Tutorial 2: Normalization¶

Week 1, Day 5: Microcircuits

By Neuromatch Academy

Content creators: Alish Dipani, Xaq Pitkow

Content reviewers: Yizhou Chen, RyeongKyung Yoon, Ruiyi Zhang, Lily Chamakura, Hlib Solodzhuk, Patrick Mineault, Alex Murphy

Production editors: Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Alex Murphy

Tutorial Objectives¶

Estimated timing of tutorial: 50 minutes

Recap: the definition of normalization is the process of bringing a set of values, each with potentially differing ranges and offsets, back into a common range such that there is not much of an extreme discrepancy between features. In modeling house prices using linear regression, features on the order of num_bathrooms (e.g. 1,2,3) are overshadowed by features such as square_ft (e.g. from 1,000 to 3,000). After applying normalization, these features are comparable and can be used effectively in such a linear regression.

In this tutorial, you will learn about the microcircuit element of normalization, which is a prominent computation in brains and machines. You will see different types of normalization, how to implement them, and observe some of its benefits for generalization.

Tutorial Learning Objectives

Understand how nonlinearities may be universal function approximators, but not all functions are simple to learn
Implement a family of normalization mechanisms
Demonstrate how normalization helps in learning and information transmission

Download slides

Setup¶

Install and import feedback gadget¶

# @title Install and import feedback gadget

!pip install vibecheck datatops --quiet

from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
    return DatatopsContentReviewContainer(
        "",  # No text prompt
        notebook_section,
        {
            "url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
            "name": "neuromatch_neuroai",
            "user_key": "wb2cxze8",
        },
    ).render()


feedback_prefix = "W1D5_T2"

Imports¶

# @title Imports

#working with data
import random
import numpy as np
from collections import OrderedDict


#plotting
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import seaborn as sns
import ipywidgets as widgets
import logging

#utils
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")

#modeling
import scipy
from sklearn.metrics import ConfusionMatrixDisplay
import scipy
import torch
import torchvision
import torch.nn.functional as F
from torchvision import transforms
from torch import nn
from torch.utils.data import Dataset, DataLoader, random_split

Figure settings¶

# @title Figure settings

logging.getLogger('matplotlib.font_manager').disabled = True

%matplotlib inline
%config InlineBackend.figure_format = 'retina' # perfrom high definition rendering for images and plots
plt.style.use("https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/nma.mplstyle")

Helper functions¶

# @title Helper functions

# Section 1

# Validation
VAL_X_LOW = 0
VAL_X_HIGH = 1

def store_grads(model, grads_mat):
  """
  Store the gradients of a PyTorch model's layers in a dictionary.

  Inputs:
  - model (torch.nn.Module): The PyTorch model whose gradients will be stored.
  - grads_mat (dict): A dictionary that will store the gradients: the keys correspond to the names of the model's layers, and the values are PyTorch tensors containing the gradients.

  Outputs:
  - grads_mat (dict): The input dictionary `grads_mat` with the updated gradient values.
  """
  for grad_layer_name, grad_layer in model.grad_layers.items():
    if grad_layer_name in grads_mat.keys():
      grads_mat[grad_layer_name] = torch.vstack((
          grads_mat[grad_layer_name],
          grad_layer.grad.detach().cpu()
      ))
    else:
      grads_mat[grad_layer_name] = grad_layer.grad.detach().cpu()
  return grads_mat

def train_sec1(model, train_dataloader, learning_rate, n_epochs, VAL_X_LOW, VAL_X_HIGH, \
          track_grads=False):
  """
  Train a model using the given dataloader, learning rate, and number of epochs.

  Inputs:
  - model (torch.nn.Module): The PyTorch model to be trained.
  - train_dataloader (torch.utils.data.DataLoader): Dataloader for training data.
  - learning_rate (float): The learning rate for the optimizer.
  - n_epochs (int): Number of epochs for training.
  - VAL_X_LOW (float): Lower bound of validation x values.
  - VAL_X_HIGH (float): Upper bound of validation x values.
  - track_grads (bool): Whether to track and store gradients during training.

  Outputs:
  - losses_iter (list of float): List of loss values for each iteration.
  - losses_epoch (list of float): List of loss values for each epoch.
  - training_dynamics_mat (torch.Tensor): Tensor containing the validation errors over training.
  - input_thresholds_tensor (torch.Tensor): Tensor containing the input threshold weights over training.
  - output_weights_tensor (torch.Tensor): Tensor containing the output layer weights over training.
  - gradients_mat (dict, optional): Dictionary containing the gradients if track_grads is True.
  """
  # Training settings
  loss_fn = nn.MSELoss()
  optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

  # Train the model
  model.train()
  losses_iter = []
  losses_epoch = []
  # training loss dynamics
  val_x = torch.arange(VAL_X_LOW+1e-2, VAL_X_HIGH, 0.01).unsqueeze(1).to(DEVICE)
  val_y = (1/val_x).to(DEVICE)
  training_dynamics_mat = None
  gradients_mat = {}
  # training weight dynamics
  input_thresholds_tensor = model.input_threshold_weights.cpu().clone().detach()
  output_weights_tensor = model.output_layer.weight.data[0].cpu().clone().detach()
  for epoch in range(n_epochs):
    epoch_loss = 0
    for X_batch, y_batch in train_dataloader:
        X_batch = X_batch.to(DEVICE)
        y_batch = y_batch.to(DEVICE)
        optimizer.zero_grad()
        y_pred = model(X_batch)
        loss = loss_fn(y_pred, y_batch)
        loss.backward()
        optimizer.step()
        with torch.no_grad():
          losses_iter.append(loss.cpu().item())
          epoch_loss += loss.cpu().item()
          if track_grads:
            gradients_mat = store_grads(model, gradients_mat)
    losses_epoch.append(epoch_loss)
    # Store training loss dynamics
    with torch.no_grad():
      val_errors = torch.log(nn.functional.mse_loss(model.predict(val_x), val_y, reduction='none'))
      val_errors = (val_errors.T).cpu()
      if training_dynamics_mat is None:
        training_dynamics_mat = val_errors
      else:
        training_dynamics_mat = torch.vstack((val_errors, training_dynamics_mat))
    # Store training weight dynamics
    epoch_ths = model.input_threshold_weights.cpu().clone().detach()
    epoch_wts = model.output_layer.weight.data[0].cpu().clone().detach()
    input_thresholds_tensor = torch.vstack((input_thresholds_tensor, epoch_ths))
    output_weights_tensor = torch.vstack((output_weights_tensor, epoch_wts))
  if track_grads:
    return losses_iter, losses_epoch, training_dynamics_mat, \
      input_thresholds_tensor, output_weights_tensor, gradients_mat
  else:
    return losses_iter, losses_epoch, training_dynamics_mat, \
      input_thresholds_tensor, output_weights_tensor

def evaluate_sec1(model, test_dataloader):
  """
  Evaluate a model using the given dataloader and return the test loss, input values, true values, and predicted values.

  Inputs:
  - model (torch.nn.Module): The PyTorch model to be evaluated.
  - test_dataloader (torch.utils.data.DataLoader): Dataloader for test data.

  Outputs:
  - test_loss (float): The loss on the test dataset.
  - x_all (torch.Tensor): Tensor containing all input values from the test dataset.
  - y_all (torch.Tensor): Tensor containing all true values from the test dataset.
  - y_pred_all (torch.Tensor): Tensor containing all predicted values from the test dataset.
  """
  # evaluate MSE after training
  model.eval()
  test_loss = 0
  x_all = None
  y_all = None
  y_pred_all = None
  with torch.no_grad():
    for X_batch, y_batch in test_dataloader:
      X_batch = X_batch.to(DEVICE)
      y_batch = y_batch.to(DEVICE)
      y_pred = model.predict(X_batch)
      # Store
      if x_all is None:
        x_all = X_batch.flatten().cpu().detach()
      else:
        x_all = torch.concat((x_all, X_batch.flatten().detach()))

      if y_all is None:
        y_all = y_batch.flatten().cpu().detach()
      else:
        y_all = torch.concat((y_all, y_batch.flatten().detach()))

      if y_pred_all is None:
        y_pred_all = y_pred.flatten().cpu().detach()
      else:
        y_pred_all = torch.concat((y_pred_all, y_pred.flatten().detach()))

      # Calculate loss
      loss = nn.functional.mse_loss(y_pred, y_batch)
      test_loss += loss.cpu()
    return test_loss, x_all, y_all, y_pred_all

# Section 2.2

def visualize_images_sec22(data, data_titles, viz_p):
  """
  Visualize pixels in given data arrays.

  Inputs:
  - data (list of numpy.array): List of numpy arrays to visualize.
  - data_titles (list of str): List of titles for each data array.
  - viz_p (int): Number of samples to visualize.

  Outputs:
  - None: Displays a plot of the data arrays.
  """
  with plt.xkcd():
      vmin = np.min([np.min(arr[: viz_p, :]) for arr in data])
      vmax = np.min([np.max(arr[: viz_p, :]) for arr in data])
      cmap = 'gray_r'
      # Plot
      height_ratios = [10, 2, 10] if len(data)==3 else [10, 2, 10, 10]
      figsize = (8, 6) if len(data)==3 else (8, 8)
      fig, axs = plt.subplots(len(data), 1, figsize=figsize, \
                              gridspec_kw={'height_ratios': height_ratios})
      cbar_ax = fig.add_axes([0.98, 0.125, 0.02, 0.75])  # Define position for colorbar
      for i, data_ in enumerate(data):
          sns.heatmap(data_[:viz_p, :].T, cmap=cmap, annot=False, cbar=(i==0), fmt=".2f", annot_kws={"size": 10},
                      ax=axs[i], linewidths=0, linecolor='black', square=True,
                      cbar_ax=None if i else cbar_ax)
          # Use single colorbar for first heatmap
          # Show axes on all sides
          axs[i].spines['top'].set_visible(True)
          axs[i].spines['right'].set_visible(True)
          axs[i].spines['bottom'].set_visible(True)
          axs[i].spines['left'].set_visible(True)
          axs[i].set_xticks([])
          axs[i].set_yticks([])
          axs[i].set_title(data_titles[i])
          axs[i].set_xlabel('')
          axs[i].set_ylabel('')
      # Set common x and y labels
      fig.text(0.5, 0, 'Samples', ha='center', fontsize=15)
      fig.text(0, 0.5, 'Pixel Intensity', va='center', rotation='vertical', fontsize=15)
      plt.subplots_adjust(wspace=5)  # Adjust the horizontal spacing between subplots
      plt.show()

def subsets(arr, k):
  """
  Generate all possible subsets from arr with length k.

  Inputs:
  - arr (numpy.array): 1-D numpy array or list.
  - k (int): Length of the subsets.

  Outputs:
  - subsets (numpy.array): Array of all possible subsets with length k.
  """
  from itertools import combinations
  return np.array(list(combinations(arr, k)))

# Section 2.3

def train_cnns(model, train_dataloader, learning_rate, momentum, n_epochs):
  """
  Train a CNN model using the given dataloader, learning rate, momentum, and number of epochs.

  Inputs:
  - model (torch.nn.Module): The CNN model to be trained.
  - train_dataloader (torch.utils.data.DataLoader): Dataloader for training data.
  - learning_rate (float): The learning rate for the optimizer.
  - momentum (float): The momentum for the optimizer.
  - n_epochs (int): Number of epochs for training.

  Outputs:
  - losses_iter (list of float): List of loss values for each iteration.
  - losses_epoch (list of float): List of loss values for each epoch.
  """
  # Training settings
  loss_fn = nn.CrossEntropyLoss()
  optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, \
                              momentum=momentum)

  # Train the model
  model.train()
  losses_iter = []
  losses_epoch = []
  # training loss dynamics
  gradients_mat = {}
  # training weight dynamics
  for epoch in tqdm(range(n_epochs)):
    epoch_loss = 0
    for X_batch, y_batch in train_dataloader:
        X_batch = X_batch.to(DEVICE)
        y_batch = y_batch.to(DEVICE)
        optimizer.zero_grad()
        y_pred = model(X_batch)
        loss = loss_fn(y_pred, y_batch)
        loss.backward()
        optimizer.step()
        with torch.no_grad():
          losses_iter.append(loss.cpu().item())
          epoch_loss += loss.cpu().item()
    losses_epoch.append(epoch_loss)
  return losses_iter, losses_epoch

def evaluate_cnns(model, test_dataloader):
  """
  Evaluate a CNN model using the given dataloader and return the test loss and accuracy.

  Inputs:
  - model (torch.nn.Module): The CNN model to be evaluated.
  - test_dataloader (torch.utils.data.DataLoader): Dataloader for test data.

  Outputs:
  - test_loss (float): The loss on the test dataset.
  - accuracy (float): The accuracy on the test dataset.
  """
  model.eval()
  test_loss = 0
  correct = 0
  total = 0
  with torch.no_grad():
    for X_batch, y_batch in test_dataloader:
      X_batch = X_batch.to(DEVICE)
      y_batch = y_batch.to(DEVICE)
      y_pred = model(X_batch).data
      # Calculate loss
      loss = nn.functional.cross_entropy(y_pred, y_batch)
      test_loss += loss.cpu().item()
      # Calculate accuracy
      # the class with the highest energy is what we choose as prediction
      _, predicted = torch.max(y_pred, 1)
      total += y_pred.size(0)
      correct += (predicted == y_batch).sum().item()
  accuracy = 100 * correct / total
  return test_loss, accuracy

def normalize_implemented(x, sigma, p, g):
  """
  Inputs:
  - x(np.ndarray): Input array (n_samples * n_dim)
  - sigma(float): Smoothing factor
  - p(int): p-norm
  - g(int): scaling factor

  Outputs:
  - xnorm (np.ndarray): normalized values.
  """
  # Raise the absolute value of x to the power p
  xp = np.power(np.abs(x), p)
  # Sum the x over the dimensions (n_dim) axis
  xp_sum = np.sum(np.power(np.abs(x), p), axis=1)
  # Correct the dimensions of xp_sum, and taking the average reduces the dimensions
  # Making xp_sum a row vector of shape (1, n_dim)
  xp_sum = np.expand_dims(xp_sum, axis=1)
  # Raise the sum to the power 1/p and add the smoothing factor (sigma)
  denominator = sigma + np.power(xp_sum, 1/p)
  # Scale the input data with a factor of g
  numerator = x*g
  # Calculate normalized x
  xnorm = numerator/denominator
  return xnorm

# Exercise solutions for correct plot output

class ReLUNet(nn.Module):
  """
  ReLUNet architecture
  Structure is as follows:
  y = Σi(ai * ReLU(θi - x))
  """
  # Define the structure of your network
  def __init__(self, n_units):
    """
    Args:
      n_units (int): Number of hidden units

    Returns:
      Nothing
    """
    super(ReLUNet, self).__init__()
    # Create input thresholds
    self.input_threshold_weights = nn.Parameter(torch.abs(torch.randn(n_units)))
    self.non_linearity = nn.ReLU()
    self.output_layer = nn.Linear(n_units, 1)
    nn.init.xavier_normal_(self.output_layer.weight)

  def forward(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    op = self.input_threshold_weights - x #prepare the input to be passed through ReLU
    op = self.non_linearity(op) #apply ReLU
    op = self.output_layer(op) #run through output layer
    return op

  # Choose the most likely label predicted by the network
  def predict(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    output = self.forward(x)
    return output

non_linearities = {
    'ReLU': nn.ReLU(),
    'ReLU6': nn.ReLU6(),
    'SoftPlus': nn.Softplus(),
    'Sigmoid': nn.Sigmoid(),
    'Tanh': nn.Tanh()
}

def HardTanh(x):
  """
  Calculates `tanh` output for the given input data.

  Inputs:
  - x (np.ndarray): input data.

  Outputs:
  - output (np.ndarray): `tanh(x)`.
  """
  min_val = -1
  max_val = 1
  output = np.copy(x)
  output[output>max_val] = max_val
  output[output<min_val] = min_val
  return output

def LeakyHardTanh(x, leak_slope=0.03):
  """
  Calculate `tanh` output for the given input data with the leaky term.

  Inputs:
  - x (np.ndarray): input data.
  - leak_slope (float, default = 0.03): leaky term.

  Outputs:
  - output (np.ndarray): `tanh(x)`.
  """
  output = np.copy(x)
  output = HardTanh(output) + leak_slope*output
  return output

def InverseLeakyHardTanh(y, leak_slope=0.03):
  """
  Calculate input into the `tanh` function with the leaky term for the given output.

  Inputs:
  - y (np.array): output of leaky tanh function.
  - leak_slope (float, default = 0.03): leaky term.

  Outputs:
  - output (np.array): input into leaky tanh function.
  """
  ycopy = np.copy(y)
  output = np.where(
      np.abs(ycopy) >= 1+leak_slope, \
      (ycopy - np.sign(ycopy))/leak_slope, \
      ycopy/(1+leak_slope)
  )
  return output

Set random seed¶

# @title Set random seed

def set_seed(seed=None, seed_torch=True):
  if seed is None:
    seed = np.random.choice(2 ** 32)
  random.seed(seed)
  np.random.seed(seed)
  if seed_torch:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True


# In case that `DataLoader` is used
def seed_worker(worker_id):
  worker_seed = torch.initial_seed() % 2**32
  np.random.seed(worker_seed)
  random.seed(worker_seed)

set_seed(seed=42, seed_torch=True)

Set device (GPU or CPU)¶

# @title Set device (GPU or CPU)

def set_device():
  device = "cuda" if torch.cuda.is_available() else "cpu"
  if device != "cuda":
    print("GPU is not enabled in this notebook. \n"
          "If you want to enable it, in the menu under `Runtime` -> \n"
          "`Hardware accelerator.` and select `GPU` from the dropdown menu")
  else:
    print("GPU is enabled in this notebook. \n"
          "If you want to disable it, in the menu under `Runtime` -> \n"
          "`Hardware accelerator.` and select `None` from the dropdown menu")

  return device

DEVICE = set_device()

GPU is not enabled in this notebook. 
If you want to enable it, in the menu under `Runtime` -> 
`Hardware accelerator.` and select `GPU` from the dropdown menu

Section 1: Can ReLUs implement normalization?¶

In this section we will explore how feasible it is to estimate a normalization-like function.

Video 1: Introduction

Youtube

Bilibili

The general form of normalization is:

\hat{x} = \frac{x}{f(||x||)}

(1)

There are indeed many options for the specific form of the denominator here; still, what we want to highlight is the essential nature of the normalization, that it is achieved by dividing the values of the vector by some function $f$ of its magnitude.

Evidence suggests that normalization provides a useful inductive bias in artificial and natural systems. However, do we need a dedicated computation (a microcircuit) that implements normalization? Can we not just let the network take care of it during the learning of the weights?

Let’s explore if a fully-connected network with ReLU nonlinearities can estimate a normalization-like function. Specifically, we will see if a fully-connected one-layer network can estimate the function $y=\frac{1}{x+\epsilon}$ .

In the cell below, we visualize train and test data.

Generate $y=\frac{1}{x+\epsilon}$ train and test dataloaders¶

$\epsilon = 0.01$

# @title Generate $y=\frac{1}{x+\epsilon}$ train and test dataloaders
# Target function y = 1/x+ε

# @markdown $\epsilon = 0.01$

N_SAMPLES = 5000
X_LOW = 0
X_HIGH = 10
TRAIN_RATIO = 0.7
EPSILON = 1e-2

range01_ratio = 0.20 # % of samples in the range 0-1
X1 = torch.distributions.uniform.Uniform(X_LOW, 1).rsample(sample_shape=torch.Size([int(N_SAMPLES*range01_ratio), 1]))
X2 = torch.distributions.uniform.Uniform(1, X_HIGH).rsample(sample_shape=torch.Size([int(N_SAMPLES*(1-range01_ratio)), 1]))
X_sec1 = torch.concatenate((X1, X2)) + EPSILON
y_sec1 = 1/X_sec1

class ReLUDataset(Dataset):
  def __init__(self, X, y):
    self.X = X
    self.y = y

  def __len__(self):
    return len(self.y)

  def __getitem__(self, idx):
    X = self.X[idx]
    y = self.y[idx]
    return X, y

dataset_sec1 = ReLUDataset(X_sec1, y_sec1)

# Define the sizes for training and testing sets
train_size = int(TRAIN_RATIO * len(dataset_sec1))
test_size = len(dataset_sec1) - train_size

# Split the dataset into training and testing sets
train_dataset_sec1, test_dataset_sec1 = random_split(dataset_sec1, [train_size, test_size])

# Dataloaders
# Create DataLoader for the training set
train_dataloader_sec1 = DataLoader(train_dataset_sec1, batch_size=len(train_dataset_sec1), shuffle=True)

# Create DataLoader for the testing set
test_dataloader_sec1 = DataLoader(test_dataset_sec1, batch_size=len(test_dataset_sec1), shuffle=False)

train_data = torch.column_stack((train_dataset_sec1.dataset.X[train_dataset_sec1.indices], train_dataset_sec1.dataset.y[train_dataset_sec1.indices]))
sorted_indices = torch.argsort(train_data[:, 0])
train_data_sorted = torch.index_select(train_data, 0, sorted_indices)

with plt.xkcd():
    plt.plot(train_data_sorted[:, 0], train_data_sorted[:, 1], 's-y', label='train')
    plt.plot(test_dataset_sec1.dataset.X[test_dataset_sec1.indices], test_dataset_sec1.dataset.y[test_dataset_sec1.indices], 'Dk', label='test', alpha=0.5, markersize=1.5)
    plt.xlabel('Input (x)')
    plt.ylabel('Output (y)')
    plt.title(r'$y=\frac{1}{x+\epsilon}$')
    plt.legend(prop={'size': 15})
    ax = plt.gca()
    for line in ax.get_lines():
      line.set_path_effects([path_effects.Normal()])
    plt.show()

Coding Exercise 1: ReLUNet¶

Let’s define a simple one-layer neural network using the following equation, with our single scalar input $x$ :

\hat{y} = \sum_{i}w_{i} \text{ReLU}(\theta_{i} - x)

(2)

Here $\theta_{i}$ is the threshold, and $w_{i}$ is the slope of neuron $i$ . $\theta_{i}$ & $w_{i}$ are learned parameters. Our network has a total of 100 neurons (the summation and aggregation over index $i$ reflects each of these 100 neurons). Your task is to complete the forward pass of the model.

###################################################################
## Fill out the following then remove
raise NotImplementedError("Student exercise: complete forward pass.")
###################################################################

class ReLUNet(nn.Module):
  """
  ReLUNet architecture
  The structure is the following:
  y = Σi(wi * ReLU(θi - x))
  """
  # Define the structure of your network
  def __init__(self, n_units):
    """
    Args:
      n_units (int): Number of hidden units

    Returns:
      Nothing
    """
    super(ReLUNet, self).__init__()
    # Create input thresholds
    self.input_threshold_weights = nn.Parameter(torch.abs(torch.randn(n_units)))
    self.non_linearity = nn.ReLU()
    self.output_layer = nn.Linear(n_units, 1)
    nn.init.xavier_normal_(self.output_layer.weight)

  def forward(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    op = ... - ... #prepare the input to be passed through ReLU
    op = self.non_linearity(...) #apply ReLU
    op = ... #run through output layer
    return op

  # Choose the most likely label predicted by the network
  def predict(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    output = self.forward(x)
    return output

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[8], line 3
      1 ###################################################################
      2 ## Fill out the following then remove
----> 3 raise NotImplementedError("Student exercise: complete forward pass.")
      4 ###################################################################
      6 class ReLUNet(nn.Module):

NotImplementedError: Student exercise: complete forward pass.

Click for solution

Now, let’s train the model and evaluate it.

Training & Evaluating model¶

# @title Training & Evaluating model

# Variables
# Model
# Training
n_epochs = 200
learning_rate = 5e-2

# Create a new ReLUNet and transfer it to the device
model = ReLUNet(100).to(DEVICE)

# Train ReLUNet
losses_iter, losses_epoch, training_dynamics_mat, \
    input_thresholds_tensor, output_weights_tensor = train_sec1(model, \
    train_dataloader_sec1, learning_rate, n_epochs, VAL_X_LOW, VAL_X_HIGH)

# Evaluate ReLUNet
test_loss, x_all, y_all, y_pred_all = evaluate_sec1(model, test_dataloader_sec1)

with plt.xkcd():
    # Plot training and evaluation performance
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    # Plot training loss per epoch
    ax[0].plot(range(1, len(losses_epoch)+1), losses_epoch, '-k')
    # plot settings
    ax[0].set_xlabel('Epochs')
    ax[0].set_ylabel('MSE Loss')
    ax[0].set_title('Training Loss per Epoch')

    # Plotting evaluation performance
    # plot errors
    y_errs = nn.functional.mse_loss(y_pred_all, y_all, reduction='none')
    ax[1].bar(x_all, y_errs, width=0.1, color='red', alpha=0.5, \
              label='error = $(y - \^y)^{2}$')
    # plot predicted values
    eval_plot_data = torch.column_stack((x_all, y_all, y_pred_all)) # Sort data for plotting
    sorted_indices = torch.argsort(eval_plot_data[:, 0])
    eval_plot_data_sorted = torch.index_select(eval_plot_data, 0, sorted_indices)
    ax[1].plot(eval_plot_data_sorted[:, 0], eval_plot_data_sorted[:, 2], 'db', label=r'$\^y$', markersize=1.5)
    # plot ground truth
    x_values = np.linspace(X_LOW+1e-2, X_HIGH+1e-2, 1000)
    y_values = 1 / x_values
    # plot settings
    ax[1].plot(x_values, y_values, '-k', alpha=0.5, label=r'$y=\frac{1}{x+\epsilon}$')
    ax[1].set_title(f'Predictions, Test Loss={test_loss:.3f}')
    ax[1].set_ylim((-0.5, 10))
    ax[1].set_xlabel('Input (x)')
    ax[1].set_ylabel('Output (y)')
    ax[1].legend()
    plt.tight_layout()
    ax = plt.gca()
    for line in ax.get_lines():
      line.set_path_effects([path_effects.Normal()])
    plt.show()

While the model learns, we see that it does not fit the test data well. Let’s find out where the model struggles during training.

Here, we plot the log mean-squared errors for values of $x$ between 0 and 1 and their progression with epochs. These are log errors (clipped at $e^{5}$ represented with blue color).

Plot Training Loss Dynamics¶

# @title Plot Training Loss Dynamics

MAX_CLIP = 5

with plt.xkcd():
    plt.figure(figsize=(10, 5))

    # Create a custom colormap for clipping
    light_pal = sns.light_palette("darkred", as_cmap=True)
    clipping_color = [0., 0.75, 1., 1.] # RGBA
    new_colors = np.vstack( (light_pal(np.arange(light_pal.N)), np.array([clipping_color])) )
    custom_cmap = sns.blend_palette(new_colors, as_cmap=True)

    ax = sns.heatmap(training_dynamics_mat.numpy(), vmax=MAX_CLIP, vmin = 0, cmap=custom_cmap)

    xptslen = training_dynamics_mat.shape[1]
    xticklabels = np.round(np.arange(VAL_X_LOW, VAL_X_HIGH + 0.05, 0.2), decimals=1)
    ax.set_xticks(np.linspace(0, xptslen, len(xticklabels)), labels=xticklabels)
    ax.set_yticks(np.arange(0, n_epochs+.1, 20), labels=np.arange(n_epochs, -0.1, -20, dtype=int))
    ax.set_xlabel('Input (x)')
    ax.set_ylabel('Epochs')
    plt.title('Log Train MSE Loss')
    plt.show()

We can see that the model has higher errors for lower values of $x$ , and as the training progresses, the errors for lower $x$ values start to decrease. Note that the losses are huge for very small values of $x$ ( $> e^5$ ).

Does it mean the model employs more resources to learn the function between 0 and 1? To check this out, let’s visualize the ReLU thresholds. Concretely, let’s look at the theta values representing the 100 neurons in our one-layer neural network. This will allow us to see where the model is placing most of the emphasis in order to learn the target function.

If you’re unsure about the terminology being used (threshold and ReLU value) then it’s useful to remember that a neural network is modelled by weights and biases (slopes and intercepts). Each layer of a neural network has an associated slope/weight which is combined with the input value and is then added to a bias (intercept) which then goes through the nonlinearity (ReLU here). The red lines in the figure below represent the bias/intercept values, while the teal lines represent the slopes/weights.

Vizualize ReLUs¶

# @title Vizualize ReLUs
# Get model weights
l1_thresholds = model.input_threshold_weights.data.cpu()
l2_slopes = model.output_layer.weight.data[0].cpu()
l2_bias = model.output_layer.bias.item()

# Visualizing
# X points
# 1 * n_samples
xpoints = torch.arange(-5, X_HIGH, 0.04).unsqueeze(1)
# zi = thetai - x
# n_samples * n_units
thetai = l1_thresholds.repeat(len(xpoints), 1)
# n_samples * n_units
zi = thetai - xpoints
# n_samples * n_units
hi = torch.maximum(zi, torch.tensor(0, dtype=torch.float32))
# n_samples * n_units
ahi = hi * l2_slopes
# y = Σi(ahi)
y_pred = torch.sum(ahi, axis=1) + l2_bias

with plt.xkcd():
    # Visualizing
    plt.title(f'Visualize ReLUs')

    # y =1/x
    # Generate x values in the range [X_LOW, X_HIGH]
    x_values = np.linspace(X_LOW+1e-2, X_HIGH+1e-2, 1000)
    # Calculate y values for the function y = 1/x
    y_values = 1 / x_values
    plt.plot(x_values, y_values, '-k', alpha=1, label=r'$y=\frac{1}{x+\epsilon}$')

    # x = 0
    # plt.axvline(x=0, c='k',label='x=0')

    # y_hat
    plt.plot(xpoints, y_pred, 'sb', markersize=7, label=r'$\^y$')

    # ReLUs
    for i in range(ahi.shape[-1]):
      plt.plot(xpoints, ahi[:, i], '-', alpha=1, color='lightblue')
    plt.plot([], [], '-', label='ReLUs', color='lightblue')

    # Thresholds
    plt.plot(l1_thresholds, np.zeros(len(l1_thresholds)), '|r', \
             markersize=15, label=r'$\theta_{i}$')

    plt.xlabel('Input (x)')
    plt.ylabel('Output (y)')
    plt.ylim((0, 5))
    plt.xlim((0, 3))
    plt.legend()
    ax = plt.gca()
    for line in ax.get_lines():
      line.set_path_effects([path_effects.Normal()])
    plt.show()

We can see that the thresholds (red lines) are bunched up between 0 and 1, which means that the model dedicates the most resources to learning the function on this interval. Let’s quantify the learning by plotting the threshold distributions and dynamics with epochs.

Here we plot the cumulative distributions of $\theta_{i}$ & $w_{i}$ . We also plot the values of the parameters as they change across epochs.

Plot Weight Dynamics¶

# @title Plot Weight Dynamics

with plt.xkcd():
    fig, ax = plt.subplots(2, 2, figsize=(12.5,10))
    ax[0, 0].set_xlabel(r'Thresholds ($\theta_{i}$)')
    thereshold_weights = model.input_threshold_weights.data.cpu()
    sns.ecdfplot(input_thresholds_tensor[0, :], color='b', ax=ax[0, 0], label='initial')
    sns.ecdfplot(thereshold_weights, color='r', ax=ax[0, 0], label='final')
    ax[0, 0].legend()
    ax[0, 1].set_xlabel('Slopes ($𝑤_{i}$)')
    slopes = model.output_layer.weight.data[0].cpu()
    sns.ecdfplot(output_weights_tensor[0, :], color='b', ax=ax[0, 1], label='initial')
    sns.ecdfplot(slopes, color='r', ax=ax[0, 1], label='final')
    fig.suptitle(r'$\hat{y} = \sum_{i}𝑤_{i} ReLU(\theta_{i} - x)$')
    ax[0, 1].legend()

    # Input thresholds
    n_cols = input_thresholds_tensor.shape[-1]
    n_rows = input_thresholds_tensor.shape[0]
    for n_col in range(n_cols):
      ax[1, 0].plot(range(n_rows), input_thresholds_tensor[:, n_col], '-k', alpha=0.5)
    ax[1, 0].set_xlabel('Epochs')
    ax[1, 0].set_ylabel('Input Thresholds')
    # Output weights
    n_cols = output_weights_tensor.shape[-1]
    n_rows = output_weights_tensor.shape[0]
    for n_col in range(n_cols):
      ax[1, 1].plot(range(n_rows), output_weights_tensor[:, n_col], '-k', alpha=0.5)
    ax[1, 1].set_xlabel('Epochs')
    ax[1, 1].set_ylabel('Output weights')
    plt.tight_layout()
    plt.show()

From the cumulative distribution plot of the thresholds ( $\theta_{i}$ ), we can see that around $80%$ of them are below $x=1$ . Hence, the model majorly struggles to learn the function between 0 and 1, where the slope changes a lot.

Since the slope changes infinite times between $x=0$ and 1, and ReLUs implement a linear function with a single slope, we would ideally need an infinite number of ReLUs units to fit the $y=\frac{1}{x+\epsilon}$ function. Hence, even though theoretically we can estimate the function, it is not empirically feasible to do so. This suggests that a microcircuit able to implement this computation would be a very useful thing to have.

Coding Exercise 1 Discussion¶

Do you think that having more slope changes in the activation function would help?

Take a minute to think on your own, then discuss in a group.

Coding Exercise 2: Test other non-linear activation functions¶

Let’s see if other non-linear activation functions perform better. Specifically, we test:

$\text{ReLU}(x) = (x)^{+} = \max(0,x)$
$\text{ReLU6}(x) = \min(\max(0,x),6)$
$\text{SoftPlus}(x, \beta=1) = \frac{1}{\beta} \log(1+e^{βx})$
$\text{Sigmoid}(x) = \sigma(x)= \frac{1}{1+e^{-x}}$
$\text{Tanh}(x) = \frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}$

We still consider the case of the one-layer neural network, except we change the activation function to be one of the above:

\hat{y} = \sum_{i}w_{i} \text{Activation}(\theta_{i} - x)

(3)

Here $\theta_{i}$ is the threshold, and $w_{i}$ is the slope of neuron $i$ . We train and evaluate each model three times and plot the mean performance across runs in order to make sure our results are not highly dependent on any specific initialization setting of the network. Your task is to complete the dictionary of the proposed non-linear functions (by defining them using torch.nn library).

###################################################################
## Fill out the following then remove
raise NotImplementedError("Student exercise: complete non-linearities.")
###################################################################
non_linearities = {
    'ReLU': nn.ReLU(),
    'ReLU6': ...,
    'SoftPlus': nn.Softplus(),
    'Sigmoid': ...,
    'Tanh': ...
}

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[13], line 3
      1 ###################################################################
      2 ## Fill out the following then remove
----> 3 raise NotImplementedError("Student exercise: complete non-linearities.")
      4 ###################################################################
      5 non_linearities = {
      6     'ReLU': nn.ReLU(),
      7     'ReLU6': ...,
   (...)
     10     'Tanh': ...
     11 }

NotImplementedError: Student exercise: complete non-linearities.

Click for solution

Now, let’s train different networks and evaluate them. Notice that the cell below will run for 1 minute approximately.

Train & Evaluate¶

# @title Train & Evaluate

class NonLinearNet(nn.Module):
  """
  NonLinearNet architecture
  The structure is the following:
  y = Σi(ai * Non-Linearity(θi - x))
  """
  # Define the structure of your network
  def __init__(self, n_units, non_linearity):
    """
    Args:
      n_units (int): Number of hidden units

    Returns:
      Nothing
    """
    super(NonLinearNet, self).__init__()
    self.n_units = n_units
    self.input_threshold_weights = nn.Parameter(torch.normal(0., 0.1, (self.n_units,)))
    self.non_linearity = non_linearity
    self.output_layer = nn.Linear(n_units, 1)
    nn.init.normal_(self.output_layer.weight, mean=0, std=0.1)

  def forward(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    # Threshold
    op = self.input_threshold_weights - x
    op = self.non_linearity(op)
    op = self.output_layer(op)
    return op

  # Choose the most likely label predicted by the network
  def predict(self, x):
    """
    Args:
      x: torch.Tensor
        Input tensor of size ([1])
    """
    output = self.forward(x)
    return output

# Model
n_units = 1

# Training
n_epochs = 100
learning_rate = 5e-3

# Experiment
n_runs = 3

nls_train_loss_epochs = {}
nls_test_losses = {}

for n_run in range(n_runs):
  for nl_name, nl in non_linearities.items():
    model = NonLinearNet(n_units, nl).to(DEVICE)
    losses_iter, losses_epoch, training_dynamics_mat, \
        input_thresholds_tensor, output_weights_tensor = train_sec1(model, \
        train_dataloader_sec1, learning_rate, n_epochs, VAL_X_LOW, VAL_X_HIGH)
    if nl_name in nls_train_loss_epochs.keys():
      nls_train_loss_epochs[nl_name] = np.vstack((nls_train_loss_epochs[nl_name], np.array(losses_epoch)))
    else:
      nls_train_loss_epochs[nl_name] = np.array(losses_epoch)

    test_loss, x_all, y_all, y_pred_all = evaluate_sec1(model, test_dataloader_sec1)
    if nl_name in nls_test_losses.keys():
      nls_test_losses[nl_name].append(test_loss.item())
    else:
      nls_test_losses[nl_name] = [test_loss.item()]

with plt.xkcd():
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    # Plot Training loss
    colors = iter(plt.cm.rainbow(np.linspace(0, 1, len(nls_train_loss_epochs))))
    for nl_ in non_linearities.keys():
      c = next(colors)
      mean_train_loss = np.mean(nls_train_loss_epochs[nl_], axis=0)
      mean_train_loss = np.log(mean_train_loss)
      ax[0].plot(range(1, len(mean_train_loss)+1), mean_train_loss, '-', color=c, label=nl_)
    ax[0].set_xlabel('Epochs')
    ax[0].set_ylabel('Log Mean MSE Loss')
    ax[0].legend()
    ax[0].set_title('Training Loss')

    # Plot loss per epoch
    colors = iter(plt.cm.rainbow(np.linspace(0, 1, len(nls_train_loss_epochs))))
    box = ax[1].boxplot(list(nls_test_losses.values()), showfliers=False, \
                        medianprops={'color':'gray'})
    for median in box['medians']:
      c = next(colors)
      median.set_color(c)
    # plt.ylim((-0.5, 5))
    ax[1].set_xticks(range(1, len(nls_test_losses)+1), labels=nls_test_losses.keys())
    ax[1].set_xlabel('Non Linearity')
    ax[1].set_ylabel('Test MSE Loss')
    ax[1].set_title('Test loss')
    plt.tight_layout()
    plt.show()

We can see that all of the proposed non-linear activation functions do not perform very well. Hence, it is beneficial to have dedicated computation that implements normalization.

Video 2: Summary

Youtube

Bilibili

Section 2: Benefits of normalization¶

Estimated timing to here from start of tutorial: 20 minutes.

In this section, we will propose a simple normalization function, which you are going to test in different environments and observe how it is connected to the concept of generalization, the overarching theme of this entire course.

Video 3: Introduction to Normalization

Youtube

Bilibili

Subsection 2.1: Explore normalization¶

This subsection is devoted to the definition of simple normalization function and the exploration of hyperparameters’ impact on its result.

Coding Exercise 2.1: Implement normalization¶

Let’s implement the normalization we saw in the video above. Specifically, we will use the following function:

\mathbf{x}_{norm} = \frac{g \mathbf{x}}{\sigma + \sqrt[p]{\Sigma_{i = 1}^{N} |x_{i}|^{p}}}

(4)

Where

$x$ is an $N$ -dimensional vector ( $x \in \mathbb{R}^N$ ),
$g$ is a scaling factor,
$\sigma$ is a smoothing (translation) factor,
$p$ defines $p$ -norm of the input vector (e.g. if $p=2$ this is an $L2$ norm)

Note that we are normalizing across dimensions (similar to Layer Normalization).

You can test your implementation by running the code cell below!

def normalize(x, sigma, p, g):
  """
  Inputs:
  - x(np.ndarray): Input array (n_samples * n_dim)
  - sigma(float): Smoothing factor
  - p(int): p-norm
  - g(int): scaling factor

  Outputs:
  - xnorm (np.ndarray): normalized values.
  """
  #################################################
  ## TODO: Implement the normalization example equation ##
  # Fill remove the following line of code once you have completed the exercise:
  raise NotImplementedError("Student exercise: complete normalization function.")
  #################################################
  # Raise the absolute value of x to the power p
  xp = ...
  # Sum the x over the dimensions (n_dim) axis
  xp_sum = ...
  # Correct the dimensions of xp_sum, and taking the average reduces the dimensions
  # Making xp_sum a row vector of shape (1, n_dim)
  xp_sum = np.expand_dims(xp_sum, axis=1)
  # Raise the sum to the power 1/p and add the smoothing factor (sigma)
  denominator = ...
  # Scale the input data with a factor of g
  numerator = ...
  # Calculate normalized x
  xnorm = numerator/denominator
  return xnorm

Click for solution

Test normalize() function

# @markdown Test `normalize()` function
def check_normalize(func):
  def np_norm(x, sigma, p, g):
    xnorm = (x*g)/(np.expand_dims(np.linalg.norm(x, ord=p, axis=1), axis=1)+sigma)
    return xnorm
  # Function to check the normalization function
  incorrect_message = "Normalize function incorrect"
  test_x = np.random.rand(3, 3)
  # Test 1
  assert np.array_equal(np_norm(test_x, 1, 1, 1), normalize(test_x, 1, 1, 1)), incorrect_message
  # Test 2
  assert np.array_equal(np_norm(test_x, 2, 0.3, 1.2), normalize(test_x, 2, 0.3, 1.2)), incorrect_message
  # Test 3
  assert np.array_equal(np_norm(test_x, 0.1, 3, 2), normalize(test_x, 0.1, 3, 2)), incorrect_message
  # Test 4
  assert np.array_equal(np_norm(test_x, 2.4, 3.2, 1.5), normalize(test_x, 2.4, 3.2, 1.5)), incorrect_message
  print('Normalize function works correctly!')

check_normalize(normalize)

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[16], line 19
     16   assert np.array_equal(np_norm(test_x, 2.4, 3.2, 1.5), normalize(test_x, 2.4, 3.2, 1.5)), incorrect_message
     17   print('Normalize function works correctly!')
---> 19 check_normalize(normalize)

Cell In[16], line 10, in check_normalize(func)
      8 test_x = np.random.rand(3, 3)
      9 # Test 1
---> 10 assert np.array_equal(np_norm(test_x, 1, 1, 1), normalize(test_x, 1, 1, 1)), incorrect_message
     11 # Test 2
     12 assert np.array_equal(np_norm(test_x, 2, 0.3, 1.2), normalize(test_x, 2, 0.3, 1.2)), incorrect_message

Cell In[15], line 15, in normalize(x, sigma, p, g)
      2 """
      3 Inputs:
      4 - x(np.ndarray): Input array (n_samples * n_dim)
   (...)
     10 - xnorm (np.ndarray): normalized values.
     11 """
     12 #################################################
     13 ## TODO: Implement the normalization example equation ##
     14 # Fill remove the following line of code once you have completed the exercise:
---> 15 raise NotImplementedError("Student exercise: complete normalization function.")
     16 #################################################
     17 # Raise the absolute value of x to the power p
     18 xp = ...

NotImplementedError: Student exercise: complete normalization function.

Interactive Demo 2.1¶

Let’s explore the effect of smoothing factor ( $\sigma$ ), p-norm ( $p$ ) and scaling factor ( $g$ ) in our normalization function:

\mathbf{x}_{norm} = \frac{g \mathbf{x}}{\sigma + \sqrt[p]{\Sigma_{i = 1}^{N} |x_{i}|^{p}}}

(5)

We will see the effect of normalization being induced on the points sampled from a 2-dimensional normal distribution.

Take a minute to play around with the values and then discuss them in the group.

Effect of smoothing factor ( $\sigma$ ), p-norm ( $p$ ) and scaling factor ( $g$ )¶

# @title Effect of smoothing factor ($\sigma$), p-norm ($p$) and scaling factor ($g$)

n_points = 1000
n_dim = 2
x_sec21 = np.random.normal(loc=0.0, scale=0.5, size=(n_points, n_dim))

@widgets.interact(sigma=widgets.FloatSlider(0.1, min=0, max=2, description='σ', layout=widgets.Layout(width='50%')),\
                  p=widgets.FloatSlider(1, min=0.1, max=5, description=r'p', layout=widgets.Layout(width='50%')), \
                  g=widgets.FloatSlider(1, min=0.1, max=2, description='g', layout=widgets.Layout(width='50%')))
def visualize_normalization(sigma, p, g):
  x_ = normalize_implemented(x_sec21, sigma, p, g)
  # Create a figure and axis
  fig, ax = plt.subplots(figsize=(5, 5))

  # Set the spines (axes lines) to intersect at the center
  ax.spines['left'].set_position('zero')
  ax.spines['bottom'].set_position('zero')
  ax.spines['right'].set_color('none')
  ax.spines['top'].set_color('none')

  # Set the ticks
  ax.xaxis.set_ticks_position('bottom')
  ax.yaxis.set_ticks_position('left')

  # Bold ticks
  for tick in ax.get_xticklabels():
      tick.set_fontweight('bold')
  for tick in ax.get_yticklabels():
      tick.set_fontweight('bold')

  ax.plot(x_sec21[:, 0], x_sec21[:, 1], '.b', markersize=5, alpha=0.5, label='Original')
  ax.plot(x_[:, 0], x_[:, 1], '.r', markersize=5, alpha=0.75, label='Normalized')
  ax.set_xlabel('$x_{1}$', loc='right', fontsize=20, fontweight='bold')
  ax.set_ylabel('$x_{2}$', loc='top', rotation=0, fontsize=20, fontweight='bold')
  ax.set_xlim((-2, 2))
  ax.set_ylim((-2, 2))
  ax.legend()
  plt.show()

Video 4: Effect of smoothing factor, p-norm and scaling factor

Youtube

Bilibili

Subsection 2.2: Estimating latent properties¶

In this subsection, we will use the normalization function to retrieve the target variable being corrupted with scaling.

Video 5: Normalization example

Youtube

Bilibili

Interactive Demo 2.2.1¶

For this demo, we have a target image ( $\mathbf{x}$ ), which we would like to infer, and a visible image ( $\mathbf{v}$ ), which is a scaled version of the target one: $\mathbf{x} = s \mathbf{v}$ . We will generate 300 different examples (we will visualize only 25 of them) of 5-dimensional vectors $\mathbf{x}$ (each of the components of the vectors is generated from an exponential distribution with $\lambda = 1$ ). Then, the scaling factor $s$ is generated from an exponential distribution with $\lambda = 1$ as well.

Play around with different hyperparameter values to get the best R-squared value.

number_samples = 300 # Number of samples
number_pixels = 5 # Number of pixels per sample

# True reflectance
reflectance = np.random.exponential(1, size=(number_samples, number_pixels))
# Illuminant intensity
illuminant_intensity = np.random.exponential(1, size=(number_samples, 1))
# Visible image
visible_image = np.repeat(illuminant_intensity, number_pixels, axis=1) * reflectance

#################################################
## TODO: Implement the normalization example equation ##
# Fill out and remove the following line of code one you have completed the exercise:
raise NotImplementedError("Student exercise: choose your parameters values.")
#################################################

# Normalized visible image
norm_visible_image = normalize(
    visible_image,
    sigma = ...,
    p = ...,
    g = ...
)

# Visualize the images
visualize_images_sec22(
    [reflectance, illuminant_intensity, visible_image, norm_visible_image],
    ['Reflectance', 'Illuminant intensity', 'Visible image', 'Normalized visible image'],
    25
)

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[18], line 14
      9 visible_image = np.repeat(illuminant_intensity, number_pixels, axis=1) * reflectance
     11 #################################################
     12 ## TODO: Implement the normalization example equation ##
     13 # Fill out and remove the following line of code one you have completed the exercise:
---> 14 raise NotImplementedError("Student exercise: choose your parameters values.")
     15 #################################################
     16 
     17 # Normalized visible image
     18 norm_visible_image = normalize(
     19     visible_image,
     20     sigma = ...,
     21     p = ...,
     22     g = ...
     23 )

NotImplementedError: Student exercise: choose your parameters values.

Click for solution

Example output:

Plot correlation of estimated reflectance with true reflectance¶

# @title Plot correlation of estimated reflectance with true reflectance

with plt.xkcd():
    fig, ax = plt.subplots(1, 2, figsize=(15, 5))
    # Plot v vs. x
    x_ = reflectance.reshape((-1, 1)).squeeze(-1)
    y_ = visible_image.reshape((-1, 1)).squeeze(-1)
    sns.regplot(x=x_, y=y_, ax=ax[0])
    ax[0].set_xlabel('Reflectance')
    ax[0].set_ylabel('Visible image')
    # Calculate R-squared and p-value
    result = scipy.stats.linregress(x_, y_)
    ax[0].set_title(f'$R^{2}$ = {(result.rvalue**2):.2f}')

    # Plot xh vs. x
    x_ = reflectance.reshape((-1, 1)).squeeze(-1)
    y_ = norm_visible_image.reshape((-1, 1)).squeeze(-1)
    sns.regplot(x=x_, y=y_, ax=ax[1])
    ax[1].set_xlabel('Reflectance')
    ax[1].set_ylabel('Normalized visible image')
    ax[1].set_ylim((-0.05, 1.05))
    # Calculate R-squared and p-value
    result = scipy.stats.linregress(x_, y_)
    ax[1].set_title(f'$R^{2}$ = {(result.rvalue**2):.2f}')

    plt.tight_layout()
    plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[19], line 17
     15 # Plot xh vs. x
     16 x_ = reflectance.reshape((-1, 1)).squeeze(-1)
---> 17 y_ = norm_visible_image.reshape((-1, 1)).squeeze(-1)
     18 sns.regplot(x=x_, y=y_, ax=ax[1])
     19 ax[1].set_xlabel('Reflectance')

NameError: name 'norm_visible_image' is not defined

Interactive Demo 2.2.2¶

Now, explore the impact of change in the hyperparemeter values on the R-squared value by using interactive sliders below.

Effect of smoothing factor ( $\sigma$ ), p-norm ( $p$ ) and scaling factor ( $g$ )¶

# @title Effect of smoothing factor ($\sigma$), p-norm ($p$) and scaling factor ($g$)
@widgets.interact(sigma=widgets.FloatSlider(0.1, min=0, max=2, description='σ', layout=widgets.Layout(width='50%')),\
                  p=widgets.FloatSlider(1, min=0.1, max=5, description=r'p', layout=widgets.Layout(width='50%')), \
                  g=widgets.FloatSlider(1, min=0.1, max=2, description='g', layout=widgets.Layout(width='50%')))
def plot_correlation_vhat_x(sigma, p, g):
  # Plot xh vs. x
  x_ = reflectance.reshape((-1, 1)).squeeze(-1)
  y_ = normalize_implemented(visible_image, sigma, p, g).reshape((-1, 1)).squeeze(-1)
  sns.regplot(x=x_, y=y_)
  plt.xlabel('Reflectance')
  plt.ylabel('Normalized visible image')
  plt.ylim((-0.05, 1.05))
  # Calculate R-squared and p-value
  result = scipy.stats.linregress(x_, y_)
  plt.title(f'$R^{2}$ = {(result.rvalue**2):.2f}')
  plt.tight_layout()
  plt.show()

Video 6: Summary

Youtube

Bilibili

Subsection 2.3: Layer Normalization¶

In this subsection, we are going to the explore normalization operation on a real application from the CIFAR10 dataset. This data subset has undergone scaling that we will aim to correct for.

Video 7: Image recognition example

Youtube

Bilibili

First, we fetch the dataset and derive only the part corresponding to the following three classes: plane, car, and cat.

