from collections.abc import Sequence
from functools import wraps
from itertools import zip_longest
from types import ModuleType
from typing import TYPE_CHECKING, Literal

import numpy as np

from pytensor.compile.sharedvalue import shared
from pytensor.graph.basic import Constant, Variable
from pytensor.scalar import ScalarVariable
from pytensor.tensor import NoneConst, get_vector_length
from pytensor.tensor.basic import as_tensor_variable, cast
from pytensor.tensor.extra_ops import broadcast_arrays, broadcast_to
from pytensor.tensor.math import maximum
from pytensor.tensor.shape import shape_padleft, specify_shape
from pytensor.tensor.type import int_dtypes
from pytensor.tensor.variable import TensorVariable


if TYPE_CHECKING:
    from pytensor.tensor.random.op import RandomVariable


def params_broadcast_shapes(param_shapes, ndims_params, use_pytensor=True):
    """Broadcast parameters that have different dimensions.

    Parameters
    ==========
    param_shapes : list of ndarray or Variable
        The shapes of each parameters to broadcast.
    ndims_params : list of int
        The expected number of dimensions for each element in `params`.
    use_pytensor : bool
        If ``True``, use PyTensor `Op`; otherwise, use NumPy.

    Returns
    =======
    bcast_shapes : list of ndarray
        The broadcasted values of `params`.
    """
    max_fn = maximum if use_pytensor else max

    rev_extra_dims = []
    for ndim_param, param_shape in zip(ndims_params, param_shapes):
        # We need this in order to use `len`
        param_shape = tuple(param_shape)
        extras = tuple(param_shape[: (len(param_shape) - ndim_param)])

        def max_bcast(x, y):
            if getattr(x, "value", x) == 1:
                return y
            if getattr(y, "value", y) == 1:
                return x
            return max_fn(x, y)

        rev_extra_dims = [
            max_bcast(a, b)
            for a, b in zip_longest(reversed(extras), rev_extra_dims, fillvalue=1)
        ]

    extra_dims = tuple(reversed(rev_extra_dims))

    bcast_shapes = [
        (extra_dims + tuple(param_shape)[-ndim_param:])
        if ndim_param > 0
        else extra_dims
        for ndim_param, param_shape in zip(ndims_params, param_shapes)
    ]

    return bcast_shapes


def broadcast_params(params, ndims_params):
    """Broadcast parameters that have different dimensions.

    >>> ndims_params = [1, 2]
    >>> mean = np.array([1, 2, 3])
    >>> cov = np.stack([np.eye(3), np.eye(3)])
    >>> params = [mean, cov]
    >>> mean_bcast, cov_bcast = broadcast_params(params, ndims_params)
    >>> mean_bcast
    array([[1, 2, 3],
           [1, 2, 3]])
    >>> cov_bcast
    array([[[1., 0., 0.],
            [0., 1., 0.],
            [0., 0., 1.]],
    <BLANKLINE>
           [[1., 0., 0.],
            [0., 1., 0.],
            [0., 0., 1.]]])

    Parameters
    ==========
    params : list of ndarray
        The parameters to broadcast.
    ndims_params : list of int
        The expected number of dimensions for each element in `params`.

    Returns
    =======
    bcast_params : list of ndarray
        The broadcasted values of `params`.
    """
    use_pytensor = False
    param_shapes = []
    for p in params:
        param_shape = tuple(
            1 if bcast else s
            for s, bcast in zip(p.shape, getattr(p, "broadcastable", (False,) * p.ndim))
        )
        use_pytensor |= isinstance(p, Variable)
        param_shapes.append(param_shape)

    shapes = params_broadcast_shapes(
        param_shapes, ndims_params, use_pytensor=use_pytensor
    )
    broadcast_to_fn = broadcast_to if use_pytensor else np.broadcast_to

    bcast_params = [
        broadcast_to_fn(param, shape) for shape, param in zip(shapes, params)
    ]

    return bcast_params


def explicit_expand_dims(
    params: Sequence[TensorVariable],
    ndim_params: Sequence[int],
    size_length: int | None = None,
) -> list[TensorVariable]:
    """Introduce explicit expand_dims in RV parameters that are implicitly broadcasted together and/or by size."""

