# Path: diffrax/custom_types.py
# class Array:
#     def __class_getitem__(cls, item):
#         class X:
#             pass
# 
#         X.__module__ = "builtins"
#         X.__qualname__ = _maybe_tuple_to_str(item)
#         return _Array[X]
# 
# class Scalar:
#     pass
# 
# Path: diffrax/misc/ad.py
# def nondifferentiable_input(x: PyTree, name: str) -> None:
#     if any(is_perturbed(xi) for xi in jax.tree_leaves(x)):
#         raise ValueError(f"Cannot differentiate {name}.")
# 
# Path: diffrax/misc/misc.py
# def force_bitcast_convert_type(val, new_type):
#     val = jnp.asarray(val)
#     intermediate_type = _itemsize_kind_type[new_type.dtype.itemsize, val.dtype.kind]
#     val = val.astype(intermediate_type)
#     return lax.bitcast_convert_type(val, new_type)
# 
# Path: diffrax/brownian/base.py
# class AbstractBrownianPath(AbstractPath):
#     "Abstract base class for all Brownian paths."
# 
#     @abc.abstractmethod
#     def evaluate(self, t0: Scalar, t1: Scalar, left: bool = True) -> Array:
#         r"""Samples a Brownian increment $w(t_1) - w(t_0)$.
# 
#         Each increment has distribution $\mathcal{N}(0, t_1 - t_0)$.
# 
#         **Arguments:**
# 
#         - `t0`: Start of interval.
#         - `t1`: End of interval.
#         - `left`: Ignored. (This determines whether to treat the path as
#             left-continuous or right-continuous at any jump points, but Brownian
#             motion has no jump points.)
# 
#         **Returns:**
# 
#         A JAX array corresponding to the increment $w(t_1) - w(t_0)$.
# 
#         Some subclasses may allow `t1=None`, in which case just the value $w(t_0)$ is
#         returned.
#         """


from typing import Tuple
from ..custom_types import Array, Scalar
from ..misc import force_bitcast_convert_type, nondifferentiable_input
from .base import AbstractBrownianPath
import equinox as eqx
import jax.numpy as jnp
import jax.random as jrandom

    3. You do not need deterministic solutions with respect to `key`. (This
       implementation will produce different results based on fluctuations in
       floating-point arithmetic.)

    Internally this operates by just sampling a fresh normal random variable over every
    interval, ignoring the correlation between samples exhibited in true Brownian
    motion. Hence the restrictions above. (They describe the general case for which the
    correlation structure isn't needed.)
    """

    shape: Tuple[int] = eqx.static_field()
    # Handled as a string because PRNGKey is actually a function, not a class, which
    # makes it appearly badly in autogenerated documentation.
    key: "jax.random.PRNGKey"  # noqa: F821

    @property
    def t0(self):
        return None

    @property
    def t1(self):
        return None

    @eqx.filter_jit
    def evaluate(self, t0: Scalar, t1: Scalar, left: bool = True) -> Array:
        del left
        nondifferentiable_input(t0, "t0")
        nondifferentiable_input(t1, "t1")
        t0_ = force_bitcast_convert_type(t0, jnp.int32)

# Path: diffrax/custom_types.py
# class Array:
#     def __class_getitem__(cls, item):
#         class X:
#             pass
# 
#         X.__module__ = "builtins"
#         X.__qualname__ = _maybe_tuple_to_str(item)
#         return _Array[X]
# 
# class Scalar:
#     pass
# 
# Path: diffrax/misc/ad.py
# def nondifferentiable_input(x: PyTree, name: str) -> None:
#     if any(is_perturbed(xi) for xi in jax.tree_leaves(x)):
#         raise ValueError(f"Cannot differentiate {name}.")
# 
# Path: diffrax/misc/misc.py
# def force_bitcast_convert_type(val, new_type):
#     val = jnp.asarray(val)
#     intermediate_type = _itemsize_kind_type[new_type.dtype.itemsize, val.dtype.kind]
#     val = val.astype(intermediate_type)
#     return lax.bitcast_convert_type(val, new_type)
# 
# Path: diffrax/brownian/base.py
# class AbstractBrownianPath(AbstractPath):
#     "Abstract base class for all Brownian paths."
# 
#     @abc.abstractmethod
#     def evaluate(self, t0: Scalar, t1: Scalar, left: bool = True) -> Array:
#         r"""Samples a Brownian increment $w(t_1) - w(t_0)$.
# 
#         Each increment has distribution $\mathcal{N}(0, t_1 - t_0)$.
# 
#         **Arguments:**
# 
#         - `t0`: Start of interval.
#         - `t1`: End of interval.
#         - `left`: Ignored. (This determines whether to treat the path as
#             left-continuous or right-continuous at any jump points, but Brownian
#             motion has no jump points.)
# 
#         **Returns:**
# 
#         A JAX array corresponding to the increment $w(t_1) - w(t_0)$.
# 
#         Some subclasses may allow `t1=None`, in which case just the value $w(t_0)$ is
#         returned.
#         """


# Path: diffrax/brownian/path.py
from typing import Tuple
from ..custom_types import Array, Scalar
from ..misc import force_bitcast_convert_type, nondifferentiable_input
from .base import AbstractBrownianPath
import equinox as eqx
import jax.numpy as jnp
import jax.random as jrandom


    3. You do not need deterministic solutions with respect to `key`. (This
       implementation will produce different results based on fluctuations in
       floating-point arithmetic.)

    Internally this operates by just sampling a fresh normal random variable over every
    interval, ignoring the correlation between samples exhibited in true Brownian
    motion. Hence the restrictions above. (They describe the general case for which the
    correlation structure isn't needed.)
    """

    shape: Tuple[int] = eqx.static_field()
    # Handled as a string because PRNGKey is actually a function, not a class, which
    # makes it appearly badly in autogenerated documentation.
    key: "jax.random.PRNGKey"  # noqa: F821

    @property
    def t0(self):
        return None

    @property
    def t1(self):
        return None

    @eqx.filter_jit
    def evaluate(self, t0: Scalar, t1: Scalar, left: bool = True) -> Array:
        del left
        nondifferentiable_input(t0, "t0")
        nondifferentiable_input(t1, "t1")
        t0_ = force_bitcast_convert_type(t0, jnp.int32)

        t1_ = force_bitcast_convert_type(t1, jnp.int32)