Get CIFAR3 scaled and unscaled dataloader¶

# @title Get CIFAR3 scaled and unscaled dataloader

set_seed(seed=42, seed_torch=True)

batch_size = 512

# CIFAR-10 dataset
cifar_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((8, 8)),
    transforms.Normalize(
        (0.49139968, 0.48215827, 0.44653124),
        (0.24703233, 0.24348505, 0.26158768))
    ])

cifar10_classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

class CIFAR3(torch.utils.data.Dataset):
    def __init__(self, dataset, classes, scale=False, scaling_limits=[0.05, 0.25]):
        self.dataset = dataset
        self.classes = classes

        # Filter indices based on chosen classes
        cls_indices = {}
        subset_ratio = 0.8
        self.indices = []
        for cls, cls_enum in self.classes.items():
          imgs_ = [i for i, label in enumerate(self.dataset.targets) if label == cls]
          self.indices += imgs_[:int(len(imgs_)*subset_ratio)]

        self.scale = scale
        self.scaling_limits = scaling_limits
        if self.scale:
            self.scale_distribution = torch.distributions.Uniform(*self.scaling_limits)
            self.scale_values = self.scale_distribution.sample((len(self.indices),))

    def __getitem__(self, index_cifar3):
        index_cifar10 = self.indices[index_cifar3]
        image, label = self.dataset[index_cifar10]
        label = self.classes[label]
        if self.scale:
            scalar = self.scale_values[index_cifar3]
            image = image * scalar
        return image, label

    def __len__(self):
        return len(self.indices)

cifar3_classes = {
    0: 0, # plane
    1: 1, # car
    3: 2, # cat
}

# CIFAR-10 datasets
cifar10_trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=cifar_transform)
cifar10_testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=cifar_transform)

# CIFAR3
cifar3_trainset = CIFAR3(cifar10_trainset, cifar3_classes)
cifar3_trainloader = torch.utils.data.DataLoader(cifar3_trainset, batch_size=batch_size,
                                          shuffle=True)
cifar3_testset = CIFAR3(cifar10_testset, cifar3_classes)
cifar3_testloader = torch.utils.data.DataLoader(cifar3_testset, batch_size=batch_size,
                                         shuffle=False)

# CIFAR3 Scaled
cifar3scaled_trainset = CIFAR3(cifar10_trainset, cifar3_classes, scale=True)
cifar3scaled_trainloader = torch.utils.data.DataLoader(cifar3scaled_trainset, batch_size=batch_size,
                                          shuffle=True)
cifar3scaled_testset = CIFAR3(cifar10_testset, cifar3_classes, scale=True)
cifar3scaled_testloader = torch.utils.data.DataLoader(cifar3scaled_testset, batch_size=batch_size,
                                         shuffle=False)

Downloading https://cave.cs.toronto.edu/kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz

  0%|          | 0/170498071 [00:00<?, ?it/s]

  0%|          | 32768/170498071 [00:00<33:50, 83933.88it/s]

  0%|          | 65536/170498071 [00:00<33:59, 83559.56it/s]

  0%|          | 98304/170498071 [00:01<34:02, 83431.92it/s]

  0%|          | 131072/170498071 [00:01<34:05, 83282.18it/s]

  0%|          | 163840/170498071 [00:01<34:06, 83214.93it/s]

  0%|          | 196608/170498071 [00:02<34:07, 83184.77it/s]

  0%|          | 229376/170498071 [00:02<34:12, 82944.12it/s]

  0%|          | 262144/170498071 [00:03<34:16, 82796.62it/s]

  0%|          | 294912/170498071 [00:03<34:14, 82850.63it/s]

  0%|          | 327680/170498071 [00:03<34:17, 82689.99it/s]

  0%|          | 360448/170498071 [00:04<36:50, 76957.92it/s]

  0%|          | 393216/170498071 [00:04<35:58, 78798.80it/s]

  0%|          | 425984/170498071 [00:05<35:17, 80313.76it/s]

  0%|          | 458752/170498071 [00:05<35:02, 80868.64it/s]

  0%|          | 491520/170498071 [00:06<34:37, 81834.56it/s]

  0%|          | 524288/170498071 [00:06<34:32, 82011.46it/s]

  0%|          | 557056/170498071 [00:06<34:27, 82198.60it/s]

  0%|          | 589824/170498071 [00:07<34:27, 82189.05it/s]

  0%|          | 622592/170498071 [00:07<34:18, 82540.39it/s]

  0%|          | 655360/170498071 [00:07<34:09, 82855.43it/s]

  0%|          | 688128/170498071 [00:08<36:56, 76610.70it/s]

  0%|          | 720896/170498071 [00:08<36:06, 78367.26it/s]

  0%|          | 753664/170498071 [00:09<35:45, 79133.08it/s]

  0%|          | 786432/170498071 [00:09<35:24, 79873.06it/s]

  0%|          | 819200/170498071 [00:10<35:12, 80303.63it/s]

  0%|          | 851968/170498071 [00:10<35:13, 80272.29it/s]

  1%|          | 884736/170498071 [00:10<35:09, 80412.16it/s]

  1%|          | 917504/170498071 [00:11<34:59, 80761.77it/s]

  1%|          | 950272/170498071 [00:11<34:55, 80916.51it/s]

  1%|          | 983040/170498071 [00:12<34:50, 81077.57it/s]

  1%|          | 1015808/170498071 [00:12<34:51, 81026.06it/s]

  1%|          | 1048576/170498071 [00:13<37:28, 75376.10it/s]

  1%|          | 1081344/170498071 [00:13<36:38, 77042.91it/s]

  1%|          | 1114112/170498071 [00:13<36:11, 77987.05it/s]

  1%|          | 1146880/170498071 [00:14<35:50, 78751.28it/s]

  1%|          | 1179648/170498071 [00:14<35:39, 79153.42it/s]

  1%|          | 1212416/170498071 [00:15<35:34, 79301.62it/s]

  1%|          | 1245184/170498071 [00:15<35:28, 79518.21it/s]

  1%|          | 1277952/170498071 [00:15<35:27, 79556.20it/s]

  1%|          | 1310720/170498071 [00:16<35:29, 79442.49it/s]

  1%|          | 1343488/170498071 [00:16<35:21, 79733.76it/s]

  1%|          | 1376256/170498071 [00:17<38:02, 74099.19it/s]

  1%|          | 1409024/170498071 [00:17<37:28, 75203.55it/s]

  1%|          | 1441792/170498071 [00:18<36:51, 76441.35it/s]

  1%|          | 1474560/170498071 [00:18<36:31, 77140.22it/s]

  1%|          | 1507328/170498071 [00:18<36:15, 77686.78it/s]

  1%|          | 1540096/170498071 [00:19<36:02, 78142.07it/s]

  1%|          | 1572864/170498071 [00:19<35:50, 78543.34it/s]

  1%|          | 1605632/170498071 [00:20<35:45, 78720.15it/s]

  1%|          | 1638400/170498071 [00:20<35:38, 78966.97it/s]

  1%|          | 1671168/170498071 [00:20<35:28, 79312.78it/s]

  1%|          | 1703936/170498071 [00:21<35:26, 79386.68it/s]

  1%|          | 1736704/170498071 [00:21<38:05, 73829.33it/s]

  1%|          | 1769472/170498071 [00:22<37:15, 75485.56it/s]

  1%|          | 1802240/170498071 [00:22<36:48, 76397.92it/s]

  1%|          | 1835008/170498071 [00:23<36:33, 76908.76it/s]

  1%|          | 1867776/170498071 [00:23<36:11, 77649.50it/s]

  1%|          | 1900544/170498071 [00:23<35:53, 78272.20it/s]

  1%|          | 1933312/170498071 [00:24<35:44, 78612.58it/s]

  1%|          | 1966080/170498071 [00:24<35:41, 78705.49it/s]

  1%|          | 1998848/170498071 [00:25<35:36, 78863.83it/s]

  1%|          | 2031616/170498071 [00:25<35:31, 79031.50it/s]

  1%|          | 2064384/170498071 [00:26<38:11, 73506.53it/s]

  1%|          | 2097152/170498071 [00:26<37:22, 75109.82it/s]

  1%|          | 2129920/170498071 [00:26<36:52, 76110.12it/s]

  1%|▏         | 2162688/170498071 [00:27<36:28, 76921.36it/s]

  1%|▏         | 2195456/170498071 [00:27<36:09, 77588.38it/s]

  1%|▏         | 2228224/170498071 [00:28<35:50, 78229.95it/s]

  1%|▏         | 2260992/170498071 [00:28<35:41, 78560.93it/s]

  1%|▏         | 2293760/170498071 [00:29<35:30, 78939.32it/s]

  1%|▏         | 2326528/170498071 [00:29<35:22, 79240.71it/s]

  1%|▏         | 2359296/170498071 [00:29<35:19, 79346.00it/s]

  1%|▏         | 2392064/170498071 [00:30<37:54, 73898.11it/s]

  1%|▏         | 2424832/170498071 [00:30<37:06, 75474.14it/s]

  1%|▏         | 2457600/170498071 [00:31<36:29, 76733.83it/s]

  1%|▏         | 2490368/170498071 [00:31<36:03, 77657.05it/s]

  1%|▏         | 2523136/170498071 [00:31<35:50, 78112.61it/s]

  1%|▏         | 2555904/170498071 [00:32<35:34, 78669.61it/s]

  2%|▏         | 2588672/170498071 [00:32<35:26, 78962.71it/s]

  2%|▏         | 2621440/170498071 [00:33<35:19, 79223.93it/s]

  2%|▏         | 2654208/170498071 [00:33<35:13, 79404.25it/s]

  2%|▏         | 2686976/170498071 [00:34<35:10, 79507.38it/s]

  2%|▏         | 2719744/170498071 [00:34<35:14, 79353.83it/s]

  2%|▏         | 2752512/170498071 [00:34<37:38, 74265.87it/s]

  2%|▏         | 2785280/170498071 [00:35<36:54, 75732.00it/s]

  2%|▏         | 2818048/170498071 [00:35<36:20, 76915.80it/s]

  2%|▏         | 2850816/170498071 [00:36<35:59, 77643.57it/s]

  2%|▏         | 2883584/170498071 [00:36<35:41, 78281.97it/s]

  2%|▏         | 2916352/170498071 [00:37<35:28, 78741.90it/s]

  2%|▏         | 2949120/170498071 [00:37<35:18, 79071.09it/s]

  2%|▏         | 2981888/170498071 [00:37<35:15, 79180.63it/s]

  2%|▏         | 3014656/170498071 [00:38<35:04, 79573.11it/s]

  2%|▏         | 3047424/170498071 [00:38<34:56, 79888.28it/s]

  2%|▏         | 3080192/170498071 [00:39<37:33, 74307.35it/s]

  2%|▏         | 3112960/170498071 [00:39<36:39, 76114.21it/s]

  2%|▏         | 3145728/170498071 [00:39<36:00, 77464.98it/s]

  2%|▏         | 3178496/170498071 [00:40<35:29, 78570.64it/s]

  2%|▏         | 3211264/170498071 [00:40<35:22, 78829.21it/s]

  2%|▏         | 3244032/170498071 [00:41<35:10, 79257.58it/s]

  2%|▏         | 3276800/170498071 [00:41<35:03, 79480.63it/s]

  2%|▏         | 3309568/170498071 [00:42<34:57, 79725.90it/s]

  2%|▏         | 3342336/170498071 [00:42<34:55, 79759.10it/s]

  2%|▏         | 3375104/170498071 [00:42<34:54, 79794.08it/s]

  2%|▏         | 3407872/170498071 [00:43<34:55, 79737.73it/s]

  2%|▏         | 3440640/170498071 [00:43<37:33, 74126.79it/s]

  2%|▏         | 3473408/170498071 [00:44<36:48, 75628.52it/s]

  2%|▏         | 3506176/170498071 [00:44<36:08, 77017.24it/s]

  2%|▏         | 3538944/170498071 [00:45<35:43, 77908.35it/s]

  2%|▏         | 3571712/170498071 [00:45<35:25, 78532.01it/s]

  2%|▏         | 3604480/170498071 [00:45<35:17, 78798.31it/s]

  2%|▏         | 3637248/170498071 [00:46<35:08, 79127.53it/s]

  2%|▏         | 3670016/170498071 [00:46<34:56, 79593.28it/s]

  2%|▏         | 3702784/170498071 [00:47<34:53, 79655.61it/s]

  2%|▏         | 3735552/170498071 [00:47<34:53, 79668.03it/s]

  2%|▏         | 3768320/170498071 [00:47<37:20, 74401.04it/s]

  2%|▏         | 3801088/170498071 [00:48<36:36, 75898.18it/s]

  2%|▏         | 3833856/170498071 [00:48<36:04, 77008.54it/s]

  2%|▏         | 3866624/170498071 [00:49<35:38, 77915.17it/s]

  2%|▏         | 3899392/170498071 [00:49<35:21, 78526.48it/s]

  2%|▏         | 3932160/170498071 [00:50<35:08, 78994.34it/s]

  2%|▏         | 3964928/170498071 [00:50<35:00, 79275.53it/s]

  2%|▏         | 3997696/170498071 [00:50<34:51, 79614.98it/s]

  2%|▏         | 4030464/170498071 [00:51<34:47, 79735.19it/s]

  2%|▏         | 4063232/170498071 [00:51<34:46, 79779.04it/s]

  2%|▏         | 4096000/170498071 [00:52<34:40, 79983.72it/s]

  2%|▏         | 4128768/170498071 [00:52<37:05, 74761.56it/s]

  2%|▏         | 4161536/170498071 [00:52<36:07, 76740.84it/s]

  2%|▏         | 4194304/170498071 [00:53<35:35, 77869.57it/s]

  2%|▏         | 4227072/170498071 [00:53<35:06, 78922.95it/s]

  2%|▏         | 4259840/170498071 [00:54<34:54, 79386.67it/s]

  3%|▎         | 4292608/170498071 [00:54<34:45, 79683.90it/s]

  3%|▎         | 4325376/170498071 [00:55<34:39, 79892.24it/s]

  3%|▎         | 4358144/170498071 [00:55<34:34, 80077.52it/s]

  3%|▎         | 4390912/170498071 [00:55<34:41, 79796.94it/s]

  3%|▎         | 4423680/170498071 [00:56<34:37, 79946.05it/s]

  3%|▎         | 4456448/170498071 [00:56<37:10, 74436.41it/s]

  3%|▎         | 4489216/170498071 [00:57<36:18, 76219.91it/s]

  3%|▎         | 4521984/170498071 [00:57<35:47, 77285.37it/s]

  3%|▎         | 4554752/170498071 [00:57<35:25, 78071.75it/s]

  3%|▎         | 4587520/170498071 [00:58<35:05, 78794.00it/s]

  3%|▎         | 4620288/170498071 [00:58<34:53, 79228.00it/s]

  3%|▎         | 4653056/170498071 [00:59<34:47, 79437.71it/s]

  3%|▎         | 4685824/170498071 [00:59<34:41, 79641.95it/s]

  3%|▎         | 4718592/170498071 [01:00<34:39, 79736.74it/s]

  3%|▎         | 4751360/170498071 [01:00<34:36, 79803.63it/s]

  3%|▎         | 4784128/170498071 [01:00<37:07, 74380.69it/s]

  3%|▎         | 4816896/170498071 [01:01<36:17, 76095.39it/s]

  3%|▎         | 4849664/170498071 [01:01<35:48, 77093.18it/s]

  3%|▎         | 4882432/170498071 [01:02<35:24, 77942.80it/s]

  3%|▎         | 4915200/170498071 [01:02<35:16, 78228.87it/s]

  3%|▎         | 4947968/170498071 [01:02<35:12, 78377.34it/s]

  3%|▎         | 4980736/170498071 [01:03<35:06, 78579.00it/s]

  3%|▎         | 5013504/170498071 [01:03<34:58, 78872.49it/s]

  3%|▎         | 5046272/170498071 [01:04<34:58, 78843.69it/s]

  3%|▎         | 5079040/170498071 [01:04<34:47, 79238.87it/s]

  3%|▎         | 5111808/170498071 [01:05<34:45, 79284.93it/s]

  3%|▎         | 5144576/170498071 [01:05<37:19, 73820.71it/s]

  3%|▎         | 5177344/170498071 [01:05<36:35, 75297.18it/s]

  3%|▎         | 5210112/170498071 [01:06<38:01, 72459.46it/s]

  3%|▎         | 5242880/170498071 [01:06<35:10, 78312.75it/s]

  3%|▎         | 5275648/170498071 [01:07<35:07, 78409.43it/s]

  3%|▎         | 5308416/170498071 [01:07<35:03, 78512.59it/s]

  3%|▎         | 5341184/170498071 [01:08<34:59, 78683.27it/s]

  3%|▎         | 5373952/170498071 [01:08<35:02, 78524.09it/s]

  3%|▎         | 5406720/170498071 [01:08<35:01, 78557.35it/s]

  3%|▎         | 5439488/170498071 [01:09<35:06, 78346.33it/s]

  3%|▎         | 5472256/170498071 [01:09<37:43, 72898.97it/s]

  3%|▎         | 5505024/170498071 [01:10<36:57, 74403.47it/s]

  3%|▎         | 5537792/170498071 [01:10<36:28, 75373.89it/s]

  3%|▎         | 5570560/170498071 [01:11<36:07, 76099.92it/s]

  3%|▎         | 5603328/170498071 [01:11<35:54, 76536.38it/s]

  3%|▎         | 5636096/170498071 [01:11<35:39, 77047.36it/s]

  3%|▎         | 5668864/170498071 [01:12<37:25, 73417.66it/s]

  3%|▎         | 5701632/170498071 [01:12<35:05, 78273.71it/s]

  3%|▎         | 5734400/170498071 [01:13<34:53, 78701.33it/s]

  3%|▎         | 5767168/170498071 [01:13<34:59, 78449.96it/s]

  3%|▎         | 5799936/170498071 [01:14<35:50, 76583.75it/s]

  3%|▎         | 5832704/170498071 [01:14<39:40, 69185.87it/s]

  3%|▎         | 5865472/170498071 [01:15<36:37, 74908.94it/s]

  3%|▎         | 5898240/170498071 [01:15<35:54, 76395.07it/s]

  3%|▎         | 5931008/170498071 [01:15<36:00, 76182.02it/s]

  3%|▎         | 5963776/170498071 [01:16<35:25, 77412.49it/s]

  4%|▎         | 5996544/170498071 [01:16<35:40, 76856.47it/s]

  4%|▎         | 6029312/170498071 [01:17<35:18, 77652.75it/s]

  4%|▎         | 6062080/170498071 [01:17<35:18, 77619.15it/s]

  4%|▎         | 6094848/170498071 [01:17<36:03, 75982.46it/s]

  4%|▎         | 6127616/170498071 [01:18<35:23, 77415.67it/s]

  4%|▎         | 6160384/170498071 [01:18<38:01, 72037.74it/s]

  4%|▎         | 6193152/170498071 [01:19<37:00, 74003.55it/s]

  4%|▎         | 6225920/170498071 [01:19<36:19, 75379.44it/s]

  4%|▎         | 6258688/170498071 [01:20<35:50, 76357.57it/s]

  4%|▎         | 6291456/170498071 [01:20<35:13, 77709.53it/s]

  4%|▎         | 6324224/170498071 [01:21<35:51, 76303.09it/s]

  4%|▎         | 6356992/170498071 [01:21<35:36, 76826.24it/s]

  4%|▎         | 6389760/170498071 [01:21<34:53, 78393.22it/s]

  4%|▍         | 6422528/170498071 [01:22<35:20, 77365.52it/s]

  4%|▍         | 6455296/170498071 [01:22<34:44, 78700.71it/s]

  4%|▍         | 6488064/170498071 [01:23<37:18, 73279.95it/s]

  4%|▍         | 6520832/170498071 [01:23<36:39, 74557.05it/s]

  4%|▍         | 6553600/170498071 [01:24<36:51, 74130.15it/s]

  4%|▍         | 6586368/170498071 [01:24<35:48, 76304.85it/s]

  4%|▍         | 6619136/170498071 [01:24<37:17, 73255.63it/s]

  4%|▍         | 6651904/170498071 [01:25<34:54, 78221.76it/s]

  4%|▍         | 6684672/170498071 [01:25<34:42, 78660.65it/s]

  4%|▍         | 6717440/170498071 [01:26<34:49, 78386.60it/s]

  4%|▍         | 6750208/170498071 [01:26<36:21, 75062.76it/s]

  4%|▍         | 6782976/170498071 [01:26<34:53, 78208.09it/s]

  4%|▍         | 6815744/170498071 [01:27<36:41, 74363.90it/s]

  4%|▍         | 6848512/170498071 [01:27<36:57, 73808.45it/s]

  4%|▍         | 6881280/170498071 [01:28<36:42, 74283.34it/s]

  4%|▍         | 6914048/170498071 [01:28<36:11, 75320.41it/s]

  4%|▍         | 6946816/170498071 [01:29<35:41, 76372.90it/s]

  4%|▍         | 6979584/170498071 [01:29<35:00, 77833.10it/s]

  4%|▍         | 7012352/170498071 [01:30<35:24, 76935.63it/s]

  4%|▍         | 7045120/170498071 [01:30<35:19, 77132.12it/s]

  4%|▍         | 7077888/170498071 [01:30<34:34, 78778.64it/s]

  4%|▍         | 7110656/170498071 [01:31<34:17, 79405.33it/s]

  4%|▍         | 7143424/170498071 [01:31<35:06, 77541.66it/s]

  4%|▍         | 7176192/170498071 [01:32<37:05, 73390.43it/s]

  4%|▍         | 7208960/170498071 [01:32<36:18, 74956.95it/s]

  4%|▍         | 7241728/170498071 [01:33<35:45, 76108.23it/s]

  4%|▍         | 7274496/170498071 [01:33<36:26, 74660.74it/s]

  4%|▍         | 7307264/170498071 [01:33<34:51, 78011.11it/s]

  4%|▍         | 7340032/170498071 [01:34<35:11, 77279.50it/s]

  4%|▍         | 7372800/170498071 [01:34<35:33, 76459.02it/s]

  4%|▍         | 7405568/170498071 [01:35<35:11, 77255.39it/s]

  4%|▍         | 7438336/170498071 [01:35<34:29, 78783.68it/s]

  4%|▍         | 7471104/170498071 [01:36<36:13, 75000.28it/s]

  4%|▍         | 7503872/170498071 [01:36<35:40, 76154.03it/s]

  4%|▍         | 7536640/170498071 [01:36<37:56, 71573.22it/s]

  4%|▍         | 7569408/170498071 [01:37<34:41, 78277.78it/s]

  4%|▍         | 7602176/170498071 [01:37<36:45, 73863.00it/s]

  4%|▍         | 7634944/170498071 [01:38<35:54, 75579.42it/s]

  4%|▍         | 7667712/170498071 [01:38<36:24, 74538.54it/s]

  5%|▍         | 7700480/170498071 [01:39<33:40, 80585.24it/s]

  5%|▍         | 7733248/170498071 [01:39<34:56, 77621.92it/s]

  5%|▍         | 7766016/170498071 [01:39<34:54, 77678.24it/s]

  5%|▍         | 7798784/170498071 [01:40<32:38, 83055.39it/s]

  5%|▍         | 7831552/170498071 [01:40<33:10, 81726.92it/s]

  5%|▍         | 7864320/170498071 [01:41<38:14, 70887.81it/s]

  5%|▍         | 7897088/170498071 [01:41<34:53, 77658.91it/s]

  5%|▍         | 7929856/170498071 [01:42<36:43, 73776.85it/s]

  5%|▍         | 7962624/170498071 [01:42<33:53, 79946.22it/s]

  5%|▍         | 7995392/170498071 [01:42<35:00, 77346.43it/s]

  5%|▍         | 8028160/170498071 [01:43<33:44, 80235.37it/s]

  5%|▍         | 8060928/170498071 [01:43<33:58, 79698.99it/s]

  5%|▍         | 8093696/170498071 [01:44<37:10, 72801.20it/s]

  5%|▍         | 8126464/170498071 [01:44<34:45, 77868.28it/s]

  5%|▍         | 8159232/170498071 [01:44<33:10, 81548.96it/s]

  5%|▍         | 8192000/170498071 [01:45<33:27, 80859.53it/s]

  5%|▍         | 8224768/170498071 [01:45<36:13, 74663.78it/s]

  5%|▍         | 8257536/170498071 [01:46<35:18, 76581.71it/s]

  5%|▍         | 8290304/170498071 [01:46<35:52, 75351.96it/s]

  5%|▍         | 8323072/170498071 [01:47<35:38, 75836.46it/s]

  5%|▍         | 8355840/170498071 [01:47<34:24, 78528.29it/s]

  5%|▍         | 8388608/170498071 [01:47<34:23, 78565.81it/s]

  5%|▍         | 8421376/170498071 [01:48<35:34, 75915.27it/s]

  5%|▍         | 8454144/170498071 [01:48<32:49, 82296.87it/s]

  5%|▍         | 8486912/170498071 [01:49<32:57, 81934.13it/s]

  5%|▍         | 8519680/170498071 [01:49<33:03, 81652.34it/s]

  5%|▌         | 8552448/170498071 [01:50<37:56, 71127.06it/s]

  5%|▌         | 8585216/170498071 [01:50<35:32, 75933.45it/s]

  5%|▌         | 8617984/170498071 [01:50<33:46, 79892.34it/s]

  5%|▌         | 8650752/170498071 [01:51<35:42, 75534.88it/s]

  5%|▌         | 8683520/170498071 [01:51<32:52, 82051.84it/s]

  5%|▌         | 8716288/170498071 [01:52<33:01, 81631.26it/s]

  5%|▌         | 8749056/170498071 [01:52<33:45, 79848.71it/s]

  5%|▌         | 8781824/170498071 [01:52<33:04, 81504.90it/s]

  5%|▌         | 8814592/170498071 [01:53<33:03, 81531.95it/s]

  5%|▌         | 8847360/170498071 [01:53<35:16, 76384.95it/s]

  5%|▌         | 8880128/170498071 [01:54<34:57, 77043.44it/s]

  5%|▌         | 8912896/170498071 [01:54<34:27, 78141.46it/s]

  5%|▌         | 8945664/170498071 [01:55<36:22, 74020.83it/s]

  5%|▌         | 8978432/170498071 [01:55<33:12, 81044.03it/s]

  5%|▌         | 9011200/170498071 [01:55<33:21, 80696.12it/s]

  5%|▌         | 9043968/170498071 [01:56<33:19, 80759.45it/s]

  5%|▌         | 9076736/170498071 [01:56<33:21, 80669.25it/s]

  5%|▌         | 9109504/170498071 [01:57<33:24, 80498.35it/s]

  5%|▌         | 9142272/170498071 [01:57<33:17, 80784.86it/s]

  5%|▌         | 9175040/170498071 [01:57<33:19, 80669.15it/s]

  5%|▌         | 9207808/170498071 [01:58<33:12, 80930.31it/s]

  5%|▌         | 9240576/170498071 [01:58<36:01, 74614.19it/s]

  5%|▌         | 9273344/170498071 [01:59<34:48, 77214.10it/s]

  5%|▌         | 9306112/170498071 [01:59<34:21, 78186.42it/s]

  5%|▌         | 9338880/170498071 [01:59<33:52, 79306.98it/s]

  5%|▌         | 9371648/170498071 [02:00<34:05, 78759.18it/s]

  6%|▌         | 9404416/170498071 [02:00<33:18, 80618.67it/s]

  6%|▌         | 9437184/170498071 [02:01<33:39, 79758.39it/s]

  6%|▌         | 9469952/170498071 [02:01<32:48, 81801.50it/s]

  6%|▌         | 9502720/170498071 [02:02<35:03, 76534.34it/s]

  6%|▌         | 9535488/170498071 [02:02<32:38, 82203.20it/s]

  6%|▌         | 9568256/170498071 [02:02<34:40, 77347.32it/s]

  6%|▌         | 9601024/170498071 [02:03<34:03, 78717.88it/s]

  6%|▌         | 9633792/170498071 [02:03<33:37, 79739.82it/s]

  6%|▌         | 9666560/170498071 [02:04<33:14, 80648.83it/s]

  6%|▌         | 9699328/170498071 [02:04<32:57, 81299.24it/s]

  6%|▌         | 9732096/170498071 [02:04<32:52, 81490.21it/s]

  6%|▌         | 9764864/170498071 [02:05<32:51, 81521.64it/s]

  6%|▌         | 9797632/170498071 [02:05<33:19, 80374.60it/s]

  6%|▌         | 9830400/170498071 [02:06<32:51, 81487.44it/s]

  6%|▌         | 9863168/170498071 [02:06<33:01, 81080.40it/s]

  6%|▌         | 9895936/170498071 [02:06<33:44, 79320.83it/s]

  6%|▌         | 9928704/170498071 [02:07<35:19, 75763.33it/s]

  6%|▌         | 9961472/170498071 [02:07<35:08, 76151.70it/s]

  6%|▌         | 9994240/170498071 [02:08<34:37, 77249.31it/s]

  6%|▌         | 10027008/170498071 [02:08<34:17, 78004.27it/s]

  6%|▌         | 10059776/170498071 [02:09<36:14, 73781.32it/s]

  6%|▌         | 10092544/170498071 [02:09<33:21, 80123.31it/s]

  6%|▌         | 10125312/170498071 [02:09<35:09, 76008.04it/s]

  6%|▌         | 10158080/170498071 [02:10<32:55, 81164.57it/s]

  6%|▌         | 10190848/170498071 [02:10<34:55, 76494.08it/s]

  6%|▌         | 10223616/170498071 [02:11<32:44, 81573.28it/s]

  6%|▌         | 10256384/170498071 [02:11<35:13, 75812.59it/s]

  6%|▌         | 10289152/170498071 [02:12<34:44, 76853.08it/s]

  6%|▌         | 10321920/170498071 [02:12<34:45, 76792.51it/s]

  6%|▌         | 10354688/170498071 [02:12<34:22, 77647.02it/s]

  6%|▌         | 10387456/170498071 [02:13<33:56, 78612.29it/s]

  6%|▌         | 10420224/170498071 [02:13<34:36, 77107.28it/s]

  6%|▌         | 10452992/170498071 [02:14<33:56, 78569.20it/s]

  6%|▌         | 10485760/170498071 [02:14<33:55, 78620.08it/s]

  6%|▌         | 10518528/170498071 [02:14<35:50, 74377.55it/s]

  6%|▌         | 10551296/170498071 [02:15<35:31, 75037.78it/s]

  6%|▌         | 10584064/170498071 [02:15<35:23, 75311.69it/s]

  6%|▌         | 10616832/170498071 [02:16<35:22, 75341.97it/s]

  6%|▌         | 10649600/170498071 [02:16<34:29, 77238.55it/s]

  6%|▋         | 10682368/170498071 [02:17<35:24, 75220.17it/s]

  6%|▋         | 10715136/170498071 [02:17<36:08, 73684.24it/s]

  6%|▋         | 10747904/170498071 [02:18<35:28, 75064.28it/s]

  6%|▋         | 10780672/170498071 [02:18<33:18, 79935.25it/s]

  6%|▋         | 10813440/170498071 [02:18<33:28, 79495.52it/s]

  6%|▋         | 10846208/170498071 [02:19<35:13, 75547.19it/s]

  6%|▋         | 10878976/170498071 [02:19<32:57, 80719.91it/s]

  6%|▋         | 10911744/170498071 [02:20<33:46, 78763.24it/s]

  6%|▋         | 10944512/170498071 [02:20<37:16, 71353.73it/s]

  6%|▋         | 10977280/170498071 [02:20<34:55, 76108.61it/s]

  6%|▋         | 11010048/170498071 [02:21<33:53, 78435.91it/s]

  6%|▋         | 11042816/170498071 [02:21<33:55, 78345.68it/s]

  6%|▋         | 11075584/170498071 [02:22<33:25, 79483.24it/s]

  7%|▋         | 11108352/170498071 [02:22<34:01, 78088.17it/s]

  7%|▋         | 11141120/170498071 [02:23<34:29, 77004.36it/s]

  7%|▋         | 11173888/170498071 [02:23<33:23, 79538.49it/s]

  7%|▋         | 11206656/170498071 [02:23<33:32, 79159.43it/s]

  7%|▋         | 11239424/170498071 [02:24<34:33, 76805.23it/s]

  7%|▋         | 11272192/170498071 [02:24<36:35, 72511.50it/s]

  7%|▋         | 11304960/170498071 [02:25<34:45, 76332.74it/s]

  7%|▋         | 11337728/170498071 [02:25<35:12, 75342.24it/s]

  7%|▋         | 11370496/170498071 [02:26<34:08, 77674.56it/s]

  7%|▋         | 11403264/170498071 [02:26<33:27, 79254.19it/s]

  7%|▋         | 11436032/170498071 [02:26<33:53, 78225.37it/s]

  7%|▋         | 11468800/170498071 [02:27<33:07, 79996.36it/s]

  7%|▋         | 11501568/170498071 [02:27<32:55, 80467.99it/s]

  7%|▋         | 11534336/170498071 [02:28<35:00, 75662.87it/s]

  7%|▋         | 11567104/170498071 [02:28<32:27, 81590.76it/s]

  7%|▋         | 11599872/170498071 [02:28<33:05, 80030.26it/s]

  7%|▋         | 11632640/170498071 [02:29<34:14, 77308.49it/s]

  7%|▋         | 11665408/170498071 [02:29<34:43, 76236.02it/s]

  7%|▋         | 11698176/170498071 [02:30<35:04, 75470.38it/s]

  7%|▋         | 11730944/170498071 [02:30<32:46, 80731.47it/s]

  7%|▋         | 11763712/170498071 [02:31<32:45, 80764.28it/s]

  7%|▋         | 11796480/170498071 [02:31<32:12, 82105.09it/s]

  7%|▋         | 11829248/170498071 [02:31<32:23, 81646.76it/s]

  7%|▋         | 11862016/170498071 [02:32<33:00, 80094.60it/s]

  7%|▋         | 11894784/170498071 [02:32<32:21, 81709.05it/s]

  7%|▋         | 11927552/170498071 [02:33<32:07, 82262.97it/s]

  7%|▋         | 11960320/170498071 [02:33<34:42, 76112.32it/s]

  7%|▋         | 11993088/170498071 [02:33<34:45, 75993.76it/s]

  7%|▋         | 12025856/170498071 [02:34<34:10, 77284.65it/s]

  7%|▋         | 12058624/170498071 [02:34<33:48, 78110.11it/s]

  7%|▋         | 12091392/170498071 [02:35<34:06, 77412.27it/s]

  7%|▋         | 12124160/170498071 [02:35<32:41, 80761.09it/s]

  7%|▋         | 12156928/170498071 [02:35<32:55, 80169.51it/s]

  7%|▋         | 12189696/170498071 [02:36<34:27, 76570.61it/s]

  7%|▋         | 12222464/170498071 [02:36<32:11, 81965.08it/s]

  7%|▋         | 12255232/170498071 [02:37<32:30, 81109.78it/s]

  7%|▋         | 12288000/170498071 [02:37<32:10, 81932.55it/s]

  7%|▋         | 12320768/170498071 [02:38<34:53, 75552.42it/s]

  7%|▋         | 12353536/170498071 [02:38<33:32, 78569.15it/s]

  7%|▋         | 12386304/170498071 [02:38<33:02, 79756.66it/s]

  7%|▋         | 12419072/170498071 [02:39<34:48, 75689.84it/s]

  7%|▋         | 12451840/170498071 [02:39<31:47, 82846.53it/s]

  7%|▋         | 12484608/170498071 [02:40<33:51, 77766.14it/s]

  7%|▋         | 12517376/170498071 [02:40<31:02, 84818.18it/s]

  7%|▋         | 12550144/170498071 [02:40<31:14, 84256.08it/s]

  7%|▋         | 12582912/170498071 [02:41<31:22, 83889.78it/s]

  7%|▋         | 12615680/170498071 [02:41<33:29, 78562.99it/s]

  7%|▋         | 12648448/170498071 [02:42<33:18, 79001.68it/s]

  7%|▋         | 12681216/170498071 [02:42<32:50, 80107.70it/s]

  7%|▋         | 12713984/170498071 [02:42<33:28, 78561.51it/s]

  7%|▋         | 12746752/170498071 [02:43<31:47, 82714.57it/s]

  7%|▋         | 12779520/170498071 [02:43<31:41, 82953.64it/s]

  8%|▊         | 12812288/170498071 [02:44<31:42, 82890.92it/s]

  8%|▊         | 12845056/170498071 [02:44<31:42, 82851.11it/s]

  8%|▊         | 12877824/170498071 [02:44<31:21, 83758.04it/s]

  8%|▊         | 12910592/170498071 [02:45<32:01, 82004.60it/s]

  8%|▊         | 12943360/170498071 [02:45<31:49, 82509.82it/s]

  8%|▊         | 12976128/170498071 [02:46<35:48, 73309.74it/s]

  8%|▊         | 13008896/170498071 [02:46<34:30, 76067.28it/s]

  8%|▊         | 13041664/170498071 [02:46<31:22, 83625.03it/s]

  8%|▊         | 13074432/170498071 [02:47<31:46, 82581.01it/s]

  8%|▊         | 13107200/170498071 [02:47<30:58, 84705.15it/s]

  8%|▊         | 13139968/170498071 [02:48<33:03, 79334.35it/s]

  8%|▊         | 13172736/170498071 [02:48<33:02, 79351.21it/s]

  8%|▊         | 13205504/170498071 [02:48<30:20, 86402.80it/s]

  8%|▊         | 13238272/170498071 [02:49<30:37, 85599.35it/s]

  8%|▊         | 13271040/170498071 [02:49<30:47, 85085.60it/s]

  8%|▊         | 13303808/170498071 [02:50<31:25, 83354.71it/s]

  8%|▊         | 13336576/170498071 [02:50<33:22, 78478.22it/s]

  8%|▊         | 13369344/170498071 [02:50<32:44, 79974.93it/s]

  8%|▊         | 13402112/170498071 [02:51<32:48, 79807.77it/s]

  8%|▊         | 13434880/170498071 [02:51<32:02, 81715.18it/s]

  8%|▊         | 13467648/170498071 [02:52<31:47, 82302.85it/s]

  8%|▊         | 13500416/170498071 [02:52<31:59, 81770.04it/s]

  8%|▊         | 13533184/170498071 [02:52<31:06, 84092.48it/s]

  8%|▊         | 13565952/170498071 [02:53<31:10, 83889.83it/s]

  8%|▊         | 13598720/170498071 [02:53<31:51, 82080.90it/s]

  8%|▊         | 13631488/170498071 [02:54<31:14, 83699.56it/s]

  8%|▊         | 13664256/170498071 [02:54<35:12, 74257.01it/s]

  8%|▊         | 13697024/170498071 [02:54<32:16, 80991.03it/s]

  8%|▊         | 13729792/170498071 [02:55<31:58, 81701.09it/s]

  8%|▊         | 13762560/170498071 [02:55<31:30, 82913.03it/s]

  8%|▊         | 13795328/170498071 [02:56<31:57, 81703.06it/s]

  8%|▊         | 13828096/170498071 [02:56<31:50, 81985.08it/s]

  8%|▊         | 13860864/170498071 [02:56<31:37, 82541.79it/s]

  8%|▊         | 13893632/170498071 [02:57<31:00, 84181.14it/s]

  8%|▊         | 13926400/170498071 [02:57<31:02, 84083.07it/s]

  8%|▊         | 13959168/170498071 [02:58<31:02, 84067.52it/s]

  8%|▊         | 13991936/170498071 [02:58<31:01, 84058.57it/s]

  8%|▊         | 14024704/170498071 [02:58<33:21, 78194.32it/s]

  8%|▊         | 14057472/170498071 [02:59<32:42, 79702.22it/s]

  8%|▊         | 14090240/170498071 [02:59<32:11, 80967.08it/s]

  8%|▊         | 14123008/170498071 [03:00<31:51, 81791.74it/s]

  8%|▊         | 14155776/170498071 [03:00<31:35, 82489.37it/s]

  8%|▊         | 14188544/170498071 [03:00<31:23, 82991.61it/s]

  8%|▊         | 14221312/170498071 [03:01<31:16, 83285.39it/s]

  8%|▊         | 14254080/170498071 [03:01<31:12, 83440.51it/s]

  8%|▊         | 14286848/170498071 [03:02<31:08, 83600.78it/s]

  8%|▊         | 14319616/170498071 [03:02<31:07, 83608.99it/s]

  8%|▊         | 14352384/170498071 [03:02<33:49, 76929.77it/s]

  8%|▊         | 14385152/170498071 [03:03<32:40, 79618.29it/s]

  8%|▊         | 14417920/170498071 [03:03<32:21, 80405.29it/s]

  8%|▊         | 14450688/170498071 [03:04<32:33, 79899.86it/s]

  8%|▊         | 14483456/170498071 [03:04<31:38, 82168.47it/s]

  9%|▊         | 14516224/170498071 [03:04<31:21, 82883.32it/s]

  9%|▊         | 14548992/170498071 [03:05<33:56, 76566.36it/s]

  9%|▊         | 14581760/170498071 [03:05<31:11, 83314.11it/s]

  9%|▊         | 14614528/170498071 [03:06<33:29, 77580.95it/s]

  9%|▊         | 14647296/170498071 [03:06<30:45, 84444.28it/s]

  9%|▊         | 14680064/170498071 [03:07<33:22, 77796.31it/s]

  9%|▊         | 14712832/170498071 [03:07<33:03, 78543.49it/s]

  9%|▊         | 14745600/170498071 [03:07<32:17, 80368.64it/s]

  9%|▊         | 14778368/170498071 [03:08<32:03, 80964.60it/s]

  9%|▊         | 14811136/170498071 [03:08<31:50, 81478.40it/s]

  9%|▊         | 14843904/170498071 [03:09<31:46, 81645.75it/s]

  9%|▊         | 14876672/170498071 [03:09<33:37, 77147.15it/s]

  9%|▊         | 14909440/170498071 [03:09<30:42, 84444.36it/s]

  9%|▉         | 14942208/170498071 [03:10<31:13, 83017.36it/s]

  9%|▉         | 14974976/170498071 [03:10<31:17, 82833.98it/s]

  9%|▉         | 15007744/170498071 [03:11<30:47, 84184.15it/s]

  9%|▉         | 15040512/170498071 [03:11<32:56, 78651.69it/s]

  9%|▉         | 15073280/170498071 [03:11<32:43, 79146.04it/s]

  9%|▉         | 15106048/170498071 [03:12<32:07, 80604.81it/s]

  9%|▉         | 15138816/170498071 [03:12<31:50, 81312.09it/s]

  9%|▉         | 15171584/170498071 [03:13<31:34, 81975.71it/s]

  9%|▉         | 15204352/170498071 [03:13<31:18, 82670.60it/s]

  9%|▉         | 15237120/170498071 [03:13<30:55, 83660.90it/s]

  9%|▉         | 15269888/170498071 [03:14<31:08, 83063.47it/s]

  9%|▉         | 15302656/170498071 [03:14<31:06, 83153.53it/s]

  9%|▉         | 15335424/170498071 [03:15<30:56, 83599.11it/s]

  9%|▉         | 15368192/170498071 [03:15<33:34, 77017.06it/s]

  9%|▉         | 15400960/170498071 [03:15<34:25, 75098.68it/s]

  9%|▉         | 15433728/170498071 [03:16<31:05, 83111.30it/s]

  9%|▉         | 15466496/170498071 [03:16<30:51, 83754.65it/s]

  9%|▉         | 15499264/170498071 [03:17<32:40, 79076.93it/s]

  9%|▉         | 15532032/170498071 [03:17<29:56, 86265.66it/s]

  9%|▉         | 15564800/170498071 [03:17<32:14, 80070.97it/s]

  9%|▉         | 15597568/170498071 [03:18<29:31, 87429.69it/s]

  9%|▉         | 15630336/170498071 [03:18<29:29, 87538.80it/s]

  9%|▉         | 15663104/170498071 [03:18<29:43, 86791.57it/s]

  9%|▉         | 15695872/170498071 [03:19<32:17, 79884.76it/s]

  9%|▉         | 15728640/170498071 [03:19<31:45, 81236.28it/s]

  9%|▉         | 15761408/170498071 [03:20<31:10, 82725.41it/s]

  9%|▉         | 15794176/170498071 [03:20<30:53, 83472.82it/s]

  9%|▉         | 15826944/170498071 [03:21<31:57, 80663.19it/s]

  9%|▉         | 15859712/170498071 [03:21<30:13, 85282.52it/s]

  9%|▉         | 15892480/170498071 [03:21<32:22, 79595.66it/s]

  9%|▉         | 15925248/170498071 [03:22<29:33, 87134.85it/s]

  9%|▉         | 15958016/170498071 [03:22<29:46, 86517.84it/s]

  9%|▉         | 15990784/170498071 [03:22<29:32, 87150.93it/s]

  9%|▉         | 16023552/170498071 [03:23<30:42, 83835.96it/s]

  9%|▉         | 16056320/170498071 [03:23<33:11, 77564.62it/s]

  9%|▉         | 16089088/170498071 [03:24<31:36, 81432.76it/s]

  9%|▉         | 16121856/170498071 [03:24<31:12, 82445.28it/s]

  9%|▉         | 16154624/170498071 [03:24<30:20, 84792.21it/s]

  9%|▉         | 16187392/170498071 [03:25<32:14, 79760.84it/s]

 10%|▉         | 16220160/170498071 [03:25<29:34, 86964.31it/s]

 10%|▉         | 16252928/170498071 [03:26<29:19, 87683.92it/s]

 10%|▉         | 16285696/170498071 [03:26<30:12, 85071.61it/s]

 10%|▉         | 16318464/170498071 [03:26<29:37, 86726.00it/s]

 10%|▉         | 16351232/170498071 [03:27<29:35, 86818.09it/s]

 10%|▉         | 16384000/170498071 [03:27<31:23, 81805.70it/s]

 10%|▉         | 16416768/170498071 [03:28<31:27, 81636.75it/s]

 10%|▉         | 16449536/170498071 [03:28<30:41, 83669.27it/s]

 10%|▉         | 16482304/170498071 [03:28<32:46, 78328.30it/s]

 10%|▉         | 16515072/170498071 [03:29<29:34, 86761.39it/s]

 10%|▉         | 16547840/170498071 [03:29<29:12, 87866.28it/s]

 10%|▉         | 16580608/170498071 [03:29<29:11, 87862.71it/s]

 10%|▉         | 16613376/170498071 [03:30<29:09, 87956.70it/s]

 10%|▉         | 16646144/170498071 [03:30<29:23, 87250.32it/s]

 10%|▉         | 16678912/170498071 [03:31<29:45, 86129.37it/s]

 10%|▉         | 16711680/170498071 [03:31<29:06, 88048.19it/s]

 10%|▉         | 16744448/170498071 [03:31<33:04, 77493.43it/s]

 10%|▉         | 16777216/170498071 [03:32<30:03, 85217.98it/s]

 10%|▉         | 16809984/170498071 [03:32<29:30, 86804.78it/s]

 10%|▉         | 16842752/170498071 [03:32<29:16, 87499.19it/s]

 10%|▉         | 16875520/170498071 [03:33<29:34, 86573.43it/s]

 10%|▉         | 16908288/170498071 [03:33<29:14, 87559.99it/s]

 10%|▉         | 16941056/170498071 [03:34<28:51, 88662.70it/s]

 10%|▉         | 16973824/170498071 [03:34<28:49, 88753.32it/s]

 10%|▉         | 17006592/170498071 [03:34<28:51, 88657.89it/s]

 10%|▉         | 17039360/170498071 [03:35<28:46, 88885.32it/s]

 10%|█         | 17072128/170498071 [03:35<30:53, 82758.79it/s]

 10%|█         | 17104896/170498071 [03:36<30:13, 84568.34it/s]

 10%|█         | 17137664/170498071 [03:36<29:38, 86253.91it/s]

 10%|█         | 17170432/170498071 [03:36<31:15, 81771.58it/s]

 10%|█         | 17203200/170498071 [03:37<28:51, 88535.03it/s]

 10%|█         | 17235968/170498071 [03:37<29:15, 87297.40it/s]

 10%|█         | 17268736/170498071 [03:37<29:04, 87839.07it/s]

 10%|█         | 17301504/170498071 [03:38<28:25, 89799.53it/s]

 10%|█         | 17334272/170498071 [03:38<28:12, 90487.28it/s]

 10%|█         | 17367040/170498071 [03:38<28:17, 90190.73it/s]

 10%|█         | 17399808/170498071 [03:39<28:16, 90217.57it/s]

 10%|█         | 17432576/170498071 [03:39<31:08, 81901.95it/s]

 10%|█         | 17465344/170498071 [03:40<29:50, 85458.54it/s]

 10%|█         | 17498112/170498071 [03:40<29:11, 87362.60it/s]

 10%|█         | 17530880/170498071 [03:40<29:14, 87200.61it/s]

 10%|█         | 17563648/170498071 [03:41<29:01, 87809.72it/s]

 10%|█         | 17596416/170498071 [03:41<28:53, 88179.51it/s]

 10%|█         | 17629184/170498071 [03:41<28:40, 88850.63it/s]

 10%|█         | 17661952/170498071 [03:42<28:18, 90003.57it/s]

 10%|█         | 17694720/170498071 [03:42<28:34, 89104.18it/s]

 10%|█         | 17727488/170498071 [03:43<28:32, 89209.33it/s]

 10%|█         | 17760256/170498071 [03:43<30:21, 83835.07it/s]

 10%|█         | 17793024/170498071 [03:43<29:46, 85496.94it/s]

 10%|█         | 17825792/170498071 [03:44<29:21, 86674.62it/s]

 10%|█         | 17858560/170498071 [03:44<29:45, 85494.25it/s]

 10%|█         | 17891328/170498071 [03:45<28:55, 87951.26it/s]

 11%|█         | 17924096/170498071 [03:45<29:15, 86895.37it/s]

 11%|█         | 17956864/170498071 [03:45<28:36, 88846.45it/s]

 11%|█         | 17989632/170498071 [03:46<30:17, 83907.80it/s]

 11%|█         | 18022400/170498071 [03:46<28:08, 90327.87it/s]

 11%|█         | 18055168/170498071 [03:46<27:59, 90773.65it/s]

 11%|█         | 18087936/170498071 [03:47<28:17, 89801.27it/s]

 11%|█         | 18120704/170498071 [03:47<31:13, 81313.16it/s]

 11%|█         | 18153472/170498071 [03:48<30:05, 84355.66it/s]

 11%|█         | 18186240/170498071 [03:48<29:27, 86154.63it/s]

 11%|█         | 18219008/170498071 [03:48<29:15, 86755.07it/s]

 11%|█         | 18251776/170498071 [03:49<29:49, 85072.79it/s]

 11%|█         | 18284544/170498071 [03:49<28:59, 87495.30it/s]

 11%|█         | 18317312/170498071 [03:49<28:47, 88074.80it/s]

 11%|█         | 18350080/170498071 [03:50<28:41, 88374.69it/s]

 11%|█         | 18382848/170498071 [03:50<30:18, 83654.16it/s]

 11%|█         | 18415616/170498071 [03:51<27:59, 90547.68it/s]

 11%|█         | 18448384/170498071 [03:51<31:52, 79513.09it/s]

 11%|█         | 18481152/170498071 [03:51<28:57, 87478.73it/s]

 11%|█         | 18513920/170498071 [03:52<28:41, 88289.46it/s]

 11%|█         | 18546688/170498071 [03:52<28:17, 89492.35it/s]

 11%|█         | 18579456/170498071 [03:52<28:31, 88748.99it/s]

 11%|█         | 18612224/170498071 [03:53<28:24, 89133.30it/s]

 11%|█         | 18644992/170498071 [03:53<28:19, 89359.96it/s]

 11%|█         | 18677760/170498071 [03:54<27:56, 90550.94it/s]

 11%|█         | 18710528/170498071 [03:54<28:20, 89246.54it/s]

 11%|█         | 18743296/170498071 [03:54<28:12, 89674.41it/s]

 11%|█         | 18776064/170498071 [03:55<29:59, 84296.25it/s]

 11%|█         | 18808832/170498071 [03:55<29:42, 85079.86it/s]

 11%|█         | 18841600/170498071 [03:55<28:48, 87744.65it/s]

 11%|█         | 18874368/170498071 [03:56<28:35, 88376.79it/s]

 11%|█         | 18907136/170498071 [03:56<28:28, 88745.85it/s]

 11%|█         | 18939904/170498071 [03:57<28:42, 87992.71it/s]

 11%|█         | 18972672/170498071 [03:57<28:09, 89692.28it/s]

 11%|█         | 19005440/170498071 [03:57<28:12, 89514.03it/s]

 11%|█         | 19038208/170498071 [03:58<28:11, 89566.06it/s]

 11%|█         | 19070976/170498071 [03:58<28:23, 88889.94it/s]

 11%|█         | 19103744/170498071 [03:58<28:20, 89026.30it/s]

 11%|█         | 19136512/170498071 [03:59<30:09, 83655.66it/s]

 11%|█         | 19169280/170498071 [03:59<29:49, 84551.80it/s]

 11%|█▏        | 19202048/170498071 [04:00<29:05, 86695.49it/s]

 11%|█▏        | 19234816/170498071 [04:00<28:49, 87466.54it/s]

 11%|█▏        | 19267584/170498071 [04:00<28:56, 87070.65it/s]

 11%|█▏        | 19300352/170498071 [04:01<28:43, 87709.39it/s]

 11%|█▏        | 19333120/170498071 [04:01<28:22, 88805.00it/s]

 11%|█▏        | 19365888/170498071 [04:01<28:18, 88983.30it/s]

 11%|█▏        | 19398656/170498071 [04:02<28:55, 87064.61it/s]

 11%|█▏        | 19431424/170498071 [04:02<28:01, 89824.50it/s]

 11%|█▏        | 19464192/170498071 [04:03<30:02, 83784.86it/s]

 11%|█▏        | 19496960/170498071 [04:03<29:46, 84531.55it/s]

 11%|█▏        | 19529728/170498071 [04:03<29:11, 86213.55it/s]

 11%|█▏        | 19562496/170498071 [04:04<29:48, 84399.46it/s]

 11%|█▏        | 19595264/170498071 [04:04<28:20, 88754.90it/s]

 12%|█▏        | 19628032/170498071 [04:04<28:14, 89029.25it/s]

 12%|█▏        | 19660800/170498071 [04:05<27:53, 90147.97it/s]

 12%|█▏        | 19693568/170498071 [04:05<27:54, 90040.54it/s]

 12%|█▏        | 19726336/170498071 [04:05<27:53, 90084.73it/s]

 12%|█▏        | 19759104/170498071 [04:06<27:50, 90231.15it/s]

 12%|█▏        | 19791872/170498071 [04:06<28:04, 89443.77it/s]

 12%|█▏        | 19824640/170498071 [04:07<31:20, 80125.83it/s]

 12%|█▏        | 19857408/170498071 [04:07<29:01, 86476.88it/s]

 12%|█▏        | 19890176/170498071 [04:07<28:19, 88641.68it/s]

 12%|█▏        | 19922944/170498071 [04:08<28:14, 88835.93it/s]

 12%|█▏        | 19955712/170498071 [04:08<28:09, 89104.28it/s]

 12%|█▏        | 19988480/170498071 [04:08<28:07, 89176.29it/s]

 12%|█▏        | 20021248/170498071 [04:09<28:04, 89311.70it/s]

 12%|█▏        | 20054016/170498071 [04:09<28:00, 89526.89it/s]

 12%|█▏        | 20086784/170498071 [04:10<27:50, 90015.18it/s]

 12%|█▏        | 20119552/170498071 [04:10<29:39, 84495.99it/s]

 12%|█▏        | 20152320/170498071 [04:10<29:23, 85267.76it/s]

 12%|█▏        | 20185088/170498071 [04:11<30:05, 83230.03it/s]

 12%|█▏        | 20217856/170498071 [04:11<27:59, 89475.52it/s]

 12%|█▏        | 20250624/170498071 [04:11<27:47, 90108.38it/s]

 12%|█▏        | 20283392/170498071 [04:12<27:32, 90921.20it/s]

 12%|█▏        | 20316160/170498071 [04:12<27:06, 92317.83it/s]

 12%|█▏        | 20348928/170498071 [04:13<27:20, 91551.89it/s]

 12%|█▏        | 20381696/170498071 [04:13<28:29, 87830.95it/s]

 12%|█▏        | 20414464/170498071 [04:13<26:36, 93992.48it/s]

 12%|█▏        | 20447232/170498071 [04:14<26:26, 94604.90it/s]

 12%|█▏        | 20480000/170498071 [04:14<26:35, 94041.59it/s]

 12%|█▏        | 20512768/170498071 [04:14<28:46, 86848.16it/s]

 12%|█▏        | 20545536/170498071 [04:15<28:11, 88659.95it/s]

 12%|█▏        | 20578304/170498071 [04:15<27:48, 89863.73it/s]

 12%|█▏        | 20611072/170498071 [04:15<27:28, 90940.02it/s]

 12%|█▏        | 20643840/170498071 [04:16<27:14, 91682.08it/s]

 12%|█▏        | 20676608/170498071 [04:16<27:10, 91864.74it/s]

 12%|█▏        | 20709376/170498071 [04:16<27:05, 92169.03it/s]

 12%|█▏        | 20742144/170498071 [04:17<27:06, 92082.89it/s]

 12%|█▏        | 20774912/170498071 [04:17<27:28, 90844.17it/s]

 12%|█▏        | 20807680/170498071 [04:18<26:58, 92463.58it/s]

 12%|█▏        | 20840448/170498071 [04:18<29:00, 85961.72it/s]

 12%|█▏        | 20873216/170498071 [04:18<28:25, 87739.96it/s]

 12%|█▏        | 20905984/170498071 [04:19<30:18, 82280.08it/s]

 12%|█▏        | 20938752/170498071 [04:19<27:14, 91487.61it/s]

 12%|█▏        | 20971520/170498071 [04:19<26:55, 92578.34it/s]

 12%|█▏        | 21004288/170498071 [04:20<26:55, 92548.51it/s]

 12%|█▏        | 21037056/170498071 [04:20<26:59, 92309.40it/s]

 12%|█▏        | 21069824/170498071 [04:20<27:01, 92175.58it/s]

 12%|█▏        | 21102592/170498071 [04:21<27:00, 92201.01it/s]

 12%|█▏        | 21135360/170498071 [04:21<27:15, 91307.05it/s]

 12%|█▏        | 21168128/170498071 [04:22<28:56, 86003.53it/s]

 12%|█▏        | 21200896/170498071 [04:22<28:25, 87560.05it/s]

 12%|█▏        | 21233664/170498071 [04:22<28:00, 88823.51it/s]

 12%|█▏        | 21266432/170498071 [04:23<29:44, 83611.41it/s]

 12%|█▏        | 21299200/170498071 [04:23<27:12, 91390.53it/s]

 13%|█▎        | 21331968/170498071 [04:23<27:04, 91796.00it/s]

 13%|█▎        | 21364736/170498071 [04:24<27:01, 91994.61it/s]

 13%|█▎        | 21397504/170498071 [04:24<26:51, 92543.34it/s]

 13%|█▎        | 21430272/170498071 [04:24<26:27, 93898.46it/s]

 13%|█▎        | 21463040/170498071 [04:25<26:25, 94023.04it/s]

 13%|█▎        | 21495808/170498071 [04:25<26:21, 94195.23it/s]

 13%|█▎        | 21528576/170498071 [04:26<28:18, 87693.48it/s]

 13%|█▎        | 21561344/170498071 [04:26<27:46, 89364.52it/s]

 13%|█▎        | 21594112/170498071 [04:26<27:24, 90542.86it/s]

