| 1 | """Director generation settings: duration, shot count, canvas, and language.""" |
| 2 | |
| 3 | from __future__ import annotations |
| 4 | |
| 5 | from typing import Any |
| 6 | |
| 7 | _UNSET = object() |
| 8 | |
| 9 | SESSION_NSHOT_KEY = "n_shots" |
| 10 | SESSION_DURATION_KEY = "duration_sec" |
| 11 | SESSION_VIDEO_WIDTH_KEY = "video_width" |
| 12 | SESSION_VIDEO_HEIGHT_KEY = "video_height" |
| 13 | SESSION_LANGUAGE_KEY = "language" |
| 14 | SESSION_LLM_TEMPERATURE_KEY = "llm_temperature" |
| 15 | SESSION_LLM_TOP_P_KEY = "llm_top_p" |
| 16 | SESSION_LLM_TOP_K_KEY = "llm_top_k" |
| 17 | |
| 18 | DEFAULT_NSHOT = 1 |
| 19 | DEFAULT_DURATION_SEC = 10 |
| 20 | DEFAULT_VIDEO_WIDTH = 1280 |
| 21 | DEFAULT_VIDEO_HEIGHT = 736 |
| 22 | DEFAULT_LANGUAGE = "zh" |
| 23 | |
| 24 | # UI / story_profile.language values |
| 25 | LANGUAGE_ZH = "zh" |
| 26 | LANGUAGE_EN = "en" |
| 27 | VALID_LANGUAGES = frozenset({LANGUAGE_ZH, LANGUAGE_EN}) |
| 28 | |
| 29 | # story_profile.dialogue_language values used by PE / shot prompts |
| 30 | DIALOGUE_LANGUAGE_BY_LANGUAGE: dict[str, str] = { |
| 31 | LANGUAGE_ZH: "Mandarin Chinese", |
| 32 | LANGUAGE_EN: "English", |
| 33 | } |
| 34 | CAPTION_LANGUAGE_BY_LANGUAGE: dict[str, str] = { |
| 35 | LANGUAGE_ZH: "Simplified Chinese", |
| 36 | LANGUAGE_EN: "English", |
| 37 | } |
| 38 | |
| 39 | # duration_sec → n_shots |
| 40 | DURATION_TO_NSHOT: dict[int, int] = { |
| 41 | 10: 1, |
| 42 | 20: 2, |
| 43 | 30: 3, |
| 44 | 60: 6, |
| 45 | 90: 9, |
| 46 | 120: 12, |
| 47 | 150: 15, |
| 48 | 180: 18, |
| 49 | } |
| 50 | NSHOT_TO_DURATION: dict[int, int] = {n: d for d, n in DURATION_TO_NSHOT.items()} |
| 51 | VALID_DURATIONS = frozenset(DURATION_TO_NSHOT) |
| 52 | VALID_NSHOTS = frozenset(NSHOT_TO_DURATION) |
| 53 | |
| 54 | |
| 55 | def duration_to_n_shots(duration_sec: int) -> int | None: |
| 56 | return DURATION_TO_NSHOT.get(int(duration_sec)) |
| 57 | |
| 58 | |
| 59 | def n_shots_to_duration(n_shots: int) -> int | None: |
| 60 | return NSHOT_TO_DURATION.get(int(n_shots)) |
| 61 | |
| 62 | |
| 63 | def normalize_duration_sec(value: Any) -> int | None: |
| 64 | try: |
| 65 | parsed = int(value) |
| 66 | except (TypeError, ValueError): |
| 67 | return None |
| 68 | return parsed if parsed in VALID_DURATIONS else None |
| 69 | |
| 70 | |
| 71 | def normalize_n_shots(value: Any) -> int | None: |
| 72 | try: |
| 73 | parsed = int(value) |
| 74 | except (TypeError, ValueError): |
| 75 | return None |
| 76 | return parsed if parsed in VALID_NSHOTS else None |
| 77 | |
| 78 | |
| 79 | def normalize_language(value: Any) -> str | None: |
| 80 | if not isinstance(value, str): |
| 81 | return None |
| 82 | cleaned = value.strip() |
| 83 | if cleaned in VALID_LANGUAGES: |
| 84 | return cleaned |
| 85 | lowered = cleaned.lower() |
| 86 | if lowered in { |
| 87 | "zh", |
| 88 | "zh-cn", |
| 89 | "zh_cn", |
| 90 | "chinese", |
| 91 | "mandarin", |
| 92 | "mandarin chinese", |
| 93 | "中文", |
| 94 | }: |
| 95 | return LANGUAGE_ZH |
| 96 | if lowered in {"en", "en-us", "en_us", "english"}: |
| 97 | return LANGUAGE_EN |
| 98 | return None |
| 99 | |
| 100 | |
