# 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
    lam_max = lam_init

    lam_final = 1
    lam_min = lam_final

    if nb_iter_bless is None:
        nb_iter_bless = np.ceil(np.log2(lam_init)).astype('int')
    lam_sequence = list(np.geomspace(lam_final, lam_init, nb_iter_bless))

    ucb_init = rls_oversample_param * diag_norm / (diag_norm + lam_init)

    rng = check_random_state(random_state)

    selected_init = rng.rand(n) <= ucb_init

    # force at least one sample to be selected
    selected_init[0] = 1
    ucb_init[0] = rls_oversample_param * 1.

    intermediate_dict_bless = CentersDictionary(
                idx=selected_init.nonzero()[0],
                X=X_data[selected_init, :],
                probs=np.ones(np.sum(selected_init)) * ucb_init[selected_init],
                lam=lam_init,
                rls_oversample=rls_oversample_param)

    # discard lam_init from the list, we already used it to initialize
    lam_new = lam_sequence.pop()
    deff_hat_new = size_final - 1


# 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

    lam_max = lam_init

    lam_final = 1
    lam_min = lam_final

    if nb_iter_bless is None:
        nb_iter_bless = np.ceil(np.log2(lam_init)).astype('int')
    lam_sequence = list(np.geomspace(lam_final, lam_init, nb_iter_bless))

    ucb_init = rls_oversample_param * diag_norm / (diag_norm + lam_init)

    rng = check_random_state(random_state)

    selected_init = rng.rand(n) <= ucb_init

    # force at least one sample to be selected
    selected_init[0] = 1
    ucb_init[0] = rls_oversample_param * 1.

    intermediate_dict_bless = CentersDictionary(
                idx=selected_init.nonzero()[0],
                X=X_data[selected_init, :],
                probs=np.ones(np.sum(selected_init)) * ucb_init[selected_init],
                lam=lam_init,
                rls_oversample=rls_oversample_param)

    # discard lam_init from the list, we already used it to initialize
    lam_new = lam_sequence.pop()
    deff_hat_new = size_final - 1


    with get_progress_bar(total=len(lam_sequence), disable=not verbose) as t: