from collections import namedtuple
from collections import deque
from pathlib import Path
import random
import math
import json
import torch

from celeste import Celeste


if __name__ == "__main__":
	# Where to read/write model data.
	model_data_root = Path("model_data/current")

	model_save_path		= model_data_root / "model.torch"
	model_archive_dir	= model_data_root / "model_archive"
	model_train_log		= model_data_root / "train_log"
	screenshot_dir		= model_data_root / "screenshots"
	model_data_root.mkdir(parents = True, exist_ok = True)
	model_archive_dir.mkdir(parents = True, exist_ok = True)
	screenshot_dir.mkdir(parents = True, exist_ok = True)


	compute_device = torch.device(
		"cuda" if torch.cuda.is_available() else "cpu"
	)


	# Celeste env properties
	n_observations = len(Celeste.state_number_map)
	n_actions = len(Celeste.action_space)


	# Epsilon-greedy parameters
	#
	# Original docs:
	# EPS_START is the starting value of epsilon
	# EPS_END is the final value of epsilon
	# EPS_DECAY controls the rate of exponential decay of epsilon, higher means a slower decay
	EPS_START = 0.9
	EPS_END = 0.05
	EPS_DECAY = 4000


	BATCH_SIZE = 1_000
	# Learning rate of target_net.
	# Controls how soft our soft update is.
	#
	# Should be between 0 and 1.
	# Large values
	# Small values do the opposite.
	#
	# A value of one makes target_net
	# change at the same rate as policy_net.
	#
	# A value of zero makes target_net
	# not change at all.
	TAU = 0.005


	# GAMMA is the discount factor as mentioned in the previous section
	GAMMA = 0.9


# Outline our network
class DQN(torch.nn.Module):
	def __init__(self, n_observations: int, n_actions: int):
		super(DQN, self).__init__()
		
		self.layers = torch.nn.Sequential(
			torch.nn.Linear(n_observations, 128),
			torch.nn.ReLU(),

			torch.nn.Linear(128, 128),
			torch.nn.ReLU(),

			torch.nn.Linear(128, 128),
			torch.nn.ReLU(),

			torch.torch.nn.Linear(128, n_actions)
		)

	# Can be called with one input, or with a batch.
	#
	# Returns tensor(
	#	[ Q(s, left), Q(s, right) ], ...
	# )
	#
	# Recall that Q(s, a) is the (expected) return of taking
	# action `a` at state `s`
	def forward(self, x):
		return self.layers(x)

Transition = namedtuple(
	"Transition",
	(
		"state",
		"action",
		"next_state",
		"reward"
	)
)


if __name__ == "__main__":
	steps_done = 0
	num_episodes = 100
	episode_number = 0
	archive_interval = 10

	# Create replay memory.
	#
	# Transition: a container for naming data (defined in util.py)
	# Memory: a deque that holds recent states as Transitions
	#	Has a fixed length, drops oldest
	#	element if maxlen is exceeded.
	memory = deque([], maxlen=50_000)

	policy_net = DQN(
		n_observations,
		n_actions
	).to(compute_device)

	target_net = DQN(
		n_observations,
		n_actions
	).to(compute_device)

	target_net.load_state_dict(policy_net.state_dict())


	optimizer = torch.optim.AdamW(
		policy_net.parameters(),
		lr = 0.01, # Hyperparameter: learning rate
		amsgrad = True
	)


	if model_save_path.is_file():
		# Load model if one exists
		checkpoint = torch.load(model_save_path)
		policy_net.load_state_dict(checkpoint["policy_state_dict"])
		target_net.load_state_dict(checkpoint["target_state_dict"])
		optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
		memory = checkpoint["memory"]
		episode_number = checkpoint["episode_number"] + 1
		steps_done = checkpoint["steps_done"]

def select_action(state, steps_done):
	"""
	Select an action using an epsilon-greedy policy.

	Sometimes use our model, sometimes sample one uniformly.

