import os, sys, platform, shutil, dpdata, json
import numpy as np
import unittest
import subprocess as sp
from common import j_loader, tests_path
from deepmd.train.trainer import DPTrainer
from deepmd.train.run_options import RunOptions
from deepmd.utils.argcheck import normalize
from deepmd.utils.compat import update_deepmd_input
from deepmd.utils.data_system import DeepmdDataSystem

from deepmd.env import GLOBAL_NP_FLOAT_PRECISION, tf

if GLOBAL_NP_FLOAT_PRECISION == np.float32:
    default_places = 4
else:
    default_places = 10


def _file_delete(file):
    if os.path.isdir(file):
        os.rmdir(file)
    elif os.path.isfile(file):
        os.remove(file)


def _subprocess_run(command):
    popen = sp.Popen(command.split(), shell=False, stdout=sp.PIPE, stderr=sp.STDOUT)
    for line in iter(popen.stdout.readline, b''):
        if hasattr(line, 'decode'):
            line = line.decode('utf-8')
        line = line.rstrip()
        print(line)
    popen.wait()
    return popen.returncode


def _init_models():
    data_file = str(tests_path / os.path.join("init_frz_model", "data"))
    frozen_model = str(tests_path / "init_frz_se_a.pb")
    ckpt = str(tests_path / "init_frz_se_a.ckpt")
    run_opt_ckpt = RunOptions(init_model=ckpt, log_level=20)
    run_opt_frz = RunOptions(init_frz_model=frozen_model, log_level=20)
    INPUT = str(tests_path / "input.json")
    jdata = j_loader(str(tests_path / os.path.join("init_frz_model", "input.json")))
    jdata["training"]["training_data"]["systems"] = data_file
    jdata["training"]["validation_data"]["systems"] = data_file
    jdata["training"]["save_ckpt"] = ckpt
    with open(INPUT, "w") as fp:
        json.dump(jdata, fp, indent=4)
    ret = _subprocess_run("dp train " + INPUT)
    np.testing.assert_equal(ret, 0, 'DP train failed!')
    ret = _subprocess_run("dp freeze -c " + str(tests_path) + " -o " + frozen_model)
    np.testing.assert_equal(ret, 0, 'DP freeze failed!')

    jdata = update_deepmd_input(jdata, warning=True, dump="input_v2_compat.json")
    jdata = normalize(jdata)
    model_ckpt = DPTrainer(jdata, run_opt=run_opt_ckpt)
    model_frz = DPTrainer(jdata, run_opt=run_opt_frz)
    rcut = model_ckpt.model.get_rcut()
    type_map = model_ckpt.model.get_type_map()
    data = DeepmdDataSystem(
        systems=[data_file],
        batch_size=1,
        test_size=1,
        rcut=rcut,
        type_map=type_map,
        trn_all_set=True
    )
    data_requirement = {'energy': {'ndof': 1,
                                   'atomic': False,
                                   'must': False,
                                   'high_prec': True,
                                   'type_sel': None,
                                   'repeat': 1,
                                   'default': 0.0},
                        'force': {'ndof': 3,
                                  'atomic': True,
                                  'must': False,
                                  'high_prec': False,
                                  'type_sel': None,
                                  'repeat': 1,
                                  'default': 0.0},
                        'virial': {'ndof': 9,
                                   'atomic': False,
                                   'must': False,
                                   'high_prec': False,
                                   'type_sel': None,
                                   'repeat': 1,
                                   'default': 0.0},
                        'atom_ener': {'ndof': 1,
                                      'atomic': True,
                                      'must': False,
                                      'high_prec': False,
                                      'type_sel': None,
                                      'repeat': 1,
                                      'default': 0.0},
                        'atom_pref': {'ndof': 1,
                                      'atomic': True,
                                      'must': False,
                                      'high_prec': False,
                                      'type_sel': None,
                                      'repeat': 3,
                                      'default': 0.0}}
    data.add_dict(data_requirement)
    stop_batch = jdata["training"]["numb_steps"]

    return INPUT, ckpt, frozen_model, model_ckpt, model_frz, data, stop_batch


INPUT, CKPT, FROZEN_MODEL, CKPT_TRAINER, FRZ_TRAINER, VALID_DATA, STOP_BATCH = _init_models()


class TestInitFrzModelA(unittest.TestCase):
    @classmethod
    def setUpClass(self):
        self.dp_ckpt = CKPT_TRAINER
        self.dp_frz = FRZ_TRAINER
        self.valid_data = VALID_DATA
        self.stop_batch = STOP_BATCH

    @classmethod
    def tearDownClass(self):
        _file_delete(INPUT)
        _file_delete(FROZEN_MODEL)
        _file_delete("out.json")
        _file_delete(str(tests_path / "checkpoint"))
        _file_delete(CKPT+".meta")
        _file_delete(CKPT+".index")
        _file_delete(CKPT+".data-00000-of-00001")
        _file_delete(CKPT+"-0.meta")
        _file_delete(CKPT+"-0.index")
        _file_delete(CKPT+"-0.data-00000-of-00001")
        _file_delete(CKPT+"-1.meta")
        _file_delete(CKPT+"-1.index")
        _file_delete(CKPT+"-1.data-00000-of-00001")
        _file_delete("input_v2_compat.json")
        _file_delete("lcurve.out")

    def test_single_frame(self):
        valid_batch = self.valid_data.get_batch()
        natoms = valid_batch['natoms_vec']
        tf.reset_default_graph()
        self.dp_ckpt.build(self.valid_data, self.stop_batch)
        self.dp_ckpt._init_session()
        feed_dict_ckpt = self.dp_ckpt.get_feed_dict(valid_batch, is_training=False)
        ckpt_rmse_ckpt = self.dp_ckpt.loss.eval(self.dp_ckpt.sess, feed_dict_ckpt, natoms)
        tf.reset_default_graph()

        self.dp_frz.build(self.valid_data, self.stop_batch)
        self.dp_frz._init_session()
        feed_dict_frz = self.dp_frz.get_feed_dict(valid_batch, is_training=False)
        ckpt_rmse_frz = self.dp_frz.loss.eval(self.dp_frz.sess, feed_dict_frz, natoms)
        tf.reset_default_graph()

        # check values
        np.testing.assert_almost_equal(ckpt_rmse_ckpt['rmse_e'], ckpt_rmse_frz['rmse_e'], default_places)
        np.testing.assert_almost_equal(ckpt_rmse_ckpt['rmse_f'], ckpt_rmse_frz['rmse_f'], default_places)
        np.testing.assert_almost_equal(ckpt_rmse_ckpt['rmse_v'], ckpt_rmse_frz['rmse_v'], default_places)


