# mypy: allow-untyped-defs
from itertools import chain
from operator import getitem
from typing import Callable, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn
from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
from torch.fx import symbolic_trace
from torch.nn.utils import parametrize

from .match_utils import apply_match, MatchAllNode
from .parametrization import BiasHook, FakeStructuredSparsity, module_contains_param
from .prune_functions import (
    prune_conv2d,
    prune_conv2d_activation_conv2d,
    prune_conv2d_activation_pool_conv2d,
    prune_conv2d_conv2d,
    prune_conv2d_pool_activation_conv2d,
    prune_conv2d_pool_flatten_linear,
    prune_linear,
    prune_linear_activation_linear,
    prune_linear_linear,
    prune_lstm_output_layernorm_linear,
    prune_lstm_output_linear,
)


def _get_supported_structured_pruning_modules():
    SUPPORTED_STRUCTURED_PRUNING_MODULES = {  # added to config if None given
        nn.Linear,
        nn.Conv2d,
        nn.LSTM,
    }
    return SUPPORTED_STRUCTURED_PRUNING_MODULES


def _get_supported_activation_functions():
    SUPPORTED_ACTIVATION_FUNCTIONS = {
        F.relu,
        F.rrelu,
        F.hardtanh,
        F.relu6,
        F.sigmoid,
        F.hardsigmoid,
        F.tanh,
        F.silu,
        F.mish,
        F.hardswish,
        F.elu,
        F.celu,
        F.selu,
        F.hardshrink,
        F.leaky_relu,
        F.logsigmoid,
        F.softplus,
        F.prelu,
        F.softsign,
        F.tanhshrink,
        F.gelu,
    }
    return SUPPORTED_ACTIVATION_FUNCTIONS


def _get_supported_activation_modules():
    SUPPORTED_ACTIVATION_MODULES = {
        nn.ReLU,
        nn.RReLU,
        nn.Hardtanh,
        nn.ReLU6,
        nn.Sigmoid,
        nn.Hardsigmoid,
        nn.Tanh,
        nn.SiLU,
        nn.Mish,
        nn.Hardswish,
        nn.ELU,
        nn.CELU,
        nn.SELU,
        nn.Hardshrink,
        nn.LeakyReLU,
        nn.LogSigmoid,
        nn.Softplus,
        nn.PReLU,
        nn.Softsign,
        nn.Tanhshrink,
        nn.GELU,
    }
    return SUPPORTED_ACTIVATION_MODULES


def _get_default_structured_pruning_patterns() -> (
    dict[
        tuple[Union[type[nn.Module], Callable, MatchAllNode, str], ...],
        Callable[..., None],
    ]
):
    """
    Returns the patterns for conv2d / linear conversion for each element in the activation functions/modules defined above.
    """
    patterns: dict[
        tuple[Union[type[nn.Module], Callable, MatchAllNode, str], ...],
        Callable[..., None],
    ] = {
        # linear -> linear
        (nn.Linear, "output"): prune_linear,
        (nn.Linear, nn.Linear): prune_linear_linear,
        # conv2d -> conv2d
        (nn.Conv2d, "output"): prune_conv2d,
        (nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
        # TODO LSTM Structured pruning does not support returned state currently.
        # Should find a way to explicitly match getitem(0) instead of getitem.
        # This will also require changing the pruning function.
        # lstm -> getitem(0) -> linear
        (nn.LSTM, getitem, nn.Linear): prune_lstm_output_linear,
        # lstm -> getitem(0) -> layernorm -> linear
        (nn.LSTM, getitem, nn.LayerNorm, nn.Linear): prune_lstm_output_layernorm_linear,
    }

