"""Local tools with explicit source records and no network or model side effects."""
import json
from pathlib import Path
from .engine import Tool
from .schema import Candidate,Measurement,ToolResult,DesignSpec
from .metrics import candidate_score,diverse_select
from .evidence import retrieval_tool
from .structure import af3_input


def registry_from_config(config):
    from .engine import CommandTool
    registry={}
    for row in config.get('commands',[]):
        registry[row['name']]=CommandTool(**row)
    if config.get('evidence_path'):
        registry['retrieve_evidence']=Tool('retrieve_evidence',retrieval_tool(config['evidence_path']),
            {'tool_calls':1},'Search the frozen passage corpus by query.',{'query':'string','k':'integer'})
    def ingest(a,s):
        p=Path(a['path']);x=json.loads(p.read_text())
        result=ToolResult.model_validate(x)
        # A manifest identifies the source of every measurement.
        if any(not m.source_id for m in result.measurements):raise ValueError('missing measurement source')
        return result
    registry['ingest_results']=Tool('ingest_results',ingest,{'tool_calls':1},
        'Read an operator-prepared ToolResult JSON file; preserve endpoint units and experiment labels.')
    def rank(a,s):
        spec=DesignSpec.model_validate(s['spec']);scores={};sequences={}
        for key,c in s['candidates'].items():
            ms=[Measurement.model_validate(m) for m in s['measurements'] if m['candidate_id']==key and m['kind']==a.get('kind','prediction')]
            try:quality=candidate_score(ms,spec.requirements)
            except ValueError:continue
            scores[key]=quality;sequences[key]=c["molecule"]["sequence"]
        selected=diverse_select(sequences,scores,k=int(a.get('k',12)),penalty=float(a.get('penalty',.2)),max_identity=float(a.get('max_identity',.8)))
        path=Path(a['output']);path.parent.mkdir(parents=True,exist_ok=True)
        path.write_text(json.dumps({'selected':selected,'eligible':len(scores)},indent=2))
        return ToolResult(artifacts={'selection':str(path.resolve())},message=f'{len(selected)} candidates selected from {len(scores)} with all required endpoints')
    registry['rank_candidates']=Tool('rank_candidates',rank,{'tool_calls':1},'Rank complete calibrated endpoint vectors and select diverse candidates.')
    def export(a,s):
        spec=DesignSpec.model_validate(s['spec']);c=Candidate.model_validate(s['candidates'][a['candidate_id']])
        all_targets={t.id:t for t in spec.targets+spec.countertargets}
        chosen=[all_targets[k] for k in a.get('target_ids',[t.id for t in spec.targets])]
        result=af3_input(spec.episode_id,chosen,c.molecule,seeds=(spec.seed,spec.seed+1,spec.seed+2))
        path=Path(a['output']);path.parent.mkdir(parents=True,exist_ok=True);path.write_text(json.dumps(result,indent=2))
        return ToolResult(artifacts={'af3_input':str(path.resolve())})
    registry['export_af3']=Tool('export_af3',export,{'tool_calls':1},'Export all target partners and a fixed peptide with explicit PTM chemistry.')
    def inspect_state(a,s):
        key=a['collection']
        if key not in {'candidates','measurements','evidence','artifacts'}:raise ValueError('invalid state collection')
        values=s[key]
        if isinstance(values,dict):
            selected={k:values[k] for k in a.get('ids',list(values)[-3:])}
        else:
            selected=[v for v in values if not a.get('candidate_id') or v['candidate_id']==a['candidate_id']][-10:]
        return ToolResult(message=json.dumps({'collection':key,'records':selected}))
    registry['inspect_state']=Tool('inspect_state',inspect_state,{'tool_calls':1},'Retrieve archived candidates, measurements, evidence, or artifacts omitted by deterministic context packing.')
    return registry