package detectors

import (
	"context"
	"crypto/sha256"
	"fmt"
	"log"
	"os"
	"regexp"
	"strings"

	"github.com/yourorg/devour/internal/quality"
)

// DuplicationDetector detects duplicate and near-duplicate code
type DuplicationDetector struct {
	*quality.BaseDetector
	similarityThreshold float64
}

// DuplicateCluster represents a cluster of similar functions
type DuplicateCluster struct {
	Functions      []quality.FunctionInfo `json:"functions"`
	Similarity     float64                `json:"similarity"`
	Representative string                 `json:"representative"`
}

// NewDuplicationDetector creates a new duplication detector
func NewDuplicationDetector(finder quality.FileFinder) *DuplicationDetector {
	return &DuplicationDetector{
		BaseDetector:        quality.NewBaseDetector("duplication", quality.SeverityT3, finder),
		similarityThreshold: 0.8,
	}
}

// Name returns the detector name
func (d *DuplicationDetector) Name() string {
	return "duplication"
}

// Severity returns the default severity
func (d *DuplicationDetector) Severity() quality.Severity {
	return quality.SeverityT3
}

// Detect runs duplication detection on the given path
func (d *DuplicationDetector) Detect(ctx context.Context, path string, config *quality.Config) ([]quality.Finding, error) {
	files, err := d.FindFiles(path, config.Language)
	if err != nil {
		return nil, fmt.Errorf("failed to find files: %w", err)
	}

	// Extract functions from all files
	var allFunctions []quality.FunctionInfo
	for _, file := range files {
		if quality.ShouldExclude(file, config.Exclude) {
			continue
		}

		functions, err := d.extractFunctions(file)
		if err != nil {
			log.Printf("Failed to extract functions from %s: %v", file, err)
			continue
		}

		allFunctions = append(allFunctions, functions...)
	}

	// Find duplicates
	clusters := d.findDuplicates(allFunctions)

	// Convert clusters to findings
	var findings []quality.Finding
	for i, cluster := range clusters {
		if len(cluster.Functions) < 2 {
			continue
		}

		finding := quality.Finding{
			ID:    fmt.Sprintf("duplication-cluster-%d", i),
			Type:  "duplication",
			Title: "Code duplication detected",
			Description: fmt.Sprintf("Found %d similar functions with %.2f similarity",
				len(cluster.Functions), cluster.Similarity),
			File:     cluster.Functions[0].File,
			Line:     cluster.Functions[0].Line,
			Severity: d.Severity(),
			Score:    len(cluster.Functions) * 2, // Score based on cluster size
			Status:   quality.StatusOpen,
			Metadata: map[string]string{
				"cluster_size": fmt.Sprintf("%d", len(cluster.Functions)),
				"similarity":   fmt.Sprintf("%.2f", cluster.Similarity),
				"functions":    d.formatFunctionList(cluster.Functions),
			},
		}
		findings = append(findings, finding)
	}

	return findings, nil
}

// extractFunctions extracts functions from a source file
func (d *DuplicationDetector) extractFunctions(filePath string) ([]quality.FunctionInfo, error) {
	content, err := os.ReadFile(filePath)
	if err != nil {
		return nil, err
	}

	contentStr := string(content)
	lines := strings.Split(contentStr, "\n")

	var functions []quality.FunctionInfo

	// Simple function extraction for Go (can be enhanced with AST parsing)
	for i, line := range lines {
		trimmed := strings.TrimSpace(line)
		if strings.HasPrefix(trimmed, "func ") {
			funcInfo := d.parseFunctionLine(trimmed, filePath, i+1, contentStr)
			if funcInfo != nil {
				functions = append(functions, *funcInfo)
			}
		}
	}

	return functions, nil
}

// parseFunctionLine parses a function declaration line
func (d *DuplicationDetector) parseFunctionLine(line, filePath string, lineNum int, content string) *quality.FunctionInfo {
	// Extract function name
	parts := strings.Fields(line)
	if len(parts) < 2 {
		return nil
	}

	funcName := parts[1]
	// Remove parentheses and receiver if present
	if idx := strings.Index(funcName, "("); idx != -1 {
		funcName = funcName[:idx]
	}

	// Find function body
	lines := strings.Split(content, "\n")
	startLine := lineNum - 1
	endLine := d.findFunctionEnd(lines, startLine)

	if endLine <= startLine {
		return nil
	}

	// Extract function body
	bodyLines := lines[startLine:endLine]
	body := strings.Join(bodyLines, "\n")
	loc := endLine - startLine

	// Create normalized version for comparison
	normalized := d.normalizeFunction(body)
	bodyHash := d.hashFunction(normalized)

	return &quality.FunctionInfo{
		Name:       funcName,
		File:       filePath,
		Line:       lineNum,
		EndLine:    endLine,
		LOC:        loc,
		Body:       body,
		Normalized: normalized,
		BodyHash:   bodyHash,
	}
}

