"""
Shared translation utilities for DazedMTLTool.
Centralized translation function used across all modules.
"""

import os
import re
import json
import unicodedata
import tiktoken
import openai
import anthropic
from openai import APIError, APIConnectionError, RateLimitError, APIStatusError
import hashlib
import threading
from dotenv import load_dotenv
from pathlib import Path
from retry import retry

# Set to True to enable debug logging (token counts, cache costs, etc.)
DEBUG = True

# Set to True to disable Claude prompt caching for baseline cost comparison.
DISABLE_CACHE = False

# Thread-local per-file token breakdown; read by calculateCost() for Claude.
_thread_local = threading.local()

# Cross-thread running total of accurate cache-discounted cost (protected by lock).
_global_accurate_cost      = 0.0
_global_accurate_cost_lock = threading.Lock()

# Tracks which distinct batch sizes have already been cache-written during this estimate run.
# Each unique numLines value maps to a distinct output_config schema → one write per size.
# Persisted to disk so sequential GUI subprocesses share state.
_estimate_written_sizes: set = set()
_ESTIMATE_SIZES_FILE = Path("log/estimate_written_sizes.json")

def _load_estimate_written_sizes():
    """Load persisted written-sizes set from disk (for GUI subprocess sharing)."""
    global _estimate_written_sizes
    try:
        if _ESTIMATE_SIZES_FILE.exists():
            with open(_ESTIMATE_SIZES_FILE, "r", encoding="utf-8") as f:
                _estimate_written_sizes = set(json.load(f))
    except Exception:
        _estimate_written_sizes = set()

def _save_estimate_written_sizes():
    """Persist written-sizes set to disk."""
    try:
        _ESTIMATE_SIZES_FILE.parent.mkdir(parents=True, exist_ok=True)
        with open(_ESTIMATE_SIZES_FILE, "w", encoding="utf-8") as f:
            json.dump(list(_estimate_written_sizes), f)
    except Exception:
        pass

def clear_estimate_written_sizes():
    """Reset the written-sizes file at the start of a new estimate run."""
    global _estimate_written_sizes
    _estimate_written_sizes = set()
    try:
        if _ESTIMATE_SIZES_FILE.exists():
            _ESTIMATE_SIZES_FILE.unlink()
    except Exception:
        pass


# ===== Placeholder Protection System =====
# Patterns to protect from translation (sound effects, control codes, etc.)
PROTECTED_PATTERNS = [
    r'\\SE\[[^\]]+\]',      # \SE[sound_effect_name]
    r'\\ME\[[^\]]+\]',      # \ME[music_effect_name]
    r'\\BGM\[[^\]]+\]',     # \BGM[background_music_name]
    r'\\BGS\[[^\]]+\]',     # \BGS[background_sound_name]
    r'_pum\[[^\]]+\]',      # _pum[name]
    r'\\VS\[[^\]]+\]',      # \VS[name]
]

def protect_script_codes(text):
    """
    Replace script codes (like \\SE[タイプライター]) with unique placeholders before translation.
    Returns: (protected_text, replacements_dict)
    """
    if not text or not isinstance(text, str):
        return text, {}

    # Normalize curly/smart quotes to ASCII equivalents BEFORE building the JSON
    # payload.  When these characters appear inside a JSON string value the AI
    # tends to treat them as regular ASCII double-quotes, which makes the value
    # appear empty (e.g. `"スキルを"リセットする` → AI sees empty + stray text).
    # This mirrors the identical normalization already applied to the AI's OUTPUT
    # inside extractTranslation's translation_table.
    quote_norm_table = str.maketrans({
        '\u201C': "'",  # " left double quotation mark
        '\u201D': "'",  # " right double quotation mark
        '\uFF02': "'",  # ＂ fullwidth quotation mark
        '\u2018': "'",  # ' left single quotation mark
        '\u2019': "'",  # ' right single quotation mark
        '\u201B': "'",  # ‛ single high-reversed-9 quotation mark
        '\u02BC': "'",  # ʼ modifier letter apostrophe
        '\uFF07': "'",  # ＇ fullwidth apostrophe
    })
    text = text.translate(quote_norm_table)

    # Convert half-width katakana (U+FF61–U+FF9F) to full-width katakana so the
    # AI recognises them as Japanese text and translates them correctly.
    # NFKC is applied only to matched half-width kana spans to avoid altering
    # intentional fullwidth Latin/digit characters elsewhere in the string.
    text = re.sub(r'[\uFF61-\uFF9F]+', lambda m: unicodedata.normalize('NFKC', m.group(0)), text)

    replacements = {}
    protected_text = text
    counter = 0
    
    # Combine all patterns
    combined_pattern = '|'.join(f'({pattern})' for pattern in PROTECTED_PATTERNS)
    
    def replace_match(match):
        nonlocal counter
        original = match.group(0)
        # Create a unique placeholder that won't be translated
        placeholder = f"__PROTECTED_{counter}__"
        replacements[placeholder] = original
        counter += 1
        return placeholder
    
    if combined_pattern:
        protected_text = re.sub(combined_pattern, replace_match, protected_text)
    
    return protected_text, replacements


def restore_script_codes(text, replacements):
    """
    Restore protected script codes from placeholders after translation.
    """
    if not text or not replacements:
        return text
    
    if isinstance(text, str):
        result = text
        for placeholder, original in replacements.items():
            result = result.replace(placeholder, original)
        return result
    elif isinstance(text, list):
        return [restore_script_codes(item, replacements) for item in text]
    else:
        return text


def validate_placeholders(original_text, translated_text, replacements):
    """
    Validate that all placeholders from the original text appear in the translation.
    Returns: (is_valid, missing_placeholders, extra_placeholders)
    """
    if not replacements:
        return True, [], []
    
    # Get all placeholders
    all_placeholders = set(replacements.keys())
    
    # Count placeholders in original
    original_counts = {}
    for placeholder in all_placeholders:
        if isinstance(original_text, str):
            original_counts[placeholder] = original_text.count(placeholder)
        elif isinstance(original_text, list):
            original_counts[placeholder] = sum(str(item).count(placeholder) for item in original_text)
    
    # Count placeholders in translation
    translated_counts = {}
    for placeholder in all_placeholders:
        if isinstance(translated_text, str):
            translated_counts[placeholder] = translated_text.count(placeholder)
        elif isinstance(translated_text, list):
            translated_counts[placeholder] = sum(str(item).count(placeholder) for item in translated_text)
    
    # Find mismatches
    missing = []
    extra = []
    for placeholder in all_placeholders:
        orig_count = original_counts.get(placeholder, 0)
        trans_count = translated_counts.get(placeholder, 0)
        
        if trans_count < orig_count:
            missing.append(f"{placeholder} (expected {orig_count}, found {trans_count})")
        elif trans_count > orig_count:
            extra.append(f"{placeholder} (expected {orig_count}, found {trans_count})")
    
    is_valid = len(missing) == 0 and len(extra) == 0
    return is_valid, missing, extra


def validate_translation_content(original_items, translated_items, langRegex):
    """
    Validate that translated items are not empty or nearly empty.
    Returns: (is_valid, invalid_indices, reasons)
    
    Rules:
    1. If original has content, translation must not be empty or just whitespace
    2. If original has Japanese text, translation must not be a single punctuation mark
    3. Translation should have meaningful content (more than 1-2 characters for substantial originals)
    """
    if not isinstance(original_items, list):
        original_items = [original_items]
        translated_items = [translated_items]
    
    invalid_indices = []
    reasons = []
    
    for i, (orig, trans) in enumerate(zip(original_items, translated_items)):
        orig_str = str(orig).strip()
        trans_str = str(trans).strip()
        
        # Skip if original is empty or placeholder
        if not orig_str or orig_str == "Placeholder Text":
            continue
        
        # Check if original has content that needs translation
        has_source_text = bool(re.search(langRegex, orig_str))
        
        if has_source_text:
            # Original has Japanese text - translation must be substantial
            
            # Check 1: Translation is empty or just whitespace
            if not trans_str:
                invalid_indices.append(i)
                reasons.append(f"Line{i+1}: Empty translation for '{orig_str[:50]}...'")
                continue
            
            # Check 2: Translation is just a single punctuation mark or very short
            # Allow control codes like \\C[27]\\V[45] but not just ":" or ""
            # Use <= 1 so real 2-char words like "No", "Go", "Hi" are not rejected
            if len(trans_str) <= 1 and not re.search(r'\\[A-Z]\[', trans_str):
                # Exception: if original is also very short (like "回" -> "x"), that's ok
                if len(orig_str) > 3:
                    invalid_indices.append(i)
                    reasons.append(f"Line{i+1}: Translation too short ('{trans_str}') for '{orig_str[:50]}...'")
                    continue
            
