#!/usr/bin/env python3
"""Deterministic, reproducible checks for ELYRION III.

Run from any directory. Results are numerical evidence for supplied proofs,
not experiments, proof-assistant verification, or global novelty certification.
"""
from __future__ import annotations
import argparse
from fractions import Fraction as F
from itertools import product
import json
from math import pi
from pathlib import Path
import platform
import time
import numpy as np
import scipy
from model import (A,B,X,C, transition, ideal_filter, finite_filter, ideal_commit,
                   run_pass, fringes, comb_profile, log_midpoint_yield, optimal_action,
                   limit_product_bounds, cubic_constant, repeated_commits,
                   hazard_example, diffusion_comb)

ROOT = Path(__file__).resolve().parents[1]


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--output', type=Path, default=ROOT/'results'/'verification.json')
    parser.add_argument('--skip-diffusion', action='store_true')
    args = parser.parse_args()
    begin=time.perf_counter()
    rng=np.random.default_rng(20261020)
    tests=[]
    def check(name: str, condition: bool, **data):
        record={'name':name, 'passed':bool(condition), **data}
        tests.append(record)
        if not condition:
            raise AssertionError(record)

    # Exact rational recurrence, including commit and mass conservation.
    rational_cases=0
    for m in range(1,7):
        for sample in range(20):
            ps=[F(int(rng.integers(1,10)),10) for _ in range(m)]
            state=[F(1),F(0),F(0),F(0)]
            expected=F(1)
            for p in ps:
                marked=state[A]*p
                state[A]-=marked; state[B]+=marked
                state=[state[B],F(0),state[X]+state[A],state[C]]
                expected*=p
            q=F(3,7)
            committed=q*state[A]
            state=[1-committed,F(0),F(0),committed]
            rational_cases+=1
            check(f'exact_fraction_pass_{m}_{sample}',
                  committed==q*expected and sum(state)==1 and min(state)>=0)

    for source,target in [(A,B),(A,X),(B,A),(A,C),(X,A)]:
        for action in [0.,.01,1.,10.,40.]:
            mat=transition(source,target,action)
            check(f'stochastic_transition_{source}_{target}_{action}',
                  np.min(mat)>=0 and np.max(abs(mat.sum(axis=0)-1))<1e-14)
    for h in [.1,1.,3.,8.,16.]:
        ff=finite_filter(h,h)
        tv=np.max(np.sum(abs(ff-ideal_filter()),axis=0))/2
        check(f'finite_filter_error_{h}',tv<=2*np.exp(-h)+1e-14, observed_tv=float(tv))

    for m in range(2,17):
        theta=2*pi*np.arange(256*m)/(256*m)
        u=optimal_action(m,.5)
        s=comb_profile(m,u,theta)
        check(f'comb_range_{m}',s.min()>=0 and s.max()<=1+1e-14)
        check(f'comb_nodes_{m}',np.max(s[::256])<1e-24)
        check(f'comb_midpoints_{m}',np.max(abs(s[128::256]-.5))<2e-11)
        check(f'cyclic_symmetry_{m}',np.max(abs(s-np.roll(s,256)))<2e-12)
        ft=np.fft.rfft(s)/len(s)
        forbidden=np.arange(len(ft))%m!=0
        check(f'forbidden_Fourier_modes_{m}',np.max(abs(ft[forbidden]))<1e-12)
        state=run_pass(fringes(m,theta),np.full(m,u),committed=.17)
        check(f'four_state_vs_product_{m}',np.max(abs(state[A]-.83*s))<2e-13)
        check(f'four_state_mass_{m}',np.max(abs(state.sum(axis=0)-1))<3e-13)
        # Removing the selection operation gives integrated dose, not the conjunction.
        no_filter=-np.expm1(-u*fringes(m,theta).sum(axis=0))
        check(f'no_filter_is_uniform_{m}',np.ptp(no_filter)<2e-12)
        small=.0005
        p0=np.prod(fringes(m,theta),axis=0)
        ss=comb_profile(m,small,theta)/small**m
        check(f'weak_gate_root_limit_{m}',
              np.max(ss-p0)<1e-12 and np.max(np.exp(-m*small/2)*p0-ss)<1e-12)
        # Noise budget at the nominal dark nodes, not a claim about all Fourier errors.
        delta=rng.uniform(-.03,.03,m)
        floor=.0001
        nodes=2*pi*np.arange(m)/m
        pert=comb_profile(m,u,nodes,phases=nodes+delta,floor=floor)
        bound=-np.expm1(-u*(floor+2*np.sin(delta/2)**2))
        check(f'dark_phase_floor_bound_{m}',np.max(pert-bound)<1e-13)

