# -*- coding: utf-8 -*-
"""Location: ./mcp-servers/python/mcp_eval_server/mcp_eval_server/tools/multilingual_tools.py
Copyright 2025
SPDX-License-Identifier: Apache-2.0
Authors: Mihai Criveti

MCP tools for multilingual evaluation.
"""

# Standard
from collections import Counter, defaultdict
import re
import statistics
from typing import Any, Dict, List, Optional

# Local
from .judge_tools import JudgeTools


class MultilingualTools:
    """Tools for multilingual and cross-cultural evaluation."""

    def __init__(self, judge_tools: Optional[JudgeTools] = None):
        """Initialize multilingual tools.

        Args:
            judge_tools: Judge tools instance for LLM evaluations
        """
        self.judge_tools = judge_tools or JudgeTools()

        # Language patterns and indicators
        self.language_patterns = {
            "english": {
                "articles": ["the", "a", "an"],
                "pronouns": ["he", "she", "it", "they", "we", "you", "i"],
                "common_words": ["and", "or", "but", "with", "from", "to", "for", "of"],
            },
            "spanish": {
                "articles": ["el", "la", "los", "las", "un", "una"],
                "pronouns": ["él", "ella", "ellos", "ellas", "nosotros", "tú", "yo"],
                "common_words": ["y", "o", "pero", "con", "de", "para", "en"],
            },
            "french": {
                "articles": ["le", "la", "les", "un", "une", "des"],
                "pronouns": ["il", "elle", "ils", "elles", "nous", "vous", "je"],
                "common_words": ["et", "ou", "mais", "avec", "de", "pour", "en"],
            },
            "german": {
                "articles": ["der", "die", "das", "ein", "eine"],
                "pronouns": ["er", "sie", "es", "wir", "ihr", "ich"],
                "common_words": ["und", "oder", "aber", "mit", "von", "für", "in"],
            },
            "chinese": {
                "particles": ["的", "了", "是", "在", "有", "这", "那"],
                "pronouns": ["我", "你", "他", "她", "它", "我们", "你们"],
                "common_words": ["和", "或", "但是", "与", "从", "为", "在"],
            },
        }

        # Cultural adaptation markers
        self.cultural_markers = {
            "formality": {
                "formal": ["respectfully", "sincerely", "please", "kindly", "would you"],
                "informal": ["hey", "yeah", "gonna", "wanna", "cool", "awesome"],
            },
            "directness": {
                "direct": ["must", "should", "need to", "have to", "required"],
                "indirect": ["might", "could", "perhaps", "maybe", "it would be nice"],
            },
            "collectivism": {
                "collective": ["we", "us", "our", "together", "community", "group"],
                "individual": ["i", "me", "my", "myself", "personal", "individual"],
            },
        }

    async def evaluate_translation_quality(
        self,
        source_text: str,
        translated_text: str,
        source_language: str,
        target_language: str,
        quality_dimensions: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Assess translation accuracy and quality.

        Args:
            source_text: Original text in source language
            translated_text: Translated text
            source_language: Source language code/name
            target_language: Target language code/name
            quality_dimensions: Aspects of translation quality to evaluate
            judge_model: Judge model for translation assessment

        Returns:
            Translation quality analysis
        """
        if quality_dimensions is None:
            quality_dimensions = ["accuracy", "fluency", "completeness", "cultural_adaptation", "terminology"]

        # Basic linguistic analysis
        linguistic_analysis = self._analyze_translation_linguistics(source_text, translated_text, source_language, target_language)

        # LLM-based quality assessment
        quality_scores = {}
        for dimension in quality_dimensions:
            score = await self._assess_translation_dimension(source_text, translated_text, dimension, source_language, target_language, judge_model)
            quality_scores[dimension] = score

        # Detect translation errors
        translation_errors = self._detect_translation_errors(source_text, translated_text, source_language, target_language)

        # Calculate overall quality
        overall_quality = statistics.mean(quality_scores.values()) if quality_scores else 0.0

