# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
#     """Turn seed into a np.random.RandomState instance
#     Parameters
#     ----------
#     seed : None | int | instance of RandomState
#         If seed is None, return the RandomState singleton used by np.random.
#         If seed is an int, return a new RandomState instance seeded with seed.
#         If seed is already a RandomState instance, return it.
#         Otherwise raise ValueError.
# 
#     Returns
#     -------
#     np.random.RandomState
# 
#     .. seealso::
# 
#         `Scikit learn source code <https://github.com/scikit-learn/scikit-learn/blob/7813f7efb/sklearn/utils/validation.py#L763>`_
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (int, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# def stable_invert_root(eigenvec, eigenval):
#     """ Given eigendecomposition of a PSD matrix, compute a representation of the pseudo-inverse square root
#     of the matrix using numerically stable operations. In particular, eigenvalues which are near-zero
#     and the associated eigenvectors are dropped from the pseudo-inverse.
#     """
#     eigenvec_thin, eigenval_thin = stable_filter(eigenvec, eigenval)
# 
#     eigenval_thin_inv_root = (1 / np.sqrt(eigenval_thin)).reshape(-1, 1)
# 
#     return eigenvec_thin, eigenval_thin_inv_root
# 
# def get_progress_bar(total=-1, disable=False, **kwargs):
#     """Helper function to get a tqdm progress bar (or a simple fallback otherwise)"""
#     class ProgBar(object):
#         def __init__(self, total=-1, disable=False, **kwargs):
#             self.disable = disable
#             self.t = 0
#             self.total = total
#             self.debug_string = ""
# 
#         def __enter__(self):
#             return self
# 
#         def __exit__(self, *args, **kwargs):
#             pass
# 
#         def set_postfix(self, **kwargs):
#             self.debug_string = ""
#             for arg in kwargs:
#                 self.debug_string += "{}={} ".format(arg, kwargs[arg])
# 
#         def update(self):
#             if not self.disable:
#                 self.t += 1
#                 print_str = "{}".format(self.t)
# 
#                 if self.total > 0:
#                     print_str += "/{}".format(self.total)
# 
#                 print_str += ": {}".format(self.debug_string)
# 
#                 if len(print_str) < 80:
#                     print_str = print_str + " " * (80 - len(print_str))
# 
#                 print(print_str, end='\r', flush=True)
# 
#             if self.t == self.total:
#                 print("")
# 
#     try:
#         from tqdm import tqdm
#         progress_bar = tqdm(total=total, disable=disable)
#     except ImportError:
#         progress_bar = ProgBar(total=total, disable=disable)
# 
#     return progress_bar
# 
# def evaluate_L_diagonal(eval_L, X):
#     """Helper function to evaluate a likelihood function on a set of points (i.e. compute the diagonal of the L matrix)"""
#     diag_eval = getattr(eval_L, "diag", None)
#     if callable(diag_eval):
#         return diag_eval(X)
#     else:
#         # inspired by sklearn.gaussian_process.kernels.PairwiseKernel
#         return np.apply_along_axis(eval_L, 1, X).ravel()


import numpy as np
from collections import namedtuple
from .utils import check_random_state, stable_invert_root, get_progress_bar, evaluate_L_diagonal
        np.random.RandomState or int or None, default None

    :param nb_iter_bless: number of iterations, defaults to log(n) if None
    :type nb_iter_bless:
        int or None, default None

    :param bool verbose: Controls verbosity of debug output, including progress bars.
        The progress bar reports:
        - lam: lambda value of the current iteration
        - m: current size of the dictionary (number of centers contained)
        - m_expected: expected size of the dictionary before sampling
        - probs_dist: (mean, max, min) of the approximate RLSs at the current iteration

    :return: An (eps, lambda)-accurate dictionary centers_dict (with high probability).
        If centers_dict contains m entries then the output fields are as follow

        centers_dict.idx`: the indices of the m selected samples in the input dataset `X`
        centers_dict.X': the (m x d) numpy.ndarray containing the selected samples
        centers_dict.probs: the probabilities (i.e. approximate RLSs) used to sample the dictionary
        lam: the final lambda accuracy
        rls_oversample: the rls_oversample used to sample the dictionary, as a proxy for the `eps`-accuracy
    :rtype:
        CentersDictionary
    """

    n, d = X_data.shape

    if nb_iter_bless is None:
        nb_iter_bless = np.ceil(np.log(n)).astype('int')


