defmodule DSPy.Variable do
  @moduledoc """
  Behaviour for all DSPy variables that can be optimized.
  """
  
  @type t :: %__MODULE__{
    id: String.t(),
    name: String.t(),
    type: variable_type(),
    value: any(),
    constraints: list(constraint()),
    metadata: map(),
    version: non_neg_integer(),
    history: list(version_entry())
  }
  
  @type variable_type :: :string | :weight | :discrete | :continuous | :structured
  @type constraint :: {constraint_type(), any()}
  @type constraint_type :: :range | :options | :pattern | :length | :custom
  @type version_entry :: {non_neg_integer(), any(), DateTime.t(), map()}
  
  @callback validate(value :: any(), constraints :: list(constraint())) :: 
    {:ok, any()} | {:error, String.t()}
  
  @callback transform(value :: any(), transformation :: map()) :: 
    {:ok, any()} | {:error, String.t()}
  
  @callback serialize(value :: any()) :: binary()
  @callback deserialize(binary()) :: {:ok, any()} | {:error, String.t()}
end

defmodule DSPy.Variable.String do
  @moduledoc """
  String variable for prompts, instructions, and text parameters.
  """
  use DSPy.Variable
  
  defstruct [:id, :name, :value, :constraints, :metadata, :version, :history,
             template: nil, 
             format: :plain,
             max_length: nil,
             min_length: nil]
  
  @impl true
  def validate(value, constraints) do
    with :ok <- validate_length(value, constraints),
         :ok <- validate_pattern(value, constraints),
         :ok <- validate_template(value, constraints) do
      {:ok, value}
    end
  end
end

defmodule DSPy.Variable.Weight do
  @moduledoc """
  Numeric weight variable for model parameters.
  """
  use DSPy.Variable
  
  defstruct [:id, :name, :value, :constraints, :metadata, :version, :history,
             shape: nil,
             dtype: :f32,
             initialization: :random_normal,
             regularization: nil]
  
  @impl true
  def validate(value, constraints) do
    with :ok <- validate_shape(value, constraints),
         :ok <- validate_range(value, constraints) do
      {:ok, value}
    end
  end
end

defmodule DSPy.Variable.Discrete do
  @moduledoc """
  Discrete choice variable for selecting from options.
  """
  use DSPy.Variable
  
  defstruct [:id, :name, :value, :constraints, :metadata, :version, :history,
             options: [],
             probabilities: nil,
             allow_multiple: false]
  
  @impl true
  def validate(value, constraints) do
    options = Keyword.get(constraints, :options, [])
    if value in options do
      {:ok, value}
    else
      {:error, "Value must be one of: #{inspect(options)}"}
    end
  end
end

defmodule DSPy.Module do
  @moduledoc """
  Enhanced module macro with variable declarations.
  """
  
  defmacro __using__(_opts) do
    quote do
      import DSPy.Module
      import DSPy.Variable.DSL
      
      Module.register_attribute(__MODULE__, :variables, accumulate: true)
      Module.register_attribute(__MODULE__, :predictors, accumulate: true)
      
      @before_compile DSPy.Module
    end
  end
  
  defmacro variable(name, type, opts \\ []) do
    quote do
      @variables {unquote(name), unquote(type), unquote(opts)}
    end
  end
end

# Example usage
defmodule ChainOfThought do
  use DSPy.Module
  
  # Declare variables
  variable :reasoning_prompt, :string,
    default: "Let's think step by step",
    constraints: [
      length: [min: 10, max: 100],
      pattern: ~r/think|reason|analyze/i
    ]
  
  variable :temperature, :continuous,
    default: 0.7,
    constraints: [
      range: {0.0, 2.0}
    ]
  
  variable :reasoning_style, :discrete,
    default: :analytical,
    options: [:analytical, :creative, :systematic, :intuitive]
  
  variable :attention_weights, :weight,
    shape: {768, 768},
    initialization: :xavier_uniform
  
  def forward(state, inputs) do
    # Variables are automatically accessible
    prompt = get_variable(state, :reasoning_prompt)
    temp = get_variable(state, :temperature)
    style = get_variable(state, :reasoning_style)
    