| 101 | def language_to_dialogue_language(language: str | None) -> str | None: |
| 102 | if not language: |
| 103 | return None |
| 104 | return DIALOGUE_LANGUAGE_BY_LANGUAGE.get(language) |
| 105 | |
| 106 | |
| 107 | def language_to_caption_language(language: str | None) -> str | None: |
| 108 | if not language: |
| 109 | return None |
| 110 | return CAPTION_LANGUAGE_BY_LANGUAGE.get(language) |
| 111 | |
| 112 | |
| 113 | def normalize_llm_temperature(value: Any) -> float | None: |
| 114 | try: |
| 115 | parsed = float(value) |
| 116 | except (TypeError, ValueError): |
| 117 | return None |
| 118 | return parsed if 0 <= parsed < 2 else None |
| 119 | |
| 120 | |
| 121 | def normalize_llm_top_p(value: Any) -> float | None: |
| 122 | try: |
| 123 | parsed = float(value) |
| 124 | except (TypeError, ValueError): |
| 125 | return None |
| 126 | return parsed if 0 <= parsed <= 1 else None |
| 127 | |
| 128 | |
| 129 | def normalize_llm_top_k(value: Any) -> int | None: |
| 130 | try: |
| 131 | parsed = int(value) |
| 132 | except (TypeError, ValueError): |
| 133 | return None |
| 134 | return parsed if 1 <= parsed <= 64 else None |
| 135 | |
| 136 | |
| 137 | def default_settings() -> dict[str, int | str]: |
| 138 | return { |
| 139 | "n_shots": DEFAULT_NSHOT, |
| 140 | "duration_sec": DEFAULT_DURATION_SEC, |
| 141 | "width": DEFAULT_VIDEO_WIDTH, |
| 142 | "height": DEFAULT_VIDEO_HEIGHT, |
| 143 | "language": DEFAULT_LANGUAGE, |
| 144 | } |
| 145 | |
| 146 | |
| 147 | def get_generation_settings(metadata: dict[str, Any] | None) -> dict[str, int | str]: |
| 148 | """Return persisted generation settings, falling back to defaults.""" |
| 149 | base = default_settings() |
| 150 | if not isinstance(metadata, dict): |
| 151 | return base |
| 152 | |
| 153 | n_shots = normalize_n_shots(metadata.get(SESSION_NSHOT_KEY)) |
| 154 | duration_sec = normalize_duration_sec(metadata.get(SESSION_DURATION_KEY)) |
| 155 | try: |
| 156 | width = int(metadata.get(SESSION_VIDEO_WIDTH_KEY)) |
| 157 | height = int(metadata.get(SESSION_VIDEO_HEIGHT_KEY)) |
| 158 | except (TypeError, ValueError): |
| 159 | width = height = 0 |
| 160 | if width > 0 and height > 0: |
| 161 | base["width"] = width |
| 162 | base["height"] = height |
| 163 | |
| 164 | language = normalize_language(metadata.get(SESSION_LANGUAGE_KEY)) |
| 165 | if language is not None: |
| 166 | base["language"] = language |
| 167 | |
| 168 | if n_shots is not None: |
| 169 | base["n_shots"] = n_shots |
| 170 | mapped = n_shots_to_duration(n_shots) |
| 171 | if mapped is not None: |
| 172 | base["duration_sec"] = mapped |
| 173 | elif duration_sec is not None: |
| 174 | base["duration_sec"] = duration_sec |
| 175 | mapped = duration_to_n_shots(duration_sec) |
| 176 | if mapped is not None: |
| 177 | base["n_shots"] = mapped |
| 178 | return base |
| 179 | |
| 180 | |
| 181 | def get_llm_sampling_settings(metadata: dict[str, Any] | None) -> dict[str, float | int]: |
| 182 | """Return only explicitly set LLM sampling params (empty dict = gateway defaults).""" |
| 183 | if not isinstance(metadata, dict): |
| 184 | return {} |
| 185 | out: dict[str, float | int] = {} |
