import sys
_module = sys.modules[__name__]
del sys
TorchCRF = _module
setup = _module
tests = _module
test_crf = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


from typing import List


from typing import Optional


import torch


import torch.nn as nn


from torch import BoolTensor


from torch import FloatTensor


from torch import LongTensor


class CRF(nn.Module):

    def __init__(self, num_labels: int, pad_idx: Optional[int]=None, use_gpu: bool=True) ->None:
        """

        :param num_labels: number of labels
        :param pad_idxL padding index. default None
        :return None
        """
        if num_labels < 1:
            raise ValueError('invalid number of labels: {0}'.format(num_labels))
        super().__init__()
        self.num_labels = num_labels
        self._use_gpu = torch.cuda.is_available() and use_gpu
        self.trans_matrix = nn.Parameter(torch.empty(num_labels, num_labels))
        self.start_trans = nn.Parameter(torch.empty(num_labels))
        self.end_trans = nn.Parameter(torch.empty(num_labels))
        self._initialize_parameters(pad_idx)

    def forward(self, h: FloatTensor, labels: LongTensor, mask: BoolTensor) ->FloatTensor:
        """

        :param h: hidden matrix (batch_size, seq_len, num_labels)
        :param labels: answer labels of each sequence
                       in mini batch (batch_size, seq_len)
        :param mask: mask tensor of each sequence
                     in mini batch (batch_size, seq_len)
        :return: The log-likelihood (batch_size)
        """
        log_numerator = self._compute_numerator_log_likelihood(h, labels, mask)
        log_denominator = self._compute_denominator_log_likelihood(h, mask)
        return log_numerator - log_denominator

    def viterbi_decode(self, h: FloatTensor, mask: BoolTensor) ->List[List[int]]:
        """
        decode labels using viterbi algorithm
        :param h: hidden matrix (batch_size, seq_len, num_labels)
        :param mask: mask tensor of each sequence
                     in mini batch (batch_size, batch_size)
        :return: labels of each sequence in mini batch
        """
        batch_size, seq_len, _ = h.size()
        seq_lens = mask.sum(dim=1)
        score = [self.start_trans.data + h[:, 0]]
        path = []
        for t in range(1, seq_len):
            previous_score = score[t - 1].view(batch_size, -1, 1)
            h_t = h[:, t].view(batch_size, 1, -1)
            score_t = previous_score + self.trans_matrix + h_t
            best_score, best_path = score_t.max(1)
            score.append(best_score)
            path.append(best_path)
        best_paths = [self._viterbi_compute_best_path(i, seq_lens, score, path) for i in range(batch_size)]
        return best_paths

    def _viterbi_compute_best_path(self, batch_idx: int, seq_lens: torch.LongTensor, score: List[FloatTensor], path: List[torch.LongTensor]) ->List[int]:
        """
        return labels using viterbi algorithm
        :param batch_idx: index of batch
        :param seq_lens: sequence lengths in mini batch (batch_size)
        :param score: transition scores of length max sequence size
                      in mini batch [(batch_size, num_labels)]
        :param path: transition paths of length max sequence size
                     in mini batch [(batch_size, num_labels)]
        :return: labels of batch_idx-th sequence
        """
        seq_end_idx = seq_lens[batch_idx] - 1
        _, best_last_label = (score[seq_end_idx][batch_idx] + self.end_trans).max(0)
        best_labels = [int(best_last_label)]
        for p in reversed(path[:seq_end_idx]):
            best_last_label = p[batch_idx][best_labels[0]]
            best_labels.insert(0, int(best_last_label))
        return best_labels

    def _compute_denominator_log_likelihood(self, h: FloatTensor, mask: BoolTensor):
        """

        compute the denominator term for the log-likelihood
        :param h: hidden matrix (batch_size, seq_len, num_labels)
        :param mask: mask tensor of each sequence
                     in mini batch (batch_size, seq_len)
        :return: The score of denominator term for the log-likelihood
        """
        device = h.device
        batch_size, seq_len, _ = h.size()
        trans = self.trans_matrix.unsqueeze(0)
        score = self.start_trans + h[:, 0]
        for t in range(1, seq_len):
            before_score = score.unsqueeze(2)
            mask_t = mask[:, t].unsqueeze(1)
            mask_t = mask_t
            h_t = h[:, t].unsqueeze(1)
            score_t = before_score + h_t + trans
            score_t = torch.logsumexp(score_t, 1)
            score = torch.where(mask_t, score_t, score)
        score += self.end_trans
        return torch.logsumexp(score, 1)

    def _compute_numerator_log_likelihood(self, h: FloatTensor, y: LongTensor, mask: BoolTensor) ->FloatTensor:
        """
        compute the numerator term for the log-likelihood
        :param h: hidden matrix (batch_size, seq_len, num_labels)
        :param y: answer labels of each sequence
                  in mini batch (batch_size, seq_len)
        :param mask: mask tensor of each sequence
                     in mini batch (batch_size, seq_len)
        :return: The score of numerator term for the log-likelihood
        """
        batch_size, seq_len, _ = h.size()
        h_unsqueezed = h.unsqueeze(-1)
        trans = self.trans_matrix.unsqueeze(-1)
        arange_b = torch.arange(batch_size)
        calc_range = seq_len - 1
        score = self.start_trans[y[:, 0]] + sum([self._calc_trans_score_for_num_llh(h_unsqueezed, y, trans, mask, t, arange_b) for t in range(calc_range)])
        last_mask_index = mask.sum(1) - 1
        last_labels = y[arange_b, last_mask_index]
        each_last_score = h[arange_b, -1, last_labels] * mask[:, -1]
        score += each_last_score + self.end_trans[last_labels]
        return score

    def _calc_trans_score_for_num_llh(self, h: FloatTensor, y: LongTensor, trans: FloatTensor, mask: BoolTensor, t: int, arange_b: FloatTensor) ->torch.Tensor:
        """
        calculate transition score for computing numberator llh
        :param h: hidden matrix (batch_size, seq_len, num_labels)
        :param y: answer labels of each sequence
                  in mini batch (batch_size, seq_len)
        :param trans: transition score
        :param mask: mask tensor of each sequence
                     in mini batch (batch_size, seq_len)
        :paramt t: index of hidden, transition, and mask matrixex
        :param arange_b: this param is seted torch.arange(batch_size)
        :param batch_size: batch size of this calculation
        """
        device = h.device
        mask_t = mask[:, t]
        mask_t = mask_t
        mask_t1 = mask[:, t + 1]
        mask_t1 = mask_t1
        h_t = h[arange_b, t, y[:, t]].squeeze(1)
        trans_t = trans[y[:, t], y[:, t + 1]].squeeze(1)
        return h_t * mask_t + trans_t * mask_t1

    def _initialize_parameters(self, pad_idx: Optional[int]) ->None:
        """
        initialize transition parameters
        :param: pad_idx: if not None, additional initialize
        :return: None
        """
        nn.init.uniform_(self.trans_matrix, -0.1, 0.1)
        nn.init.uniform_(self.start_trans, -0.1, 0.1)
        nn.init.uniform_(self.end_trans, -0.1, 0.1)
        if pad_idx is not None:
            self.start_trans[pad_idx] = -10000.0
            self.trans_matrix[pad_idx, :] = -10000.0
            self.trans_matrix[:, pad_idx] = -10000.0
            self.trans_matrix[pad_idx, pad_idx] = 0.0

