pytorch_state_dict(StateDict) ->
    % Serialize PyTorch state dict
    term_to_binary(StateDict).

deserialize_pytorch_state_dict(PickleData) ->
    % Deserialize PyTorch state dict
    try
        StateDict = binary_to_term(PickleData),
        {ok, StateDict}
    catch
        error:Reason -> {error, Reason}
    end.

convert_from_pytorch_format(StateDict) ->
    % Convert from PyTorch format
    StateDict.

convert_hf_config_to_mlx(Config) ->
    % Convert HuggingFace config to MLX architecture
    Config.

convert_to_tf_graph(Model) ->
    % Convert MLX model to TensorFlow graph
    {ok, #{}}.

create_tf_saved_model(TFGraph, SignatureName) ->
    % Create TensorFlow SavedModel
    #{}.

save_tf_saved_model(SavedModel, FilePath) ->
    % Save TensorFlow SavedModel
    ok.

load_tf_saved_model(SavedModelPath) ->
    % Load TensorFlow SavedModel
    {ok, #{}}.

convert_from_tf_saved_model(SavedModel, Options) ->
    % Convert TensorFlow SavedModel to MLX
    {ok, #{}}.

extract_local_weights(Model, NodeId) ->
    % Extract weights for specific node in distributed setup
    Model.

serialize_weights(Weights) ->
    term_to_binary(Weights).

compress_data(Data, CompressionLevel) ->
    zlib:gzip(Data).

apply_storage_quantization(Weights, CompressionLevel) ->
    % Apply quantization for storage
    Weights.

choose_optimal_compression(LayerName, Weight, CompressionLevel) ->
    % Choose optimal compression for layer
    Weight.

detect_compression_format(CompressedModel) ->
    % Detect compression format
    {ok, gzip}.

decompress_with_format(CompressedModel, Format) ->
    % Decompress with specific format
    case Format of
        gzip -> zlib:gunzip(CompressedModel);
        _ -> CompressedModel
    end.

read_layer_from_stream(FileHandle, LayerName, BufferSize) ->
    % Read layer from file stream
    case file:read(FileHandle, BufferSize) of
        {ok, Data} ->
            {ok, Data, file:position(FileHandle, cur)};
        eof ->
            {error, eof};
        {error, Reason} ->
            {error, Reason}
    end.