            # Check 3: For longer originals (>10 chars), translation should be more than just 1-2 chars
            # unless it's a special case like numbers or codes
            if len(orig_str) > 10 and len(trans_str) <= 2:
                # Allow if it contains control codes or is just a replacement word
                if not re.search(r'\\[A-Z]\[', trans_str) and not trans_str.isalnum():
                    invalid_indices.append(i)
                    reasons.append(f"Line{i+1}: Translation suspiciously short ('{trans_str}') for '{orig_str[:50]}...'")
                    continue

            # Check 4: Runaway translation - translation is excessively long relative to original
            # Catches cases where the model repeats words endlessly (e.g. "it hurts it hurts it hurts...")
            if len(orig_str) > 10 and len(trans_str) > len(orig_str) * 10:
                invalid_indices.append(i)
                reasons.append(f"Line{i+1}: Runaway translation (output {len(trans_str)} chars vs input {len(orig_str)} chars) for '{orig_str[:50]}...'")
                continue
    
    is_valid = len(invalid_indices) == 0
    return is_valid, invalid_indices, reasons

# Load .env, strip accidental whitespace, set base URL / org / API key.
# Handles the Gemini compatibility layer as a special case.
load_dotenv()
api_provider = os.getenv("API_PROVIDER", "openai").lower()
env_api = os.getenv("api", "").strip()
if api_provider == "gemini":
    # Use Google Generative Language compatibility endpoint when running Gemini
    openai.base_url = "https://generativelanguage.googleapis.com/v1beta/openai/"
    openai.organization = None
else:
    if env_api:
        openai.base_url = env_api
    # Support both 'organization' (gui/.env.example) and legacy 'org' names
    org = os.getenv("organization") or os.getenv("org")
    if org:
        openai.organization = org.strip()

# Always set API key from 'key' env var (trim whitespace)
openai.api_key = os.getenv("key", "").strip()

# Translation cache management
CACHE_FILE = Path("log/translation_cache.json")
CACHE_LOCK = threading.Lock()
_cache = None

def clear_cache():
    """Clear the translation cache (called at start of each run)"""
    global _cache
    with CACHE_LOCK:
        _cache = {}
        # Also clear the cache file on disk
        try:
            if CACHE_FILE.exists():
                CACHE_FILE.unlink()
        except Exception:
            pass

def load_cache():
    """Load the translation cache from disk"""
    global _cache
    if _cache is not None:
        return _cache
    
    with CACHE_LOCK:
        if _cache is not None:  # Double-check after acquiring lock
            return _cache
        
        # Try to load existing cache from disk
        _cache = {}
        try:
            if CACHE_FILE.exists():
                with open(CACHE_FILE, "r", encoding="utf-8") as f:
                    _cache = json.load(f)
        except Exception:
            # If cache file is corrupted or unreadable, start fresh
            _cache = {}
        
        return _cache

def save_cache():
    """Save the translation cache to disk"""
    global _cache
    if _cache is None:
        return
    
    try:
        CACHE_FILE.parent.mkdir(parents=True, exist_ok=True)
        # Use atomic write
        tmp_file = CACHE_FILE.with_suffix(".tmp")
        with open(tmp_file, "w", encoding="utf-8") as f:
            json.dump(_cache, f, ensure_ascii=False, indent=2)
        tmp_file.replace(CACHE_FILE)
    except Exception:
        pass

def get_cache_key(payload, language):
    """Generate a cache key for a payload (can be single string or JSON batch)"""
    # Use hash to keep keys short but unique
    payload_str = str(payload) if payload is not None else ""
    combined = f"{payload_str}|{language}"
    return hashlib.md5(combined.encode("utf-8")).hexdigest()

def get_cached_translation(payload, language):
    """Get cached translation if it exists"""
    cache = load_cache()
    key = get_cache_key(payload, language)
    return cache.get(key)

def cache_translation(payload, translation, language):
    """Cache a translation payload and its response"""
    global _cache
    cache = load_cache()
    key = get_cache_key(payload, language)
    
    with CACHE_LOCK:
        cache[key] = translation
        # Save every 10 new entries to avoid excessive disk writes
        if len(cache) % 10 == 0:
            save_cache()


# Variable translation map (code 122 <-> code 111 consistency)
VAR_MAP_FILE = Path("log/var_translation_map.json")
VAR_MAP_LOCK = threading.Lock()
_var_map = None

def clear_var_map():
    """Clear the variable translation map (called at start of each run)"""
    global _var_map
    with VAR_MAP_LOCK:
        _var_map = {}
        try:
            if VAR_MAP_FILE.exists():
                VAR_MAP_FILE.unlink()
        except Exception:
            pass

def _load_var_map():
    """Load the variable translation map from disk (always re-reads to pick up
    entries written by other subprocesses)."""
    global _var_map
    _var_map = {}
    try:
        if VAR_MAP_FILE.exists():
            with open(VAR_MAP_FILE, "r", encoding="utf-8") as f:
                _var_map = json.load(f)
    except Exception:
        _var_map = {}
    return _var_map

def _save_var_map():
    """Save the variable translation map to disk.
    Re-reads the file first and merges so entries from other subprocesses
    are never lost."""
    global _var_map
    if _var_map is None:
        return
    try:
        VAR_MAP_FILE.parent.mkdir(parents=True, exist_ok=True)
        # Re-read the on-disk version and merge our entries on top
        disk_map = {}
        try:
            if VAR_MAP_FILE.exists():
                with open(VAR_MAP_FILE, "r", encoding="utf-8") as f:
                    disk_map = json.load(f)
        except Exception:
            disk_map = {}
        disk_map.update(_var_map)
        _var_map = disk_map
        tmp_file = VAR_MAP_FILE.with_suffix(".tmp")
        with open(tmp_file, "w", encoding="utf-8") as f:
            json.dump(_var_map, f, ensure_ascii=False, indent=2)
        tmp_file.replace(VAR_MAP_FILE)
    except Exception:
        pass

def get_var_translation(original):
    """Look up a cached variable translation. Returns the translation or None."""
    with VAR_MAP_LOCK:
        m = _load_var_map()
        return m.get(original)

def set_var_translation(original, translated):
    """Store a variable translation and persist to disk.
    Skips if the translation is identical to the original (untranslated).
    """
    if original == translated:
        return
    with VAR_MAP_LOCK:
        m = _load_var_map()
        m[original] = translated
        _save_var_map()

def set_var_translations_batch(pairs):
    """Store multiple variable translations at once and persist to disk.
    pairs: list of (original, translated) tuples
    Skips pairs where the translation is identical to the original (untranslated).
    """
    with VAR_MAP_LOCK:
        m = _load_var_map()
        for original, translated in pairs:
            if original != translated:
                m[original] = translated
        _save_var_map()


class TranslationConfig:
    """Configuration class to hold all translation settings"""
    
    def __init__(self, 
                 model=None,
                 language=None,
                 prompt=None,
                 vocab=None,
                 langRegex=None,
                 batchSize=None,
                 maxHistory=10,
                 estimateMode=False,
                 logFilePath="log/translationHistory.txt",
                 mismatchLogPath="log/mismatchHistory.txt"):
        
        # Load from environment if not provided
        self.model = model or os.getenv("model")
        self.language = (language or os.getenv("language", "english")).capitalize()
        
        # Load prompt and vocab files if not provided
        if prompt is None:
            try:
                self.prompt = Path("prompt.txt").read_text(encoding="utf-8")
            except FileNotFoundError:
                self.prompt = ""
        else:
            self.prompt = prompt
            
        if vocab is None:
            try:
                self.vocab = Path("vocab.txt").read_text(encoding="utf-8")
            except FileNotFoundError:
                self.vocab = ""
        else:
            self.vocab = vocab
        
        # Set language regex (default is Japanese)
        self.langRegex = langRegex or r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+"
        
        # Set batch size — derive from pricing config unless explicitly supplied
        if batchSize is None:
            self.batchSize = getPricingConfig(self.model)["batchSize"]
        else:
            self.batchSize = batchSize
            
        self.maxHistory = maxHistory
        self.estimateMode = estimateMode
        self.logFilePath = logFilePath
        self.mismatchLogPath = mismatchLogPath


def getPricingConfig(model):
    """
    Get pricing configuration for a given model.
    