    batch_dims = [
        param.type.ndim - ndim_param for param, ndim_param in zip(params, ndim_params)
    ]

    if size_length is not None:
        max_batch_dims = size_length
    else:
        max_batch_dims = max(batch_dims, default=0)

    new_params = []
    for new_param, batch_dim in zip(params, batch_dims):
        missing_dims = max_batch_dims - batch_dim
        if missing_dims:
            new_param = shape_padleft(new_param, missing_dims)
        new_params.append(new_param)

    return new_params


def compute_batch_shape(
    params: Sequence[TensorVariable], ndims_params: Sequence[int]
) -> TensorVariable:
    params = explicit_expand_dims(params, ndims_params)
    batch_params = [
        param[(..., *(0,) * core_ndim)]
        for param, core_ndim in zip(params, ndims_params)
    ]
    return broadcast_arrays(*batch_params)[0].shape


def normalize_size_param(
    shape: int | np.ndarray | Variable | Sequence | None,
) -> Variable:
    """Create an PyTensor value for a ``RandomVariable`` ``size`` parameter."""
    if shape is None or NoneConst.equals(shape):
        return NoneConst
    elif isinstance(shape, int):
        shape = as_tensor_variable([shape], ndim=1)
    elif not isinstance(shape, np.ndarray | Variable | Sequence):
        raise TypeError(
            "Parameter size must be None, an integer, or a sequence with integers."
        )
    else:
        shape = cast(as_tensor_variable(shape, ndim=1, dtype="int64"), "int64")

        if not isinstance(shape, Constant):
            # This should help ensure that the length of non-constant `size`s
            # will be available after certain types of cloning (e.g. the kind
            # `Scan` performs)
            shape = specify_shape(shape, (get_vector_length(shape),))

    assert not any(s is None for s in shape.type.shape)
    assert shape.dtype in int_dtypes

    return shape


class RandomStream:
    """Module component with similar interface to `numpy.random.Generator`.

    Attributes
    ----------
    seed: None or int
        A default seed to initialize the `Generator` instances after build.
    state_updates: list
        A list of pairs of the form ``(input_r, output_r)``.  This will be
        over-ridden by the module instance to contain stream generators.
    default_instance_seed: int
        Instance variable should take None or integer value. Used to seed the
        random number generator that provides seeds for member streams.
    gen_seedgen: numpy.random.Generator
        `Generator` instance that `RandomStream.gen` uses to seed new
        streams.
    rng_ctor: type
        Constructor used to create the underlying RNG objects.  The default
        is `np.random.default_rng`.

    """

    def __init__(
        self,
        seed: int | None = None,
        namespace: ModuleType | None = None,
        rng_ctor: Literal[np.random.Generator] = np.random.default_rng,
    ):
        if namespace is None:
            from pytensor.tensor.random import basic  # pylint: disable=import-self

            self.namespaces = [(basic, set(basic.__all__))]
        else:
            self.namespaces = [(namespace, set(namespace.__all__))]

        self.default_instance_seed = seed
        self.state_updates = []
        self.gen_seedgen = np.random.SeedSequence(seed)
        self.rng_ctor = rng_ctor

    def __getattr__(self, obj):
        ns_obj = next(
            (
                getattr(ns, obj)
                for ns, all_ in self.namespaces
                if obj in all_ and hasattr(ns, obj)
            ),
            None,
        )

        if ns_obj is None:
            raise AttributeError(f"No attribute {obj}.")

