# 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)


import numpy as np
import scipy.linalg as la
import scipy.sparse as sp
from dppy.utils import check_random_state
# coding: utf8
""" Implementation of full and banded matrix models for :math:`\beta`-Ensemble:

- Hermite Ensemble (full + tridiagonal)
- Laguerre Ensemble (full + tridiagonal)
- Jacobi Ensemble (full + tridiagonal)
- Circular Ensemble (full + quindiagonal)
- Ginibre Ensemble (full)

.. seealso:

    `Documentation on ReadTheDocs <https://dppy.readthedocs.io/en/latest/continuous_dpps/beta_ensembles.sampling.html>`_
"""




###########
# Hermite #
###########
def hermite_sampler_full(N, beta=2,
                         random_state=None):


# 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)


# Path: dppy/random_matrices.py
import numpy as np
import scipy.linalg as la
import scipy.sparse as sp
from dppy.utils import check_random_state

# coding: utf8
""" Implementation of full and banded matrix models for :math:`\beta`-Ensemble:

- Hermite Ensemble (full + tridiagonal)
- Laguerre Ensemble (full + tridiagonal)
- Jacobi Ensemble (full + tridiagonal)
- Circular Ensemble (full + quindiagonal)
- Ginibre Ensemble (full)

.. seealso:

    `Documentation on ReadTheDocs <https://dppy.readthedocs.io/en/latest/continuous_dpps/beta_ensembles.sampling.html>`_
"""




###########
# Hermite #
###########
def hermite_sampler_full(N, beta=2,
                         random_state=None):


    rng = check_random_state(random_state)