# Path: dppy/utils.py
# def check_random_state(seed: object) -> np.random.RandomState:
#     """Turn seed into a np.random.RandomState instance
#     Parameters
#     ----------
#     seed : None | int | instance of RandomState
#         If seed is None, return the RandomState singleton used by np.random.
#         If seed is an int, return a new RandomState instance seeded with seed.
#         If seed is already a RandomState instance, return it.
#         Otherwise raise ValueError.
# 
#     Returns
#     -------
#     np.random.RandomState
# 
#     .. seealso::
# 
#         `Scikit learn source code <https://github.com/scikit-learn/scikit-learn/blob/7813f7efb/sklearn/utils/validation.py#L763>`_
#     """
#     if seed is None or seed is np.random:
#         return np.random.mtrand._rand
#     if isinstance(seed, (int, np.integer)):
#         return np.random.RandomState(seed)
#     if isinstance(seed, np.random.RandomState):
#         return seed
#     raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
#                      ' instance' % seed)
# 
# def stable_invert_root(eigenvec, eigenval):
#     """ Given eigendecomposition of a PSD matrix, compute a representation of the pseudo-inverse square root
#     of the matrix using numerically stable operations. In particular, eigenvalues which are near-zero
#     and the associated eigenvectors are dropped from the pseudo-inverse.
#     """
#     eigenvec_thin, eigenval_thin = stable_filter(eigenvec, eigenval)
# 
#     eigenval_thin_inv_root = (1 / np.sqrt(eigenval_thin)).reshape(-1, 1)
# 
#     return eigenvec_thin, eigenval_thin_inv_root
# 
# def get_progress_bar(total=-1, disable=False, **kwargs):
#     """Helper function to get a tqdm progress bar (or a simple fallback otherwise)"""
#     class ProgBar(object):
#         def __init__(self, total=-1, disable=False, **kwargs):
#             self.disable = disable
#             self.t = 0
#             self.total = total
#             self.debug_string = ""
# 
#         def __enter__(self):
#             return self
# 
#         def __exit__(self, *args, **kwargs):
#             pass
# 
#         def set_postfix(self, **kwargs):
#             self.debug_string = ""
#             for arg in kwargs:
#                 self.debug_string += "{}={} ".format(arg, kwargs[arg])
# 
#         def update(self):
#             if not self.disable:
#                 self.t += 1
#                 print_str = "{}".format(self.t)
# 
#                 if self.total > 0:
#                     print_str += "/{}".format(self.total)
# 
#                 print_str += ": {}".format(self.debug_string)
# 
#                 if len(print_str) < 80:
#                     print_str = print_str + " " * (80 - len(print_str))
# 
#                 print(print_str, end='\r', flush=True)
# 
#             if self.t == self.total:
#                 print("")
# 
#     try:
#         from tqdm import tqdm
#         progress_bar = tqdm(total=total, disable=disable)
#     except ImportError:
#         progress_bar = ProgBar(total=total, disable=disable)
# 
#     return progress_bar
# 
# def evaluate_L_diagonal(eval_L, X):
#     """Helper function to evaluate a likelihood function on a set of points (i.e. compute the diagonal of the L matrix)"""
#     diag_eval = getattr(eval_L, "diag", None)
#     if callable(diag_eval):
#         return diag_eval(X)
#     else:
#         # inspired by sklearn.gaussian_process.kernels.PairwiseKernel
#         return np.apply_along_axis(eval_L, 1, X).ravel()


# Path: dppy/bless.py
import numpy as np
from collections import namedtuple
from .utils import check_random_state, stable_invert_root, get_progress_bar, evaluate_L_diagonal

        np.random.RandomState or int or None, default None

    :param nb_iter_bless: number of iterations, defaults to log(n) if None
    :type nb_iter_bless:
        int or None, default None

    :param bool verbose: Controls verbosity of debug output, including progress bars.
        The progress bar reports:
        - lam: lambda value of the current iteration
        - m: current size of the dictionary (number of centers contained)
        - m_expected: expected size of the dictionary before sampling
        - probs_dist: (mean, max, min) of the approximate RLSs at the current iteration

    :return: An (eps, lambda)-accurate dictionary centers_dict (with high probability).
        If centers_dict contains m entries then the output fields are as follow

        centers_dict.idx`: the indices of the m selected samples in the input dataset `X`
        centers_dict.X': the (m x d) numpy.ndarray containing the selected samples
        centers_dict.probs: the probabilities (i.e. approximate RLSs) used to sample the dictionary
        lam: the final lambda accuracy
        rls_oversample: the rls_oversample used to sample the dictionary, as a proxy for the `eps`-accuracy
    :rtype:
        CentersDictionary
    """

    n, d = X_data.shape

    if nb_iter_bless is None:
        nb_iter_bless = np.ceil(np.log(n)).astype('int')


    diag_norm = np.asarray(evaluate_L_diagonal(eval_L, X_data))