| 186 | temp = normalize_llm_temperature(metadata.get(SESSION_LLM_TEMPERATURE_KEY)) |
| 187 | if temp is not None: |
| 188 | out["temperature"] = temp |
| 189 | top_p = normalize_llm_top_p(metadata.get(SESSION_LLM_TOP_P_KEY)) |
| 190 | if top_p is not None: |
| 191 | out["top_p"] = top_p |
| 192 | top_k = normalize_llm_top_k(metadata.get(SESSION_LLM_TOP_K_KEY)) |
| 193 | if top_k is not None: |
| 194 | out["top_k"] = top_k |
| 195 | return out |
| 196 | |
| 197 | |
| 198 | def get_llm_sampling_for_api(metadata: dict[str, Any] | None) -> dict[str, float | int | None]: |
| 199 | """API-facing view: keys always present, null when unset.""" |
| 200 | sampling = get_llm_sampling_settings(metadata) |
| 201 | return { |
| 202 | "temperature": sampling.get("temperature"), |
| 203 | "top_p": sampling.get("top_p"), |
| 204 | "top_k": sampling.get("top_k"), |
| 205 | } |
| 206 | |
| 207 | |
| 208 | def apply_llm_sampling_settings( |
| 209 | metadata: dict[str, Any], |
| 210 | *, |
| 211 | temperature: Any = _UNSET, |
| 212 | top_p: Any = _UNSET, |
| 213 | top_k: Any = _UNSET, |
| 214 | ) -> dict[str, float | int | None]: |
| 215 | """Persist optional LLM sampling params. Pass ``None`` to clear a field.""" |
| 216 | resolved = get_llm_sampling_for_api(metadata) |
| 217 | |
| 218 | if temperature is not _UNSET: |
| 219 | if temperature is None or temperature == "": |
| 220 | metadata.pop(SESSION_LLM_TEMPERATURE_KEY, None) |
| 221 | resolved["temperature"] = None |
| 222 | else: |
| 223 | normalized = normalize_llm_temperature(temperature) |
| 224 | if normalized is None: |
| 225 | raise ValueError(f"invalid temperature: {temperature}") |
| 226 | metadata[SESSION_LLM_TEMPERATURE_KEY] = normalized |
| 227 | resolved["temperature"] = normalized |
| 228 | |
| 229 | if top_p is not _UNSET: |
| 230 | if top_p is None or top_p == "": |
| 231 | metadata.pop(SESSION_LLM_TOP_P_KEY, None) |
| 232 | resolved["top_p"] = None |
| 233 | else: |
| 234 | normalized = normalize_llm_top_p(top_p) |
| 235 | if normalized is None: |
| 236 | raise ValueError(f"invalid top_p: {top_p}") |
| 237 | metadata[SESSION_LLM_TOP_P_KEY] = normalized |
| 238 | resolved["top_p"] = normalized |
| 239 | |
| 240 | if top_k is not _UNSET: |
| 241 | if top_k is None or top_k == "": |
| 242 | metadata.pop(SESSION_LLM_TOP_K_KEY, None) |
| 243 | resolved["top_k"] = None |
| 244 | else: |
| 245 | normalized = normalize_llm_top_k(top_k) |
| 246 | if normalized is None: |
| 247 | raise ValueError(f"invalid top_k: {top_k}") |
| 248 | metadata[SESSION_LLM_TOP_K_KEY] = normalized |
| 249 | resolved["top_k"] = normalized |
| 250 | |
| 251 | return resolved |
| 252 | |
| 253 | |
| 254 | def apply_llm_sampling_from_wire(metadata: dict[str, Any], wire: dict[str, Any] | None) -> bool: |
| 255 | """Apply LLM sampling overrides from a WS message envelope. Returns True if updated.""" |
| 256 | if not isinstance(wire, dict): |
| 257 | return False |
| 258 | updates: dict[str, Any] = {} |
| 259 | for src, dst in ( |
| 260 | ("temperature", "temperature"), |
| 261 | ("topP", "top_p"), |
| 262 | ("top_p", "top_p"), |
| 263 | ("topK", "top_k"), |