    Args:
        model: The model name string
        
    Returns:
        dict: Dictionary containing inputAPICost, outputAPICost, batchSize, and frequencyPenalty
    """
    # Pricing - Depends on the model https://openai.com/pricing ($ Price Per 1M)
    # Batch Size - GPT 3.5 Struggles past 15 lines per request. GPT4 struggles past 50 lines per request
    # If you are getting a MISMATCH LENGTH error, lower the batch size.
    if "gpt-3.5" in model:
        return {
            "inputAPICost": 3.00,
            "outputAPICost": 5.00,
            "batchSize": 10,
            "frequencyPenalty": 0.2
        }
    elif "gpt-4.1-mini" in model:
        return {
            "inputAPICost": 0.40,
            "outputAPICost": 1.60,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "gpt-4.1" in model:
        return {
            "inputAPICost": 2.00,
            "outputAPICost": 8.00,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "gpt-5" in model:
        return {
            "inputAPICost": 1.25,
            "outputAPICost": 10.00,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "deepseek" in model:
        return {
            "inputAPICost": 0.27,
            "outputAPICost": 1.10,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "claude-opus" in model or model == "claude-3-opus":
        return {
            "inputAPICost": 15.00,
            "outputAPICost": 75.00,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "claude-haiku" in model or "haiku" in model:
        return {
            "inputAPICost": 0.80,
            "outputAPICost": 4.00,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "sonnet" in model or "claude" in model:
        return {
            "inputAPICost": 3.00,
            "outputAPICost": 15.00,
            "batchSize": 30,
            "frequencyPenalty": 0.05
        }
    elif "gemini-2.0-flash-lite" in model:
        return {
            "inputAPICost": 0.075,
            "outputAPICost": 0.30,
            "batchSize": 30,
            "frequencyPenalty": 0.0
        }
    elif "gemini-2.0-flash" in model:
        return {
            "inputAPICost": 0.10,
            "outputAPICost": 0.40,
            "batchSize": 30,
            "frequencyPenalty": 0.0
        }
    elif "gemini-2.5-flash-lite" in model:
        return {
            "inputAPICost": 0.10,
            "outputAPICost": 0.40,
            "batchSize": 30,
            "frequencyPenalty": 0.0
        }
    elif "gemini-2.5-flash" in model:
        return {
            "inputAPICost": 0.30,
            "outputAPICost": 2.50,
            "batchSize": 30,
            "frequencyPenalty": 0.0
        }
    elif "gemini-2.5-pro" in model:
        return {
            "inputAPICost": 1.25,
            "outputAPICost": 10.00,
            "batchSize": 30,
            "frequencyPenalty": 0.0
        }
    else:
        # Fallback to environment variables
        return {
            "inputAPICost": float(os.getenv("input_cost", 3.00)),
            "outputAPICost": float(os.getenv("output_cost", 6.00)),
            "batchSize": int(os.getenv("batchsize", 10)),
            "frequencyPenalty": float(os.getenv("frequency_penalty", 0.2))
        }


def batchList(inputList, batchSize):
    """Split a list into batches of specified size"""
    if not isinstance(batchSize, int) or batchSize <= 0:
        raise ValueError("batchSize must be a positive integer")
    
    return [inputList[i : i + batchSize] for i in range(0, len(inputList), batchSize)]


def parseVocabWithCategories(vocabText):
    """Parse vocabulary text and extract terms with their categories."""
    pairs = []
    seen = set()
    currentCategory = None
    
    for line in vocabText.splitlines():
        line = line.strip()
        if not line or line.startswith('```') or line.startswith('Here are some vocabulary'):
            continue
        
        # Check if this is a category header
        if line.startswith('#'):
            currentCategory = line
            continue
            
        # Parse vocabulary term - extract both Japanese and English parts
        # Format: "Japanese term (English translation)" or "Japanese term – English translation"
        m = re.match(r'^(.+?)(?:\s?[\(–]\s*(.+?)[\)]?\s*$)', line)
        if m and ('(' in line or '–' in line):  # Only process lines that actually have parentheses or dashes
            japanese_term = m.group(1).strip()
            english_term = m.group(2).strip().rstrip(')')  # Remove trailing parenthesis if exists
            
            # Create a tuple with both terms for matching
            term_pair = (japanese_term, english_term)
            if term_pair not in seen:
                pairs.append((term_pair, line, currentCategory))
                seen.add(term_pair)
        elif line and not line.startswith('#'):
            # Fallback for lines without parentheses - treat as single term
            term = line.strip()
            if term and term not in seen:
                pairs.append((term, line, currentCategory))
                seen.add(term)
    
    return pairs


def _japanese_term_in_text(term, text):
    """
    Check if a Japanese term appears in text as a standalone word, not as a
    substring of a longer run of the same script (katakana/hiragana/kanji).
    E.g. 'キス' will NOT match inside 'テキスト' because both neighbours are katakana.
    Falls back to plain substring check for non-Japanese or mixed terms.
    """
    if term not in text:
        return False
    KATAKANA = r'ァ-ヴーｦ-ﾟ'
    HIRAGANA = r'ぁ-ゔ'
    KANJI = r'一-龠'
    if re.search(rf'[{KATAKANA}]', term) and not re.search(rf'[{HIRAGANA}{KANJI}]', term):
        pattern = rf'(?<![{KATAKANA}]){re.escape(term)}(?![{KATAKANA}])'
    elif re.search(rf'[{HIRAGANA}]', term) and not re.search(rf'[{KATAKANA}{KANJI}]', term):
        pattern = rf'(?<![{HIRAGANA}]){re.escape(term)}(?![{HIRAGANA}])'
    elif re.search(rf'[{KANJI}]', term) and not re.search(rf'[{KATAKANA}{HIRAGANA}]', term):
        pattern = rf'(?<![{KANJI}]){re.escape(term)}(?![{KANJI}])'
    else:
        return True  # mixed-script term: plain substring match already confirmed above
    return bool(re.search(pattern, text))


def buildMatchedVocabText(vocabPairs, subbedText, history=None):
    """Build formatted vocabulary text with matched terms organized by category."""
    matchedCategories = {}

    # Prepare text to search - combine subbedText and history
    textToSearch = str(subbedText)
    if history:
        if isinstance(history, list):
            textToSearch += " " + " ".join(str(h) for h in history)
        else:
            textToSearch += " " + str(history)

    # Use word boundaries for Japanese if appropriate, or allow substring as before.
    for term, line, category in vocabPairs:
        # Check if term is a tuple (Japanese, English) or a single term
        term_found = False
        if isinstance(term, tuple):
            # Check both Japanese and English terms
            japanese_term, english_term = term
            if _japanese_term_in_text(japanese_term, textToSearch) or english_term in textToSearch:
                term_found = True
        else:
            # Single term check
            if _japanese_term_in_text(term, textToSearch) if re.search(r'[一-龠ぁ-ゔァ-ヴーｦ-ﾟ｡-ﾟ]', str(term)) else term in textToSearch:
                term_found = True
        
        # Always include "# Game Characters" category and all its terms regardless of matches
        if term_found or (category and category.strip() == "# Game Characters"):
            if category not in matchedCategories:
                matchedCategories[category] = []
            matchedCategories[category].append(line)

    # Format matched vocabulary with categories
    if matchedCategories:
        formattedLines = ["Here are some vocabulary and terms so that you know the proper spelling and translation.\n"]
        for category, lines in matchedCategories.items():
            if category:  # Only add category header if it exists
                formattedLines.append(category)
            formattedLines.extend(lines)
            formattedLines.append("")  # Add blank line between categories
        matchedVocabText = f"\n{chr(10).join(formattedLines).rstrip()}\n"
    else:
        matchedVocabText = ""
    
    return matchedVocabText


_BASE_VOCAB_SEPARATOR = "# ── Base Vocabulary"  # Prefix of the separator line written by _save_vocab


def createContext(config, subbedText, formatType, history=None):
    """Create system and user messages for translation.

    Returns (static_system, vocab_text, user) so that callers can keep the
    static prompt and the per-batch vocab list separate.  This lets Claude
    prompt-caching mark only the stable prefix with cache_control, avoiding
    cache invalidation caused by changing vocabulary matches.

    Cached in static_system (never changes between batches):
      - prompt.txt content
      - # Game Characters entries
      - Base vocabulary (vocab_base.txt: honorifics, elements, demons, etc.)

    Dynamic in vocab_text (matched per-batch):
      - All other game-specific vocab terms (items, worldbuilding, etc.)
    """
    vocab_full = config.vocab

    # Split game-specific vocab from the static base vocab appended by _save_vocab
    base_vocab_text = ""
    sep_idx = vocab_full.find(_BASE_VOCAB_SEPARATOR)
    if sep_idx != -1:
        # Strip the separator header line itself; keep the sections underneath
        base_section = vocab_full[sep_idx:]
        # Skip the separator line
        newline_idx = base_section.find("\n")
        base_vocab_text = base_section[newline_idx + 1:].strip() if newline_idx != -1 else ""
        vocab_game = vocab_full[:sep_idx]
    else:
        vocab_game = vocab_full

    vocabPairs = parseVocabWithCategories(vocab_game)

    # Split character entries (stable, cached) from everything else (dynamic)
    CHAR_CAT = "# Game Characters"
    char_lines   = [line for _, line, cat in vocabPairs if cat and cat.strip() == CHAR_CAT]
    non_char_pairs = [(term, line, cat) for term, line, cat in vocabPairs
                      if not (cat and cat.strip() == CHAR_CAT)]

    matchedVocabText = buildMatchedVocabText(non_char_pairs, subbedText, history)

    static_system = config.prompt.replace("English", config.language)

    # Append character glossary to the stable system prompt so it is cached
    if char_lines:
        char_block = f"\n{CHAR_CAT}\n" + "\n".join(char_lines)
        static_system = static_system.rstrip() + "\n" + char_block + "\n"