    # Use variables in computation
    # ...
  end
end

defmodule DSPy.Variable.Registry do
  @moduledoc """
  GenServer that manages all variables in the system.
  """
  use GenServer
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def register_variable(module, name, type, opts) do
    GenServer.call(__MODULE__, {:register, module, name, type, opts})
  end
  
  def get_variable(module, name) do
    GenServer.call(__MODULE__, {:get, module, name})
  end
  
  def update_variable(module, name, value, metadata \\ %{}) do
    GenServer.call(__MODULE__, {:update, module, name, value, metadata})
  end
  
  def get_variables_for_module(module) do
    GenServer.call(__MODULE__, {:get_all, module})
  end
  
  def get_variables_by_type(type) do
    GenServer.call(__MODULE__, {:get_by_type, type})
  end
  
  # Implementation handles versioning, history, and notifications
end

defmodule DSPy.Optimizer.Behaviour do
  @moduledoc """
  Enhanced optimizer behaviour with variable support.
  """
  
  @callback compile(program :: module(), opts :: keyword()) :: 
    {:ok, optimized_program :: module()} | {:error, term()}
  
  @callback optimize_variables(variables :: list(DSPy.Variable.t()), opts :: keyword()) ::
    {:ok, updates :: list({variable_id :: String.t(), new_value :: any()})} | 
    {:error, term()}
    
  @callback supports_variable_type?(type :: atom()) :: boolean()
end

defmodule DSPy.Optimizer.VariableOptimizer do
  @moduledoc """
  Mixin for optimizers to handle variable optimization.
  """
  
  defmacro __using__(_opts) do
    quote do
      def optimize_program(program, trainset, opts \\ []) do
        # Get all variables from the program
        variables = DSPy.Variable.Registry.get_variables_for_module(program)
        
        # Group by type for efficient optimization
        grouped = Enum.group_by(variables, & &1.type)
        
        # Optimize each group
        updates = Enum.flat_map(grouped, fn {type, vars} ->
          if supports_variable_type?(type) do
            {:ok, type_updates} = optimize_variable_group(type, vars, trainset, opts)
            type_updates
          else
            []
          end
        end)
        
        # Apply updates atomically
        apply_variable_updates(program, updates)
      end
      
      defp optimize_variable_group(type, variables, trainset, opts) do
        strategy = get_optimization_strategy(type)
        strategy.optimize(variables, trainset, opts)
      end
    end
  end
end

defmodule DSPy.Optimizer.BayesianOptimizer do
  @moduledoc """
  Bayesian optimization for continuous and discrete variables.
  """
  use DSPy.Optimizer.VariableOptimizer
  @behaviour DSPy.Optimizer.Behaviour
  
  def supports_variable_type?(type) when type in [:continuous, :discrete], do: true
  def supports_variable_type?(_), do: false
  
  def optimize_variables(variables, opts) do
    # Bayesian optimization logic
    acquisition_fn = opts[:acquisition_fn] || :expected_improvement
    
    surrogate_model = build_surrogate_model(variables)
    
    candidates = generate_candidates(variables, surrogate_model, acquisition_fn)
    
    # Evaluate candidates
    results = Enum.map(candidates, fn candidate ->
      score = evaluate_candidate(candidate, opts[:metric])
      {candidate, score}
    end)
    
    # Select best
    best = Enum.max_by(results, fn {_, score} -> score end)
    {:ok, best}
  end
end

defmodule DSPy.Optimizer.PromptOptimizer do
  @moduledoc """
  Specialized optimizer for string/prompt variables.
  """
  use DSPy.Optimizer.VariableOptimizer
  @behaviour DSPy.Optimizer.Behaviour
  
  def supports_variable_type?(:string), do: true
  def supports_variable_type?(_), do: false
  
  def optimize_variables(variables, opts) do
    # Use LLM to generate prompt variants
    variants = Enum.flat_map(variables, fn var ->
      generate_prompt_variants(var, opts[:num_variants] || 10)
    end)
    
    # Evaluate variants
    scored = Enum.map(variants, fn {var_id, variant} ->
      score = evaluate_prompt(variant, opts[:trainset], opts[:metric])
      {var_id, variant, score}
    end)
    
    # Select best per variable
    updates = scored
    |> Enum.group_by(fn {var_id, _, _} -> var_id end)
    |> Enum.map(fn {var_id, group} ->
      {_, best_variant, _} = Enum.max_by(group, fn {_, _, score} -> score end)
      {var_id, best_variant}
    end)
    
    {:ok, updates}
  end
  
  defp generate_prompt_variants(variable, num_variants) do
    # Use an LLM to generate variations
    prompt = """
    Generate #{num_variants} variations of this prompt:
    "#{variable.value}"
    
