

def mpii_get_sequence_info(subject_id, sequence):

    switcher = {
        "1 1": [6416,25],
        "1 2": [12430,50],
        "2 1": [6502,25],
        "2 2": [6081,25],
        "3 1": [12488,50],
        "3 2": [12283,50],
        "4 1": [6171,25],
        "4 2": [6675,25],
        "5 1": [12820,50],
        "5 2": [12312,50],
        "6 1": [6188,25],
        "6 2": [6145,25],
        "7 1": [6239,25],
        "7 2": [6320,25],
        "8 1": [6468,25],
        "8 2": [6054,25],

    }
    return switcher.get(subject_id+" "+sequence)


import torch
import numpy as np
import hashlib
from torch.autograd import Variable
import os

def deterministic_random(min_value, max_value, data):
    digest = hashlib.sha256(data.encode()).digest()
    raw_value = int.from_bytes(digest[:4], byteorder='little', signed=False)
    return int(raw_value / (2 ** 32 - 1) * (max_value - min_value)) + min_value


def mpjpe_cal(predicted, target):
    assert predicted.shape == target.shape
    return torch.mean(torch.norm(predicted - target, dim=len(target.shape) - 1))


def test_calculation(predicted, target, action, error_sum, data_type, subject, MAE=False):
    error_sum = mpjpe_by_action_p1(predicted, target, action, error_sum)
    if not MAE:
        error_sum = mpjpe_by_action_p2(predicted, target, action, error_sum)

    return error_sum


def mpjpe_by_action_p1(predicted, target, action, action_error_sum):
    assert predicted.shape == target.shape
    batch_num = predicted.size(0)
    frame_num = predicted.size(1)
    dist = torch.mean(torch.norm(predicted - target, dim=len(target.shape) - 1), dim=len(target.shape) - 2)

    if len(set(list(action))) == 1:
        end_index = action[0].find(' ')
        if end_index != -1:
            action_name = action[0][:end_index]
        else:
            action_name = action[0]

        action_error_sum[action_name]['p1'].update(torch.mean(dist).item()*batch_num*frame_num, batch_num*frame_num)
    else:
        for i in range(batch_num):
            end_index = action[i].find(' ')
            if end_index != -1:
                action_name = action[i][:end_index]
            else:
                action_name = action[i]

            action_error_sum[action_name]['p1'].update(torch.mean(dist[i]).item()*frame_num, frame_num)

    return action_error_sum


def mpjpe_by_action_p2(predicted, target, action, action_error_sum):
    assert predicted.shape == target.shape
    num = predicted.size(0)
    pred = predicted.detach().cpu().numpy().reshape(-1, predicted.shape[-2], predicted.shape[-1])
    gt = target.detach().cpu().numpy().reshape(-1, target.shape[-2], target.shape[-1])
    dist = p_mpjpe(pred, gt)
    if len(set(list(action))) == 1:
        end_index = action[0].find(' ')
        if end_index != -1:
            action_name = action[0][:end_index]
        else:
            action_name = action[0]
        action_error_sum[action_name]['p2'].update(np.mean(dist) * num, num)
    else:
        for i in range(num):
            end_index = action[i].find(' ')
            if end_index != -1:
                action_name = action[i][:end_index]
            else:
                action_name = action[i]
            action_error_sum[action_name]['p2'].update(np.mean(dist), 1)

    return action_error_sum


def p_mpjpe(predicted, target):
    assert predicted.shape == target.shape

    muX = np.mean(target, axis=1, keepdims=True)
    muY = np.mean(predicted, axis=1, keepdims=True)

    X0 = target - muX
    Y0 = predicted - muY

    normX = np.sqrt(np.sum(X0 ** 2, axis=(1, 2), keepdims=True))
    normY = np.sqrt(np.sum(Y0 ** 2, axis=(1, 2), keepdims=True))

