# Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Dict, Optional

import mmengine
import torch.nn as nn
from mmengine.hooks import Hook
from mmengine.logging import MMLogger
from mmengine.registry import HOOKS

from mmcv.cnn.rfsearch.utils import get_single_padding, write_to_json
from .operator import BaseConvRFSearchOp

logger = MMLogger.get_current_instance()


@HOOKS.register_module()
class RFSearchHook(Hook):
    """Rcecptive field search via dilation rates.

    Please refer to `RF-Next: Efficient Receptive Field
    Search for Convolutional Neural Networks
    <https://arxiv.org/abs/2206.06637>`_ for more details.


    Args:
        mode (str, optional): It can be set to the following types:
            'search', 'fixed_single_branch', or 'fixed_multi_branch'.
            Defaults to 'search'.
        config (Dict, optional): config dict of search.
            By default this config contains "search",
            and config["search"] must include:

            - "step": recording the current searching step.
            - "max_step": The maximum number of searching steps
              to update the structures.
            - "search_interval": The interval (epoch/iteration)
              between two updates.
            - "exp_rate": The controller of the sparsity of search space.
            - "init_alphas": The value for initializing weights of each branch.
            - "mmin": The minimum dilation rate.
            - "mmax": The maximum dilation rate.
            - "num_branches": The controller of the size of
              search space (the number of branches).
            - "skip_layer": The modules in skip_layer will be ignored
              during the receptive field search.
        rfstructure_file (str, optional): Path to load searched receptive
            fields of the model. Defaults to None.
        by_epoch (bool, optional): Determine to perform step by epoch or
            by iteration. If set to True, it will step by epoch. Otherwise, by
            iteration. Defaults to True.
        verbose (bool): Determines whether to print rf-next related logging
            messages. Defaults to True.
    """

    def __init__(self,
                 mode: str = 'search',
                 config: Dict = {},
                 rfstructure_file: Optional[str] = None,
                 by_epoch: bool = True,
                 verbose: bool = True):
        assert mode in ['search', 'fixed_single_branch', 'fixed_multi_branch']
        assert config is not None
        self.config = config
        self.config['structure'] = {}
        self.verbose = verbose
        if rfstructure_file is not None:
            rfstructure = mmengine.load(rfstructure_file)['structure']
            self.config['structure'] = rfstructure
        self.mode = mode
        self.num_branches = self.config['search']['num_branches']
        self.by_epoch = by_epoch

    def init_model(self, model: nn.Module):
        """init model with search ability.

        Args:
            model (nn.Module): pytorch model

        Raises:
            NotImplementedError: only support three modes:
                search/fixed_single_branch/fixed_multi_branch
        """
        if self.verbose:
            logger.info('RFSearch init begin.')
        if self.mode == 'search':
            if self.config['structure']:
                self.set_model(model, search_op='Conv2d')
            self.wrap_model(model, search_op='Conv2d')
        elif self.mode == 'fixed_single_branch':
            self.set_model(model, search_op='Conv2d')
        elif self.mode == 'fixed_multi_branch':
            self.set_model(model, search_op='Conv2d')
            self.wrap_model(model, search_op='Conv2d')
        else:
            raise NotImplementedError
        if self.verbose:
            logger.info('RFSearch init end.')

    def after_train_epoch(self, runner):
        """Performs a dilation searching step after one training epoch."""
        if self.by_epoch and self.mode == 'search':
            self.step(runner.model, runner.work_dir)

    def after_train_iter(self, runner):
        """Performs a dilation searching step after one training iteration."""
        if not self.by_epoch and self.mode == 'search':
            self.step(runner.model, runner.work_dir)

    def step(self, model: nn.Module, work_dir: str):
        """Performs a dilation searching step.

