"""
Shared configuration for the IR Generation pipeline.
Reads API keys from a secrets.yaml file, falling back to environment variables.
"""

import os
import sys
import json
import yaml

# ---- Paths ----
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
WORKSPACE_DIR = os.path.dirname(BASE_DIR)
PROJECT_ROOT = os.path.dirname(WORKSPACE_DIR)
PROJECT_OUTPUT = os.path.join(PROJECT_ROOT, "output")

# Subdirectories under PROJECT_OUTPUT
IR_OUTPUT = os.path.join(PROJECT_OUTPUT, "ir")
FINAL_OUTPUT = os.path.join(PROJECT_OUTPUT, "final")

# Legacy paths (maintained for doc_parser integration)
DOC_PARSER_OUTPUT = os.path.join(WORKSPACE_DIR, "doc_parser_skill", "output")
PROMPTS_DIR = os.path.join(BASE_DIR, "prompts")
TESTS_DIR = os.path.join(BASE_DIR, "tests")
OUTPUT_DIR = IR_OUTPUT  # backward compatibility alias

# Input file (the parsed PRD JSON) — must be set via env var or CLI
# No hardcoded default to avoid silently processing the wrong document.
INPUT_JSON = os.environ.get("IR_INPUT_JSON", None)


def set_input_file(path: str) -> None:
    """Override the default input JSON path."""
    global INPUT_JSON
    INPUT_JSON = path

# Secrets file (shared with workspace-document-analyzer)
# .openclaw/workspace/skills/ir_generation_new_skill -> .openclaw/workspace-document-analyzer
OPENCLAW_HOME = os.path.dirname(os.path.dirname(WORKSPACE_DIR))
SECRETS_YAML = os.path.join(
    OPENCLAW_HOME, "workspace-document-analyzer", "config", "secrets.yaml",
)

# Intermediate outputs (all under PROJECT_OUTPUT/ir/)
SEMANTIC_INDEX_R1_JSON = os.path.join(IR_OUTPUT, "semantic_index_r1.json")
SEMANTIC_INDEX_R2_JSON = os.path.join(IR_OUTPUT, "semantic_index_r2.json")
SEMANTIC_INDEX_R3_JSON = os.path.join(IR_OUTPUT, "semantic_index_r3.json")
SEMANTIC_INDEX_JSON = os.path.join(IR_OUTPUT, "semantic_index.json")
IR_FRAGMENTS_JSON = os.path.join(IR_OUTPUT, "ir_fragments.json")
PATH_ENUM_JSON = os.path.join(IR_OUTPUT, "path_enumeration.json")
IR_AUTOCOMPLETE_FRAGMENTS_JSON = os.path.join(IR_OUTPUT, "ir_autocomplete_fragments.json")

# Final deliverables (under PROJECT_OUTPUT/final/)
IR_FINAL_JSON = os.path.join(FINAL_OUTPUT, "ir_final.json")
IR_AUDIT_REPORT_MD = os.path.join(FINAL_OUTPUT, "ir_audit_report.md")

# ---- LLM API ----
# Choose provider: "deepseek" | "dashscope"
LLM_PROVIDER = os.environ.get("IR_PROVIDER", "deepseek")

# Model names per provider
PROVIDER_MODELS = {
    "deepseek": os.environ.get("IR_MODEL", "deepseek-v4-flash"),
    "dashscope": os.environ.get("IR_MODEL", "qwen-max"),
}
MODEL_NAME = PROVIDER_MODELS.get(LLM_PROVIDER, PROVIDER_MODELS["deepseek"])

# Maximum tokens for LLM responses
MAX_TOKENS = int(os.environ.get("IR_MAX_TOKENS", "16000"))
TEMPERATURE = float(os.environ.get("IR_TEMPERATURE", "0.1"))

# ---- Iteration & Quality ----
MAX_RETRIES_PER_STAGE = int(os.environ.get("IR_MAX_RETRIES", "3"))
COVERAGE_TARGET = float(os.environ.get("IR_COVERAGE_TARGET", "0.95"))

# Stage 1 ensemble temperatures (parallel multi-temperature generation)
ENSEMBLE_TEMPERATURES = [
    float(os.environ.get("IR_ENSEMBLE_T1", "0.0")),
    float(os.environ.get("IR_ENSEMBLE_T2", "0.3")),
    float(os.environ.get("IR_ENSEMBLE_T3", "0.7")),
]


def _load_secrets() -> dict[str, dict[str, str]]:
    """Load provider credentials from secrets.yaml.

    Returns a dict like: {"deepseek": {"apiKey": "...", "baseUrl": "..."}, ...}
    """
    if os.path.isfile(SECRETS_YAML):
        with open(SECRETS_YAML, "r", encoding="utf-8") as f:
            return yaml.safe_load(f) or {}
    return {}


def _get_provider_config(provider: str) -> dict[str, str]:
    """Get {apiKey, baseUrl} for a provider from secrets, with env-var fallback."""
    secrets = _load_secrets()
    entry = secrets.get(provider, {})

    env_prefix = provider.upper()
    api_key = (
        os.environ.get(f"{env_prefix}_API_KEY")
        or entry.get("apiKey", "")
    )
    base_url = (
        os.environ.get(f"{env_prefix}_BASE_URL")
        or entry.get("baseUrl", "https://api.deepseek.com/v1")
    )

    if not api_key:
        raise RuntimeError(
            f"No API key found for provider '{provider}'. "
            f"Check {SECRETS_YAML} or set {env_prefix}_API_KEY."
        )
    return {"apiKey": api_key, "baseUrl": base_url}


def llm_client():
    """Return an OpenAI-compatible client configured from secrets.yaml."""
    from openai import OpenAI

    cfg = _get_provider_config(LLM_PROVIDER)
    return OpenAI(base_url=cfg["baseUrl"], api_key=cfg["apiKey"])


def load_input_document(path: str | None = None) -> dict:
    """Load the parsed PRD JSON document.

    Args:
        path: Explicit file path. If None, reads from IR_INPUT_JSON env var.

    Raises:
        FileNotFoundError: If no path is configured.
        SystemExit: If the configured path does not exist.
    """
    path = path or INPUT_JSON
    if not path:
        print("错误: 未指定输入文件。请通过以下任一方式指定:", file=sys.stderr)
        print("  1. 设置环境变量: IR_INPUT_JSON=<path>", file=sys.stderr)
        print("  2. 通过 main.py: python main.py --input <path>", file=sys.stderr)
        print("  3. 通过 step 脚本: python step1_semantic_index.py --input <path>", file=sys.stderr)
        print("  4. 程序调用: config.set_input_file(<path>)", file=sys.stderr)
        sys.exit(1)
    if not os.path.isfile(path):
        print(f"错误: 输入文件不存在: {path}", file=sys.stderr)
        sys.exit(1)
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)


def save_json(data, path: str) -> None:
    """Save data as formatted JSON."""
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, "w", encoding="utf-8") as f:
        json.dump(data, f, ensure_ascii=False, indent=2)


def load_json(path: str) -> dict:
    """Load a JSON file."""
    with open(path, "r", encoding="utf-8") as f:
        return json.load(f)