    # Append base vocab (honorifics, elements, etc.) to static_system so it is
    # cached with the system prompt and never re-evaluated per batch.
    if base_vocab_text:
        static_system = static_system.rstrip() + "\n\n" + base_vocab_text + "\n"

    if formatType == "json":
        user = f"```json\n{subbedText}\n```"
    else:
        user = subbedText

    return static_system, matchedVocabText, user


def createTranslationSchema(numLines):
    """Create a JSON schema for translation response based on number of lines."""
    properties = {}
    required = []
    for i in range(1, numLines + 1):
        line_key = f"Line{i}"
        properties[line_key] = {"type": "string"}
        required.append(line_key)
    return {
        "type": "object",
        "properties": properties,
        "required": required,
        "additionalProperties": False,
    }


def translateText(system, user, history, penalty, formatType, model, numLines=None, vocab_text=""):
    """Send translation request to the selected API.

    system:     Static system prompt (prompt.txt). Cached by Claude.
    vocab_text: Per-batch vocabulary (dynamic, never cached to avoid cache busting).
    """
    # Ensure system content is not empty
    if not system or not str(system).strip():
        raise ValueError("System content cannot be empty")
    
    _is_claude = model and any(x in model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
    _is_deepseek = model and "deepseek" in model.lower()

    # Build message list.
    # Claude: static prompt gets cache_control; vocab appended uncached so it
    # never busts the cache. Requires ≥2048 tokens for Sonnet 4.6 to qualify.
    # Other providers: combine into one plain string.
    if _is_claude:
        if DISABLE_CACHE:
            # No cache_control — sends as a plain content block for a real uncached run.
            combined_system = system + vocab_text
            content_blocks = [{"type": "text", "text": f"```\n{combined_system}\n```"}]
        else:
            # Only the static prompt goes in the system content blocks.
            # Vocab and history are moved to messages so they don't bust the
            # Anthropic prefix cache (the entire system parameter is part of
            # the cache key, not just blocks up to cache_control).
            content_blocks = [{"type": "text", "text": f"```\n{system}\n```", "cache_control": {"type": "ephemeral", "ttl": "1h"}}]
        msg = [{"role": "system", "content": content_blocks}]
    else:
        combined_system = system + vocab_text
        msg = [{"role": "system", "content": f"```\n{combined_system}\n```"}]

    # History
    if isinstance(history, list):
        # Filter out empty or None history items to prevent API errors
        valid_history = [h for h in history if h and str(h).strip()]
        if valid_history:
            msg.append({"role": "system", "content": "Translation History:\n```"})
            msg.extend([{"role": "assistant", "content": h} for h in valid_history])
            msg.append({"role": "system", "content": "```"})
    else:
        if history and str(history).strip():
            msg.append({"role": "assistant", "content": history})

    # Response format per provider:
    # OpenAI/Gemini: json_schema  |  Deepseek: json_object  |  text: omit entirely

    if formatType == "json" and numLines is not None:
        if _is_deepseek:
            # Deepseek: use json_object (no strict schema support)
            responseFormat = {"type": "json_object"}
        else:
            # OpenAI, Claude, Gemini: use json_schema with strict enforcement
            responseFormat = {
                "type": "json_schema",
                "json_schema": {"name": "translation_response", "strict": True, "schema": createTranslationSchema(numLines)}
            }
    else:
        responseFormat = {"type": "text"}

    # Content to TL - ensure user content is not empty
    if not user or not str(user).strip():
        raise ValueError("User content cannot be empty")
    msg.append({"role": "user", "content": f"```\n{user}\n```"})

    # Debug: Check for any empty messages before API call
    for i, message in enumerate(msg):
        if not message.get("content") or not str(message.get("content")).strip():
            raise ValueError(f"Message {i} has empty content: {message}")

    # --- API Call Logic ---
    api_provider = os.getenv("API_PROVIDER", "openai").lower()

    # Omit response_format for plain text — some providers reject {"type": "text"}.
    params = {
        "model": model,
        "messages": msg,
    }
    if responseFormat.get("type") != "text":
        params["response_format"] = responseFormat

    # Provider-specific parameters
    if api_provider == "gemini":
        params["temperature"] = 0
        
        # Handle thinking budget for Gemini
        thinking_budget_str = os.getenv("GEMINI_THINKING_BUDGET")
        if thinking_budget_str:
            try:
                thinking_budget = int(thinking_budget_str)
                params["extra_body"] = {
                    'google': {
                        'thinking_config': {
                            'thinking_budget': thinking_budget
                        }
                    }
                }
            except (ValueError, TypeError):
                pass
        
    # frequency_penalty is unsupported on the Gemini OpenAI compat layer
    elif _is_claude:
        params["temperature"] = 0
        # cache_control is set on the system message content block above.
    else:  # Default to OpenAI behavior
        if "gpt-5" in model:
            params["reasoning_effort"] = "minimal"
        else:
            params["temperature"] = 0
            params["frequency_penalty"] = penalty

    # Use native Anthropic SDK — the OpenAI compat endpoint strips cache_control
    # and never returns cache_read/creation_input_tokens.
    if _is_claude:
        # system blocks were built above (with cache_control on static prompt).
        ant_system = list(msg[0]["content"])

        # Convert remaining messages to native format.
        # Skip system wrapper lines; collect assistant history and user content.
        history_items = []
        native_msgs   = []
        for m in msg[1:]:
            role    = m["role"]
            content = m.get("content", "")
            if role == "system":
                pass  # skip "Translation History:\n```" / "```" wrapper lines
            elif role == "assistant":
                history_items.append(str(content))
            elif role == "user":
                native_msgs.append({"role": "user", "content": str(content)})

        # Vocab goes into messages as a user turn so it doesn't bust the
        # Anthropic prefix cache (the entire system parameter is the key).
        if vocab_text and vocab_text.strip():
            native_msgs.insert(0, {"role": "user", "content": vocab_text.strip()})
            native_msgs.insert(1, {"role": "assistant", "content": "Understood."})

        # History also goes into messages, NOT ant_system.
        if history_items:
            history_block = "Translation History:\n```\n" + "\n".join(history_items) + "\n```"
            native_msgs.insert(0, {"role": "user", "content": history_block})
            native_msgs.insert(1, {"role": "assistant", "content": "Understood."})

        if not native_msgs:
            raise ValueError("No user message found for Anthropic native call")

        ant_client = anthropic.Anthropic(api_key=openai.api_key)
        try:
            ant_kwargs = dict(
                model=model,
                max_tokens=16384,
                temperature=0,
                system=ant_system,
                messages=native_msgs,
            )
            if formatType == "json" and numLines is not None:
                ant_kwargs["output_config"] = {
                    "format": {
                        "type": "json_schema",
                        "schema": createTranslationSchema(numLines),
                    }
                }

            ant_resp = ant_client.messages.create(**ant_kwargs)
        except Exception as e:
            raise Exception(f"Anthropic API error: {e}")

        _ant_text = ant_resp.content[0].text if ant_resp.content else ""
        _u = ant_resp.usage
        _cr  = getattr(_u, "cache_read_input_tokens",     0) or 0
        _cw  = getattr(_u, "cache_creation_input_tokens", 0) or 0
        _inp = getattr(_u, "input_tokens",  0) or 0
        _out = getattr(_u, "output_tokens", 0) or 0

        # input_tokens (native SDK) = non-cached portion; add cache fields for true total.
        _total_prompt = _inp + _cr + _cw

        class _AnthropicCompat:
            class _Usage:
                def __init__(self, prompt, completion, cr, cw):
                    self.prompt_tokens               = prompt
                    self.completion_tokens           = completion
                    self.cache_read_input_tokens     = cr
                    self.cache_creation_input_tokens = cw
                @property
                def model_extra(self):
                    return {
                        "cache_read_input_tokens":     self.cache_read_input_tokens,
                        "cache_creation_input_tokens": self.cache_creation_input_tokens,
                    }
            class _Choice:
                class _Msg:
                    def __init__(self, c): self.content = c
                def __init__(self, c):
                    self.message = _AnthropicCompat._Choice._Msg(c)
            def __init__(self, text, prompt, output, cr, cw):
                self.choices = [_AnthropicCompat._Choice(text)]
                self.usage   = _AnthropicCompat._Usage(prompt, output, cr, cw)

        return _AnthropicCompat(_ant_text, _total_prompt, _out, _cr, _cw)