        # Assess preservation of meaning
        meaning_preservation = await self._assess_meaning_preservation(source_text, translated_text, judge_model)

        return {
            "overall_quality": overall_quality,
            "quality_scores": quality_scores,
            "linguistic_analysis": linguistic_analysis,
            "translation_errors": translation_errors,
            "meaning_preservation": meaning_preservation,
            "analysis": {
                "source_language": source_language,
                "target_language": target_language,
                "source_length": len(source_text),
                "translated_length": len(translated_text),
                "length_ratio": len(translated_text) / len(source_text) if source_text else 0,
                "quality_dimensions": quality_dimensions,
                "error_count": len(translation_errors),
            },
            "recommendations": self._generate_translation_recommendations(overall_quality, translation_errors, quality_scores),
        }

    async def measure_cross_lingual_consistency(
        self,
        base_text: str,
        base_language: str,
        translated_versions: Dict[str, str],
        consistency_metrics: List[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Check consistency across languages.

        Args:
            base_text: Original text in base language
            base_language: Base language code/name
            translated_versions: Dictionary of language -> translated text
            consistency_metrics: Metrics to evaluate consistency
            judge_model: Judge model for consistency assessment

        Returns:
            Cross-lingual consistency analysis
        """
        if consistency_metrics is None:
            consistency_metrics = ["semantic_consistency", "factual_consistency", "tone_consistency", "style_consistency"]

        # Analyze consistency between each translation and base
        language_comparisons = {}
        for target_lang, translated_text in translated_versions.items():
            comparison = await self._compare_cross_lingual_texts(base_text, translated_text, base_language, target_lang, consistency_metrics, judge_model)
            language_comparisons[target_lang] = comparison

        # Analyze consistency between all translation pairs
        pairwise_comparisons = {}
        languages = list(translated_versions.keys())
        for i, lang1 in enumerate(languages):
            for lang2 in languages[i + 1 :]:
                pair_key = f"{lang1}_{lang2}"
                comparison = await self._compare_translation_pair(translated_versions[lang1], translated_versions[lang2], lang1, lang2, consistency_metrics, judge_model)
                pairwise_comparisons[pair_key] = comparison

        # Calculate overall consistency metrics
        metric_consistency = {}
        for metric in consistency_metrics:
            base_scores = [comp["consistency_scores"][metric] for comp in language_comparisons.values() if metric in comp["consistency_scores"]]
            pair_scores = [comp["consistency_scores"][metric] for comp in pairwise_comparisons.values() if metric in comp["consistency_scores"]]

            all_scores = base_scores + pair_scores
            if all_scores:
                metric_consistency[metric] = {
                    "mean_consistency": statistics.mean(all_scores),
                    "std_consistency": statistics.stdev(all_scores) if len(all_scores) > 1 else 0.0,
                    "min_consistency": min(all_scores),
                    "max_consistency": max(all_scores),
                }

        # Overall cross-lingual consistency
        mean_consistencies = [mc["mean_consistency"] for mc in metric_consistency.values()]
        overall_consistency = statistics.mean(mean_consistencies) if mean_consistencies else 0.0

        return {
            "overall_consistency": overall_consistency,
            "metric_consistency": metric_consistency,
            "language_comparisons": language_comparisons,
            "pairwise_comparisons": pairwise_comparisons,
            "analysis": {
                "base_language": base_language,
                "target_languages": list(translated_versions.keys()),
                "total_languages": len(translated_versions) + 1,  # +1 for base
                "consistency_metrics": consistency_metrics,
                "least_consistent_metric": min(metric_consistency.items(), key=lambda x: x[1]["mean_consistency"])[0] if metric_consistency else None,
            },
            "recommendations": self._generate_consistency_recommendations(overall_consistency, metric_consistency, language_comparisons),
        }

    async def assess_cultural_adaptation(
        self,
        text: str,
        target_culture: str,
        cultural_dimensions: List[str] = None,
        reference_text: Optional[str] = None,
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Evaluate cultural appropriateness and adaptation.