 13%|█▎        | 21626880/170498071 [04:27<27:09, 91367.01it/s]

 13%|█▎        | 21659648/170498071 [04:27<26:55, 92148.55it/s]

 13%|█▎        | 21692416/170498071 [04:27<26:42, 92835.12it/s]

 13%|█▎        | 21725184/170498071 [04:28<26:39, 93027.51it/s]

 13%|█▎        | 21757952/170498071 [04:28<26:34, 93298.73it/s]

 13%|█▎        | 21790720/170498071 [04:28<26:28, 93596.66it/s]

 13%|█▎        | 21823488/170498071 [04:29<26:23, 93903.93it/s]

 13%|█▎        | 21856256/170498071 [04:29<28:33, 86722.87it/s]

 13%|█▎        | 21889024/170498071 [04:30<27:59, 88488.31it/s]

 13%|█▎        | 21921792/170498071 [04:30<27:39, 89531.09it/s]

 13%|█▎        | 21954560/170498071 [04:30<27:44, 89241.98it/s]

 13%|█▎        | 21987328/170498071 [04:31<27:31, 89923.30it/s]

 13%|█▎        | 22020096/170498071 [04:31<27:01, 91543.48it/s]

 13%|█▎        | 22052864/170498071 [04:31<26:59, 91650.96it/s]

 13%|█▎        | 22085632/170498071 [04:32<26:59, 91618.85it/s]

 13%|█▎        | 22118400/170498071 [04:32<26:54, 91887.07it/s]

 13%|█▎        | 22151168/170498071 [04:32<26:50, 92124.46it/s]

 13%|█▎        | 22183936/170498071 [04:33<26:46, 92346.29it/s]

 13%|█▎        | 22216704/170498071 [04:33<29:23, 84061.15it/s]

 13%|█▎        | 22249472/170498071 [04:34<28:05, 87954.05it/s]

 13%|█▎        | 22282240/170498071 [04:34<27:16, 90554.72it/s]

 13%|█▎        | 22315008/170498071 [04:34<27:02, 91311.50it/s]

 13%|█▎        | 22347776/170498071 [04:35<26:49, 92049.33it/s]

 13%|█▎        | 22380544/170498071 [04:35<27:28, 89855.83it/s]

 13%|█▎        | 22413312/170498071 [04:35<26:39, 92585.94it/s]

 13%|█▎        | 22446080/170498071 [04:36<26:20, 93674.10it/s]

 13%|█▎        | 22478848/170498071 [04:36<26:19, 93740.37it/s]

 13%|█▎        | 22511616/170498071 [04:36<26:36, 92688.58it/s]

 13%|█▎        | 22544384/170498071 [04:37<30:09, 81761.86it/s]

 13%|█▎        | 22577152/170498071 [04:37<27:20, 90145.06it/s]

 13%|█▎        | 22609920/170498071 [04:37<26:44, 92147.67it/s]

 13%|█▎        | 22642688/170498071 [04:38<27:29, 89635.83it/s]

 13%|█▎        | 22675456/170498071 [04:38<26:47, 91947.86it/s]

 13%|█▎        | 22708224/170498071 [04:39<26:33, 92770.23it/s]

 13%|█▎        | 22740992/170498071 [04:39<28:33, 86239.09it/s]

 13%|█▎        | 22773760/170498071 [04:39<26:21, 93414.71it/s]

 13%|█▎        | 22806528/170498071 [04:40<26:10, 94046.93it/s]

 13%|█▎        | 22839296/170498071 [04:40<26:19, 93468.46it/s]

 13%|█▎        | 22872064/170498071 [04:40<28:26, 86491.60it/s]

 13%|█▎        | 22904832/170498071 [04:41<28:02, 87746.35it/s]

 13%|█▎        | 22937600/170498071 [04:41<27:43, 88719.72it/s]

 13%|█▎        | 22970368/170498071 [04:41<27:28, 89481.25it/s]

 13%|█▎        | 23003136/170498071 [04:42<27:32, 89276.08it/s]

 14%|█▎        | 23035904/170498071 [04:42<27:22, 89788.86it/s]

 14%|█▎        | 23068672/170498071 [04:43<27:02, 90848.00it/s]

 14%|█▎        | 23101440/170498071 [04:43<26:57, 91104.00it/s]

 14%|█▎        | 23134208/170498071 [04:43<29:43, 82634.55it/s]

 14%|█▎        | 23166976/170498071 [04:44<26:28, 92731.12it/s]

 14%|█▎        | 23199744/170498071 [04:44<26:24, 92968.97it/s]

 14%|█▎        | 23232512/170498071 [04:44<28:36, 85811.31it/s]

 14%|█▎        | 23265280/170498071 [04:45<28:14, 86910.94it/s]

 14%|█▎        | 23298048/170498071 [04:45<27:55, 87828.92it/s]

 14%|█▎        | 23330816/170498071 [04:46<27:42, 88535.97it/s]

 14%|█▎        | 23363584/170498071 [04:46<29:30, 83085.43it/s]

 14%|█▎        | 23396352/170498071 [04:46<27:09, 90292.97it/s]

 14%|█▎        | 23429120/170498071 [04:47<27:00, 90764.85it/s]

 14%|█▍        | 23461888/170498071 [04:47<27:44, 88311.78it/s]

 14%|█▍        | 23494656/170498071 [04:47<27:44, 88318.63it/s]

 14%|█▍        | 23527424/170498071 [04:48<27:09, 90168.28it/s]

 14%|█▍        | 23560192/170498071 [04:48<29:05, 84185.70it/s]

 14%|█▍        | 23592960/170498071 [04:49<28:57, 84535.28it/s]

 14%|█▍        | 23625728/170498071 [04:49<28:24, 86153.88it/s]

 14%|█▍        | 23658496/170498071 [04:49<28:26, 86039.19it/s]

 14%|█▍        | 23691264/170498071 [04:50<28:03, 87223.85it/s]

 14%|█▍        | 23724032/170498071 [04:50<27:59, 87391.73it/s]

 14%|█▍        | 23756800/170498071 [04:50<27:58, 87431.50it/s]

 14%|█▍        | 23789568/170498071 [04:51<28:10, 86789.62it/s]

 14%|█▍        | 23822336/170498071 [04:51<28:07, 86933.91it/s]

 14%|█▍        | 23855104/170498071 [04:52<27:53, 87643.85it/s]

 14%|█▍        | 23887872/170498071 [04:52<28:40, 85238.21it/s]

 14%|█▍        | 23920640/170498071 [04:52<29:55, 81616.66it/s]

 14%|█▍        | 23953408/170498071 [04:53<29:44, 82136.74it/s]

 14%|█▍        | 23986176/170498071 [04:53<29:02, 84066.16it/s]

 14%|█▍        | 24018944/170498071 [04:54<28:53, 84499.23it/s]

 14%|█▍        | 24051712/170498071 [04:54<28:38, 85216.51it/s]

 14%|█▍        | 24084480/170498071 [04:54<28:49, 84664.27it/s]

 14%|█▍        | 24117248/170498071 [04:55<30:24, 80230.70it/s]

 14%|█▍        | 24150016/170498071 [04:55<27:59, 87136.61it/s]

 14%|█▍        | 24182784/170498071 [04:55<27:51, 87527.29it/s]

 14%|█▍        | 24215552/170498071 [04:56<28:02, 86929.00it/s]

 14%|█▍        | 24248320/170498071 [04:56<30:33, 79763.15it/s]

 14%|█▍        | 24281088/170498071 [04:57<31:39, 76975.96it/s]

 14%|█▍        | 24313856/170498071 [04:57<28:53, 84311.58it/s]

 14%|█▍        | 24346624/170498071 [04:57<29:07, 83624.37it/s]

 14%|█▍        | 24379392/170498071 [04:58<28:27, 85593.34it/s]

 14%|█▍        | 24412160/170498071 [04:58<28:47, 84581.51it/s]

 14%|█▍        | 24444928/170498071 [04:59<28:11, 86370.41it/s]

 14%|█▍        | 24477696/170498071 [04:59<27:56, 87079.17it/s]

 14%|█▍        | 24510464/170498071 [04:59<28:38, 84948.45it/s]

 14%|█▍        | 24543232/170498071 [05:00<28:08, 86456.84it/s]

 14%|█▍        | 24576000/170498071 [05:00<28:08, 86433.54it/s]

 14%|█▍        | 24608768/170498071 [05:01<30:41, 79213.05it/s]

 14%|█▍        | 24641536/170498071 [05:01<29:29, 82448.89it/s]

 14%|█▍        | 24674304/170498071 [05:01<30:25, 79883.01it/s]

 14%|█▍        | 24707072/170498071 [05:02<28:22, 85616.45it/s]

 15%|█▍        | 24739840/170498071 [05:02<30:03, 80823.15it/s]

 15%|█▍        | 24772608/170498071 [05:03<27:40, 87748.87it/s]

 15%|█▍        | 24805376/170498071 [05:03<27:28, 88362.31it/s]

 15%|█▍        | 24838144/170498071 [05:03<29:49, 81386.15it/s]

 15%|█▍        | 24870912/170498071 [05:04<27:13, 89154.53it/s]

 15%|█▍        | 24903680/170498071 [05:04<27:23, 88612.44it/s]

 15%|█▍        | 24936448/170498071 [05:05<30:01, 80784.64it/s]

 15%|█▍        | 24969216/170498071 [05:05<30:05, 80612.66it/s]

 15%|█▍        | 25001984/170498071 [05:05<29:25, 82424.31it/s]

 15%|█▍        | 25034752/170498071 [05:06<27:44, 87389.96it/s]

 15%|█▍        | 25067520/170498071 [05:06<28:16, 85715.83it/s]

 15%|█▍        | 25100288/170498071 [05:06<29:47, 81352.23it/s]

 15%|█▍        | 25133056/170498071 [05:07<27:15, 88907.80it/s]

 15%|█▍        | 25165824/170498071 [05:07<28:42, 84350.11it/s]

 15%|█▍        | 25198592/170498071 [05:08<29:19, 82579.66it/s]

 15%|█▍        | 25231360/170498071 [05:08<28:08, 86028.48it/s]

 15%|█▍        | 25264128/170498071 [05:08<30:06, 80404.73it/s]

 15%|█▍        | 25296896/170498071 [05:09<29:07, 83101.39it/s]

 15%|█▍        | 25329664/170498071 [05:09<28:25, 85100.85it/s]

 15%|█▍        | 25362432/170498071 [05:10<29:06, 83092.22it/s]

 15%|█▍        | 25395200/170498071 [05:10<28:40, 84341.10it/s]

 15%|█▍        | 25427968/170498071 [05:10<29:03, 83222.47it/s]

 15%|█▍        | 25460736/170498071 [05:11<27:26, 88097.79it/s]

 15%|█▍        | 25493504/170498071 [05:11<29:02, 83196.13it/s]

 15%|█▍        | 25526272/170498071 [05:11<27:39, 87360.22it/s]

 15%|█▍        | 25559040/170498071 [05:12<26:58, 89555.80it/s]

 15%|█▌        | 25591808/170498071 [05:12<29:13, 82628.23it/s]

 15%|█▌        | 25624576/170498071 [05:13<31:03, 77748.61it/s]

 15%|█▌        | 25657344/170498071 [05:13<28:07, 85841.43it/s]

 15%|█▌        | 25690112/170498071 [05:13<28:31, 84628.82it/s]

 15%|█▌        | 25722880/170498071 [05:14<27:57, 86304.98it/s]

 15%|█▌        | 25755648/170498071 [05:14<28:26, 84793.58it/s]

 15%|█▌        | 25788416/170498071 [05:15<27:58, 86231.36it/s]

 15%|█▌        | 25821184/170498071 [05:15<29:58, 80425.57it/s]

 15%|█▌        | 25853952/170498071 [05:15<27:50, 86591.80it/s]

 15%|█▌        | 25886720/170498071 [05:16<27:28, 87710.83it/s]

 15%|█▌        | 25919488/170498071 [05:16<27:36, 87293.42it/s]

 15%|█▌        | 25952256/170498071 [05:17<31:00, 77695.52it/s]

 15%|█▌        | 25985024/170498071 [05:17<28:59, 83079.18it/s]

 15%|█▌        | 26017792/170498071 [05:17<29:48, 80786.46it/s]

 15%|█▌        | 26050560/170498071 [05:18<28:09, 85511.36it/s]

 15%|█▌        | 26083328/170498071 [05:18<30:05, 79969.68it/s]

 15%|█▌        | 26116096/170498071 [05:18<27:26, 87677.70it/s]

 15%|█▌        | 26148864/170498071 [05:19<27:59, 85961.28it/s]

 15%|█▌        | 26181632/170498071 [05:19<27:29, 87473.91it/s]

 15%|█▌        | 26214400/170498071 [05:20<27:23, 87788.03it/s]

 15%|█▌        | 26247168/170498071 [05:20<27:28, 87523.65it/s]

 15%|█▌        | 26279936/170498071 [05:20<28:08, 85411.01it/s]

 15%|█▌        | 26312704/170498071 [05:21<30:26, 78936.33it/s]

 15%|█▌        | 26345472/170498071 [05:21<28:47, 83428.28it/s]

 15%|█▌        | 26378240/170498071 [05:22<28:07, 85384.53it/s]

 15%|█▌        | 26411008/170498071 [05:22<28:00, 85723.32it/s]

 16%|█▌        | 26443776/170498071 [05:22<27:47, 86407.65it/s]

 16%|█▌        | 26476544/170498071 [05:23<27:34, 87032.75it/s]

 16%|█▌        | 26509312/170498071 [05:23<27:44, 86503.92it/s]

 16%|█▌        | 26542080/170498071 [05:23<27:21, 87720.05it/s]

 16%|█▌        | 26574848/170498071 [05:24<27:17, 87869.26it/s]

 16%|█▌        | 26607616/170498071 [05:24<29:26, 81466.31it/s]

 16%|█▌        | 26640384/170498071 [05:25<29:05, 82429.35it/s]

 16%|█▌        | 26673152/170498071 [05:25<30:45, 77942.72it/s]

 16%|█▌        | 26705920/170498071 [05:25<27:44, 86404.73it/s]

 16%|█▌        | 26738688/170498071 [05:26<29:42, 80643.26it/s]

 16%|█▌        | 26771456/170498071 [05:26<27:06, 88392.69it/s]

 16%|█▌        | 26804224/170498071 [05:27<27:04, 88479.99it/s]

 16%|█▌        | 26836992/170498071 [05:27<27:57, 85627.87it/s]

 16%|█▌        | 26869760/170498071 [05:27<29:36, 80829.76it/s]

 16%|█▌        | 26902528/170498071 [05:28<28:11, 84888.67it/s]

 16%|█▌        | 26935296/170498071 [05:28<26:57, 88766.50it/s]

 16%|█▌        | 26968064/170498071 [05:29<28:57, 82598.35it/s]

 16%|█▌        | 27000832/170498071 [05:29<28:34, 83694.51it/s]

 16%|█▌        | 27033600/170498071 [05:29<28:20, 84369.49it/s]

 16%|█▌        | 27066368/170498071 [05:30<28:14, 84620.90it/s]

 16%|█▌        | 27099136/170498071 [05:30<28:09, 84873.45it/s]

 16%|█▌        | 27131904/170498071 [05:30<28:42, 83238.01it/s]

 16%|█▌        | 27164672/170498071 [05:31<27:49, 85842.01it/s]

 16%|█▌        | 27197440/170498071 [05:31<27:49, 85842.89it/s]

 16%|█▌        | 27230208/170498071 [05:32<27:46, 85970.94it/s]

 16%|█▌        | 27262976/170498071 [05:32<28:03, 85063.66it/s]

 16%|█▌        | 27295744/170498071 [05:32<27:38, 86344.51it/s]

 16%|█▌        | 27328512/170498071 [05:33<30:02, 79420.18it/s]

 16%|█▌        | 27361280/170498071 [05:33<30:04, 79332.26it/s]

 16%|█▌        | 27394048/170498071 [05:34<29:56, 79640.74it/s]

 16%|█▌        | 27426816/170498071 [05:34<28:07, 84807.20it/s]

 16%|█▌        | 27459584/170498071 [05:34<27:41, 86107.33it/s]

 16%|█▌        | 27492352/170498071 [05:35<29:05, 81948.73it/s]

 16%|█▌        | 27525120/170498071 [05:35<27:27, 86799.79it/s]

 16%|█▌        | 27557888/170498071 [05:35<27:14, 87458.73it/s]

 16%|█▌        | 27590656/170498071 [05:36<27:16, 87341.75it/s]

 16%|█▌        | 27623424/170498071 [05:36<27:19, 87119.94it/s]

 16%|█▌        | 27656192/170498071 [05:37<29:23, 81008.06it/s]

 16%|█▌        | 27688960/170498071 [05:37<28:44, 82809.12it/s]

 16%|█▋        | 27721728/170498071 [05:37<28:19, 84028.90it/s]

 16%|█▋        | 27754496/170498071 [05:38<27:58, 85056.07it/s]

 16%|█▋        | 27787264/170498071 [05:38<27:44, 85745.51it/s]

 16%|█▋        | 27820032/170498071 [05:39<28:09, 84460.29it/s]

 16%|█▋        | 27852800/170498071 [05:39<27:11, 87429.29it/s]

 16%|█▋        | 27885568/170498071 [05:39<27:09, 87502.74it/s]

 16%|█▋        | 27918336/170498071 [05:40<27:49, 85400.64it/s]

 16%|█▋        | 27951104/170498071 [05:40<27:05, 87695.28it/s]

 16%|█▋        | 27983872/170498071 [05:41<28:45, 82600.16it/s]

 16%|█▋        | 28016640/170498071 [05:41<28:59, 81927.57it/s]

 16%|█▋        | 28049408/170498071 [05:41<27:39, 85851.89it/s]

 16%|█▋        | 28082176/170498071 [05:42<27:28, 86394.43it/s]

 16%|█▋        | 28114944/170498071 [05:42<27:35, 86031.87it/s]

 17%|█▋        | 28147712/170498071 [05:42<27:32, 86118.90it/s]

 17%|█▋        | 28180480/170498071 [05:43<26:52, 88278.21it/s]

 17%|█▋        | 28213248/170498071 [05:43<26:48, 88442.51it/s]

 17%|█▋        | 28246016/170498071 [05:44<26:49, 88396.27it/s]

 17%|█▋        | 28278784/170498071 [05:44<26:47, 88488.60it/s]

 17%|█▋        | 28311552/170498071 [05:44<26:45, 88554.24it/s]

 17%|█▋        | 28344320/170498071 [05:45<28:48, 82236.02it/s]

 17%|█▋        | 28377088/170498071 [05:45<28:28, 83190.24it/s]

 17%|█▋        | 28409856/170498071 [05:45<27:43, 85403.32it/s]

 17%|█▋        | 28442624/170498071 [05:46<27:28, 86159.58it/s]

 17%|█▋        | 28475392/170498071 [05:46<27:15, 86812.60it/s]

 17%|█▋        | 28508160/170498071 [05:47<27:12, 86980.06it/s]

 17%|█▋        | 28540928/170498071 [05:47<27:48, 85090.84it/s]

 17%|█▋        | 28573696/170498071 [05:47<27:12, 86950.13it/s]

 17%|█▋        | 28606464/170498071 [05:48<26:50, 88127.67it/s]

 17%|█▋        | 28639232/170498071 [05:48<26:57, 87724.87it/s]

 17%|█▋        | 28672000/170498071 [05:48<26:59, 87569.19it/s]

 17%|█▋        | 28704768/170498071 [05:49<29:08, 81104.31it/s]

 17%|█▋        | 28737536/170498071 [05:49<28:35, 82626.68it/s]

 17%|█▋        | 28770304/170498071 [05:50<28:11, 83766.87it/s]

 17%|█▋        | 28803072/170498071 [05:50<28:00, 84305.02it/s]

 17%|█▋        | 28835840/170498071 [05:50<27:49, 84858.52it/s]

 17%|█▋        | 28868608/170498071 [05:51<27:43, 85124.50it/s]

 17%|█▋        | 28901376/170498071 [05:51<27:38, 85366.98it/s]

 17%|█▋        | 28934144/170498071 [05:52<27:32, 85670.90it/s]

 17%|█▋        | 28966912/170498071 [05:52<27:27, 85914.66it/s]

 17%|█▋        | 28999680/170498071 [05:52<27:24, 86063.21it/s]

 17%|█▋        | 29032448/170498071 [05:53<29:20, 80342.66it/s]

 17%|█▋        | 29065216/170498071 [05:53<28:41, 82162.09it/s]

 17%|█▋        | 29097984/170498071 [05:54<28:11, 83597.60it/s]

 17%|█▋        | 29130752/170498071 [05:54<27:58, 84212.76it/s]

 17%|█▋        | 29163520/170498071 [05:54<27:47, 84758.59it/s]

 17%|█▋        | 29196288/170498071 [05:55<27:42, 84983.21it/s]

 17%|█▋        | 29229056/170498071 [05:55<27:41, 85048.02it/s]

 17%|█▋        | 29261824/170498071 [05:56<27:43, 84883.59it/s]

 17%|█▋        | 29294592/170498071 [05:56<27:32, 85452.45it/s]

 17%|█▋        | 29327360/170498071 [05:56<27:25, 85780.85it/s]

 17%|█▋        | 29360128/170498071 [05:57<29:30, 79736.47it/s]

 17%|█▋        | 29392896/170498071 [05:57<28:40, 81992.85it/s]

 17%|█▋        | 29425664/170498071 [05:57<28:15, 83227.25it/s]

 17%|█▋        | 29458432/170498071 [05:58<27:54, 84247.86it/s]

 17%|█▋        | 29491200/170498071 [05:58<27:35, 85194.36it/s]

 17%|█▋        | 29523968/170498071 [05:59<27:21, 85857.74it/s]

 17%|█▋        | 29556736/170498071 [05:59<27:08, 86548.43it/s]

 17%|█▋        | 29589504/170498071 [05:59<27:02, 86842.18it/s]

 17%|█▋        | 29622272/170498071 [06:00<26:52, 87366.19it/s]

 17%|█▋        | 29655040/170498071 [06:00<26:44, 87779.44it/s]

 17%|█▋        | 29687808/170498071 [06:00<26:49, 87470.37it/s]

 17%|█▋        | 29720576/170498071 [06:01<28:47, 81469.92it/s]

 17%|█▋        | 29753344/170498071 [06:01<28:14, 83082.79it/s]

 17%|█▋        | 29786112/170498071 [06:02<27:54, 84035.57it/s]

 17%|█▋        | 29818880/170498071 [06:02<27:37, 84861.52it/s]

 18%|█▊        | 29851648/170498071 [06:02<27:23, 85574.86it/s]

 18%|█▊        | 29884416/170498071 [06:03<27:13, 86067.12it/s]

 18%|█▊        | 29917184/170498071 [06:03<27:05, 86511.14it/s]

 18%|█▊        | 29949952/170498071 [06:04<27:07, 86345.34it/s]

 18%|█▊        | 29982720/170498071 [06:04<27:03, 86548.94it/s]

 18%|█▊        | 30015488/170498071 [06:04<26:57, 86856.31it/s]

 18%|█▊        | 30048256/170498071 [06:05<28:56, 80878.19it/s]

 18%|█▊        | 30081024/170498071 [06:05<28:15, 82814.63it/s]

 18%|█▊        | 30113792/170498071 [06:06<27:43, 84404.44it/s]

 18%|█▊        | 30146560/170498071 [06:06<27:32, 84926.33it/s]

 18%|█▊        | 30179328/170498071 [06:06<27:13, 85924.97it/s]

 18%|█▊        | 30212096/170498071 [06:07<29:10, 80162.56it/s]

 18%|█▊        | 30244864/170498071 [06:07<26:29, 88240.48it/s]

 18%|█▊        | 30277632/170498071 [06:07<26:18, 88857.47it/s]

 18%|█▊        | 30310400/170498071 [06:08<26:25, 88413.45it/s]

 18%|█▊        | 30343168/170498071 [06:08<26:31, 88068.25it/s]

 18%|█▊        | 30375936/170498071 [06:09<26:27, 88276.20it/s]

 18%|█▊        | 30408704/170498071 [06:09<28:36, 81610.56it/s]

 18%|█▊        | 30441472/170498071 [06:09<28:04, 83160.29it/s]

 18%|█▊        | 30474240/170498071 [06:10<27:36, 84513.68it/s]

 18%|█▊        | 30507008/170498071 [06:10<27:22, 85235.45it/s]

 18%|█▊        | 30539776/170498071 [06:11<27:09, 85891.72it/s]

 18%|█▊        | 30572544/170498071 [06:11<26:59, 86411.94it/s]

 18%|█▊        | 30605312/170498071 [06:11<26:51, 86786.97it/s]

 18%|█▊        | 30638080/170498071 [06:12<26:43, 87203.84it/s]

 18%|█▊        | 30670848/170498071 [06:12<26:45, 87096.12it/s]

 18%|█▊        | 30703616/170498071 [06:12<26:32, 87798.43it/s]

 18%|█▊        | 30736384/170498071 [06:13<28:50, 80781.57it/s]

 18%|█▊        | 30769152/170498071 [06:13<27:51, 83591.24it/s]

 18%|█▊        | 30801920/170498071 [06:14<27:21, 85101.63it/s]

 18%|█▊        | 30834688/170498071 [06:14<26:59, 86212.54it/s]

 18%|█▊        | 30867456/170498071 [06:14<26:42, 87114.82it/s]

 18%|█▊        | 30900224/170498071 [06:15<26:25, 88037.63it/s]

 18%|█▊        | 30932992/170498071 [06:15<26:14, 88616.31it/s]

 18%|█▊        | 30965760/170498071 [06:15<26:05, 89147.49it/s]

 18%|█▊        | 30998528/170498071 [06:16<25:56, 89609.54it/s]

 18%|█▊        | 31031296/170498071 [06:16<25:48, 90072.80it/s]

 18%|█▊        | 31064064/170498071 [06:17<27:43, 83802.70it/s]

 18%|█▊        | 31096832/170498071 [06:17<27:07, 85673.14it/s]

 18%|█▊        | 31129600/170498071 [06:17<26:39, 87152.67it/s]

 18%|█▊        | 31162368/170498071 [06:18<26:16, 88374.26it/s]

 18%|█▊        | 31195136/170498071 [06:18<26:01, 89184.10it/s]

 18%|█▊        | 31227904/170498071 [06:18<25:49, 89886.34it/s]

 18%|█▊        | 31260672/170498071 [06:19<25:45, 90096.97it/s]

 18%|█▊        | 31293440/170498071 [06:19<25:45, 90049.08it/s]

 18%|█▊        | 31326208/170498071 [06:19<25:45, 90031.87it/s]

 18%|█▊        | 31358976/170498071 [06:20<25:46, 89955.41it/s]

 18%|█▊        | 31391744/170498071 [06:20<25:42, 90190.91it/s]

 18%|█▊        | 31424512/170498071 [06:21<27:37, 83930.38it/s]

 18%|█▊        | 31457280/170498071 [06:21<27:01, 85762.93it/s]

 18%|█▊        | 31490048/170498071 [06:21<26:37, 87038.89it/s]

 18%|█▊        | 31522816/170498071 [06:22<26:13, 88303.19it/s]

 19%|█▊        | 31555584/170498071 [06:22<26:05, 88758.00it/s]

 19%|█▊        | 31588352/170498071 [06:22<25:55, 89285.48it/s]

 19%|█▊        | 31621120/170498071 [06:23<25:53, 89410.49it/s]

 19%|█▊        | 31653888/170498071 [06:23<25:57, 89135.76it/s]

 19%|█▊        | 31686656/170498071 [06:24<26:02, 88811.59it/s]

 19%|█▊        | 31719424/170498071 [06:24<26:04, 88718.97it/s]

 19%|█▊        | 31752192/170498071 [06:24<28:03, 82424.95it/s]

 19%|█▊        | 31784960/170498071 [06:25<27:27, 84177.06it/s]

 19%|█▊        | 31817728/170498071 [06:25<27:14, 84835.98it/s]

 19%|█▊        | 31850496/170498071 [06:26<26:48, 86174.37it/s]

 19%|█▊        | 31883264/170498071 [06:26<26:42, 86509.56it/s]

 19%|█▊        | 31916032/170498071 [06:26<26:32, 87040.92it/s]

 19%|█▊        | 31948800/170498071 [06:27<26:27, 87288.85it/s]

 19%|█▉        | 31981568/170498071 [06:27<26:22, 87513.12it/s]

 19%|█▉        | 32014336/170498071 [06:27<26:15, 87918.97it/s]

 19%|█▉        | 32047104/170498071 [06:28<26:10, 88158.43it/s]

 19%|█▉        | 32079872/170498071 [06:28<26:13, 87982.76it/s]

 19%|█▉        | 32112640/170498071 [06:29<28:07, 81991.57it/s]

 19%|█▉        | 32145408/170498071 [06:29<27:26, 84006.07it/s]

 19%|█▉        | 32178176/170498071 [06:29<27:07, 84988.08it/s]

 19%|█▉        | 32210944/170498071 [06:30<26:47, 86034.77it/s]

 19%|█▉        | 32243712/170498071 [06:30<26:29, 86954.17it/s]

 19%|█▉        | 32276480/170498071 [06:30<26:20, 87468.57it/s]

 19%|█▉        | 32309248/170498071 [06:31<26:15, 87694.31it/s]

 19%|█▉        | 32342016/170498071 [06:31<26:09, 88025.11it/s]

 19%|█▉        | 32374784/170498071 [06:32<26:05, 88216.98it/s]

 19%|█▉        | 32407552/170498071 [06:32<26:01, 88430.17it/s]

 19%|█▉        | 32440320/170498071 [06:32<27:56, 82364.40it/s]

 19%|█▉        | 32473088/170498071 [06:33<27:14, 84470.40it/s]

 19%|█▉        | 32505856/170498071 [06:33<26:46, 85894.53it/s]

 19%|█▉        | 32538624/170498071 [06:33<26:22, 87193.69it/s]

 19%|█▉        | 32571392/170498071 [06:34<26:18, 87398.45it/s]

 19%|█▉        | 32604160/170498071 [06:34<26:17, 87402.45it/s]

 19%|█▉        | 32636928/170498071 [06:35<25:59, 88382.10it/s]

 19%|█▉        | 32669696/170498071 [06:35<26:05, 88026.26it/s]

 19%|█▉        | 32702464/170498071 [06:35<26:09, 87786.29it/s]

 19%|█▉        | 32735232/170498071 [06:36<26:04, 88060.52it/s]

 19%|█▉        | 32768000/170498071 [06:36<26:07, 87892.97it/s]

 19%|█▉        | 32800768/170498071 [06:37<28:06, 81668.84it/s]

 19%|█▉        | 32833536/170498071 [06:37<27:38, 83013.76it/s]

 19%|█▉        | 32866304/170498071 [06:37<27:20, 83916.27it/s]

 19%|█▉        | 32899072/170498071 [06:38<27:03, 84759.11it/s]

 19%|█▉        | 32931840/170498071 [06:38<26:57, 85046.13it/s]

 19%|█▉        | 32964608/170498071 [06:38<26:48, 85510.06it/s]

 19%|█▉        | 32997376/170498071 [06:39<26:49, 85450.63it/s]

 19%|█▉        | 33030144/170498071 [06:39<26:49, 85390.61it/s]

 19%|█▉        | 33062912/170498071 [06:40<26:48, 85428.21it/s]

 19%|█▉        | 33095680/170498071 [06:40<26:48, 85440.88it/s]

 19%|█▉        | 33128448/170498071 [06:40<28:48, 79480.12it/s]

 19%|█▉        | 33161216/170498071 [06:41<28:09, 81306.72it/s]

 19%|█▉        | 33193984/170498071 [06:41<27:43, 82548.32it/s]

 19%|█▉        | 33226752/170498071 [06:42<27:19, 83747.88it/s]

 20%|█▉        | 33259520/170498071 [06:42<27:07, 84307.21it/s]

 20%|█▉        | 33292288/170498071 [06:42<26:58, 84786.67it/s]

 20%|█▉        | 33325056/170498071 [06:43<26:50, 85173.20it/s]

 20%|█▉        | 33357824/170498071 [06:43<26:42, 85568.04it/s]

 20%|█▉        | 33390592/170498071 [06:44<26:38, 85785.66it/s]

 20%|█▉        | 33423360/170498071 [06:44<26:36, 85875.15it/s]

 20%|█▉        | 33456128/170498071 [06:44<28:28, 80227.98it/s]

 20%|█▉        | 33488896/170498071 [06:45<27:51, 81960.40it/s]

 20%|█▉        | 33521664/170498071 [06:45<27:26, 83217.24it/s]

 20%|█▉        | 33554432/170498071 [06:45<27:04, 84309.13it/s]

 20%|█▉        | 33587200/170498071 [06:46<26:49, 85061.05it/s]

 20%|█▉        | 33619968/170498071 [06:46<26:33, 85879.69it/s]

 20%|█▉        | 33652736/170498071 [06:47<26:25, 86332.84it/s]

 20%|█▉        | 33685504/170498071 [06:47<26:17, 86726.04it/s]

 20%|█▉        | 33718272/170498071 [06:47<26:09, 87126.18it/s]

 20%|█▉        | 33751040/170498071 [06:48<26:03, 87465.95it/s]

 20%|█▉        | 33783808/170498071 [06:48<26:02, 87475.26it/s]

 20%|█▉        | 33816576/170498071 [06:49<27:52, 81737.75it/s]

 20%|█▉        | 33849344/170498071 [06:49<27:11, 83744.87it/s]

 20%|█▉        | 33882112/170498071 [06:49<26:43, 85208.23it/s]

 20%|█▉        | 33914880/170498071 [06:50<26:21, 86347.20it/s]

 20%|█▉        | 33947648/170498071 [06:50<26:03, 87335.30it/s]

 20%|█▉        | 33980416/170498071 [06:50<25:57, 87639.99it/s]

 20%|█▉        | 34013184/170498071 [06:51<25:43, 88404.81it/s]

 20%|█▉        | 34045952/170498071 [06:51<25:40, 88597.72it/s]

 20%|█▉        | 34078720/170498071 [06:52<25:41, 88507.00it/s]

 20%|██        | 34111488/170498071 [06:52<25:37, 88699.93it/s]

 20%|██        | 34144256/170498071 [06:52<27:28, 82726.20it/s]

 20%|██        | 34177024/170498071 [06:53<26:52, 84531.62it/s]

 20%|██        | 34209792/170498071 [06:53<26:31, 85639.26it/s]

 20%|██        | 34242560/170498071 [06:53<26:07, 86946.78it/s]

 20%|██        | 34275328/170498071 [06:54<25:54, 87607.29it/s]

 20%|██        | 34308096/170498071 [06:54<25:40, 88400.79it/s]

 20%|██        | 34340864/170498071 [06:55<25:32, 88856.75it/s]

 20%|██        | 34373632/170498071 [06:55<25:28, 89064.56it/s]

 20%|██        | 34406400/170498071 [06:55<25:14, 89864.57it/s]

 20%|██        | 34439168/170498071 [06:56<25:10, 90079.75it/s]

 20%|██        | 34471936/170498071 [06:56<25:04, 90394.98it/s]

 20%|██        | 34504704/170498071 [06:56<26:51, 84370.13it/s]

 20%|██        | 34537472/170498071 [06:57<26:12, 86484.08it/s]

 20%|██        | 34570240/170498071 [06:57<25:46, 87890.80it/s]

 20%|██        | 34603008/170498071 [06:58<25:27, 88958.36it/s]

 20%|██        | 34635776/170498071 [06:58<25:09, 90019.66it/s]

 20%|██        | 34668544/170498071 [06:58<24:59, 90557.79it/s]

 20%|██        | 34701312/170498071 [06:59<24:51, 91053.22it/s]

 20%|██        | 34734080/170498071 [06:59<24:45, 91386.59it/s]

 20%|██        | 34766848/170498071 [06:59<24:37, 91871.34it/s]

 20%|██        | 34799616/170498071 [07:00<24:38, 91780.89it/s]

 20%|██        | 34832384/170498071 [07:00<26:22, 85725.87it/s]

 20%|██        | 34865152/170498071 [07:00<25:45, 87762.45it/s]

 20%|██        | 34897920/170498071 [07:01<25:14, 89559.39it/s]

 20%|██        | 34930688/170498071 [07:01<24:55, 90642.82it/s]

 21%|██        | 34963456/170498071 [07:01<24:44, 91276.21it/s]

 21%|██        | 34996224/170498071 [07:02<24:37, 91711.96it/s]

 21%|██        | 35028992/170498071 [07:02<24:26, 92397.75it/s]

 21%|██        | 35061760/170498071 [07:03<24:25, 92394.78it/s]

 21%|██        | 35094528/170498071 [07:03<24:23, 92495.34it/s]

 21%|██        | 35127296/170498071 [07:03<24:24, 92446.01it/s]

 21%|██        | 35160064/170498071 [07:04<26:10, 86155.04it/s]

 21%|██        | 35192832/170498071 [07:04<25:31, 88352.43it/s]

 21%|██        | 35225600/170498071 [07:04<25:07, 89728.27it/s]

 21%|██        | 35258368/170498071 [07:05<24:51, 90667.18it/s]

 21%|██        | 35291136/170498071 [07:05<24:44, 91051.02it/s]

 21%|██        | 35323904/170498071 [07:05<24:32, 91805.56it/s]

 21%|██        | 35356672/170498071 [07:06<24:22, 92373.36it/s]

 21%|██        | 35389440/170498071 [07:06<24:15, 92838.29it/s]

 21%|██        | 35422208/170498071 [07:06<24:10, 93114.75it/s]

 21%|██        | 35454976/170498071 [07:07<24:16, 92712.25it/s]

 21%|██        | 35487744/170498071 [07:07<24:21, 92393.23it/s]

 21%|██        | 35520512/170498071 [07:08<26:06, 86143.42it/s]

 21%|██        | 35553280/170498071 [07:08<25:33, 88012.74it/s]

 21%|██        | 35586048/170498071 [07:08<25:07, 89468.77it/s]

 21%|██        | 35618816/170498071 [07:09<24:45, 90822.08it/s]

 21%|██        | 35651584/170498071 [07:09<24:27, 91896.71it/s]

 21%|██        | 35684352/170498071 [07:09<24:20, 92321.81it/s]

 21%|██        | 35717120/170498071 [07:10<24:10, 92891.33it/s]

 21%|██        | 35749888/170498071 [07:10<24:07, 93066.43it/s]

 21%|██        | 35782656/170498071 [07:10<23:57, 93746.17it/s]

 21%|██        | 35815424/170498071 [07:11<23:59, 93549.85it/s]

 21%|██        | 35848192/170498071 [07:11<25:51, 86785.07it/s]

 21%|██        | 35880960/170498071 [07:12<25:15, 88853.49it/s]

 21%|██        | 35913728/170498071 [07:12<24:52, 90159.63it/s]

 21%|██        | 35946496/170498071 [07:12<24:35, 91210.30it/s]

 21%|██        | 35979264/170498071 [07:13<24:25, 91805.99it/s]

 21%|██        | 36012032/170498071 [07:13<24:17, 92244.83it/s]

 21%|██        | 36044800/170498071 [07:13<24:10, 92686.30it/s]

 21%|██        | 36077568/170498071 [07:14<24:09, 92722.21it/s]

 21%|██        | 36110336/170498071 [07:14<24:02, 93160.68it/s]

 21%|██        | 36143104/170498071 [07:14<23:58, 93418.98it/s]

 21%|██        | 36175872/170498071 [07:15<23:55, 93557.64it/s]

 21%|██        | 36208640/170498071 [07:15<25:41, 87140.73it/s]

 21%|██▏       | 36241408/170498071 [07:16<25:03, 89282.18it/s]

 21%|██▏       | 36274176/170498071 [07:16<24:46, 90298.86it/s]

 21%|██▏       | 36306944/170498071 [07:16<24:35, 90955.25it/s]

 21%|██▏       | 36339712/170498071 [07:17<24:27, 91435.12it/s]

 21%|██▏       | 36372480/170498071 [07:17<24:20, 91859.91it/s]

 21%|██▏       | 36405248/170498071 [07:17<24:13, 92235.41it/s]

 21%|██▏       | 36438016/170498071 [07:18<24:08, 92546.74it/s]

 21%|██▏       | 36470784/170498071 [07:18<24:09, 92478.87it/s]

 21%|██▏       | 36503552/170498071 [07:18<24:05, 92713.88it/s]

 21%|██▏       | 36536320/170498071 [07:19<25:51, 86361.42it/s]

 21%|██▏       | 36569088/170498071 [07:19<25:17, 88240.39it/s]

 21%|██▏       | 36601856/170498071 [07:19<24:53, 89639.05it/s]

 21%|██▏       | 36634624/170498071 [07:20<24:32, 90910.99it/s]

 22%|██▏       | 36667392/170498071 [07:20<24:21, 91594.73it/s]

 22%|██▏       | 36700160/170498071 [07:21<24:12, 92107.18it/s]

 22%|██▏       | 36732928/170498071 [07:21<24:01, 92791.95it/s]

 22%|██▏       | 36765696/170498071 [07:21<23:53, 93269.19it/s]

 22%|██▏       | 36798464/170498071 [07:22<23:46, 93723.74it/s]

 22%|██▏       | 36831232/170498071 [07:22<23:41, 94004.15it/s]

 22%|██▏       | 36864000/170498071 [07:22<23:42, 93936.50it/s]

 22%|██▏       | 36896768/170498071 [07:23<25:30, 87269.25it/s]

 22%|██▏       | 36929536/170498071 [07:23<24:53, 89438.75it/s]

 22%|██▏       | 36962304/170498071 [07:23<24:28, 90920.46it/s]

 22%|██▏       | 36995072/170498071 [07:24<24:12, 91914.86it/s]

 22%|██▏       | 37027840/170498071 [07:24<24:01, 92559.72it/s]

 22%|██▏       | 37060608/170498071 [07:24<23:53, 93063.57it/s]

 22%|██▏       | 37093376/170498071 [07:25<23:50, 93272.82it/s]

 22%|██▏       | 37126144/170498071 [07:25<23:43, 93703.91it/s]

 22%|██▏       | 37158912/170498071 [07:25<23:40, 93838.23it/s]

 22%|██▏       | 37191680/170498071 [07:26<23:39, 93892.78it/s]

 22%|██▏       | 37224448/170498071 [07:26<25:24, 87402.55it/s]

 22%|██▏       | 37257216/170498071 [07:27<24:53, 89215.30it/s]

 22%|██▏       | 37289984/170498071 [07:27<24:32, 90445.48it/s]

 22%|██▏       | 37322752/170498071 [07:27<24:19, 91240.46it/s]

 22%|██▏       | 37355520/170498071 [07:28<24:13, 91630.97it/s]

 22%|██▏       | 37388288/170498071 [07:28<24:06, 92031.92it/s]

 22%|██▏       | 37421056/170498071 [07:28<24:01, 92320.08it/s]

 22%|██▏       | 37453824/170498071 [07:29<24:00, 92383.84it/s]

 22%|██▏       | 37486592/170498071 [07:29<23:57, 92503.18it/s]

 22%|██▏       | 37519360/170498071 [07:29<23:53, 92777.22it/s]

 22%|██▏       | 37552128/170498071 [07:30<25:40, 86317.12it/s]

 22%|██▏       | 37584896/170498071 [07:30<25:09, 88072.01it/s]

 22%|██▏       | 37617664/170498071 [07:31<24:47, 89340.20it/s]

 22%|██▏       | 37650432/170498071 [07:31<24:27, 90503.16it/s]

 22%|██▏       | 37683200/170498071 [07:31<24:09, 91603.54it/s]

 22%|██▏       | 37715968/170498071 [07:32<23:54, 92562.86it/s]

 22%|██▏       | 37748736/170498071 [07:32<23:49, 92858.34it/s]

 22%|██▏       | 37781504/170498071 [07:32<23:49, 92828.80it/s]

 22%|██▏       | 37814272/170498071 [07:33<23:49, 92794.63it/s]

 22%|██▏       | 37847040/170498071 [07:33<23:48, 92845.99it/s]

 22%|██▏       | 37879808/170498071 [07:33<23:45, 93035.94it/s]

 22%|██▏       | 37912576/170498071 [07:34<25:28, 86732.78it/s]

 22%|██▏       | 37945344/170498071 [07:34<25:04, 88122.79it/s]

 22%|██▏       | 37978112/170498071 [07:35<24:44, 89253.09it/s]

 22%|██▏       | 38010880/170498071 [07:35<24:32, 89953.87it/s]

 22%|██▏       | 38043648/170498071 [07:35<24:21, 90634.48it/s]

 22%|██▏       | 38076416/170498071 [07:36<24:12, 91173.53it/s]

 22%|██▏       | 38109184/170498071 [07:36<24:06, 91505.12it/s]

 22%|██▏       | 38141952/170498071 [07:36<24:02, 91764.76it/s]

 22%|██▏       | 38174720/170498071 [07:37<23:58, 91991.58it/s]

 22%|██▏       | 38207488/170498071 [07:37<23:55, 92140.52it/s]

 22%|██▏       | 38240256/170498071 [07:37<25:43, 85712.52it/s]

 22%|██▏       | 38273024/170498071 [07:38<25:10, 87536.46it/s]

 22%|██▏       | 38305792/170498071 [07:38<24:43, 89130.00it/s]

 22%|██▏       | 38338560/170498071 [07:39<24:22, 90371.65it/s]

 23%|██▎       | 38371328/170498071 [07:39<24:08, 91215.84it/s]

 23%|██▎       | 38404096/170498071 [07:39<24:00, 91673.18it/s]

 23%|██▎       | 38436864/170498071 [07:40<23:56, 91909.37it/s]

 23%|██▎       | 38469632/170498071 [07:40<23:54, 92054.58it/s]

 23%|██▎       | 38502400/170498071 [07:40<23:50, 92240.31it/s]

 23%|██▎       | 38535168/170498071 [07:41<23:45, 92573.55it/s]

 23%|██▎       | 38567936/170498071 [07:41<23:42, 92772.69it/s]

 23%|██▎       | 38600704/170498071 [07:41<25:28, 86297.61it/s]

 23%|██▎       | 38633472/170498071 [07:42<24:56, 88136.39it/s]

 23%|██▎       | 38666240/170498071 [07:42<24:34, 89427.96it/s]

 23%|██▎       | 38699008/170498071 [07:43<24:21, 90155.98it/s]

 23%|██▎       | 38731776/170498071 [07:43<24:10, 90821.97it/s]

 23%|██▎       | 38764544/170498071 [07:43<24:08, 90933.61it/s]

 23%|██▎       | 38797312/170498071 [07:44<24:11, 90732.78it/s]

 23%|██▎       | 38830080/170498071 [07:44<24:11, 90707.01it/s]

 23%|██▎       | 38862848/170498071 [07:44<24:11, 90720.27it/s]

 23%|██▎       | 38895616/170498071 [07:45<24:07, 90933.17it/s]

 23%|██▎       | 38928384/170498071 [07:45<25:55, 84593.62it/s]

 23%|██▎       | 38961152/170498071 [07:45<25:12, 86955.94it/s]

 23%|██▎       | 38993920/170498071 [07:46<24:45, 88543.82it/s]

 23%|██▎       | 39026688/170498071 [07:46<24:24, 89741.77it/s]

 23%|██▎       | 39059456/170498071 [07:47<24:09, 90675.70it/s]

 23%|██▎       | 39092224/170498071 [07:47<23:59, 91306.75it/s]

 23%|██▎       | 39124992/170498071 [07:47<23:52, 91689.27it/s]

 23%|██▎       | 39157760/170498071 [07:48<23:50, 91818.44it/s]

 23%|██▎       | 39190528/170498071 [07:48<23:48, 91919.32it/s]

 23%|██▎       | 39223296/170498071 [07:48<23:47, 91961.07it/s]

 23%|██▎       | 39256064/170498071 [07:49<25:33, 85604.19it/s]

 23%|██▎       | 39288832/170498071 [07:49<24:58, 87557.50it/s]

 23%|██▎       | 39321600/170498071 [07:49<24:38, 88745.62it/s]

 23%|██▎       | 39354368/170498071 [07:50<24:23, 89599.69it/s]

 23%|██▎       | 39387136/170498071 [07:50<24:14, 90129.95it/s]

 23%|██▎       | 39419904/170498071 [07:51<24:07, 90563.66it/s]

 23%|██▎       | 39452672/170498071 [07:51<24:00, 90946.49it/s]

 23%|██▎       | 39485440/170498071 [07:51<23:56, 91173.64it/s]

 23%|██▎       | 39518208/170498071 [07:52<23:52, 91433.88it/s]

 23%|██▎       | 39550976/170498071 [07:52<23:46, 91776.15it/s]

 23%|██▎       | 39583744/170498071 [07:52<23:46, 91773.11it/s]

 23%|██▎       | 39616512/170498071 [07:53<25:28, 85609.14it/s]

 23%|██▎       | 39649280/170498071 [07:53<24:54, 87570.04it/s]

 23%|██▎       | 39682048/170498071 [07:53<24:31, 88926.13it/s]

 23%|██▎       | 39714816/170498071 [07:54<24:14, 89898.96it/s]

 23%|██▎       | 39747584/170498071 [07:54<24:06, 90409.60it/s]

 23%|██▎       | 39780352/170498071 [07:55<23:53, 91187.98it/s]

 23%|██▎       | 39813120/170498071 [07:55<23:40, 91995.79it/s]

 23%|██▎       | 39845888/170498071 [07:55<23:31, 92581.73it/s]

 23%|██▎       | 39878656/170498071 [07:56<23:22, 93125.43it/s]

 23%|██▎       | 39911424/170498071 [07:56<23:19, 93320.64it/s]

 23%|██▎       | 39944192/170498071 [07:56<24:57, 87183.76it/s]

 23%|██▎       | 39976960/170498071 [07:57<24:23, 89170.54it/s]

 23%|██▎       | 40009728/170498071 [07:57<23:59, 90648.84it/s]

 23%|██▎       | 40042496/170498071 [07:57<23:42, 91676.90it/s]

 24%|██▎       | 40075264/170498071 [07:58<23:28, 92589.63it/s]

 24%|██▎       | 40108032/170498071 [07:58<23:18, 93214.03it/s]

 24%|██▎       | 40140800/170498071 [07:58<23:12, 93597.19it/s]

 24%|██▎       | 40173568/170498071 [07:59<23:11, 93676.48it/s]

 24%|██▎       | 40206336/170498071 [07:59<23:10, 93671.75it/s]

 24%|██▎       | 40239104/170498071 [07:59<23:10, 93693.65it/s]

 24%|██▎       | 40271872/170498071 [08:00<23:06, 93895.00it/s]

 24%|██▎       | 40304640/170498071 [08:00<24:49, 87427.90it/s]

 24%|██▎       | 40337408/170498071 [08:01<24:18, 89251.15it/s]

 24%|██▎       | 40370176/170498071 [08:01<23:56, 90563.78it/s]

 24%|██▎       | 40402944/170498071 [08:01<23:42, 91455.42it/s]

 24%|██▎       | 40435712/170498071 [08:02<23:35, 91858.89it/s]

 24%|██▎       | 40468480/170498071 [08:02<23:31, 92095.52it/s]

 24%|██▍       | 40501248/170498071 [08:02<23:28, 92262.34it/s]

 24%|██▍       | 40534016/170498071 [08:03<23:25, 92448.14it/s]

 24%|██▍       | 40566784/170498071 [08:03<23:23, 92575.59it/s]

 24%|██▍       | 40599552/170498071 [08:03<23:20, 92733.28it/s]

 24%|██▍       | 40632320/170498071 [08:04<25:09, 86005.88it/s]

 24%|██▍       | 40665088/170498071 [08:04<24:41, 87615.61it/s]

 24%|██▍       | 40697856/170498071 [08:05<24:20, 88889.70it/s]

 24%|██▍       | 40730624/170498071 [08:05<24:00, 90111.23it/s]

 24%|██▍       | 40763392/170498071 [08:05<23:44, 91051.76it/s]

 24%|██▍       | 40796160/170498071 [08:06<23:33, 91743.25it/s]

 24%|██▍       | 40828928/170498071 [08:06<23:27, 92113.34it/s]

 24%|██▍       | 40861696/170498071 [08:06<23:23, 92393.90it/s]

 24%|██▍       | 40894464/170498071 [08:07<23:19, 92582.16it/s]

 24%|██▍       | 40927232/170498071 [08:07<23:18, 92617.42it/s]

 24%|██▍       | 40960000/170498071 [08:07<23:18, 92648.75it/s]

 24%|██▍       | 40992768/170498071 [08:08<25:01, 86253.28it/s]

 24%|██▍       | 41025536/170498071 [08:08<24:26, 88316.81it/s]

 24%|██▍       | 41058304/170498071 [08:09<24:01, 89775.39it/s]

 24%|██▍       | 41091072/170498071 [08:09<23:47, 90655.00it/s]

 24%|██▍       | 41123840/170498071 [08:09<23:34, 91460.08it/s]

 24%|██▍       | 41156608/170498071 [08:10<23:25, 92027.04it/s]

 24%|██▍       | 41189376/170498071 [08:10<23:19, 92369.05it/s]

 24%|██▍       | 41222144/170498071 [08:10<23:19, 92378.55it/s]

 24%|██▍       | 41254912/170498071 [08:11<23:18, 92429.89it/s]

 24%|██▍       | 41287680/170498071 [08:11<23:12, 92779.40it/s]

 24%|██▍       | 41320448/170498071 [08:11<24:54, 86411.29it/s]

 24%|██▍       | 41353216/170498071 [08:12<24:19, 88458.41it/s]

 24%|██▍       | 41385984/170498071 [08:12<23:59, 89680.13it/s]

 24%|██▍       | 41418752/170498071 [08:13<23:43, 90669.72it/s]

 24%|██▍       | 41451520/170498071 [08:13<23:34, 91258.86it/s]

 24%|██▍       | 41484288/170498071 [08:13<23:24, 91878.61it/s]

 24%|██▍       | 41517056/170498071 [08:14<23:18, 92210.56it/s]

 24%|██▍       | 41549824/170498071 [08:14<23:14, 92444.42it/s]

 24%|██▍       | 41582592/170498071 [08:14<23:12, 92568.43it/s]

 24%|██▍       | 41615360/170498071 [08:15<23:10, 92659.07it/s]

 24%|██▍       | 41648128/170498071 [08:15<24:53, 86246.29it/s]

 24%|██▍       | 41680896/170498071 [08:15<24:24, 87950.73it/s]

 24%|██▍       | 41713664/170498071 [08:16<24:03, 89222.93it/s]

 24%|██▍       | 41746432/170498071 [08:16<23:44, 90364.58it/s]

 25%|██▍       | 41779200/170498071 [08:16<23:29, 91306.50it/s]

 25%|██▍       | 41811968/170498071 [08:17<23:18, 92030.67it/s]

 25%|██▍       | 41844736/170498071 [08:17<23:11, 92471.14it/s]

 25%|██▍       | 41877504/170498071 [08:18<23:05, 92845.41it/s]

 25%|██▍       | 41910272/170498071 [08:18<23:03, 92938.24it/s]

 25%|██▍       | 41943040/170498071 [08:18<23:01, 93039.70it/s]

 25%|██▍       | 41975808/170498071 [08:19<23:01, 93063.96it/s]

 25%|██▍       | 42008576/170498071 [08:19<24:43, 86624.44it/s]

 25%|██▍       | 42041344/170498071 [08:19<24:09, 88621.16it/s]

 25%|██▍       | 42074112/170498071 [08:20<23:47, 89973.06it/s]

 25%|██▍       | 42106880/170498071 [08:20<23:29, 91101.28it/s]

 25%|██▍       | 42139648/170498071 [08:20<23:14, 92045.90it/s]