    # Call API (reaches here only for non-Claude providers)
    try:
        response = openai.chat.completions.create(**params)
    except APIStatusError as e:
        # Handle HTTP status errors (404, 500, etc.)
        if e.status_code == 404:
            raise Exception(f"API endpoint not found (404) - check your API_PROVIDER and base URL settings. Error: {e}")
        elif e.status_code >= 500:
            raise Exception(f"API server error ({e.status_code}) - retrying... Error: {e}")
        elif e.status_code == 400 and formatType == "json" and "json_schema" in str(responseFormat):
            # Only fall back to json_object if the error is NOT "Input should be 'json_schema'"
            # (that message means json_schema IS required and json_object would also be rejected)
            if "input should be 'json_schema'" in str(e).lower() or "input should be \"json_schema\"" in str(e).lower():
                raise Exception(f"API status error ({e.status_code}): {e}")
            # Provider doesn't support json_schema (e.g. Claude) — fall back to json_object
            responseFormat = {"type": "json_object"}
            params["response_format"] = responseFormat
            try:
                response = openai.chat.completions.create(**params)
            except APIStatusError as fallback_error:
                if fallback_error.status_code == 400 and "input should be 'json_schema'" in str(fallback_error).lower():
                    raise Exception(f"API requires json_schema response format but rejected the schema. Original error: {e}")
                raise Exception(f"API call failed: {e}. Fallback also failed: {fallback_error}")
            except Exception as fallback_error:
                raise Exception(f"API call failed: {e}. Fallback also failed: {fallback_error}")
        elif e.status_code == 400 and "input should be 'json_schema'" in str(e).lower():
            # response_format.type was rejected (e.g. sent "text" or "json_object" to a model
            # that only accepts json_schema). Remove response_format and retry with no constraint.
            params.pop("response_format", None)
            try:
                response = openai.chat.completions.create(**params)
            except Exception as fallback_error:
                raise Exception(f"API call failed: {e}. Fallback also failed: {fallback_error}")
        else:
            raise Exception(f"API status error ({e.status_code}): {e}")
    except (APIConnectionError, RateLimitError) as e:
        # These should always be retried
        raise Exception(f"API connection/rate limit error - retrying... Error: {e}")
    except Exception as e:
        # Check if it's a 404 error or other HTTP error that should be retried
        error_str = str(e).lower()
        if "404" in error_str or "not found" in error_str:
            raise Exception(f"API returned 404 Not Found - check your API configuration. Original error: {e}")
        
        # If structured output fails, fallback to json_object (unless the error
        # explicitly states json_schema is required — falling back would just fail again)
        if formatType == "json" and "json_schema" in str(responseFormat) and \
                "input should be 'json_schema'" not in error_str:
            responseFormat = {"type": "json_object"}
            params["response_format"] = responseFormat
            try:
                response = openai.chat.completions.create(**params)
            except Exception as fallback_error:
                # If fallback also fails, raise the original error for retry
                raise Exception(f"API call failed: {e}. Fallback also failed: {fallback_error}")
        else:
            raise e
    
    # Validate response before returning
    if not response or not hasattr(response, 'choices') or not response.choices:
        raise Exception("API returned invalid or empty response - retrying...")
    
    return response


def cleanTranslatedText(translatedText, language):
    """Clean and format translated text"""
    placeholders = {
        f"{language} Translation: ": "",
        "Translation: ": "",
        "っ": "",
        "〜": "~",
        "ッ": "",
        "。": ".",
        # Note: 「 and 」 are NOT replaced here — replacing them with ASCII " would
        # corrupt raw JSON strings before extraction.  They are handled per-line
        # in _clean_extracted_line() after JSON parsing.
        "—": "―",
        "】": "]",
        "【": "[",
        "é": "e",
        "’": "'",
        "this guy": "this bastard",
        "This guy": "This bastard",
        "```json": "",
        "```": "",
    }
    
    for target, replacement in placeholders.items():
        translatedText = translatedText.replace(target, replacement)

    # Remove Repeating Characters
    pattern = re.compile(r"(.)\s*\1(?:\s*\1){" + str(20 - 1) + r",}")
    translatedText = pattern.sub(lambda match: match.group(0).replace(" ", "")[:20], translatedText)

    # Elongate Long Dashes (Since GPT Ignores them...)
    translatedText = elongateCharacters(translatedText)
    return translatedText


def elongateCharacters(text):
    """Replace ー sequences with elongated characters"""
    # Define a pattern to match one character followed by two or more ー characters.
    # The lookbehind is restricted to non-ー Japanese/CJK characters so that:
    #   - standalone ー separators (e.g. "ーーーーーーーーーー") are left untouched
    #   - ー sequences preceded by a JSON quote or other non-Japanese char are not corrupted
    pattern = r"(?<=([\u3040-\u309F\u30A0-\u30FB\u30FD-\u30FF\u4E00-\u9FEF\uFF61-\uFF9F]))ー{2,}"

    # Define a replacement function that elongates the captured character
    def repl(match):
        char = match.group(1)  # The character before the ー sequence
        count = len(match.group(0)) - 1  # Number of ー characters
        return char * count  # Replace ー sequence with the character repeated

    # Use re.sub() to replace the pattern in the text
    return re.sub(pattern, repl, text)


def extractTranslation(translatedTextList, isList, pbar=None):
    """Extract translation from JSON response.

    This function is resilient to a few common model mistakes:
    - Wraps output in code fences or outer quotes
    - Uses smart quotes instead of straight quotes
    - Inserts an extra leading quote in values (e.g. :""Word" -> :"Word")
    - Trailing commas before } or ]

    If strict JSON parsing fails, falls back to a regex-based extractor that
    captures LineN values in numeric order.
    """
    s = str(translatedTextList or "").strip()

    # Fast exit
    if not s:
        return None

    # Remove code fences if present
    if s.startswith("```"):
        s = re.sub(r"^```(?:json)?\s*", "", s, flags=re.IGNORECASE)
        s = re.sub(r"\s*```$", "", s)

    # Trim wrapping quotes around the whole JSON blob (common in logs)
    if len(s) >= 2 and s[0] == s[-1] and s[0] in {'"', "'"}:
        # Only strip if it still looks like JSON inside
        if s[1:2] == "{" and s[-2:-1] == "}":
            s = s[1:-1]

    # Normalize a broad set of Unicode “smart” quotes to ASCII equivalents.
    translation_table = {
        0x201C: "'",  # “ left double quotation mark
        0x201D: "'",  # ” right double quotation mark
        0xFF02: "'",  # ＂ fullwidth quotation mark

        0x2018: "'",  # ‘ left single quotation mark
        0x2019: "'",  # ’ right single quotation mark
        0x201B: "'",  # ‛ single high-reversed-9 quotation mark
        0x02BC: "'",  # ʼ modifier letter apostrophe
        0xFF07: "'",  # ＇ fullwidth apostrophe
    }
    s = s.translate(translation_table)

    # Remove trailing commas before object/array closures
    s = re.sub(r",(\s*[}\]])", r"\1", s)

    # Repair common doubled leading quote in values: :""Word" -> :"Word"
    # Ensure we don't alter legitimate empty strings (:"")
    s = re.sub(r":\s*\"\"(?=[^\",}\]\s])", r':"', s)

    # Attempt strict parse first
    try:
        lineDict = json.loads(s)

        # Handle array-based schema: {"translations": ["...", ...]}
        if isinstance(lineDict, dict) and "translations" in lineDict and isinstance(lineDict["translations"], list):
            stringList = [str(v) for v in lineDict["translations"]]
            return stringList if isList else (stringList[0] if stringList else None)

        # Build list in numeric order if keys are LineN
        numeric_keys = []
        for k in lineDict.keys():
            m = re.fullmatch(r"Line(\d+)", str(k))
            if m:
                numeric_keys.append(int(m.group(1)))

        if numeric_keys:
            stringList = [lineDict.get(f"Line{n}", "") for n in sorted(numeric_keys)]
        else:
            # Fallback to values order if no LineN keys found
            stringList = list(lineDict.values())

        return stringList if isList else (stringList[0] if stringList else None)

    except Exception as e:
        # Fallback: regex-based extraction tolerant to one or two opening quotes
        # Captures escaped quotes within values too
        try:
            pairs = re.findall(r'"Line(\d+)"\s*:\s*"{1,2}((?:\\.|[^"\\])*)"', s)
            if not pairs:
                raise ValueError("No LineN pairs found")

            # Sort numerically and unescape JSON string content
            items = []
            for n_str, v in sorted(((int(n), v) for n, v in pairs), key=lambda x: x[0]):
                try:
                    # Decode JSON escapes reliably by round-tripping as a JSON string
                    decoded = json.loads(f'"{v}"')
                except Exception:
                    decoded = v
                items.append(decoded)

            return items if isList else (items[0] if items else None)
        except Exception as e2:
            if pbar:
                pbar.write(f"extractTranslation Error: {e2} after JSON error {e} on String {translatedTextList}")
            return None


def calculateCost(inputTokens, outputTokens, model):
    """
    Calculate the cost of translation based on token usage and model pricing.

    For Claude models the cost is derived from the actual cache token breakdown
    recorded by translateAI, so cache discounts are reflected accurately:
      - Cache reads:  10 % of the base input rate
      - Cache writes: 125 % of the base input rate
      - Regular input: 100 % of the base input rate

    Call pattern (no module changes required):
      Per-file call: file_cost_ready flag is True → read thread-local per-file
                     accumulators (which span all translateAI calls for the file),
                     compute cost, reset accumulators, clear flag, return cost.
      TOTAL call:    file_cost_ready is False (already cleared) → return the
                     cross-thread _global_accurate_cost running sum.