        @wraps(ns_obj)
        def meta_obj(*args, **kwargs):
            return self.gen(ns_obj, *args, **kwargs)

        setattr(self, obj, meta_obj)
        return getattr(self, obj)

    def updates(self):
        return list(self.state_updates)

    def seed(self, seed=None):
        """
        Re-initialize each random stream.

        Parameters
        ----------
        seed : None or integer
            Each random stream will be assigned a unique state that depends
            deterministically on this value.

        Returns
        -------
        None

        """
        if seed is None:
            seed = self.default_instance_seed

        self.gen_seedgen = np.random.SeedSequence(seed)
        old_r_seeds = self.gen_seedgen.spawn(len(self.state_updates))

        for (old_r, new_r), old_r_seed in zip(self.state_updates, old_r_seeds):
            old_r.set_value(self.rng_ctor(old_r_seed), borrow=True)

    def gen(self, op: "RandomVariable", *args, **kwargs) -> TensorVariable:
        r"""Generate a draw from `op` seeded from this `RandomStream`.

        Parameters
        ----------
        op
            A `RandomVariable` instance
        args
            Positional arguments passed to `op`.
        kwargs
            Keyword arguments passed to `op`.

        Returns
        -------
        The symbolic random draw performed by `op`.  This function stores
        the updated `RandomType`\s for use at compile time.

        """
        if "rng" in kwargs:
            raise ValueError(
                "The `rng` option cannot be used with a variate in a `RandomStream`"
            )

        # Generate a new random state
        (seed,) = self.gen_seedgen.spawn(1)
        rng = shared(self.rng_ctor(seed), borrow=True)

        # Generate the sample
        out = op(*args, **kwargs, rng=rng)

        # This is the value that should be used to replace the old state
        # (i.e. `rng`) after `out` is sampled/evaluated.
        # The updates mechanism in `pytensor.function` is supposed to perform
        # this replace action.
        new_rng = out.owner.outputs[0]

        self.state_updates.append((rng, new_rng))

        rng.default_update = new_rng

        return out


def supp_shape_from_ref_param_shape(
    *,
    ndim_supp: int,
    dist_params: Sequence[Variable],
    param_shapes: Sequence[tuple[ScalarVariable, ...]] | None = None,
    ref_param_idx: int,
) -> TensorVariable | tuple[ScalarVariable, ...]:
    """Extract the support shape of a multivariate `RandomVariable` from the shape of a reference parameter.

    Several multivariate `RandomVariable`s have a support shape determined by the last dimensions of a parameter.
    For example `multivariate_normal(zeros(5, 3), eye(3)) has a support shape of (3,) that is determined by the
    last dimension of the mean or the covariance.

    Parameters
    ----------
    ndim_supp: int
        Support dimensionality of the `RandomVariable`.
        (e.g. a multivariate normal draw is 1D, so `ndim_supp = 1`).
    dist_params: list of `pytensor.graph.basic.Variable`
        The distribution parameters.
    param_shapes: list of tuple of `ScalarVariable` (optional)
        Symbolic shapes for each distribution parameter.  These will
        be used in place of distribution parameter-generated shapes.
    ref_param_idx: int
        The index of the distribution parameter to use as a reference

    Returns
    -------
    out: tuple
        Representing the support shape for a `RandomVariable` with the given `dist_params`.

    Notes
    _____
    This helper is no longer necessary when using signatures in `RandomVariable` subclasses.


    """
    if ndim_supp <= 0:
        raise ValueError("ndim_supp must be greater than 0")
    if param_shapes is not None:
        ref_param = param_shapes[ref_param_idx]
        return tuple(ref_param[i] for i in range(-ndim_supp, 0))
    else:
        ref_param = dist_params[ref_param_idx]
        if ref_param.ndim < ndim_supp:
            raise ValueError(
                "Reference parameter does not match the expected dimensions; "
                f"{ref_param} has less than {ndim_supp} dim(s)."
            )
        return ref_param.shape[-ndim_supp:]