 25%|██▍       | 42172416/170498071 [08:21<23:04, 92655.89it/s]

 25%|██▍       | 42205184/170498071 [08:21<22:59, 92991.52it/s]

 25%|██▍       | 42237952/170498071 [08:21<22:54, 93284.10it/s]

 25%|██▍       | 42270720/170498071 [08:22<22:54, 93304.70it/s]

 25%|██▍       | 42303488/170498071 [08:22<22:54, 93279.91it/s]

 25%|██▍       | 42336256/170498071 [08:23<24:37, 86742.15it/s]

 25%|██▍       | 42369024/170498071 [08:23<24:06, 88554.76it/s]

 25%|██▍       | 42401792/170498071 [08:23<23:43, 90008.56it/s]

 25%|██▍       | 42434560/170498071 [08:24<23:23, 91215.24it/s]

 25%|██▍       | 42467328/170498071 [08:24<23:10, 92081.52it/s]

 25%|██▍       | 42500096/170498071 [08:24<23:02, 92557.73it/s]

 25%|██▍       | 42532864/170498071 [08:25<23:00, 92700.24it/s]

 25%|██▍       | 42565632/170498071 [08:25<22:56, 92944.46it/s]

 25%|██▍       | 42598400/170498071 [08:25<22:57, 92839.39it/s]

 25%|██▌       | 42631168/170498071 [08:26<22:57, 92835.38it/s]

 25%|██▌       | 42663936/170498071 [08:26<22:56, 92847.89it/s]

 25%|██▌       | 42696704/170498071 [08:27<24:34, 86693.35it/s]

 25%|██▌       | 42729472/170498071 [08:27<23:58, 88851.41it/s]

 25%|██▌       | 42762240/170498071 [08:27<23:34, 90291.74it/s]

 25%|██▌       | 42795008/170498071 [08:28<23:13, 91624.91it/s]

 25%|██▌       | 42827776/170498071 [08:28<23:00, 92465.84it/s]

 25%|██▌       | 42860544/170498071 [08:28<22:49, 93193.23it/s]

 25%|██▌       | 42893312/170498071 [08:29<22:45, 93444.62it/s]

 25%|██▌       | 42926080/170498071 [08:29<22:40, 93770.62it/s]

 25%|██▌       | 42958848/170498071 [08:29<22:37, 93961.00it/s]

 25%|██▌       | 42991616/170498071 [08:30<22:34, 94104.91it/s]

 25%|██▌       | 43024384/170498071 [08:30<24:15, 87593.35it/s]

 25%|██▌       | 43057152/170498071 [08:30<23:49, 89149.25it/s]

 25%|██▌       | 43089920/170498071 [08:31<23:27, 90504.76it/s]

 25%|██▌       | 43122688/170498071 [08:31<23:14, 91313.92it/s]

 25%|██▌       | 43155456/170498071 [08:32<23:06, 91874.42it/s]

 25%|██▌       | 43188224/170498071 [08:32<22:53, 92687.05it/s]

 25%|██▌       | 43220992/170498071 [08:32<22:51, 92813.54it/s]

 25%|██▌       | 43253760/170498071 [08:33<22:50, 92875.11it/s]

 25%|██▌       | 43286528/170498071 [08:33<22:52, 92670.92it/s]

 25%|██▌       | 43319296/170498071 [08:33<22:53, 92566.95it/s]

 25%|██▌       | 43352064/170498071 [08:34<24:31, 86381.49it/s]

 25%|██▌       | 43384832/170498071 [08:34<23:56, 88495.98it/s]

 25%|██▌       | 43417600/170498071 [08:34<23:35, 89795.53it/s]

 25%|██▌       | 43450368/170498071 [08:35<23:17, 90894.47it/s]

 26%|██▌       | 43483136/170498071 [08:35<23:06, 91580.14it/s]

 26%|██▌       | 43515904/170498071 [08:35<23:00, 91992.72it/s]

 26%|██▌       | 43548672/170498071 [08:36<22:57, 92181.74it/s]

 26%|██▌       | 43581440/170498071 [08:36<22:55, 92300.18it/s]

 26%|██▌       | 43614208/170498071 [08:37<22:52, 92438.22it/s]

 26%|██▌       | 43646976/170498071 [08:37<22:51, 92500.55it/s]

 26%|██▌       | 43679744/170498071 [08:37<22:52, 92430.62it/s]

 26%|██▌       | 43712512/170498071 [08:38<24:32, 86095.26it/s]

 26%|██▌       | 43745280/170498071 [08:38<24:05, 87674.72it/s]

 26%|██▌       | 43778048/170498071 [08:38<23:39, 89269.31it/s]

 26%|██▌       | 43810816/170498071 [08:39<23:20, 90469.46it/s]

 26%|██▌       | 43843584/170498071 [08:39<23:02, 91580.86it/s]

 26%|██▌       | 43876352/170498071 [08:39<22:54, 92094.53it/s]

 26%|██▌       | 43909120/170498071 [08:40<22:42, 92927.20it/s]

 26%|██▌       | 43941888/170498071 [08:40<22:32, 93598.98it/s]

 26%|██▌       | 43974656/170498071 [08:40<22:26, 93934.02it/s]

 26%|██▌       | 44007424/170498071 [08:41<22:20, 94339.39it/s]

 26%|██▌       | 44040192/170498071 [08:41<23:54, 88137.88it/s]

 26%|██▌       | 44072960/170498071 [08:42<23:18, 90389.87it/s]

 26%|██▌       | 44105728/170498071 [08:42<22:57, 91756.04it/s]

 26%|██▌       | 44138496/170498071 [08:42<22:39, 92912.99it/s]

 26%|██▌       | 44171264/170498071 [08:43<22:26, 93784.03it/s]

 26%|██▌       | 44204032/170498071 [08:43<22:18, 94389.80it/s]

 26%|██▌       | 44236800/170498071 [08:43<22:09, 94934.96it/s]

 26%|██▌       | 44269568/170498071 [08:44<22:09, 94956.07it/s]

 26%|██▌       | 44302336/170498071 [08:44<22:02, 95442.10it/s]

 26%|██▌       | 44335104/170498071 [08:44<22:02, 95398.88it/s]

 26%|██▌       | 44367872/170498071 [08:45<22:01, 95446.82it/s]

 26%|██▌       | 44400640/170498071 [08:45<23:38, 88925.98it/s]

 26%|██▌       | 44433408/170498071 [08:45<23:06, 90949.42it/s]

 26%|██▌       | 44466176/170498071 [08:46<22:47, 92190.39it/s]

 26%|██▌       | 44498944/170498071 [08:46<22:36, 92919.35it/s]

 26%|██▌       | 44531712/170498071 [08:46<22:22, 93854.51it/s]

 26%|██▌       | 44564480/170498071 [08:47<22:16, 94206.03it/s]

 26%|██▌       | 44597248/170498071 [08:47<22:14, 94358.63it/s]

 26%|██▌       | 44630016/170498071 [08:48<22:08, 94775.95it/s]

 26%|██▌       | 44662784/170498071 [08:48<22:09, 94644.06it/s]

 26%|██▌       | 44695552/170498071 [08:48<22:09, 94641.61it/s]

 26%|██▌       | 44728320/170498071 [08:49<23:47, 88110.86it/s]

 26%|██▋       | 44761088/170498071 [08:49<23:21, 89693.81it/s]

 26%|██▋       | 44793856/170498071 [08:49<23:01, 91016.20it/s]

 26%|██▋       | 44826624/170498071 [08:50<22:48, 91832.47it/s]

 26%|██▋       | 44859392/170498071 [08:50<22:35, 92661.62it/s]

 26%|██▋       | 44892160/170498071 [08:50<22:27, 93212.51it/s]

 26%|██▋       | 44924928/170498071 [08:51<22:21, 93576.18it/s]

 26%|██▋       | 44957696/170498071 [08:51<22:12, 94230.33it/s]

 26%|██▋       | 44990464/170498071 [08:51<22:07, 94516.78it/s]

 26%|██▋       | 45023232/170498071 [08:52<22:04, 94745.78it/s]

 26%|██▋       | 45056000/170498071 [08:52<21:58, 95112.09it/s]

 26%|██▋       | 45088768/170498071 [08:53<23:36, 88528.17it/s]

 26%|██▋       | 45121536/170498071 [08:53<23:04, 90577.19it/s]

 26%|██▋       | 45154304/170498071 [08:53<22:40, 92108.47it/s]

 27%|██▋       | 45187072/170498071 [08:54<22:22, 93316.88it/s]

 27%|██▋       | 45219840/170498071 [08:54<22:12, 94008.28it/s]

 27%|██▋       | 45252608/170498071 [08:54<22:03, 94643.92it/s]

 27%|██▋       | 45285376/170498071 [08:55<21:57, 95065.24it/s]

 27%|██▋       | 45318144/170498071 [08:55<21:52, 95365.48it/s]

 27%|██▋       | 45350912/170498071 [08:55<21:50, 95522.25it/s]

 27%|██▋       | 45383680/170498071 [08:56<21:49, 95526.30it/s]

 27%|██▋       | 45416448/170498071 [08:56<23:21, 89260.82it/s]

 27%|██▋       | 45449216/170498071 [08:56<22:48, 91353.89it/s]

 27%|██▋       | 45481984/170498071 [08:57<22:29, 92663.07it/s]

 27%|██▋       | 45514752/170498071 [08:57<22:14, 93672.48it/s]

 27%|██▋       | 45547520/170498071 [08:57<22:05, 94251.27it/s]

 27%|██▋       | 45580288/170498071 [08:58<22:01, 94561.77it/s]

 27%|██▋       | 45613056/170498071 [08:58<21:59, 94652.90it/s]

 27%|██▋       | 45645824/170498071 [08:58<21:55, 94900.87it/s]

 27%|██▋       | 45678592/170498071 [08:59<21:55, 94915.86it/s]

 27%|██▋       | 45711360/170498071 [08:59<21:57, 94711.96it/s]

 27%|██▋       | 45744128/170498071 [09:00<23:38, 87941.64it/s]

 27%|██▋       | 45776896/170498071 [09:00<23:09, 89774.36it/s]

 27%|██▋       | 45809664/170498071 [09:00<22:48, 91120.30it/s]

 27%|██▋       | 45842432/170498071 [09:01<22:33, 92076.20it/s]

 27%|██▋       | 45875200/170498071 [09:01<22:23, 92742.07it/s]

 27%|██▋       | 45907968/170498071 [09:01<22:12, 93488.30it/s]

 27%|██▋       | 45940736/170498071 [09:02<22:04, 94017.84it/s]

 27%|██▋       | 45973504/170498071 [09:02<22:00, 94289.61it/s]

 27%|██▋       | 46006272/170498071 [09:02<21:54, 94673.89it/s]

 27%|██▋       | 46039040/170498071 [09:03<21:52, 94842.84it/s]

 27%|██▋       | 46071808/170498071 [09:03<21:51, 94877.14it/s]

 27%|██▋       | 46104576/170498071 [09:03<23:26, 88422.03it/s]

 27%|██▋       | 46137344/170498071 [09:04<22:54, 90467.33it/s]

 27%|██▋       | 46170112/170498071 [09:04<22:31, 91993.00it/s]

 27%|██▋       | 46202880/170498071 [09:04<22:14, 93161.80it/s]

 27%|██▋       | 46235648/170498071 [09:05<22:03, 93920.07it/s]

 27%|██▋       | 46268416/170498071 [09:05<21:57, 94276.12it/s]

 27%|██▋       | 46301184/170498071 [09:05<21:50, 94795.43it/s]

 27%|██▋       | 46333952/170498071 [09:06<21:46, 95066.45it/s]

 27%|██▋       | 46366720/170498071 [09:06<21:45, 95082.31it/s]

 27%|██▋       | 46399488/170498071 [09:07<21:45, 95073.79it/s]

 27%|██▋       | 46432256/170498071 [09:07<23:24, 88305.72it/s]

 27%|██▋       | 46465024/170498071 [09:07<22:57, 90062.57it/s]

 27%|██▋       | 46497792/170498071 [09:08<22:41, 91072.87it/s]

 27%|██▋       | 46530560/170498071 [09:08<22:26, 92036.21it/s]

 27%|██▋       | 46563328/170498071 [09:08<22:17, 92633.15it/s]

 27%|██▋       | 46596096/170498071 [09:09<22:13, 92941.87it/s]

 27%|██▋       | 46628864/170498071 [09:09<22:06, 93375.09it/s]

 27%|██▋       | 46661632/170498071 [09:09<22:01, 93734.87it/s]

 27%|██▋       | 46694400/170498071 [09:10<21:56, 94051.23it/s]

 27%|██▋       | 46727168/170498071 [09:10<21:51, 94367.91it/s]

 27%|██▋       | 46759936/170498071 [09:10<21:46, 94706.73it/s]

 27%|██▋       | 46792704/170498071 [09:11<23:21, 88247.64it/s]

 27%|██▋       | 46825472/170498071 [09:11<22:50, 90206.99it/s]

 27%|██▋       | 46858240/170498071 [09:12<22:24, 91953.62it/s]

 28%|██▊       | 46891008/170498071 [09:12<22:10, 92917.19it/s]

 28%|██▊       | 46923776/170498071 [09:12<21:57, 93825.65it/s]

 28%|██▊       | 46956544/170498071 [09:13<21:50, 94239.32it/s]

 28%|██▊       | 46989312/170498071 [09:13<21:41, 94907.19it/s]

 28%|██▊       | 47022080/170498071 [09:13<21:35, 95333.59it/s]

 28%|██▊       | 47054848/170498071 [09:14<21:34, 95375.46it/s]

 28%|██▊       | 47087616/170498071 [09:14<21:30, 95616.97it/s]

 28%|██▊       | 47120384/170498071 [09:14<23:04, 89122.14it/s]

 28%|██▊       | 47153152/170498071 [09:15<22:28, 91451.04it/s]

 28%|██▊       | 47185920/170498071 [09:15<22:05, 93026.31it/s]

 28%|██▊       | 47218688/170498071 [09:15<21:47, 94284.73it/s]

 28%|██▊       | 47251456/170498071 [09:16<21:33, 95276.24it/s]

 28%|██▊       | 47284224/170498071 [09:16<21:20, 96255.04it/s]

 28%|██▊       | 47316992/170498071 [09:16<21:13, 96739.32it/s]

 28%|██▊       | 47349760/170498071 [09:17<21:08, 97107.69it/s]

 28%|██▊       | 47382528/170498071 [09:17<21:03, 97421.24it/s]

 28%|██▊       | 47415296/170498071 [09:17<21:01, 97595.59it/s]

 28%|██▊       | 47448064/170498071 [09:18<22:36, 90721.34it/s]

 28%|██▊       | 47480832/170498071 [09:18<22:10, 92432.10it/s]

 28%|██▊       | 47513600/170498071 [09:18<21:50, 93821.04it/s]

 28%|██▊       | 47546368/170498071 [09:19<21:31, 95231.41it/s]

 28%|██▊       | 47579136/170498071 [09:19<21:17, 96252.22it/s]

 28%|██▊       | 47611904/170498071 [09:19<21:09, 96834.99it/s]

 28%|██▊       | 47644672/170498071 [09:20<21:03, 97205.27it/s]

 28%|██▊       | 47677440/170498071 [09:20<20:59, 97505.01it/s]

 28%|██▊       | 47710208/170498071 [09:20<20:57, 97613.87it/s]

 28%|██▊       | 47742976/170498071 [09:21<20:55, 97802.13it/s]

 28%|██▊       | 47775744/170498071 [09:21<20:51, 98025.36it/s]

 28%|██▊       | 47808512/170498071 [09:22<22:25, 91179.37it/s]

 28%|██▊       | 47841280/170498071 [09:22<21:52, 93426.50it/s]

 28%|██▊       | 47874048/170498071 [09:22<21:33, 94811.40it/s]

 28%|██▊       | 47906816/170498071 [09:23<21:18, 95854.76it/s]

 28%|██▊       | 47939584/170498071 [09:23<21:04, 96920.86it/s]

 28%|██▊       | 47972352/170498071 [09:23<20:55, 97609.50it/s]

 28%|██▊       | 48005120/170498071 [09:24<20:48, 98113.04it/s]

 28%|██▊       | 48037888/170498071 [09:24<20:44, 98389.59it/s]

 28%|██▊       | 48070656/170498071 [09:24<20:47, 98143.87it/s]

 28%|██▊       | 48103424/170498071 [09:25<20:44, 98333.36it/s]

 28%|██▊       | 48136192/170498071 [09:25<22:12, 91861.00it/s]

 28%|██▊       | 48168960/170498071 [09:25<21:40, 94060.40it/s]

 28%|██▊       | 48201728/170498071 [09:26<21:22, 95384.53it/s]

 28%|██▊       | 48234496/170498071 [09:26<21:06, 96518.29it/s]

 28%|██▊       | 48267264/170498071 [09:26<20:55, 97362.39it/s]

 28%|██▊       | 48300032/170498071 [09:27<20:49, 97827.19it/s]

 28%|██▊       | 48332800/170498071 [09:27<20:42, 98283.34it/s]

 28%|██▊       | 48365568/170498071 [09:27<20:34, 98965.22it/s]

 28%|██▊       | 48398336/170498071 [09:28<20:31, 99139.40it/s]

 28%|██▊       | 48431104/170498071 [09:28<20:28, 99373.12it/s]

 28%|██▊       | 48463872/170498071 [09:28<20:26, 99478.30it/s]

 28%|██▊       | 48496640/170498071 [09:29<21:56, 92654.83it/s]

 28%|██▊       | 48529408/170498071 [09:29<21:27, 94719.40it/s]

 28%|██▊       | 48562176/170498071 [09:29<21:05, 96339.82it/s]

 29%|██▊       | 48594944/170498071 [09:30<20:50, 97487.30it/s]

 29%|██▊       | 48627712/170498071 [09:30<20:44, 97910.93it/s]

 29%|██▊       | 48660480/170498071 [09:30<20:34, 98733.12it/s]

 29%|██▊       | 48693248/170498071 [09:31<20:32, 98865.66it/s]

 29%|██▊       | 48726016/170498071 [09:31<20:30, 98980.60it/s]

 29%|██▊       | 48758784/170498071 [09:31<20:24, 99396.38it/s]

 29%|██▊       | 48791552/170498071 [09:32<20:22, 99526.29it/s]

 29%|██▊       | 48824320/170498071 [09:32<21:58, 92289.37it/s]

 29%|██▊       | 48857088/170498071 [09:32<21:29, 94364.64it/s]

 29%|██▊       | 48889856/170498071 [09:33<21:10, 95742.95it/s]

 29%|██▊       | 48922624/170498071 [09:33<20:59, 96532.71it/s]

 29%|██▊       | 48955392/170498071 [09:33<20:53, 96931.42it/s]

 29%|██▊       | 48988160/170498071 [09:34<20:47, 97431.51it/s]

 29%|██▉       | 49020928/170498071 [09:34<20:45, 97551.99it/s]

 29%|██▉       | 49053696/170498071 [09:34<20:44, 97572.50it/s]

 29%|██▉       | 49086464/170498071 [09:35<20:44, 97550.78it/s]

 29%|██▉       | 49119232/170498071 [09:35<20:43, 97584.37it/s]

 29%|██▉       | 49152000/170498071 [09:35<20:42, 97688.69it/s]

 29%|██▉       | 49184768/170498071 [09:36<22:14, 90899.90it/s]

 29%|██▉       | 49217536/170498071 [09:36<21:46, 92814.48it/s]

 29%|██▉       | 49250304/170498071 [09:36<21:24, 94359.87it/s]

 29%|██▉       | 49283072/170498071 [09:37<21:10, 95395.88it/s]

 29%|██▉       | 49315840/170498071 [09:37<21:00, 96172.32it/s]

 29%|██▉       | 49348608/170498071 [09:37<20:51, 96788.65it/s]

 29%|██▉       | 49381376/170498071 [09:38<20:42, 97453.62it/s]

 29%|██▉       | 49414144/170498071 [09:38<20:39, 97686.63it/s]

 29%|██▉       | 49446912/170498071 [09:38<20:38, 97755.63it/s]

 29%|██▉       | 49479680/170498071 [09:39<20:39, 97643.02it/s]

 29%|██▉       | 49512448/170498071 [09:39<22:08, 91070.79it/s]

 29%|██▉       | 49545216/170498071 [09:40<21:39, 93094.94it/s]

 29%|██▉       | 49577984/170498071 [09:40<21:19, 94520.89it/s]

 29%|██▉       | 49610752/170498071 [09:40<21:05, 95509.55it/s]

 29%|██▉       | 49643520/170498071 [09:41<20:52, 96512.90it/s]

 29%|██▉       | 49676288/170498071 [09:41<20:46, 96943.48it/s]

 29%|██▉       | 49709056/170498071 [09:41<20:40, 97376.81it/s]

 29%|██▉       | 49741824/170498071 [09:42<20:39, 97456.59it/s]

 29%|██▉       | 49774592/170498071 [09:42<20:35, 97699.78it/s]

 29%|██▉       | 49807360/170498071 [09:42<20:29, 98142.02it/s]

 29%|██▉       | 49840128/170498071 [09:43<21:52, 91956.17it/s]

 29%|██▉       | 49872896/170498071 [09:43<21:20, 94216.26it/s]

 29%|██▉       | 49905664/170498071 [09:43<20:56, 96003.09it/s]

 29%|██▉       | 49938432/170498071 [09:44<20:39, 97236.05it/s]

 29%|██▉       | 49971200/170498071 [09:44<20:27, 98157.42it/s]

 29%|██▉       | 50003968/170498071 [09:44<20:19, 98778.48it/s]

 29%|██▉       | 50036736/170498071 [09:45<20:52, 96205.90it/s]

 29%|██▉       | 50069504/170498071 [09:45<20:13, 99221.12it/s]

 29%|██▉       | 50102272/170498071 [09:45<19:57, 100546.31it/s]

 29%|██▉       | 50135040/170498071 [09:46<20:14, 99064.41it/s]

 29%|██▉       | 50167808/170498071 [09:46<19:51, 100976.28it/s]

 29%|██▉       | 50200576/170498071 [09:46<22:21, 89694.08it/s]

 29%|██▉       | 50233344/170498071 [09:47<20:45, 96527.17it/s]

 29%|██▉       | 50266112/170498071 [09:47<20:30, 97693.32it/s]

 30%|██▉       | 50298880/170498071 [09:47<20:22, 98350.83it/s]

 30%|██▉       | 50331648/170498071 [09:48<20:48, 96233.80it/s]

 30%|██▉       | 50364416/170498071 [09:48<19:57, 100284.97it/s]

 30%|██▉       | 50397184/170498071 [09:48<19:57, 100323.09it/s]

 30%|██▉       | 50429952/170498071 [09:49<20:11, 99129.39it/s]

 30%|██▉       | 50462720/170498071 [09:49<20:07, 99423.78it/s]

 30%|██▉       | 50495488/170498071 [09:49<19:50, 100779.40it/s]

 30%|██▉       | 50528256/170498071 [09:50<21:21, 93603.08it/s]

 30%|██▉       | 50561024/170498071 [09:50<21:51, 91433.08it/s]

 30%|██▉       | 50593792/170498071 [09:50<20:33, 97193.58it/s]

 30%|██▉       | 50626560/170498071 [09:51<20:10, 98998.53it/s]

 30%|██▉       | 50659328/170498071 [09:51<20:05, 99387.23it/s]

 30%|██▉       | 50692096/170498071 [09:51<20:02, 99608.65it/s]

 30%|██▉       | 50724864/170498071 [09:52<20:02, 99616.13it/s]

 30%|██▉       | 50757632/170498071 [09:52<19:57, 99990.24it/s]

 30%|██▉       | 50790400/170498071 [09:52<19:56, 100051.61it/s]

 30%|██▉       | 50823168/170498071 [09:53<19:57, 99926.69it/s]

 30%|██▉       | 50855936/170498071 [09:53<19:57, 99917.29it/s]

 30%|██▉       | 50888704/170498071 [09:53<21:29, 92768.38it/s]

 30%|██▉       | 50921472/170498071 [09:54<20:58, 95033.89it/s]

 30%|██▉       | 50954240/170498071 [09:54<20:40, 96397.80it/s]

 30%|██▉       | 50987008/170498071 [09:54<20:24, 97632.42it/s]

 30%|██▉       | 51019776/170498071 [09:55<20:15, 98255.90it/s]

 30%|██▉       | 51052544/170498071 [09:55<20:10, 98651.61it/s]

 30%|██▉       | 51085312/170498071 [09:55<20:04, 99134.40it/s]

 30%|██▉       | 51118080/170498071 [09:56<20:00, 99454.59it/s]

 30%|███       | 51150848/170498071 [09:56<19:59, 99535.35it/s]

 30%|███       | 51183616/170498071 [09:56<19:55, 99808.29it/s]

 30%|███       | 51216384/170498071 [09:57<21:40, 91705.39it/s]

 30%|███       | 51249152/170498071 [09:57<21:07, 94112.69it/s]

 30%|███       | 51281920/170498071 [09:57<20:30, 96867.73it/s]

 30%|███       | 51314688/170498071 [09:58<20:28, 96980.45it/s]

 30%|███       | 51347456/170498071 [09:58<20:26, 97144.47it/s]

 30%|███       | 51380224/170498071 [09:58<20:21, 97496.39it/s]

 30%|███       | 51412992/170498071 [09:59<20:25, 97187.81it/s]

 30%|███       | 51445760/170498071 [09:59<20:25, 97132.70it/s]

 30%|███       | 51478528/170498071 [09:59<20:25, 97111.22it/s]

 30%|███       | 51511296/170498071 [10:00<20:28, 96826.27it/s]

 30%|███       | 51544064/170498071 [10:00<22:04, 89840.84it/s]

 30%|███       | 51576832/170498071 [10:00<21:33, 91903.76it/s]

 30%|███       | 51609600/170498071 [10:01<21:16, 93167.05it/s]

 30%|███       | 51642368/170498071 [10:01<21:02, 94140.90it/s]

 30%|███       | 51675136/170498071 [10:01<20:53, 94824.14it/s]

 30%|███       | 51707904/170498071 [10:02<20:50, 95019.18it/s]

 30%|███       | 51740672/170498071 [10:02<20:45, 95357.55it/s]

 30%|███       | 51773440/170498071 [10:03<20:46, 95243.54it/s]

 30%|███       | 51806208/170498071 [10:03<20:43, 95469.36it/s]

 30%|███       | 51838976/170498071 [10:03<20:43, 95448.84it/s]

 30%|███       | 51871744/170498071 [10:04<20:39, 95719.87it/s]

 30%|███       | 51904512/170498071 [10:04<22:13, 88948.20it/s]

 30%|███       | 51937280/170498071 [10:04<21:44, 90863.68it/s]

 30%|███       | 51970048/170498071 [10:05<21:26, 92167.40it/s]

 31%|███       | 52002816/170498071 [10:05<21:13, 93056.16it/s]

 31%|███       | 52035584/170498071 [10:05<21:06, 93572.10it/s]

 31%|███       | 52068352/170498071 [10:06<21:00, 93990.58it/s]

 31%|███       | 52101120/170498071 [10:06<20:55, 94320.63it/s]

 31%|███       | 52133888/170498071 [10:06<20:55, 94274.36it/s]

 31%|███       | 52166656/170498071 [10:07<20:54, 94310.84it/s]

 31%|███       | 52199424/170498071 [10:07<20:54, 94270.60it/s]

 31%|███       | 52232192/170498071 [10:08<22:27, 87739.71it/s]

 31%|███       | 52264960/170498071 [10:08<22:00, 89565.66it/s]

 31%|███       | 52297728/170498071 [10:08<21:38, 91049.43it/s]

 31%|███       | 52330496/170498071 [10:09<21:23, 92074.90it/s]

 31%|███       | 52363264/170498071 [10:09<21:13, 92795.24it/s]

 31%|███       | 52396032/170498071 [10:09<21:05, 93333.01it/s]

 31%|███       | 52428800/170498071 [10:10<20:59, 93778.95it/s]

 31%|███       | 52461568/170498071 [10:10<20:54, 94098.06it/s]

 31%|███       | 52494336/170498071 [10:10<20:49, 94424.06it/s]

 31%|███       | 52527104/170498071 [10:11<20:45, 94694.84it/s]

 31%|███       | 52559872/170498071 [10:11<20:41, 95007.62it/s]

 31%|███       | 52592640/170498071 [10:11<22:13, 88436.63it/s]

 31%|███       | 52625408/170498071 [10:12<21:39, 90687.92it/s]

 31%|███       | 52658176/170498071 [10:12<21:18, 92144.13it/s]

 31%|███       | 52690944/170498071 [10:12<21:05, 93092.83it/s]

 31%|███       | 52723712/170498071 [10:13<20:53, 93952.95it/s]

 31%|███       | 52756480/170498071 [10:13<20:44, 94593.54it/s]

 31%|███       | 52789248/170498071 [10:13<20:40, 94881.28it/s]

 31%|███       | 52822016/170498071 [10:14<20:34, 95302.12it/s]

 31%|███       | 52854784/170498071 [10:14<20:28, 95782.45it/s]

 31%|███       | 52887552/170498071 [10:15<21:50, 89766.69it/s]

 31%|███       | 52920320/170498071 [10:15<21:39, 90454.71it/s]

 31%|███       | 52953088/170498071 [10:15<21:03, 93064.60it/s]

 31%|███       | 52985856/170498071 [10:16<20:50, 93944.45it/s]

 31%|███       | 53018624/170498071 [10:16<20:42, 94555.38it/s]

 31%|███       | 53051392/170498071 [10:16<20:37, 94927.14it/s]

 31%|███       | 53084160/170498071 [10:17<20:34, 95129.18it/s]

 31%|███       | 53116928/170498071 [10:17<20:31, 95302.68it/s]

 31%|███       | 53149696/170498071 [10:17<20:30, 95393.51it/s]

 31%|███       | 53182464/170498071 [10:18<20:30, 95376.09it/s]

 31%|███       | 53215232/170498071 [10:18<20:27, 95534.98it/s]

 31%|███       | 53248000/170498071 [10:18<20:24, 95772.55it/s]

 31%|███▏      | 53280768/170498071 [10:19<21:55, 89096.18it/s]

 31%|███▏      | 53313536/170498071 [10:19<21:30, 90795.47it/s]

 31%|███▏      | 53346304/170498071 [10:19<21:05, 92544.79it/s]

 31%|███▏      | 53379072/170498071 [10:20<20:54, 93361.68it/s]

 31%|███▏      | 53411840/170498071 [10:20<20:44, 94100.23it/s]

 31%|███▏      | 53444608/170498071 [10:20<20:38, 94478.71it/s]

 31%|███▏      | 53477376/170498071 [10:21<20:33, 94877.39it/s]

 31%|███▏      | 53510144/170498071 [10:21<20:31, 95029.59it/s]

 31%|███▏      | 53542912/170498071 [10:21<20:28, 95239.64it/s]

 31%|███▏      | 53575680/170498071 [10:22<20:26, 95300.21it/s]

 31%|███▏      | 53608448/170498071 [10:22<21:54, 88927.54it/s]

 31%|███▏      | 53641216/170498071 [10:23<21:25, 90898.09it/s]

 31%|███▏      | 53673984/170498071 [10:23<21:01, 92586.26it/s]

 31%|███▏      | 53706752/170498071 [10:23<20:46, 93689.35it/s]

 32%|███▏      | 53739520/170498071 [10:24<20:34, 94602.33it/s]

 32%|███▏      | 53772288/170498071 [10:24<20:27, 95115.57it/s]

 32%|███▏      | 53805056/170498071 [10:24<20:20, 95584.91it/s]

 32%|███▏      | 53837824/170498071 [10:25<20:16, 95907.67it/s]

 32%|███▏      | 53870592/170498071 [10:25<20:15, 95913.76it/s]

 32%|███▏      | 53903360/170498071 [10:25<20:12, 96194.30it/s]

 32%|███▏      | 53936128/170498071 [10:26<21:43, 89399.43it/s]

 32%|███▏      | 53968896/170498071 [10:26<21:14, 91431.78it/s]

 32%|███▏      | 54001664/170498071 [10:26<20:53, 92930.18it/s]

 32%|███▏      | 54034432/170498071 [10:27<20:38, 94026.95it/s]

 32%|███▏      | 54067200/170498071 [10:27<20:28, 94737.24it/s]

 32%|███▏      | 54099968/170498071 [10:27<20:22, 95244.99it/s]

 32%|███▏      | 54132736/170498071 [10:28<20:16, 95685.81it/s]

 32%|███▏      | 54165504/170498071 [10:28<20:14, 95761.63it/s]

 32%|███▏      | 54198272/170498071 [10:28<20:08, 96255.89it/s]

 32%|███▏      | 54231040/170498071 [10:29<20:03, 96618.22it/s]

 32%|███▏      | 54263808/170498071 [10:29<20:00, 96824.22it/s]

 32%|███▏      | 54296576/170498071 [10:30<21:28, 90205.58it/s]

 32%|███▏      | 54329344/170498071 [10:30<21:01, 92078.17it/s]

 32%|███▏      | 54362112/170498071 [10:30<20:41, 93516.86it/s]

 32%|███▏      | 54394880/170498071 [10:31<20:28, 94515.44it/s]

 32%|███▏      | 54427648/170498071 [10:31<20:17, 95327.68it/s]

 32%|███▏      | 54460416/170498071 [10:31<20:13, 95606.23it/s]

 32%|███▏      | 54493184/170498071 [10:32<20:07, 96070.82it/s]

 32%|███▏      | 54525952/170498071 [10:32<20:03, 96384.30it/s]

 32%|███▏      | 54558720/170498071 [10:32<19:59, 96688.89it/s]

 32%|███▏      | 54591488/170498071 [10:33<19:52, 97160.58it/s]

 32%|███▏      | 54624256/170498071 [10:33<21:15, 90826.00it/s]

 32%|███▏      | 54657024/170498071 [10:33<20:48, 92798.51it/s]

 32%|███▏      | 54689792/170498071 [10:34<20:31, 94045.09it/s]

 32%|███▏      | 54722560/170498071 [10:34<20:17, 95083.68it/s]

 32%|███▏      | 54755328/170498071 [10:34<20:09, 95727.29it/s]

 32%|███▏      | 54788096/170498071 [10:35<20:01, 96291.07it/s]

 32%|███▏      | 54820864/170498071 [10:35<19:58, 96493.05it/s]

 32%|███▏      | 54853632/170498071 [10:35<19:55, 96703.85it/s]

 32%|███▏      | 54886400/170498071 [10:36<19:54, 96780.53it/s]

 32%|███▏      | 54919168/170498071 [10:36<19:51, 96987.68it/s]

 32%|███▏      | 54951936/170498071 [10:36<19:50, 97067.77it/s]

 32%|███▏      | 54984704/170498071 [10:37<21:21, 90148.34it/s]

 32%|███▏      | 55017472/170498071 [10:37<20:57, 91825.79it/s]

 32%|███▏      | 55050240/170498071 [10:37<20:38, 93193.58it/s]

 32%|███▏      | 55083008/170498071 [10:38<20:29, 93897.42it/s]

 32%|███▏      | 55115776/170498071 [10:38<20:20, 94512.45it/s]

 32%|███▏      | 55148544/170498071 [10:38<20:14, 94938.83it/s]

 32%|███▏      | 55181312/170498071 [10:39<20:12, 95139.68it/s]

 32%|███▏      | 55214080/170498071 [10:39<20:09, 95301.45it/s]

 32%|███▏      | 55246848/170498071 [10:40<20:08, 95379.06it/s]

 32%|███▏      | 55279616/170498071 [10:40<20:39, 92940.99it/s]

 32%|███▏      | 55312384/170498071 [10:40<21:25, 89608.71it/s]

 32%|███▏      | 55345152/170498071 [10:41<21:04, 91071.08it/s]

 32%|███▏      | 55377920/170498071 [10:41<20:50, 92079.62it/s]

 32%|███▏      | 55410688/170498071 [10:41<20:37, 92981.50it/s]

 33%|███▎      | 55443456/170498071 [10:42<20:32, 93355.32it/s]

 33%|███▎      | 55476224/170498071 [10:42<22:52, 83800.07it/s]

 33%|███▎      | 55508992/170498071 [10:42<19:52, 96400.13it/s]

 33%|███▎      | 55541760/170498071 [10:43<19:42, 97189.39it/s]

 33%|███▎      | 55574528/170498071 [10:43<19:48, 96715.77it/s]

 33%|███▎      | 55607296/170498071 [10:43<20:39, 92724.59it/s]

 33%|███▎      | 55640064/170498071 [10:44<21:10, 90368.58it/s]

 33%|███▎      | 55672832/170498071 [10:44<20:52, 91696.46it/s]

 33%|███▎      | 55705600/170498071 [10:45<21:51, 87541.12it/s]

 33%|███▎      | 55738368/170498071 [10:45<20:17, 94262.08it/s]

 33%|███▎      | 55771136/170498071 [10:45<19:57, 95767.66it/s]

 33%|███▎      | 55803904/170498071 [10:46<20:00, 95530.50it/s]

 33%|███▎      | 55836672/170498071 [10:46<20:04, 95206.12it/s]

 33%|███▎      | 55869440/170498071 [10:46<20:06, 95042.54it/s]

 33%|███▎      | 55902208/170498071 [10:47<21:51, 87374.02it/s]

 33%|███▎      | 55934976/170498071 [10:47<19:50, 96249.04it/s]

 33%|███▎      | 55967744/170498071 [10:47<19:46, 96533.40it/s]

 33%|███▎      | 56000512/170498071 [10:48<21:29, 88818.32it/s]

 33%|███▎      | 56033280/170498071 [10:48<21:40, 88014.56it/s]

 33%|███▎      | 56066048/170498071 [10:48<20:42, 92066.10it/s]

 33%|███▎      | 56098816/170498071 [10:49<20:37, 92430.64it/s]

 33%|███▎      | 56131584/170498071 [10:49<20:31, 92865.36it/s]

 33%|███▎      | 56164352/170498071 [10:49<20:28, 93104.31it/s]

 33%|███▎      | 56197120/170498071 [10:50<20:27, 93134.01it/s]

 33%|███▎      | 56229888/170498071 [10:50<20:26, 93185.44it/s]

 33%|███▎      | 56262656/170498071 [10:51<20:27, 93055.36it/s]

 33%|███▎      | 56295424/170498071 [10:51<20:27, 93004.75it/s]

 33%|███▎      | 56328192/170498071 [10:51<22:03, 86287.03it/s]

 33%|███▎      | 56360960/170498071 [10:52<21:41, 87688.35it/s]

 33%|███▎      | 56393728/170498071 [10:52<21:28, 88529.21it/s]

 33%|███▎      | 56426496/170498071 [10:52<21:15, 89436.54it/s]

 33%|███▎      | 56459264/170498071 [10:53<21:08, 89907.44it/s]

 33%|███▎      | 56492032/170498071 [10:53<21:03, 90265.53it/s]

 33%|███▎      | 56524800/170498071 [10:53<20:59, 90525.63it/s]

 33%|███▎      | 56557568/170498071 [10:54<20:57, 90633.29it/s]

 33%|███▎      | 56590336/170498071 [10:54<21:09, 89702.54it/s]

 33%|███▎      | 56623104/170498071 [10:55<20:47, 91272.74it/s]

 33%|███▎      | 56655872/170498071 [10:55<20:45, 91426.26it/s]

 33%|███▎      | 56688640/170498071 [10:55<22:16, 85144.87it/s]

 33%|███▎      | 56721408/170498071 [10:56<21:53, 86641.73it/s]

 33%|███▎      | 56754176/170498071 [10:56<21:30, 88136.80it/s]

 33%|███▎      | 56786944/170498071 [10:56<21:22, 88679.12it/s]

 33%|███▎      | 56819712/170498071 [10:57<21:06, 89779.46it/s]

 33%|███▎      | 56852480/170498071 [10:57<20:55, 90534.47it/s]

 33%|███▎      | 56885248/170498071 [10:58<20:54, 90547.48it/s]

 33%|███▎      | 56918016/170498071 [10:58<20:48, 91005.10it/s]

 33%|███▎      | 56950784/170498071 [10:58<20:45, 91166.76it/s]

 33%|███▎      | 56983552/170498071 [10:59<21:22, 88519.20it/s]

 33%|███▎      | 57016320/170498071 [10:59<23:19, 81095.22it/s]

 33%|███▎      | 57049088/170498071 [10:59<21:33, 87733.70it/s]

 33%|███▎      | 57081856/170498071 [11:00<21:05, 89650.82it/s]

 33%|███▎      | 57114624/170498071 [11:00<21:31, 87804.97it/s]

 34%|███▎      | 57147392/170498071 [11:00<20:44, 91062.60it/s]

 34%|███▎      | 57180160/170498071 [11:01<20:42, 91215.88it/s]

 34%|███▎      | 57212928/170498071 [11:01<20:43, 91074.58it/s]

 34%|███▎      | 57245696/170498071 [11:02<20:43, 91054.82it/s]

 34%|███▎      | 57278464/170498071 [11:02<20:40, 91243.71it/s]

 34%|███▎      | 57311232/170498071 [11:02<20:38, 91409.71it/s]

 34%|███▎      | 57344000/170498071 [11:03<20:34, 91627.03it/s]

 34%|███▎      | 57376768/170498071 [11:03<22:07, 85187.31it/s]

 34%|███▎      | 57409536/170498071 [11:03<21:43, 86784.52it/s]

 34%|███▎      | 57442304/170498071 [11:04<21:23, 88088.28it/s]

 34%|███▎      | 57475072/170498071 [11:04<21:10, 88939.89it/s]

 34%|███▎      | 57507840/170498071 [11:05<21:01, 89584.03it/s]

 34%|███▎      | 57540608/170498071 [11:05<21:07, 89140.42it/s]

 34%|███▍      | 57573376/170498071 [11:05<20:41, 90985.73it/s]

 34%|███▍      | 57606144/170498071 [11:06<20:37, 91205.43it/s]

 34%|███▍      | 57638912/170498071 [11:06<20:33, 91486.25it/s]

 34%|███▍      | 57671680/170498071 [11:06<20:32, 91516.73it/s]

 34%|███▍      | 57704448/170498071 [11:07<22:05, 85087.90it/s]

 34%|███▍      | 57737216/170498071 [11:07<21:39, 86803.84it/s]

 34%|███▍      | 57769984/170498071 [11:07<21:20, 88028.55it/s]

 34%|███▍      | 57802752/170498071 [11:08<21:06, 89005.29it/s]

 34%|███▍      | 57835520/170498071 [11:08<22:38, 82921.69it/s]

 34%|███▍      | 57868288/170498071 [11:09<20:35, 91197.93it/s]

 34%|███▍      | 57901056/170498071 [11:09<20:23, 92021.31it/s]

 34%|███▍      | 57933824/170498071 [11:09<20:23, 92007.70it/s]

 34%|███▍      | 57966592/170498071 [11:10<20:22, 92082.55it/s]

 34%|███▍      | 57999360/170498071 [11:10<20:23, 91948.92it/s]

 34%|███▍      | 58032128/170498071 [11:10<22:02, 85032.42it/s]

 34%|███▍      | 58064896/170498071 [11:11<21:38, 86568.62it/s]

 34%|███▍      | 58097664/170498071 [11:11<21:20, 87773.16it/s]

 34%|███▍      | 58130432/170498071 [11:12<21:10, 88475.57it/s]

 34%|███▍      | 58163200/170498071 [11:12<21:35, 86707.09it/s]

 34%|███▍      | 58195968/170498071 [11:12<20:55, 89440.45it/s]

 34%|███▍      | 58228736/170498071 [11:13<20:37, 90697.24it/s]

 34%|███▍      | 58261504/170498071 [11:13<20:35, 90829.87it/s]

 34%|███▍      | 58294272/170498071 [11:13<20:39, 90556.92it/s]

 34%|███▍      | 58327040/170498071 [11:14<20:38, 90535.75it/s]

 34%|███▍      | 58359808/170498071 [11:14<20:42, 90259.22it/s]

 34%|███▍      | 58392576/170498071 [11:15<22:19, 83663.57it/s]

 34%|███▍      | 58425344/170498071 [11:15<21:54, 85282.07it/s]

 34%|███▍      | 58458112/170498071 [11:15<21:34, 86559.51it/s]

 34%|███▍      | 58490880/170498071 [11:16<21:20, 87489.19it/s]

 34%|███▍      | 58523648/170498071 [11:16<21:10, 88130.27it/s]

 34%|███▍      | 58556416/170498071 [11:16<21:06, 88385.51it/s]

 34%|███▍      | 58589184/170498071 [11:17<21:02, 88623.28it/s]

 34%|███▍      | 58621952/170498071 [11:17<20:59, 88796.29it/s]

 34%|███▍      | 58654720/170498071 [11:17<21:00, 88729.54it/s]

 34%|███▍      | 58687488/170498071 [11:18<21:03, 88460.91it/s]

 34%|███▍      | 58720256/170498071 [11:18<22:42, 82053.54it/s]

 34%|███▍      | 58753024/170498071 [11:19<22:29, 82796.62it/s]

 34%|███▍      | 58785792/170498071 [11:19<21:50, 85273.37it/s]

 34%|███▍      | 58818560/170498071 [11:19<21:41, 85809.46it/s]

 35%|███▍      | 58851328/170498071 [11:20<21:36, 86088.98it/s]

 35%|███▍      | 58884096/170498071 [11:20<21:32, 86373.76it/s]

 35%|███▍      | 58916864/170498071 [11:21<21:34, 86194.40it/s]

 35%|███▍      | 58949632/170498071 [11:21<21:32, 86305.99it/s]

 35%|███▍      | 58982400/170498071 [11:21<21:34, 86113.14it/s]

 35%|███▍      | 59015168/170498071 [11:22<21:38, 85836.40it/s]

 35%|███▍      | 59047936/170498071 [11:22<21:41, 85660.32it/s]

 35%|███▍      | 59080704/170498071 [11:23<23:34, 78750.21it/s]

 35%|███▍      | 59113472/170498071 [11:23<22:43, 81661.74it/s]

 35%|███▍      | 59146240/170498071 [11:23<22:22, 82945.59it/s]

 35%|███▍      | 59179008/170498071 [11:24<22:05, 84001.03it/s]

 35%|███▍      | 59211776/170498071 [11:24<21:56, 84533.93it/s]

 35%|███▍      | 59244544/170498071 [11:24<21:47, 85077.69it/s]

 35%|███▍      | 59277312/170498071 [11:25<22:15, 83308.65it/s]

 35%|███▍      | 59310080/170498071 [11:25<21:40, 85465.24it/s]

 35%|███▍      | 59342848/170498071 [11:26<21:24, 86514.68it/s]

 35%|███▍      | 59375616/170498071 [11:26<21:29, 86195.68it/s]

 35%|███▍      | 59408384/170498071 [11:27<24:47, 74680.16it/s]

 35%|███▍      | 59441152/170498071 [11:27<22:23, 82675.56it/s]

 35%|███▍      | 59473920/170498071 [11:27<22:28, 82360.02it/s]

 35%|███▍      | 59506688/170498071 [11:28<21:54, 84446.86it/s]

 35%|███▍      | 59539456/170498071 [11:28<22:39, 81637.67it/s]

 35%|███▍      | 59572224/170498071 [11:28<21:31, 85867.87it/s]

 35%|███▍      | 59604992/170498071 [11:29<21:21, 86539.77it/s]

 35%|███▍      | 59637760/170498071 [11:29<21:43, 85046.72it/s]

 35%|███▍      | 59670528/170498071 [11:30<21:24, 86255.47it/s]

 35%|███▌      | 59703296/170498071 [11:30<21:29, 85922.99it/s]

 35%|███▌      | 59736064/170498071 [11:30<24:00, 76901.32it/s]

 35%|███▌      | 59768832/170498071 [11:31<22:38, 81487.75it/s]

 35%|███▌      | 59801600/170498071 [11:31<22:36, 81602.94it/s]

 35%|███▌      | 59834368/170498071 [11:32<21:57, 83987.73it/s]

 35%|███▌      | 59867136/170498071 [11:32<23:18, 79104.37it/s]

 35%|███▌      | 59899904/170498071 [11:32<21:18, 86507.06it/s]

 35%|███▌      | 59932672/170498071 [11:33<21:07, 87206.26it/s]

 35%|███▌      | 59965440/170498071 [11:33<21:26, 85921.70it/s]

 35%|███▌      | 59998208/170498071 [11:33<21:11, 86932.37it/s]

 35%|███▌      | 60030976/170498071 [11:34<21:08, 87083.62it/s]

 35%|███▌      | 60063744/170498071 [11:34<21:11, 86871.30it/s]

 35%|███▌      | 60096512/170498071 [11:35<22:43, 80953.54it/s]

 35%|███▌      | 60129280/170498071 [11:35<22:14, 82728.92it/s]

 35%|███▌      | 60162048/170498071 [11:35<21:52, 84081.90it/s]

 35%|███▌      | 60194816/170498071 [11:36<21:34, 85236.44it/s]

 35%|███▌      | 60227584/170498071 [11:36<22:25, 81944.79it/s]

 35%|███▌      | 60260352/170498071 [11:37<21:09, 86811.12it/s]

 35%|███▌      | 60293120/170498071 [11:37<20:49, 88167.15it/s]

 35%|███▌      | 60325888/170498071 [11:37<20:49, 88145.94it/s]

 35%|███▌      | 60358656/170498071 [11:38<20:50, 88087.17it/s]

 35%|███▌      | 60391424/170498071 [11:38<22:26, 81777.80it/s]

 35%|███▌      | 60424192/170498071 [11:38<22:02, 83227.35it/s]

 35%|███▌      | 60456960/170498071 [11:39<21:25, 85591.97it/s]

 35%|███▌      | 60489728/170498071 [11:39<21:26, 85517.87it/s]

 35%|███▌      | 60522496/170498071 [11:40<21:15, 86227.73it/s]

 36%|███▌      | 60555264/170498071 [11:40<20:53, 87677.86it/s]

 36%|███▌      | 60588032/170498071 [11:40<20:52, 87734.69it/s]

 36%|███▌      | 60620800/170498071 [11:41<20:52, 87743.92it/s]

 36%|███▌      | 60653568/170498071 [11:41<20:49, 87903.68it/s]

 36%|███▌      | 60686336/170498071 [11:41<20:48, 87934.99it/s]

 36%|███▌      | 60719104/170498071 [11:42<20:49, 87834.73it/s]

 36%|███▌      | 60751872/170498071 [11:42<20:50, 87765.80it/s]

 36%|███▌      | 60784640/170498071 [11:43<22:26, 81484.69it/s]

 36%|███▌      | 60817408/170498071 [11:43<21:58, 83188.98it/s]

 36%|███▌      | 60850176/170498071 [11:43<21:39, 84371.48it/s]

 36%|███▌      | 60882944/170498071 [11:44<21:21, 85562.08it/s]

 36%|███▌      | 60915712/170498071 [11:44<21:10, 86244.49it/s]

 36%|███▌      | 60948480/170498071 [11:45<21:02, 86785.10it/s]

 36%|███▌      | 60981248/170498071 [11:45<20:57, 87060.97it/s]

 36%|███▌      | 61014016/170498071 [11:45<20:55, 87222.21it/s]

 36%|███▌      | 61046784/170498071 [11:46<21:54, 83267.75it/s]

 36%|███▌      | 61079552/170498071 [11:46<20:43, 87993.14it/s]

 36%|███▌      | 61112320/170498071 [11:46<22:03, 82630.75it/s]

 36%|███▌      | 61145088/170498071 [11:47<21:38, 84216.12it/s]

 36%|███▌      | 61177856/170498071 [11:47<21:19, 85425.72it/s]

 36%|███▌      | 61210624/170498071 [11:48<21:07, 86224.65it/s]

 36%|███▌      | 61243392/170498071 [11:48<20:58, 86801.96it/s]

 36%|███▌      | 61276160/170498071 [11:48<20:53, 87155.37it/s]

 36%|███▌      | 61308928/170498071 [11:49<21:38, 84087.55it/s]

 36%|███▌      | 61341696/170498071 [11:49<20:39, 88067.77it/s]

 36%|███▌      | 61374464/170498071 [11:50<21:19, 85288.02it/s]

 36%|███▌      | 61407232/170498071 [11:50<20:21, 89294.94it/s]

 36%|███▌      | 61440000/170498071 [11:50<20:11, 90021.93it/s]

 36%|███▌      | 61472768/170498071 [11:51<22:20, 81328.03it/s]

 36%|███▌      | 61505536/170498071 [11:51<21:27, 84631.53it/s]

 36%|███▌      | 61538304/170498071 [11:51<21:08, 85909.67it/s]

 36%|███▌      | 61571072/170498071 [11:52<20:40, 87773.98it/s]

 36%|███▌      | 61603840/170498071 [11:52<20:36, 88084.27it/s]

 36%|███▌      | 61636608/170498071 [11:53<21:06, 85938.86it/s]

 36%|███▌      | 61669376/170498071 [11:53<20:35, 88094.20it/s]

 36%|███▌      | 61702144/170498071 [11:53<20:22, 88971.92it/s]

 36%|███▌      | 61734912/170498071 [11:54<20:28, 88549.21it/s]

 36%|███▌      | 61767680/170498071 [11:54<20:30, 88365.63it/s]

 36%|███▌      | 61800448/170498071 [11:54<22:10, 81722.76it/s]

 36%|███▋      | 61833216/170498071 [11:55<22:25, 80777.57it/s]

 36%|███▋      | 61865984/170498071 [11:55<21:28, 84330.88it/s]

 36%|███▋      | 61898752/170498071 [11:56<21:36, 83753.82it/s]

 36%|███▋      | 61931520/170498071 [11:56<20:47, 87020.05it/s]

 36%|███▋      | 61964288/170498071 [11:56<20:48, 86942.42it/s]

 36%|███▋      | 61997056/170498071 [11:57<20:46, 87043.83it/s]

 36%|███▋      | 62029824/170498071 [11:57<20:46, 86997.13it/s]

 36%|███▋      | 62062592/170498071 [11:57<20:44, 87103.69it/s]

 36%|███▋      | 62095360/170498071 [11:58<21:15, 84991.01it/s]

 36%|███▋      | 62128128/170498071 [11:58<22:04, 81810.46it/s]

 36%|███▋      | 62160896/170498071 [11:59<21:37, 83490.53it/s]

 36%|███▋      | 62193664/170498071 [11:59<21:34, 83676.58it/s]

 36%|███▋      | 62226432/170498071 [11:59<20:59, 85988.22it/s]

 37%|███▋      | 62259200/170498071 [12:00<20:53, 86335.77it/s]

 37%|███▋      | 62291968/170498071 [12:00<21:47, 82747.57it/s]

 37%|███▋      | 62324736/170498071 [12:01<20:35, 87533.26it/s]

 37%|███▋      | 62357504/170498071 [12:01<20:55, 86137.75it/s]

 37%|███▋      | 62390272/170498071 [12:01<20:14, 89022.39it/s]

 37%|███▋      | 62423040/170498071 [12:02<20:17, 88780.29it/s]

 37%|███▋      | 62455808/170498071 [12:02<20:19, 88585.34it/s]

 37%|███▋      | 62488576/170498071 [12:03<23:29, 76630.19it/s]

 37%|███▋      | 62521344/170498071 [12:03<21:09, 85031.08it/s]

 37%|███▋      | 62554112/170498071 [12:03<20:43, 86792.96it/s]

 37%|███▋      | 62586880/170498071 [12:04<20:37, 87198.54it/s]

 37%|███▋      | 62619648/170498071 [12:04<20:33, 87465.08it/s]

 37%|███▋      | 62652416/170498071 [12:04<20:28, 87768.69it/s]