    allocation_trials=0
    for m in [2,3,4,6,8,12]:
        u=optimal_action(m,.5)
        x=2*pi*(np.arange(m)+.5)/m
        for j in range(100):
            alloc=rng.dirichlet(np.ones(m))*m*u
            worst=float(comb_profile(m,alloc,x).min())
            allocation_trials+=1
            check(f'max_min_allocation_{m}_{j}',worst<=.5+1e-12)

    grid_allocation_count=0
    for m in [2,3,4]:
        quanta=12
        best=0.0
        x=2*pi*(np.arange(m)+.5)/m
        for alloc in product(range(quanta+1),repeat=m-1):
            last=quanta-sum(alloc)
            if last<0: continue
            a=np.asarray((*alloc,last),dtype=float)/4
            value=float(comb_profile(m,a,x).min())
            best=max(best,value)
            grid_allocation_count+=1
        exact=float(np.exp(log_midpoint_yield(m,quanta/(4*m))))
        check(f'exhaustive_allocation_grid_{m}',abs(best-exact)<1e-13,
              maximum=float(best))

    for j in range(100):
        a,b,g=rng.uniform(.001,20,3)
        separate=(-np.expm1(-a*g))*(-np.expm1(-b*g))
        merged=-np.expm1(-(a+b)*g)
        check(f'repeated_node_consolidation_{j}',separate<=merged+1e-14)

    for case in range(100):
        m=int(rng.integers(2,10))
        theta=2*pi*np.arange(257)/257
        allocations=rng.uniform(.01,5,(3,m))
        separate_remaining=np.ones(len(theta))
        for aa in allocations:
            separate_remaining*=1-comb_profile(m,aa,theta)
        combined=comb_profile(m,allocations.sum(axis=0),theta)
        check(f'multipass_merge_domination_{case}',
              np.max((1-separate_remaining)-combined)<1e-13)

    constants={str(q):cubic_constant(q) for q in [.1,.5,.9,.99]}
    dose_rows=[]
    for q in [.1,.5,.9,.99]:
        c=constants[str(q)]
        for m in [2,3,4,8,16,32,64,128,256]:
            u=optimal_action(m,q)
            dose_rows.append(dict(m=m,q=q,action=u,total_action=m*u,ratio=u/m**2))
            check(f'dose_inverse_{q}_{m}',abs(np.exp(log_midpoint_yield(m,u))-q)<2e-12)
            lower=m*(-np.log1p(-np.sqrt(q)))/(2*np.sin(pi/(2*m))**2)
            check(f'two_nearest_gate_lower_bound_{q}_{m}',m*u+2e-12*max(1.0,lower)>=lower)
        check(f'cubic_limit_{q}',abs(dose_rows[-1]['ratio']/c-1)<5e-5)
    for c in [.025,.05,.1,.25,.6,1.0]:
        for r in [1,2,4,8]:
            lo,hi=limit_product_bounds(c,r)
            ref=limit_product_bounds(c,64)[1]
            check(f'analytic_product_tail_{c}_{r}',lo-2e-15<=ref<=hi+2e-15)

