use std::collections::HashMap;

use std::env;

use std::fmt::Display;

use thiserror::Error;

use crate::bug;

use crate::stats::RewriterStats;

use tracing::trace;

use uniplate::Uniplate;

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

use crate::{

    ast::Expression,

    rule_engine::resolve_rules::{

        get_rule_priorities, get_rules_vec, ResolveRulesError as ResolveError,

    },

    Model,

};

#[derive(Debug)]

struct RuleResult<'a> {

    rule: &'a Rule<'a>,

    reduction: Reduction,

}

/// Represents errors that can occur during the model rewriting process.

///

/// This enum captures errors that occur when trying to resolve or apply rules in the model.

#[derive(Debug, Error)]

pub enum RewriteError {

    ResolveRulesError(ResolveError),

}

impl Display for RewriteError {

}

impl From<ResolveError> for RewriteError {

}

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

///

/// # Returns

/// - true if the environment variable is set to "1".

/// - false if the environment variable is not set or set to any other value.

    }

/// Rewrites the given model by applying a set of rules to all its constraints.

///

/// This function iteratively applies transformations to the model's constraints using the specified rule sets.

/// It returns a modified version of the model with all applicable rules applied, ensuring that any side-effects

/// such as updates to the symbol table and top-level constraints are properly reflected in the returned model.

///

/// # Parameters

/// - `model`: A reference to the [`Model`] to be rewritten. The function will clone this model to produce a modified version.

/// - `rule_sets`: A vector of references to [`RuleSet`]s that define the rules to be applied to the model's constraints.

///   Each `RuleSet` is expected to contain a collection of rules that can transform one or more constraints

///   within the model. The lifetime parameter `'a` ensures that the rules' references are valid for the

///   duration of the function execution.

///

/// # Returns

/// - `Ok(Model)`: If successful, it returns a modified copy of the [`Model`] after all applicable rules have been

///   applied. This new model includes any side-effects such as updates to the symbol table or modifications

///   to the constraints.

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

///   it returns a [`RewriteError`] with details about the failure.

///

/// # Side-Effects

/// - When the model is rewritten, related data structures such as the symbol table (which tracks variable names and types)

///   or other top-level constraints may also be updated to reflect these changes. These updates are applied to the returned model,

///   ensuring that all related components stay consistent and aligned with the changes made during the rewrite.

/// - The function collects statistics about the rewriting process, including the number of rule applications

///   and the total runtime of the rewriter. These statistics are then stored in the model's context for

///   performance monitoring and analysis.

///

/// # Example

/// - Using `rewrite_model` with the Expression `a + min(x, y)`

///

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

///   A model containing the expression is created. The variables of the model are represented by a SymbolTable and contain a,x,y.

///   The contraints of the initail model is the expression itself.

///

///   After getting the rules by their priorities and getting additional statistics the while loop of single interations is executed.

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

///

///   The loop is exited only when no more rules can be applied, when rewrite_iteration returns None and [`while let Some(step) = None`] occurs

///

///

///   Will result in side effects ((d<=x ^ d<=y) being the [`new_top`] and the model will now be a conjuction of that and (a+d)

///   Rewritten expression: ((a + d) ^ (d<=x ^ d<=y))

///

/// # Performance Considerations

/// - The function checks if optimizations are enabled before applying rules, which may affect the performance

///   of the rewriting process.

/// - Depending on the size of the model and the number of rules, the rewriting process might take a significant

///   amount of time. Use the statistics collected (`rewriter_run_time` and `rewriter_rule_application_attempts`)

///   to monitor and optimize performance.

///

/// # 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_rule_priorities`]: Retrieves the priorities for the given rules.

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

    //the while loop is exited when None is returned implying the sub-expression is clean

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

///

/// This function recursively traverses the provided expression, applying any applicable rules from the given set.

/// If a rule is successfully applied to the expression or any of its sub-expressions, it returns a `Reduction`

/// containing the new expression, modified top-level constraints, and any changes to symbols. If no rules can be

/// applied at any level, it returns `None`.

///

/// # Parameters

/// - `expression`: A reference to the [`Expression`] to be rewritten. This is the main expression that the function

///   attempts to modify using the given rules.

/// - `model`: A reference to the [`Model`] that provides context and additional constraints for evaluating the rules.

/// - `rules`: A vector of references to [`Rule`]s that define the transformations to apply to the expression.

/// - `apply_optimizations`: A boolean flag that indicates whether optimization checks should be applied during the rewriting process.

///   If `true`, the function skips already "clean" (fully optimized or processed) expressions and marks them accordingly

///   to avoid redundant work.

/// - `stats`: A mutable reference to [`RewriterStats`] to collect statistics about the rule application process, such as

///   the number of rules applied and the time taken for each iteration.

///

/// # Returns

/// - `Some(<Reduction>)`: A [`Reduction`] containing the new expression and any associated modifications if a rule was applied

///   to `expr` or one of its sub-expressions.

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

///

/// # Side-Effects

/// - If `apply_optimizations` is enabled, the function will skip "clean" expressions and mark successfully rewritten

///   expressions as "dirty". This is done to avoid unnecessary recomputation of expressions that have already been

///   optimized or processed.

///

/// # Example

/// - Recursively applying [`rewrite_iteration`]  to [`a + min(x, y)`]

///

///   Initially [`if apply_optimizations && expression.is_clean()`] is not true yet since intially our expression is dirty.

///

///   [`apply_results`] returns a null vector since no rules can be applied at the top level.

