"""Validated runtime configuration derived from the user-editable ``space_config``.

Only fork-author customization belongs in ``space_config.py``. This module owns
normalization, safety fallbacks, derived values, runtime-only constants, and
model-download allow patterns so ``app.py`` can focus on Gradio composition.
"""
from __future__ import annotations

import os

from space_config import (
    AUTO_DURATION_ENABLED,
    AUTO_DURATION_MAX_SECONDS,
    AUTO_DURATION_MIN_SECONDS,
    A2V_ENABLED,
    DEFAULT_ATTENTION_BACKEND,
    DEFAULT_DURATION_SECONDS,
    DEFAULT_LORA_STRENGTH,
    DEFAULT_RANDOMIZE_SEED,
    DEFAULT_RESOLUTION,
    DEFAULT_SEED,
    DEFAULT_SELECTED_LORAS,
    DEFAULT_USE_AUTO_DURATION,
    DEFAULT_USE_DIFFUSION_DECODER,
    DIFFUSION_DECODER_ENABLED,
    EXPERIMENTAL_MAX_SECONDS,
    FRAME_RATE,
    FULL_SFT_STAGE2_LORA_REPO_ID,
    FULL_SFT_STAGE2_LORA_REVISION,
    FULL_SFT_STAGE2_LORA_STRENGTH,
    FULL_SFT_TRANSFORMER_PATH,
    FULL_SFT_TRANSFORMER_REPO_ID,
    FULL_SFT_TRANSFORMER_REVISION,
    IC_COLORIZER_ENABLED,
    IC_PIXEL_UPSCALER_ENABLED,
    IC_INOUTPAINT_ENABLED,
    MODEL_QUANTIZATION_POLICY,
    MODEL_REPO_ID,
    MODEL_REVISION,
    MODEL_RUNTIME_PROFILE,
    PROMPT_ENHANCER_ENABLED,
    PROMPT_ENHANCER_QUANTIZATION_POLICY,
    RESOLUTIONS,
    STANDARD_MAX_SECONDS,
    ZEROGPU_GPU_SIZE,
)

from . import app_helpers

CANONICAL_MODEL_REPO_ID = "Lightricks/LTX-2.5-Diffusers"
CANONICAL_MODEL_REVISION = "549f18015d588ce728aad02aebff9212d94f598e"
MAX_FRAMES = int(round(EXPERIMENTAL_MAX_SECONDS * FRAME_RATE))
# LTX temporal grid is 8k+1; canonical 30s/24fps endpoint is 721f.
MAX_FRAMES = int(round((MAX_FRAMES - 1) / 8) * 8 + 1)
GPU_CONCURRENCY_ID = "ltx25-gpu"
FULL_SFT_STAGE2_ADAPTER_NAME = "stage_2_distilled"

ZERO_GPU_ENV_KEYS = ("SPACES_ZERO_GPU", "ZEROGPU_V2", "SPACE_ZERO_GPU", "ZERO_GPU")
ZERO_GPU_TEXT_ENV_KEYS = (
    "SPACE_HARDWARE", "HF_SPACE_HARDWARE", "SPACE_RUNTIME", "SPACES_RUNTIME", "HF_SPACE_RUNTIME"
)
TRUE_VALUES = {"1", "true", "t", "yes", "y", "on", "zerogpu", "zero"}

IS_ZEROGPU = app_helpers.detect_zerogpu_env(
    os.environ,
    bool_keys=ZERO_GPU_ENV_KEYS,
    text_keys=ZERO_GPU_TEXT_ENV_KEYS,
    true_values=TRUE_VALUES,
)
ATTENTION_BACKEND = os.environ.get("LTX25_ATTENTION_BACKEND", DEFAULT_ATTENTION_BACKEND).strip().lower()
CONFIG_WARNINGS: list[str] = []


def _safe_config_float(name: str, value, default: float, minimum: float, maximum: float) -> float:
    return app_helpers.safe_config_float(name, value, default, minimum, maximum, warnings=CONFIG_WARNINGS)


def _safe_config_int(name: str, value, default: int) -> int:
    return app_helpers.safe_config_int(name, value, default, warnings=CONFIG_WARNINGS)


def _safe_config_bool(value, default: bool) -> bool:
    return app_helpers.safe_config_bool(value, default, warnings=CONFIG_WARNINGS, true_values=TRUE_VALUES)