 37%|███▋      | 62685184/170498071 [12:05<20:25, 87981.14it/s]

 37%|███▋      | 62717952/170498071 [12:05<22:00, 81635.39it/s]

 37%|███▋      | 62750720/170498071 [12:06<20:04, 89470.05it/s]

 37%|███▋      | 62783488/170498071 [12:06<21:38, 82973.45it/s]

 37%|███▋      | 62816256/170498071 [12:06<21:17, 84278.14it/s]

 37%|███▋      | 62849024/170498071 [12:07<20:51, 86021.00it/s]

 37%|███▋      | 62881792/170498071 [12:07<20:40, 86721.13it/s]

 37%|███▋      | 62914560/170498071 [12:07<20:37, 86959.92it/s]

 37%|███▋      | 62947328/170498071 [12:08<20:35, 87031.75it/s]

 37%|███▋      | 62980096/170498071 [12:08<20:32, 87206.01it/s]

 37%|███▋      | 63012864/170498071 [12:09<20:34, 87096.51it/s]

 37%|███▋      | 63045632/170498071 [12:09<20:32, 87205.36it/s]

 37%|███▋      | 63078400/170498071 [12:09<20:32, 87186.62it/s]

 37%|███▋      | 63111168/170498071 [12:10<20:33, 87066.82it/s]

 37%|███▋      | 63143936/170498071 [12:10<20:33, 87046.55it/s]

 37%|███▋      | 63176704/170498071 [12:11<23:10, 77207.12it/s]

 37%|███▋      | 63209472/170498071 [12:11<21:33, 82973.70it/s]

 37%|███▋      | 63242240/170498071 [12:11<22:08, 80729.63it/s]

 37%|███▋      | 63275008/170498071 [12:12<20:52, 85606.78it/s]

 37%|███▋      | 63307776/170498071 [12:12<21:41, 82346.16it/s]

 37%|███▋      | 63340544/170498071 [12:12<20:34, 86777.40it/s]

 37%|███▋      | 63373312/170498071 [12:13<22:02, 81005.29it/s]

 37%|███▋      | 63406080/170498071 [12:13<20:13, 88224.60it/s]

 37%|███▋      | 63438848/170498071 [12:14<20:07, 88663.04it/s]

 37%|███▋      | 63471616/170498071 [12:14<20:56, 85206.69it/s]

 37%|███▋      | 63504384/170498071 [12:14<21:44, 82027.18it/s]

 37%|███▋      | 63537152/170498071 [12:15<21:43, 82071.88it/s]

 37%|███▋      | 63569920/170498071 [12:15<21:14, 83901.50it/s]

 37%|███▋      | 63602688/170498071 [12:16<21:08, 84301.60it/s]

 37%|███▋      | 63635456/170498071 [12:16<21:59, 80987.08it/s]

 37%|███▋      | 63668224/170498071 [12:16<20:52, 85285.27it/s]

 37%|███▋      | 63700992/170498071 [12:17<20:37, 86288.03it/s]

 37%|███▋      | 63733760/170498071 [12:17<20:40, 86085.01it/s]

 37%|███▋      | 63766528/170498071 [12:18<20:42, 85928.06it/s]

 37%|███▋      | 63799296/170498071 [12:18<21:49, 81497.78it/s]

 37%|███▋      | 63832064/170498071 [12:18<23:05, 76997.27it/s]

 37%|███▋      | 63864832/170498071 [12:19<21:40, 82013.93it/s]

 37%|███▋      | 63897600/170498071 [12:19<21:16, 83482.91it/s]

 37%|███▋      | 63930368/170498071 [12:20<21:23, 83044.53it/s]

 38%|███▊      | 63963136/170498071 [12:20<21:09, 83898.35it/s]

 38%|███▊      | 63995904/170498071 [12:20<22:44, 78026.13it/s]

 38%|███▊      | 64028672/170498071 [12:21<20:49, 85194.84it/s]

 38%|███▊      | 64061440/170498071 [12:21<20:41, 85712.35it/s]

 38%|███▊      | 64094208/170498071 [12:21<20:48, 85242.75it/s]

 38%|███▊      | 64126976/170498071 [12:22<21:23, 82858.27it/s]

 38%|███▊      | 64159744/170498071 [12:22<20:59, 84402.06it/s]

 38%|███▊      | 64192512/170498071 [12:23<23:56, 74018.60it/s]

 38%|███▊      | 64225280/170498071 [12:23<22:28, 78788.81it/s]

 38%|███▊      | 64258048/170498071 [12:24<21:14, 83362.92it/s]

 38%|███▊      | 64290816/170498071 [12:24<21:06, 83868.30it/s]

 38%|███▊      | 64323584/170498071 [12:24<20:49, 84994.04it/s]

 38%|███▊      | 64356352/170498071 [12:25<20:51, 84777.78it/s]

 38%|███▊      | 64389120/170498071 [12:25<21:21, 82790.70it/s]

 38%|███▊      | 64421888/170498071 [12:25<20:55, 84497.70it/s]

 38%|███▊      | 64454656/170498071 [12:26<20:51, 84709.59it/s]

 38%|███▊      | 64487424/170498071 [12:26<20:48, 84915.66it/s]

 38%|███▊      | 64520192/170498071 [12:27<23:03, 76600.81it/s]

 38%|███▊      | 64552960/170498071 [12:27<21:39, 81556.67it/s]

 38%|███▊      | 64585728/170498071 [12:28<21:23, 82510.99it/s]

 38%|███▊      | 64618496/170498071 [12:28<21:31, 81992.60it/s]

 38%|███▊      | 64651264/170498071 [12:28<22:18, 79061.14it/s]

 38%|███▊      | 64684032/170498071 [12:29<20:28, 86153.40it/s]

 38%|███▊      | 64716800/170498071 [12:29<22:28, 78443.93it/s]

 38%|███▊      | 64749568/170498071 [12:29<20:38, 85397.64it/s]

 38%|███▊      | 64782336/170498071 [12:30<21:12, 83068.42it/s]

 38%|███▊      | 64815104/170498071 [12:30<21:00, 83854.05it/s]

 38%|███▊      | 64847872/170498071 [12:31<19:32, 90096.29it/s]

 38%|███▊      | 64880640/170498071 [12:31<22:42, 77544.17it/s]

 38%|███▊      | 64913408/170498071 [12:31<20:44, 84867.96it/s]

 38%|███▊      | 64946176/170498071 [12:32<22:01, 79875.61it/s]

 38%|███▊      | 64978944/170498071 [12:32<21:00, 83734.10it/s]

 38%|███▊      | 65011712/170498071 [12:33<21:28, 81877.69it/s]

 38%|███▊      | 65044480/170498071 [12:33<20:13, 86878.64it/s]

 38%|███▊      | 65077248/170498071 [12:33<20:50, 84305.91it/s]

 38%|███▊      | 65110016/170498071 [12:34<20:45, 84641.26it/s]

 38%|███▊      | 65142784/170498071 [12:34<20:03, 87569.06it/s]

 38%|███▊      | 65175552/170498071 [12:35<20:34, 85332.58it/s]

 38%|███▊      | 65208320/170498071 [12:35<23:08, 75832.81it/s]

 38%|███▊      | 65241088/170498071 [12:35<21:03, 83281.28it/s]

 38%|███▊      | 65273856/170498071 [12:36<20:54, 83859.81it/s]

 38%|███▊      | 65306624/170498071 [12:36<22:10, 79066.14it/s]

 38%|███▊      | 65339392/170498071 [12:37<21:44, 80589.87it/s]

 38%|███▊      | 65372160/170498071 [12:37<20:27, 85637.76it/s]

 38%|███▊      | 65404928/170498071 [12:37<20:09, 86911.79it/s]

 38%|███▊      | 65437696/170498071 [12:38<20:05, 87174.67it/s]

 38%|███▊      | 65470464/170498071 [12:38<21:11, 82621.01it/s]

 38%|███▊      | 65503232/170498071 [12:39<21:07, 82866.65it/s]

 38%|███▊      | 65536000/170498071 [12:39<20:13, 86530.18it/s]

 38%|███▊      | 65568768/170498071 [12:39<22:39, 77160.79it/s]

 38%|███▊      | 65601536/170498071 [12:40<22:39, 77165.01it/s]

 38%|███▊      | 65634304/170498071 [12:40<20:45, 84170.29it/s]

 39%|███▊      | 65667072/170498071 [12:41<20:32, 85022.75it/s]

 39%|███▊      | 65699840/170498071 [12:41<22:09, 78821.37it/s]

 39%|███▊      | 65732608/170498071 [12:41<20:17, 86050.45it/s]

 39%|███▊      | 65765376/170498071 [12:42<20:08, 86653.39it/s]

 39%|███▊      | 65798144/170498071 [12:42<20:54, 83446.35it/s]

 39%|███▊      | 65830912/170498071 [12:43<21:45, 80172.64it/s]

 39%|███▊      | 65863680/170498071 [12:43<20:42, 84183.80it/s]

 39%|███▊      | 65896448/170498071 [12:43<21:32, 80928.76it/s]

 39%|███▊      | 65929216/170498071 [12:44<22:38, 76984.41it/s]

 39%|███▊      | 65961984/170498071 [12:44<20:39, 84315.95it/s]

 39%|███▊      | 65994752/170498071 [12:44<20:27, 85127.55it/s]

 39%|███▊      | 66027520/170498071 [12:45<20:30, 84910.80it/s]

 39%|███▊      | 66060288/170498071 [12:45<21:03, 82635.20it/s]

 39%|███▉      | 66093056/170498071 [12:46<21:18, 81688.96it/s]

 39%|███▉      | 66125824/170498071 [12:46<20:16, 85808.00it/s]

 39%|███▉      | 66158592/170498071 [12:46<20:40, 84136.86it/s]

 39%|███▉      | 66191360/170498071 [12:47<20:19, 85534.22it/s]

 39%|███▉      | 66224128/170498071 [12:47<22:14, 78149.34it/s]

 39%|███▉      | 66256896/170498071 [12:48<21:29, 80842.60it/s]

 39%|███▉      | 66289664/170498071 [12:48<22:30, 77155.07it/s]

 39%|███▉      | 66322432/170498071 [12:49<21:49, 79556.68it/s]

 39%|███▉      | 66355200/170498071 [12:49<21:18, 81429.32it/s]

 39%|███▉      | 66387968/170498071 [12:49<20:57, 82781.23it/s]

 39%|███▉      | 66420736/170498071 [12:50<20:18, 85389.53it/s]

 39%|███▉      | 66453504/170498071 [12:50<19:18, 89784.93it/s]

 39%|███▉      | 66486272/170498071 [12:50<19:54, 87081.87it/s]

 39%|███▉      | 66519040/170498071 [12:51<19:33, 88618.55it/s]

 39%|███▉      | 66551808/170498071 [12:51<20:26, 84746.29it/s]

 39%|███▉      | 66584576/170498071 [12:52<22:24, 77279.22it/s]

 39%|███▉      | 66617344/170498071 [12:52<20:20, 85143.06it/s]

 39%|███▉      | 66650112/170498071 [12:52<21:00, 82353.75it/s]

 39%|███▉      | 66682880/170498071 [12:53<20:02, 86307.29it/s]

 39%|███▉      | 66715648/170498071 [12:53<20:23, 84818.12it/s]

 39%|███▉      | 66748416/170498071 [12:54<21:24, 80773.07it/s]

 39%|███▉      | 66781184/170498071 [12:54<20:27, 84519.92it/s]

 39%|███▉      | 66813952/170498071 [12:54<19:35, 88193.91it/s]

 39%|███▉      | 66846720/170498071 [12:55<20:27, 84450.94it/s]

 39%|███▉      | 66879488/170498071 [12:55<19:33, 88307.84it/s]

 39%|███▉      | 66912256/170498071 [12:56<22:25, 76977.94it/s]

 39%|███▉      | 66945024/170498071 [12:56<20:41, 83425.44it/s]

 39%|███▉      | 66977792/170498071 [12:56<21:30, 80246.38it/s]

 39%|███▉      | 67010560/170498071 [12:57<20:23, 84552.02it/s]

 39%|███▉      | 67043328/170498071 [12:57<20:47, 82900.33it/s]

 39%|███▉      | 67076096/170498071 [12:57<20:01, 86107.53it/s]

 39%|███▉      | 67108864/170498071 [12:58<20:30, 84045.16it/s]

 39%|███▉      | 67141632/170498071 [12:58<20:15, 85055.10it/s]

 39%|███▉      | 67174400/170498071 [12:59<18:46, 91733.79it/s]

 39%|███▉      | 67207168/170498071 [12:59<19:32, 88084.75it/s]

 39%|███▉      | 67239936/170498071 [12:59<19:05, 90174.90it/s]

 39%|███▉      | 67272704/170498071 [13:00<20:41, 83138.69it/s]

 39%|███▉      | 67305472/170498071 [13:00<21:40, 79359.84it/s]

 39%|███▉      | 67338240/170498071 [13:01<21:02, 81736.56it/s]

 40%|███▉      | 67371008/170498071 [13:01<20:06, 85470.20it/s]

 40%|███▉      | 67403776/170498071 [13:01<19:54, 86304.18it/s]

 40%|███▉      | 67436544/170498071 [13:02<19:44, 87009.74it/s]

 40%|███▉      | 67469312/170498071 [13:02<20:12, 84947.46it/s]

 40%|███▉      | 67502080/170498071 [13:02<20:01, 85731.02it/s]

 40%|███▉      | 67534848/170498071 [13:03<19:51, 86413.00it/s]

 40%|███▉      | 67567616/170498071 [13:03<19:10, 89504.43it/s]

 40%|███▉      | 67600384/170498071 [13:04<20:26, 83881.69it/s]

 40%|███▉      | 67633152/170498071 [13:04<19:42, 87021.13it/s]

 40%|███▉      | 67665920/170498071 [13:04<20:23, 84069.70it/s]

 40%|███▉      | 67698688/170498071 [13:05<20:36, 83163.34it/s]

 40%|███▉      | 67731456/170498071 [13:05<18:52, 90756.85it/s]

 40%|███▉      | 67764224/170498071 [13:05<20:15, 84540.71it/s]

 40%|███▉      | 67796992/170498071 [13:06<20:01, 85445.76it/s]

 40%|███▉      | 67829760/170498071 [13:06<19:53, 86039.95it/s]

 40%|███▉      | 67862528/170498071 [13:07<19:47, 86442.82it/s]

 40%|███▉      | 67895296/170498071 [13:07<19:05, 89557.45it/s]

 40%|███▉      | 67928064/170498071 [13:07<20:38, 82842.48it/s]

 40%|███▉      | 67960832/170498071 [13:08<20:19, 84114.86it/s]

 40%|███▉      | 67993600/170498071 [13:08<19:59, 85487.26it/s]

 40%|███▉      | 68026368/170498071 [13:08<19:19, 88372.61it/s]

 40%|███▉      | 68059136/170498071 [13:09<20:12, 84470.92it/s]

 40%|███▉      | 68091904/170498071 [13:09<19:52, 85885.47it/s]

 40%|███▉      | 68124672/170498071 [13:10<19:06, 89292.15it/s]

 40%|███▉      | 68157440/170498071 [13:10<19:06, 89238.34it/s]

 40%|███▉      | 68190208/170498071 [13:10<19:06, 89231.80it/s]

 40%|████      | 68222976/170498071 [13:11<19:12, 88774.04it/s]

 40%|████      | 68255744/170498071 [13:11<19:13, 88663.29it/s]

 40%|████      | 68288512/170498071 [13:12<20:39, 82463.78it/s]

 40%|████      | 68321280/170498071 [13:12<20:44, 82130.59it/s]

 40%|████      | 68354048/170498071 [13:12<20:08, 84535.84it/s]

 40%|████      | 68386816/170498071 [13:13<19:45, 86157.48it/s]

 40%|████      | 68419584/170498071 [13:13<18:14, 93288.06it/s]

 40%|████      | 68452352/170498071 [13:13<19:36, 86742.98it/s]

 40%|████      | 68485120/170498071 [13:14<18:26, 92197.40it/s]

 40%|████      | 68517888/170498071 [13:14<18:12, 93375.62it/s]

 40%|████      | 68550656/170498071 [13:15<19:49, 85680.66it/s]

 40%|████      | 68583424/170498071 [13:15<18:01, 94215.93it/s]

 40%|████      | 68616192/170498071 [13:15<20:12, 84001.27it/s]

 40%|████      | 68648960/170498071 [13:16<19:09, 88633.14it/s]

 40%|████      | 68681728/170498071 [13:16<19:01, 89232.91it/s]

 40%|████      | 68714496/170498071 [13:16<19:13, 88219.44it/s]

 40%|████      | 68747264/170498071 [13:17<19:21, 87597.38it/s]

 40%|████      | 68780032/170498071 [13:17<18:37, 91022.43it/s]

 40%|████      | 68812800/170498071 [13:17<18:35, 91190.91it/s]

 40%|████      | 68845568/170498071 [13:18<19:57, 84888.46it/s]

 40%|████      | 68878336/170498071 [13:18<18:20, 92308.21it/s]

 40%|████      | 68911104/170498071 [13:18<18:35, 91057.28it/s]

 40%|████      | 68943872/170498071 [13:19<18:38, 90778.27it/s]

 40%|████      | 68976640/170498071 [13:19<20:10, 83855.44it/s]

 40%|████      | 69009408/170498071 [13:20<19:49, 85343.54it/s]

 40%|████      | 69042176/170498071 [13:20<19:29, 86732.80it/s]

 41%|████      | 69074944/170498071 [13:20<19:04, 88646.56it/s]

 41%|████      | 69107712/170498071 [13:21<19:06, 88468.99it/s]

 41%|████      | 69140480/170498071 [13:21<19:01, 88798.09it/s]

 41%|████      | 69173248/170498071 [13:22<20:19, 83113.13it/s]

 41%|████      | 69206016/170498071 [13:22<18:33, 90929.98it/s]

 41%|████      | 69238784/170498071 [13:22<18:31, 91132.01it/s]

 41%|████      | 69271552/170498071 [13:23<18:40, 90340.37it/s]

 41%|████      | 69304320/170498071 [13:23<20:12, 83469.25it/s]

 41%|████      | 69337088/170498071 [13:24<21:21, 78942.44it/s]

 41%|████      | 69369856/170498071 [13:24<20:35, 81836.91it/s]

 41%|████      | 69402624/170498071 [13:24<20:09, 83576.48it/s]

 41%|████      | 69435392/170498071 [13:25<18:34, 90686.30it/s]

 41%|████      | 69468160/170498071 [13:25<18:43, 89920.23it/s]

 41%|████      | 69500928/170498071 [13:25<18:40, 90173.81it/s]

 41%|████      | 69533696/170498071 [13:26<19:27, 86465.12it/s]

 41%|████      | 69566464/170498071 [13:26<20:18, 82803.36it/s]

 41%|████      | 69599232/170498071 [13:26<19:20, 86947.41it/s]

 41%|████      | 69632000/170498071 [13:27<18:38, 90213.91it/s]

 41%|████      | 69664768/170498071 [13:27<21:07, 79566.72it/s]

 41%|████      | 69697536/170498071 [13:28<19:23, 86605.43it/s]

 41%|████      | 69730304/170498071 [13:28<19:12, 87459.74it/s]

 41%|████      | 69763072/170498071 [13:28<19:06, 87883.32it/s]

 41%|████      | 69795840/170498071 [13:29<18:44, 89561.03it/s]

 41%|████      | 69828608/170498071 [13:29<18:44, 89529.31it/s]

 41%|████      | 69861376/170498071 [13:30<19:34, 85703.95it/s]

 41%|████      | 69894144/170498071 [13:30<18:23, 91206.26it/s]

 41%|████      | 69926912/170498071 [13:30<19:11, 87340.28it/s]

 41%|████      | 69959680/170498071 [13:31<18:43, 89458.48it/s]

 41%|████      | 69992448/170498071 [13:31<20:56, 80014.48it/s]

 41%|████      | 70025216/170498071 [13:31<19:28, 86018.00it/s]

 41%|████      | 70057984/170498071 [13:32<20:09, 83062.11it/s]

 41%|████      | 70090752/170498071 [13:32<18:21, 91176.76it/s]

 41%|████      | 70123520/170498071 [13:33<19:02, 87828.38it/s]

 41%|████      | 70156288/170498071 [13:33<18:52, 88620.14it/s]

 41%|████      | 70189056/170498071 [13:33<19:25, 86077.67it/s]

 41%|████      | 70221824/170498071 [13:34<18:33, 90030.86it/s]

 41%|████      | 70254592/170498071 [13:34<17:38, 94745.25it/s]

 41%|████      | 70287360/170498071 [13:34<19:01, 87779.69it/s]

 41%|████      | 70320128/170498071 [13:35<19:10, 87087.99it/s]

 41%|████▏     | 70352896/170498071 [13:35<19:43, 84652.98it/s]

 41%|████▏     | 70385664/170498071 [13:35<19:10, 87037.99it/s]

 41%|████▏     | 70418432/170498071 [13:36<18:18, 91075.29it/s]

 41%|████▏     | 70451200/170498071 [13:36<17:38, 94494.86it/s]

 41%|████▏     | 70483968/170498071 [13:36<17:27, 95452.07it/s]

 41%|████▏     | 70516736/170498071 [13:37<18:11, 91617.12it/s]

 41%|████▏     | 70549504/170498071 [13:37<18:29, 90092.01it/s]

 41%|████▏     | 70582272/170498071 [13:38<17:38, 94392.03it/s]

 41%|████▏     | 70615040/170498071 [13:38<17:14, 96560.42it/s]

 41%|████▏     | 70647808/170498071 [13:38<18:28, 90040.99it/s]

 41%|████▏     | 70680576/170498071 [13:39<18:17, 90956.64it/s]

 41%|████▏     | 70713344/170498071 [13:39<18:18, 90874.48it/s]

 41%|████▏     | 70746112/170498071 [13:39<17:47, 93409.48it/s]

 42%|████▏     | 70778880/170498071 [13:40<17:44, 93701.71it/s]

 42%|████▏     | 70811648/170498071 [13:40<17:40, 94014.58it/s]

 42%|████▏     | 70844416/170498071 [13:40<17:56, 92552.64it/s]

 42%|████▏     | 70877184/170498071 [13:41<17:31, 94702.66it/s]

 42%|████▏     | 70909952/170498071 [13:41<17:21, 95633.64it/s]

 42%|████▏     | 70942720/170498071 [13:41<18:55, 87682.85it/s]

 42%|████▏     | 70975488/170498071 [13:42<17:25, 95177.98it/s]

 42%|████▏     | 71008256/170498071 [13:42<18:27, 89864.90it/s]

 42%|████▏     | 71041024/170498071 [13:43<19:29, 85051.02it/s]

 42%|████▏     | 71073792/170498071 [13:43<17:28, 94803.75it/s]

 42%|████▏     | 71106560/170498071 [13:43<17:44, 93354.68it/s]

 42%|████▏     | 71139328/170498071 [13:44<18:41, 88564.71it/s]

 42%|████▏     | 71172096/170498071 [13:44<16:59, 97384.26it/s]

 42%|████▏     | 71204864/170498071 [13:44<17:09, 96485.43it/s]

 42%|████▏     | 71237632/170498071 [13:45<17:05, 96785.25it/s]

 42%|████▏     | 71270400/170498071 [13:45<17:24, 94996.26it/s]

 42%|████▏     | 71303168/170498071 [13:45<18:19, 90239.79it/s]

 42%|████▏     | 71335936/170498071 [13:46<17:19, 95431.14it/s]

 42%|████▏     | 71368704/170498071 [13:46<18:29, 89309.02it/s]

 42%|████▏     | 71401472/170498071 [13:46<18:09, 90935.13it/s]

 42%|████▏     | 71434240/170498071 [13:47<17:55, 92092.79it/s]

 42%|████▏     | 71467008/170498071 [13:47<18:03, 91420.21it/s]

 42%|████▏     | 71499776/170498071 [13:47<17:51, 92366.86it/s]

 42%|████▏     | 71532544/170498071 [13:48<17:26, 94533.07it/s]

 42%|████▏     | 71565312/170498071 [13:48<18:44, 87978.89it/s]

 42%|████▏     | 71598080/170498071 [13:48<17:03, 96652.90it/s]

 42%|████▏     | 71630848/170498071 [13:49<17:44, 92842.14it/s]

 42%|████▏     | 71663616/170498071 [13:49<17:21, 94934.11it/s]

 42%|████▏     | 71696384/170498071 [13:50<18:25, 89401.14it/s]

 42%|████▏     | 71729152/170498071 [13:50<18:03, 91186.20it/s]

 42%|████▏     | 71761920/170498071 [13:50<17:52, 92048.17it/s]

 42%|████▏     | 71794688/170498071 [13:51<18:32, 88734.30it/s]

 42%|████▏     | 71827456/170498071 [13:51<17:15, 95294.92it/s]

 42%|████▏     | 71860224/170498071 [13:51<17:41, 92884.38it/s]

 42%|████▏     | 71892992/170498071 [13:52<17:40, 93003.66it/s]

 42%|████▏     | 71925760/170498071 [13:52<16:58, 96763.87it/s]

 42%|████▏     | 71958528/170498071 [13:52<17:04, 96153.12it/s]

 42%|████▏     | 71991296/170498071 [13:53<18:27, 88905.57it/s]

 42%|████▏     | 72024064/170498071 [13:53<18:57, 86559.27it/s]

 42%|████▏     | 72056832/170498071 [13:54<18:06, 90563.66it/s]

 42%|████▏     | 72089600/170498071 [13:54<17:37, 93048.82it/s]

 42%|████▏     | 72122368/170498071 [13:54<18:10, 90229.15it/s]

 42%|████▏     | 72155136/170498071 [13:55<17:03, 96049.01it/s]

 42%|████▏     | 72187904/170498071 [13:55<17:22, 94267.81it/s]

 42%|████▏     | 72220672/170498071 [13:55<17:17, 94732.05it/s]

 42%|████▏     | 72253440/170498071 [13:56<17:13, 95029.81it/s]

 42%|████▏     | 72286208/170498071 [13:56<17:26, 93888.27it/s]

 42%|████▏     | 72318976/170498071 [13:56<18:47, 87043.26it/s]

 42%|████▏     | 72351744/170498071 [13:57<17:52, 91477.70it/s]

 42%|████▏     | 72384512/170498071 [13:57<18:16, 89450.67it/s]

 42%|████▏     | 72417280/170498071 [13:58<19:13, 85026.45it/s]

 42%|████▏     | 72450048/170498071 [13:58<18:36, 87784.27it/s]

 43%|████▎     | 72482816/170498071 [13:58<18:10, 89904.02it/s]

 43%|████▎     | 72515584/170498071 [13:58<16:46, 97301.52it/s]

 43%|████▎     | 72548352/170498071 [13:59<17:02, 95837.72it/s]

 43%|████▎     | 72581120/170498071 [13:59<17:08, 95183.20it/s]

 43%|████▎     | 72613888/170498071 [14:00<17:07, 95234.53it/s]

 43%|████▎     | 72646656/170498071 [14:00<18:06, 90076.04it/s]

 43%|████▎     | 72679424/170498071 [14:00<17:16, 94404.07it/s]

 43%|████▎     | 72712192/170498071 [14:01<18:33, 87829.37it/s]

 43%|████▎     | 72744960/170498071 [14:01<19:12, 84784.91it/s]

 43%|████▎     | 72777728/170498071 [14:01<17:19, 94046.05it/s]

 43%|████▎     | 72810496/170498071 [14:02<17:36, 92469.70it/s]

 43%|████▎     | 72843264/170498071 [14:02<17:13, 94457.40it/s]

 43%|████▎     | 72876032/170498071 [14:02<17:16, 94165.21it/s]

 43%|████▎     | 72908800/170498071 [14:03<18:06, 89791.97it/s]

 43%|████▎     | 72941568/170498071 [14:03<16:56, 95929.43it/s]

 43%|████▎     | 72974336/170498071 [14:03<16:50, 96484.25it/s]

 43%|████▎     | 73007104/170498071 [14:04<17:59, 90303.38it/s]

 43%|████▎     | 73039872/170498071 [14:04<16:59, 95569.57it/s]

 43%|████▎     | 73072640/170498071 [14:05<18:26, 88039.85it/s]

 43%|████▎     | 73105408/170498071 [14:05<19:08, 84803.48it/s]

 43%|████▎     | 73138176/170498071 [14:05<17:10, 94465.97it/s]

 43%|████▎     | 73170944/170498071 [14:06<17:22, 93384.25it/s]

 43%|████▎     | 73203712/170498071 [14:06<18:12, 89079.55it/s]

 43%|████▎     | 73236480/170498071 [14:06<17:34, 92275.80it/s]

 43%|████▎     | 73269248/170498071 [14:07<18:01, 89922.00it/s]

 43%|████▎     | 73302016/170498071 [14:07<16:08, 100323.43it/s]

 43%|████▎     | 73334784/170498071 [14:07<16:44, 96765.18it/s]

 43%|████▎     | 73367552/170498071 [14:08<16:50, 96092.23it/s]

 43%|████▎     | 73400320/170498071 [14:08<17:59, 89965.62it/s]

 43%|████▎     | 73433088/170498071 [14:08<18:28, 87540.19it/s]

 43%|████▎     | 73465856/170498071 [14:09<18:25, 87801.70it/s]

 43%|████▎     | 73498624/170498071 [14:09<17:36, 91820.99it/s]

 43%|████▎     | 73531392/170498071 [14:09<16:36, 97326.78it/s]

 43%|████▎     | 73564160/170498071 [14:10<16:59, 95044.69it/s]

 43%|████▎     | 73596928/170498071 [14:10<17:33, 91994.27it/s]

 43%|████▎     | 73629696/170498071 [14:11<17:46, 90863.00it/s]

 43%|████▎     | 73662464/170498071 [14:11<16:17, 99025.76it/s]

 43%|████▎     | 73695232/170498071 [14:11<17:41, 91178.04it/s]

 43%|████▎     | 73728000/170498071 [14:12<16:42, 96487.67it/s]

 43%|████▎     | 73760768/170498071 [14:12<17:48, 90505.49it/s]

 43%|████▎     | 73793536/170498071 [14:12<18:32, 86923.38it/s]

 43%|████▎     | 73826304/170498071 [14:13<16:39, 96759.38it/s]

 43%|████▎     | 73859072/170498071 [14:13<17:46, 90612.55it/s]

 43%|████▎     | 73891840/170498071 [14:13<16:27, 97866.81it/s]

 43%|████▎     | 73924608/170498071 [14:14<17:56, 89744.91it/s]

 43%|████▎     | 73957376/170498071 [14:14<17:40, 91017.89it/s]

 43%|████▎     | 73990144/170498071 [14:14<16:16, 98785.27it/s]

 43%|████▎     | 74022912/170498071 [14:15<17:44, 90614.01it/s]

 43%|████▎     | 74055680/170498071 [14:15<16:26, 97778.46it/s]

 43%|████▎     | 74088448/170498071 [14:16<18:46, 85587.33it/s]

 43%|████▎     | 74121216/170498071 [14:16<18:22, 87428.35it/s]

 43%|████▎     | 74153984/170498071 [14:16<18:05, 88789.51it/s]

 44%|████▎     | 74186752/170498071 [14:17<17:09, 93535.35it/s]

 44%|████▎     | 74219520/170498071 [14:17<17:23, 92299.17it/s]

 44%|████▎     | 74252288/170498071 [14:17<18:16, 87765.95it/s]

 44%|████▎     | 74285056/170498071 [14:18<16:51, 95143.92it/s]

 44%|████▎     | 74317824/170498071 [14:18<18:18, 87517.25it/s]

 44%|████▎     | 74350592/170498071 [14:18<17:33, 91236.82it/s]

 44%|████▎     | 74383360/170498071 [14:19<16:44, 95722.38it/s]

 44%|████▎     | 74416128/170498071 [14:19<19:16, 83056.94it/s]

 44%|████▎     | 74448896/170498071 [14:20<18:16, 87586.13it/s]

 44%|████▎     | 74481664/170498071 [14:20<17:35, 90971.00it/s]

 44%|████▎     | 74514432/170498071 [14:20<17:01, 93979.71it/s]

 44%|████▎     | 74547200/170498071 [14:21<18:24, 86856.48it/s]

 44%|████▎     | 74579968/170498071 [14:21<17:37, 90704.73it/s]

 44%|████▍     | 74612736/170498071 [14:21<17:53, 89279.83it/s]

 44%|████▍     | 74645504/170498071 [14:22<17:38, 90521.86it/s]

 44%|████▍     | 74678272/170498071 [14:22<16:23, 97399.25it/s]

 44%|████▍     | 74711040/170498071 [14:22<16:44, 95364.99it/s]

 44%|████▍     | 74743808/170498071 [14:23<17:01, 93781.29it/s]

 44%|████▍     | 74776576/170498071 [14:23<19:26, 82081.77it/s]

 44%|████▍     | 74809344/170498071 [14:24<18:04, 88204.61it/s]

 44%|████▍     | 74842112/170498071 [14:24<18:19, 87035.49it/s]

 44%|████▍     | 74874880/170498071 [14:24<17:49, 89419.29it/s]

 44%|████▍     | 74907648/170498071 [14:25<17:27, 91258.15it/s]

 44%|████▍     | 74940416/170498071 [14:25<17:28, 91125.22it/s]

 44%|████▍     | 74973184/170498071 [14:25<18:48, 84656.57it/s]

 44%|████▍     | 75005952/170498071 [14:26<17:03, 93330.68it/s]

 44%|████▍     | 75038720/170498071 [14:26<17:07, 92913.73it/s]

 44%|████▍     | 75071488/170498071 [14:26<17:31, 90795.77it/s]

 44%|████▍     | 75104256/170498071 [14:27<19:44, 80516.93it/s]

 44%|████▍     | 75137024/170498071 [14:27<17:56, 88619.92it/s]

 44%|████▍     | 75169792/170498071 [14:28<17:31, 90633.36it/s]

 44%|████▍     | 75202560/170498071 [14:28<17:59, 88264.97it/s]

 44%|████▍     | 75235328/170498071 [14:28<17:34, 90362.72it/s]

 44%|████▍     | 75268096/170498071 [14:29<17:33, 90422.66it/s]

 44%|████▍     | 75300864/170498071 [14:29<17:35, 90209.18it/s]

 44%|████▍     | 75333632/170498071 [14:29<18:49, 84290.59it/s]

 44%|████▍     | 75366400/170498071 [14:30<18:24, 86094.13it/s]

 44%|████▍     | 75399168/170498071 [14:30<18:10, 87216.76it/s]

 44%|████▍     | 75431936/170498071 [14:31<17:38, 89778.82it/s]

 44%|████▍     | 75464704/170498071 [14:31<19:22, 81771.78it/s]

 44%|████▍     | 75497472/170498071 [14:31<18:45, 84436.71it/s]

 44%|████▍     | 75530240/170498071 [14:32<17:27, 90673.19it/s]

 44%|████▍     | 75563008/170498071 [14:32<18:09, 87153.54it/s]

 44%|████▍     | 75595776/170498071 [14:32<17:09, 92221.23it/s]

 44%|████▍     | 75628544/170498071 [14:33<18:30, 85455.40it/s]

 44%|████▍     | 75661312/170498071 [14:33<17:43, 89215.49it/s]

 44%|████▍     | 75694080/170498071 [14:34<17:02, 92762.06it/s]

 44%|████▍     | 75726848/170498071 [14:34<18:36, 84893.47it/s]

 44%|████▍     | 75759616/170498071 [14:34<17:48, 88697.80it/s]

 44%|████▍     | 75792384/170498071 [14:35<19:32, 80746.80it/s]

 44%|████▍     | 75825152/170498071 [14:35<18:52, 83619.25it/s]

 44%|████▍     | 75857920/170498071 [14:35<17:14, 91505.10it/s]

 45%|████▍     | 75890688/170498071 [14:36<18:08, 86916.11it/s]

 45%|████▍     | 75923456/170498071 [14:36<17:15, 91303.08it/s]

 45%|████▍     | 75956224/170498071 [14:37<18:38, 84501.66it/s]

 45%|████▍     | 75988992/170498071 [14:37<17:09, 91759.88it/s]

 45%|████▍     | 76021760/170498071 [14:37<17:37, 89304.78it/s]

 45%|████▍     | 76054528/170498071 [14:38<18:09, 86716.08it/s]

 45%|████▍     | 76087296/170498071 [14:38<17:53, 87967.29it/s]

 45%|████▍     | 76120064/170498071 [14:39<19:41, 79894.04it/s]

 45%|████▍     | 76152832/170498071 [14:39<19:05, 82359.20it/s]

 45%|████▍     | 76185600/170498071 [14:39<19:16, 81529.09it/s]

 45%|████▍     | 76218368/170498071 [14:40<18:56, 82967.35it/s]

 45%|████▍     | 76251136/170498071 [14:40<18:41, 84054.03it/s]

 45%|████▍     | 76283904/170498071 [14:40<18:31, 84778.39it/s]

 45%|████▍     | 76316672/170498071 [14:41<17:36, 89159.54it/s]

 45%|████▍     | 76349440/170498071 [14:41<17:40, 88814.79it/s]

 45%|████▍     | 76382208/170498071 [14:42<18:16, 85863.43it/s]

 45%|████▍     | 76414976/170498071 [14:42<17:38, 88900.63it/s]

 45%|████▍     | 76447744/170498071 [14:42<17:50, 87834.10it/s]

 45%|████▍     | 76480512/170498071 [14:43<19:40, 79662.41it/s]

 45%|████▍     | 76513280/170498071 [14:43<19:44, 79328.74it/s]

 45%|████▍     | 76546048/170498071 [14:44<19:53, 78688.69it/s]

 45%|████▍     | 76578816/170498071 [14:44<19:27, 80418.51it/s]

 45%|████▍     | 76611584/170498071 [14:44<17:54, 87356.91it/s]

 45%|████▍     | 76644352/170498071 [14:45<18:40, 83759.85it/s]

 45%|████▍     | 76677120/170498071 [14:45<18:33, 84239.70it/s]

 45%|████▍     | 76709888/170498071 [14:46<18:29, 84556.89it/s]

 45%|████▌     | 76742656/170498071 [14:46<17:47, 87795.48it/s]

 45%|████▌     | 76775424/170498071 [14:46<17:59, 86851.67it/s]

 45%|████▌     | 76808192/170498071 [14:47<19:29, 80130.23it/s]

 45%|████▌     | 76840960/170498071 [14:47<20:23, 76532.04it/s]

 45%|████▌     | 76873728/170498071 [14:48<18:34, 83975.15it/s]

 45%|████▌     | 76906496/170498071 [14:48<19:44, 79010.85it/s]

 45%|████▌     | 76939264/170498071 [14:48<18:21, 84904.04it/s]

 45%|████▌     | 76972032/170498071 [14:49<18:44, 83172.01it/s]

 45%|████▌     | 77004800/170498071 [14:49<18:38, 83605.20it/s]

 45%|████▌     | 77037568/170498071 [14:49<18:32, 84029.07it/s]

 45%|████▌     | 77070336/170498071 [14:50<18:04, 86127.18it/s]

 45%|████▌     | 77103104/170498071 [14:50<17:53, 87032.26it/s]

 45%|████▌     | 77135872/170498071 [14:51<17:58, 86538.41it/s]

 45%|████▌     | 77168640/170498071 [14:51<20:01, 77677.35it/s]

 45%|████▌     | 77201408/170498071 [14:51<19:01, 81763.31it/s]

 45%|████▌     | 77234176/170498071 [14:52<19:50, 78346.87it/s]

 45%|████▌     | 77266944/170498071 [14:52<18:17, 84936.33it/s]

 45%|████▌     | 77299712/170498071 [14:53<19:14, 80695.55it/s]

 45%|████▌     | 77332480/170498071 [14:53<17:57, 86465.35it/s]

 45%|████▌     | 77365248/170498071 [14:53<18:02, 86042.45it/s]

 45%|████▌     | 77398016/170498071 [14:54<17:49, 87057.26it/s]

 45%|████▌     | 77430784/170498071 [14:54<18:40, 83021.97it/s]

 45%|████▌     | 77463552/170498071 [14:55<17:54, 86547.43it/s]

 45%|████▌     | 77496320/170498071 [14:55<19:27, 79689.72it/s]

 45%|████▌     | 77529088/170498071 [14:56<20:23, 75986.36it/s]

 45%|████▌     | 77561856/170498071 [14:56<18:33, 83468.03it/s]

 46%|████▌     | 77594624/170498071 [14:56<18:32, 83495.40it/s]

 46%|████▌     | 77627392/170498071 [14:57<18:46, 82477.14it/s]

 46%|████▌     | 77660160/170498071 [14:57<19:44, 78358.98it/s]

 46%|████▌     | 77692928/170498071 [14:57<18:06, 85402.15it/s]

 46%|████▌     | 77725696/170498071 [14:58<18:57, 81530.37it/s]

 46%|████▌     | 77758464/170498071 [14:58<18:50, 82046.97it/s]

 46%|████▌     | 77791232/170498071 [14:59<18:07, 85230.21it/s]

 46%|████▌     | 77824000/170498071 [14:59<19:29, 79215.44it/s]

 46%|████▌     | 77856768/170498071 [14:59<19:22, 79702.66it/s]

 46%|████▌     | 77889536/170498071 [15:00<20:08, 76621.99it/s]

 46%|████▌     | 77922304/170498071 [15:00<18:41, 82528.63it/s]

 46%|████▌     | 77955072/170498071 [15:01<20:00, 77070.69it/s]

 46%|████▌     | 77987840/170498071 [15:01<19:36, 78603.34it/s]

 46%|████▌     | 78020608/170498071 [15:01<18:02, 85395.82it/s]

 46%|████▌     | 78053376/170498071 [15:02<18:04, 85265.66it/s]

 46%|████▌     | 78086144/170498071 [15:02<18:13, 84521.32it/s]

 46%|████▌     | 78118912/170498071 [15:03<18:17, 84198.73it/s]

 46%|████▌     | 78151680/170498071 [15:03<18:45, 82023.72it/s]

 46%|████▌     | 78184448/170498071 [15:04<19:50, 77522.70it/s]

 46%|████▌     | 78217216/170498071 [15:04<19:18, 79651.40it/s]

 46%|████▌     | 78249984/170498071 [15:04<19:03, 80706.96it/s]

 46%|████▌     | 78282752/170498071 [15:05<20:15, 75859.24it/s]

 46%|████▌     | 78315520/170498071 [15:05<18:26, 83281.19it/s]

 46%|████▌     | 78348288/170498071 [15:05<18:27, 83198.90it/s]

 46%|████▌     | 78381056/170498071 [15:06<18:41, 82170.64it/s]

 46%|████▌     | 78413824/170498071 [15:06<18:21, 83625.56it/s]

 46%|████▌     | 78446592/170498071 [15:07<19:33, 78426.86it/s]

 46%|████▌     | 78479360/170498071 [15:07<18:00, 85202.47it/s]

 46%|████▌     | 78512128/170498071 [15:08<19:24, 78971.17it/s]

 46%|████▌     | 78544896/170498071 [15:08<20:04, 76316.66it/s]

 46%|████▌     | 78577664/170498071 [15:08<19:15, 79551.78it/s]

 46%|████▌     | 78610432/170498071 [15:09<18:16, 83812.10it/s]

 46%|████▌     | 78643200/170498071 [15:09<18:08, 84365.65it/s]

 46%|████▌     | 78675968/170498071 [15:10<18:34, 82412.81it/s]

 46%|████▌     | 78708736/170498071 [15:10<19:25, 78759.12it/s]

 46%|████▌     | 78741504/170498071 [15:10<17:52, 85539.28it/s]

 46%|████▌     | 78774272/170498071 [15:11<17:50, 85661.74it/s]

 46%|████▌     | 78807040/170498071 [15:11<17:59, 84942.42it/s]

 46%|████▌     | 78839808/170498071 [15:11<18:02, 84661.77it/s]

 46%|████▋     | 78872576/170498071 [15:12<19:51, 76905.28it/s]

 46%|████▋     | 78905344/170498071 [15:12<19:05, 79955.32it/s]

 46%|████▋     | 78938112/170498071 [15:13<19:17, 79103.65it/s]

 46%|████▋     | 78970880/170498071 [15:13<18:26, 82692.17it/s]

 46%|████▋     | 79003648/170498071 [15:14<18:16, 83437.83it/s]

 46%|████▋     | 79036416/170498071 [15:14<18:00, 84620.38it/s]

 46%|████▋     | 79069184/170498071 [15:14<17:59, 84678.25it/s]

 46%|████▋     | 79101952/170498071 [15:15<17:59, 84686.80it/s]

 46%|████▋     | 79134720/170498071 [15:15<18:20, 83047.63it/s]

 46%|████▋     | 79167488/170498071 [15:15<17:57, 84722.92it/s]

 46%|████▋     | 79200256/170498071 [15:16<19:04, 79763.48it/s]

 46%|████▋     | 79233024/170498071 [15:16<18:39, 81532.71it/s]

 46%|████▋     | 79265792/170498071 [15:17<18:10, 83660.18it/s]

 47%|████▋     | 79298560/170498071 [15:17<17:56, 84684.38it/s]

 47%|████▋     | 79331328/170498071 [15:17<17:43, 85700.51it/s]

 47%|████▋     | 79364096/170498071 [15:18<17:35, 86363.78it/s]

 47%|████▋     | 79396864/170498071 [15:18<17:36, 86220.70it/s]

 47%|████▋     | 79429632/170498071 [15:19<18:31, 81914.16it/s]

 47%|████▋     | 79462400/170498071 [15:19<17:21, 87407.93it/s]

 47%|████▋     | 79495168/170498071 [15:19<17:05, 88711.01it/s]

 47%|████▋     | 79527936/170498071 [15:20<18:23, 82472.13it/s]

 47%|████▋     | 79560704/170498071 [15:20<18:06, 83724.00it/s]

 47%|████▋     | 79593472/170498071 [15:21<18:32, 81720.76it/s]

 47%|████▋     | 79626240/170498071 [15:21<17:30, 86508.01it/s]

 47%|████▋     | 79659008/170498071 [15:21<17:20, 87320.30it/s]

 47%|████▋     | 79691776/170498071 [15:22<17:44, 85274.10it/s]

 47%|████▋     | 79724544/170498071 [15:22<17:12, 87900.12it/s]

 47%|████▋     | 79757312/170498071 [15:22<18:05, 83571.35it/s]

 47%|████▋     | 79790080/170498071 [15:23<17:08, 88210.16it/s]

 47%|████▋     | 79822848/170498071 [15:23<17:00, 88891.17it/s]

 47%|████▋     | 79855616/170498071 [15:24<17:27, 86495.75it/s]

 47%|████▋     | 79888384/170498071 [15:24<19:11, 78701.94it/s]

 47%|████▋     | 79921152/170498071 [15:24<17:48, 84787.36it/s]

 47%|████▋     | 79953920/170498071 [15:25<18:20, 82274.28it/s]

 47%|████▋     | 79986688/170498071 [15:25<17:15, 87447.29it/s]

 47%|████▋     | 80019456/170498071 [15:25<17:03, 88412.90it/s]

 47%|████▋     | 80052224/170498071 [15:26<17:15, 87381.84it/s]

 47%|████▋     | 80084992/170498071 [15:26<17:03, 88330.15it/s]

 47%|████▋     | 80117760/170498071 [15:27<17:03, 88280.71it/s]

 47%|████▋     | 80150528/170498071 [15:27<17:04, 88221.11it/s]

 47%|████▋     | 80183296/170498071 [15:27<17:06, 87991.06it/s]

 47%|████▋     | 80216064/170498071 [15:28<18:21, 81963.30it/s]

 47%|████▋     | 80248832/170498071 [15:28<18:03, 83287.33it/s]

 47%|████▋     | 80281600/170498071 [15:29<17:45, 84683.62it/s]

 47%|████▋     | 80314368/170498071 [15:29<17:39, 85088.57it/s]

 47%|████▋     | 80347136/170498071 [15:29<17:34, 85511.75it/s]

 47%|████▋     | 80379904/170498071 [15:30<17:29, 85846.77it/s]

 47%|████▋     | 80412672/170498071 [15:30<17:22, 86393.21it/s]

 47%|████▋     | 80445440/170498071 [15:30<17:19, 86671.53it/s]

 47%|████▋     | 80478208/170498071 [15:31<17:13, 87107.91it/s]

 47%|████▋     | 80510976/170498071 [15:31<17:08, 87525.16it/s]

 47%|████▋     | 80543744/170498071 [15:32<17:03, 87922.64it/s]

 47%|████▋     | 80576512/170498071 [15:32<18:18, 81826.71it/s]

 47%|████▋     | 80609280/170498071 [15:32<17:54, 83645.57it/s]

 47%|████▋     | 80642048/170498071 [15:33<17:37, 85005.05it/s]

 47%|████▋     | 80674816/170498071 [15:33<18:17, 81851.58it/s]

 47%|████▋     | 80707584/170498071 [15:33<17:09, 87204.54it/s]

 47%|████▋     | 80740352/170498071 [15:34<16:54, 88434.08it/s]

 47%|████▋     | 80773120/170498071 [15:34<17:19, 86277.57it/s]

 47%|████▋     | 80805888/170498071 [15:35<16:59, 87975.76it/s]

 47%|████▋     | 80838656/170498071 [15:35<18:13, 81983.00it/s]

 47%|████▋     | 80871424/170498071 [15:35<16:38, 89733.36it/s]

 47%|████▋     | 80904192/170498071 [15:36<17:51, 83646.42it/s]

 47%|████▋     | 80936960/170498071 [15:36<17:32, 85095.81it/s]

 47%|████▋     | 80969728/170498071 [15:37<17:20, 86026.63it/s]

 48%|████▊     | 81002496/170498071 [15:37<17:12, 86682.92it/s]

 48%|████▊     | 81035264/170498071 [15:37<17:11, 86765.92it/s]

 48%|████▊     | 81068032/170498071 [15:38<17:07, 87006.25it/s]

 48%|████▊     | 81100800/170498071 [15:38<17:04, 87259.57it/s]

 48%|████▊     | 81133568/170498071 [15:38<17:04, 87214.31it/s]

 48%|████▊     | 81166336/170498071 [15:39<17:03, 87321.37it/s]

 48%|████▊     | 81199104/170498071 [15:39<17:00, 87464.49it/s]

 48%|████▊     | 81231872/170498071 [15:40<16:57, 87696.15it/s]

 48%|████▊     | 81264640/170498071 [15:40<18:12, 81692.84it/s]

 48%|████▊     | 81297408/170498071 [15:40<17:47, 83533.83it/s]

 48%|████▊     | 81330176/170498071 [15:41<17:31, 84834.47it/s]

 48%|████▊     | 81362944/170498071 [15:41<17:19, 85770.11it/s]

 48%|████▊     | 81395712/170498071 [15:41<17:12, 86285.17it/s]

 48%|████▊     | 81428480/170498071 [15:42<17:05, 86828.27it/s]

 48%|████▊     | 81461248/170498071 [15:42<17:01, 87142.70it/s]

 48%|████▊     | 81494016/170498071 [15:43<16:59, 87273.25it/s]

 48%|████▊     | 81526784/170498071 [15:43<16:56, 87504.65it/s]

 48%|████▊     | 81559552/170498071 [15:43<16:56, 87494.92it/s]

 48%|████▊     | 81592320/170498071 [15:44<18:11, 81446.69it/s]

 48%|████▊     | 81625088/170498071 [15:44<17:46, 83328.59it/s]

 48%|████▊     | 81657856/170498071 [15:45<17:32, 84444.32it/s]

 48%|████▊     | 81690624/170498071 [15:45<17:22, 85161.32it/s]

 48%|████▊     | 81723392/170498071 [15:45<17:15, 85762.83it/s]

 48%|████▊     | 81756160/170498071 [15:46<17:07, 86335.01it/s]

 48%|████▊     | 81788928/170498071 [15:46<16:59, 86984.59it/s]

 48%|████▊     | 81821696/170498071 [15:46<17:01, 86804.58it/s]

 48%|████▊     | 81854464/170498071 [15:47<16:57, 87129.96it/s]

 48%|████▊     | 81887232/170498071 [15:47<16:52, 87480.50it/s]

 48%|████▊     | 81920000/170498071 [15:48<16:50, 87661.46it/s]

 48%|████▊     | 81952768/170498071 [15:48<18:02, 81781.28it/s]

 48%|████▊     | 81985536/170498071 [15:48<17:41, 83417.57it/s]

 48%|████▊     | 82018304/170498071 [15:49<17:25, 84633.72it/s]

 48%|████▊     | 82051072/170498071 [15:49<17:15, 85391.44it/s]

 48%|████▊     | 82083840/170498071 [15:50<17:09, 85898.15it/s]

 48%|████▊     | 82116608/170498071 [15:50<17:05, 86147.49it/s]

 48%|████▊     | 82149376/170498071 [15:50<16:59, 86616.62it/s]

 48%|████▊     | 82182144/170498071 [15:51<17:01, 86458.90it/s]

 48%|████▊     | 82214912/170498071 [15:51<16:54, 86986.72it/s]

 48%|████▊     | 82247680/170498071 [15:51<16:58, 86668.66it/s]

 48%|████▊     | 82280448/170498071 [15:52<18:12, 80711.85it/s]

 48%|████▊     | 82313216/170498071 [15:52<17:47, 82616.45it/s]

 48%|████▊     | 82345984/170498071 [15:53<17:27, 84146.35it/s]

 48%|████▊     | 82378752/170498071 [15:53<17:15, 85131.64it/s]

 48%|████▊     | 82411520/170498071 [15:53<16:59, 86370.14it/s]

 48%|████▊     | 82444288/170498071 [15:54<16:51, 87092.67it/s]

 48%|████▊     | 82477056/170498071 [15:54<16:43, 87686.07it/s]

 48%|████▊     | 82509824/170498071 [15:54<16:38, 88081.90it/s]

 48%|████▊     | 82542592/170498071 [15:55<16:36, 88232.68it/s]

 48%|████▊     | 82575360/170498071 [15:55<16:34, 88372.59it/s]

 48%|████▊     | 82608128/170498071 [15:56<17:42, 82693.63it/s]

 48%|████▊     | 82640896/170498071 [15:56<17:17, 84702.19it/s]

 48%|████▊     | 82673664/170498071 [15:56<17:00, 86093.69it/s]

 49%|████▊     | 82706432/170498071 [15:57<16:48, 87052.67it/s]

 49%|████▊     | 82739200/170498071 [15:57<16:38, 87874.07it/s]

 49%|████▊     | 82771968/170498071 [15:57<16:34, 88235.79it/s]

 49%|████▊     | 82804736/170498071 [15:58<16:33, 88272.83it/s]

 49%|████▊     | 82837504/170498071 [15:58<16:31, 88450.93it/s]

 49%|████▊     | 82870272/170498071 [15:59<16:27, 88751.45it/s]

 49%|████▊     | 82903040/170498071 [15:59<16:28, 88581.45it/s]

 49%|████▊     | 82935808/170498071 [15:59<16:27, 88706.62it/s]

 49%|████▊     | 82968576/170498071 [16:00<17:37, 82766.39it/s]

 49%|████▊     | 83001344/170498071 [16:00<17:12, 84703.79it/s]

 49%|████▊     | 83034112/170498071 [16:01<16:56, 86029.72it/s]

 49%|████▊     | 83066880/170498071 [16:01<16:46, 86871.40it/s]

 49%|████▊     | 83099648/170498071 [16:01<16:38, 87486.14it/s]

 49%|████▉     | 83132416/170498071 [16:02<16:36, 87694.82it/s]

 49%|████▉     | 83165184/170498071 [16:02<16:34, 87839.81it/s]

 49%|████▉     | 83197952/170498071 [16:02<16:32, 87966.54it/s]

