import dpdata,os,sys,unittest
import numpy as np
from deepmd.env import tf
import pickle
from common import Data,gen_data, j_loader

from deepmd.utils.data_system import DataSystem
from deepmd.descriptor import DescrptSeA
from deepmd.fit import EnerFitting
from deepmd.model import EnerModel
from deepmd.utils.type_embed import TypeEmbedNet
from deepmd.common import j_must_have

GLOBAL_ENER_FLOAT_PRECISION = tf.float64
GLOBAL_TF_FLOAT_PRECISION = tf.float64
GLOBAL_NP_FLOAT_PRECISION = np.float64

class TestModel(tf.test.TestCase):
    def setUp(self) :
        gen_data()

    def test_model(self):
        jfile = 'water_se_a_type.json'
        jdata = j_loader(jfile)

        systems = j_must_have(jdata, 'systems')
        set_pfx = j_must_have(jdata, 'set_prefix')
        batch_size = j_must_have(jdata, 'batch_size')
        test_size = j_must_have(jdata, 'numb_test')
        batch_size = 1
        test_size = 1
        stop_batch = j_must_have(jdata, 'stop_batch')
        rcut = j_must_have (jdata['model']['descriptor'], 'rcut')
        
        data = DataSystem(systems, set_pfx, batch_size, test_size, rcut, run_opt = None)
        
        test_data = data.get_test ()
        numb_test = 1

        jdata['model']['descriptor'].pop('type', None)        
        descrpt = DescrptSeA(**jdata['model']['descriptor'], uniform_seed = True)
        jdata['model']['fitting_net']['descrpt'] = descrpt
        fitting = EnerFitting(**jdata['model']['fitting_net'], uniform_seed = True)
        typeebd_param = jdata['model']['type_embedding']
        typeebd = TypeEmbedNet(
            neuron = typeebd_param['neuron'],
            resnet_dt = typeebd_param['resnet_dt'],
            seed = typeebd_param['seed'], 
            uniform_seed = True)
        model = EnerModel(descrpt, fitting, typeebd)

        # model._compute_dstats([test_data['coord']], [test_data['box']], [test_data['type']], [test_data['natoms_vec']], [test_data['default_mesh']])
        input_data = {'coord' : [test_data['coord']], 
                      'box': [test_data['box']], 
                      'type': [test_data['type']],
                      'natoms_vec' : [test_data['natoms_vec']],
                      'default_mesh' : [test_data['default_mesh']]
        }
        model._compute_input_stat(input_data)
        model.descrpt.bias_atom_e = data.compute_energy_shift()

        t_prop_c           = tf.placeholder(tf.float32, [5],    name='t_prop_c')
        t_energy           = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name='t_energy')
        t_force            = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_force')
        t_virial           = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_virial')
        t_atom_ener        = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='t_atom_ener')
        t_coord            = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None], name='i_coord')
        t_type             = tf.placeholder(tf.int32,   [None], name='i_type')
        t_natoms           = tf.placeholder(tf.int32,   [model.ntypes+2], name='i_natoms')
        t_box              = tf.placeholder(GLOBAL_TF_FLOAT_PRECISION, [None, 9], name='i_box')
        t_mesh             = tf.placeholder(tf.int32,   [None], name='i_mesh')
        is_training        = tf.placeholder(tf.bool)
        t_fparam = None
        inputs_dict = {}

        model_pred \
            = model.build (t_coord, 
                           t_type, 
                           t_natoms, 
                           t_box, 
                           t_mesh,
                           inputs_dict,
                           suffix = "se_a_type", 
                           reuse = False)
        energy = model_pred['energy']
        force  = model_pred['force']
        virial = model_pred['virial']
        atom_ener =  model_pred['atom_ener']

        feed_dict_test = {t_prop_c:        test_data['prop_c'],
                          t_energy:        test_data['energy']              [:numb_test],
                          t_force:         np.reshape(test_data['force']    [:numb_test, :], [-1]),
                          t_virial:        np.reshape(test_data['virial']   [:numb_test, :], [-1]),
                          t_atom_ener:     np.reshape(test_data['atom_ener'][:numb_test, :], [-1]),
                          t_coord:         np.reshape(test_data['coord']    [:numb_test, :], [-1]),
                          t_box:           test_data['box']                 [:numb_test, :],
                          t_type:          np.reshape(test_data['type']     [:numb_test, :], [-1]),
                          t_natoms:        test_data['natoms_vec'],
                          t_mesh:          test_data['default_mesh'],
                          is_training:     False}

        sess = self.test_session().__enter__()
        sess.run(tf.global_variables_initializer())
        [e, f, v] = sess.run([energy, force, virial], 
                             feed_dict = feed_dict_test)
        # print(sess.run(model.type_embedding))
        # np.savetxt('tmp.out', sess.run(descrpt.dout, feed_dict = feed_dict_test), fmt='%.10e')
        # # print(sess.run(model.atype_embed, feed_dict = feed_dict_test))
        # print(sess.run(fitting.inputs, feed_dict = feed_dict_test))
        # print(sess.run(fitting.outs, feed_dict = feed_dict_test))
        # print(sess.run(fitting.atype_embed, feed_dict = feed_dict_test))

        e = e.reshape([-1])
        f = f.reshape([-1])
        v = v.reshape([-1])
        np.savetxt('e.out', e.reshape([1, -1]), delimiter=',')
        np.savetxt('f.out', f.reshape([1, -1]), delimiter=',')
        np.savetxt('v.out', v.reshape([1, -1]), delimiter=',')

        refe = [6.049065170680415804e+01]
        reff = [1.021832439441947293e-01,1.122650466359011306e-01,3.927874278714531091e-03,1.407089812207832635e-01,1.312473824343091400e-01,-1.228371057389851181e-02,-1.109672154547165501e-01,6.582735820731049070e-02,1.251568633647655391e-03,7.933758749748777428e-02,-1.831777072317984367e-01,-6.173090134630876760e-03,-2.703597126460742794e-01,4.817856571062521104e-02,1.491963457594796399e-02,5.909711543832503466e-02,-1.743406457563475287e-01,-1.642276779780762769e-03]
        refv = [-6.932736357193732823e-01,1.453756052949563837e-01,2.138263139115256783e-02,1.453756052949564392e-01,-3.880901656480436612e-01,-7.782259726407755700e-03,2.138263139115256437e-02,-7.782259726407749628e-03,-1.225285973678705374e-03]

        refe = np.reshape(refe, [-1])
        reff = np.reshape(reff, [-1])
        refv = np.reshape(refv, [-1])

        places = 10
        np.testing.assert_almost_equal(e, refe, places)
        np.testing.assert_almost_equal(f, reff, places)
        np.testing.assert_almost_equal(v, refv, places)