QUANTIZATION_POLICY = str(MODEL_QUANTIZATION_POLICY or "nf4_auto").strip().lower()
if QUANTIZATION_POLICY not in {"nf4_auto", "repo_native"}:
    CONFIG_WARNINGS.append(
        f"Unsupported MODEL_QUANTIZATION_POLICY={MODEL_QUANTIZATION_POLICY!r}; using safe default 'nf4_auto'."
    )
    QUANTIZATION_POLICY = "nf4_auto"

RUNTIME_PROFILE = str(MODEL_RUNTIME_PROFILE or "distilled_nf4").strip().lower()
if RUNTIME_PROFILE not in {"distilled_nf4", "full_sft_nf4"}:
    CONFIG_WARNINGS.append(
        f"Unsupported MODEL_RUNTIME_PROFILE={MODEL_RUNTIME_PROFILE!r}; using safe default 'distilled_nf4'."
    )
    RUNTIME_PROFILE = "distilled_nf4"
IS_FULL_SFT_PROFILE = RUNTIME_PROFILE == "full_sft_nf4"

EFFECTIVE_ZEROGPU_GPU_SIZE = str(ZEROGPU_GPU_SIZE or "large").strip().lower()
if EFFECTIVE_ZEROGPU_GPU_SIZE not in {"large", "xlarge"}:
    CONFIG_WARNINGS.append(f"Unsupported ZEROGPU_GPU_SIZE={ZEROGPU_GPU_SIZE!r}; using canonical 'large'.")
    EFFECTIVE_ZEROGPU_GPU_SIZE = "large"

EFFECTIVE_A2V_ENABLED = bool(A2V_ENABLED)
EFFECTIVE_IC_COLORIZER_ENABLED = bool(IC_COLORIZER_ENABLED)
IC_COLORIZER_AVAILABLE = EFFECTIVE_IC_COLORIZER_ENABLED
EFFECTIVE_IC_PIXEL_UPSCALER_ENABLED = bool(IC_PIXEL_UPSCALER_ENABLED)
IC_PIXEL_UPSCALER_AVAILABLE = EFFECTIVE_IC_PIXEL_UPSCALER_ENABLED and not IS_FULL_SFT_PROFILE
EFFECTIVE_IC_INOUTPAINT_ENABLED = bool(IC_INOUTPAINT_ENABLED)
IC_INOUTPAINT_AVAILABLE = EFFECTIVE_IC_INOUTPAINT_ENABLED and not IS_FULL_SFT_PROFILE
FULL_SFT_TRANSFORMER_REPO_EFFECTIVE = str(FULL_SFT_TRANSFORMER_REPO_ID or MODEL_REPO_ID).strip()
FULL_SFT_TRANSFORMER_REVISION_EFFECTIVE = str(FULL_SFT_TRANSFORMER_REVISION or MODEL_REVISION or "").strip() or None
FULL_SFT_STAGE2_LORA_REPO_EFFECTIVE = str(FULL_SFT_STAGE2_LORA_REPO_ID or MODEL_REPO_ID).strip()
FULL_SFT_STAGE2_LORA_REVISION_EFFECTIVE = str(FULL_SFT_STAGE2_LORA_REVISION or MODEL_REVISION or "").strip() or None

