#!/usr/bin/env python2

import cPickle
import importlib
import logging
import operator
import os
import sys
from functools import reduce

from theano import tensor

from blocks import roles
from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule, Momentum
from blocks.extensions import Printing, FinishAfter, SimpleExtension
from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring
import blocks
blocks.config.default_seed = 123

try:
    from blocks.extras.extensions.plotting import Plot
    use_plot = True
except ImportError:
    use_plot = False
    
from blocks.filter import VariableFilter
from blocks.graph import ComputationGraph, apply_dropout, apply_noise
from blocks.main_loop import MainLoop
from blocks.model import Model


logger = logging.getLogger(__name__)


class ElementwiseRemoveNotFinite(StepRule):
    """A step rule that replaces non-finite coefficients by zeros.

    Replaces non-finite elements (such as ``inf`` or ``NaN``) in a step
    (the parameter update of a single shared variable)
    with a scaled version of the parameters being updated instead.

    Parameters
    ----------
    scaler : float, optional
        The scaling applied to the parameter in case the step contains
        non-finite elements. Defaults to 0.1.

    Notes
    -----
    This trick was originally used in the GroundHog_ framework.

    .. _GroundHog: https://github.com/lisa-groundhog/GroundHog

    """
    def __init__(self, scaler=0.1):
        self.scaler = scaler

    def compute_step(self, param, previous_step):
        not_finite = tensor.isnan(previous_step) + tensor.isinf(previous_step)
        step = tensor.switch(not_finite, self.scaler * param, previous_step)

        return step, []

class SaveLoadParams(SimpleExtension):
    def __init__(self, path, model, **kwargs):
        super(SaveLoadParams, self).__init__(**kwargs)

        self.path = path
        self.model = model
    
    def do_save(self):
        with open(self.path, 'w') as f:
            logger.info('Saving parameters to %s...'%self.path)
            cPickle.dump(self.model.get_param_values(), f, protocol=cPickle.HIGHEST_PROTOCOL)
            logger.info('Done saving.')
    
    def do_load(self):
        try:
            with open(self.path, 'r') as f:
                logger.info('Loading parameters from %s...'%self.path)
                self.model.set_param_values(cPickle.load(f))
        except IOError:
            pass

    def do(self, which_callback, *args):
        if which_callback == 'before_training':
            self.do_load()
        else:
            self.do_save()

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print >> sys.stderr, 'Usage: %s config' % sys.argv[0]
        sys.exit(1)
    model_name = sys.argv[1]
    config = importlib.import_module('.%s' % model_name, 'config')

    logger.info('# Configuration: %s' % config.__name__)
    for key in dir(config):
        if not key.startswith('__') and isinstance(getattr(config, key), (int, str, list, tuple)):
            logger.info('    %20s %s' % (key, str(getattr(config, key))))

    model = config.Model(config)
    model.initialize()

    stream = config.Stream(config)
    inputs = stream.inputs()
    req_vars = model.cost.inputs

    train_stream = stream.train(req_vars)
    valid_stream = stream.valid(req_vars)

    cost = model.cost(**inputs)
    cg = ComputationGraph(cost)
    monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables))

    valid_monitored = monitored
    if hasattr(model, 'valid_cost'):
        valid_cost = model.valid_cost(**inputs)
        valid_cg = ComputationGraph(valid_cost)
        valid_monitored = set([valid_cost] + VariableFilter(roles=[roles.COST])(valid_cg.variables))

    if hasattr(config, 'dropout') and config.dropout < 1.0:
        cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout)
    if hasattr(config, 'noise') and config.noise > 0.0:
        cg = apply_noise(cg, config.noise_inputs(cg), config.noise)
    cost = cg.outputs[0]
    cg = Model(cost)

    logger.info('# Parameter shapes:')
    parameters_size = 0
    for key, value in cg.get_params().iteritems():
        logger.info('    %20s %s' % (value.get_value().shape, key))
        parameters_size += reduce(operator.mul, value.get_value().shape, 1)
    logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.get_params())))

    if hasattr(config, 'step_rule'):
        step_rule = config.step_rule
    else:
        step_rule = AdaDelta()

    params = cg.parameters
    algorithm = GradientDescent(
        cost=cost,
        step_rule=CompositeRule([
                ElementwiseRemoveNotFinite(),
                step_rule
            ]),
        params=params)
    
    plot_vars = [['valid_' + x.name for x in valid_monitored]]
    logger.info('Plotted variables: %s' % str(plot_vars))

    dump_path = os.path.join('model_data', model_name) + '.pkl'
    logger.info('Dump path: %s' % dump_path)

    extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000),
                DataStreamMonitoring(valid_monitored, valid_stream,
                                     prefix='valid',
                                     every_n_batches=1000),
                Printing(every_n_batches=1000),

                SaveLoadParams(dump_path, cg,
                               before_training=True,        # before training -> load params
                               every_n_batches=1000,        # every N batches -> save params
                               after_epoch=True,            # after epoch -> save params
                               after_training=True,         # after training -> save params
                               ),
                ]
    
    if use_plot:
        extensions.append(Plot(model_name, channels=plot_vars, every_n_batches=500))

    main_loop = MainLoop(
        model=cg,
        data_stream=train_stream,
        algorithm=algorithm,
        extensions=extensions)
    main_loop.run()
    main_loop.profile.report()