import re, json, os
from datasets import load_dataset
HOME=os.environ["HOME"]
MEDX=f"{HOME}/pentabrid/datasets/MedXpertQA/Text/test.jsonl"
OUT=f"{HOME}/pentabrid/datasets/mcr_clinician_baseline.jsonl"
def dequote(s):
    s=str(s)
    for ch in ['\u201c','\u201d','\u201e','\u201f','\u2033','"']: s=s.replace(ch,'')
    return re.sub(r'[ \t]+',' ',s).strip()
def words(s): return re.findall(r"[a-z0-9]+", str(s).lower())
def grams(t,n): return set(tuple(t[i:i+n]) for i in range(len(t)-n+1)) if len(t)>=n else set()
G13=set(); nref=0
with open(MEDX) as f:
    for line in f:
        line=line.strip()
        if not line: continue
        r=json.loads(line); parts=[str(v) for v in r.values() if isinstance(v,str)]
        for v in r.values():
            if isinstance(v,dict): parts+=[str(x) for x in v.values()]
        G13|=grams(words(" ".join(parts)),13); nref+=1
print(f"decontam ref: {nref} MedXpertQA questions, {len(G13)} 13-grams")
mcr=load_dataset("zou-lab/MedCaseReasoning",split="train")
kept=[]; dc=0
for ex in mcr:
    cp=str(ex.get("case_prompt","")).strip(); dr=dequote(ex.get("diagnostic_reasoning","")); dx=str(ex.get("final_diagnosis","")).strip()
    if not cp or not dr or not dx: continue
    if grams(words(cp),13)&G13: dc+=1; continue
    instr=cp+"\n\nReason through the differential diagnosis step by step, then give the single most likely diagnosis on a final line as 'Diagnosis: <name>'."
    output="<think>\n"+dr+"\n</think>\n\nDiagnosis: "+dx
    kept.append({"instruction":instr,"input":"","output":output,"source":"medcasereasoning"})
with open(OUT,"w") as f:
    for r in kept: f.write(json.dumps(r,ensure_ascii=False)+"\n")
print(f"kept={len(kept)} dropped_contam={dc} -> {OUT} ({os.path.getsize(OUT)/1e6:.1f} MB)")