        Args:
            text: Text to assess for cultural adaptation
            target_culture: Target culture/region for adaptation
            cultural_dimensions: Aspects of cultural adaptation to evaluate
            reference_text: Optional reference text for comparison
            judge_model: Judge model for cultural assessment

        Returns:
            Cultural adaptation analysis
        """
        if cultural_dimensions is None:
            cultural_dimensions = ["formality", "directness", "context_level", "hierarchy", "collectivism", "time_orientation"]

        # Analyze cultural markers in text
        cultural_markers = self._analyze_cultural_markers(text)

        # Assess each cultural dimension
        dimension_scores = {}
        for dimension in cultural_dimensions:
            score = await self._assess_cultural_dimension(text, target_culture, dimension, judge_model)
            dimension_scores[dimension] = score

        # Detect cultural mismatches
        cultural_mismatches = self._detect_cultural_mismatches(text, target_culture)

        # Compare with reference if provided
        reference_comparison = None
        if reference_text:
            reference_comparison = await self._compare_cultural_adaptation(text, reference_text, target_culture, judge_model)

        # Overall cultural adaptation score
        overall_adaptation = statistics.mean(dimension_scores.values()) if dimension_scores else 0.0

        return {
            "overall_adaptation": overall_adaptation,
            "dimension_scores": dimension_scores,
            "cultural_markers": cultural_markers,
            "cultural_mismatches": cultural_mismatches,
            "reference_comparison": reference_comparison,
            "analysis": {
                "target_culture": target_culture,
                "cultural_dimensions": cultural_dimensions,
                "adaptation_issues": len(cultural_mismatches),
                "weakest_dimension": min(dimension_scores.items(), key=lambda x: x[1])[0] if dimension_scores else None,
                "strongest_dimension": max(dimension_scores.items(), key=lambda x: x[1])[0] if dimension_scores else None,
            },
            "recommendations": self._generate_cultural_recommendations(overall_adaptation, cultural_mismatches, dimension_scores),
        }

    async def detect_language_mixing(
        self,
        text: str,
        expected_language: str,
        mixing_tolerance: float = 0.05,
        detection_method: str = "pattern_based",
        judge_model: str = "gpt-4o-mini",
    ) -> Dict[str, Any]:
        """Identify inappropriate code-switching or language mixing.

        Args:
            text: Text to analyze for language mixing
            expected_language: Expected primary language
            mixing_tolerance: Acceptable level of language mixing (0-1)
            detection_method: Method for detecting language mixing
            judge_model: Judge model for language assessment

        Returns:
            Language mixing analysis
        """
        # Detect languages present in text
        detected_languages = self._detect_languages_in_text(text)

        # Analyze code-switching patterns
        code_switching = self._analyze_code_switching(text, expected_language)

        # Calculate mixing metrics
        primary_language_ratio = detected_languages.get(expected_language, 0.0)
        other_languages_ratio = 1.0 - primary_language_ratio
        mixing_appropriate = other_languages_ratio <= mixing_tolerance

        # Classify mixing types
        mixing_classification = self._classify_language_mixing(code_switching, expected_language)

        # LLM assessment of mixing appropriateness
        if detection_method == "llm_based":
            llm_assessment = await self._llm_assess_language_mixing(text, expected_language, judge_model)
        else:
            llm_assessment = {"appropriateness": 0.5, "reasoning": "Pattern-based analysis only"}

        return {
            "mixing_appropriate": mixing_appropriate,
            "primary_language_ratio": primary_language_ratio,
            "other_languages_ratio": other_languages_ratio,
            "detected_languages": detected_languages,
            "code_switching": code_switching,
            "mixing_classification": mixing_classification,
            "llm_assessment": llm_assessment,
            "analysis": {
                "expected_language": expected_language,
                "mixing_tolerance": mixing_tolerance,
                "detection_method": detection_method,
                "total_switches": len(code_switching),
                "most_mixed_language": max(detected_languages.items(), key=lambda x: x[1] if x[0] != expected_language else 0)[0] if len(detected_languages) > 1 else None,
            },
            "recommendations": self._generate_mixing_recommendations(mixing_appropriate, other_languages_ratio, mixing_classification),
        }

    # Helper methods for multilingual evaluation

    def _analyze_translation_linguistics(self, source_text: str, translated_text: str, _source_lang: str, _target_lang: str) -> Dict[str, Any]:
        """Analyze linguistic features of translation.