 49%|████▉     | 83230720/170498071 [16:03<16:45, 86825.66it/s]

 49%|████▉     | 83263488/170498071 [16:03<16:23, 88701.35it/s]

 49%|████▉     | 83296256/170498071 [16:04<17:37, 82422.37it/s]

 49%|████▉     | 83329024/170498071 [16:04<17:16, 84091.48it/s]

 49%|████▉     | 83361792/170498071 [16:04<17:01, 85288.27it/s]

 49%|████▉     | 83394560/170498071 [16:05<16:46, 86533.74it/s]

 49%|████▉     | 83427328/170498071 [16:05<16:39, 87126.19it/s]

 49%|████▉     | 83460096/170498071 [16:05<16:33, 87615.93it/s]

 49%|████▉     | 83492864/170498071 [16:06<16:26, 88160.96it/s]

 49%|████▉     | 83525632/170498071 [16:06<16:26, 88196.60it/s]

 49%|████▉     | 83558400/170498071 [16:07<16:27, 88073.13it/s]

 49%|████▉     | 83591168/170498071 [16:07<16:29, 87822.01it/s]

 49%|████▉     | 83623936/170498071 [16:07<16:30, 87747.64it/s]

 49%|████▉     | 83656704/170498071 [16:08<17:37, 82098.07it/s]

 49%|████▉     | 83689472/170498071 [16:08<17:11, 84119.66it/s]

 49%|████▉     | 83722240/170498071 [16:08<16:53, 85605.28it/s]

 49%|████▉     | 83755008/170498071 [16:09<16:38, 86833.49it/s]

 49%|████▉     | 83787776/170498071 [16:09<16:29, 87653.15it/s]

 49%|████▉     | 83820544/170498071 [16:10<16:21, 88297.45it/s]

 49%|████▉     | 83853312/170498071 [16:10<16:18, 88506.23it/s]

 49%|████▉     | 83886080/170498071 [16:10<16:16, 88734.83it/s]

 49%|████▉     | 83918848/170498071 [16:11<16:11, 89080.66it/s]

 49%|████▉     | 83951616/170498071 [16:11<16:08, 89340.28it/s]

 49%|████▉     | 83984384/170498071 [16:11<17:18, 83290.16it/s]

 49%|████▉     | 84017152/170498071 [16:12<16:52, 85453.20it/s]

 49%|████▉     | 84049920/170498071 [16:12<16:34, 86962.47it/s]

 49%|████▉     | 84082688/170498071 [16:13<16:21, 88045.80it/s]

 49%|████▉     | 84115456/170498071 [16:13<16:12, 88799.64it/s]

 49%|████▉     | 84148224/170498071 [16:13<16:04, 89541.22it/s]

 49%|████▉     | 84180992/170498071 [16:14<15:57, 90156.85it/s]

 49%|████▉     | 84213760/170498071 [16:14<15:50, 90742.03it/s]

 49%|████▉     | 84246528/170498071 [16:14<15:46, 91151.99it/s]

 49%|████▉     | 84279296/170498071 [16:15<15:44, 91258.97it/s]

 49%|████▉     | 84312064/170498071 [16:15<16:53, 85014.17it/s]

 49%|████▉     | 84344832/170498071 [16:16<16:31, 86909.36it/s]

 49%|████▉     | 84377600/170498071 [16:16<16:15, 88316.17it/s]

 50%|████▉     | 84410368/170498071 [16:16<16:02, 89455.43it/s]

 50%|████▉     | 84443136/170498071 [16:17<15:56, 89932.90it/s]

 50%|████▉     | 84475904/170498071 [16:17<15:52, 90311.15it/s]

 50%|████▉     | 84508672/170498071 [16:17<15:44, 91040.73it/s]

 50%|████▉     | 84541440/170498071 [16:18<15:47, 90713.05it/s]

 50%|████▉     | 84574208/170498071 [16:18<15:45, 90872.21it/s]

 50%|████▉     | 84606976/170498071 [16:18<15:47, 90641.11it/s]

 50%|████▉     | 84639744/170498071 [16:19<15:44, 90933.65it/s]

 50%|████▉     | 84672512/170498071 [16:19<16:52, 84803.38it/s]

 50%|████▉     | 84705280/170498071 [16:20<16:29, 86704.84it/s]

 50%|████▉     | 84738048/170498071 [16:20<16:14, 88005.59it/s]

 50%|████▉     | 84770816/170498071 [16:20<16:03, 88962.90it/s]

 50%|████▉     | 84803584/170498071 [16:21<15:56, 89618.56it/s]

 50%|████▉     | 84836352/170498071 [16:21<15:51, 90023.30it/s]

 50%|████▉     | 84869120/170498071 [16:21<15:57, 89391.23it/s]

 50%|████▉     | 84901888/170498071 [16:22<15:42, 90837.54it/s]

 50%|████▉     | 84934656/170498071 [16:22<15:42, 90797.22it/s]

 50%|████▉     | 84967424/170498071 [16:22<15:41, 90868.85it/s]

 50%|████▉     | 85000192/170498071 [16:23<16:50, 84583.96it/s]

 50%|████▉     | 85032960/170498071 [16:23<16:30, 86295.02it/s]

 50%|████▉     | 85065728/170498071 [16:24<16:14, 87640.63it/s]

 50%|████▉     | 85098496/170498071 [16:24<16:03, 88605.58it/s]

 50%|████▉     | 85131264/170498071 [16:24<17:13, 82632.26it/s]

 50%|████▉     | 85164032/170498071 [16:25<15:34, 91268.80it/s]

 50%|████▉     | 85196800/170498071 [16:25<15:24, 92233.61it/s]

 50%|████▉     | 85229568/170498071 [16:25<15:28, 91848.50it/s]

 50%|█████     | 85262336/170498071 [16:26<15:30, 91604.94it/s]

 50%|█████     | 85295104/170498071 [16:26<15:30, 91528.50it/s]

 50%|█████     | 85327872/170498071 [16:26<15:30, 91520.26it/s]

 50%|█████     | 85360640/170498071 [16:27<16:38, 85271.03it/s]

 50%|█████     | 85393408/170498071 [16:27<16:18, 86970.29it/s]

 50%|█████     | 85426176/170498071 [16:28<16:04, 88159.70it/s]

 50%|█████     | 85458944/170498071 [16:28<15:55, 88981.26it/s]

 50%|█████     | 85491712/170498071 [16:28<15:50, 89449.08it/s]

 50%|█████     | 85524480/170498071 [16:29<15:46, 89770.94it/s]

 50%|█████     | 85557248/170498071 [16:29<15:46, 89741.08it/s]

 50%|█████     | 85590016/170498071 [16:29<15:43, 89978.40it/s]

 50%|█████     | 85622784/170498071 [16:30<15:44, 89871.15it/s]

 50%|█████     | 85655552/170498071 [16:30<15:41, 90105.46it/s]

 50%|█████     | 85688320/170498071 [16:31<16:48, 84111.55it/s]

 50%|█████     | 85721088/170498071 [16:31<16:30, 85631.48it/s]

 50%|█████     | 85753856/170498071 [16:31<16:15, 86855.13it/s]

 50%|█████     | 85786624/170498071 [16:32<16:05, 87723.31it/s]

 50%|█████     | 85819392/170498071 [16:32<16:04, 87764.98it/s]

 50%|█████     | 85852160/170498071 [16:32<16:01, 87995.83it/s]

 50%|█████     | 85884928/170498071 [16:33<16:03, 87799.03it/s]

 50%|█████     | 85917696/170498071 [16:33<16:05, 87630.00it/s]

 50%|█████     | 85950464/170498071 [16:34<16:02, 87831.23it/s]

 50%|█████     | 85983232/170498071 [16:34<16:00, 88034.04it/s]

 50%|█████     | 86016000/170498071 [16:34<16:02, 87800.42it/s]

 50%|█████     | 86048768/170498071 [16:35<17:14, 81594.59it/s]

 50%|█████     | 86081536/170498071 [16:35<16:52, 83340.51it/s]

 51%|█████     | 86114304/170498071 [16:36<16:35, 84804.43it/s]

 51%|█████     | 86147072/170498071 [16:36<16:26, 85510.44it/s]

 51%|█████     | 86179840/170498071 [16:36<16:19, 86121.45it/s]

 51%|█████     | 86212608/170498071 [16:37<16:11, 86716.54it/s]

 51%|█████     | 86245376/170498071 [16:37<16:07, 87051.56it/s]

 51%|█████     | 86278144/170498071 [16:37<16:06, 87114.64it/s]

 51%|█████     | 86310912/170498071 [16:38<16:05, 87153.14it/s]

 51%|█████     | 86343680/170498071 [16:38<16:05, 87145.53it/s]

 51%|█████     | 86376448/170498071 [16:39<17:13, 81364.70it/s]

 51%|█████     | 86409216/170498071 [16:39<16:54, 82888.53it/s]

 51%|█████     | 86441984/170498071 [16:39<16:37, 84271.16it/s]

 51%|█████     | 86474752/170498071 [16:40<16:27, 85044.20it/s]

 51%|█████     | 86507520/170498071 [16:40<16:22, 85520.46it/s]

 51%|█████     | 86540288/170498071 [16:41<16:15, 86053.64it/s]

 51%|█████     | 86573056/170498071 [16:41<16:13, 86199.51it/s]

 51%|█████     | 86605824/170498071 [16:41<16:10, 86435.80it/s]

 51%|█████     | 86638592/170498071 [16:42<16:06, 86743.67it/s]

 51%|█████     | 86671360/170498071 [16:42<16:07, 86645.72it/s]

 51%|█████     | 86704128/170498071 [16:43<17:17, 80767.43it/s]

 51%|█████     | 86736896/170498071 [16:43<16:52, 82689.38it/s]

 51%|█████     | 86769664/170498071 [16:43<16:35, 84117.06it/s]

 51%|█████     | 86802432/170498071 [16:44<16:26, 84875.23it/s]

 51%|█████     | 86835200/170498071 [16:44<16:16, 85646.11it/s]

 51%|█████     | 86867968/170498071 [16:44<16:08, 86356.28it/s]

 51%|█████     | 86900736/170498071 [16:45<16:01, 86908.29it/s]

 51%|█████     | 86933504/170498071 [16:45<15:55, 87411.99it/s]

 51%|█████     | 86966272/170498071 [16:45<15:53, 87597.02it/s]

 51%|█████     | 86999040/170498071 [16:46<15:51, 87786.58it/s]

 51%|█████     | 87031808/170498071 [16:46<15:49, 87934.75it/s]

 51%|█████     | 87064576/170498071 [16:47<17:00, 81749.81it/s]

 51%|█████     | 87097344/170498071 [16:47<16:38, 83527.06it/s]

 51%|█████     | 87130112/170498071 [16:47<16:23, 84748.14it/s]

 51%|█████     | 87162880/170498071 [16:48<16:10, 85828.88it/s]

 51%|█████     | 87195648/170498071 [16:48<16:02, 86505.48it/s]

 51%|█████     | 87228416/170498071 [16:49<16:02, 86491.58it/s]

 51%|█████     | 87261184/170498071 [16:49<16:01, 86527.52it/s]

 51%|█████     | 87293952/170498071 [16:49<16:01, 86514.47it/s]

 51%|█████     | 87326720/170498071 [16:50<16:00, 86626.59it/s]

 51%|█████     | 87359488/170498071 [16:50<15:58, 86741.63it/s]

 51%|█████▏    | 87392256/170498071 [16:51<17:08, 80781.00it/s]

 51%|█████▏    | 87425024/170498071 [16:51<16:46, 82496.73it/s]

 51%|█████▏    | 87457792/170498071 [16:51<16:32, 83680.33it/s]

 51%|█████▏    | 87490560/170498071 [16:52<16:25, 84255.16it/s]

 51%|█████▏    | 87523328/170498071 [16:52<16:18, 84775.21it/s]

 51%|█████▏    | 87556096/170498071 [16:52<16:13, 85162.73it/s]

 51%|█████▏    | 87588864/170498071 [16:53<16:11, 85337.22it/s]

 51%|█████▏    | 87621632/170498071 [16:53<16:09, 85512.07it/s]

 51%|█████▏    | 87654400/170498071 [16:54<16:04, 85907.10it/s]

 51%|█████▏    | 87687168/170498071 [16:54<16:01, 86142.36it/s]

 51%|█████▏    | 87719936/170498071 [16:54<15:59, 86233.09it/s]

 51%|█████▏    | 87752704/170498071 [16:55<17:08, 80432.56it/s]

 51%|█████▏    | 87785472/170498071 [16:55<16:45, 82265.36it/s]

 52%|█████▏    | 87818240/170498071 [16:56<16:30, 83488.06it/s]

 52%|█████▏    | 87851008/170498071 [16:56<16:17, 84562.69it/s]

 52%|█████▏    | 87883776/170498071 [16:56<16:09, 85199.36it/s]

 52%|█████▏    | 87916544/170498071 [16:57<16:02, 85770.78it/s]

 52%|█████▏    | 87949312/170498071 [16:57<16:01, 85849.16it/s]

 52%|█████▏    | 87982080/170498071 [16:57<15:54, 86406.76it/s]

 52%|█████▏    | 88014848/170498071 [16:58<16:04, 85479.15it/s]

 52%|█████▏    | 88047616/170498071 [16:58<16:06, 85344.63it/s]

 52%|█████▏    | 88080384/170498071 [16:59<17:19, 79292.15it/s]

 52%|█████▏    | 88113152/170498071 [16:59<16:58, 80893.65it/s]

 52%|█████▏    | 88145920/170498071 [16:59<16:42, 82106.44it/s]

 52%|█████▏    | 88178688/170498071 [17:00<16:30, 83069.50it/s]

 52%|█████▏    | 88211456/170498071 [17:00<16:20, 83936.97it/s]

 52%|█████▏    | 88244224/170498071 [17:01<16:14, 84390.30it/s]

 52%|█████▏    | 88276992/170498071 [17:01<16:11, 84626.23it/s]

 52%|█████▏    | 88309760/170498071 [17:01<16:09, 84807.26it/s]

 52%|█████▏    | 88342528/170498071 [17:02<16:06, 84991.69it/s]

 52%|█████▏    | 88375296/170498071 [17:02<16:01, 85393.83it/s]

 52%|█████▏    | 88408064/170498071 [17:03<17:13, 79444.59it/s]

 52%|█████▏    | 88440832/170498071 [17:03<17:35, 77707.67it/s]

 52%|█████▏    | 88473600/170498071 [17:03<16:26, 83119.51it/s]

 52%|█████▏    | 88506368/170498071 [17:04<16:06, 84866.43it/s]

 52%|█████▏    | 88539136/170498071 [17:04<16:01, 85268.11it/s]

 52%|█████▏    | 88571904/170498071 [17:05<16:43, 81602.95it/s]

 52%|█████▏    | 88604672/170498071 [17:05<15:49, 86268.08it/s]

 52%|█████▏    | 88637440/170498071 [17:05<15:38, 87249.76it/s]

 52%|█████▏    | 88670208/170498071 [17:06<15:39, 87120.73it/s]

 52%|█████▏    | 88702976/170498071 [17:06<15:43, 86689.96it/s]

 52%|█████▏    | 88735744/170498071 [17:06<15:42, 86704.59it/s]

 52%|█████▏    | 88768512/170498071 [17:07<16:56, 80442.12it/s]

 52%|█████▏    | 88801280/170498071 [17:07<16:33, 82226.76it/s]

 52%|█████▏    | 88834048/170498071 [17:08<16:20, 83269.83it/s]

 52%|█████▏    | 88866816/170498071 [17:08<16:05, 84523.52it/s]

 52%|█████▏    | 88899584/170498071 [17:08<15:55, 85366.99it/s]

 52%|█████▏    | 88932352/170498071 [17:09<15:45, 86242.59it/s]

 52%|█████▏    | 88965120/170498071 [17:09<15:43, 86435.36it/s]

 52%|█████▏    | 88997888/170498071 [17:10<15:41, 86578.79it/s]

 52%|█████▏    | 89030656/170498071 [17:10<15:40, 86586.82it/s]

 52%|█████▏    | 89063424/170498071 [17:10<15:39, 86686.72it/s]

 52%|█████▏    | 89096192/170498071 [17:11<16:49, 80671.23it/s]

 52%|█████▏    | 89128960/170498071 [17:11<16:24, 82658.32it/s]

 52%|█████▏    | 89161728/170498071 [17:12<16:09, 83881.57it/s]

 52%|█████▏    | 89194496/170498071 [17:12<17:14, 78591.50it/s]

 52%|█████▏    | 89227264/170498071 [17:12<15:38, 86586.12it/s]

 52%|█████▏    | 89260032/170498071 [17:13<15:24, 87871.12it/s]

 52%|█████▏    | 89292800/170498071 [17:13<15:27, 87573.36it/s]

 52%|█████▏    | 89325568/170498071 [17:13<15:53, 85164.65it/s]

 52%|█████▏    | 89358336/170498071 [17:14<15:26, 87608.17it/s]

 52%|█████▏    | 89391104/170498071 [17:14<15:21, 88041.75it/s]

 52%|█████▏    | 89423872/170498071 [17:15<16:42, 80881.56it/s]

 52%|█████▏    | 89456640/170498071 [17:15<16:23, 82401.80it/s]

 52%|█████▏    | 89489408/170498071 [17:15<15:57, 84617.75it/s]

 53%|█████▎    | 89522176/170498071 [17:16<15:45, 85601.35it/s]

 53%|█████▎    | 89554944/170498071 [17:16<15:43, 85803.30it/s]

 53%|█████▎    | 89587712/170498071 [17:17<15:40, 86002.17it/s]

 53%|█████▎    | 89620480/170498071 [17:17<15:36, 86372.91it/s]

 53%|█████▎    | 89653248/170498071 [17:17<15:36, 86350.33it/s]

 53%|█████▎    | 89686016/170498071 [17:18<15:37, 86217.94it/s]

 53%|█████▎    | 89718784/170498071 [17:18<15:47, 85289.40it/s]

 53%|█████▎    | 89751552/170498071 [17:18<15:35, 86324.63it/s]

 53%|█████▎    | 89784320/170498071 [17:19<16:44, 80317.75it/s]

 53%|█████▎    | 89817088/170498071 [17:19<17:38, 76242.95it/s]

 53%|█████▎    | 89849856/170498071 [17:20<15:54, 84466.94it/s]

 53%|█████▎    | 89882624/170498071 [17:20<15:39, 85824.88it/s]

 53%|█████▎    | 89915392/170498071 [17:20<16:03, 83611.63it/s]

 53%|█████▎    | 89948160/170498071 [17:21<15:32, 86421.71it/s]

 53%|█████▎    | 89980928/170498071 [17:21<15:19, 87519.42it/s]

 53%|█████▎    | 90013696/170498071 [17:22<15:42, 85386.63it/s]

 53%|█████▎    | 90046464/170498071 [17:22<15:13, 88062.28it/s]

 53%|█████▎    | 90079232/170498071 [17:22<15:13, 88060.72it/s]

 53%|█████▎    | 90112000/170498071 [17:23<16:02, 83531.94it/s]

 53%|█████▎    | 90144768/170498071 [17:23<16:17, 82211.71it/s]

 53%|█████▎    | 90177536/170498071 [17:23<15:49, 84552.58it/s]

 53%|█████▎    | 90210304/170498071 [17:24<15:36, 85764.08it/s]

 53%|█████▎    | 90243072/170498071 [17:24<15:31, 86145.40it/s]

 53%|█████▎    | 90275840/170498071 [17:25<15:24, 86766.26it/s]

 53%|█████▎    | 90308608/170498071 [17:25<15:21, 87005.21it/s]

 53%|█████▎    | 90341376/170498071 [17:25<15:18, 87281.97it/s]

 53%|█████▎    | 90374144/170498071 [17:26<15:16, 87466.73it/s]

 53%|█████▎    | 90406912/170498071 [17:26<15:14, 87562.75it/s]

 53%|█████▎    | 90439680/170498071 [17:27<16:01, 83250.98it/s]

 53%|█████▎    | 90472448/170498071 [17:27<17:29, 76223.42it/s]

 53%|█████▎    | 90505216/170498071 [17:27<15:42, 84889.56it/s]

 53%|█████▎    | 90537984/170498071 [17:28<15:25, 86390.08it/s]

 53%|█████▎    | 90570752/170498071 [17:28<15:24, 86409.15it/s]

 53%|█████▎    | 90603520/170498071 [17:28<15:24, 86391.39it/s]

 53%|█████▎    | 90636288/170498071 [17:29<15:24, 86422.07it/s]

 53%|█████▎    | 90669056/170498071 [17:29<15:24, 86352.70it/s]

 53%|█████▎    | 90701824/170498071 [17:30<15:23, 86366.03it/s]

 53%|█████▎    | 90734592/170498071 [17:30<15:24, 86306.89it/s]

 53%|█████▎    | 90767360/170498071 [17:30<15:22, 86449.54it/s]

 53%|█████▎    | 90800128/170498071 [17:31<16:30, 80465.33it/s]

 53%|█████▎    | 90832896/170498071 [17:31<16:09, 82140.80it/s]

 53%|█████▎    | 90865664/170498071 [17:32<15:54, 83454.77it/s]

 53%|█████▎    | 90898432/170498071 [17:32<15:43, 84351.49it/s]

 53%|█████▎    | 90931200/170498071 [17:32<15:35, 85045.46it/s]

 53%|█████▎    | 90963968/170498071 [17:33<15:31, 85383.66it/s]

 53%|█████▎    | 90996736/170498071 [17:33<15:26, 85779.35it/s]

 53%|█████▎    | 91029504/170498071 [17:34<16:35, 79849.65it/s]

 53%|█████▎    | 91062272/170498071 [17:34<15:10, 87263.72it/s]

 53%|█████▎    | 91095040/170498071 [17:34<15:02, 87995.35it/s]

 53%|█████▎    | 91127808/170498071 [17:35<15:05, 87606.92it/s]

 53%|█████▎    | 91160576/170498071 [17:35<16:17, 81204.07it/s]

 53%|█████▎    | 91193344/170498071 [17:35<15:53, 83193.05it/s]

 54%|█████▎    | 91226112/170498071 [17:36<15:33, 84889.14it/s]

 54%|█████▎    | 91258880/170498071 [17:36<15:23, 85838.14it/s]

 54%|█████▎    | 91291648/170498071 [17:37<15:15, 86529.66it/s]

 54%|█████▎    | 91324416/170498071 [17:37<15:09, 87051.11it/s]

 54%|█████▎    | 91357184/170498071 [17:37<15:06, 87309.81it/s]

 54%|█████▎    | 91389952/170498071 [17:38<15:05, 87363.73it/s]

 54%|█████▎    | 91422720/170498071 [17:38<15:03, 87524.43it/s]

 54%|█████▎    | 91455488/170498071 [17:38<15:01, 87709.98it/s]

 54%|█████▎    | 91488256/170498071 [17:39<16:09, 81503.04it/s]

 54%|█████▎    | 91521024/170498071 [17:39<15:46, 83408.86it/s]

 54%|█████▎    | 91553792/170498071 [17:40<15:33, 84560.60it/s]

 54%|█████▎    | 91586560/170498071 [17:40<15:24, 85332.84it/s]

 54%|█████▎    | 91619328/170498071 [17:40<15:16, 86057.66it/s]

 54%|█████▍    | 91652096/170498071 [17:41<15:08, 86745.59it/s]

 54%|█████▍    | 91684864/170498071 [17:41<15:05, 87072.68it/s]

 54%|█████▍    | 91717632/170498071 [17:42<14:59, 87562.68it/s]

 54%|█████▍    | 91750400/170498071 [17:42<14:56, 87822.23it/s]

 54%|█████▍    | 91783168/170498071 [17:42<14:54, 88011.13it/s]

 54%|█████▍    | 91815936/170498071 [17:43<14:52, 88184.83it/s]

 54%|█████▍    | 91848704/170498071 [17:43<15:55, 82283.49it/s]

 54%|█████▍    | 91881472/170498071 [17:43<15:34, 84140.36it/s]

 54%|█████▍    | 91914240/170498071 [17:44<15:19, 85508.31it/s]

 54%|█████▍    | 91947008/170498071 [17:44<15:11, 86224.08it/s]

 54%|█████▍    | 91979776/170498071 [17:45<15:03, 86899.95it/s]

 54%|█████▍    | 92012544/170498071 [17:45<15:00, 87180.21it/s]

 54%|█████▍    | 92045312/170498071 [17:45<14:54, 87659.04it/s]

 54%|█████▍    | 92078080/170498071 [17:46<14:53, 87739.10it/s]

 54%|█████▍    | 92110848/170498071 [17:46<14:52, 87868.33it/s]

 54%|█████▍    | 92143616/170498071 [17:46<14:52, 87747.67it/s]

 54%|█████▍    | 92176384/170498071 [17:47<15:58, 81694.27it/s]

 54%|█████▍    | 92209152/170498071 [17:47<15:36, 83619.54it/s]

 54%|█████▍    | 92241920/170498071 [17:48<15:20, 84975.62it/s]

 54%|█████▍    | 92274688/170498071 [17:48<15:11, 85828.15it/s]

 54%|█████▍    | 92307456/170498071 [17:48<15:05, 86365.85it/s]

 54%|█████▍    | 92340224/170498071 [17:49<14:58, 86952.83it/s]

 54%|█████▍    | 92372992/170498071 [17:49<14:55, 87216.55it/s]

 54%|█████▍    | 92405760/170498071 [17:49<14:51, 87571.43it/s]

 54%|█████▍    | 92438528/170498071 [17:50<14:51, 87531.77it/s]

 54%|█████▍    | 92471296/170498071 [17:50<14:47, 87945.22it/s]

 54%|█████▍    | 92504064/170498071 [17:51<15:53, 81814.64it/s]

 54%|█████▍    | 92536832/170498071 [17:51<15:30, 83769.71it/s]

 54%|█████▍    | 92569600/170498071 [17:51<15:17, 84917.82it/s]

 54%|█████▍    | 92602368/170498071 [17:52<15:06, 85956.75it/s]

 54%|█████▍    | 92635136/170498071 [17:52<14:59, 86610.13it/s]

 54%|█████▍    | 92667904/170498071 [17:53<14:54, 87055.52it/s]

 54%|█████▍    | 92700672/170498071 [17:53<14:47, 87690.54it/s]

 54%|█████▍    | 92733440/170498071 [17:53<14:43, 88060.62it/s]

 54%|█████▍    | 92766208/170498071 [17:54<14:39, 88409.85it/s]

 54%|█████▍    | 92798976/170498071 [17:54<14:36, 88637.21it/s]

 54%|█████▍    | 92831744/170498071 [17:54<14:34, 88832.45it/s]

 54%|█████▍    | 92864512/170498071 [17:55<15:32, 83233.25it/s]

 54%|█████▍    | 92897280/170498071 [17:55<15:14, 84843.12it/s]

 55%|█████▍    | 92930048/170498071 [17:56<14:58, 86313.47it/s]

 55%|█████▍    | 92962816/170498071 [17:56<14:48, 87259.32it/s]

 55%|█████▍    | 92995584/170498071 [17:56<14:39, 88076.71it/s]

 55%|█████▍    | 93028352/170498071 [17:57<14:36, 88376.43it/s]

 55%|█████▍    | 93061120/170498071 [17:57<14:34, 88597.74it/s]

 55%|█████▍    | 93093888/170498071 [17:57<14:34, 88516.23it/s]

 55%|█████▍    | 93126656/170498071 [17:58<14:33, 88605.30it/s]

 55%|█████▍    | 93159424/170498071 [17:58<14:32, 88682.00it/s]

 55%|█████▍    | 93192192/170498071 [17:59<15:36, 82505.35it/s]

 55%|█████▍    | 93224960/170498071 [17:59<15:18, 84122.77it/s]

 55%|█████▍    | 93257728/170498071 [17:59<15:02, 85546.12it/s]

 55%|█████▍    | 93290496/170498071 [18:00<14:53, 86426.72it/s]

 55%|█████▍    | 93323264/170498071 [18:00<14:44, 87265.64it/s]

 55%|█████▍    | 93356032/170498071 [18:00<14:39, 87668.21it/s]

 55%|█████▍    | 93388800/170498071 [18:01<14:34, 88191.06it/s]

 55%|█████▍    | 93421568/170498071 [18:01<14:29, 88630.80it/s]

 55%|█████▍    | 93454336/170498071 [18:02<14:24, 89132.35it/s]

 55%|█████▍    | 93487104/170498071 [18:02<14:20, 89462.14it/s]

 55%|█████▍    | 93519872/170498071 [18:02<14:20, 89417.84it/s]

 55%|█████▍    | 93552640/170498071 [18:03<15:23, 83331.55it/s]

 55%|█████▍    | 93585408/170498071 [18:03<15:02, 85213.77it/s]

 55%|█████▍    | 93618176/170498071 [18:03<14:50, 86372.93it/s]

 55%|█████▍    | 93650944/170498071 [18:04<14:39, 87348.22it/s]

 55%|█████▍    | 93683712/170498071 [18:04<14:36, 87684.76it/s]

 55%|█████▍    | 93716480/170498071 [18:05<14:33, 87863.73it/s]

 55%|█████▍    | 93749248/170498071 [18:05<14:31, 88025.08it/s]

 55%|█████▌    | 93782016/170498071 [18:05<14:26, 88502.35it/s]

 55%|█████▌    | 93814784/170498071 [18:06<14:22, 88883.87it/s]

 55%|█████▌    | 93847552/170498071 [18:06<14:20, 89043.16it/s]

 55%|█████▌    | 93880320/170498071 [18:07<15:24, 82846.72it/s]

 55%|█████▌    | 93913088/170498071 [18:07<15:01, 84917.45it/s]

 55%|█████▌    | 93945856/170498071 [18:07<14:46, 86385.81it/s]

 55%|█████▌    | 93978624/170498071 [18:08<14:35, 87421.49it/s]

 55%|█████▌    | 94011392/170498071 [18:08<14:28, 88068.17it/s]

 55%|█████▌    | 94044160/170498071 [18:08<14:21, 88706.57it/s]

 55%|█████▌    | 94076928/170498071 [18:09<14:17, 89119.19it/s]

 55%|█████▌    | 94109696/170498071 [18:09<14:16, 89163.19it/s]

 55%|█████▌    | 94142464/170498071 [18:09<14:14, 89367.62it/s]

 55%|█████▌    | 94175232/170498071 [18:10<14:11, 89601.26it/s]

 55%|█████▌    | 94208000/170498071 [18:10<14:10, 89648.14it/s]

 55%|█████▌    | 94240768/170498071 [18:11<15:16, 83213.08it/s]

 55%|█████▌    | 94273536/170498071 [18:11<14:53, 85288.68it/s]

 55%|█████▌    | 94306304/170498071 [18:11<14:40, 86549.81it/s]

 55%|█████▌    | 94339072/170498071 [18:12<14:29, 87630.62it/s]

 55%|█████▌    | 94371840/170498071 [18:12<14:20, 88459.66it/s]

 55%|█████▌    | 94404608/170498071 [18:12<14:17, 88781.13it/s]

 55%|█████▌    | 94437376/170498071 [18:13<14:14, 88963.79it/s]

 55%|█████▌    | 94470144/170498071 [18:13<14:10, 89424.92it/s]

 55%|█████▌    | 94502912/170498071 [18:14<14:09, 89509.03it/s]

 55%|█████▌    | 94535680/170498071 [18:14<14:05, 89846.86it/s]

 55%|█████▌    | 94568448/170498071 [18:14<15:08, 83603.99it/s]

 55%|█████▌    | 94601216/170498071 [18:15<14:46, 85588.24it/s]

 56%|█████▌    | 94633984/170498071 [18:15<14:31, 87085.28it/s]

 56%|█████▌    | 94666752/170498071 [18:15<14:22, 87946.46it/s]

 56%|█████▌    | 94699520/170498071 [18:16<14:14, 88719.25it/s]

 56%|█████▌    | 94732288/170498071 [18:16<14:08, 89327.85it/s]

 56%|█████▌    | 94765056/170498071 [18:17<14:06, 89463.34it/s]

 56%|█████▌    | 94797824/170498071 [18:17<14:02, 89886.25it/s]

 56%|█████▌    | 94830592/170498071 [18:17<13:58, 90191.66it/s]

 56%|█████▌    | 94863360/170498071 [18:18<13:56, 90462.29it/s]

 56%|█████▌    | 94896128/170498071 [18:18<14:54, 84505.83it/s]

 56%|█████▌    | 94928896/170498071 [18:18<14:30, 86804.41it/s]

 56%|█████▌    | 94961664/170498071 [18:19<14:19, 87901.83it/s]

 56%|█████▌    | 94994432/170498071 [18:19<14:11, 88701.75it/s]

 56%|█████▌    | 95027200/170498071 [18:19<14:04, 89393.98it/s]

 56%|█████▌    | 95059968/170498071 [18:20<13:58, 89954.77it/s]

 56%|█████▌    | 95092736/170498071 [18:20<13:54, 90309.51it/s]

 56%|█████▌    | 95125504/170498071 [18:21<13:53, 90438.26it/s]

 56%|█████▌    | 95158272/170498071 [18:21<13:51, 90561.37it/s]

 56%|█████▌    | 95191040/170498071 [18:21<13:50, 90663.51it/s]

 56%|█████▌    | 95223808/170498071 [18:22<13:49, 90729.96it/s]

 56%|█████▌    | 95256576/170498071 [18:22<14:54, 84152.60it/s]

 56%|█████▌    | 95289344/170498071 [18:22<14:33, 86067.98it/s]

 56%|█████▌    | 95322112/170498071 [18:23<14:15, 87894.18it/s]

 56%|█████▌    | 95354880/170498071 [18:23<14:04, 88959.91it/s]

 56%|█████▌    | 95387648/170498071 [18:24<13:57, 89683.28it/s]

 56%|█████▌    | 95420416/170498071 [18:24<13:52, 90229.17it/s]

 56%|█████▌    | 95453184/170498071 [18:24<13:52, 90184.25it/s]

 56%|█████▌    | 95485952/170498071 [18:25<13:47, 90702.65it/s]

 56%|█████▌    | 95518720/170498071 [18:25<13:43, 91007.63it/s]

 56%|█████▌    | 95551488/170498071 [18:25<13:41, 91221.76it/s]

 56%|█████▌    | 95584256/170498071 [18:26<14:38, 85232.85it/s]

 56%|█████▌    | 95617024/170498071 [18:26<14:22, 86831.89it/s]

 56%|█████▌    | 95649792/170498071 [18:26<14:10, 88027.09it/s]

 56%|█████▌    | 95682560/170498071 [18:27<13:57, 89382.89it/s]

 56%|█████▌    | 95715328/170498071 [18:27<13:44, 90747.62it/s]

 56%|█████▌    | 95748096/170498071 [18:28<13:38, 91357.57it/s]

 56%|█████▌    | 95780864/170498071 [18:28<13:32, 91915.23it/s]

 56%|█████▌    | 95813632/170498071 [18:28<13:30, 92147.80it/s]

 56%|█████▌    | 95846400/170498071 [18:29<13:28, 92380.72it/s]

 56%|█████▌    | 95879168/170498071 [18:29<13:29, 92232.66it/s]

 56%|█████▋    | 95911936/170498071 [18:29<13:23, 92794.52it/s]

 56%|█████▋    | 95944704/170498071 [18:30<14:26, 86015.38it/s]

 56%|█████▋    | 95977472/170498071 [18:30<14:09, 87758.40it/s]

 56%|█████▋    | 96010240/170498071 [18:30<13:55, 89141.92it/s]

 56%|█████▋    | 96043008/170498071 [18:31<13:45, 90141.31it/s]

 56%|█████▋    | 96075776/170498071 [18:31<13:38, 90900.68it/s]

 56%|█████▋    | 96108544/170498071 [18:32<13:34, 91292.20it/s]

 56%|█████▋    | 96141312/170498071 [18:32<13:32, 91495.20it/s]

 56%|█████▋    | 96174080/170498071 [18:32<13:28, 91879.20it/s]

 56%|█████▋    | 96206848/170498071 [18:33<13:27, 92007.22it/s]

 56%|█████▋    | 96239616/170498071 [18:33<13:27, 92017.19it/s]

 56%|█████▋    | 96272384/170498071 [18:33<14:26, 85665.14it/s]

 56%|█████▋    | 96305152/170498071 [18:34<14:09, 87383.52it/s]

 57%|█████▋    | 96337920/170498071 [18:34<13:49, 89387.69it/s]

 57%|█████▋    | 96370688/170498071 [18:34<13:44, 89904.69it/s]

 57%|█████▋    | 96403456/170498071 [18:35<13:37, 90635.71it/s]

 57%|█████▋    | 96436224/170498071 [18:35<13:31, 91298.21it/s]

 57%|█████▋    | 96468992/170498071 [18:36<13:27, 91707.18it/s]

 57%|█████▋    | 96501760/170498071 [18:36<13:26, 91726.25it/s]

 57%|█████▋    | 96534528/170498071 [18:36<13:21, 92284.80it/s]

 57%|█████▋    | 96567296/170498071 [18:37<13:19, 92478.56it/s]

 57%|█████▋    | 96600064/170498071 [18:37<14:15, 86345.03it/s]

 57%|█████▋    | 96632832/170498071 [18:37<13:53, 88670.37it/s]

 57%|█████▋    | 96665600/170498071 [18:38<13:39, 90143.71it/s]

 57%|█████▋    | 96698368/170498071 [18:38<13:27, 91362.20it/s]

 57%|█████▋    | 96731136/170498071 [18:38<13:21, 92006.20it/s]

 57%|█████▋    | 96763904/170498071 [18:39<13:16, 92593.84it/s]

 57%|█████▋    | 96796672/170498071 [18:39<13:12, 93008.65it/s]

 57%|█████▋    | 96829440/170498071 [18:39<13:10, 93153.63it/s]

 57%|█████▋    | 96862208/170498071 [18:40<13:06, 93646.65it/s]

 57%|█████▋    | 96894976/170498071 [18:40<13:05, 93678.33it/s]

 57%|█████▋    | 96927744/170498071 [18:40<13:03, 93918.21it/s]

 57%|█████▋    | 96960512/170498071 [18:41<13:58, 87721.15it/s]

 57%|█████▋    | 96993280/170498071 [18:41<13:39, 89678.35it/s]

 57%|█████▋    | 97026048/170498071 [18:42<13:27, 90953.06it/s]

 57%|█████▋    | 97058816/170498071 [18:42<13:15, 92368.90it/s]

 57%|█████▋    | 97091584/170498071 [18:42<13:09, 92984.54it/s]

 57%|█████▋    | 97124352/170498071 [18:43<13:14, 92363.82it/s]

 57%|█████▋    | 97157120/170498071 [18:43<13:07, 93159.62it/s]

 57%|█████▋    | 97189888/170498071 [18:43<12:56, 94397.67it/s]

 57%|█████▋    | 97222656/170498071 [18:44<12:56, 94314.19it/s]

 57%|█████▋    | 97255424/170498071 [18:44<12:58, 94065.88it/s]

 57%|█████▋    | 97288192/170498071 [18:44<13:54, 87753.47it/s]

 57%|█████▋    | 97320960/170498071 [18:45<13:35, 89782.98it/s]

 57%|█████▋    | 97353728/170498071 [18:45<13:21, 91296.39it/s]

 57%|█████▋    | 97386496/170498071 [18:46<13:13, 92084.36it/s]

 57%|█████▋    | 97419264/170498071 [18:46<13:04, 93095.97it/s]

 57%|█████▋    | 97452032/170498071 [18:46<13:01, 93492.92it/s]

 57%|█████▋    | 97484800/170498071 [18:47<13:00, 93547.84it/s]

 57%|█████▋    | 97517568/170498071 [18:47<12:57, 93850.68it/s]

 57%|█████▋    | 97550336/170498071 [18:47<12:56, 93961.37it/s]

 57%|█████▋    | 97583104/170498071 [18:48<12:52, 94396.24it/s]

 57%|█████▋    | 97615872/170498071 [18:48<12:52, 94304.35it/s]

 57%|█████▋    | 97648640/170498071 [18:48<13:48, 87910.86it/s]

 57%|█████▋    | 97681408/170498071 [18:49<13:31, 89760.66it/s]

 57%|█████▋    | 97714176/170498071 [18:49<13:17, 91234.17it/s]

 57%|█████▋    | 97746944/170498071 [18:49<13:11, 91963.67it/s]

 57%|█████▋    | 97779712/170498071 [18:50<13:09, 92067.12it/s]

 57%|█████▋    | 97812480/170498071 [18:50<13:07, 92280.34it/s]

 57%|█████▋    | 97845248/170498071 [18:50<13:05, 92480.10it/s]

 57%|█████▋    | 97878016/170498071 [18:51<13:00, 93006.64it/s]

 57%|█████▋    | 97910784/170498071 [18:51<13:00, 92991.27it/s]

 57%|█████▋    | 97943552/170498071 [18:52<12:57, 93332.84it/s]

 57%|█████▋    | 97976320/170498071 [18:52<13:52, 87066.82it/s]

 57%|█████▋    | 98009088/170498071 [18:52<13:35, 88918.11it/s]

 58%|█████▊    | 98041856/170498071 [18:53<13:21, 90383.23it/s]

 58%|█████▊    | 98074624/170498071 [18:53<13:15, 91088.48it/s]

 58%|█████▊    | 98107392/170498071 [18:53<13:08, 91847.77it/s]

 58%|█████▊    | 98140160/170498071 [18:54<13:03, 92318.40it/s]

 58%|█████▊    | 98172928/170498071 [18:54<13:00, 92705.08it/s]

 58%|█████▊    | 98205696/170498071 [18:54<12:57, 92935.84it/s]

 58%|█████▊    | 98238464/170498071 [18:55<12:53, 93363.47it/s]

 58%|█████▊    | 98271232/170498071 [18:55<12:48, 93976.69it/s]

 58%|█████▊    | 98304000/170498071 [18:55<12:49, 93763.62it/s]

 58%|█████▊    | 98336768/170498071 [18:56<13:47, 87252.77it/s]

 58%|█████▊    | 98369536/170498071 [18:56<13:26, 89488.18it/s]

 58%|█████▊    | 98402304/170498071 [18:57<13:18, 90253.88it/s]

 58%|█████▊    | 98435072/170498071 [18:57<13:11, 91068.05it/s]

 58%|█████▊    | 98467840/170498071 [18:57<13:02, 92039.31it/s]

 58%|█████▊    | 98500608/170498071 [18:58<12:56, 92671.38it/s]

 58%|█████▊    | 98533376/170498071 [18:58<12:51, 93226.82it/s]

 58%|█████▊    | 98566144/170498071 [18:58<12:51, 93215.72it/s]

 58%|█████▊    | 98598912/170498071 [18:59<12:46, 93801.10it/s]

 58%|█████▊    | 98631680/170498071 [18:59<12:44, 94013.11it/s]

 58%|█████▊    | 98664448/170498071 [18:59<13:41, 87484.23it/s]

 58%|█████▊    | 98697216/170498071 [19:00<13:22, 89481.87it/s]

 58%|█████▊    | 98729984/170498071 [19:00<13:09, 90881.73it/s]

 58%|█████▊    | 98762752/170498071 [19:01<13:01, 91816.09it/s]

 58%|█████▊    | 98795520/170498071 [19:01<12:58, 92095.42it/s]

 58%|█████▊    | 98828288/170498071 [19:01<12:56, 92303.01it/s]

 58%|█████▊    | 98861056/170498071 [19:02<12:53, 92651.11it/s]

 58%|█████▊    | 98893824/170498071 [19:02<12:49, 93103.03it/s]

 58%|█████▊    | 98926592/170498071 [19:02<12:46, 93357.77it/s]

 58%|█████▊    | 98959360/170498071 [19:03<12:48, 93085.80it/s]

 58%|█████▊    | 98992128/170498071 [19:03<13:46, 86522.81it/s]

 58%|█████▊    | 99024896/170498071 [19:03<13:25, 88714.26it/s]

 58%|█████▊    | 99057664/170498071 [19:04<13:12, 90109.85it/s]

 58%|█████▊    | 99090432/170498071 [19:04<13:04, 90992.24it/s]

 58%|█████▊    | 99123200/170498071 [19:04<13:00, 91428.28it/s]

 58%|█████▊    | 99155968/170498071 [19:05<12:53, 92192.53it/s]

 58%|█████▊    | 99188736/170498071 [19:05<12:50, 92513.13it/s]

 58%|█████▊    | 99221504/170498071 [19:06<12:46, 92955.90it/s]

 58%|█████▊    | 99254272/170498071 [19:06<12:45, 93014.68it/s]

 58%|█████▊    | 99287040/170498071 [19:06<12:44, 93193.07it/s]

 58%|█████▊    | 99319808/170498071 [19:07<12:42, 93298.88it/s]

 58%|█████▊    | 99352576/170498071 [19:07<13:41, 86556.39it/s]

 58%|█████▊    | 99385344/170498071 [19:07<13:28, 87924.39it/s]

 58%|█████▊    | 99418112/170498071 [19:08<13:11, 89814.26it/s]

 58%|█████▊    | 99450880/170498071 [19:08<13:05, 90483.20it/s]

 58%|█████▊    | 99483648/170498071 [19:08<12:59, 91068.78it/s]

 58%|█████▊    | 99516416/170498071 [19:09<12:54, 91647.23it/s]

 58%|█████▊    | 99549184/170498071 [19:09<12:50, 92132.60it/s]

 58%|█████▊    | 99581952/170498071 [19:09<12:47, 92345.05it/s]

 58%|█████▊    | 99614720/170498071 [19:10<12:44, 92685.36it/s]

 58%|█████▊    | 99647488/170498071 [19:10<12:45, 92596.70it/s]

 58%|█████▊    | 99680256/170498071 [19:11<13:42, 86128.67it/s]

 58%|█████▊    | 99713024/170498071 [19:11<13:21, 88295.26it/s]

 59%|█████▊    | 99745792/170498071 [19:11<13:09, 89592.64it/s]

 59%|█████▊    | 99778560/170498071 [19:12<12:59, 90696.84it/s]

 59%|█████▊    | 99811328/170498071 [19:12<12:52, 91511.54it/s]

 59%|█████▊    | 99844096/170498071 [19:12<12:46, 92229.97it/s]

 59%|█████▊    | 99876864/170498071 [19:13<12:43, 92538.83it/s]

 59%|█████▊    | 99909632/170498071 [19:13<12:39, 92879.57it/s]

 59%|█████▊    | 99942400/170498071 [19:13<12:39, 92919.82it/s]

 59%|█████▊    | 99975168/170498071 [19:14<12:38, 92919.90it/s]

 59%|█████▊    | 100007936/170498071 [19:14<12:31, 93765.50it/s]

 59%|█████▊    | 100040704/170498071 [19:15<13:26, 87406.90it/s]

 59%|█████▊    | 100073472/170498071 [19:15<13:06, 89572.47it/s]

 59%|█████▊    | 100106240/170498071 [19:15<12:53, 90996.27it/s]

 59%|█████▊    | 100139008/170498071 [19:16<12:47, 91720.05it/s]

 59%|█████▉    | 100171776/170498071 [19:16<12:40, 92483.85it/s]

 59%|█████▉    | 100204544/170498071 [19:16<12:34, 93195.42it/s]

 59%|█████▉    | 100237312/170498071 [19:17<12:32, 93413.43it/s]

 59%|█████▉    | 100270080/170498071 [19:17<12:28, 93817.38it/s]

 59%|█████▉    | 100302848/170498071 [19:17<12:27, 93945.92it/s]

 59%|█████▉    | 100335616/170498071 [19:18<12:25, 94162.67it/s]

 59%|█████▉    | 100368384/170498071 [19:18<13:15, 88176.41it/s]

 59%|█████▉    | 100401152/170498071 [19:18<12:57, 90206.98it/s]

 59%|█████▉    | 100433920/170498071 [19:19<12:43, 91780.61it/s]

 59%|█████▉    | 100466688/170498071 [19:19<12:50, 90839.56it/s]

 59%|█████▉    | 100499456/170498071 [19:19<12:23, 94117.33it/s]

 59%|█████▉    | 100532224/170498071 [19:20<12:22, 94228.71it/s]

 59%|█████▉    | 100564992/170498071 [19:20<12:21, 94286.48it/s]

 59%|█████▉    | 100597760/170498071 [19:21<12:17, 94826.11it/s]

 59%|█████▉    | 100630528/170498071 [19:21<12:15, 94945.95it/s]

 59%|█████▉    | 100663296/170498071 [19:21<12:21, 94183.42it/s]

 59%|█████▉    | 100696064/170498071 [19:22<13:14, 87831.81it/s]

 59%|█████▉    | 100728832/170498071 [19:22<13:02, 89126.00it/s]

 59%|█████▉    | 100761600/170498071 [19:22<12:50, 90551.37it/s]

 59%|█████▉    | 100794368/170498071 [19:23<12:42, 91388.44it/s]

 59%|█████▉    | 100827136/170498071 [19:23<12:39, 91758.53it/s]

 59%|█████▉    | 100859904/170498071 [19:23<12:32, 92527.34it/s]

 59%|█████▉    | 100892672/170498071 [19:24<12:27, 93059.13it/s]

 59%|█████▉    | 100925440/170498071 [19:24<12:26, 93252.67it/s]

 59%|█████▉    | 100958208/170498071 [19:24<12:25, 93258.61it/s]

 59%|█████▉    | 100990976/170498071 [19:25<12:24, 93316.66it/s]

 59%|█████▉    | 101023744/170498071 [19:25<12:23, 93463.89it/s]

 59%|█████▉    | 101056512/170498071 [19:26<13:17, 87036.06it/s]

 59%|█████▉    | 101089280/170498071 [19:26<13:00, 88876.23it/s]

 59%|█████▉    | 101122048/170498071 [19:26<12:48, 90310.39it/s]

 59%|█████▉    | 101154816/170498071 [19:27<12:42, 90959.39it/s]

 59%|█████▉    | 101187584/170498071 [19:27<12:34, 91847.36it/s]

 59%|█████▉    | 101220352/170498071 [19:27<12:28, 92527.90it/s]

 59%|█████▉    | 101253120/170498071 [19:28<12:28, 92568.65it/s]

 59%|█████▉    | 101285888/170498071 [19:28<12:24, 92963.69it/s]

 59%|█████▉    | 101318656/170498071 [19:28<12:28, 92374.35it/s]

 59%|█████▉    | 101351424/170498071 [19:29<12:16, 93843.92it/s]

 59%|█████▉    | 101384192/170498071 [19:29<13:13, 87074.64it/s]

 59%|█████▉    | 101416960/170498071 [19:30<12:57, 88816.90it/s]

 60%|█████▉    | 101449728/170498071 [19:30<12:45, 90252.94it/s]

 60%|█████▉    | 101482496/170498071 [19:30<12:37, 91103.27it/s]

 60%|█████▉    | 101515264/170498071 [19:31<12:32, 91720.46it/s]

 60%|█████▉    | 101548032/170498071 [19:31<12:28, 92143.74it/s]

 60%|█████▉    | 101580800/170498071 [19:31<12:24, 92588.67it/s]

 60%|█████▉    | 101613568/170498071 [19:32<12:19, 93136.85it/s]

 60%|█████▉    | 101646336/170498071 [19:32<12:19, 93117.70it/s]

 60%|█████▉    | 101679104/170498071 [19:32<12:18, 93190.63it/s]

 60%|█████▉    | 101711872/170498071 [19:33<12:16, 93426.14it/s]

 60%|█████▉    | 101744640/170498071 [19:33<13:08, 87164.06it/s]

 60%|█████▉    | 101777408/170498071 [19:33<12:50, 89155.07it/s]

 60%|█████▉    | 101810176/170498071 [19:34<12:38, 90547.74it/s]

 60%|█████▉    | 101842944/170498071 [19:34<12:35, 90829.17it/s]

 60%|█████▉    | 101875712/170498071 [19:35<12:20, 92625.89it/s]

 60%|█████▉    | 101908480/170498071 [19:35<12:32, 91207.43it/s]

 60%|█████▉    | 101941248/170498071 [19:35<12:06, 94414.17it/s]

 60%|█████▉    | 101974016/170498071 [19:36<12:03, 94708.63it/s]

 60%|█████▉    | 102006784/170498071 [19:36<12:03, 94688.52it/s]

 60%|█████▉    | 102039552/170498071 [19:36<12:04, 94462.92it/s]

 60%|█████▉    | 102072320/170498071 [19:37<12:56, 88170.27it/s]

 60%|█████▉    | 102105088/170498071 [19:37<12:36, 90452.97it/s]

 60%|█████▉    | 102137856/170498071 [19:37<12:27, 91443.83it/s]

 60%|█████▉    | 102170624/170498071 [19:38<12:19, 92433.12it/s]

 60%|█████▉    | 102203392/170498071 [19:38<12:13, 93161.55it/s]

 60%|█████▉    | 102236160/170498071 [19:38<12:12, 93229.93it/s]

 60%|█████▉    | 102268928/170498071 [19:39<12:08, 93683.83it/s]

 60%|██████    | 102301696/170498071 [19:39<12:06, 93827.35it/s]

 60%|██████    | 102334464/170498071 [19:39<12:03, 94160.43it/s]

 60%|██████    | 102367232/170498071 [19:40<12:02, 94236.81it/s]

 60%|██████    | 102400000/170498071 [19:40<12:02, 94303.14it/s]

 60%|██████    | 102432768/170498071 [19:41<12:51, 88232.18it/s]

 60%|██████    | 102465536/170498071 [19:41<12:33, 90347.62it/s]

 60%|██████    | 102498304/170498071 [19:41<13:22, 84761.33it/s]

 60%|██████    | 102531072/170498071 [19:42<11:58, 94651.90it/s]

 60%|██████    | 102563840/170498071 [19:42<11:56, 94869.50it/s]

 60%|██████    | 102596608/170498071 [19:42<11:42, 96713.26it/s]

 60%|██████    | 102629376/170498071 [19:43<11:46, 96082.15it/s]

 60%|██████    | 102662144/170498071 [19:43<11:47, 95834.58it/s]

 60%|██████    | 102694912/170498071 [19:43<11:45, 96130.28it/s]

 60%|██████    | 102727680/170498071 [19:44<11:44, 96133.32it/s]

 60%|██████    | 102760448/170498071 [19:44<12:38, 89277.86it/s]

 60%|██████    | 102793216/170498071 [19:44<12:23, 91046.14it/s]

 60%|██████    | 102825984/170498071 [19:45<12:12, 92397.88it/s]

 60%|██████    | 102858752/170498071 [19:45<12:05, 93182.11it/s]

 60%|██████    | 102891520/170498071 [19:45<11:58, 94033.93it/s]

 60%|██████    | 102924288/170498071 [19:46<12:18, 91519.84it/s]

 60%|██████    | 102957056/170498071 [19:46<11:55, 94441.35it/s]

 60%|██████    | 102989824/170498071 [19:46<11:45, 95707.95it/s]

 60%|██████    | 103022592/170498071 [19:47<11:43, 95878.33it/s]

 60%|██████    | 103055360/170498071 [19:47<11:44, 95668.00it/s]

 60%|██████    | 103088128/170498071 [19:48<12:39, 88730.41it/s]

 60%|██████    | 103120896/170498071 [19:48<12:28, 90052.34it/s]

 61%|██████    | 103153664/170498071 [19:48<12:17, 91329.44it/s]

 61%|██████    | 103186432/170498071 [19:49<12:09, 92236.37it/s]

 61%|██████    | 103219200/170498071 [19:49<12:04, 92809.35it/s]

 61%|██████    | 103251968/170498071 [19:49<11:59, 93450.66it/s]

 61%|██████    | 103284736/170498071 [19:50<11:55, 93953.86it/s]

 61%|██████    | 103317504/170498071 [19:50<11:53, 94196.80it/s]

