"""ACDL Terraform adapter — compile a Target Stack IR instance to Terraform.

ARCHITECTURE.md §12.2: the adapter translates the IR-typed L1 interface
to a Terraform variable/output block, the L2 thin-composition tree to a
root module that calls the L1 modules, the IR-typed relationships to
Terraform module references, and emits a Terraform plan from the IR.

The adapter is a THIN LAYER; it does not own L1/L2 content — it only
translates. Substrate-agnostic in, Terraform out.

Spike scope (Phase 09): handles one L1 (l1-s3, IR type aws:s3:bucket).
L2 thin-composition + relationships land in Phase 10.

CLI: adapter.py <ir_instance.json> <out_dir>
"""

import json
import os
import sys


# IR type -> Terraform resource type. The only substrate-specific table.
# As more L1s land, this grows; the L1 content + IR do not change.
TYPE_MAP = {
    "aws:s3:bucket": "aws_s3_bucket",
}


def _tf_block(block_type, name, body_lines, indent=2):
    head = f'{block_type} "{name}" {{'
    body = "\n".join(f"  {l}" for l in body_lines)
    return f"{head}\n{body}\n}}\n"


def _emit_resource(resource):
    rtype = resource["type"]
    rid = resource["id"]
    tf_type = TYPE_MAP.get(rtype)
    if not tf_type:
        raise ValueError(f"unknown IR type {rtype!r} (adapter spike handles aws:s3:bucket only)")
    body = []
    inputs = resource.get("inputs", {})
    # S3 bucket: bucket_name -> bucket arg; region -> provider (handled separately)
    if "bucket_name" in inputs:
        body.append(f'bucket = "{inputs["bucket_name"]}"')
    # NFR: versioning (default true)
    nfrs = resource.get("nfrs", {})
    versioning = nfrs.get("versioning", True) if isinstance(nfrs, dict) else True
    body.append("versioning {")
    body.append(f'  enabled = {"true" if versioning else "false"}')
    body.append("}")
    return _tf_block("resource", f'aws_s3_bucket.{rid}', body) if False else _resource_block(rid, tf_type, body)


def _resource_block(rid, tf_type, body):
    """Emit a top-level resource block."""
    head = f'resource "{tf_type}" "{rid}" {{'
    body_str = "\n".join(f"  {l}" for l in body)
    return f"{head}\n{body_str}\n}}\n"


def _emit_output(output_name, value_expr):
    return f'output "{output_name}" {{\n  value = {value_expr}\n}}\n'


def adapt(ir_instance, out_dir):
    """Emit main.tf + terraform.tf + providers.tf to out_dir for the IR instance."""
    os.makedirs(out_dir, exist_ok=True)
    stack = ir_instance["stack"]
    resources = ir_instance["resources"]

    # --- providers.tf: aws provider, region from the first resource's inputs.region ---
    region = "us-east-1"
    for r in resources:
        if "region" in r.get("inputs", {}):
            region = r["inputs"]["region"]
            break
    providers_tf = (
        f'provider "aws" {{\n'
        f'  region = "{region}"\n'
        f'}}\n'
    )

    # --- terraform.tf: required_version + required_providers + S3 backend (no DynamoDB lock per D-P09-1) ---
    terraform_tf = (
        'terraform {\n'
        '  required_version = ">= 1.9, < 1.10"\n'
        '  required_providers {\n'
        '    aws = {\n'
        '      source  = "hashicorp/aws"\n'
        '      version = "~> 5.0"\n'
        '    }\n'
        '  }\n'
        '  backend "s3" {\n'
        '    bucket = "acdl-tfstate-581513795199-us-east-1"\n'
        '    key    = "spike/l1-s3/terraform.tfstate"\n'
        '    region = "us-east-1"\n'
        '  }\n'
        '}\n'
    )

    # --- main.tf: resources + outputs ---
    main_tf_parts = []
    for r in resources:
        main_tf_parts.append(_emit_resource(r))
        rid = r["id"]
        outputs = r.get("outputs", {})
        for out_name in outputs:
            if out_name == "bucket_arn":
                main_tf_parts.append(_emit_output("bucket_arn", f"aws_s3_bucket.{rid}.arn"))
            elif out_name == "bucket_name":
                main_tf_parts.append(_emit_output("bucket_name", f"aws_s3_bucket.{rid}.id"))
    main_tf = "\n".join(main_tf_parts)

    with open(os.path.join(out_dir, "main.tf"), "w") as fh:
        fh.write(main_tf)
    with open(os.path.join(out_dir, "terraform.tf"), "w") as fh:
        fh.write(terraform_tf)
    with open(os.path.join(out_dir, "providers.tf"), "w") as fh:
        fh.write(providers_tf)
    return out_dir


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("usage: adapter.py <ir_instance.json> <out_dir>", file=sys.stderr)
        sys.exit(2)
    with open(sys.argv[1], "r") as fh:
        ir = json.load(fh)
    adapt(ir, sys.argv[2])
    print(f"adapter: emitted terraform to {sys.argv[2]}", file=sys.stderr)