        Args:
            source_text: Original text in source language
            translated_text: Translated text
            _source_lang: Source language code/name
            _target_lang: Target language code/name

        Returns:
            Dictionary containing linguistic analysis metrics
        """
        return {
            "word_count_ratio": len(translated_text.split()) / len(source_text.split()) if source_text.split() else 0,
            "character_count_ratio": len(translated_text) / len(source_text) if source_text else 0,
            "sentence_count_ratio": len(re.split(r"[.!?]+", translated_text)) / len(re.split(r"[.!?]+", source_text)) if re.split(r"[.!?]+", source_text) else 0,
            "punctuation_preserved": self._check_punctuation_preservation(source_text, translated_text),
        }

    def _check_punctuation_preservation(self, source: str, translation: str) -> float:
        """Check how well punctuation is preserved in translation.

        Args:
            source: Original source text
            translation: Translated text

        Returns:
            Punctuation preservation score (0-1)
        """
        source_punct = re.findall(r"[.!?,:;]", source)
        trans_punct = re.findall(r"[.!?,:;]", translation)

        if not source_punct:
            return 1.0 if not trans_punct else 0.5

        # Simple preservation check
        punct_ratio = len(trans_punct) / len(source_punct)
        return min(1.0, punct_ratio) if punct_ratio <= 1.5 else max(0.0, 2.0 - punct_ratio)

    async def _assess_translation_dimension(self, source: str, translation: str, dimension: str, source_lang: str, target_lang: str, judge_model: str) -> float:
        """Assess a specific dimension of translation quality.

        Args:
            source: Original source text
            translation: Translated text
            dimension: Quality dimension to assess
            source_lang: Source language code/name
            target_lang: Target language code/name
            judge_model: Judge model for assessment

        Returns:
            Quality score for the specified dimension (0-1)
        """
        criteria = [
            {
                "name": f"translation_{dimension}",
                "description": f"Rate the {dimension} of this translation from {source_lang} to {target_lang}",
                "scale": "1-5",
                "weight": 1.0,
            }
        ]

        rubric = {
            "criteria": criteria,
            "scale_description": {
                "1": f"Very poor {dimension}",
                "2": f"Poor {dimension}",
                "3": f"Acceptable {dimension}",
                "4": f"Good {dimension}",
                "5": f"Excellent {dimension}",
            },
        }

        try:
            result = await self.judge_tools.evaluate_response(
                response=f"Source ({source_lang}): {source}\nTranslation ({target_lang}): {translation}",
                criteria=criteria,
                rubric=rubric,
                judge_model=judge_model,
            )

            return (result["overall_score"] - 1) / 4  # Convert to 0-1
        except Exception:
            return 0.5  # Default score if assessment fails

    def _detect_translation_errors(self, source: str, translation: str, _source_lang: str, _target_lang: str) -> List[Dict[str, Any]]:
        """Detect common translation errors.

