import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import rltorch
import rltorch.network as rn
import rltorch.memory as M
import rltorch.env as E
from rltorch.action_selector import ArgMaxSelector
from tensorboardX import SummaryWriter
import torch.multiprocessing as mp

#
## Networks
#
class Value(nn.Module):
  def __init__(self, state_size, action_size):
    super(Value, self).__init__()
    self.state_size = state_size
    self.action_size = action_size
    
    self.conv1 = nn.Conv2d(4, 32, kernel_size = (8, 8), stride = (4, 4))
    self.conv_norm1 = nn.LayerNorm([32, 19, 19])
    self.conv2 = nn.Conv2d(32, 64, kernel_size = (4, 4), stride = (2, 2))    
    self.conv_norm2 = nn.LayerNorm([64, 8, 8])
    self.conv3 = nn.Conv2d(64, 64, kernel_size = (3, 3), stride = (1, 1))
    self.conv_norm3 = nn.LayerNorm([64, 6, 6])
    
    self.fc1 = rn.NoisyLinear(64 * 6 * 6, 384)
    self.fc_norm = nn.LayerNorm(384)
    
    self.value_fc = rn.NoisyLinear(384, 384)
    self.value_fc_norm = nn.LayerNorm(384)
    self.value = rn.NoisyLinear(384, 1)
    
    self.advantage_fc = rn.NoisyLinear(384, 384)
    self.advantage_fc_norm = nn.LayerNorm(384)
    self.advantage = rn.NoisyLinear(384, action_size)

  
  def forward(self, x):
    x = F.relu(self.conv_norm1(self.conv1(x)))
    x = F.relu(self.conv_norm2(self.conv2(x)))
    x = F.relu(self.conv_norm3(self.conv3(x)))
    
    # Makes batch_size dimension again
    x = x.view(-1, 64 * 6 * 6)
    x = F.relu(self.fc_norm(self.fc1(x)))
    
    state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
    state_value = self.value(state_value)
    
    advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
    advantage = self.advantage(advantage)
    
    x = state_value + advantage - advantage.mean()
    
    # For debugging purposes...
    if torch.isnan(x).any().item():
      print("WARNING NAN IN MODEL DETECTED")
    
    return x
    
#
## Configuration
#
config = {}
config['seed'] = 901
config['environment_name'] = 'PongNoFrameskip-v4'
config['memory_size'] = 5000
config['total_training_episodes'] = 500
config['total_evaluation_episodes'] = 10
config['learning_rate'] = 1e-4
config['target_sync_tau'] = 1e-3
config['discount_rate'] = 0.99
config['exploration_rate'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.1, end_value = 0.01, iterations = 5000)
config['replay_skip'] = 4
config['batch_size'] = 32 * (config['replay_skip'] + 1)
# How many episodes between printing out the episode stats
config['print_stat_n_eps'] = 1 
config['disable_cuda'] = False
# Prioritized vs Random Sampling
# 0 - Random sampling
# 1 - Only the highest prioirities
config['prioritized_replay_sampling_priority'] = 0.6
# How important are the weights for the loss?
# 0 - Treat all losses equally
# 1 - Lower the importance of high losses
# Should ideally start from 0 and move your way to 1 to prevent overfitting
config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)

if __name__ == "__main__":
  # To not hit file descriptor memory limit
  torch.multiprocessing.set_sharing_strategy('file_system') 

  # Setting up the environment
  rltorch.set_seed(config['seed'])
  print("Setting up environment...", end = " ")
  env = E.FrameStack(E.TorchWrap(
    E.ProcessFrame(E.FireResetEnv(gym.make(config['environment_name'])), 
      resize_shape = (80, 80), crop_bounds = [34, 194, 15, 145], grayscale = True))
  , 4)
  env.seed(config['seed'])
  print("Done.")
      
  state_size = env.observation_space.shape[0]
  action_size = env.action_space.n

  # Logging
  logger = rltorch.log.Logger()
  logwriter = rltorch.log.LogWriter(SummaryWriter())

  # Setting up the networks
  device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
  net = rn.Network(Value(state_size, action_size), 
                      torch.optim.Adam, config, device = device, name = "DQN")
  target_net = rn.TargetNetwork(net, device = device)
  net.model.share_memory()
  target_net.model.share_memory()

  # Actor takes a net and uses it to produce actions from given states
  actor = ArgMaxSelector(net, action_size, device = device)
  # Memory stores experiences for later training
  memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])

  # Runner performs a certain number of steps in the environment
  runner = rltorch.mp.EnvironmentRun(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)

  # Agent is what performs the training
  agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
    
  print("Training...")
  train(runner, agent, config, logger = logger, logwriter = logwriter) 

  # For profiling...
  # import cProfile
  # cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
  # python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...

  print("Training Finished.")
  runner.terminate() # We don't need the extra process anymore

  print("Evaluating...")
  rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
  print("Evaulations Done.")

  logwriter.close() # We don't need to write anything out to disk anymore