| 264 | ("top_k", "top_k"), |
| 265 | ): |
| 266 | if src in wire: |
| 267 | updates[dst] = wire[src] |
| 268 | if not updates: |
| 269 | return False |
| 270 | apply_llm_sampling_settings(metadata, **updates) |
| 271 | return True |
| 272 | |
| 273 | |
| 274 | def apply_generation_settings( |
| 275 | metadata: dict[str, Any], |
| 276 | *, |
| 277 | n_shots: int | None = None, |
| 278 | duration_sec: int | None = None, |
| 279 | width: int | None = None, |
| 280 | height: int | None = None, |
| 281 | language: str | None = None, |
| 282 | ) -> dict[str, int | str]: |
| 283 | """Persist Director generation settings and return the resolved values.""" |
| 284 | resolved = get_generation_settings(metadata) |
| 285 | |
| 286 | if duration_sec is not None: |
| 287 | normalized_duration = normalize_duration_sec(duration_sec) |
| 288 | if normalized_duration is None: |
| 289 | raise ValueError(f"invalid duration_sec: {duration_sec}") |
| 290 | resolved["duration_sec"] = normalized_duration |
| 291 | resolved["n_shots"] = duration_to_n_shots(normalized_duration) or DEFAULT_NSHOT |
| 292 | elif n_shots is not None: |
| 293 | normalized_n = normalize_n_shots(n_shots) |
| 294 | if normalized_n is None: |
| 295 | raise ValueError(f"invalid n_shots: {n_shots}") |
| 296 | resolved["n_shots"] = normalized_n |
| 297 | resolved["duration_sec"] = n_shots_to_duration(normalized_n) or DEFAULT_DURATION_SEC |
| 298 | |
| 299 | metadata[SESSION_NSHOT_KEY] = resolved["n_shots"] |
| 300 | metadata[SESSION_DURATION_KEY] = resolved["duration_sec"] |
| 301 | if width is not None: |
| 302 | parsed_width = int(width) |
| 303 | if parsed_width <= 0: |
| 304 | raise ValueError(f"invalid width: {width}") |
| 305 | resolved["width"] = parsed_width |
| 306 | if height is not None: |
| 307 | parsed_height = int(height) |
| 308 | if parsed_height <= 0: |
| 309 | raise ValueError(f"invalid height: {height}") |
| 310 | resolved["height"] = parsed_height |
| 311 | metadata[SESSION_VIDEO_WIDTH_KEY] = resolved["width"] |
| 312 | metadata[SESSION_VIDEO_HEIGHT_KEY] = resolved["height"] |
| 313 | if language is not None: |
| 314 | normalized_language = normalize_language(language) |
| 315 | if normalized_language is None: |
| 316 | raise ValueError(f"invalid language: {language}") |
| 317 | resolved["language"] = normalized_language |
| 318 | metadata[SESSION_LANGUAGE_KEY] = resolved["language"] |
| 319 | return resolved |
| 320 | |
| 321 | |
| 322 | def resolve_n_shots_from_wire(data: dict[str, Any] | None) -> int | None: |
| 323 | """Resolve explicit shot count from WebSocket envelope or inbound message metadata.""" |
| 324 | if not isinstance(data, dict): |
| 325 | return None |
| 326 | for key in ("nShot", "nshot", "n_shots", "nShots"): |
| 327 | raw = data.get(key) |
| 328 | if raw is None or raw == "": |
| 329 | continue |
| 330 | parsed = normalize_n_shots(raw) |
| 331 | if parsed is not None: |
| 332 | return parsed |
| 333 | duration = normalize_duration_sec(data.get("durationSec") or data.get("duration_sec")) |
| 334 | if duration is not None: |
| 335 | return duration_to_n_shots(duration) |
| 336 | return None |
| 337 | |
| 338 |