    hazard_rows=[]
    for action in [.2,.1,.05,.025]:
        data=hazard_example(action)
        check(f'universal_hazard_bound_{action}',data['error']<=data['bound']+1e-13)
        check(f'universal_hazard_contraction_{action}',data['error']<=data['hazard_error']+1e-13)
        check(f'universal_hazard_probability_{action}',
              data['written'].min()>=0 and data['written'].max()<1)
        hazard_rows.append({k:v for k,v in data.items() if not isinstance(v,np.ndarray)})
    check('hazard_example_convergence',all(hazard_rows[i+1]['error']<hazard_rows[i]['error']
                                          for i in range(len(hazard_rows)-1)))
    for case in range(20):
        q=rng.uniform(.001,.4,15)
        scale=.03
        coeff=float(rng.uniform(.001,.2))
        logs,count,frac=repeated_commits(q,coeff,scale)
        remaining=np.ones(15)
        whole=int(np.floor(coeff/scale))
        for _ in range(whole): remaining*=1-q
        remaining*=1-frac*q
        check(f'collapsed_finite_passes_{case}',np.max(abs(np.exp(logs)-remaining))<1e-13)

    # A single-pass retention ceiling is NOT a multi-pass impossibility theorem.
    single_lossy=(.8**4)*.9
    two_lossy=1-(1-single_lossy)**2
    check('retention_ceiling_is_single_pass_only',single_lossy<.5<two_lossy,
          one_pass=single_lossy,two_passes=two_lossy)

    diffusion_rows=[]
    if not args.skip_diffusion:
        for m in [2,3,4,6]:
            u=optimal_action(m,.5)
            for d in [0.,1e-6,1e-4,1e-3]:
                data=diffusion_comb(m,u,d)
                check(f'diffusion_positivity_{m}_{d}',data['survival'].min()>-1e-12)
                check(f'diffusion_subprobability_{m}_{d}',data['survival'].max()<1+1e-12)
                check(f'diffusion_dark_bound_{m}_{d}',np.max(data['dark']-data['bounds'])<2e-11)
                if d==0:
                    ref=comb_profile(m,u,data['theta'])
                    check(f'diffusion_zero_reduction_{m}',np.max(abs(ref-data['survival']))<1e-11)
                refinement=diffusion_comb(m,u,d,n=128*m)
                grid_error=float(abs(data['survival']-refinement['survival'][::2]).max())
                check(f'diffusion_grid_refinement_{m}_{d}',grid_error<.002)
                diffusion_rows.append(dict(m=m,diffusion=d,action=u,time=data['time'],
                      max_dark=float(data['dark'].max()),min_bright=float(data['bright'].min()),
                      max_dark_bound=float(data['bounds'].max()),grid_error=grid_error,
                      n=data['n']))

    example_m=8
    ex_u=optimal_action(example_m,.5)
    dark=.01
    report=dict(
        project='EVE / ELYRION III',version='3.0',date='2026-10-20',seed=20261020,
        epistemic_status='ANALYTIC_PROOFS_PLUS_EXACT_RATIONAL_AND_FLOATING_TESTS; NO_EXPERIMENTS',
        tests_passed=sum(t['passed'] for t in tests),tests_total=len(tests),
        exact_rational_pass_cases=rational_cases,random_allocation_trials=allocation_trials,
        exhaustive_grid_allocations=grid_allocation_count,
        cubic_constants=constants,dose_table=dose_rows,hazard_examples=hazard_rows,
        diffusion_examples=diffusion_rows,
        m8_dark01_budget=dict(action=ex_u,total_action=example_m*ex_u,
                  extinction_floor=-np.log1p(-dark)/ex_u,
                  phase_radians=float(np.arccos(1+np.log1p(-dark)/ex_u))),
        environment=dict(python=platform.python_version(),numpy=np.__version__,scipy=scipy.__version__),
        elapsed_seconds=time.perf_counter()-begin,tests=tests,
        limitations=['No actual material is identified or characterized.',
                     'Floating tests and tail evaluations are not interval certificates.',
                     'Diffusion computations discretize the continuum PDE; refinement is numerical.',
                     'Cubic optimality is for the declared exact-node serial gate class only.',
                     'Positive 3D universality has no efficient runtime guarantee.'])
    args.output.parent.mkdir(parents=True,exist_ok=True)
    args.output.write_text(json.dumps(report,indent=2),encoding='utf-8')
    print(json.dumps({k:report[k] for k in ['tests_passed','tests_total','exact_rational_pass_cases',
                                          'exhaustive_grid_allocations','elapsed_seconds']},indent=2))
    print('Results:',args.output)

if __name__=='__main__':
    main()