        Args:
            source: Original source text
            translation: Translated text
            _source_lang: Source language code/name
            _target_lang: Target language code/name

        Returns:
            List of detected translation errors with details
        """
        errors = []

        # Check for obvious issues
        if not translation.strip():
            errors.append({"type": "empty_translation", "severity": "critical"})

        if translation == source:
            errors.append({"type": "untranslated", "severity": "critical"})

        # Check for length anomalies
        length_ratio = len(translation) / len(source) if source else 0
        if length_ratio < 0.3:
            errors.append({"type": "too_short", "severity": "high", "ratio": length_ratio})
        elif length_ratio > 3.0:
            errors.append({"type": "too_long", "severity": "medium", "ratio": length_ratio})

        # Check for repeated text (possible translation error)
        words = translation.split()
        if len(words) > 10:
            word_counts = Counter(words)
            repeated_words = [word for word, count in word_counts.items() if count > len(words) * 0.1]
            if repeated_words:
                errors.append({"type": "excessive_repetition", "severity": "medium", "words": repeated_words})

        return errors

    async def _assess_meaning_preservation(self, source: str, translation: str, judge_model: str) -> Dict[str, Any]:
        """Assess how well meaning is preserved in translation.

        Args:
            source: Original source text
            translation: Translated text
            judge_model: Judge model for assessment

        Returns:
            Dictionary containing meaning preservation analysis
        """
        criteria = [
            {
                "name": "meaning_preservation",
                "description": "How well does the translation preserve the original meaning?",
                "scale": "1-5",
                "weight": 1.0,
            }
        ]

        try:
            result = await self.judge_tools.evaluate_response(
                response=f"Original: {source}\nTranslation: {translation}",
                criteria=criteria,
                rubric={
                    "criteria": criteria,
                    "scale_description": {
                        "1": "Meaning completely lost",
                        "2": "Meaning mostly lost",
                        "3": "Meaning partially preserved",
                        "4": "Meaning mostly preserved",
                        "5": "Meaning fully preserved",
                    },
                },
                judge_model=judge_model,
            )

            return {
                "preservation_score": (result["overall_score"] - 1) / 4,
                "reasoning": result.get("reasoning", {}),
            }
        except Exception:
            return {"preservation_score": 0.5, "reasoning": {"error": "Assessment failed"}}

    def _detect_languages_in_text(self, text: str) -> Dict[str, float]:
        """Detect languages present in text using pattern matching.

        Args:
            text: Text to analyze for language detection

        Returns:
            Dictionary mapping languages to their presence ratios
        """
        detected = defaultdict(int)
        words = text.lower().split()
        total_words = len(words)

        if total_words == 0:
            return {}

        for language, patterns in self.language_patterns.items():
            matches = 0
            for _pattern_type, pattern_words in patterns.items():
                for word in words:
                    if word in pattern_words:
                        matches += 1
            detected[language] = matches

        # Convert to ratios
        if detected:
            total_detected = sum(detected.values())
            return {lang: count / total_detected for lang, count in detected.items()}
        return {"unknown": 1.0}

    def _analyze_code_switching(self, text: str, primary_language: str) -> List[Dict[str, Any]]:
        """Analyze code-switching patterns in text.

        Args:
            text: Text to analyze for code-switching
            primary_language: Expected primary language

        Returns:
            List of detected code-switching instances
        """
        # Simple code-switching detection based on language patterns
        words = text.split()
        switches = []

        current_lang = primary_language

        for i, word in enumerate(words):
            word_lower = word.lower().strip(".,!?")
            detected_lang = self._detect_word_language(word_lower)

            if detected_lang not in (current_lang, "unknown"):
                switches.append(
                    {
                        "position": i,
                        "word": word,
                        "from_language": current_lang,
                        "to_language": detected_lang,
                        "context": " ".join(words[max(0, i - 2) : i + 3]),
                    }
                )
                current_lang = detected_lang

        return switches

    def _detect_word_language(self, word: str) -> str:
        """Detect language of a single word.

        Args:
            word: Word to analyze for language detection

        Returns:
            Detected language code or 'unknown'
        """
        for language, patterns in self.language_patterns.items():
            for pattern_words in patterns.values():
                if word in pattern_words:
                    return language
        return "unknown"

    def _classify_language_mixing(self, switches: List[Dict], _primary_lang: str) -> Dict[str, Any]:
        """Classify types of language mixing.

