"""

"""

import os

from pytest import fixture

from pathlib import Path

import gdown

from bpnet.cli.train import bpnet_train

from bpnet.cli.contrib import bpnet_contrib

import gin

import keras.backend as K

@fixture(scope='session')

def test_dir():

    import inspect

    filename = inspect.getframeinfo(inspect.currentframe()).filename

    return Path(os.path.dirname(os.path.abspath(filename)))

@fixture(scope='session')

def data_dir(test_dir):

    return test_dir / 'data'

@fixture(scope='session')

def fasta_file(data_dir):

    return data_dir / 'dummy/dummy.fa'

@fixture(scope='session')

def regions(data_dir):

    return data_dir / 'dummy/peaks.bed'

@fixture(scope='session')

def genome_file(data_dir):

    return data_dir / 'dummy/dummy.genome'

@fixture(scope='session')

def dataspec_task1(data_dir):

    return data_dir / 'dataspec.task1.yml'

@fixture(scope='session')

def dataspec_bias(data_dir):

    return data_dir / 'dataspec.w-bias.yml'

@fixture(scope='session')

def dataspec_bed6(data_dir):

    return data_dir / 'dataspec.bed6.yml'

@fixture(scope='session')

def config_gin(data_dir):

    return data_dir / 'config.gin'

@fixture(scope='session')

def modisco_config_gin(data_dir):

    return data_dir / 'modisco-config.gin'

@fixture(scope="session")

def trained_model(data_dir, dataspec_task1, config_gin):

    K.clear_session()

    gin.clear_config()

    bpnet_train(dataspec=dataspec_task1,

                output_dir=data_dir,

                run_id='trained_model',

                premade='bpnet9',

                config=str(config_gin),

                num_workers=1,

                overwrite=True

                )

    return data_dir / 'trained_model'

@fixture(scope="session")

def trained_model_w_bias(config_gin, data_dir, dataspec_bias):

    K.clear_session()

    gin.clear_config()

    bpnet_train(dataspec=str(dataspec_bias),

                output_dir=str(data_dir),

                run_id='trained_model_w_bias',

                premade='bpnet9',

                config=str(config_gin),

                num_workers=1,

                overwrite=True,

                )

    return data_dir / 'trained_model_w_bias'

@fixture(scope='session')

def contrib_score_grad(trained_model):

    K.clear_session()

    fpath = trained_model / 'imp-score.grad.fixture.h5'

    bpnet_contrib(str(trained_model),

                  str(fpath),

                  method='grad',

                  overwrite=True)

    return fpath

@fixture(scope='session')

def contrib_score_grad_null(trained_model):

    K.clear_session()

    fpath = trained_model / 'imp-score.grad.null.fixture.h5'

    bpnet_contrib(str(trained_model),

                  str(fpath),

                  method='grad',

                  shuffle_seq=True,

                  max_regions=16,

                  overwrite=True)

    return fpath

@fixture(scope='session')

def download_dir():

    dir_path = '/tmp/bpnet'

    os.makedirs(dir_path, exist_ok=True)

    return Path(dir_path)

@fixture(scope='session')

def contrib_file(download_dir):

    """Download the contributon file

    """

    contrib_file = download_dir / 'contrib.deeplift.h5'

    url = 'https://drive.google.com/uc?id=1-70VlFvcOCwwt4SrEXoqkaXyBQPnlQGZ'

    md5 = '56e456f0d1aeffc9d3fcdfead0520c17'

    gdown.cached_download(url, str(contrib_file), md5=md5)

    return contrib_file

@fixture(scope='session')

def modisco_dir(download_dir):

    """Download the contributon file

    """

    _modisco_dir = download_dir / 'Oct4'

    _modisco_dir.mkdir(exist_ok=True)

    path = _modisco_dir / 'modisco-run.subset-contrib-file.npy'

    url = 'https://drive.google.com/uc?id=11uW8WaJ2EZuXQXUPq9g_TqrmWYTF-59V'

    md5 = '5b1425618cf87127f5a02cf54b2e361a'

    gdown.cached_download(url, str(path), md5=md5)

    path = _modisco_dir / 'modisco-run.kwargs.json'

    url = 'https://drive.google.com/uc?id=1zExhfQZ0-3irlpK6RrG1M-FBXvjHuQdm'

    md5 = '6bb33a8a0a2d0745ea9bf1ab2f5d241d'

    gdown.cached_download(url, str(path), md5=md5)

    path = _modisco_dir / 'modisco.h5'

    url = 'https://drive.google.com/uc?id=10owbBEB3PasIBSnMJ6KZQmJjbsyzhEgG'

    md5 = '8132fdbe7095748e8b229e81db45a6c9'

    gdown.cached_download(url, str(path), md5=md5)

    return _modisco_dir

@fixture(scope='session')

def mf(modisco_dir):

    """ModiscoFile

    """

    from bpnet.modisco.files import ModiscoFile

    mf = ModiscoFile(modisco_dir / 'modisco.h5')

    return mf

@fixture(scope='session')

def mfg(mf):

    """ModiscoFile

    """

    from bpnet.modisco.files import ModiscoFileGroup

    return ModiscoFileGroup({"Oct4": mf, "Sox2": mf})