    for activation in chain(
        _get_supported_activation_functions(), _get_supported_activation_modules()
    ):
        patterns.update(
            {
                # linear -> activation -> linear
                (nn.Linear, activation, nn.Linear): prune_linear_activation_linear,
                # conv2d -> activation -> conv2d
                (nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
                # conv2d -> activation -> pool -> conv2d
                (
                    nn.Conv2d,
                    activation,
                    nn.AvgPool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    F.avg_pool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    nn.MaxPool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    F.max_pool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                # conv2d -> pool -> activation -> conv2d
                (
                    nn.Conv2d,
                    nn.AvgPool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    F.avg_pool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    nn.MaxPool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    F.max_pool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                # conv2d -> adaptive pool -> flatten -> linear
                (
                    nn.Conv2d,
                    nn.AdaptiveAvgPool2d,
                    nn.Flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveAvgPool2d,
                    torch.flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveMaxPool2d,
                    nn.Flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveMaxPool2d,
                    torch.flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
            }
        )
    return patterns


class BaseStructuredSparsifier(BaseSparsifier):
    r"""Base class for structured pruning.

    Abstract methods that need to be implemented:
        - update_mask: Function to compute a new mask for all keys in the
            `groups` attribute.

    Args:
        - defaults [dict]: default configurations will be attached to the
            configuration. Only the keys that don't exist in the `config` will
            be updated.
    """

    def __init__(self, defaults, patterns=None):
        super().__init__(defaults)
        if patterns is None:
            patterns = _get_default_structured_pruning_patterns()
        self.patterns = patterns

    def make_config_from_model(
        self,
        model: nn.Module,
        SUPPORTED_MODULES: Optional[set[type]] = None,
    ) -> None:
        if SUPPORTED_MODULES is None:
            SUPPORTED_MODULES = _get_supported_structured_pruning_modules()
        super().make_config_from_model(model, SUPPORTED_MODULES=SUPPORTED_MODULES)

    def _prepare(self, *args, **kwargs) -> None:
        r"""This function will attach the FakeStructuredSparsity parameterizations
        and BiasHooks at the appropriate points in the model.
        """
        for config in self.groups:
            module = config["module"]
            tensor_name = config["tensor_name"]
            parametrization = config.get("parametrization", FakeStructuredSparsity)
            tensor = getattr(module, tensor_name)

            mask = config.get(
                "mask",
                torch.ones(tensor.shape[0], dtype=torch.bool, device=tensor.device),
            )
            self.state[config["tensor_fqn"]]["mask"] = mask
            parametrize.register_parametrization(
                module, tensor_name, parametrization(mask)
            )

            # if linear / conv, we add in bias hooks
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                prune_bias = config.get("prune_bias", True)
                if module.bias is not None:
                    module.register_parameter(
                        "_bias", nn.Parameter(module.bias.detach())
                    )
                    module.bias = None
                    module.prune_bias = prune_bias

                module.register_forward_hook(
                    BiasHook(module.parametrizations.weight[0], prune_bias)  # type: ignore[union-attr, index]
                )

    def prune(self) -> None:
        r"""
        This function will FX symbolically trace the model and then find instances of the patterns
        defined in self.patterns (by default SUPPORTED_STRUCTURED_PRUNING_PATTERNS ).

        For each pattern, it will apply to corresponding conversion function, which will modify the output
        and input size expected by the modules within the pattern
        """

        self.traced = symbolic_trace(self.model)
        modules = dict(self.traced.named_modules())

        # Right now we check for matches simply by iterating across all the patterns
        # if this is slow we can store patterns in a trie-structure and modify this code for faster lookup
        for node in self.traced.graph.nodes:
            for pattern, convert_fn in self.patterns.items():
                matched = apply_match(modules, pattern, node, [])
                if matched is None:
                    continue

                first_module = modules.get(node.target)
                # check if first module exists and has appropriate parameterization, otherwise skip
                if (
                    first_module is not None
                    and parametrize.is_parametrized(first_module)
                    and module_contains_param(first_module, FakeStructuredSparsity)
                ):
                    convert_block = []
                    for node in matched:
                        if node.op == "call_module":
                            convert_block.append(modules.get(node.target))
                        elif node.op == "call_function":
                            convert_block.append(node.target)
                    convert_fn(*convert_block)

        for module in self.traced.modules():
            if module_contains_param(module, FakeStructuredSparsity):
                raise Exception(  # noqa: TRY002
                    f"Error: {module} still contains FakeStructuredSparsity parametrizations!"
                )

        self.traced.graph.lint()
        self.traced.recompile()
        return self.traced  # type: ignore[return-value]