        Args:
            switches: List of code-switching instances
            _primary_lang: Primary language for classification

        Returns:
            Dictionary containing mixing classification details
        """
        if not switches:
            return {"type": "monolingual", "switches": 0}

        switch_count = len(switches)
        languages_involved = set([switch["from_language"] for switch in switches] + [switch["to_language"] for switch in switches])

        if switch_count <= 2:
            mixing_type = "minimal"
        elif switch_count <= 5:
            mixing_type = "moderate"
        else:
            mixing_type = "extensive"

        return {
            "type": mixing_type,
            "switches": switch_count,
            "languages_involved": list(languages_involved),
            "switch_density": switch_count / 100,  # switches per 100 words (approximate)
        }

    def _analyze_cultural_markers(self, text: str) -> Dict[str, Any]:
        """Analyze cultural markers in text.

        Args:
            text: Text to analyze for cultural markers

        Returns:
            Dictionary containing cultural marker analysis
        """
        markers = {}
        text_lower = text.lower()

        for dimension, patterns in self.cultural_markers.items():
            dimension_scores = {}
            for style, words in patterns.items():
                count = sum(1 for word in words if word in text_lower)
                dimension_scores[style] = count
            markers[dimension] = dimension_scores

        return markers

    def _detect_cultural_mismatches(self, text: str, target_culture: str) -> List[Dict[str, Any]]:
        """Detect cultural mismatches for target culture.

        Args:
            text: Text to analyze for cultural mismatches
            target_culture: Target culture to check against

        Returns:
            List of detected cultural mismatches
        """
        # Simplified cultural mismatch detection
        mismatches = []

        # Example: Western cultures might prefer direct communication
        if target_culture.lower() in ["american", "german", "dutch"]:
            indirect_indicators = ["perhaps", "maybe", "it might be", "could possibly"]
            for indicator in indirect_indicators:
                if indicator in text.lower():
                    mismatches.append(
                        {
                            "type": "too_indirect",
                            "indicator": indicator,
                            "suggestion": "Consider more direct phrasing",
                        }
                    )

        return mismatches

    # Recommendation generation methods

    def _generate_translation_recommendations(self, quality: float, errors: List, scores: Dict) -> List[str]:
        """Generate recommendations for improving translation quality.

        Args:
            quality: Overall translation quality score
            errors: List of detected translation errors
            scores: Quality scores by dimension

        Returns:
            List of recommendation strings
        """
        recommendations = []

        if quality < 0.7:
            recommendations.append("Overall translation quality needs improvement")

        if errors:
            error_types = [e["type"] for e in errors]
            recommendations.append(f"Address translation errors: {', '.join(set(error_types))}")

        low_scoring_dimensions = [dim for dim, score in scores.items() if score < 0.6]
        if low_scoring_dimensions:
            recommendations.append(f"Focus on improving: {', '.join(low_scoring_dimensions)}")

        return recommendations

    def _generate_consistency_recommendations(self, consistency: float, metrics: Dict, _comparisons: Dict) -> List[str]:
        """Generate recommendations for improving cross-lingual consistency.

        Args:
            consistency: Overall consistency score
            metrics: Consistency metrics by type
            _comparisons: Cross-lingual comparison results

        Returns:
            List of recommendation strings
        """
        recommendations = []

        if consistency < 0.8:
            recommendations.append("Improve cross-lingual consistency")

        inconsistent_metrics = [metric for metric, data in metrics.items() if data["mean_consistency"] < 0.7]
        if inconsistent_metrics:
            recommendations.append(f"Focus on consistency in: {', '.join(inconsistent_metrics)}")

        return recommendations

    def _generate_cultural_recommendations(self, adaptation: float, mismatches: List, scores: Dict) -> List[str]:
        """Generate recommendations for improving cultural adaptation.

