import warnings

from .._utils.class_inspect import get_params
from .multi_nifti_masker import MultiNiftiMasker
from .nifti_masker import NiftiMasker


def check_embedded_nifti_masker(estimator, multi_subject=True):
    """Base function for using a masker within a BaseEstimator class

    This creates a masker from instance parameters :
    - If instance contains a mask image in mask parameter,
    we use this image as new masker mask_img, forwarding instance parameters to
    new masker : smoothing_fwhm, standardize, detrend, low_pass= high_pass,
    t_r, target_affine, target_shape, mask_strategy, mask_args,
    - If instance contains a masker in mask parameter, we use a copy of
    this masker, overriding all instance masker related parameters.
    In all case, we forward system parameters of instance to new masker :
    memory, memory_level, verbose, n_jobs

    Parameters
    ----------
    instance: object, instance of BaseEstimator
        The object that gives us the values of the parameters

    multi_subject: boolean
        Indicates whether to return a MultiNiftiMasker or a NiftiMasker
        (the default is True)

    Returns
    -------
    masker: MultiNiftiMasker or NiftiMasker
        New masker
    """
    masker_type = MultiNiftiMasker if multi_subject else NiftiMasker
    estimator_params = get_params(masker_type, estimator)
    mask = getattr(estimator, 'mask', None)

    if isinstance(mask, (NiftiMasker, MultiNiftiMasker)):
        # Creating (Multi)NiftiMasker from provided masker
        masker_params = get_params(masker_type, mask)
        new_masker_params = masker_params
    else:
        # Creating (Multi)NiftiMasker
        # with parameters extracted from estimator
        new_masker_params = estimator_params
        new_masker_params['mask_img'] = mask
    # Forwarding system parameters of instance to new masker in all case
    if multi_subject and hasattr(estimator, 'n_jobs'):
        # For MultiNiftiMasker only
        new_masker_params['n_jobs'] = estimator.n_jobs
    new_masker_params['memory'] = estimator.memory
    new_masker_params['memory_level'] = max(0, estimator.memory_level - 1)
    new_masker_params['verbose'] = estimator.verbose

    # Raising warning if masker override parameters
    conflict_string = ""
    for param_key in sorted(estimator_params):
        if new_masker_params[param_key] != estimator_params[param_key]:
            conflict_string += ("Parameter {0} :\n"
                                "    Masker parameter {1}"
                                " - overriding estimator parameter {2}\n"
                                ).format(param_key,
                                         new_masker_params[param_key],
                                         estimator_params[param_key])

    if conflict_string != "":
        warn_str = ("Overriding provided-default estimator parameters with"
                    " provided masker parameters :\n"
                    "{0:s}").format(conflict_string)
        warnings.warn(warn_str)
    masker = masker_type(**new_masker_params)

    # Forwarding potential attribute of provided masker
    if hasattr(mask, 'mask_img_'):
        # Allow free fit of returned mask
        masker.mask_img = mask.mask_img_

    return masker