    Constraints: #{inspect(variable.constraints)}
    Each variation should maintain the same intent but use different phrasing.
    """
    
    # Call LLM and parse results
    # ...
  end
end

defmodule DSPy.Variable.Composite do
  @moduledoc """
  Compose multiple variables into complex structures.
  """
  
  defstruct [:variables, :composition_fn, :constraints]
  
  def compose(variables, composition_fn, opts \\ []) do
    %__MODULE__{
      variables: variables,
      composition_fn: composition_fn,
      constraints: opts[:constraints] || []
    }
  end
  
  def evaluate(composite, variable_values) do
    composite.composition_fn.(variable_values)
  end
end

# Example: Composing a complex prompt from multiple variables
defmodule AdvancedChainOfThought do
  use DSPy.Module
  
  variable :task_description, :string
  variable :reasoning_style, :discrete, options: [:analytical, :creative]
  variable :output_format, :string
  
  composite_variable :full_prompt, [:task_description, :reasoning_style, :output_format],
    fn values ->
      """
      Task: #{values.task_description}
      
      Approach this #{values.reasoning_style}ly.
      
      #{values.output_format}
      """
    end
end

defmodule DSPy.Variable.Constraints do
  @moduledoc """
  Constraint system for variables.
  """
  
  def validate_constraint(value, {:range, {min, max}}) do
    if value >= min and value <= max do
      :ok
    else
      {:error, "Value #{value} outside range [#{min}, #{max}]"}
    end
  end
  
  def validate_constraint(value, {:options, allowed}) do
    if value in allowed do
      :ok
    else
      {:error, "Value must be one of: #{inspect(allowed)}"}
    end
  end
  
  def validate_constraint(value, {:pattern, regex}) do
    if Regex.match?(regex, to_string(value)) do
      :ok
    else
      {:error, "Value does not match pattern: #{inspect(regex)}"}
    end
  end
  
  def validate_constraint(value, {:custom, validator_fn}) do
    validator_fn.(value)
  end
  
  def validate_all(value, constraints) do
    Enum.reduce_while(constraints, :ok, fn constraint, _ ->
      case validate_constraint(value, constraint) do
        :ok -> {:cont, :ok}
        error -> {:halt, error}
      end
    end)
  end
end

defmodule DSPy.Variable.History do
  @moduledoc """
  Track variable changes over time.
  """
  
  defstruct [:variable_id, :entries, :max_entries]
  
  def new(variable_id, max_entries \\ 100) do
    %__MODULE__{
      variable_id: variable_id,
      entries: [],
      max_entries: max_entries
    }
  end
  
  def add_entry(history, value, metadata) do
    entry = {
      System.unique_integer([:positive]),
      value,
      DateTime.utc_now(),
      metadata
    }
    
    entries = [entry | history.entries] |> Enum.take(history.max_entries)
    %{history | entries: entries}
  end
  
  def get_trajectory(history, metric_fn) do
    history.entries
    |> Enum.reverse()
    |> Enum.map(fn {version, value, time, meta} ->
      score = meta[:score] || metric_fn.(value)
      %{version: version, value: value, time: time, score: score}
    end)
  end
end

defmodule DSPy.Predict do
  @moduledoc """
  Enhanced Predict with variable support.
  """
  
  defmacro predict(signature, opts \\ []) do
    quote do
      # Check if any variables are referenced in the signature
      variables = extract_variables_from_signature(unquote(signature))
      
      # Create a predictor that uses current variable values
      fn state ->
        # Resolve variable values
        resolved_signature = resolve_signature_variables(
          unquote(signature), 
          state.variables
        )
        
        # Standard prediction logic with resolved values
        DSPy.Predict.Base.predict(resolved_signature, unquote(opts))
      end
    end
  end
end

defmodule DSPy.Teleprompter.VariableAware do
  @moduledoc """
  Base teleprompter that can optimize variables.
  """
  
  def compile(student, trainset, opts \\ []) do
    # Extract all variables
    variables = DSPy.Variable.Registry.get_variables_for_module(student)
    
    # Separate optimizable variables
    {prompt_vars, other_vars} = Enum.split_with(variables, & &1.type == :string)
    
    # Optimize prompts
    optimized_prompts = optimize_prompts(prompt_vars, trainset, opts)
    
    # Optimize other variables
    optimized_others = optimize_other_variables(other_vars, trainset, opts)
    
    # Create new program with optimized variables
    create_optimized_program(student, optimized_prompts ++ optimized_others)
  end
end