import torch
from torch import nn
import logging
import numpy as np


def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
	if maxlen is None:
		maxlen = lengths.max()
	row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
	matrix = torch.unsqueeze(lengths, dim=-1)
	mask = row_vector < matrix
	mask = mask.detach()
	
	return mask.type(dtype).to(device) if device is not None else mask.type(dtype)


def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):

	if length_dim == 0:
		raise ValueError("length_dim cannot be 0: {}".format(length_dim))
	
	if not isinstance(lengths, list):
		lengths = lengths.tolist()
	bs = int(len(lengths))
	if maxlen is None:
		if xs is None:
			maxlen = int(max(lengths))
		else:
			maxlen = xs.size(length_dim)
	else:
		assert xs is None
		assert maxlen >= int(max(lengths))
	
	seq_range = torch.arange(0, maxlen, dtype=torch.int64)
	seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
	seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
	mask = seq_range_expand >= seq_length_expand
	
	if xs is not None:
		assert xs.size(0) == bs, (xs.size(0), bs)
		
		if length_dim < 0:
			length_dim = xs.dim() + length_dim
		# ind = (:, None, ..., None, :, , None, ..., None)
		ind = tuple(
			slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
		)
		mask = mask[ind].expand_as(xs).to(xs.device)
	return mask



class CifPredictorV2(nn.Module):
	def __init__(self,
	             idim: int,
	             l_order: int,
	             r_order: int,
	             threshold: float = 1.0,
	             dropout: float = 0.1,
	             smooth_factor: float = 1.0,
	             noise_threshold: float = 0,
	             tail_threshold: float = 0.0,
	             ):
		super(CifPredictorV2, self).__init__()
		
		self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
		self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
		self.cif_output = nn.Linear(idim, 1)
		self.dropout = torch.nn.Dropout(p=dropout)
		self.threshold = threshold
		self.smooth_factor = smooth_factor
		self.noise_threshold = noise_threshold
		self.tail_threshold = tail_threshold
	
	def forward(self, hidden: torch.Tensor,
	            mask: torch.Tensor,
	            ):
		h = hidden
		context = h.transpose(1, 2)
		queries = self.pad(context)
		output = torch.relu(self.cif_conv1d(queries))
		output = output.transpose(1, 2)
		
		output = self.cif_output(output)
		alphas = torch.sigmoid(output)
		alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
		mask = mask.transpose(-1, -2).float()
		alphas = alphas * mask
		
		alphas = alphas.squeeze(-1)
		
		token_num = alphas.sum(-1)
		
		acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
		
		return acoustic_embeds, token_num, alphas, cif_peak
	
	def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
		b, t, d = hidden.size()
		tail_threshold = self.tail_threshold
		
		zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
		ones_t = torch.ones_like(zeros_t)
		mask_1 = torch.cat([mask, zeros_t], dim=1)
		mask_2 = torch.cat([ones_t, mask], dim=1)
		mask = mask_2 - mask_1
		tail_threshold = mask * tail_threshold
		alphas = torch.cat([alphas, tail_threshold], dim=1)
		
		zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
		hidden = torch.cat([hidden, zeros], dim=1)
		token_num = alphas.sum(dim=-1)
		token_num_floor = torch.floor(token_num)
		
		return hidden, alphas, token_num_floor

@torch.jit.script
def cif(hidden, alphas, threshold: float):
	batch_size, len_time, hidden_size = hidden.size()
	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
	
	# loop varss
	integrate = torch.zeros([batch_size], device=hidden.device)
	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
	# intermediate vars along time
	list_fires = []
	list_frames = []
	
	for t in range(len_time):
		alpha = alphas[:, t]
		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
		
		integrate += alpha
		list_fires.append(integrate)
		
		fire_place = integrate >= threshold
		integrate = torch.where(fire_place,
		                        integrate - torch.ones([batch_size], device=hidden.device),
		                        integrate)
		cur = torch.where(fire_place,
		                  distribution_completion,
		                  alpha)
		remainds = alpha - cur
		
		frame += cur[:, None] * hidden[:, t, :]
		list_frames.append(frame)
		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
		                    remainds[:, None] * hidden[:, t, :],
		                    frame)
	
	fires = torch.stack(list_fires, 1)
	frames = torch.stack(list_frames, 1)
	list_ls = []
	len_labels = torch.round(alphas.sum(-1)).int()
	max_label_len = len_labels.max()
	for b in range(batch_size):
		fire = fires[b, :]
		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
		list_ls.append(torch.cat([l, pad_l], 0))
	return torch.stack(list_ls, 0), fires


def CifPredictorV2_test():
	x = torch.rand([2, 21, 2])
	x_len = torch.IntTensor([6, 21])
	
	mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
	x = x * mask[:, :, None]
	
	predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
	# cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
	predictor_scripts.save('test.pt')
	loaded = torch.jit.load('test.pt')
	cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
	# print(cif_output)
	print(predictor_scripts.code)
	# predictor = CifPredictorV2(2, 1, 1)
	# cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
	print(cif_output)


def CifPredictorV2_export_test():
	x = torch.rand([2, 21, 2])
	x_len = torch.IntTensor([6, 21])
	
	mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
	x = x * mask[:, :, None]
	
	# predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1))
	# cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :])
	predictor = CifPredictorV2(2, 1, 1)
	predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :]))
	predictor_trace.save('test_trace.pt')
	loaded = torch.jit.load('test_trace.pt')
	
	x = torch.rand([3, 30, 2])
	x_len = torch.IntTensor([6, 20, 30])
	mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype)
	x = x * mask[:, :, None]
	cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :])
	print(cif_output)
	# print(predictor_trace.code)
	# predictor = CifPredictorV2(2, 1, 1)
	# cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :])
	# print(cif_output)


if __name__ == '__main__':
	# CifPredictorV2_test()
	CifPredictorV2_export_test()