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

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 
(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."""

    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


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

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

_identity