EFFECTIVE_DEFAULT_DURATION_SECONDS = _safe_config_float(
    "DEFAULT_DURATION_SECONDS", DEFAULT_DURATION_SECONDS, 1.0, 1.0, STANDARD_MAX_SECONDS
)
EFFECTIVE_DEFAULT_LORA_STRENGTH = _safe_config_float(
    "DEFAULT_LORA_STRENGTH", DEFAULT_LORA_STRENGTH, 1.0, 0.0, 2.0
)
EFFECTIVE_FULL_SFT_STAGE2_LORA_STRENGTH = _safe_config_float(
    "FULL_SFT_STAGE2_LORA_STRENGTH", FULL_SFT_STAGE2_LORA_STRENGTH, 1.0, 0.0, 2.0
)
EFFECTIVE_DEFAULT_SEED = _safe_config_int("DEFAULT_SEED", DEFAULT_SEED, 42)
EFFECTIVE_DEFAULT_RANDOMIZE_SEED = _safe_config_bool(DEFAULT_RANDOMIZE_SEED, True)
EFFECTIVE_DIFFUSION_DECODER_ENABLED = _safe_config_bool(DIFFUSION_DECODER_ENABLED, True)
EFFECTIVE_PROMPT_ENHANCER_ENABLED = _safe_config_bool(PROMPT_ENHANCER_ENABLED, True)
EFFECTIVE_AUTO_DURATION_ENABLED = _safe_config_bool(AUTO_DURATION_ENABLED, True)
EFFECTIVE_DEFAULT_USE_AUTO_DURATION = _safe_config_bool(DEFAULT_USE_AUTO_DURATION, False) and EFFECTIVE_AUTO_DURATION_ENABLED
EFFECTIVE_AUTO_DURATION_MIN_SECONDS = _safe_config_float(
    "AUTO_DURATION_MIN_SECONDS", AUTO_DURATION_MIN_SECONDS, 1.0, 0.5, STANDARD_MAX_SECONDS - 0.5
)
EFFECTIVE_AUTO_DURATION_MAX_SECONDS = _safe_config_float(
    "AUTO_DURATION_MAX_SECONDS", AUTO_DURATION_MAX_SECONDS, 8.0, 1.0, STANDARD_MAX_SECONDS
)
if EFFECTIVE_AUTO_DURATION_MIN_SECONDS >= EFFECTIVE_AUTO_DURATION_MAX_SECONDS:
    CONFIG_WARNINGS.append(
        "AUTO_DURATION_MIN_SECONDS must be less than AUTO_DURATION_MAX_SECONDS; using 1.0s / 8.0s."
    )
    EFFECTIVE_AUTO_DURATION_MIN_SECONDS = 1.0
    EFFECTIVE_AUTO_DURATION_MAX_SECONDS = 8.0

PROMPT_ENHANCER_POLICY = str(PROMPT_ENHANCER_QUANTIZATION_POLICY or "nf4_auto").strip().lower()
if PROMPT_ENHANCER_POLICY not in {"nf4_auto", "repo_native"}:
    CONFIG_WARNINGS.append(
        f"Unsupported PROMPT_ENHANCER_QUANTIZATION_POLICY={PROMPT_ENHANCER_QUANTIZATION_POLICY!r}; "
        "using safe default 'nf4_auto'."
    )
    PROMPT_ENHANCER_POLICY = "nf4_auto"

EFFECTIVE_DEFAULT_USE_DIFFUSION_DECODER = (
    _safe_config_bool(DEFAULT_USE_DIFFUSION_DECODER, False) and EFFECTIVE_DIFFUSION_DECODER_ENABLED
)
try:
    app_helpers.parse_resolution_value(str(DEFAULT_RESOLUTION), resolutions=RESOLUTIONS)
    EFFECTIVE_DEFAULT_RESOLUTION = str(DEFAULT_RESOLUTION).strip()
except Exception:
    EFFECTIVE_DEFAULT_RESOLUTION = next(iter(RESOLUTIONS), "512 × 512 · validated")
    CONFIG_WARNINGS.append(
        f"Invalid DEFAULT_RESOLUTION={DEFAULT_RESOLUTION!r}; using {EFFECTIVE_DEFAULT_RESOLUTION!r}."
    )

if isinstance(DEFAULT_SELECTED_LORAS, (list, tuple, set)):
    EFFECTIVE_DEFAULT_SELECTED_LORAS = [str(x) for x in DEFAULT_SELECTED_LORAS if str(x).strip()]
else:
    CONFIG_WARNINGS.append("DEFAULT_SELECTED_LORAS must be a list/tuple/set; using no default LoRAs.")
    EFFECTIVE_DEFAULT_SELECTED_LORAS = []

ALLOW_PATTERNS = [
    "model_index.json", "vae/*", "audio_vae/*", "vocoder/*", "connectors/*", "text_encoder/*",
    "tokenizer/*", "scheduler/*", "latent_upsampler/*",
]
if IS_FULL_SFT_PROFILE:
    if (
        FULL_SFT_TRANSFORMER_REPO_EFFECTIVE == str(MODEL_REPO_ID).strip()
        and FULL_SFT_TRANSFORMER_REVISION_EFFECTIVE == (str(MODEL_REVISION or "").strip() or None)
    ):
        ALLOW_PATTERNS.append(f"{str(FULL_SFT_TRANSFORMER_PATH or 'transformer_full').strip().strip('/')}/*")
else:
    ALLOW_PATTERNS.append("transformer/*")
if EFFECTIVE_DIFFUSION_DECODER_ENABLED:
    ALLOW_PATTERNS.append("diffusion_decoder/*")
if EFFECTIVE_AUTO_DURATION_ENABLED:
    ALLOW_PATTERNS.append("duration_head/*")