        Args:
            adaptation: Overall cultural adaptation score
            mismatches: List of detected cultural mismatches
            scores: Cultural adaptation scores by dimension

        Returns:
            List of recommendation strings
        """
        recommendations = []

        if adaptation < 0.7:
            recommendations.append("Improve cultural adaptation for target audience")

        if mismatches:
            recommendations.append("Address cultural mismatches identified")

        weak_dimensions = [dim for dim, score in scores.items() if score < 0.6]
        if weak_dimensions:
            recommendations.append(f"Strengthen cultural adaptation in: {', '.join(weak_dimensions)}")

        return recommendations

    def _generate_mixing_recommendations(self, appropriate: bool, ratio: float, classification: Dict) -> List[str]:
        """Generate recommendations for language mixing.

        Args:
            appropriate: Whether language mixing is appropriate
            ratio: Ratio of other languages to primary language
            classification: Language mixing classification details

        Returns:
            List of recommendation strings
        """
        recommendations = []

        if not appropriate:
            recommendations.append("Reduce inappropriate language mixing")

        if ratio > 0.2:
            recommendations.append("Consider maintaining primary language consistency")

        if classification["type"] == "extensive":
            recommendations.append("Minimize code-switching for better readability")

        return recommendations

    # Additional placeholder methods for complex operations
    async def _compare_cross_lingual_texts(self, _base: str, _target: str, base_lang: str, target_lang: str, metrics: List[str], _judge_model: str) -> Dict[str, Any]:
        """Compare texts across languages for consistency.

        Args:
            _base: Base text in base language
            _target: Target text in target language
            base_lang: Base language code/name
            target_lang: Target language code/name
            metrics: Consistency metrics to evaluate
            _judge_model: Judge model for assessment

        Returns:
            Dictionary containing cross-lingual comparison results
        """
        consistency_scores = {}
        for metric in metrics:
            # Simplified consistency scoring
            consistency_scores[metric] = 0.8  # Placeholder

        return {
            "consistency_scores": consistency_scores,
            "languages": f"{base_lang}-{target_lang}",
        }

    async def _compare_translation_pair(self, _text1: str, _text2: str, _lang1: str, _lang2: str, metrics: List[str], _judge_model: str) -> Dict[str, Any]:
        """Compare a pair of translations for consistency.

        Args:
            _text1: First translation text
            _text2: Second translation text
            _lang1: Language of first text
            _lang2: Language of second text
            metrics: Consistency metrics to evaluate
            _judge_model: Judge model for assessment

        Returns:
            Dictionary containing translation pair comparison results
        """
        consistency_scores = {}
        for metric in metrics:
            consistency_scores[metric] = 0.75  # Placeholder

        return {"consistency_scores": consistency_scores}

    async def _assess_cultural_dimension(self, _text: str, _culture: str, _dimension: str, _judge_model: str) -> float:
        """Assess cultural adaptation for specific dimension.

        Args:
            _text: Text to assess for cultural adaptation
            _culture: Target culture for adaptation
            _dimension: Cultural dimension to evaluate
            _judge_model: Judge model for assessment

        Returns:
            Cultural adaptation score for the dimension
        """
        return 0.7  # Placeholder

    async def _compare_cultural_adaptation(self, _text: str, _reference: str, _culture: str, _judge_model: str) -> Dict[str, Any]:
        """Compare cultural adaptation with reference.

        Args:
            _text: Text to assess for cultural adaptation
            _reference: Reference text for comparison
            _culture: Target culture for adaptation
            _judge_model: Judge model for assessment

        Returns:
            Dictionary containing cultural adaptation comparison
        """
        return {"comparison_score": 0.8, "differences": []}

    async def _llm_assess_language_mixing(self, _text: str, _expected_lang: str, _judge_model: str) -> Dict[str, Any]:
        """LLM assessment of language mixing appropriateness.

        Args:
            _text: Text to assess for language mixing
            _expected_lang: Expected primary language
            _judge_model: Judge model for assessment

        Returns:
            Dictionary containing language mixing assessment
        """
        return {"appropriateness": 0.7, "reasoning": "Mixed languages detected"}
