use std::env;

use itertools::Itertools;

use uniplate::Uniplate;

use crate::ast::{Expression, ReturnType};

use crate::bug;

use crate::rule_engine::{get_rules, Reduction, RuleSet};

use crate::stats::RewriterStats;

use crate::Model;

use super::resolve_rules::RuleData;

use super::rewriter_common::{log_rule_application, RewriteError, RuleResult};

/// Checks if the OPTIMIZATIONS environment variable is set to "1".

    }

/// Rewrites the given model by applying a set of rules to all its constraints, until no more rules can be applied.

///

/// Rules are applied in order of priority (from highest to lowest)

/// Rules can:

/// - Apply transformations to the constraints in the model (or their sub-expressions)

/// - Add new constraints to the model

/// - Modify the symbol table (e.g. add new variables)

///

/// # Parameters

/// - `model`: A reference to the [`Model`] to be rewritten.

/// - `rule_sets`: A vector of references to [`RuleSet`]s to be applied.

///

/// Each `RuleSet` contains a map of rules to priorities.

///

/// # Returns

/// - `Ok(Model)`: If successful, it returns a modified copy of the [`Model`]

/// - `Err(RewriteError)`: If an error occurs during rule application (e.g., invalid rules)

///

/// # Side-Effects

/// - Rules can apply side-effects to the model (e.g. adding new constraints or variables).

///   The original model is cloned and a modified copy is returned.

/// - Rule engine statistics (e.g. number of rule applications, run time) are collected and stored in the new model's context.

///

/// # Example

/// - Using `rewrite_model` with the constraint `(a + min(x, y)) = b`

///

///   Original model:

///   ```text

///   model: {

///     constraints: [(a + min(x, y) + 42 - 10) = b],

///     symbols: [a, b, x, y]

///   }

///   rule_sets: [{

///       name: "MyRuleSet",

///       rules: [

///         min_to_var: 10,

///         const_eval: 20

///       ]

///     }]

///   ```

///

///   Rules:

///   - `min_to_var`: min([a, b]) ~> c ; c <= a & c <= b & (c = a \/ c = b)

///   - `const_eval`: c1 + c2 ~> (c1 + c2) ; c1, c2 are constants

///

///   Result:

///   ```text

///   model: {

///     constraints: [

///       (a + aux + 32) = b,

///       aux <= x,

///       aux <= y,

///       aux = x \/ aux = y

///     ],

///     symbols: [a, b, x, y, aux]

///   }

///   ```

///

///   Process:

///   1. We traverse the expression tree until a rule can be applied.

///   2. If multiple rules can be applied to the same expression, the higher priority one goes first.

///      In this case, `const_eval` is applied before `min_to_var`.

///   3. The rule `min_to_var` adds a new variable `aux` and new constraints to the model.

///   4. When no more rules can be applied, the resulting model is returned.

///

///   Details for this process can be found in [`rewrite_iteration`] documentation.

///

/// # Performance Considerations

/// - We recursively traverse the tree multiple times to check if any rules can be applied.

/// - Expressions are cloned on each rule application

///

/// This can be expensive for large models

///

/// # Panics

/// - This function may panic if the model's context is unavailable or if there is an issue with locking the context.

///

/// # See Also

/// - [`get_rules`]: Resolves the rules from the provided rule sets and sorts them by priority.

/// - [`rewrite_iteration`]: Executes a single iteration of rewriting the model using the specified rules.

        }

        // If new constraints are added, continue processing them in the next iterations.

    }

/// Checks if all expressions in `Vec<Expr>` are booleans.

/// All top-level constraints in a model should be boolean expressions.

/// Attempts to apply a set of rules to the given expression and its sub-expressions in the model.

///

/// 1. Checks if the expression is "clean" (all possible rules have been applied).

/// 2. Tries to apply rules to the top-level expression, in oprder of priority.

/// 3. If no rules can be applied to the top-level expression, recurses into its sub-expressions.

///

/// When a successful rule application is found, immediately returns a `Reduction` and stops.

/// The `Reduction` contains the new expression and any side-effects (e.g., new constraints, variables).

/// If no rule applications are possible in this expression tree, returns `None`.

///

/// # Parameters

/// - `expression`: The [`Expression`] to be rewritten.

/// - `model`: The root [`Model`] for access to the context and symbol table.