    Falls back to naive token × rate calculation for non-Claude models.
    """
    _is_claude = model and any(x in model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
    if _is_claude:
        if getattr(_thread_local, 'file_cost_ready', False):
            # Per-file call: compute from accumulators (may be 0 for disk-cached files),
            # reset everything, return the file cost.
            cr  = getattr(_thread_local, 'file_cache_read',  0)
            cw  = getattr(_thread_local, 'file_cache_write', 0)
            reg = getattr(_thread_local, 'file_regular',     0)
            out = getattr(_thread_local, 'file_output',      0)
            pricing  = getPricingConfig(model)
            br  = pricing["inputAPICost"]  / 1_000_000
            orr = pricing["outputAPICost"] / 1_000_000
            cost = cr * br * 0.10 + cw * br * 2.00 + reg * br + out * orr
            _thread_local.file_cache_read  = 0
            _thread_local.file_cache_write = 0
            _thread_local.file_regular     = 0
            _thread_local.file_output      = 0
            _thread_local.file_cost_ready  = False
            return cost
        # TOTAL call (flag already cleared): return the cross-thread running total.
        # If _global_accurate_cost is 0 it means no real API calls were made
        # (e.g. estimate mode), so fall through to the naive calculation below.
        with _global_accurate_cost_lock:
            accurate = _global_accurate_cost
        if accurate > 0:
            return accurate

    # Non-Claude, estimate mode, or no accurate data: naive calculation.
    # For Claude models, use the accumulated static_system token count (the portion
    # that is always cache-written at the 1hr TTL rate = 2x input rate).
    # Remaining tokens are billed at the regular input rate.
    pricing = getPricingConfig(model)
    _is_claude_naive = model and any(x in model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
    if _is_claude_naive:
        static_tok  = getattr(_thread_local, 'estimate_static_tokens', 0)
        regular_tok = getattr(_thread_local, 'estimate_regular_tokens', 0)
        batch_count = max(1, getattr(_thread_local, 'estimate_batch_count', 1))
        _thread_local.estimate_static_tokens  = 0
        _thread_local.estimate_regular_tokens = 0
        _thread_local.estimate_batch_count    = 0
        # If cache is disabled, every batch is a write (2x) — no reads ever.
        # Otherwise: each distinct batch size (= distinct output_config schema) gets exactly
        # one cache write on first use; all subsequent batches of that size are reads (0.10x).
        # Load from disk first so GUI subprocesses (one per file) share warm-cache state.
        global _estimate_written_sizes
        if DISABLE_CACHE:
            write_batches = batch_count
            read_batches  = 0
        else:
            _load_estimate_written_sizes()
            seen_sizes = getattr(_thread_local, 'estimate_seen_sizes', set())
            new_sizes = seen_sizes - _estimate_written_sizes
            write_batches = len(new_sizes)  # one write per newly-seen size
            read_batches  = batch_count - write_batches
            _estimate_written_sizes.update(new_sizes)
            _save_estimate_written_sizes()
            _thread_local.estimate_seen_sizes = set()
        write_cost   = (write_batches * static_tok / 1_000_000) * pricing["inputAPICost"] * 2.0
        read_cost    = (read_batches  * static_tok / 1_000_000) * pricing["inputAPICost"] * 0.10
        regular_cost = (regular_tok / 1_000_000) * pricing["inputAPICost"]
        inputCost    = write_cost + read_cost + regular_cost
    else:
        inputCost = (inputTokens / 1_000_000) * pricing["inputAPICost"]
    outputCost = (outputTokens / 1_000_000) * pricing["outputAPICost"]
    return inputCost + outputCost


def countTokens(system, user, history):
    """Count tokens for cost estimation"""
    inputTotalTokens = 0
    outputTotalTokens = 0
    enc = tiktoken.encoding_for_model("gpt-4")

    # Input
    if isinstance(history, list):
        for line in history:
            inputTotalTokens += len(enc.encode(line))
    else:
        inputTotalTokens += len(enc.encode(history))
    inputTotalTokens += len(enc.encode(system))
    inputTotalTokens += len(enc.encode(user))

    # Output
    outputTotalTokens += round(len(enc.encode(user)) * 2.5)

    return [inputTotalTokens, outputTotalTokens]


@retry(exceptions=Exception, tries=5, delay=5)
def translateAI(text, history, config, filename=None, pbar=None, lock=None, mismatchList=None):
    """
    Main translation entry point used by all modules.

    Returns [translatedText, [inputTokens, outputTokens]].
    """
    if not text:
        return [text, [0, 0]]

    # Use TRANSLATION_RUN_LOG env var as log path if set.
    run_log = os.getenv("TRANSLATION_RUN_LOG")
    if run_log:
        # Make sure parent dir exists
        try:
            Path(run_log).parent.mkdir(parents=True, exist_ok=True)
        except Exception:
            pass
        config.logFilePath = run_log

    # Ensure log directory exists for the configured path
    try:
        Path(config.logFilePath).parent.mkdir(parents=True, exist_ok=True)
    except Exception:
        pass

    # Token tracking: [input, output].
    totalTokens = [0, 0]

    # Init per-file accumulators on first call on this thread (never reset here —
    # they span all translateAI calls for a file; reset by calculateCost).
    if not hasattr(_thread_local, 'file_cache_read'):
        _thread_local.file_cache_read  = 0
        _thread_local.file_cache_write = 0
        _thread_local.file_regular     = 0
        _thread_local.file_output      = 0
    # Snapshot accumulators so end-of-call delta only counts tokens from this call.
    _prev_cr  = _thread_local.file_cache_read
    _prev_cw  = _thread_local.file_cache_write
    _prev_reg = _thread_local.file_regular
    _prev_out = _thread_local.file_output
    _thread_local.file_cost_ready = False  # will be set True at end of translateAI
    
    if isinstance(text, list):
        formatType = "json"
        tList = batchList(text, config.batchSize)
    else:
        formatType = "text"
        tList = [text]

    for index, tItem in enumerate(tList):
        # Check if text contains target language
        if not re.search(config.langRegex, str(tItem)):
            if pbar is not None:
                pbar.update(len(tItem) if isinstance(tItem, list) else 1)
            if isinstance(tItem, list):
                for j in range(len(tItem)):
                    tItem[j] = cleanTranslatedText(tItem[j], config.language)
                tList[index] = tItem
            else:
                tList[index] = cleanTranslatedText(tItem, config.language)
            history = tItem[-config.maxHistory:] if isinstance(tItem, list) else tItem
            continue

        # Protect script codes before translation
        protected_items = []
        all_replacements = {}
        
        if isinstance(tItem, list):
            for j in range(len(tItem)):
                if not tItem[j] or not str(tItem[j]).strip():
                    protected_items.append("Placeholder Text")
                    all_replacements[j] = {}
                else:
                    protected_text, replacements = protect_script_codes(tItem[j])
                    protected_items.append(protected_text)
                    all_replacements[j] = replacements
        else:
            if not tItem or not str(tItem).strip():
                protected_items = "Placeholder Text"
                all_replacements[0] = {}
            else:
                protected_items, all_replacements[0] = protect_script_codes(tItem)
        
        # Filter out corrupted/mojibake text (U+FFFD) from the batch before API call
        corrupted_map = {}  # original_index -> original_text
        if isinstance(tItem, list):
            for j in range(len(tItem)):
                if tItem[j] and "\ufffd" in str(tItem[j]):
                    corrupted_map[j] = tItem[j]
        elif tItem and "\ufffd" in str(tItem):
            # Single corrupted string - skip translation entirely
            tList[index] = tItem
            if pbar is not None:
                pbar.update(1)
            history = tItem
            continue

        # Filter out items that have content but no Japanese — they need no translation
        # and the AI tends to empty them (e.g. "「………」" -> "").  Apply the same
        # cleanup that would happen post-translation and restore them afterwards.
        no_japanese_map = {}  # original_index -> already-cleaned text
        if isinstance(tItem, list):
            for j in range(len(tItem)):
                if j in corrupted_map:
                    continue
                item_str = str(tItem[j]).strip() if tItem[j] else ""
                if item_str and item_str != "Placeholder Text" and not re.search(config.langRegex, item_str):
                    cleaned = cleanTranslatedText(tItem[j], config.language)
                    cleaned = cleaned.replace("「", '"').replace("」", '"').strip()
                    no_japanese_map[j] = cleaned