 61%|██████    | 103350272/170498071 [19:50<11:52, 94233.82it/s]

 61%|██████    | 103383040/170498071 [19:51<11:52, 94143.17it/s]

 61%|██████    | 103415808/170498071 [19:51<11:49, 94532.54it/s]

 61%|██████    | 103448576/170498071 [19:51<12:43, 87847.25it/s]

 61%|██████    | 103481344/170498071 [19:52<12:28, 89506.12it/s]

 61%|██████    | 103514112/170498071 [19:52<12:17, 90788.83it/s]

 61%|██████    | 103546880/170498071 [19:53<12:11, 91466.64it/s]

 61%|██████    | 103579648/170498071 [19:53<12:06, 92084.80it/s]

 61%|██████    | 103612416/170498071 [19:53<12:05, 92203.56it/s]

 61%|██████    | 103645184/170498071 [19:54<12:05, 92200.91it/s]

 61%|██████    | 103677952/170498071 [19:54<11:59, 92895.56it/s]

 61%|██████    | 103710720/170498071 [19:54<11:58, 92902.47it/s]

 61%|██████    | 103743488/170498071 [19:55<11:55, 93330.21it/s]

 61%|██████    | 103776256/170498071 [19:55<12:52, 86417.81it/s]

 61%|██████    | 103809024/170498071 [19:55<12:34, 88412.37it/s]

 61%|██████    | 103841792/170498071 [19:56<12:23, 89602.54it/s]

 61%|██████    | 103874560/170498071 [19:56<12:15, 90614.44it/s]

 61%|██████    | 103907328/170498071 [19:56<12:07, 91500.57it/s]

 61%|██████    | 103940096/170498071 [19:57<12:02, 92149.49it/s]

 61%|██████    | 103972864/170498071 [19:57<11:59, 92477.15it/s]

 61%|██████    | 104005632/170498071 [19:58<11:57, 92729.02it/s]

 61%|██████    | 104038400/170498071 [19:58<11:56, 92722.35it/s]

 61%|██████    | 104071168/170498071 [19:58<11:57, 92628.38it/s]

 61%|██████    | 104103936/170498071 [19:59<11:57, 92506.75it/s]

 61%|██████    | 104136704/170498071 [19:59<12:49, 86191.05it/s]

 61%|██████    | 104169472/170498071 [19:59<12:33, 88050.44it/s]

 61%|██████    | 104202240/170498071 [20:00<12:19, 89596.89it/s]

 61%|██████    | 104235008/170498071 [20:00<12:12, 90462.45it/s]

 61%|██████    | 104267776/170498071 [20:00<12:06, 91195.73it/s]

 61%|██████    | 104300544/170498071 [20:01<12:02, 91602.19it/s]

 61%|██████    | 104333312/170498071 [20:01<12:00, 91812.85it/s]

 61%|██████    | 104366080/170498071 [20:02<12:00, 91849.97it/s]

 61%|██████    | 104398848/170498071 [20:02<11:58, 92038.83it/s]

 61%|██████▏   | 104431616/170498071 [20:02<11:59, 91871.55it/s]

 61%|██████▏   | 104464384/170498071 [20:03<12:52, 85478.84it/s]

 61%|██████▏   | 104497152/170498071 [20:03<12:37, 87153.41it/s]

 61%|██████▏   | 104529920/170498071 [20:03<12:23, 88743.43it/s]

 61%|██████▏   | 104562688/170498071 [20:04<12:14, 89730.52it/s]

 61%|██████▏   | 104595456/170498071 [20:04<12:08, 90523.03it/s]

 61%|██████▏   | 104628224/170498071 [20:04<12:00, 91452.08it/s]

 61%|██████▏   | 104660992/170498071 [20:05<11:57, 91815.12it/s]

 61%|██████▏   | 104693760/170498071 [20:05<11:53, 92244.54it/s]

 61%|██████▏   | 104726528/170498071 [20:06<11:51, 92379.49it/s]

 61%|██████▏   | 104759296/170498071 [20:06<11:51, 92447.79it/s]

 61%|██████▏   | 104792064/170498071 [20:06<12:39, 86457.10it/s]

 61%|██████▏   | 104824832/170498071 [20:07<12:24, 88229.80it/s]

 62%|██████▏   | 104857600/170498071 [20:07<12:18, 88917.25it/s]

 62%|██████▏   | 104890368/170498071 [20:07<11:59, 91170.70it/s]

 62%|██████▏   | 104923136/170498071 [20:08<11:53, 91935.14it/s]

 62%|██████▏   | 104955904/170498071 [20:08<11:50, 92312.24it/s]

 62%|██████▏   | 104988672/170498071 [20:08<11:45, 92894.93it/s]

 62%|██████▏   | 105021440/170498071 [20:09<11:41, 93306.20it/s]

 62%|██████▏   | 105054208/170498071 [20:09<11:40, 93408.14it/s]

 62%|██████▏   | 105086976/170498071 [20:09<11:38, 93629.37it/s]

 62%|██████▏   | 105119744/170498071 [20:10<11:35, 93959.80it/s]

 62%|██████▏   | 105152512/170498071 [20:10<12:25, 87667.96it/s]

 62%|██████▏   | 105185280/170498071 [20:11<12:09, 89555.83it/s]

 62%|██████▏   | 105218048/170498071 [20:11<11:58, 90866.85it/s]

 62%|██████▏   | 105250816/170498071 [20:11<11:45, 92461.50it/s]

 62%|██████▏   | 105283584/170498071 [20:12<11:40, 93052.86it/s]

 62%|██████▏   | 105316352/170498071 [20:12<11:36, 93565.94it/s]

 62%|██████▏   | 105349120/170498071 [20:12<11:37, 93345.52it/s]

 62%|██████▏   | 105381888/170498071 [20:13<11:36, 93490.98it/s]

 62%|██████▏   | 105414656/170498071 [20:13<11:32, 94017.36it/s]

 62%|██████▏   | 105447424/170498071 [20:13<11:38, 93180.59it/s]

 62%|██████▏   | 105480192/170498071 [20:14<12:30, 86608.30it/s]

 62%|██████▏   | 105512960/170498071 [20:14<12:17, 88084.03it/s]

 62%|██████▏   | 105545728/170498071 [20:15<12:07, 89319.99it/s]

 62%|██████▏   | 105578496/170498071 [20:15<12:02, 89869.28it/s]

 62%|██████▏   | 105611264/170498071 [20:15<11:57, 90478.72it/s]

 62%|██████▏   | 105644032/170498071 [20:16<11:52, 90985.08it/s]

 62%|██████▏   | 105676800/170498071 [20:16<11:49, 91323.81it/s]

 62%|██████▏   | 105709568/170498071 [20:16<11:49, 91257.75it/s]

 62%|██████▏   | 105742336/170498071 [20:17<11:49, 91242.17it/s]

 62%|██████▏   | 105775104/170498071 [20:17<11:49, 91277.86it/s]

 62%|██████▏   | 105807872/170498071 [20:17<11:50, 91007.63it/s]

 62%|██████▏   | 105840640/170498071 [20:18<12:43, 84659.64it/s]

 62%|██████▏   | 105873408/170498071 [20:18<12:26, 86535.96it/s]

 62%|██████▏   | 105906176/170498071 [20:19<12:16, 87682.49it/s]

 62%|██████▏   | 105938944/170498071 [20:19<12:07, 88733.37it/s]

 62%|██████▏   | 105971712/170498071 [20:19<12:03, 89238.19it/s]

 62%|██████▏   | 106004480/170498071 [20:20<11:58, 89793.15it/s]

 62%|██████▏   | 106037248/170498071 [20:20<11:53, 90336.41it/s]

 62%|██████▏   | 106070016/170498071 [20:20<11:50, 90649.12it/s]

 62%|██████▏   | 106102784/170498071 [20:21<11:49, 90795.04it/s]

 62%|██████▏   | 106135552/170498071 [20:21<11:43, 91432.53it/s]

 62%|██████▏   | 106168320/170498071 [20:22<13:33, 79063.21it/s]

 62%|██████▏   | 106201088/170498071 [20:22<12:08, 88221.35it/s]

 62%|██████▏   | 106233856/170498071 [20:22<11:52, 90212.16it/s]

 62%|██████▏   | 106266624/170498071 [20:23<11:50, 90344.20it/s]

 62%|██████▏   | 106299392/170498071 [20:23<11:47, 90709.19it/s]

 62%|██████▏   | 106332160/170498071 [20:23<11:48, 90546.12it/s]

 62%|██████▏   | 106364928/170498071 [20:24<11:46, 90728.63it/s]

 62%|██████▏   | 106397696/170498071 [20:24<11:47, 90595.55it/s]

 62%|██████▏   | 106430464/170498071 [20:24<11:46, 90712.65it/s]

 62%|██████▏   | 106463232/170498071 [20:25<11:44, 90838.45it/s]

 62%|██████▏   | 106496000/170498071 [20:25<11:43, 90946.42it/s]

 62%|██████▏   | 106528768/170498071 [20:26<12:36, 84556.93it/s]

 63%|██████▎   | 106561536/170498071 [20:26<12:19, 86407.16it/s]

 63%|██████▎   | 106594304/170498071 [20:26<12:07, 87833.52it/s]

 63%|██████▎   | 106627072/170498071 [20:27<11:59, 88725.57it/s]

 63%|██████▎   | 106659840/170498071 [20:27<11:54, 89327.80it/s]

 63%|██████▎   | 106692608/170498071 [20:27<11:51, 89650.19it/s]

 63%|██████▎   | 106725376/170498071 [20:28<11:47, 90128.83it/s]

 63%|██████▎   | 106758144/170498071 [20:28<11:44, 90438.25it/s]

 63%|██████▎   | 106790912/170498071 [20:28<11:41, 90785.29it/s]

 63%|██████▎   | 106823680/170498071 [20:29<11:42, 90693.51it/s]

 63%|██████▎   | 106856448/170498071 [20:29<12:31, 84737.14it/s]

 63%|██████▎   | 106889216/170498071 [20:30<12:13, 86725.49it/s]

 63%|██████▎   | 106921984/170498071 [20:30<12:02, 87976.70it/s]

 63%|██████▎   | 106954752/170498071 [20:30<11:54, 88972.25it/s]

 63%|██████▎   | 106987520/170498071 [20:31<11:47, 89809.95it/s]

 63%|██████▎   | 107020288/170498071 [20:31<11:41, 90508.06it/s]

 63%|██████▎   | 107053056/170498071 [20:31<11:37, 91011.26it/s]

 63%|██████▎   | 107085824/170498071 [20:32<11:35, 91240.26it/s]

 63%|██████▎   | 107118592/170498071 [20:32<11:30, 91793.35it/s]

 63%|██████▎   | 107151360/170498071 [20:32<11:29, 91857.71it/s]

 63%|██████▎   | 107184128/170498071 [20:33<12:20, 85540.70it/s]

 63%|██████▎   | 107216896/170498071 [20:33<12:05, 87253.11it/s]

 63%|██████▎   | 107249664/170498071 [20:34<11:54, 88545.70it/s]

 63%|██████▎   | 107282432/170498071 [20:34<11:45, 89621.54it/s]

 63%|██████▎   | 107315200/170498071 [20:34<11:38, 90478.59it/s]

 63%|██████▎   | 107347968/170498071 [20:35<11:34, 90938.11it/s]

 63%|██████▎   | 107380736/170498071 [20:35<11:30, 91442.42it/s]

 63%|██████▎   | 107413504/170498071 [20:35<11:30, 91404.31it/s]

 63%|██████▎   | 107446272/170498071 [20:36<11:27, 91777.02it/s]

 63%|██████▎   | 107479040/170498071 [20:36<11:23, 92264.65it/s]

 63%|██████▎   | 107511808/170498071 [20:36<11:21, 92463.07it/s]

 63%|██████▎   | 107544576/170498071 [20:37<12:11, 86118.52it/s]

 63%|██████▎   | 107577344/170498071 [20:37<11:51, 88428.11it/s]

 63%|██████▎   | 107610112/170498071 [20:38<11:42, 89460.80it/s]

 63%|██████▎   | 107642880/170498071 [20:38<11:35, 90406.81it/s]

 63%|██████▎   | 107675648/170498071 [20:38<11:30, 90936.07it/s]

 63%|██████▎   | 107708416/170498071 [20:39<11:25, 91621.77it/s]

 63%|██████▎   | 107741184/170498071 [20:39<11:25, 91499.12it/s]

 63%|██████▎   | 107773952/170498071 [20:39<11:25, 91536.66it/s]

 63%|██████▎   | 107806720/170498071 [20:40<11:23, 91689.99it/s]

 63%|██████▎   | 107839488/170498071 [20:40<11:21, 91893.49it/s]

 63%|██████▎   | 107872256/170498071 [20:41<12:10, 85675.74it/s]

 63%|██████▎   | 107905024/170498071 [20:41<11:52, 87870.24it/s]

 63%|██████▎   | 107937792/170498071 [20:41<11:38, 89501.80it/s]

 63%|██████▎   | 107970560/170498071 [20:42<11:30, 90559.89it/s]

 63%|██████▎   | 108003328/170498071 [20:42<11:24, 91340.05it/s]

 63%|██████▎   | 108036096/170498071 [20:42<11:20, 91756.71it/s]

 63%|██████▎   | 108068864/170498071 [20:43<11:17, 92096.65it/s]

 63%|██████▎   | 108101632/170498071 [20:43<11:14, 92457.25it/s]

 63%|██████▎   | 108134400/170498071 [20:43<11:12, 92711.42it/s]

 63%|██████▎   | 108167168/170498071 [20:44<11:12, 92677.95it/s]

 63%|██████▎   | 108199936/170498071 [20:44<11:08, 93145.25it/s]

 63%|██████▎   | 108232704/170498071 [20:44<11:56, 86918.88it/s]

 63%|██████▎   | 108265472/170498071 [20:45<11:39, 88956.98it/s]

 64%|██████▎   | 108298240/170498071 [20:45<11:29, 90240.58it/s]

 64%|██████▎   | 108331008/170498071 [20:46<11:21, 91245.01it/s]

 64%|██████▎   | 108363776/170498071 [20:46<11:14, 92062.23it/s]

 64%|██████▎   | 108396544/170498071 [20:46<11:13, 92267.00it/s]

 64%|██████▎   | 108429312/170498071 [20:47<11:11, 92477.10it/s]

 64%|██████▎   | 108462080/170498071 [20:47<11:07, 92948.44it/s]

 64%|██████▎   | 108494848/170498071 [20:47<11:07, 92925.85it/s]

 64%|██████▎   | 108527616/170498071 [20:48<12:03, 85705.12it/s]

 64%|██████▎   | 108560384/170498071 [20:48<11:46, 87722.09it/s]

 64%|██████▎   | 108593152/170498071 [20:48<11:27, 90054.03it/s]

 64%|██████▎   | 108625920/170498071 [20:49<11:21, 90764.16it/s]

 64%|██████▎   | 108658688/170498071 [20:49<11:16, 91379.40it/s]

 64%|██████▎   | 108691456/170498071 [20:50<11:19, 90988.81it/s]

 64%|██████▍   | 108724224/170498071 [20:50<11:06, 92632.27it/s]

 64%|██████▍   | 108756992/170498071 [20:50<11:07, 92550.56it/s]

 64%|██████▍   | 108789760/170498071 [20:51<11:06, 92628.61it/s]

 64%|██████▍   | 108822528/170498071 [20:51<11:06, 92479.99it/s]

 64%|██████▍   | 108855296/170498071 [20:51<11:05, 92688.63it/s]

 64%|██████▍   | 108888064/170498071 [20:52<11:53, 86295.20it/s]

 64%|██████▍   | 108920832/170498071 [20:52<11:39, 88035.58it/s]

 64%|██████▍   | 108953600/170498071 [20:52<11:37, 88232.98it/s]

 64%|██████▍   | 108986368/170498071 [20:53<11:27, 89473.62it/s]

 64%|██████▍   | 109019136/170498071 [20:53<11:11, 91568.25it/s]

 64%|██████▍   | 109051904/170498071 [20:53<11:07, 91989.64it/s]

 64%|██████▍   | 109084672/170498071 [20:54<11:05, 92288.67it/s]

 64%|██████▍   | 109117440/170498071 [20:54<11:04, 92305.01it/s]

 64%|██████▍   | 109150208/170498071 [20:55<11:03, 92436.64it/s]

 64%|██████▍   | 109182976/170498071 [20:55<11:05, 92106.62it/s]

 64%|██████▍   | 109215744/170498071 [20:55<11:05, 92144.30it/s]

 64%|██████▍   | 109248512/170498071 [20:56<12:02, 84762.39it/s]

 64%|██████▍   | 109281280/170498071 [20:56<11:46, 86675.77it/s]

 64%|██████▍   | 109314048/170498071 [20:56<11:26, 89143.80it/s]

 64%|██████▍   | 109346816/170498071 [20:57<11:20, 89841.42it/s]

 64%|██████▍   | 109379584/170498071 [20:57<11:16, 90353.42it/s]

 64%|██████▍   | 109412352/170498071 [20:57<11:12, 90795.34it/s]

 64%|██████▍   | 109445120/170498071 [20:58<11:06, 91561.22it/s]

 64%|██████▍   | 109477888/170498071 [20:58<11:08, 91262.47it/s]

 64%|██████▍   | 109510656/170498071 [20:59<11:07, 91339.68it/s]

 64%|██████▍   | 109543424/170498071 [20:59<11:08, 91230.57it/s]

 64%|██████▍   | 109576192/170498071 [20:59<12:00, 84585.34it/s]

 64%|██████▍   | 109608960/170498071 [21:00<11:44, 86448.29it/s]

 64%|██████▍   | 109641728/170498071 [21:00<11:34, 87671.15it/s]

 64%|██████▍   | 109674496/170498071 [21:00<12:00, 84415.72it/s]

 64%|██████▍   | 109707264/170498071 [21:01<11:11, 90581.95it/s]

 64%|██████▍   | 109740032/170498071 [21:01<11:02, 91688.22it/s]

 64%|██████▍   | 109772800/170498071 [21:02<11:01, 91744.70it/s]

 64%|██████▍   | 109805568/170498071 [21:02<11:02, 91637.59it/s]

 64%|██████▍   | 109838336/170498071 [21:02<11:03, 91484.13it/s]

 64%|██████▍   | 109871104/170498071 [21:03<11:03, 91380.09it/s]

 64%|██████▍   | 109903872/170498071 [21:03<11:40, 86540.14it/s]

 64%|██████▍   | 109936640/170498071 [21:03<11:42, 86244.99it/s]

 64%|██████▍   | 109969408/170498071 [21:04<11:29, 87728.07it/s]

 65%|██████▍   | 110002176/170498071 [21:04<11:21, 88768.02it/s]

 65%|██████▍   | 110034944/170498071 [21:04<11:14, 89594.01it/s]

 65%|██████▍   | 110067712/170498071 [21:05<11:10, 90060.65it/s]

 65%|██████▍   | 110100480/170498071 [21:05<11:15, 89355.63it/s]

 65%|██████▍   | 110133248/170498071 [21:06<11:01, 91315.25it/s]

 65%|██████▍   | 110166016/170498071 [21:06<11:01, 91250.28it/s]

 65%|██████▍   | 110198784/170498071 [21:06<10:59, 91416.39it/s]

 65%|██████▍   | 110231552/170498071 [21:07<10:57, 91648.44it/s]

 65%|██████▍   | 110264320/170498071 [21:07<11:43, 85606.56it/s]

 65%|██████▍   | 110297088/170498071 [21:07<11:28, 87499.55it/s]

 65%|██████▍   | 110329856/170498071 [21:08<11:16, 88887.32it/s]

 65%|██████▍   | 110362624/170498071 [21:08<11:08, 89930.91it/s]

 65%|██████▍   | 110395392/170498071 [21:08<11:02, 90657.29it/s]

 65%|██████▍   | 110428160/170498071 [21:09<10:58, 91195.91it/s]

 65%|██████▍   | 110460928/170498071 [21:09<10:55, 91615.17it/s]

 65%|██████▍   | 110493696/170498071 [21:10<10:53, 91868.07it/s]

 65%|██████▍   | 110526464/170498071 [21:10<10:51, 92047.65it/s]

 65%|██████▍   | 110559232/170498071 [21:10<10:51, 91949.59it/s]

 65%|██████▍   | 110592000/170498071 [21:11<10:49, 92198.27it/s]

 65%|██████▍   | 110624768/170498071 [21:11<11:35, 86066.45it/s]

 65%|██████▍   | 110657536/170498071 [21:11<11:20, 87924.32it/s]

 65%|██████▍   | 110690304/170498071 [21:12<11:46, 84603.03it/s]

 65%|██████▍   | 110723072/170498071 [21:12<10:55, 91238.36it/s]

 65%|██████▍   | 110755840/170498071 [21:12<10:45, 92529.39it/s]

 65%|██████▍   | 110788608/170498071 [21:13<10:44, 92693.45it/s]

 65%|██████▍   | 110821376/170498071 [21:13<10:42, 92890.28it/s]

 65%|██████▌   | 110854144/170498071 [21:14<10:48, 91921.22it/s]

 65%|██████▌   | 110886912/170498071 [21:14<10:43, 92639.44it/s]

 65%|██████▌   | 110919680/170498071 [21:14<10:35, 93705.94it/s]

 65%|██████▌   | 110952448/170498071 [21:15<11:20, 87456.18it/s]

 65%|██████▌   | 110985216/170498071 [21:15<11:03, 89720.78it/s]

 65%|██████▌   | 111017984/170498071 [21:15<10:49, 91567.96it/s]

 65%|██████▌   | 111050752/170498071 [21:16<10:46, 92002.53it/s]

 65%|██████▌   | 111083520/170498071 [21:16<11:11, 88432.50it/s]

 65%|██████▌   | 111116288/170498071 [21:16<10:55, 90610.05it/s]

 65%|██████▌   | 111149056/170498071 [21:17<10:39, 92734.78it/s]

 65%|██████▌   | 111181824/170498071 [21:17<10:01, 98663.88it/s]

 65%|██████▌   | 111214592/170498071 [21:17<10:50, 91126.17it/s]

 65%|██████▌   | 111247360/170498071 [21:18<10:43, 92066.08it/s]

 65%|██████▌   | 111280128/170498071 [21:18<10:23, 94964.84it/s]

 65%|██████▌   | 111312896/170498071 [21:19<11:07, 88723.26it/s]

 65%|██████▌   | 111345664/170498071 [21:19<10:10, 96814.13it/s]

 65%|██████▌   | 111378432/170498071 [21:19<10:34, 93110.66it/s]

 65%|██████▌   | 111411200/170498071 [21:20<10:39, 92333.89it/s]

 65%|██████▌   | 111443968/170498071 [21:20<10:29, 93820.76it/s]

 65%|██████▌   | 111476736/170498071 [21:20<10:12, 96381.98it/s]

 65%|██████▌   | 111509504/170498071 [21:21<09:51, 99766.30it/s]

 65%|██████▌   | 111542272/170498071 [21:21<10:28, 93822.22it/s]

 65%|██████▌   | 111575040/170498071 [21:21<10:08, 96762.00it/s]

 65%|██████▌   | 111607808/170498071 [21:22<09:34, 102542.33it/s]

 65%|██████▌   | 111640576/170498071 [21:22<10:35, 92660.22it/s]

 65%|██████▌   | 111673344/170498071 [21:22<10:57, 89410.70it/s]

 66%|██████▌   | 111706112/170498071 [21:23<10:15, 95480.47it/s]

 66%|██████▌   | 111738880/170498071 [21:23<10:03, 97316.69it/s]

 66%|██████▌   | 111771648/170498071 [21:23<10:56, 89521.18it/s]

 66%|██████▌   | 111804416/170498071 [21:24<10:35, 92335.93it/s]

 66%|██████▌   | 111837184/170498071 [21:24<10:11, 95975.08it/s]

 66%|██████▌   | 111869952/170498071 [21:24<10:30, 93015.31it/s]

 66%|██████▌   | 111902720/170498071 [21:25<10:23, 94013.43it/s]

 66%|██████▌   | 111935488/170498071 [21:25<09:38, 101193.83it/s]

 66%|██████▌   | 111968256/170498071 [21:25<10:31, 92723.66it/s]

 66%|██████▌   | 112001024/170498071 [21:26<10:18, 94654.54it/s]

 66%|██████▌   | 112033792/170498071 [21:26<10:08, 96041.46it/s]

 66%|██████▌   | 112066560/170498071 [21:27<11:38, 83659.31it/s]

 66%|██████▌   | 112099328/170498071 [21:27<10:20, 94102.83it/s]

 66%|██████▌   | 112132096/170498071 [21:27<10:05, 96360.00it/s]

 66%|██████▌   | 112164864/170498071 [21:28<10:33, 92136.42it/s]

 66%|██████▌   | 112197632/170498071 [21:28<09:34, 101515.84it/s]

 66%|██████▌   | 112230400/170498071 [21:28<10:11, 95241.54it/s]

 66%|██████▌   | 112263168/170498071 [21:29<10:41, 90823.79it/s]

 66%|██████▌   | 112295936/170498071 [21:29<09:53, 98022.23it/s]

 66%|██████▌   | 112328704/170498071 [21:29<10:17, 94137.99it/s]

 66%|██████▌   | 112361472/170498071 [21:30<10:38, 91048.87it/s]

 66%|██████▌   | 112394240/170498071 [21:30<10:28, 92513.79it/s]

 66%|██████▌   | 112427008/170498071 [21:30<10:08, 95384.98it/s]

 66%|██████▌   | 112459776/170498071 [21:31<09:34, 100966.12it/s]

 66%|██████▌   | 112492544/170498071 [21:31<09:40, 99874.07it/s]

 66%|██████▌   | 112525312/170498071 [21:31<10:09, 95177.93it/s]

 66%|██████▌   | 112558080/170498071 [21:32<10:33, 91525.31it/s]

 66%|██████▌   | 112590848/170498071 [21:32<10:10, 94790.00it/s]

 66%|██████▌   | 112623616/170498071 [21:32<09:34, 100682.56it/s]

 66%|██████▌   | 112656384/170498071 [21:33<11:13, 85883.26it/s]

 66%|██████▌   | 112689152/170498071 [21:33<10:40, 90251.02it/s]

 66%|██████▌   | 112721920/170498071 [21:33<09:59, 96363.82it/s]

 66%|██████▌   | 112754688/170498071 [21:34<10:38, 90505.08it/s]

 66%|██████▌   | 112787456/170498071 [21:34<10:23, 92544.55it/s]

 66%|██████▌   | 112820224/170498071 [21:35<10:49, 88783.11it/s]

 66%|██████▌   | 112852992/170498071 [21:35<09:12, 104290.91it/s]

 66%|██████▌   | 112885760/170498071 [21:35<09:26, 101654.31it/s]

 66%|██████▌   | 112918528/170498071 [21:35<09:35, 100108.61it/s]

 66%|██████▌   | 112951296/170498071 [21:36<10:31, 91122.83it/s]

 66%|██████▋   | 112984064/170498071 [21:36<10:38, 90084.56it/s]

 66%|██████▋   | 113016832/170498071 [21:37<10:09, 94267.49it/s]

 66%|██████▋   | 113049600/170498071 [21:37<09:59, 95881.00it/s]

 66%|██████▋   | 113082368/170498071 [21:37<10:07, 94520.70it/s]

 66%|██████▋   | 113115136/170498071 [21:38<10:39, 89760.54it/s]

 66%|██████▋   | 113147904/170498071 [21:38<10:15, 93149.43it/s]

 66%|██████▋   | 113180672/170498071 [21:38<10:07, 94371.98it/s]

 66%|██████▋   | 113213440/170498071 [21:39<09:51, 96886.40it/s]

 66%|██████▋   | 113246208/170498071 [21:39<09:29, 100586.51it/s]

 66%|██████▋   | 113278976/170498071 [21:39<10:25, 91444.08it/s]

 66%|██████▋   | 113311744/170498071 [21:40<10:10, 93613.90it/s]

 66%|██████▋   | 113344512/170498071 [21:40<10:38, 89520.39it/s]

 66%|██████▋   | 113377280/170498071 [21:40<09:55, 95955.68it/s]

 67%|██████▋   | 113410048/170498071 [21:41<10:28, 90776.16it/s]

 67%|██████▋   | 113442816/170498071 [21:41<10:03, 94546.36it/s]

 67%|██████▋   | 113475584/170498071 [21:41<09:39, 98452.14it/s]

 67%|██████▋   | 113508352/170498071 [21:42<10:03, 94368.96it/s]

 67%|██████▋   | 113541120/170498071 [21:42<09:21, 101429.16it/s]

 67%|██████▋   | 113573888/170498071 [21:42<09:15, 102559.95it/s]

 67%|██████▋   | 113606656/170498071 [21:43<10:09, 93293.08it/s]

 67%|██████▋   | 113639424/170498071 [21:43<09:48, 96677.72it/s]

 67%|██████▋   | 113672192/170498071 [21:43<10:35, 89371.20it/s]

 67%|██████▋   | 113704960/170498071 [21:44<10:08, 93327.13it/s]

 67%|██████▋   | 113737728/170498071 [21:44<09:19, 101453.41it/s]

 67%|██████▋   | 113770496/170498071 [21:44<10:08, 93186.03it/s]

 67%|██████▋   | 113803264/170498071 [21:45<09:21, 101030.29it/s]

 67%|██████▋   | 113836032/170498071 [21:45<09:15, 101958.54it/s]

 67%|██████▋   | 113868800/170498071 [21:45<09:54, 95254.80it/s]

 67%|██████▋   | 113901568/170498071 [21:46<09:53, 95421.04it/s]

 67%|██████▋   | 113934336/170498071 [21:46<09:04, 103875.07it/s]

 67%|██████▋   | 113967104/170498071 [21:46<09:40, 97412.76it/s]

 67%|██████▋   | 113999872/170498071 [21:47<09:05, 103584.77it/s]

 67%|██████▋   | 114032640/170498071 [21:47<10:36, 88748.29it/s]

 67%|██████▋   | 114065408/170498071 [21:48<10:07, 92969.41it/s]

 67%|██████▋   | 114098176/170498071 [21:48<09:19, 100849.60it/s]

 67%|██████▋   | 114130944/170498071 [21:48<09:16, 101211.78it/s]

 67%|██████▋   | 114163712/170498071 [21:49<10:06, 92921.93it/s]

 67%|██████▋   | 114196480/170498071 [21:49<09:57, 94240.03it/s]

 67%|██████▋   | 114229248/170498071 [21:49<09:28, 98986.04it/s]

 67%|██████▋   | 114262016/170498071 [21:49<09:17, 100798.03it/s]

 67%|██████▋   | 114294784/170498071 [21:50<08:57, 104561.32it/s]

 67%|██████▋   | 114327552/170498071 [21:50<09:23, 99726.03it/s]

 67%|██████▋   | 114360320/170498071 [21:51<10:24, 89888.78it/s]

 67%|██████▋   | 114393088/170498071 [21:51<09:32, 97917.69it/s]

 67%|██████▋   | 114425856/170498071 [21:51<09:18, 100322.20it/s]

 67%|██████▋   | 114458624/170498071 [21:52<10:08, 92082.41it/s]

 67%|██████▋   | 114491392/170498071 [21:52<09:51, 94756.27it/s]

 67%|██████▋   | 114524160/170498071 [21:52<09:07, 102184.72it/s]

 67%|██████▋   | 114556928/170498071 [21:53<09:36, 97009.89it/s]

 67%|██████▋   | 114589696/170498071 [21:53<09:38, 96650.75it/s]

 67%|██████▋   | 114622464/170498071 [21:53<09:26, 98627.72it/s]

 67%|██████▋   | 114655232/170498071 [21:53<09:19, 99757.55it/s]

 67%|██████▋   | 114688000/170498071 [21:54<09:32, 97547.24it/s]

 67%|██████▋   | 114720768/170498071 [21:54<10:24, 89273.67it/s]

 67%|██████▋   | 114753536/170498071 [21:55<09:55, 93665.49it/s]

 67%|██████▋   | 114786304/170498071 [21:55<09:45, 95195.65it/s]

 67%|██████▋   | 114819072/170498071 [21:55<09:42, 95523.17it/s]

 67%|██████▋   | 114851840/170498071 [21:56<09:53, 93716.80it/s]

 67%|██████▋   | 114884608/170498071 [21:56<09:45, 95059.68it/s]

 67%|██████▋   | 114917376/170498071 [21:56<09:41, 95512.19it/s]

 67%|██████▋   | 114950144/170498071 [21:57<09:02, 102361.88it/s]

 67%|██████▋   | 114982912/170498071 [21:57<09:45, 94831.06it/s]

 67%|██████▋   | 115015680/170498071 [21:57<08:55, 103698.81it/s]

 67%|██████▋   | 115048448/170498071 [21:58<09:44, 94879.59it/s]

 67%|██████▋   | 115081216/170498071 [21:58<10:19, 89510.96it/s]

 68%|██████▊   | 115113984/170498071 [21:58<09:30, 97058.57it/s]

 68%|██████▊   | 115146752/170498071 [21:59<09:58, 92415.95it/s]

 68%|██████▊   | 115179520/170498071 [21:59<09:10, 100551.98it/s]

 68%|██████▊   | 115212288/170498071 [21:59<09:20, 98578.29it/s]

 68%|██████▊   | 115245056/170498071 [22:00<09:30, 96807.85it/s]

 68%|██████▊   | 115277824/170498071 [22:00<09:10, 100322.75it/s]

 68%|██████▊   | 115310592/170498071 [22:00<09:22, 98066.93it/s]

 68%|██████▊   | 115343360/170498071 [22:01<10:00, 91846.41it/s]

 68%|██████▊   | 115376128/170498071 [22:01<10:10, 90304.08it/s]

 68%|██████▊   | 115408896/170498071 [22:01<10:15, 89497.55it/s]

 68%|██████▊   | 115441664/170498071 [22:02<10:31, 87169.39it/s]

 68%|██████▊   | 115474432/170498071 [22:02<09:36, 95493.07it/s]

 68%|██████▊   | 115507200/170498071 [22:02<08:48, 104015.37it/s]

 68%|██████▊   | 115539968/170498071 [22:03<08:59, 101890.58it/s]

 68%|██████▊   | 115572736/170498071 [22:03<09:39, 94776.18it/s]

 68%|██████▊   | 115605504/170498071 [22:03<09:03, 101047.38it/s]

 68%|██████▊   | 115638272/170498071 [22:04<09:53, 92416.24it/s]

 68%|██████▊   | 115671040/170498071 [22:04<09:00, 101384.54it/s]

 68%|██████▊   | 115703808/170498071 [22:04<09:02, 100984.61it/s]

 68%|██████▊   | 115736576/170498071 [22:05<10:26, 87407.24it/s]

 68%|██████▊   | 115769344/170498071 [22:05<09:38, 94527.78it/s]

 68%|██████▊   | 115802112/170498071 [22:06<10:05, 90274.06it/s]

 68%|██████▊   | 115834880/170498071 [22:06<09:33, 95327.01it/s]

 68%|██████▊   | 115867648/170498071 [22:06<09:20, 97464.49it/s]

 68%|██████▊   | 115900416/170498071 [22:07<09:51, 92281.41it/s]

 68%|██████▊   | 115933184/170498071 [22:07<09:12, 98772.63it/s]

 68%|██████▊   | 115965952/170498071 [22:07<09:53, 91904.11it/s]

 68%|██████▊   | 115998720/170498071 [22:08<09:49, 92410.71it/s]

 68%|██████▊   | 116031488/170498071 [22:08<09:10, 98872.55it/s]

 68%|██████▊   | 116064256/170498071 [22:08<09:48, 92496.33it/s]

 68%|██████▊   | 116097024/170498071 [22:09<09:34, 94666.23it/s]

 68%|██████▊   | 116129792/170498071 [22:09<09:48, 92435.53it/s]

 68%|██████▊   | 116162560/170498071 [22:09<09:47, 92432.51it/s]

 68%|██████▊   | 116195328/170498071 [22:10<09:35, 94403.23it/s]

 68%|██████▊   | 116228096/170498071 [22:10<09:22, 96498.67it/s]

 68%|██████▊   | 116260864/170498071 [22:10<10:05, 89617.02it/s]

 68%|██████▊   | 116293632/170498071 [22:11<09:41, 93284.14it/s]

 68%|██████▊   | 116326400/170498071 [22:11<10:05, 89485.20it/s]

 68%|██████▊   | 116359168/170498071 [22:11<09:30, 94851.98it/s]

 68%|██████▊   | 116391936/170498071 [22:12<09:12, 97992.09it/s]

 68%|██████▊   | 116424704/170498071 [22:12<10:12, 88227.31it/s]

 68%|██████▊   | 116457472/170498071 [22:13<09:41, 92961.69it/s]

 68%|██████▊   | 116490240/170498071 [22:13<09:04, 99222.88it/s]

 68%|██████▊   | 116523008/170498071 [22:13<09:45, 92117.03it/s]

 68%|██████▊   | 116555776/170498071 [22:14<09:21, 96133.74it/s]

 68%|██████▊   | 116588544/170498071 [22:14<08:54, 100838.43it/s]

 68%|██████▊   | 116621312/170498071 [22:14<09:20, 96045.35it/s]

 68%|██████▊   | 116654080/170498071 [22:15<08:59, 99753.70it/s]

 68%|██████▊   | 116686848/170498071 [22:15<09:40, 92672.99it/s]

 68%|██████▊   | 116719616/170498071 [22:15<09:18, 96260.12it/s]

 68%|██████▊   | 116752384/170498071 [22:16<09:59, 89687.82it/s]

 68%|██████▊   | 116785152/170498071 [22:16<09:30, 94076.94it/s]

 69%|██████▊   | 116817920/170498071 [22:16<09:05, 98460.46it/s]

 69%|██████▊   | 116850688/170498071 [22:17<09:44, 91795.16it/s]

 69%|██████▊   | 116883456/170498071 [22:17<09:22, 95389.16it/s]

 69%|██████▊   | 116916224/170498071 [22:17<09:16, 96282.47it/s]

 69%|██████▊   | 116948992/170498071 [22:18<09:01, 98876.48it/s]

 69%|██████▊   | 116981760/170498071 [22:18<08:50, 100893.07it/s]

 69%|██████▊   | 117014528/170498071 [22:18<09:19, 95601.33it/s]

 69%|██████▊   | 117047296/170498071 [22:19<08:53, 100199.31it/s]

 69%|██████▊   | 117080064/170498071 [22:19<10:02, 88665.14it/s]

 69%|██████▊   | 117112832/170498071 [22:19<09:50, 90459.57it/s]

 69%|██████▊   | 117145600/170498071 [22:20<09:17, 95689.31it/s]

 69%|██████▊   | 117178368/170498071 [22:20<08:41, 102234.62it/s]

 69%|██████▊   | 117211136/170498071 [22:20<09:06, 97506.16it/s]

 69%|██████▉   | 117243904/170498071 [22:21<09:18, 95330.82it/s]

 69%|██████▉   | 117276672/170498071 [22:21<09:06, 97333.77it/s]

 69%|██████▉   | 117309440/170498071 [22:21<09:27, 93700.03it/s]

 69%|██████▉   | 117342208/170498071 [22:22<09:18, 95187.56it/s]

 69%|██████▉   | 117374976/170498071 [22:22<08:32, 103683.53it/s]

 69%|██████▉   | 117407744/170498071 [22:22<08:44, 101226.31it/s]

 69%|██████▉   | 117440512/170498071 [22:23<09:27, 93516.47it/s]

 69%|██████▉   | 117473280/170498071 [22:23<09:37, 91804.60it/s]

 69%|██████▉   | 117506048/170498071 [22:23<09:22, 94270.45it/s]

 69%|██████▉   | 117538816/170498071 [22:24<09:49, 89911.54it/s]

 69%|██████▉   | 117571584/170498071 [22:24<09:12, 95722.17it/s]

 69%|██████▉   | 117604352/170498071 [22:25<09:55, 88835.16it/s]

 69%|██████▉   | 117637120/170498071 [22:25<09:06, 96798.44it/s]

 69%|██████▉   | 117669888/170498071 [22:25<09:06, 96597.87it/s]

 69%|██████▉   | 117702656/170498071 [22:26<09:10, 95833.34it/s]

 69%|██████▉   | 117735424/170498071 [22:26<09:05, 96660.53it/s]

 69%|██████▉   | 117768192/170498071 [22:26<09:43, 90392.14it/s]

 69%|██████▉   | 117800960/170498071 [22:27<09:58, 88015.70it/s]

 69%|██████▉   | 117833728/170498071 [22:27<09:17, 94520.99it/s]

 69%|██████▉   | 117866496/170498071 [22:27<09:13, 95052.87it/s]

 69%|██████▉   | 117899264/170498071 [22:28<09:49, 89288.38it/s]

 69%|██████▉   | 117932032/170498071 [22:28<09:18, 94163.07it/s]

 69%|██████▉   | 117964800/170498071 [22:28<09:57, 87919.80it/s]

 69%|██████▉   | 117997568/170498071 [22:29<08:34, 102103.48it/s]

 69%|██████▉   | 118030336/170498071 [22:29<08:29, 102938.41it/s]

 69%|██████▉   | 118063104/170498071 [22:29<08:36, 101488.21it/s]

 69%|██████▉   | 118095872/170498071 [22:30<08:45, 99641.49it/s]

 69%|██████▉   | 118128640/170498071 [22:30<10:07, 86264.79it/s]

 69%|██████▉   | 118161408/170498071 [22:30<09:41, 89995.35it/s]

 69%|██████▉   | 118194176/170498071 [22:31<08:43, 99942.48it/s]

 69%|██████▉   | 118226944/170498071 [22:31<08:43, 99772.20it/s]

 69%|██████▉   | 118259712/170498071 [22:31<09:26, 92179.73it/s]

 69%|██████▉   | 118292480/170498071 [22:32<08:40, 100328.87it/s]

 69%|██████▉   | 118325248/170498071 [22:32<08:32, 101844.30it/s]

 69%|██████▉   | 118358016/170498071 [22:32<09:19, 93266.98it/s]

 69%|██████▉   | 118390784/170498071 [22:33<09:02, 96137.53it/s]

 69%|██████▉   | 118423552/170498071 [22:33<08:30, 102031.04it/s]

 69%|██████▉   | 118456320/170498071 [22:33<09:18, 93147.12it/s]

 69%|██████▉   | 118489088/170498071 [22:34<09:01, 95990.27it/s]

 70%|██████▉   | 118521856/170498071 [22:34<09:40, 89480.83it/s]

 70%|██████▉   | 118554624/170498071 [22:35<09:04, 95476.85it/s]

 70%|██████▉   | 118587392/170498071 [22:35<08:37, 100260.05it/s]

 70%|██████▉   | 118620160/170498071 [22:35<08:32, 101257.88it/s]

 70%|██████▉   | 118652928/170498071 [22:36<09:28, 91255.71it/s]

 70%|██████▉   | 118685696/170498071 [22:36<08:27, 102023.01it/s]

 70%|██████▉   | 118718464/170498071 [22:36<09:05, 94953.38it/s]

 70%|██████▉   | 118751232/170498071 [22:36<08:27, 102020.13it/s]

 70%|██████▉   | 118784000/170498071 [22:37<08:27, 101975.71it/s]

 70%|██████▉   | 118816768/170498071 [22:37<09:15, 93094.30it/s]

 70%|██████▉   | 118849536/170498071 [22:38<09:01, 95451.49it/s]

 70%|██████▉   | 118882304/170498071 [22:38<09:02, 95108.35it/s]

 70%|██████▉   | 118915072/170498071 [22:38<08:51, 97083.40it/s]

 70%|██████▉   | 118947840/170498071 [22:39<08:56, 96106.64it/s]

 70%|██████▉   | 118980608/170498071 [22:39<08:46, 97773.28it/s]

 70%|██████▉   | 119013376/170498071 [22:39<08:51, 96814.53it/s]

 70%|██████▉   | 119046144/170498071 [22:40<08:45, 97929.36it/s]

 70%|██████▉   | 119078912/170498071 [22:40<09:00, 95207.46it/s]

 70%|██████▉   | 119111680/170498071 [22:40<09:28, 90325.46it/s]

 70%|██████▉   | 119144448/170498071 [22:41<09:25, 90820.64it/s]

 70%|██████▉   | 119177216/170498071 [22:41<09:34, 89284.58it/s]

 70%|██████▉   | 119209984/170498071 [22:41<09:18, 91761.88it/s]

 70%|██████▉   | 119242752/170498071 [22:42<09:24, 90747.19it/s]

 70%|██████▉   | 119275520/170498071 [22:42<08:32, 99884.27it/s]

 70%|██████▉   | 119308288/170498071 [22:42<08:52, 96086.06it/s]

 70%|██████▉   | 119341056/170498071 [22:43<08:43, 97683.58it/s]

 70%|███████   | 119373824/170498071 [22:43<08:32, 99706.96it/s]

 70%|███████   | 119406592/170498071 [22:43<09:28, 89915.36it/s]

 70%|███████   | 119439360/170498071 [22:44<08:39, 98204.64it/s]

 70%|███████   | 119472128/170498071 [22:44<09:15, 91884.09it/s]

 70%|███████   | 119504896/170498071 [22:44<09:06, 93344.27it/s]

 70%|███████   | 119537664/170498071 [22:45<09:29, 89440.42it/s]

 70%|███████   | 119570432/170498071 [22:45<08:46, 96757.49it/s]

 70%|███████   | 119603200/170498071 [22:45<08:44, 96951.85it/s]

 70%|███████   | 119635968/170498071 [22:46<09:04, 93385.75it/s]

 70%|███████   | 119668736/170498071 [22:46<08:36, 98321.38it/s]

 70%|███████   | 119701504/170498071 [22:47<08:42, 97283.93it/s]

 70%|███████   | 119734272/170498071 [22:47<09:02, 93546.64it/s]

 70%|███████   | 119767040/170498071 [22:47<08:48, 95906.68it/s]

 70%|███████   | 119799808/170498071 [22:48<09:01, 93665.91it/s]

 70%|███████   | 119832576/170498071 [22:48<08:49, 95636.31it/s]

 70%|███████   | 119865344/170498071 [22:48<09:00, 93625.50it/s]

 70%|███████   | 119898112/170498071 [22:49<08:39, 97468.10it/s]

 70%|███████   | 119930880/170498071 [22:49<09:00, 93537.18it/s]

 70%|███████   | 119963648/170498071 [22:49<08:23, 100348.48it/s]

 70%|███████   | 119996416/170498071 [22:50<09:06, 92420.37it/s]

 70%|███████   | 120029184/170498071 [22:50<08:34, 98185.35it/s]

 70%|███████   | 120061952/170498071 [22:50<08:23, 100220.33it/s]

 70%|███████   | 120094720/170498071 [22:51<08:19, 100907.46it/s]

 70%|███████   | 120127488/170498071 [22:51<08:24, 99869.34it/s]

 70%|███████   | 120160256/170498071 [22:51<09:06, 92088.67it/s]

 70%|███████   | 120193024/170498071 [22:52<09:05, 92207.63it/s]

 71%|███████   | 120225792/170498071 [22:52<08:47, 95272.40it/s]

 71%|███████   | 120258560/170498071 [22:52<08:36, 97190.31it/s]

 71%|███████   | 120291328/170498071 [22:53<09:21, 89433.72it/s]

 71%|███████   | 120324096/170498071 [22:53<08:56, 93602.13it/s]

 71%|███████   | 120356864/170498071 [22:53<08:51, 94330.68it/s]

 71%|███████   | 120389632/170498071 [22:54<08:36, 97017.92it/s]

 71%|███████   | 120422400/170498071 [22:54<08:11, 101970.99it/s]

 71%|███████   | 120455168/170498071 [22:54<08:02, 103732.67it/s]

 71%|███████   | 120487936/170498071 [22:55<08:15, 100858.70it/s]

 71%|███████   | 120520704/170498071 [22:55<08:55, 93337.30it/s]

 71%|███████   | 120553472/170498071 [22:55<08:45, 94961.73it/s]

 71%|███████   | 120586240/170498071 [22:56<09:19, 89222.39it/s]

 71%|███████   | 120619008/170498071 [22:56<08:54, 93305.90it/s]

 71%|███████   | 120651776/170498071 [22:56<08:52, 93606.25it/s]

 71%|███████   | 120684544/170498071 [22:57<08:05, 102703.67it/s]

 71%|███████   | 120717312/170498071 [22:57<08:02, 103084.78it/s]

 71%|███████   | 120750080/170498071 [22:57<08:44, 94879.26it/s]

 71%|███████   | 120782848/170498071 [22:58<08:09, 101474.83it/s]

 71%|███████   | 120815616/170498071 [22:58<08:06, 102174.78it/s]

 71%|███████   | 120848384/170498071 [22:58<08:53, 93016.11it/s]

 71%|███████   | 120881152/170498071 [22:59<08:39, 95526.47it/s]

 71%|███████   | 120913920/170498071 [22:59<08:33, 96515.86it/s]

 71%|███████   | 120946688/170498071 [22:59<08:45, 94365.52it/s]

 71%|███████   | 120979456/170498071 [23:00<08:30, 97000.98it/s]

 71%|███████   | 121012224/170498071 [23:00<09:11, 89795.36it/s]

 71%|███████   | 121044992/170498071 [23:00<08:23, 98293.10it/s]

 71%|███████   | 121077760/170498071 [23:01<08:15, 99699.79it/s]

 71%|███████   | 121110528/170498071 [23:01<08:12, 100223.82it/s]

 71%|███████   | 121143296/170498071 [23:01<08:15, 99585.45it/s]

 71%|███████   | 121176064/170498071 [23:02<09:18, 88347.32it/s]

 71%|███████   | 121208832/170498071 [23:02<09:22, 87666.68it/s]

 71%|███████   | 121241600/170498071 [23:03<08:24, 97727.29it/s]

 71%|███████   | 121274368/170498071 [23:03<08:48, 93134.81it/s]

 71%|███████   | 121307136/170498071 [23:03<08:44, 93791.78it/s]

 71%|███████   | 121339904/170498071 [23:04<08:49, 92768.20it/s]

 71%|███████   | 121372672/170498071 [23:04<08:27, 96736.98it/s]

 71%|███████   | 121405440/170498071 [23:04<08:05, 101171.87it/s]

 71%|███████   | 121438208/170498071 [23:05<08:29, 96246.99it/s]

 71%|███████   | 121470976/170498071 [23:05<08:37, 94709.94it/s]

 71%|███████▏  | 121503744/170498071 [23:05<07:54, 103177.17it/s]

 71%|███████▏  | 121536512/170498071 [23:06<08:38, 94402.45it/s]

 71%|███████▏  | 121569280/170498071 [23:06<08:51, 92116.39it/s]

 71%|███████▏  | 121602048/170498071 [23:06<08:21, 97497.89it/s]

 71%|███████▏  | 121634816/170498071 [23:07<08:14, 98903.41it/s]

 71%|███████▏  | 121667584/170498071 [23:07<08:15, 98472.18it/s]

 71%|███████▏  | 121700352/170498071 [23:07<08:16, 98297.94it/s]

 71%|███████▏  | 121733120/170498071 [23:08<08:21, 97163.83it/s]

 71%|███████▏  | 121765888/170498071 [23:08<08:15, 98373.95it/s]

 71%|███████▏  | 121798656/170498071 [23:08<08:16, 98011.73it/s]

 71%|███████▏  | 121831424/170498071 [23:09<08:22, 96839.33it/s]

 71%|███████▏  | 121864192/170498071 [23:09<08:53, 91115.68it/s]

 71%|███████▏  | 121896960/170498071 [23:09<08:43, 92870.22it/s]

 72%|███████▏  | 121929728/170498071 [23:10<08:35, 94289.77it/s]

 72%|███████▏  | 121962496/170498071 [23:10<08:34, 94245.72it/s]

 72%|███████▏  | 121995264/170498071 [23:10<08:24, 96092.23it/s]

 72%|███████▏  | 122028032/170498071 [23:11<08:23, 96332.27it/s]

 72%|███████▏  | 122060800/170498071 [23:11<08:20, 96693.60it/s]

 72%|███████▏  | 122093568/170498071 [23:11<08:19, 96938.25it/s]

 72%|███████▏  | 122126336/170498071 [23:12<08:19, 96876.40it/s]

 72%|███████▏  | 122159104/170498071 [23:12<08:17, 97103.59it/s]

 72%|███████▏  | 122191872/170498071 [23:12<08:24, 95682.33it/s]

 72%|███████▏  | 122224640/170498071 [23:13<08:53, 90495.61it/s]

 72%|███████▏  | 122257408/170498071 [23:13<08:43, 92197.88it/s]

 72%|███████▏  | 122290176/170498071 [23:14<08:35, 93434.96it/s]

 72%|███████▏  | 122322944/170498071 [23:14<08:30, 94398.75it/s]

 72%|███████▏  | 122355712/170498071 [23:14<08:25, 95148.67it/s]

 72%|███████▏  | 122388480/170498071 [23:15<08:25, 95162.63it/s]

 72%|███████▏  | 122421248/170498071 [23:15<08:23, 95463.28it/s]

 72%|███████▏  | 122454016/170498071 [23:15<08:22, 95553.77it/s]

 72%|███████▏  | 122486784/170498071 [23:16<08:22, 95588.09it/s]

 72%|███████▏  | 122519552/170498071 [23:16<08:19, 95973.77it/s]

 72%|███████▏  | 122552320/170498071 [23:16<08:57, 89221.68it/s]

 72%|███████▏  | 122585088/170498071 [23:17<08:45, 91109.34it/s]

 72%|███████▏  | 122617856/170498071 [23:17<08:38, 92414.85it/s]

 72%|███████▏  | 122650624/170498071 [23:17<08:32, 93348.99it/s]

 72%|███████▏  | 122683392/170498071 [23:18<08:32, 93288.87it/s]

 72%|███████▏  | 122716160/170498071 [23:18<08:23, 94910.84it/s]

 72%|███████▏  | 122748928/170498071 [23:18<08:20, 95375.53it/s]

 72%|███████▏  | 122781696/170498071 [23:19<08:20, 95256.32it/s]

 72%|███████▏  | 122814464/170498071 [23:19<08:19, 95400.49it/s]

 72%|███████▏  | 122847232/170498071 [23:19<08:19, 95353.73it/s]

 72%|███████▏  | 122880000/170498071 [23:20<08:20, 95072.13it/s]

 72%|███████▏  | 122912768/170498071 [23:20<08:58, 88307.74it/s]

 72%|███████▏  | 122945536/170498071 [23:21<08:46, 90392.97it/s]

 72%|███████▏  | 122978304/170498071 [23:21<08:38, 91713.88it/s]

 72%|███████▏  | 123011072/170498071 [23:21<08:31, 92845.77it/s]

 72%|███████▏  | 123043840/170498071 [23:22<08:26, 93605.00it/s]

 72%|███████▏  | 123076608/170498071 [23:22<08:24, 93974.20it/s]

 72%|███████▏  | 123109376/170498071 [23:22<08:22, 94262.90it/s]

 72%|███████▏  | 123142144/170498071 [23:23<08:20, 94565.05it/s]

 72%|███████▏  | 123174912/170498071 [23:23<08:25, 93610.18it/s]

 72%|███████▏  | 123207680/170498071 [23:23<08:20, 94567.34it/s]

 72%|███████▏  | 123240448/170498071 [23:24<08:54, 88478.93it/s]

 72%|███████▏  | 123273216/170498071 [23:24<08:43, 90155.61it/s]

 72%|███████▏  | 123305984/170498071 [23:24<08:37, 91220.95it/s]

 72%|███████▏  | 123338752/170498071 [23:25<08:33, 91892.83it/s]

 72%|███████▏  | 123371520/170498071 [23:25<08:29, 92415.19it/s]