/// - `rules`: A max-heap of [`RuleData`] containing rules, priorities, and metadata. Ordered by rule priority.

/// - `apply_optimizations`: If `true`, skip already "clean" expressions to avoid redundant work.

/// - `stats`: A mutable reference to [`RewriterStats`] to collect statistics

///

/// # Returns

/// - `Some(<Reduction>)`: If a rule is successfully applied to the expression or any of its sub-expressions.

///                        Contains the new expression and any side-effects to apply to the model.

/// - `None`: If no rule is applicable to the expression or any of its sub-expressions.

///

/// # Example

///

/// - Rewriting the expression `a + min(x, y)`:

///

///   Input:

///   ```text

///   expression: a + min(x, y)

///   rules: [min_to_var]

///   model: {

///     constraints: [(a + min(x, y)) = b],

///     symbols: [a, b, x, y]

///   }

///   apply_optimizations: true

///   ```

///

///   Process:

///   1. Initially, the expression is dirty, so we proceed with the rewrite.

///   2. No rules can be applied to the top-level expression `a + min(x, y)`.

///      Try its children: `a` and `min(x, y)`.

///   3. No rules can be applied to `a`. Mark it as clean and return None.

///   4. The rule `min_to_var` can be applied to `min(x, y)`. Return the `Reduction`.

///      ```text

///      Reduction {

///        new_expression: aux,

///        new_top: [aux <= x, aux <= y, aux = x \/ aux = y],

///        symbols: [a, b, x, y, aux]

///      }

///      ```

///   5. Update the parent expression `a + min(x, y)` with the new child `a + aux`.

///      Add new constraints and variables to the model.

///   6. No more rules can be applied to this expression. Mark it as clean and return a pure `Reduction`.

        // Skip processing this expression if it's clean

        // If a rule is applied, mark the expression as dirty

    }

    // If all children are clean, mark this expression as clean

/// Tries to apply rules to an expression and returns a list of successful applications.

///

/// The expression or model is NOT modified directly.

/// We create a list of `RuleResult`s containing the reductions and pass it to `choose_rewrite` to select one to apply.

///

/// # Parameters

/// - `expression`: A reference to the [`Expression`] to evaluate.

/// - `model`: A reference to the [`Model`] for access to the symbol table and context.

/// - `rules`: A vector of references to [`Rule`]s to try.

/// - `stats`: A mutable reference to [`RewriterStats`] used to track the number of rule applications and other statistics.

///

/// # Returns

/// - A `Vec<RuleResult>` containing all successful rule applications to the expression.

///   Each `RuleResult` contains the rule that was applied and the resulting `Reduction`.

///

/// # Side-Effects

/// - The function updates the provided `stats` with the number of rule application attempts and successful applications.

/// - Debug or trace logging may be performed to track which rules were applicable or not for a given expression.

///

/// # Example

/// let applicable_rules = apply_all_rules(&expr, &model, &rules, &mut stats);

/// if !applicable_rules.is_empty() {

///     for result in applicable_rules {

///         println!("Rule applied: {:?}", result.rule_data.rule);

///     }

/// }

///

/// ## Note

/// - Rules are applied only to the given expression, not its children.

///

/// # See Also

/// - [`choose_rewrite`]: Chooses a single reduction from the rule results provided by `apply_all_rules`.

        {

            Err(_) => {

                    rule_data.rule,

                    expression,

                    rule_data

                );

            }

        }

    }

/// Chooses the first applicable rule result from a list of rule applications.

///

/// Currently, applies the rule with the highest priority.

/// If multiple rules have the same priority, logs an error message and panics.

///

/// # Parameters

/// - `results`: A slice of [`RuleResult`]s to consider.

/// -  `initial_expression`: [`Expression`] before the rule application.

///

/// # Returns

/// - `Some(<Reduction>)`: If there is at least one successful rule application, returns a [`Reduction`] to apply.

/// - `None`: If there are no successful rule applications (i.e. `results` is empty).

///

/// # Example

///

/// let rule_results = vec![rule1_result, rule2_result];

/// if let Some(reduction) = choose_rewrite(&rule_results) {

///   // Process the chosen reduction

/// }

///

                // Multiple rules with the same priority

                bug!("{}", message);

        }

            // Keep old behaviour: log a message and apply the highest priority rule