        # Combine skip sets and rebuild protected_items / all_replacements
        skip_indices = set(corrupted_map.keys()) | set(no_japanese_map.keys())
        if isinstance(tItem, list) and skip_indices:
            clean_indices = [j for j in range(len(tItem)) if j not in skip_indices]

            if not clean_indices:
                # Every item is either corrupted or untranslatable — reassemble and move on
                result = []
                for j in range(len(tItem)):
                    if j in corrupted_map:
                        result.append(corrupted_map[j])
                    elif j in no_japanese_map:
                        result.append(no_japanese_map[j])
                    else:
                        result.append(tItem[j])
                tList[index] = result
                if pbar is not None:
                    pbar.update(len(tItem))
                history = result[-config.maxHistory:]
                continue

            # Rebuild protected_items and all_replacements for translatable items only
            protected_items = [protected_items[j] for j in clean_indices]
            new_replacements = {}
            for new_idx, old_idx in enumerate(clean_indices):
                new_replacements[new_idx] = all_replacements.get(old_idx, {})
            all_replacements = new_replacements

        # Build filtered tItem for validation (excludes skipped items)
        if isinstance(tItem, list) and skip_indices:
            clean_tItem = [tItem[j] for j in range(len(tItem)) if j not in skip_indices]
        else:
            clean_tItem = tItem

        # Format for translation
        if isinstance(tItem, list):
            payload = {f"Line{i+1}": string for i, string in enumerate(protected_items)}
            payload = json.dumps(payload, indent=4, ensure_ascii=False)
            subbedT = payload
        else:
            subbedT = protected_items

        # Check cache for this exact payload
        cached_result = get_cached_translation(subbedT, config.language)
        if cached_result is not None:
            if isinstance(tItem, list):
                tList[index] = cached_result
                history = cached_result[-config.maxHistory:]
            else:
                tList[index] = cached_result
                history = cached_result
            
            if lock and pbar is not None:
                with lock:
                    pbar.update(len(tItem) if isinstance(tItem, list) else 1)
            
            continue

        # Create context — static_system is the stable prompt.txt content;
        # vocab_text is the per-batch matched vocabulary (dynamic).
        static_system, vocab_text, user = createContext(config, subbedT, formatType, history)

        # Calculate estimate if in estimate mode
        if config.estimateMode:
            estimate = countTokens(static_system + vocab_text, user, history)
            totalTokens[0] += estimate[0]
            totalTokens[1] += estimate[1]

            # Track exact cache write size (static_system, constant across batches)
            # and accumulate non-cached (vocab + user + history) tokens per batch.
            _is_claude_est = config.model and any(x in config.model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
            if _is_claude_est:
                # Use Anthropic's count_tokens API once to get the exact cached token count.
                # Only called on the first batch; result reused for all subsequent batches.
                if not getattr(_thread_local, 'estimate_static_tokens', 0):
                    try:
                        _ant_count_client = anthropic.Anthropic(api_key=openai.api_key)
                        backtick = chr(96) * 3
                        _sys_block = [{"type": "text", "text": backtick + "\n" + static_system + "\n" + backtick, "cache_control": {"type": "ephemeral", "ttl": "1h"}}]
                        _count_resp = _ant_count_client.beta.messages.count_tokens(
                            betas=["token-counting-2024-11-01"],
                            model=config.model,
                            system=_sys_block,
                            messages=[{"role": "user", "content": "x"}]
                        )
                        _thread_local.estimate_static_tokens = _count_resp.input_tokens
                    except Exception:
                        # Fallback to tiktoken if count_tokens fails
                        enc = tiktoken.encoding_for_model("gpt-4")
                        _thread_local.estimate_static_tokens = len(enc.encode(static_system))
                regular_tok = max(0, estimate[0] - getattr(_thread_local, 'estimate_static_tokens', 0))
                _thread_local.estimate_regular_tokens = getattr(_thread_local, 'estimate_regular_tokens', 0) + regular_tok
                _thread_local.estimate_batch_count = getattr(_thread_local, 'estimate_batch_count', 0) + 1
                # Track unique batch sizes seen this file (each maps to a distinct schema)
                _size = len(clean_tItem) if isinstance(clean_tItem, list) else 1
                _seen = getattr(_thread_local, 'estimate_seen_sizes', set())
                _seen.add(_size)
                _thread_local.estimate_seen_sizes = _seen
            
            # Cache the payload with original text as placeholder for future estimates
            if isinstance(tItem, list):
                cache_translation(subbedT, tItem, config.language)
            else:
                cache_translation(subbedT, [tItem], config.language)
            
            continue

        # --- Translation and Validation Retry Block ---
        max_retries = 2  # 1 initial attempt + 2 retries
        final_translations = None
        last_raw_translation = ""
        numLines = len(clean_tItem) if isinstance(tItem, list) else None

        for attempt in range(max_retries + 1):
            is_valid = True

            # On retries, prepend the correction note to the USER message so the
            # cached static_system block is never modified (avoids cache busting).
            current_user = user
            if attempt > 0:
                retry_note = (
                    f"IMPORTANT: Your previous attempt was incorrect or incomplete. Please ensure:\n"
                    f"1. The entire output is translated to {config.language} with no untranslated characters\n"
                    f"2. The JSON structure is correct with NO EMPTY or near-empty translations\n"
                    f"   - Every line with Japanese text MUST be fully translated\n"
                    f"   - Do NOT leave translations empty (\"\") or as single punctuation marks (\":\")\n"
                    f"3. ALL placeholders (like __PROTECTED_0__, __PROTECTED_1__, etc.) are preserved EXACTLY as they appear in the input\n"
                    f"   - Do not modify, translate, or remove any __PROTECTED_N__ placeholders\n"
                    f"   - Keep them in the exact same position in your translation\n\n"
                )
                current_user = retry_note + user
                if pbar:
                    pbar.write(f"Retrying translation... (Attempt {attempt + 1}/{max_retries + 1})")

            # Translate
            try:
                response = translateText(static_system, current_user, history, 0.05, formatType, config.model, numLines, vocab_text=vocab_text)
            except Exception as api_err:
                err_msg = f"[API_ERROR] {api_err}"
                # Print to stdout so the GUI captures it immediately
                print(err_msg, flush=True)
                if pbar:
                    pbar.write(err_msg)
                # Also write to the translation log file for persistence
                try:
                    Path(config.logFilePath).parent.mkdir(parents=True, exist_ok=True)
                    with open(config.logFilePath, "a", encoding="utf-8") as _lf:
                        _lf.write(f"{err_msg}\n")
                        _lf.flush()
                except Exception:
                    pass
                raise  # Let retry decorator handle it
            translatedText = response.choices[0].message.content
            last_raw_translation = translatedText

            # Update token count for this attempt
            totalTokens[0] += response.usage.prompt_tokens
            totalTokens[1] += response.usage.completion_tokens

            # --- Cache cost tracking (Claude only) ---
            _is_claude_model = config.model and any(x in config.model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
            if _is_claude_model:
                usage = response.usage

                # Read cache fields from _AnthropicCompat._Usage; fall back to model_extra.
                def _get_usage_field(field):
                    v = getattr(usage, field, None)
                    if v is None:
                        v = (getattr(usage, "model_extra", None) or {}).get(field)
                    return int(v) if v else 0

                batch_cache_read  = _get_usage_field("cache_read_input_tokens")
                batch_cache_write = _get_usage_field("cache_creation_input_tokens")
                batch_prompt_total = getattr(usage, "prompt_tokens", 0) or 0
                batch_regular = max(0, batch_prompt_total - batch_cache_read - batch_cache_write)
                batch_output  = getattr(usage, "completion_tokens", 0) or 0

                # Accumulate into per-file thread-local counters.
                _thread_local.file_cache_read  += batch_cache_read
                _thread_local.file_cache_write += batch_cache_write
                _thread_local.file_regular     += batch_regular
                _thread_local.file_output      += batch_output

            # --- Debug Token Logging ---
            if DEBUG:
                try:
                    _dbg_dir = Path("log")
                    _dbg_dir.mkdir(parents=True, exist_ok=True)
                    with open(_dbg_dir / "debug.log", "a", encoding="utf-8") as _dbf:
                        _dbf.write(f"\n--- Batch ({len(clean_tItem) if isinstance(tItem, list) else 1} lines) ---\n")
                        _dbf.write(f"Prompt: {response.usage.prompt_tokens} tokens | Output: {response.usage.completion_tokens} tokens\n")
                        if hasattr(response.usage, "cache_read_input_tokens"):
                            cr = getattr(response.usage, "cache_read_input_tokens", 0) or 0
                            cw = getattr(response.usage, "cache_creation_input_tokens", 0) or 0
                            cache_status = "HIT" if cr > 0 else ("WRITE" if cw > 0 else "MISS")
                            _dbf.write(f"Cache: {cache_status} (read={cr}, write={cw})\n")
                        _dbf.flush()
                except Exception:
                    pass

            # Clean the translation first for consistency
            cleaned_text = cleanTranslatedText(translatedText, config.language)

            # Process and validate translation result
            if cleaned_text:
                if isinstance(tItem, list):
                    extracted = extractTranslation(cleaned_text, True, pbar)

