import math
import torch
import torch.nn.functional as F
import traceback

from typing import Literal, overload
from functools import partial
from einops import rearrange

from torch import Tensor, einsum, nn
from torch.distributions import Categorical
from torch.nn.utils.rnn import pad_sequence
from torch.utils.checkpoint import checkpoint
from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassPrecision
from torchvision.models import resnet18

from ..data import get_symmap

def _create_mask(l, device):
	"""1 is valid region and 0 is invalid."""
	seq = torch.arange(max(l), device=device).unsqueeze(0)  # (1 t)
	stop = torch.tensor(l, device=device).unsqueeze(1)  # (b 1)
	return (seq < stop).float()  # (b t)

def list_to_tensor(x_list: list[Tensor], pattern="t b c -> b t c"):
	"""
	Args:
		x_list: [(t d)]
	Returns:
		x: (? ? ?)
		m: (? ? ?), same as x
	"""
	l = list(map(len, x_list))
	x = rearrange(pad_sequence(x_list), pattern)
	m = _create_mask(l, x_list[0].device)
	m = m.t().unsqueeze(-1)  # (t b 1)
	m = rearrange(m, pattern)
	m = m.to(x)
	return x, m

class Model(nn.Module):
	def __init__(
		self,
		n_tokens: int = 0, # number of token types
		n_len: int = 6, # how long a sequence can be
		d_model: int = 512,
	):
		super().__init__()


		_symmap = get_symmap()
		self.symmap = { f'{v}': k for k, v in _symmap.items() }
		self.symmap['0'] = ""

		if n_tokens == 0:
			n_tokens = len(_symmap.keys())

		self.n_tokens = n_tokens
		self.n_len = n_len + 2 # start/stop tokens
		self.d_model = d_model

		self.resnet = resnet18(pretrained=False)
		self.resnet.fc = nn.Linear( self.d_model, self.n_tokens * self.n_len )
		
		self.criterion = nn.CTCLoss(zero_infinity=True)

	def forward(
		self,
		
		image,
		text = None,

		sampling_temperature: float = 1.0,
	):
		x_list = torch.stack( image, dim=0 )

		x = self.resnet( x_list )
		y = x.view(x.size(0), self.n_len, self.n_tokens)
		
		# pred = y.argmax(dim=2)
		pred = Categorical(logits=y / sampling_temperature).sample()

		answer = [ "".join([ self.symmap[f'{x.item()}'] for x in t ]) for t in pred ]
		
		if text is not None:
			y_list = rearrange(pad_sequence(text), "t b -> b t")
			
			loss = 0
			for i in range(self.n_len):
				loss += F.cross_entropy( y[:, i], y_list[:, i] )

			self.loss = dict(
				nll=loss
			)

		return answer

def example_usage():
	from ..config import cfg
	cfg.trainer.backend = "local"
	cfg.trainer.check_for_oom = False

	from functools import partial

	from einops import repeat

	from ..emb.qnt import decode_to_file
	from ..engines import Engine, Engines
	from tqdm import tqdm, trange

	from .ar import AR
	from .nar import NAR

	device = "cpu"
	x8 = partial(repeat, pattern="t -> t l", l=2) 
	symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
	def tokenize(content, lang_marker="en"):
		split = content.split(" ")
		phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
		return torch.tensor([*map(symmap.get, phones)]).to()

	kwargs = {
		'n_tokens': 1024,
		'd_model': 1024,
		'n_heads': 16,
		'n_layers': 12,
	}
	models = { "ar": AR(**kwargs).to(device), "nar": NAR(**kwargs).to(device) }
	engines = Engines({ name: Engine(model=model, optimizer=torch.optim.AdamW(model.parameters(), lr=1e-4)) for name, model in models.items() })

	train = True


	
	
	def sample( name, steps=400 ):
		AR = None
		NAR = None

		engines.eval()
		for name, engine in engines.items():
			if name[:2] == "ar":
				AR = engine
			elif name[:3] == "nar":
				NAR = engine

		resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
		resps_list = [r.unsqueeze(-1) for r in resps_list]		
		codes = NAR( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 ) 

		decode_to_file(resps_list[0], f"./data/ar.{name}.wav", device=device)
		decode_to_file(codes[0], f"./data/ar+nar.{name}.wav", device=device)
	
	if train:
		sample("init", 15)

		engines.train()
		t = trange(60)
		for i in t:
			stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list}, device="cpu")
			t.set_description(f"{stats}")
	else:
		for name, engine in engines.items():
			engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))

	sample("final")
	

if __name__ == "__main__":
	example_usage()