# AutoLoRA Training Pipeline
# Replaces: autolora repo (1,500 lines) with config + make targets
#
# Prerequisites:
#   pip install axolotl mlx-lm lm-evaluation-harness pyyaml
#
# Targets:
#   make train-cloud   -- QLoRA on cloud GPU via Axolotl
#   make train-local   -- LoRA on Apple Silicon via MLX
#   make eval          -- Standard benchmarks via lm-eval-harness
#   make vibes         -- Hand-picked prompts through Ollama, human review
#   make ingest        -- Pull heartbeat trajectories into training data
#   make curated       -- Regenerate curated exemplar dataset

PYTHON      ?= python3
MODEL       ?= timmy:v0.1-q4
BASELINE    ?= hermes3:latest
OLLAMA_URL  ?= http://localhost:11434
OUTPUT      ?= output

# -- Training --

train-cloud: ## QLoRA fine-tune on cloud GPU (Axolotl)
	axolotl train axolotl.yaml

train-local: ## LoRA fine-tune on Apple Silicon (MLX)
	$(PYTHON) -m mlx_lm.lora --config mlx-lora.yaml

# -- Evaluation --

eval: ## Run standard benchmarks against Ollama model
	lm_eval --model local-completions \
		--model_args "model=$(MODEL),base_url=$(OLLAMA_URL)/v1,tokenized_requests=False" \
		--tasks hellaswag,truthfulqa_mc2,arc_challenge,winogrande \
		--output_path evals_archive/$(MODEL)/
	@echo "Results in evals_archive/$(MODEL)/"

eval-baseline: ## Run same benchmarks against baseline for comparison
	lm_eval --model local-completions \
		--model_args "model=$(BASELINE),base_url=$(OLLAMA_URL)/v1,tokenized_requests=False" \
		--tasks hellaswag,truthfulqa_mc2,arc_challenge,winogrande \
		--output_path evals_archive/$(BASELINE)/

vibes: ## Run vibes check -- hand-picked prompts, human review
	@echo "=== Vibes Check: $(MODEL) ==="
	@echo "Date: $$(date '+%Y-%m-%d %H:%M')" > $(OUTPUT)/vibes-$(MODEL).md
	@echo "Model: $(MODEL)" >> $(OUTPUT)/vibes-$(MODEL).md
	@echo "" >> $(OUTPUT)/vibes-$(MODEL).md
	@$(PYTHON) -c "import yaml, subprocess, sys; prompts = yaml.safe_load(open('data/prompts_vibes.yaml'))['prompts']; f = open('$(OUTPUT)/vibes-$(MODEL).md', 'a'); [(sys.stdout.write(f'  [{p[\"id\"]}] {p[\"category\"]}...'), sys.stdout.flush(), f.write(f'## [{p[\"id\"]}] {p[\"category\"]}\n'), f.write(f'PROMPT: {p[\"prompt\"]}\n'), f.write(f'EXPECTED: {p[\"expected\"]}\n\n'), f.write('RESPONSE:\n'), f.write(subprocess.run(['ollama', 'run', '$(MODEL)', p['prompt']], capture_output=True, text=True, timeout=120).stdout), f.write('\n\nSCORE: ___/5\n\n---\n\n'), print(' done')) for p in prompts]; f.close()"
	@echo "Output: $(OUTPUT)/vibes-$(MODEL).md -- fill in scores manually."

adversary-value-violations: ## Run 200-prompt value-violations adversary suite
	@mkdir -p $(OUTPUT)/adversary-value-violations
	$(PYTHON) run_adversary_eval.py \
		--suite data/prompts_adversary_value_violations.yaml \
		--model $(MODEL) \
		--output-dir $(OUTPUT)/adversary-value-violations
	@echo "Output: $(OUTPUT)/adversary-value-violations"

# -- Data Pipeline --

ingest: ## Pull heartbeat trajectories into training data
	$(PYTHON) ingest_trajectories.py \
		--trajectories ~/.nexus/trajectories/ \
		--curated data/curated_dataset.jsonl \
		--output data/merged_training_data.jsonl
	@echo "Merged dataset ready. Convert for MLX with: make convert"

curated: ## Regenerate curated exemplar dataset
	$(PYTHON) build_curated.py
	@echo "Curated dataset regenerated."

convert: ## Convert merged dataset to MLX format (train/valid split)
	@mkdir -p data/mlx_curated
	$(PYTHON) -c "import json; lines = open('data/merged_training_data.jsonl').readlines(); sessions = [json.loads(l) for l in lines]; ROLE_MAP = {'system':'system','human':'user','gpt':'assistant','tool':'user'}; converted = [{'messages': [{'role': ROLE_MAP.get(t.get('from',''),'user'), 'content': t.get('value','')} for t in s.get('conversations',[])]} for s in sessions]; split = max(1, int(len(converted)*0.9)); open('data/mlx_curated/train.jsonl','w').writelines(json.dumps(c)+'\n' for c in converted[:split]); open('data/mlx_curated/valid.jsonl','w').writelines(json.dumps(c)+'\n' for c in converted[split:]); print(f'train: {split}, valid: {len(converted)-split}')"

# -- Helpers --

.PHONY: train-cloud train-local eval eval-baseline vibes adversary-value-violations ingest curated convert help

help: ## Show this help
	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | \
		awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-16s\033[0m %s\n", $$1, $$2}'