                    # Check 1: Mismatch in length -> still a hard failure
                    if extracted is None or len(clean_tItem) != len(extracted):
                        is_valid = False
                        if pbar:
                            pbar.write(f"Length mismatch: expected {len(clean_tItem)}, got {len(extracted) if extracted else 0}")
                    else:
                        # Check 2: Validate placeholders are preserved
                        # Flatten all_replacements for batch validation
                        all_protected_text = protected_items  # The list we sent
                        placeholder_valid, missing, extra = validate_placeholders(all_protected_text, extracted, 
                                                                                  {k: v for replacements in all_replacements.values() for k, v in replacements.items()})
                        
                        if not placeholder_valid:
                            is_valid = False
                            if pbar:
                                if missing:
                                    pbar.write(f"Missing placeholders: {', '.join(missing)}")
                                if extra:
                                    pbar.write(f"Extra placeholders: {', '.join(extra)}")
                        else:
                            # Check 3: Validate that translations are not empty or nearly empty
                            content_valid, invalid_indices, content_reasons = validate_translation_content(
                                clean_tItem, extracted, config.langRegex
                            )
                            
                            if not content_valid:
                                is_valid = False
                                if pbar:
                                    pbar.write(f"Invalid translation content detected:")
                                    for reason in content_reasons[:5]:  # Show first 5 issues
                                        pbar.write(f"  - {reason}")
                                    if len(content_reasons) > 5:
                                        pbar.write(f"  ... and {len(content_reasons) - 5} more issues")
                            else:
                                # Set translations (line count matches, placeholders valid, and content is good)
                                # Strip "Placeholder Text" from individual lines (AI placeholder for untranslatable input)
                                # Also apply the 「→" / 」→" replacements here per-line (safe now that JSON is parsed)
                                def _clean_extracted_line(line):
                                    if not isinstance(line, str):
                                        return line
                                    line = line.replace("Placeholder Text", "").strip()
                                    line = line.replace("「", '"').replace("」", '"')
                                    return line
                                final_translations = [_clean_extracted_line(line) for line in extracted]
                else:
                    # Single string: validate placeholders
                    placeholder_valid, missing, extra = validate_placeholders(protected_items, cleaned_text, all_replacements[0])
                    
                    if not placeholder_valid:
                        is_valid = False
                        if pbar:
                            if missing:
                                pbar.write(f"Missing placeholders: {', '.join(missing)}")
                            if extra:
                                pbar.write(f"Extra placeholders: {', '.join(extra)}")
                    else:
                        # Validate content for single string
                        final_cleaned = cleaned_text.replace("Placeholder Text", "")
                        content_valid, _, content_reasons = validate_translation_content(
                            tItem, final_cleaned, config.langRegex
                        )
                        
                        if not content_valid:
                            is_valid = False
                            if pbar:
                                pbar.write(f"Invalid translation content:")
                                for reason in content_reasons:
                                    pbar.write(f"  - {reason}")
                        else:
                            # Accept output - all validations passed
                            final_translations = final_cleaned
            else:
                is_valid = False
                if pbar: pbar.write(f"AI Refused: {tItem}\n")

            # If translation is valid, break the retry loop
            if is_valid:
                break
        
        # --- End of Retry Block ---

        # After the loop, handle the final result
        if final_translations is not None: # Success case
            # Restore protected script codes
            if isinstance(tItem, list):
                for j in range(len(final_translations)):
                    if j in all_replacements:
                        final_translations[j] = restore_script_codes(final_translations[j], all_replacements[j])
                
                # Re-insert corrupted / no-japanese originals at their original positions
                if corrupted_map or no_japanese_map:
                    expanded = []
                    clean_idx = 0
                    for j in range(len(tItem)):
                        if j in corrupted_map:
                            expanded.append(corrupted_map[j])
                        elif j in no_japanese_map:
                            expanded.append(no_japanese_map[j])
                        else:
                            expanded.append(final_translations[clean_idx])
                            clean_idx += 1
                    final_translations = expanded
            else:
                final_translations = restore_script_codes(final_translations, all_replacements[0])
            
            formatted_output = last_raw_translation
            try:
                parsed_json = json.loads(last_raw_translation)
                # Normalize array-based output to LineN format for log readability
                if isinstance(parsed_json, dict) and "translations" in parsed_json and isinstance(parsed_json["translations"], list):
                    parsed_json = {f"Line{i+1}": v for i, v in enumerate(parsed_json["translations"])}
                formatted_output = json.dumps(parsed_json, indent=4, ensure_ascii=False)
            except (json.JSONDecodeError, ValueError):
                pass
            
            # Only open and write to log file when we have something to log
            try:
                with open(config.logFilePath, "a", encoding="utf-8") as logFile:
                    logFile.write(f"Input:\n{subbedT}\n")
                    logFile.write(f"Output:\n{formatted_output}\n")
                    logFile.flush()  # Ensure data is written to disk immediately
            except Exception:
                pass  # Don't fail if logging fails

            # Cache the entire payload and its translation
            if not config.estimateMode:
                cache_translation(subbedT, final_translations, config.language)

            if isinstance(tItem, list):
                tList[index] = final_translations
                history = final_translations[-config.maxHistory:]
            else:
                tList[index] = final_translations
                history = final_translations

            if lock and pbar is not None:
                with lock:
                    pbar.update(len(tItem) if isinstance(tItem, list) else 1)

        else: # Failure case after all retries
            if pbar: pbar.write(f"Translation failed after {max_retries + 1} attempts. Check mismatch log.")

            # Emit a machine-readable marker on stdout so the GUI worker
            # thread can detect the mismatch reliably (stdout is captured
            # synchronously, unlike file-tail polling which can be racy).
            try:
                print(f"MISMATCH_EVENT:{filename}", flush=True)
            except Exception:
                pass

            formatted_mismatch_output = last_raw_translation
            try:
                parsed_json = json.loads(last_raw_translation)
                if isinstance(parsed_json, dict) and "translations" in parsed_json and isinstance(parsed_json["translations"], list):
                    parsed_json = {f"Line{i+1}": v for i, v in enumerate(parsed_json["translations"])}
                formatted_mismatch_output = json.dumps(parsed_json, indent=4, ensure_ascii=False)
            except (json.JSONDecodeError, ValueError):
                pass
            with open(config.mismatchLogPath, "a+", encoding="utf-8") as mismatchFile:
                mismatchFile.write(f"Failed after retries: {filename}\n")
                mismatchFile.write(f"Input:\n{subbedT}\n")
                mismatchFile.write(f"Final Output:\n{formatted_mismatch_output}\n")
                mismatchFile.flush()  # Ensure data is written to disk immediately

            # Also write to the main translation log so the GUI log viewer can display it
            try:
                with open(config.logFilePath, "a", encoding="utf-8") as logFile:
                    logFile.write(f"[MISMATCH] Failed after retries: {filename}\n")
                    logFile.write(f"[MISMATCH] Input:\n")
                    for mline in subbedT.splitlines():
                        logFile.write(f"[MISMATCH] {mline}\n")
                    logFile.write(f"[MISMATCH] Final Output:\n")
                    for mline in formatted_mismatch_output.splitlines():
                        logFile.write(f"[MISMATCH] {mline}\n")
                    logFile.flush()
            except Exception:
                pass  # Don't fail if logging fails

            if filename and mismatchList is not None and filename not in mismatchList:
                mismatchList.append(filename)
            
            tList[index] = tItem
            history = text[-config.maxHistory:] if isinstance(text, list) else text

    # Combine if multilist
    if tList and isinstance(tList[0], list):
        tList = [t for sublist in tList for t in sublist]
    
    # Save cache after processing (for both estimate and translation modes)
    save_cache()

    # For Claude: accumulate only this call's delta into the cross-thread total.
    # file_* accumulators hold full per-file totals; calculateCost() reads them.
    _is_claude_final = config.model and any(x in config.model.lower() for x in ("claude", "sonnet", "haiku", "opus"))
    if _is_claude_final and not config.estimateMode:
        _pricing = getPricingConfig(config.model)
        _br = _pricing["inputAPICost"] / 1_000_000
        _or = _pricing["outputAPICost"] / 1_000_000
        # Delta = tokens added in this call only (not earlier calls for same file).
        _delta_cr  = getattr(_thread_local, 'file_cache_read',  0) - _prev_cr
        _delta_cw  = getattr(_thread_local, 'file_cache_write', 0) - _prev_cw
        _delta_reg = getattr(_thread_local, 'file_regular',     0) - _prev_reg
        _delta_out = getattr(_thread_local, 'file_output',      0) - _prev_out
        _call_cost = (
            _delta_cr  * _br * 0.10 +
            _delta_cw  * _br * 2.00 +
            _delta_reg * _br +
            _delta_out * _or
        )
        global _global_accurate_cost
        with _global_accurate_cost_lock:
            _global_accurate_cost += _call_cost
        _thread_local.file_cost_ready = True  # signals calculateCost to use file accumulators

    # Return result
    if formatType == "json":
        return [tList, totalTokens]
    else:
        return [tList[0], totalTokens]