import typing
from typing import Any, Callable, List, Optional, Sequence, Union

import jax
import jax.nn as jnn
import jax.random as jrandom

from ..custom_types import Array
from ..module import Module, static_field
from .linear import Linear


def _identity(x):
    return x


if getattr(typing, "GENERATING_DOCUMENTATION", False):

    def relu(_):
        pass

    jnn.relu = relu
    _identity.__qualname__ = "identity"  # Renamed for nicer documentation.


class MLP(Module):
    """Standard Multi-Layer Perceptron; also known as a feed-forward network."""

    layers: List[Linear]
    activation: Callable
    final_activation: Callable
    in_size: int = static_field()
    out_size: int = static_field()
    width_size: int = static_field()
    depth: int = static_field()

    def __init__(
        self,
        in_size: int,
        out_size: int,
        width_size: int,
        depth: int,
        activation: Callable = jnn.relu,
        final_activation: Callable = _identity,
        *,
        key: "jax.random.PRNGKey",
        **kwargs,
    ):
        """**Arguments**:

        - `in_size`: The size of the input layer.
        - `out_size`: The size of the output layer.
        - `width_size`: The size of each hidden layer.
        - `depth`: The number of hidden layers.
        - `activation`: The activation function after each hidden layer. Defaults to
            ReLU.
        - `final_activation`: The activation function after the output layer. Defaults
            to the identity.
        - `key`: A `jax.random.PRNGKey` used to provide randomness for parameter
            initialisation. (Keyword only argument.)
        """

        super().__init__(**kwargs)
        keys = jrandom.split(key, depth + 1)
        layers = []
        if depth == 0:
            layers.append(Linear(in_size, out_size, key=keys[0]))
        else:
            layers.append(Linear(in_size, width_size, key=keys[0]))
            for i in range(depth - 1):
                layers.append(Linear(width_size, width_size, key=keys[i + 1]))
            layers.append(Linear(width_size, out_size, key=keys[-1]))
        self.layers = layers
        self.in_size = in_size
        self.out_size = out_size
        self.width_size = width_size
        self.depth = depth
        self.activation = activation
        self.final_activation = final_activation

    def __call__(
        self, x: Array, *, key: Optional["jax.random.PRNGKey"] = None
    ) -> Array:
        """**Arguments:**

        - `x`: A JAX array with shape `(in_size,)`.
        - `key`: Ignored; provided for compatibility with the rest of the Equinox API.
            (Keyword only argument.)

        **Returns:**

        A JAX array with shape `(out_size,)`.
        """
        for layer in self.layers[:-1]:
            x = layer(x)
            x = self.activation(x)
        x = self.layers[-1](x)
        x = self.final_activation(x)
        return x


class Sequential(Module):
    """A sequence of [`equinox.Module`][]s applied in order.

    !!! note

        Activation functions can be added by wrapping them in [`equinox.nn.Lambda`][].
    """

    layers: Sequence[Module]

    def __call__(self, x: Any, *, key: Optional["jax.random.PRNGKey"] = None) -> Any:
        """**Arguments:**

        - `x`: Argument passed to the first member of the sequence.
        - `key`: A `jax.random.PRNGKey`, which will be split and passed to every layer
            to provide any desired randomness. (Optional. Keyword only argument.)

        **Returns:**

        The output of the last member of the sequence.
        """

        if key is None:
            keys = [None] * len(self.layers)
        else:
            keys = jrandom.split(key, len(self.layers))
        for layer, key in zip(self.layers, keys):
            x = layer(x, key=key)
        return x

    def __getitem__(self, i: Union[int, slice]) -> Module:
        if isinstance(i, int):
            return self.layers[i]
        elif isinstance(i, slice):
            return Sequential(self.layers[i])
        else:
            raise TypeError(f"Indexing with type {type(i)} is not supported")

    def __iter__(self):
        yield from self.layers

    def __len__(self):
        return len(self.layers)


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

- `layers`: A sequence of [`equinox.Module`][]s.
"""


class Lambda(Module):
    """Wraps a callable (e.g. an activation function) for use with
    [`equinox.nn.Sequential`][].

    Precisely, this just adds an extra `key` argument (that is ignored). Given some
    function `fn`, then `Lambda` is essentially a convenience for `lambda x, key: f(x)`.

    !!! Example

        ```python
           model = eqx.nn.Sequential(
               [
                   eqx.nn.Linear(...),
                   eqx.nn.Lambda(jax.nn.relu),
                   ...
               ]
           )
        ```
    """

    fn: Callable

    def __call__(
        self, x: Array, *, key: Optional["jax.random.PRNGKey"] = None
    ) -> Array:
        """**Arguments:**

        - `x`: The input JAX array.
        - `key`: Ignored.

        **Returns:**

        The output of the `fn(x)` operation.
        """
        return self.fn(x)


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

- `fn`: A callable to be wrapped in [`equinox.Module`][].
"""

@@ -66,16 +66,23 @@
 equence,
+ Tuple,
  Union%0A%0A
@@ -3157,24 +3157,113 @@
 rs: 
-Sequence%5BModule%5D
+Tuple%5BModule, ...%5D%0A%0A    def __init__(self, layers: Sequence%5BModule%5D):%0A        self.layers = tuple(layers)
 %0A%0A  

make sequential module immutable