// findFunctionEnd finds the end line of a function
func (d *DuplicationDetector) findFunctionEnd(lines []string, startLine int) int {
	if startLine >= len(lines) {
		return startLine
	}

	braceCount := 0
	for i := startLine; i < len(lines); i++ {
		line := lines[i]
		braceCount += strings.Count(line, "{")
		braceCount += strings.Count(line, "}")

		if braceCount == 0 && i > startLine {
			return i
		}
	}

	return len(lines)
}

// normalizeFunction normalizes a function for comparison
func (d *DuplicationDetector) normalizeFunction(body string) string {
	// Remove comments
	body = regexp.MustCompile(`//.*`).ReplaceAllString(body, "")
	body = regexp.MustCompile(`/\*[\s\S]*?\*/`).ReplaceAllString(body, "")

	// Normalize whitespace
	body = regexp.MustCompile(`\s+`).ReplaceAllString(body, " ")
	body = strings.TrimSpace(body)

	// Normalize variable names (simple approach)
	body = regexp.MustCompile(`\b[a-z][a-zA-Z0-9]*\b`).ReplaceAllString(body, "VAR")

	return body
}

// hashFunction creates a hash of the normalized function
func (d *DuplicationDetector) hashFunction(normalized string) string {
	hash := sha256.Sum256([]byte(normalized))
	return fmt.Sprintf("%x", hash)
}

// findDuplicates finds duplicate functions using similarity analysis
func (d *DuplicationDetector) findDuplicates(functions []quality.FunctionInfo) []DuplicateCluster {
	var clusters []DuplicateCluster

	// Group by exact hash first
	hashGroups := make(map[string][]quality.FunctionInfo)
	for _, fn := range functions {
		hashGroups[fn.BodyHash] = append(hashGroups[fn.BodyHash], fn)
	}

	// Create clusters from exact duplicates
	for _, group := range hashGroups {
		if len(group) >= 2 {
			cluster := DuplicateCluster{
				Functions:      group,
				Similarity:     1.0,
				Representative: group[0].Name,
			}
			clusters = append(clusters, cluster)
		}
	}

	// Find near-duplicates using similarity
	processed := make(map[int]bool)
	for i, fn1 := range functions {
		if processed[i] {
			continue
		}

		var similar []quality.FunctionInfo
		similar = append(similar, fn1)

		for j, fn2 := range functions {
			if i == j || processed[j] {
				continue
			}

			similarity := d.calculateSimilarity(fn1.Normalized, fn2.Normalized)
			if similarity >= d.similarityThreshold {
				similar = append(similar, fn2)
				processed[j] = true
			}
		}

		if len(similar) >= 2 {
			cluster := DuplicateCluster{
				Functions:      similar,
				Similarity:     d.similarityThreshold,
				Representative: similar[0].Name,
			}
			clusters = append(clusters, cluster)
		}

		processed[i] = true
	}

	return clusters
}

// calculateSimilarity calculates similarity between two strings
func (d *DuplicationDetector) calculateSimilarity(s1, s2 string) float64 {
	if s1 == s2 {
		return 1.0
	}

	// Simple Levenshtein distance-based similarity
	distance := d.levenshteinDistance(s1, s2)
	maxLen := max(len(s1), len(s2))
	if maxLen == 0 {
		return 1.0
	}

	return 1.0 - float64(distance)/float64(maxLen)
}

// levenshteinDistance calculates the Levenshtein distance between two strings
func (d *DuplicationDetector) levenshteinDistance(s1, s2 string) int {
	m, n := len(s1), len(s2)
	if m < n {
		s1, s2 = s2, s1
		m, n = n, m
	}

	if n == 0 {
		return m
	}

	prev := make([]int, n+1)
	for i := range prev {
		prev[i] = i
	}

	for i := 1; i <= m; i++ {
		current := make([]int, n+1)
		current[0] = i

		for j := 1; j <= n; j++ {
			cost := 0
			if s1[i-1] != s2[j-1] {
				cost = 1
			}

			current[j] = min(
				prev[j]+1,      // deletion
				current[j-1]+1, // insertion
				prev[j-1]+cost, // substitution
			)
		}

		prev = current
	}

	return prev[n]
}

// formatFunctionList formats a list of functions for metadata
func (d *DuplicationDetector) formatFunctionList(functions []quality.FunctionInfo) string {
	var names []string
	for _, fn := range functions {
		names = append(names, fmt.Sprintf("%s:%d", fn.Name, fn.Line))
	}
	return strings.Join(names, ",")
}

// min returns the minimum of three integers
func min(a, b, c int) int {
	if a < b {
		if a < c {
			return a
		}
		return c
	}
	if b < c {
		return b
	}
	return c
}

// max returns the maximum of two integers
func max(a, b int) int {
	if a > b {
		return a
	}
	return b
}