	P(random action) starts at EPS_START and decays to EPS_END.
	Decay rate is controlled by EPS_DECAY.
	"""

	# Random number 0 <= x < 1
	sample = random.random()

	# Calculate random step threshhold
	eps_threshold = (
		EPS_END + (EPS_START - EPS_END) *
		math.exp(
			-1.0 * steps_done /
			EPS_DECAY
		)
	)

	if sample > eps_threshold:
		with torch.no_grad():
			# t.max(1) will return the largest column value of each row.
			# second column on max result is index of where max element was
			# found, so we pick action with the larger expected reward.
			return policy_net(state).max(1)[1].view(1, 1).item()

	else:
		return random.randint( 0, n_actions-1 )


def optimize_model():

	if len(memory) < BATCH_SIZE:
		raise Exception(f"Not enough elements in memory for a batch of {BATCH_SIZE}")



	# Get a random sample of transitions
	batch = random.sample(memory, BATCH_SIZE)

	# Conversion.
	# Transposes batch, turning an array of Transitions
	# into a Transition of arrays.
	batch = Transition(*zip(*batch))

	# Conversion.
	# Combine states, actions, and rewards into their own tensors.
	state_batch = torch.cat(batch.state)
	action_batch = torch.cat(batch.action)
	reward_batch = torch.cat(batch.reward)



	# Compute a mask of non_final_states.
	# Each element of this tensor corresponds to an element in the batch.
	# True if this is a final state, False if it isn't.
	#
	# We use this to select non-final states later.
	non_final_mask = torch.tensor(
		tuple(map(
			lambda s: s is not None,
			batch.next_state
		))
	)

	non_final_next_states = torch.cat(
		[s for s in batch.next_state if s is not None]
	)



	# How .gather works:
	# if out = a.gather(1, b),
	# out[i, j] = a[ i ][ b[i,j] ]
	#
	# a is "input," b is "index"
	# If this doesn't make sense, RTFD.

	# Compute Q(s_t, a).
	#	- Use policy_net to compute Q(s_t) for each state in the batch.
	#		This gives a tensor of [ Q(state, left), Q(state, right) ]
	#
	#	- Action batch is a tensor that looks like [ [0], [1], [1], ... ]
	#		listing the action that was taken in each transition.
	#		0 => we went left, 1 => we went right.
	#
	#	This aligns nicely with the output of policy_net. We use
	#	action_batch to index the output of policy_net's prediction.
	#
	#	This gives us a tensor that contains the return we expect to get
	#	at that state if we follow the model's advice.

	state_action_values = policy_net(state_batch).gather(1, action_batch)



	# Compute V(s_t+1) for all next states.
	# V(s_t+1) = max_a ( Q(s_t+1, a) )
	# = the maximum reward over all possible actions at state s_t+1.
	next_state_values = torch.zeros(BATCH_SIZE, device = compute_device)

	# Don't compute gradient for operations in this block.
	# If you don't understand what this means, RTFD.
	with torch.no_grad():

		# Note the use of non_final_mask here.
		# States that are final do not have their reward set by the line
		# below, so their reward stays at zero.
		#
		# States that are not final get their predicted value
		# set to the best value the model predicts.
		#
		#
		# Expected values of action are selected with the "older" target net,
		# and their best reward (over possible actions) is selected with max(1)[0].

		next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]


	# TODO: What does this mean?
	# "Compute expected Q values"
	expected_state_action_values = reward_batch + (next_state_values * GAMMA)



	# Compute Huber loss between predicted reward and expected reward.
	# Pytorch is will account for this when we compute the gradient of loss.
	#
	# loss is a single-element tensor (i.e, a scalar).
	criterion = torch.nn.SmoothL1Loss()
	loss = criterion(
		state_action_values,
		expected_state_action_values.unsqueeze(1)
	)


	# We can now run a step of backpropagation on our model.

	# TODO: what does this do?
	#
	# Calling .backward() multiple times will accumulate parameter gradients.
	# Thus, we reset the gradient before each step.
	optimizer.zero_grad()

	# Compute the gradient of loss wrt... something?
	# TODO: what does this do, we never use loss again?!
	loss.backward()


	# Prevent vanishing and exploding gradients.
	# Forces gradients to be in [-clip_value, +clip_value]
	torch.nn.utils.clip_grad_value_(  # type: ignore
		policy_net.parameters(),
		clip_value = 100
	)