///   After calling function [`children`] on the expression a vector of sub-expression [`[a, min(x, y)]`] is returned.

///

///   The function iterates through the vector of the children from the top expression and calls itself.

///

///   [rewrite_iteration] on on the child [`a`] returns None, but on [`min(x, y)`] returns a [`Reduction`] object [`red`].

///   In this case, a rule (min simplification) can apply:

///   - d is added to the SymbolTable and the variables field is updated in the model. new_top is the side effects: (d<=x ^ d<=y)

///   - [`red = Reduction::new(new_expression = d, new_top, symbols)`];

///   - [`sub[1] = red.new_expression`] - Updates the second element in the vector of sub-expressions from [`min(x, y)`] to [`d`]

///

///   Since a child expression [`min(x, y)`] was rewritten to d, the parent expression [`a + min(x, y)`] is updated with the new child [`a+d`].

///   New [`Reduction`] is returned containing the modifications

///

///   The condition [`Some(step) = Some(new reduction)`] in the while loop in [`rewrite_model`] is met -> side effects are applied.

///

///   No more rules in our example can apply to the modified model -> mark all the children as clean and return a pure [`Reduction`].

///   [`return Some(Reduction::pure(expression))`]

///

///   On the last execution of rewrite_iteration condition [`apply_optimizations && expression.is_clean()`] is met, [`None`] is returned.

///

///

/// # Notes

/// - This function works recursively, meaning it traverses all sub-expressions within the given `expression` to find the

///   first rule that can be applied. If a rule is applied, it immediately returns the modified expression and stops

///   further traversal for that branch.

        // 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

/// Applies all the given rules to a specific expression within the model.

///

/// This function iterates through the provided rules and attempts to apply each rule to the given `expression`.

/// If a rule is successfully applied, it creates a [`RuleResult`] containing the original rule and the resulting

/// [`Reduction`]. The statistics (`stats`) are updated to reflect the number of rule application attempts and successful

/// applications.

///

/// The function does not modify the provided `expression` directly. Instead, it collects all applicable rule results

/// into a vector, which can then be used for further processing or selection (e.g., with [`choose_rewrite`]).

///

/// # Parameters

/// - `expression`: A reference to the [`Expression`] that will be evaluated against the given rules. This is the main

///   target for rule transformations and is expected to remain unchanged during the function execution.

/// - `model`: A reference to the [`Model`] that provides context for rule evaluation, such as constraints and symbols.

///   Rules may depend on information in the model to determine if they can be applied.

/// - `rules`: A vector of references to [`Rule`]s that define the transformations to be applied to the expression.

///   Each rule is applied independently, and all applicable rules are collected.

/// - `stats`: A mutable reference to [`RewriterStats`] used to track statistics about rule application, such as

///   the number of attempts and successful applications.

///

/// # Returns

/// - A `Vec<RuleResult>` containing all rule applications that were successful. Each element in the vector represents

///   a rule that was applied to the given `expression` along with the resulting transformation.

/// - An empty vector if no rules were applicable to the expression.

///

/// # 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);

///     }

/// }

///

///

/// # Notes

/// - This function does not modify the input `expression` or `model` directly. The returned `RuleResult` vector

///   provides information about successful transformations, allowing the caller to decide how to process them.

/// - The function performs independent rule applications. If rules have dependencies or should be applied in a

///   specific order, consider handling that logic outside of this function.

///

/// # See Also

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

            Err(_) => {

                    rule.name,

                    rule.rule_sets,

                    expression

                );

            }

        }

    }

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

///

/// This function selects a reduction from the provided `RuleResult` list, prioritizing the first rule

/// that successfully transforms the expression. This strategy can be modified in the future to incorporate

/// more complex selection criteria, such as prioritizing rules based on cost, complexity, or other heuristic metrics.

///

/// The function also checks the priorities of all the applicable rules and detects if there are multiple rules of the same proirity

///

/// # Parameters

/// - `results`: A slice of [`RuleResult`] containing potential rule applications to be considered. Each element

///   represents a rule that was successfully applied to the expression, along with the resulting transformation.

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

///

/// # Returns

/// - `Some(<Reduction>)`: Returns a [`Reduction`] representing the first rule's application if there is at least one

///   rule that produced a successful transformation.

/// - `None`: If no rule applications are available in the `results` slice (i.e., it is empty), it returns `None`.

///

/// # Example

///

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

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

/// Process the chosen reduction

/// }

///

        new_top=%red.new_top,

        rule.name,

        rule.rule_sets,

        initial_expression,

        red.new_expression

    );

    // Return the first result for now

/// Function filters all the applicable rules based on their priority.

/// In the case where there are multiple rules of the same prioriy, a bug! is thrown listing all those duplicates.

/// Otherwise, if there are multiple rules applicable but they all have different priorities, a warning message is dispalyed.

///

/// # Parameters

/// - `rules`: a vector of [`Rule`] containing all the applicable rules and their metadata for a specific expression.

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

/// - `new_expr`: [`Expression`] after the rule transformation.

///

    //iterates over each rule_set and groups by the rule priority

    }

    //filters the map, retaining only entries where there is more than 1 rule of the same priority

        //accumulates all duplicates into a formatted message

        bug!("{}", message);

    //no duplicate rules of the same priorities were found in the set of applicable rules

    } else {

    }

            target: "rule_engine",

            rule.name,

            rule.rule_sets,

            expression,

            new_expression,

        );