    X0 /= normX
    Y0 /= normY

    H = np.matmul(X0.transpose(0, 2, 1), Y0)
    U, s, Vt = np.linalg.svd(H)
    V = Vt.transpose(0, 2, 1)
    R = np.matmul(V, U.transpose(0, 2, 1))

    sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
    V[:, :, -1] *= sign_detR
    s[:, -1] *= sign_detR.flatten()
    R = np.matmul(V, U.transpose(0, 2, 1))

    tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)

    a = tr * normX / normY
    t = muX - a * np.matmul(muY, R)

    predicted_aligned = a * np.matmul(predicted, R) + t

    return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape) - 1), axis=len(target.shape) - 2)


def define_actions( action ):

  actions = ["Directions","Discussion","Eating","Greeting",
           "Phoning","Photo","Posing","Purchases",
           "Sitting","SittingDown","Smoking","Waiting",
           "WalkDog","Walking","WalkTogether"]

  if action == "All" or action == "all" or action == '*':
    return actions

  if not action in actions:
    raise( ValueError, "Unrecognized action: %s" % action )

  return [action]


def define_error_list(actions):
    error_sum = {}
    error_sum.update({actions[i]: {'p1':AccumLoss(), 'p2':AccumLoss()} for i in range(len(actions))})
    return error_sum


class AccumLoss(object):
    def __init__(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val
        self.count += n
        self.avg = self.sum / self.count


def get_varialbe(split, target):
    num = len(target)
    var = []
    if split == 'train':
        for i in range(num):
            temp = Variable(target[i], requires_grad=False).contiguous().type(torch.cuda.FloatTensor)
            var.append(temp)
    else:
        for i in range(num):
            temp = Variable(target[i]).contiguous().cuda().type(torch.cuda.FloatTensor)
            var.append(temp)

    return var


def print_error(data_type, action_error_sum, is_train):
    mean_error_p1, mean_error_p2 = print_error_action(action_error_sum, is_train)

    return mean_error_p1, mean_error_p2


def print_error_action(action_error_sum, is_train):
    mean_error_each = {'p1': 0.0, 'p2': 0.0}
    mean_error_all = {'p1': AccumLoss(), 'p2': AccumLoss()}

    if is_train == 0:
        print("{0:=^12} {1:=^10} {2:=^8}".format("Action", "p#1 mm", "p#2 mm"))

    for action, value in action_error_sum.items():
        if is_train == 0:
            print("{0:<12} ".format(action), end="")

        mean_error_each['p1'] = action_error_sum[action]['p1'].avg * 1000.0
        mean_error_all['p1'].update(mean_error_each['p1'], 1)

        mean_error_each['p2'] = action_error_sum[action]['p2'].avg * 1000.0
        mean_error_all['p2'].update(mean_error_each['p2'], 1)

        if is_train == 0:
            print("{0:>6.2f} {1:>10.2f}".format(mean_error_each['p1'], mean_error_each['p2']))

    if is_train == 0:
        print("{0:<12} {1:>6.2f} {2:>10.2f}".format("Average", mean_error_all['p1'].avg, \
            mean_error_all['p2'].avg))

    return mean_error_all['p1'].avg, mean_error_all['p2'].avg


def save_model(previous_name, save_dir,epoch, data_threshold, model, model_name):
    # if os.path.exists(previous_name):
    #     os.remove(previous_name)

    torch.save(model.state_dict(),
               '%s/%s_%d_%d.pth' % (save_dir, model_name, epoch, data_threshold * 100))

    previous_name = '%s/%s_%d_%d.pth' % (save_dir, model_name, epoch, data_threshold * 100)
    return previous_name

def save_model_new(save_dir,epoch, data_threshold, lr, optimizer, model, model_name):
    # if os.path.exists(previous_name):
    #     os.remove(previous_name)

    # torch.save(model.state_dict(),
    #            '%s/%s_%d_%d.pth' % (save_dir, model_name, epoch, data_threshold * 100))
    torch.save({
                'epoch': epoch,
                'lr': lr,
                'optimizer': optimizer.state_dict(),
                'model_pos': model.state_dict(),
            },
               '%s/%s_%d_%d.pth' % (save_dir, model_name, epoch, data_threshold * 100))