        Args:
            model (nn.Module): pytorch model
            work_dir (str): Directory to save the searching results.
        """
        self.config['search']['step'] += 1
        if (self.config['search']['step']
            ) % self.config['search']['search_interval'] == 0 and (self.config[
                'search']['step']) < self.config['search']['max_step']:
            self.estimate_and_expand(model)
            for name, module in model.named_modules():
                if isinstance(module, BaseConvRFSearchOp):
                    self.config['structure'][name] = module.op_layer.dilation

            write_to_json(
                self.config,
                os.path.join(
                    work_dir,
                    'local_search_config_step%d.json' %
                    self.config['search']['step'],
                ),
            )

    def estimate_and_expand(self, model: nn.Module):
        """estimate and search for RFConvOp.

        Args:
            model (nn.Module): pytorch model
        """
        for module in model.modules():
            if isinstance(module, BaseConvRFSearchOp):
                module.estimate_rates()
                module.expand_rates()

    def wrap_model(self,
                   model: nn.Module,
                   search_op: str = 'Conv2d',
                   prefix: str = ''):
        """wrap model to support searchable conv op.

        Args:
            model (nn.Module): pytorch model
            search_op (str): The module that uses RF search.
                Defaults to 'Conv2d'.
            init_rates (int, optional): Set to other initial dilation rates.
                Defaults to None.
            prefix (str): Prefix for function recursion. Defaults to ''.
        """
        op = 'torch.nn.' + search_op
        for name, module in model.named_children():
            if prefix == '':
                fullname = 'module.' + name
            else:
                fullname = prefix + '.' + name
            if self.config['search']['skip_layer'] is not None:
                if any(layer in fullname
                       for layer in self.config['search']['skip_layer']):
                    continue
            if isinstance(module, eval(op)):
                if 1 < module.kernel_size[0] and \
                    0 != module.kernel_size[0] % 2 or \
                    1 < module.kernel_size[1] and \
                        0 != module.kernel_size[1] % 2:
                    moduleWrap = eval(search_op + 'RFSearchOp')(
                        module, self.config['search'], self.verbose)
                    moduleWrap = moduleWrap.to(module.weight.device)
                    if self.verbose:
                        logger.info('Wrap model %s to %s.' %
                                    (str(module), str(moduleWrap)))
                    setattr(model, name, moduleWrap)
            elif not isinstance(module, BaseConvRFSearchOp):
                self.wrap_model(module, search_op, fullname)

    def set_model(self,
                  model: nn.Module,
                  search_op: str = 'Conv2d',
                  init_rates: Optional[int] = None,
                  prefix: str = ''):
        """set model based on config.

        Args:
            model (nn.Module): pytorch model
            config (Dict): config file
            search_op (str): The module that uses RF search.
                Defaults to 'Conv2d'.
            init_rates (int, optional):  Set to other initial dilation rates.
                Defaults to None.
            prefix (str): Prefix for function recursion. Defaults to ''.
        """
        op = 'torch.nn.' + search_op
        for name, module in model.named_children():
            if prefix == '':
                fullname = 'module.' + name
            else:
                fullname = prefix + '.' + name
            if self.config['search']['skip_layer'] is not None:
                if any(layer in fullname
                       for layer in self.config['search']['skip_layer']):
                    continue
            if isinstance(module, eval(op)):
                if 1 < module.kernel_size[0] and \
                    0 != module.kernel_size[0] % 2 or \
                    1 < module.kernel_size[1] and \
                        0 != module.kernel_size[1] % 2:
                    if isinstance(self.config['structure'][fullname], int):
                        self.config['structure'][fullname] = [
                            self.config['structure'][fullname],
                            self.config['structure'][fullname]
                        ]
                    module.dilation = (
                        self.config['structure'][fullname][0],
                        self.config['structure'][fullname][1],
                    )
                    module.padding = (
                        get_single_padding(
                            module.kernel_size[0], module.stride[0],
                            self.config['structure'][fullname][0]),
                        get_single_padding(
                            module.kernel_size[1], module.stride[1],
                            self.config['structure'][fullname][1]))
                    setattr(model, name, module)
                    if self.verbose:
                        logger.info(
                            'Set module %s dilation as: [%d %d]' %
                            (fullname, module.dilation[0], module.dilation[1]))
            elif not isinstance(module, BaseConvRFSearchOp):
                self.set_model(module, search_op, init_rates, fullname)
