# 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

    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)
        key = jrandom.fold_in(self.key, t0_)
        key = jrandom.fold_in(key, t1_)
        return jrandom.normal(key, self.shape) * jnp.sqrt(t1 - t0)


UnsafeBrownianPath.__init__.__doc__ = """
**Arguments:**

- `shape`: What shape each individual Brownian sample should be.

# 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


    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)
        key = jrandom.fold_in(self.key, t0_)
        key = jrandom.fold_in(key, t1_)
        return jrandom.normal(key, self.shape) * jnp.sqrt(t1 - t0)


UnsafeBrownianPath.__init__.__doc__ = """
**Arguments:**

- `shape`: What shape each individual Brownian sample should be.

- `key`: A random key.