	# Perform a single optimizer step.
	#
	# Uses the current gradient, which is stored
	# in the .grad attribute of the parameter.
	optimizer.step()

	return loss



def on_state_before(celeste):
	global steps_done

	# Conversion to pytorch

	state = celeste.state

	pt_state = torch.tensor(
		[getattr(state, x) for x in Celeste.state_number_map],
		dtype = torch.float32,
		device = compute_device
	).unsqueeze(0)

	action = None
	while (action) is None or ((not state.can_dash) and (str_action not in ["left", "right"])):
		action = select_action(
			pt_state,
			steps_done
		)
		str_action = Celeste.action_space[action]
	steps_done += 1


	# For manual testing
	#str_action = ""
	#while str_action not in Celeste.action_space:
	#	str_action = input("action> ")
	#action = Celeste.action_space.index(str_action)

	print(str_action)
	celeste.act(str_action)

	return state, action



def on_state_after(celeste, before_out):
	global episode_number

	state, action = before_out
	next_state = celeste.state

	pt_state = torch.tensor(
		[getattr(state, x) for x in Celeste.state_number_map],
		dtype = torch.float32,
		device = compute_device
	).unsqueeze(0)

	pt_action = torch.tensor(
		[[ action ]],
		device = compute_device,
		dtype = torch.long
	)

	if next_state.deaths != 0:
		pt_next_state = None
		reward = 0

	else:
		pt_next_state = torch.tensor(
			[getattr(next_state, x) for x in Celeste.state_number_map],
			dtype = torch.float32,
			device = compute_device
		).unsqueeze(0)


		if state.next_point == next_state.next_point:
			reward = state.dist - next_state.dist

			# Clip rewards that are too large
			if reward > 1:
				reward = 1
			else:
				reward = 0

		else:
			# Reward for reaching a point
			reward = 1

	pt_reward = torch.tensor([reward], device = compute_device)


	# Add this state transition to memory.
	memory.append(
		Transition(
			pt_state,		# last state
			pt_action,
			pt_next_state,	# next state
			pt_reward
		)
	)

	print("==> ", int(reward))
	print("")


	loss = None
	# Only train the network if we have enough
	# transitions in memory to do so.
	if len(memory) >= BATCH_SIZE:
		loss = optimize_model()

		# Soft update target_net weights
		target_net_state = target_net.state_dict()
		policy_net_state = policy_net.state_dict()
		for key in policy_net_state:
			target_net_state[key] = (
				policy_net_state[key] * TAU +
				target_net_state[key] * (1-TAU)
			)
		target_net.load_state_dict(target_net_state)

	# Move on to the next episode once we reach
	# a terminal state.
	if (next_state.deaths != 0):
		s = celeste.state
		with model_train_log.open("a") as f:
			f.write(json.dumps({
				"checkpoints": s.next_point,
				"state_count": s.state_count,
				"loss": None if loss is None else loss.item()
			}) + "\n")
 

		# Save model
		torch.save({
			"policy_state_dict": policy_net.state_dict(),
			"target_state_dict": target_net.state_dict(),
			"optimizer_state_dict": optimizer.state_dict(),
			"memory": memory,
			"episode_number": episode_number,
			"steps_done": steps_done
		}, model_save_path)


		# Clean up screenshots
		shots = Path("/home/mark/Desktop").glob("hackcel_*.png")

		target = screenshot_dir / Path(f"{episode_number}")
		target.mkdir(parents = True)

		for s in shots:
			s.rename(target / s.name)

		# Save a prediction graph
		if episode_number % archive_interval == 0:
			torch.save({
				"policy_state_dict": policy_net.state_dict(),
				"target_state_dict": target_net.state_dict(),
				"optimizer_state_dict": optimizer.state_dict(),
				"memory": memory,
				"episode_number": episode_number,
				"steps_done": steps_done
			}, model_archive_dir / f"{episode_number}.torch")


		print("Game over. Resetting.")
		episode_number += 1
		celeste.reset()


if __name__ == "__main__":
	c = Celeste()

	c.update_loop(
		on_state_before,
		on_state_after
	)