from torch.utils.tensorboard import SummaryWriter
from collections import defaultdict
import json
import os
import shutil
import torch
import torchvision
import numpy as np
from termcolor import colored
from datetime import datetime

FORMAT_CONFIG = {
    'rl': {
        'train': [
            ('episode', 'E', 'int'), ('step', 'S', 'int'),
            ('duration', 'D', 'time'), ('episode_reward', 'R', 'float'),
            ('batch_reward', 'BR', 'float'), ('actor_loss', 'ALOSS', 'float'),
            ('critic_loss', 'CLOSS', 'float'), ('ae_loss', 'RLOSS', 'float')
        ],
        'eval': [('step', 'S', 'int'), ('episode_reward', 'ER', 'float')]
    }
}


class AverageMeter(object):
    def __init__(self):
        self._sum = 0
        self._count = 0

    def update(self, value, n=1):
        self._sum += value
        self._count += n

    def value(self):
        return self._sum / max(1, self._count)


class MetersGroup(object):
    def __init__(self, file_name, formating):
        self._file_name = file_name
        if os.path.exists(file_name):
            os.remove(file_name)
        self._formating = formating
        self._meters = defaultdict(AverageMeter)

    def log(self, key, value, n=1):
        self._meters[key].update(value, n)

    def _prime_meters(self):
        data = dict()
        for key, meter in self._meters.items():
            if key.startswith('train'):
                key = key[len('train') + 1:]
            else:
                key = key[len('eval') + 1:]
            key = key.replace('/', '_')
            data[key] = meter.value()
        return data

    def _dump_to_file(self, data):
        with open(self._file_name, 'a') as f:
            f.write(json.dumps(data) + '\n')

    def _format(self, key, value, ty):
        template = '%s: '
        if ty == 'int':
            template += '%d'
        elif ty == 'float':
            template += '%.04f'
        elif ty == 'time':
            template += '%.01f s'
        else:
            raise 'invalid format type: %s' % ty
        return template % (key, value)

    def _dump_to_console(self, data, prefix):
        prefix = colored(prefix, 'yellow' if prefix == 'train' else 'green')
        pieces = ['{:5}'.format(prefix)]
        for key, disp_key, ty in self._formating:
            value = data.get(key, 0)
            pieces.append(self._format(disp_key, value, ty))
        print('| %s' % (' | '.join(pieces)))

    def dump(self, step, prefix):
        if len(self._meters) == 0:
            return
        data = self._prime_meters()
        data['step'] = step
        self._dump_to_file(data)
        self._dump_to_console(data, prefix)
        self._meters.clear()


class Logger(object):
    def __init__(self, log_dir, use_tb=True, config='rl'):
        self._log_dir = log_dir
        now = datetime.now()
        dt_string = now.strftime("%d_%m_%Y-%H_%M_%S")
        if use_tb:
            tb_dir = os.path.join(log_dir, 'runs/tb_'+dt_string)
            if os.path.exists(tb_dir):
                shutil.rmtree(tb_dir)
            self._sw = SummaryWriter(tb_dir)
        else:
            self._sw = None
        self._train_mg = MetersGroup(
            os.path.join(log_dir, 'train.log'),
            formating=FORMAT_CONFIG[config]['train']
        )
        self._eval_mg = MetersGroup(
            os.path.join(log_dir, 'eval.log'),
            formating=FORMAT_CONFIG[config]['eval']
        )

    def _try_sw_log(self, key, value, step):
        if self._sw is not None:
            self._sw.add_scalar(key, value, step)

    def _try_sw_log_image(self, key, image, step):
        if self._sw is not None:
            assert image.dim() == 3
            grid = torchvision.utils.make_grid(image.unsqueeze(1))
            self._sw.add_image(key, grid, step)

    def _try_sw_log_video(self, key, frames, step):
        if self._sw is not None:
            frames = torch.from_numpy(np.array(frames))
            frames = frames.unsqueeze(0)
            self._sw.add_video(key, frames, step, fps=30)

    def _try_sw_log_histogram(self, key, histogram, step):
        if self._sw is not None:
            self._sw.add_histogram(key, histogram, step)

    def log(self, key, value, step, n=1):
        assert key.startswith('train') or key.startswith('eval')
        if type(value) == torch.Tensor:
            value = value.item()
        self._try_sw_log(key, value / n, step)
        mg = self._train_mg if key.startswith('train') else self._eval_mg
        mg.log(key, value, n)

    def log_param(self, key, param, step):
        self.log_histogram(key + '_w', param.weight.data, step)
        if hasattr(param.weight, 'grad') and param.weight.grad is not None:
            self.log_histogram(key + '_w_g', param.weight.grad.data, step)
        if hasattr(param, 'bias'):
            self.log_histogram(key + '_b', param.bias.data, step)
            if hasattr(param.bias, 'grad') and param.bias.grad is not None:
                self.log_histogram(key + '_b_g', param.bias.grad.data, step)

    def log_image(self, key, image, step):
        assert key.startswith('train') or key.startswith('eval')
        self._try_sw_log_image(key, image, step)

    def log_video(self, key, frames, step):
        assert key.startswith('train') or key.startswith('eval')
        self._try_sw_log_video(key, frames, step)

    def log_histogram(self, key, histogram, step):
        assert key.startswith('train') or key.startswith('eval')
        self._try_sw_log_histogram(key, histogram, step)

    def dump(self, step):
        self._train_mg.dump(step, 'train')
        self._eval_mg.dump(step, 'eval')