 72%|███████▏  | 123404288/170498071 [23:25<08:27, 92756.67it/s]

 72%|███████▏  | 123437056/170498071 [23:26<08:26, 92944.31it/s]

 72%|███████▏  | 123469824/170498071 [23:26<08:23, 93366.41it/s]

 72%|███████▏  | 123502592/170498071 [23:27<08:22, 93591.97it/s]

 72%|███████▏  | 123535360/170498071 [23:27<08:21, 93578.37it/s]

 72%|███████▏  | 123568128/170498071 [23:27<09:11, 85068.36it/s]

 72%|███████▏  | 123600896/170498071 [23:28<09:13, 84662.76it/s]

 73%|███████▎  | 123633664/170498071 [23:28<08:33, 91325.32it/s]

 73%|███████▎  | 123666432/170498071 [23:28<09:06, 85621.13it/s]

 73%|███████▎  | 123699200/170498071 [23:29<08:15, 94460.61it/s]

 73%|███████▎  | 123731968/170498071 [23:29<08:11, 95156.34it/s]

 73%|███████▎  | 123764736/170498071 [23:29<08:13, 94665.32it/s]

 73%|███████▎  | 123797504/170498071 [23:30<08:15, 94314.03it/s]

 73%|███████▎  | 123830272/170498071 [23:30<08:16, 94057.70it/s]

 73%|███████▎  | 123863040/170498071 [23:31<08:29, 91589.35it/s]

 73%|███████▎  | 123895808/170498071 [23:31<08:18, 93411.12it/s]

 73%|███████▎  | 123928576/170498071 [23:31<08:57, 86599.53it/s]

 73%|███████▎  | 123961344/170498071 [23:32<08:41, 89180.04it/s]

 73%|███████▎  | 123994112/170498071 [23:32<08:34, 90432.78it/s]

 73%|███████▎  | 124026880/170498071 [23:32<08:29, 91148.11it/s]

 73%|███████▎  | 124059648/170498071 [23:33<08:26, 91735.58it/s]

 73%|███████▎  | 124092416/170498071 [23:33<08:25, 91868.15it/s]

 73%|███████▎  | 124125184/170498071 [23:33<08:24, 91980.42it/s]

 73%|███████▎  | 124157952/170498071 [23:34<08:23, 91988.89it/s]

 73%|███████▎  | 124190720/170498071 [23:34<08:36, 89693.23it/s]

 73%|███████▎  | 124223488/170498071 [23:34<08:18, 92820.66it/s]

 73%|███████▎  | 124256256/170498071 [23:35<08:56, 86137.42it/s]

 73%|███████▎  | 124289024/170498071 [23:35<08:47, 87611.39it/s]

 73%|███████▎  | 124321792/170498071 [23:36<08:40, 88652.65it/s]

 73%|███████▎  | 124354560/170498071 [23:36<08:36, 89253.79it/s]

 73%|███████▎  | 124387328/170498071 [23:36<08:34, 89599.26it/s]

 73%|███████▎  | 124420096/170498071 [23:37<08:32, 89950.87it/s]

 73%|███████▎  | 124452864/170498071 [23:37<08:30, 90224.13it/s]

 73%|███████▎  | 124485632/170498071 [23:37<08:27, 90647.28it/s]

 73%|███████▎  | 124518400/170498071 [23:38<08:27, 90680.17it/s]

 73%|███████▎  | 124551168/170498071 [23:38<08:27, 90541.10it/s]

 73%|███████▎  | 124583936/170498071 [23:39<08:27, 90449.08it/s]

 73%|███████▎  | 124616704/170498071 [23:39<09:05, 84078.84it/s]

 73%|███████▎  | 124649472/170498071 [23:39<09:22, 81453.55it/s]

 73%|███████▎  | 124682240/170498071 [23:40<08:44, 87289.88it/s]

 73%|███████▎  | 124715008/170498071 [23:40<08:36, 88632.02it/s]

 73%|███████▎  | 124747776/170498071 [23:40<08:36, 88648.18it/s]

 73%|███████▎  | 124780544/170498071 [23:41<08:34, 88823.12it/s]

 73%|███████▎  | 124813312/170498071 [23:41<08:33, 88978.28it/s]

 73%|███████▎  | 124846080/170498071 [23:42<08:32, 89088.29it/s]

 73%|███████▎  | 124878848/170498071 [23:42<08:30, 89303.55it/s]

 73%|███████▎  | 124911616/170498071 [23:42<08:28, 89655.82it/s]

 73%|███████▎  | 124944384/170498071 [23:43<09:04, 83679.47it/s]

 73%|███████▎  | 124977152/170498071 [23:43<08:53, 85399.63it/s]

 73%|███████▎  | 125009920/170498071 [23:43<08:43, 86936.71it/s]

 73%|███████▎  | 125042688/170498071 [23:44<08:37, 87822.54it/s]

 73%|███████▎  | 125075456/170498071 [23:44<08:47, 86168.46it/s]

 73%|███████▎  | 125108224/170498071 [23:45<08:34, 88299.32it/s]

 73%|███████▎  | 125140992/170498071 [23:45<08:26, 89521.44it/s]

 73%|███████▎  | 125173760/170498071 [23:45<08:25, 89694.99it/s]

 73%|███████▎  | 125206528/170498071 [23:46<08:23, 89944.56it/s]

 73%|███████▎  | 125239296/170498071 [23:46<08:23, 89854.31it/s]

 73%|███████▎  | 125272064/170498071 [23:46<09:00, 83706.20it/s]

 73%|███████▎  | 125304832/170498071 [23:47<08:49, 85341.24it/s]

 74%|███████▎  | 125337600/170498071 [23:47<08:40, 86822.62it/s]

 74%|███████▎  | 125370368/170498071 [23:48<08:33, 87863.45it/s]

 74%|███████▎  | 125403136/170498071 [23:48<08:28, 88690.16it/s]

 74%|███████▎  | 125435904/170498071 [23:48<08:26, 88990.54it/s]

 74%|███████▎  | 125468672/170498071 [23:49<08:23, 89410.01it/s]

 74%|███████▎  | 125501440/170498071 [23:49<08:21, 89680.32it/s]

 74%|███████▎  | 125534208/170498071 [23:49<08:20, 89852.57it/s]

 74%|███████▎  | 125566976/170498071 [23:50<08:18, 90081.77it/s]

 74%|███████▎  | 125599744/170498071 [23:50<08:18, 89998.43it/s]

 74%|███████▎  | 125632512/170498071 [23:51<08:55, 83774.07it/s]

 74%|███████▎  | 125665280/170498071 [23:51<08:44, 85472.31it/s]

 74%|███████▎  | 125698048/170498071 [23:51<08:36, 86764.55it/s]

 74%|███████▎  | 125730816/170498071 [23:52<08:30, 87740.62it/s]

 74%|███████▍  | 125763584/170498071 [23:52<08:25, 88548.99it/s]

 74%|███████▍  | 125796352/170498071 [23:52<08:23, 88715.34it/s]

 74%|███████▍  | 125829120/170498071 [23:53<08:21, 89009.84it/s]

 74%|███████▍  | 125861888/170498071 [23:53<08:19, 89347.76it/s]

 74%|███████▍  | 125894656/170498071 [23:53<08:16, 89897.51it/s]

 74%|███████▍  | 125927424/170498071 [23:54<08:18, 89338.02it/s]

 74%|███████▍  | 125960192/170498071 [23:54<08:49, 84161.62it/s]

 74%|███████▍  | 125992960/170498071 [23:55<08:38, 85896.21it/s]

 74%|███████▍  | 126025728/170498071 [23:55<08:30, 87120.27it/s]

 74%|███████▍  | 126058496/170498071 [23:55<08:25, 87887.15it/s]

 74%|███████▍  | 126091264/170498071 [23:56<08:21, 88629.31it/s]

 74%|███████▍  | 126124032/170498071 [23:56<08:19, 88924.27it/s]

 74%|███████▍  | 126156800/170498071 [23:56<08:14, 89718.52it/s]

 74%|███████▍  | 126189568/170498071 [23:57<08:11, 90183.32it/s]

 74%|███████▍  | 126222336/170498071 [23:57<08:09, 90404.32it/s]

 74%|███████▍  | 126255104/170498071 [23:58<08:08, 90506.94it/s]

 74%|███████▍  | 126287872/170498071 [23:58<08:06, 90816.86it/s]

 74%|███████▍  | 126320640/170498071 [23:58<08:45, 84066.55it/s]

 74%|███████▍  | 126353408/170498071 [23:59<08:35, 85695.38it/s]

 74%|███████▍  | 126386176/170498071 [23:59<08:27, 86839.13it/s]

 74%|███████▍  | 126418944/170498071 [23:59<08:21, 87808.47it/s]

 74%|███████▍  | 126451712/170498071 [24:00<08:17, 88567.13it/s]

 74%|███████▍  | 126484480/170498071 [24:00<08:14, 89052.94it/s]

 74%|███████▍  | 126517248/170498071 [24:01<08:12, 89255.03it/s]

 74%|███████▍  | 126550016/170498071 [24:01<08:11, 89372.11it/s]

 74%|███████▍  | 126582784/170498071 [24:01<08:10, 89513.67it/s]

 74%|███████▍  | 126615552/170498071 [24:02<08:10, 89420.90it/s]

 74%|███████▍  | 126648320/170498071 [24:02<08:46, 83338.91it/s]

 74%|███████▍  | 126681088/170498071 [24:02<08:34, 85161.29it/s]

 74%|███████▍  | 126713856/170498071 [24:03<08:26, 86522.11it/s]

 74%|███████▍  | 126746624/170498071 [24:03<08:19, 87576.22it/s]

 74%|███████▍  | 126779392/170498071 [24:04<08:15, 88300.84it/s]

 74%|███████▍  | 126812160/170498071 [24:04<08:10, 89020.31it/s]

 74%|███████▍  | 126844928/170498071 [24:04<08:07, 89502.92it/s]

 74%|███████▍  | 126877696/170498071 [24:05<08:07, 89449.91it/s]

 74%|███████▍  | 126910464/170498071 [24:05<08:06, 89564.40it/s]

 74%|███████▍  | 126943232/170498071 [24:05<08:04, 89825.34it/s]

 74%|███████▍  | 126976000/170498071 [24:06<08:05, 89672.34it/s]

 74%|███████▍  | 127008768/170498071 [24:06<08:40, 83602.30it/s]

 75%|███████▍  | 127041536/170498071 [24:07<08:29, 85369.15it/s]

 75%|███████▍  | 127074304/170498071 [24:07<08:20, 86842.70it/s]

 75%|███████▍  | 127107072/170498071 [24:07<08:14, 87789.92it/s]

 75%|███████▍  | 127139840/170498071 [24:08<08:09, 88541.68it/s]

 75%|███████▍  | 127172608/170498071 [24:08<08:05, 89317.52it/s]

 75%|███████▍  | 127205376/170498071 [24:08<08:02, 89749.12it/s]

 75%|███████▍  | 127238144/170498071 [24:09<07:59, 90285.88it/s]

 75%|███████▍  | 127270912/170498071 [24:09<07:57, 90468.61it/s]

 75%|███████▍  | 127303680/170498071 [24:09<07:56, 90665.83it/s]

 75%|███████▍  | 127336448/170498071 [24:10<08:31, 84463.02it/s]

 75%|███████▍  | 127369216/170498071 [24:10<08:20, 86110.77it/s]

 75%|███████▍  | 127401984/170498071 [24:11<08:11, 87762.15it/s]

 75%|███████▍  | 127434752/170498071 [24:11<08:06, 88576.35it/s]

 75%|███████▍  | 127467520/170498071 [24:11<08:01, 89358.37it/s]

 75%|███████▍  | 127500288/170498071 [24:12<07:57, 90013.83it/s]

 75%|███████▍  | 127533056/170498071 [24:12<07:55, 90452.66it/s]

 75%|███████▍  | 127565824/170498071 [24:12<07:51, 91002.72it/s]

 75%|███████▍  | 127598592/170498071 [24:13<07:49, 91399.66it/s]

 75%|███████▍  | 127631360/170498071 [24:13<07:46, 91913.95it/s]

 75%|███████▍  | 127664128/170498071 [24:14<08:19, 85829.15it/s]

 75%|███████▍  | 127696896/170498071 [24:14<08:08, 87564.76it/s]

 75%|███████▍  | 127729664/170498071 [24:14<08:00, 89070.81it/s]

 75%|███████▍  | 127762432/170498071 [24:15<07:54, 90070.05it/s]

 75%|███████▍  | 127795200/170498071 [24:15<07:50, 90771.37it/s]

 75%|███████▍  | 127827968/170498071 [24:15<07:47, 91324.33it/s]

 75%|███████▍  | 127860736/170498071 [24:16<07:45, 91521.63it/s]

 75%|███████▌  | 127893504/170498071 [24:16<07:43, 91951.64it/s]

 75%|███████▌  | 127926272/170498071 [24:16<07:41, 92322.51it/s]

 75%|███████▌  | 127959040/170498071 [24:17<07:39, 92495.63it/s]

 75%|███████▌  | 127991808/170498071 [24:17<07:39, 92432.68it/s]

 75%|███████▌  | 128024576/170498071 [24:18<08:14, 85894.33it/s]

 75%|███████▌  | 128057344/170498071 [24:18<08:03, 87748.47it/s]

 75%|███████▌  | 128090112/170498071 [24:18<07:55, 89184.27it/s]

 75%|███████▌  | 128122880/170498071 [24:19<07:49, 90175.04it/s]

 75%|███████▌  | 128155648/170498071 [24:19<07:43, 91263.70it/s]

 75%|███████▌  | 128188416/170498071 [24:19<07:40, 91900.71it/s]

 75%|███████▌  | 128221184/170498071 [24:20<07:36, 92520.95it/s]

 75%|███████▌  | 128253952/170498071 [24:20<07:34, 92846.35it/s]

 75%|███████▌  | 128286720/170498071 [24:20<07:32, 93237.42it/s]

 75%|███████▌  | 128319488/170498071 [24:21<07:31, 93408.51it/s]

 75%|███████▌  | 128352256/170498071 [24:21<08:03, 87091.97it/s]

 75%|███████▌  | 128385024/170498071 [24:21<07:53, 88859.13it/s]

 75%|███████▌  | 128417792/170498071 [24:22<07:46, 90160.38it/s]

 75%|███████▌  | 128450560/170498071 [24:22<07:41, 91205.54it/s]

 75%|███████▌  | 128483328/170498071 [24:23<07:36, 91936.31it/s]

 75%|███████▌  | 128516096/170498071 [24:23<07:33, 92547.64it/s]

 75%|███████▌  | 128548864/170498071 [24:23<07:32, 92739.87it/s]

 75%|███████▌  | 128581632/170498071 [24:24<07:30, 92988.11it/s]

 75%|███████▌  | 128614400/170498071 [24:24<07:28, 93389.02it/s]

 75%|███████▌  | 128647168/170498071 [24:24<07:28, 93332.46it/s]

 75%|███████▌  | 128679936/170498071 [24:25<07:27, 93433.27it/s]

 75%|███████▌  | 128712704/170498071 [24:25<07:59, 87079.38it/s]

 76%|███████▌  | 128745472/170498071 [24:25<07:49, 88866.40it/s]

 76%|███████▌  | 128778240/170498071 [24:26<07:42, 90198.89it/s]

 76%|███████▌  | 128811008/170498071 [24:26<07:36, 91289.95it/s]

 76%|███████▌  | 128843776/170498071 [24:26<07:32, 92042.44it/s]

 76%|███████▌  | 128876544/170498071 [24:27<07:29, 92552.04it/s]

 76%|███████▌  | 128909312/170498071 [24:27<07:26, 93246.73it/s]

 76%|███████▌  | 128942080/170498071 [24:27<07:22, 93901.11it/s]

 76%|███████▌  | 128974848/170498071 [24:28<07:20, 94286.82it/s]

 76%|███████▌  | 129007616/170498071 [24:28<07:18, 94703.43it/s]

 76%|███████▌  | 129040384/170498071 [24:29<07:48, 88554.27it/s]

 76%|███████▌  | 129073152/170498071 [24:29<07:37, 90634.46it/s]

 76%|███████▌  | 129105920/170498071 [24:29<07:29, 92171.26it/s]

 76%|███████▌  | 129138688/170498071 [24:30<07:23, 93320.43it/s]

 76%|███████▌  | 129171456/170498071 [24:30<07:18, 94291.71it/s]

 76%|███████▌  | 129204224/170498071 [24:30<07:15, 94829.27it/s]

 76%|███████▌  | 129236992/170498071 [24:31<07:13, 95222.74it/s]

 76%|███████▌  | 129269760/170498071 [24:31<07:12, 95219.68it/s]

 76%|███████▌  | 129302528/170498071 [24:31<07:12, 95320.09it/s]

 76%|███████▌  | 129335296/170498071 [24:32<07:11, 95391.63it/s]

 76%|███████▌  | 129368064/170498071 [24:32<07:44, 88599.42it/s]

 76%|███████▌  | 129400832/170498071 [24:32<07:33, 90662.00it/s]

 76%|███████▌  | 129433600/170498071 [24:33<07:25, 92126.64it/s]

 76%|███████▌  | 129466368/170498071 [24:33<07:20, 93150.98it/s]

 76%|███████▌  | 129499136/170498071 [24:33<07:18, 93603.32it/s]

 76%|███████▌  | 129531904/170498071 [24:34<07:17, 93680.97it/s]

 76%|███████▌  | 129564672/170498071 [24:34<07:14, 94303.54it/s]

 76%|███████▌  | 129597440/170498071 [24:35<07:12, 94522.13it/s]

 76%|███████▌  | 129630208/170498071 [24:35<07:12, 94525.81it/s]

 76%|███████▌  | 129662976/170498071 [24:35<07:11, 94681.61it/s]

 76%|███████▌  | 129695744/170498071 [24:36<07:10, 94698.83it/s]

 76%|███████▌  | 129728512/170498071 [24:36<07:42, 88125.84it/s]

 76%|███████▌  | 129761280/170498071 [24:36<07:32, 90027.92it/s]

 76%|███████▌  | 129794048/170498071 [24:37<07:25, 91405.26it/s]

 76%|███████▌  | 129826816/170498071 [24:37<07:20, 92242.68it/s]

 76%|███████▌  | 129859584/170498071 [24:37<07:16, 93180.22it/s]

 76%|███████▌  | 129892352/170498071 [24:38<07:14, 93446.04it/s]

 76%|███████▌  | 129925120/170498071 [24:38<07:12, 93875.80it/s]

 76%|███████▌  | 129957888/170498071 [24:38<07:10, 94205.97it/s]

 76%|███████▌  | 129990656/170498071 [24:39<07:08, 94423.70it/s]

 76%|███████▋  | 130023424/170498071 [24:39<07:09, 94321.93it/s]

 76%|███████▋  | 130056192/170498071 [24:40<07:38, 88153.02it/s]

 76%|███████▋  | 130088960/170498071 [24:40<07:28, 90197.09it/s]

 76%|███████▋  | 130121728/170498071 [24:40<07:20, 91702.15it/s]

 76%|███████▋  | 130154496/170498071 [24:41<07:15, 92544.48it/s]

 76%|███████▋  | 130187264/170498071 [24:41<07:12, 93160.32it/s]

 76%|███████▋  | 130220032/170498071 [24:41<07:10, 93587.05it/s]

 76%|███████▋  | 130252800/170498071 [24:42<07:07, 94159.98it/s]

 76%|███████▋  | 130285568/170498071 [24:42<07:05, 94432.63it/s]

 76%|███████▋  | 130318336/170498071 [24:42<07:04, 94763.50it/s]

 76%|███████▋  | 130351104/170498071 [24:43<07:02, 95073.52it/s]

 76%|███████▋  | 130383872/170498071 [24:43<06:58, 95754.46it/s]

 76%|███████▋  | 130416640/170498071 [24:43<07:30, 89021.22it/s]

 77%|███████▋  | 130449408/170498071 [24:44<07:21, 90772.45it/s]

 77%|███████▋  | 130482176/170498071 [24:44<07:14, 92082.72it/s]

 77%|███████▋  | 130514944/170498071 [24:44<07:09, 93192.23it/s]

 77%|███████▋  | 130547712/170498071 [24:45<07:04, 94205.57it/s]

 77%|███████▋  | 130580480/170498071 [24:45<07:00, 94977.25it/s]

 77%|███████▋  | 130613248/170498071 [24:45<06:57, 95590.63it/s]

 77%|███████▋  | 130646016/170498071 [24:46<06:57, 95359.20it/s]

 77%|███████▋  | 130678784/170498071 [24:46<06:57, 95388.10it/s]

 77%|███████▋  | 130711552/170498071 [24:46<06:56, 95502.73it/s]

 77%|███████▋  | 130744320/170498071 [24:47<07:26, 89085.12it/s]

 77%|███████▋  | 130777088/170498071 [24:47<07:16, 91091.56it/s]

 77%|███████▋  | 130809856/170498071 [24:48<07:08, 92719.36it/s]

 77%|███████▋  | 130842624/170498071 [24:48<07:03, 93695.45it/s]

 77%|███████▋  | 130875392/170498071 [24:48<06:59, 94528.23it/s]

 77%|███████▋  | 130908160/170498071 [24:49<06:55, 95279.08it/s]

 77%|███████▋  | 130940928/170498071 [24:49<06:53, 95741.00it/s]

 77%|███████▋  | 130973696/170498071 [24:49<06:51, 95993.26it/s]

 77%|███████▋  | 131006464/170498071 [24:50<06:50, 96209.63it/s]

 77%|███████▋  | 131039232/170498071 [24:50<06:48, 96487.73it/s]

 77%|███████▋  | 131072000/170498071 [24:50<06:47, 96760.31it/s]

 77%|███████▋  | 131104768/170498071 [24:51<07:15, 90380.88it/s]

 77%|███████▋  | 131137536/170498071 [24:51<07:04, 92680.77it/s]

 77%|███████▋  | 131170304/170498071 [24:51<06:58, 94027.44it/s]

 77%|███████▋  | 131203072/170498071 [24:52<06:51, 95564.45it/s]

 77%|███████▋  | 131235840/170498071 [24:52<06:48, 96161.95it/s]

 77%|███████▋  | 131268608/170498071 [24:52<06:43, 97134.15it/s]

 77%|███████▋  | 131301376/170498071 [24:53<06:42, 97307.78it/s]

 77%|███████▋  | 131334144/170498071 [24:53<06:41, 97577.71it/s]

 77%|███████▋  | 131366912/170498071 [24:53<06:40, 97721.82it/s]

 77%|███████▋  | 131399680/170498071 [24:54<06:38, 98016.56it/s]

 77%|███████▋  | 131432448/170498071 [24:54<07:09, 90851.82it/s]

 77%|███████▋  | 131465216/170498071 [24:54<06:59, 92960.57it/s]

 77%|███████▋  | 131497984/170498071 [24:55<06:54, 94172.07it/s]

 77%|███████▋  | 131530752/170498071 [24:55<06:49, 95165.07it/s]

 77%|███████▋  | 131563520/170498071 [24:55<06:46, 95697.05it/s]

 77%|███████▋  | 131596288/170498071 [24:56<06:45, 96030.36it/s]

 77%|███████▋  | 131629056/170498071 [24:56<06:43, 96278.90it/s]

 77%|███████▋  | 131661824/170498071 [24:56<06:41, 96681.70it/s]

 77%|███████▋  | 131694592/170498071 [24:57<06:41, 96681.85it/s]

 77%|███████▋  | 131727360/170498071 [24:57<06:41, 96655.70it/s]

 77%|███████▋  | 131760128/170498071 [24:58<07:11, 89870.78it/s]

 77%|███████▋  | 131792896/170498071 [24:58<07:02, 91566.94it/s]

 77%|███████▋  | 131825664/170498071 [24:58<06:55, 93069.96it/s]

 77%|███████▋  | 131858432/170498071 [24:59<06:50, 94194.23it/s]

 77%|███████▋  | 131891200/170498071 [24:59<06:49, 94358.27it/s]

 77%|███████▋  | 131923968/170498071 [24:59<06:46, 95003.94it/s]

 77%|███████▋  | 131956736/170498071 [25:00<06:44, 95245.69it/s]

 77%|███████▋  | 131989504/170498071 [25:00<06:43, 95517.47it/s]

 77%|███████▋  | 132022272/170498071 [25:00<06:43, 95380.91it/s]

 77%|███████▋  | 132055040/170498071 [25:01<06:43, 95302.98it/s]

 77%|███████▋  | 132087808/170498071 [25:01<06:41, 95601.68it/s]

 77%|███████▋  | 132120576/170498071 [25:01<07:10, 89131.11it/s]

 78%|███████▊  | 132153344/170498071 [25:02<06:59, 91304.69it/s]

 78%|███████▊  | 132186112/170498071 [25:02<06:54, 92517.36it/s]

 78%|███████▊  | 132218880/170498071 [25:02<06:50, 93246.84it/s]

 78%|███████▊  | 132251648/170498071 [25:03<06:46, 94061.27it/s]

 78%|███████▊  | 132284416/170498071 [25:03<06:45, 94215.79it/s]

 78%|███████▊  | 132317184/170498071 [25:03<06:43, 94582.17it/s]

 78%|███████▊  | 132349952/170498071 [25:04<06:42, 94721.44it/s]

 78%|███████▊  | 132382720/170498071 [25:04<06:41, 94905.78it/s]

 78%|███████▊  | 132415488/170498071 [25:05<06:39, 95210.00it/s]

 78%|███████▊  | 132448256/170498071 [25:05<07:09, 88530.53it/s]

 78%|███████▊  | 132481024/170498071 [25:05<06:59, 90664.45it/s]

 78%|███████▊  | 132513792/170498071 [25:06<06:52, 92018.39it/s]

 78%|███████▊  | 132546560/170498071 [25:06<06:48, 92930.79it/s]

 78%|███████▊  | 132579328/170498071 [25:06<06:45, 93588.08it/s]

 78%|███████▊  | 132612096/170498071 [25:07<06:44, 93637.94it/s]

 78%|███████▊  | 132644864/170498071 [25:07<06:41, 94170.33it/s]

 78%|███████▊  | 132677632/170498071 [25:07<06:42, 94016.58it/s]

 78%|███████▊  | 132710400/170498071 [25:08<06:41, 94147.45it/s]

 78%|███████▊  | 132743168/170498071 [25:08<06:41, 93950.80it/s]

 78%|███████▊  | 132775936/170498071 [25:08<06:42, 93816.17it/s]

 78%|███████▊  | 132808704/170498071 [25:09<07:11, 87331.37it/s]

 78%|███████▊  | 132841472/170498071 [25:09<07:02, 89148.19it/s]

 78%|███████▊  | 132874240/170498071 [25:10<06:55, 90629.56it/s]

 78%|███████▊  | 132907008/170498071 [25:10<06:50, 91569.89it/s]

 78%|███████▊  | 132939776/170498071 [25:10<06:46, 92319.46it/s]

 78%|███████▊  | 132972544/170498071 [25:11<06:44, 92705.27it/s]

 78%|███████▊  | 133005312/170498071 [25:11<06:42, 93154.62it/s]

 78%|███████▊  | 133038080/170498071 [25:11<06:40, 93480.44it/s]

 78%|███████▊  | 133070848/170498071 [25:12<06:37, 94272.35it/s]

 78%|███████▊  | 133103616/170498071 [25:12<06:37, 94050.40it/s]

 78%|███████▊  | 133136384/170498071 [25:12<07:04, 87960.58it/s]

 78%|███████▊  | 133169152/170498071 [25:13<06:54, 90116.22it/s]

 78%|███████▊  | 133201920/170498071 [25:13<06:47, 91509.75it/s]

 78%|███████▊  | 133234688/170498071 [25:13<06:40, 92948.46it/s]

 78%|███████▊  | 133267456/170498071 [25:14<06:37, 93559.69it/s]

 78%|███████▊  | 133300224/170498071 [25:14<06:35, 94160.16it/s]

 78%|███████▊  | 133332992/170498071 [25:14<06:32, 94781.37it/s]

 78%|███████▊  | 133365760/170498071 [25:15<06:30, 95112.19it/s]

 78%|███████▊  | 133398528/170498071 [25:15<06:29, 95357.78it/s]

 78%|███████▊  | 133431296/170498071 [25:15<06:27, 95660.39it/s]

 78%|███████▊  | 133464064/170498071 [25:16<06:54, 89340.38it/s]

 78%|███████▊  | 133496832/170498071 [25:16<06:45, 91264.30it/s]

 78%|███████▊  | 133529600/170498071 [25:17<06:38, 92795.43it/s]

 78%|███████▊  | 133562368/170498071 [25:17<06:34, 93655.70it/s]

 78%|███████▊  | 133595136/170498071 [25:17<06:30, 94573.25it/s]

 78%|███████▊  | 133627904/170498071 [25:18<06:27, 95211.52it/s]

 78%|███████▊  | 133660672/170498071 [25:18<06:23, 96043.30it/s]

 78%|███████▊  | 133693440/170498071 [25:18<06:22, 96288.10it/s]

 78%|███████▊  | 133726208/170498071 [25:19<06:20, 96648.63it/s]

 78%|███████▊  | 133758976/170498071 [25:19<06:19, 96859.72it/s]

 78%|███████▊  | 133791744/170498071 [25:19<06:18, 96899.60it/s]

 78%|███████▊  | 133824512/170498071 [25:20<06:46, 90310.43it/s]

 79%|███████▊  | 133857280/170498071 [25:20<06:36, 92339.98it/s]

 79%|███████▊  | 133890048/170498071 [25:20<06:29, 93997.92it/s]

 79%|███████▊  | 133922816/170498071 [25:21<06:25, 94959.04it/s]

 79%|███████▊  | 133955584/170498071 [25:21<06:21, 95704.06it/s]

 79%|███████▊  | 133988352/170498071 [25:21<06:18, 96342.72it/s]

 79%|███████▊  | 134021120/170498071 [25:22<06:17, 96614.18it/s]

 79%|███████▊  | 134053888/170498071 [25:22<06:16, 96835.42it/s]

 79%|███████▊  | 134086656/170498071 [25:22<06:15, 96873.80it/s]

 79%|███████▊  | 134119424/170498071 [25:23<06:14, 97064.05it/s]

 79%|███████▊  | 134152192/170498071 [25:23<06:42, 90218.65it/s]

 79%|███████▊  | 134184960/170498071 [25:23<06:33, 92292.12it/s]

 79%|███████▊  | 134217728/170498071 [25:24<06:27, 93552.62it/s]

 79%|███████▊  | 134250496/170498071 [25:24<06:23, 94514.58it/s]

 79%|███████▉  | 134283264/170498071 [25:25<06:19, 95382.26it/s]

 79%|███████▉  | 134316032/170498071 [25:25<06:17, 95846.45it/s]

 79%|███████▉  | 134348800/170498071 [25:25<06:15, 96219.59it/s]

 79%|███████▉  | 134381568/170498071 [25:26<06:13, 96618.21it/s]

 79%|███████▉  | 134414336/170498071 [25:26<06:13, 96529.43it/s]

 79%|███████▉  | 134447104/170498071 [25:26<06:12, 96693.37it/s]

 79%|███████▉  | 134479872/170498071 [25:27<06:12, 96630.14it/s]

 79%|███████▉  | 134512640/170498071 [25:27<06:40, 89777.40it/s]

 79%|███████▉  | 134545408/170498071 [25:27<06:32, 91626.76it/s]

 79%|███████▉  | 134578176/170498071 [25:28<06:27, 92590.05it/s]

 79%|███████▉  | 134610944/170498071 [25:28<06:24, 93428.99it/s]

 79%|███████▉  | 134643712/170498071 [25:28<06:21, 93912.05it/s]

 79%|███████▉  | 134676480/170498071 [25:29<06:21, 93796.66it/s]

 79%|███████▉  | 134709248/170498071 [25:29<06:22, 93562.39it/s]

 79%|███████▉  | 134742016/170498071 [25:29<06:21, 93780.98it/s]

 79%|███████▉  | 134774784/170498071 [25:30<06:19, 94044.03it/s]

 79%|███████▉  | 134807552/170498071 [25:30<06:19, 94114.24it/s]

 79%|███████▉  | 134840320/170498071 [25:31<06:46, 87642.59it/s]

 79%|███████▉  | 134873088/170498071 [25:31<06:37, 89601.73it/s]

 79%|███████▉  | 134905856/170498071 [25:31<06:30, 91037.22it/s]

 79%|███████▉  | 134938624/170498071 [25:32<06:26, 91998.44it/s]

 79%|███████▉  | 134971392/170498071 [25:32<06:23, 92535.08it/s]

 79%|███████▉  | 135004160/170498071 [25:32<06:21, 93056.56it/s]

 79%|███████▉  | 135036928/170498071 [25:33<06:19, 93406.82it/s]

 79%|███████▉  | 135069696/170498071 [25:33<06:17, 93783.60it/s]

 79%|███████▉  | 135102464/170498071 [25:33<06:17, 93845.92it/s]

 79%|███████▉  | 135135232/170498071 [25:34<06:15, 94160.02it/s]

 79%|███████▉  | 135168000/170498071 [25:34<06:21, 92570.19it/s]

 79%|███████▉  | 135200768/170498071 [25:34<06:39, 88330.57it/s]

 79%|███████▉  | 135233536/170498071 [25:35<06:54, 85136.66it/s]

 79%|███████▉  | 135266304/170498071 [25:35<06:22, 92218.86it/s]

 79%|███████▉  | 135299072/170498071 [25:35<06:15, 93862.95it/s]

 79%|███████▉  | 135331840/170498071 [25:36<06:13, 94166.57it/s]

 79%|███████▉  | 135364608/170498071 [25:36<06:11, 94481.81it/s]

 79%|███████▉  | 135397376/170498071 [25:36<06:10, 94630.96it/s]

 79%|███████▉  | 135430144/170498071 [25:37<06:10, 94546.84it/s]

 79%|███████▉  | 135462912/170498071 [25:37<06:10, 94661.95it/s]

 79%|███████▉  | 135495680/170498071 [25:38<06:09, 94761.52it/s]

 79%|███████▉  | 135528448/170498071 [25:38<06:37, 87975.24it/s]

 80%|███████▉  | 135561216/170498071 [25:38<06:29, 89692.36it/s]

 80%|███████▉  | 135593984/170498071 [25:39<06:23, 90993.71it/s]

 80%|███████▉  | 135626752/170498071 [25:39<06:17, 92351.29it/s]

 80%|███████▉  | 135659520/170498071 [25:39<06:13, 93168.54it/s]

 80%|███████▉  | 135692288/170498071 [25:40<06:10, 93820.58it/s]

 80%|███████▉  | 135725056/170498071 [25:40<06:10, 93851.28it/s]

 80%|███████▉  | 135757824/170498071 [25:40<06:09, 93912.45it/s]

 80%|███████▉  | 135790592/170498071 [25:41<06:08, 94086.47it/s]

 80%|███████▉  | 135823360/170498071 [25:41<06:08, 94044.90it/s]

 80%|███████▉  | 135856128/170498071 [25:42<06:36, 87417.52it/s]

 80%|███████▉  | 135888896/170498071 [25:42<06:27, 89303.93it/s]

 80%|███████▉  | 135921664/170498071 [25:42<06:19, 91043.45it/s]

 80%|███████▉  | 135954432/170498071 [25:43<06:16, 91753.34it/s]

 80%|███████▉  | 135987200/170498071 [25:43<06:11, 92940.67it/s]

 80%|███████▉  | 136019968/170498071 [25:43<06:08, 93671.23it/s]

 80%|███████▉  | 136052736/170498071 [25:44<06:09, 93216.08it/s]

 80%|███████▉  | 136085504/170498071 [25:44<06:25, 89201.88it/s]

 80%|███████▉  | 136118272/170498071 [25:44<05:58, 95925.09it/s]

 80%|███████▉  | 136151040/170498071 [25:45<05:53, 97035.30it/s]

 80%|███████▉  | 136183808/170498071 [25:45<05:55, 96573.06it/s]

 80%|███████▉  | 136216576/170498071 [25:45<06:23, 89420.70it/s]

 80%|███████▉  | 136249344/170498071 [25:46<06:15, 91176.16it/s]

 80%|███████▉  | 136282112/170498071 [25:46<06:10, 92274.59it/s]

 80%|███████▉  | 136314880/170498071 [25:46<06:07, 93078.63it/s]

 80%|███████▉  | 136347648/170498071 [25:47<06:05, 93509.40it/s]

 80%|███████▉  | 136380416/170498071 [25:47<06:04, 93548.74it/s]

 80%|████████  | 136413184/170498071 [25:47<06:04, 93571.58it/s]

 80%|████████  | 136445952/170498071 [25:48<06:03, 93763.86it/s]

 80%|████████  | 136478720/170498071 [25:48<06:06, 92786.03it/s]

 80%|████████  | 136511488/170498071 [25:49<06:01, 93924.51it/s]

 80%|████████  | 136544256/170498071 [25:49<06:28, 87454.22it/s]

 80%|████████  | 136577024/170498071 [25:49<06:19, 89386.93it/s]

 80%|████████  | 136609792/170498071 [25:50<06:13, 90683.48it/s]

 80%|████████  | 136642560/170498071 [25:50<06:09, 91627.06it/s]

 80%|████████  | 136675328/170498071 [25:50<06:05, 92483.62it/s]

 80%|████████  | 136708096/170498071 [25:51<06:07, 91828.53it/s]

 80%|████████  | 136740864/170498071 [25:51<06:00, 93696.62it/s]

 80%|████████  | 136773632/170498071 [25:51<06:30, 86410.46it/s]

 80%|████████  | 136806400/170498071 [25:52<05:53, 95316.85it/s]

 80%|████████  | 136839168/170498071 [25:52<05:50, 96006.13it/s]

 80%|████████  | 136871936/170498071 [25:52<05:53, 95243.00it/s]

 80%|████████  | 136904704/170498071 [25:53<06:21, 88133.20it/s]

 80%|████████  | 136937472/170498071 [25:53<06:12, 89995.92it/s]

 80%|████████  | 136970240/170498071 [25:54<06:07, 91252.48it/s]

 80%|████████  | 137003008/170498071 [25:54<06:03, 92268.82it/s]

 80%|████████  | 137035776/170498071 [25:54<06:00, 92766.37it/s]

 80%|████████  | 137068544/170498071 [25:55<05:59, 92937.19it/s]

 80%|████████  | 137101312/170498071 [25:55<05:58, 93162.94it/s]

 80%|████████  | 137134080/170498071 [25:55<05:57, 93425.03it/s]

 80%|████████  | 137166848/170498071 [25:56<05:56, 93510.98it/s]

 80%|████████  | 137199616/170498071 [25:56<05:56, 93524.89it/s]

 80%|████████  | 137232384/170498071 [25:56<06:22, 87080.07it/s]

 81%|████████  | 137265152/170498071 [25:57<06:12, 89175.10it/s]

 81%|████████  | 137297920/170498071 [25:57<06:07, 90347.57it/s]

 81%|████████  | 137330688/170498071 [25:57<06:02, 91528.85it/s]

 81%|████████  | 137363456/170498071 [25:58<05:57, 92655.52it/s]

 81%|████████  | 137396224/170498071 [25:58<05:54, 93284.72it/s]

 81%|████████  | 137428992/170498071 [25:59<05:52, 93904.74it/s]

 81%|████████  | 137461760/170498071 [25:59<05:50, 94208.26it/s]

 81%|████████  | 137494528/170498071 [25:59<05:49, 94465.54it/s]

 81%|████████  | 137527296/170498071 [26:00<05:48, 94637.65it/s]

 81%|████████  | 137560064/170498071 [26:00<06:14, 88039.82it/s]

 81%|████████  | 137592832/170498071 [26:00<06:04, 90353.92it/s]

 81%|████████  | 137625600/170498071 [26:01<05:57, 91951.22it/s]

 81%|████████  | 137658368/170498071 [26:01<05:52, 93236.05it/s]

 81%|████████  | 137691136/170498071 [26:01<05:48, 94170.30it/s]

 81%|████████  | 137723904/170498071 [26:02<05:46, 94718.32it/s]

 81%|████████  | 137756672/170498071 [26:02<05:43, 95402.59it/s]

 81%|████████  | 137789440/170498071 [26:02<05:42, 95441.50it/s]

 81%|████████  | 137822208/170498071 [26:03<05:41, 95717.41it/s]

 81%|████████  | 137854976/170498071 [26:03<05:39, 96012.82it/s]

 81%|████████  | 137887744/170498071 [26:03<05:39, 95979.50it/s]

 81%|████████  | 137920512/170498071 [26:04<06:05, 89192.56it/s]

 81%|████████  | 137953280/170498071 [26:04<05:56, 91168.53it/s]

 81%|████████  | 137986048/170498071 [26:05<05:50, 92661.18it/s]

 81%|████████  | 138018816/170498071 [26:05<05:46, 93604.25it/s]

 81%|████████  | 138051584/170498071 [26:05<05:44, 94274.25it/s]

 81%|████████  | 138084352/170498071 [26:06<05:41, 94897.63it/s]

 81%|████████  | 138117120/170498071 [26:06<05:38, 95563.35it/s]

 81%|████████  | 138149888/170498071 [26:06<05:37, 95886.76it/s]

 81%|████████  | 138182656/170498071 [26:07<05:37, 95817.02it/s]

 81%|████████  | 138215424/170498071 [26:07<05:37, 95593.27it/s]

 81%|████████  | 138248192/170498071 [26:07<06:01, 89105.51it/s]

 81%|████████  | 138280960/170498071 [26:08<05:54, 90826.28it/s]

 81%|████████  | 138313728/170498071 [26:08<05:49, 92162.00it/s]

 81%|████████  | 138346496/170498071 [26:08<05:45, 93075.58it/s]

 81%|████████  | 138379264/170498071 [26:09<05:41, 93960.07it/s]

 81%|████████  | 138412032/170498071 [26:09<05:39, 94537.84it/s]

 81%|████████  | 138444800/170498071 [26:09<05:38, 94618.16it/s]

 81%|████████  | 138477568/170498071 [26:10<05:37, 95003.36it/s]

 81%|████████  | 138510336/170498071 [26:10<05:36, 95016.14it/s]

 81%|████████▏ | 138543104/170498071 [26:10<05:37, 94741.15it/s]

 81%|████████▏ | 138575872/170498071 [26:11<05:38, 94255.91it/s]

 81%|████████▏ | 138608640/170498071 [26:11<06:05, 87185.59it/s]

 81%|████████▏ | 138641408/170498071 [26:12<05:57, 89098.80it/s]

 81%|████████▏ | 138674176/170498071 [26:12<05:52, 90264.62it/s]

 81%|████████▏ | 138706944/170498071 [26:12<05:48, 91325.61it/s]

 81%|████████▏ | 138739712/170498071 [26:13<05:46, 91650.73it/s]

 81%|████████▏ | 138772480/170498071 [26:13<05:45, 91850.30it/s]

 81%|████████▏ | 138805248/170498071 [26:13<05:44, 92117.46it/s]

 81%|████████▏ | 138838016/170498071 [26:14<05:43, 92050.54it/s]

 81%|████████▏ | 138870784/170498071 [26:14<05:43, 91976.43it/s]

 81%|████████▏ | 138903552/170498071 [26:14<05:43, 91926.44it/s]

 81%|████████▏ | 138936320/170498071 [26:15<06:11, 85066.27it/s]

 82%|████████▏ | 138969088/170498071 [26:15<06:04, 86565.31it/s]

 82%|████████▏ | 139001856/170498071 [26:16<05:58, 87896.30it/s]

 82%|████████▏ | 139034624/170498071 [26:16<05:56, 88275.39it/s]

 82%|████████▏ | 139067392/170498071 [26:16<05:54, 88694.08it/s]

 82%|████████▏ | 139100160/170498071 [26:17<05:52, 88993.74it/s]

 82%|████████▏ | 139132928/170498071 [26:17<05:50, 89482.71it/s]

 82%|████████▏ | 139165696/170498071 [26:17<05:50, 89430.28it/s]

 82%|████████▏ | 139198464/170498071 [26:18<05:50, 89322.66it/s]

 82%|████████▏ | 139231232/170498071 [26:18<05:49, 89471.82it/s]

 82%|████████▏ | 139264000/170498071 [26:18<05:50, 89184.75it/s]

 82%|████████▏ | 139296768/170498071 [26:19<06:17, 82627.40it/s]

 82%|████████▏ | 139329536/170498071 [26:19<06:11, 83976.13it/s]

 82%|████████▏ | 139362304/170498071 [26:20<06:05, 85273.81it/s]

 82%|████████▏ | 139395072/170498071 [26:20<06:00, 86184.64it/s]

 82%|████████▏ | 139427840/170498071 [26:20<05:58, 86717.58it/s]

 82%|████████▏ | 139460608/170498071 [26:21<05:55, 87236.72it/s]

 82%|████████▏ | 139493376/170498071 [26:21<05:54, 87502.17it/s]

 82%|████████▏ | 139526144/170498071 [26:22<05:52, 87767.00it/s]

 82%|████████▏ | 139558912/170498071 [26:22<05:52, 87686.05it/s]

 82%|████████▏ | 139591680/170498071 [26:22<05:52, 87576.55it/s]

 82%|████████▏ | 139624448/170498071 [26:23<06:18, 81531.23it/s]

 82%|████████▏ | 139657216/170498071 [26:23<06:09, 83400.02it/s]

 82%|████████▏ | 139689984/170498071 [26:24<06:03, 84709.93it/s]

 82%|████████▏ | 139722752/170498071 [26:24<05:58, 85786.13it/s]

 82%|████████▏ | 139755520/170498071 [26:24<05:56, 86227.38it/s]

 82%|████████▏ | 139788288/170498071 [26:25<05:53, 86783.33it/s]

 82%|████████▏ | 139821056/170498071 [26:25<05:51, 87275.03it/s]

 82%|████████▏ | 139853824/170498071 [26:25<05:49, 87698.47it/s]

 82%|████████▏ | 139886592/170498071 [26:26<05:48, 87833.86it/s]

 82%|████████▏ | 139919360/170498071 [26:26<05:47, 87996.27it/s]

 82%|████████▏ | 139952128/170498071 [26:27<06:13, 81736.59it/s]

 82%|████████▏ | 139984896/170498071 [26:27<06:03, 83989.63it/s]

 82%|████████▏ | 140017664/170498071 [26:27<05:56, 85485.29it/s]

 82%|████████▏ | 140050432/170498071 [26:28<05:52, 86488.43it/s]

 82%|████████▏ | 140083200/170498071 [26:28<05:48, 87172.93it/s]

 82%|████████▏ | 140115968/170498071 [26:28<05:46, 87782.72it/s]

 82%|████████▏ | 140148736/170498071 [26:29<05:43, 88366.28it/s]

 82%|████████▏ | 140181504/170498071 [26:29<05:42, 88623.56it/s]

 82%|████████▏ | 140214272/170498071 [26:30<05:40, 88947.46it/s]

 82%|████████▏ | 140247040/170498071 [26:30<05:40, 88940.88it/s]

 82%|████████▏ | 140279808/170498071 [26:30<05:40, 88786.00it/s]

 82%|████████▏ | 140312576/170498071 [26:31<06:05, 82523.94it/s]

 82%|████████▏ | 140345344/170498071 [26:31<05:55, 84730.30it/s]

 82%|████████▏ | 140378112/170498071 [26:31<05:49, 86116.41it/s]

 82%|████████▏ | 140410880/170498071 [26:32<05:45, 87074.68it/s]

 82%|████████▏ | 140443648/170498071 [26:32<05:41, 87992.94it/s]

 82%|████████▏ | 140476416/170498071 [26:33<05:39, 88552.16it/s]

 82%|████████▏ | 140509184/170498071 [26:33<05:36, 89094.92it/s]

 82%|████████▏ | 140541952/170498071 [26:33<05:34, 89479.47it/s]

 82%|████████▏ | 140574720/170498071 [26:34<05:33, 89641.84it/s]

 82%|████████▏ | 140607488/170498071 [26:34<05:32, 89776.70it/s]

 82%|████████▏ | 140640256/170498071 [26:34<05:58, 83401.54it/s]

 83%|████████▎ | 140673024/170498071 [26:35<05:50, 85112.92it/s]

 83%|████████▎ | 140705792/170498071 [26:35<05:44, 86420.18it/s]

 83%|████████▎ | 140738560/170498071 [26:36<05:41, 87059.20it/s]

 83%|████████▎ | 140771328/170498071 [26:36<05:38, 87788.61it/s]

 83%|████████▎ | 140804096/170498071 [26:36<05:36, 88154.59it/s]

 83%|████████▎ | 140836864/170498071 [26:37<05:35, 88368.00it/s]

 83%|████████▎ | 140869632/170498071 [26:37<05:33, 88788.04it/s]

 83%|████████▎ | 140902400/170498071 [26:37<05:31, 89399.45it/s]

 83%|████████▎ | 140935168/170498071 [26:38<05:29, 89608.60it/s]

 83%|████████▎ | 140967936/170498071 [26:38<05:27, 90266.95it/s]

 83%|████████▎ | 141000704/170498071 [26:39<05:50, 84183.05it/s]

 83%|████████▎ | 141033472/170498071 [26:39<05:42, 85928.33it/s]

 83%|████████▎ | 141066240/170498071 [26:39<05:35, 87844.31it/s]

 83%|████████▎ | 141099008/170498071 [26:40<05:30, 88872.48it/s]

 83%|████████▎ | 141131776/170498071 [26:40<05:26, 89839.36it/s]

 83%|████████▎ | 141164544/170498071 [26:40<05:25, 90231.07it/s]

 83%|████████▎ | 141197312/170498071 [26:41<05:23, 90495.91it/s]

 83%|████████▎ | 141230080/170498071 [26:41<05:22, 90705.22it/s]

 83%|████████▎ | 141262848/170498071 [26:41<05:20, 91296.85it/s]

 83%|████████▎ | 141295616/170498071 [26:42<05:18, 91569.69it/s]

 83%|████████▎ | 141328384/170498071 [26:42<05:42, 85133.07it/s]

 83%|████████▎ | 141361152/170498071 [26:43<05:34, 86997.61it/s]

 83%|████████▎ | 141393920/170498071 [26:43<05:29, 88286.95it/s]

 83%|████████▎ | 141426688/170498071 [26:43<05:26, 89141.81it/s]

 83%|████████▎ | 141459456/170498071 [26:44<05:24, 89365.38it/s]

 83%|████████▎ | 141492224/170498071 [26:44<05:22, 89871.41it/s]

 83%|████████▎ | 141524992/170498071 [26:44<05:20, 90406.41it/s]

 83%|████████▎ | 141557760/170498071 [26:45<05:19, 90443.14it/s]

 83%|████████▎ | 141590528/170498071 [26:45<05:18, 90714.98it/s]

 83%|████████▎ | 141623296/170498071 [26:45<05:17, 90981.49it/s]

 83%|████████▎ | 141656064/170498071 [26:46<05:40, 84788.48it/s]

 83%|████████▎ | 141688832/170498071 [26:46<05:31, 86935.63it/s]

 83%|████████▎ | 141721600/170498071 [26:47<05:24, 88729.91it/s]

 83%|████████▎ | 141754368/170498071 [26:47<05:20, 89638.75it/s]

 83%|████████▎ | 141787136/170498071 [26:47<05:17, 90467.76it/s]

 83%|████████▎ | 141819904/170498071 [26:48<05:13, 91458.57it/s]

 83%|████████▎ | 141852672/170498071 [26:48<05:11, 92047.21it/s]

 83%|████████▎ | 141885440/170498071 [26:48<05:09, 92364.28it/s]

 83%|████████▎ | 141918208/170498071 [26:49<05:07, 92793.97it/s]

 83%|████████▎ | 141950976/170498071 [26:49<05:07, 92748.33it/s]

 83%|████████▎ | 141983744/170498071 [26:49<05:06, 93052.95it/s]

 83%|████████▎ | 142016512/170498071 [26:50<05:28, 86741.44it/s]

 83%|████████▎ | 142049280/170498071 [26:50<05:20, 88761.91it/s]

 83%|████████▎ | 142082048/170498071 [26:51<05:16, 89655.03it/s]

 83%|████████▎ | 142114816/170498071 [26:51<05:13, 90539.67it/s]

 83%|████████▎ | 142147584/170498071 [26:51<05:10, 91255.43it/s]

 83%|████████▎ | 142180352/170498071 [26:52<05:09, 91628.85it/s]

 83%|████████▎ | 142213120/170498071 [26:52<05:08, 91795.02it/s]

 83%|████████▎ | 142245888/170498071 [26:52<05:07, 91850.87it/s]

 83%|████████▎ | 142278656/170498071 [26:53<05:07, 91843.12it/s]

 83%|████████▎ | 142311424/170498071 [26:53<05:08, 91498.41it/s]

 83%|████████▎ | 142344192/170498071 [26:54<05:29, 85444.34it/s]

 84%|████████▎ | 142376960/170498071 [26:54<05:22, 87243.65it/s]

 84%|████████▎ | 142409728/170498071 [26:54<05:15, 88938.83it/s]

 84%|████████▎ | 142442496/170498071 [26:55<05:12, 89693.44it/s]

 84%|████████▎ | 142475264/170498071 [26:55<05:11, 90077.54it/s]

 84%|████████▎ | 142508032/170498071 [26:55<05:09, 90474.02it/s]

 84%|████████▎ | 142540800/170498071 [26:56<05:07, 90972.29it/s]

 84%|████████▎ | 142573568/170498071 [26:56<05:06, 91194.20it/s]

 84%|████████▎ | 142606336/170498071 [26:56<05:04, 91459.07it/s]

 84%|████████▎ | 142639104/170498071 [26:57<05:05, 91157.09it/s]

 84%|████████▎ | 142671872/170498071 [26:57<05:05, 91228.39it/s]

 84%|████████▎ | 142704640/170498071 [26:58<05:28, 84654.93it/s]

 84%|████████▎ | 142737408/170498071 [26:58<05:19, 86770.01it/s]

 84%|████████▎ | 142770176/170498071 [26:58<05:15, 88004.19it/s]

 84%|████████▍ | 142802944/170498071 [26:59<05:11, 88814.56it/s]

 84%|████████▍ | 142835712/170498071 [26:59<05:08, 89535.06it/s]

 84%|████████▍ | 142868480/170498071 [26:59<05:07, 89910.61it/s]

 84%|████████▍ | 142901248/170498071 [27:00<05:05, 90219.84it/s]

 84%|████████▍ | 142934016/170498071 [27:00<05:03, 90673.29it/s]

 84%|████████▍ | 142966784/170498071 [27:00<05:03, 90784.14it/s]

 84%|████████▍ | 142999552/170498071 [27:01<05:03, 90643.77it/s]

 84%|████████▍ | 143032320/170498071 [27:01<05:25, 84420.66it/s]

 84%|████████▍ | 143065088/170498071 [27:02<05:17, 86366.24it/s]

 84%|████████▍ | 143097856/170498071 [27:02<05:12, 87722.93it/s]

 84%|████████▍ | 143130624/170498071 [27:02<05:08, 88664.89it/s]

 84%|████████▍ | 143163392/170498071 [27:03<05:06, 89299.31it/s]

 84%|████████▍ | 143196160/170498071 [27:03<05:04, 89592.54it/s]

 84%|████████▍ | 143228928/170498071 [27:03<05:03, 89854.69it/s]

 84%|████████▍ | 143261696/170498071 [27:04<05:02, 90125.53it/s]

 84%|████████▍ | 143294464/170498071 [27:04<05:03, 89542.35it/s]

 84%|████████▍ | 143327232/170498071 [27:04<04:59, 90779.80it/s]

 84%|████████▍ | 143360000/170498071 [27:05<04:59, 90662.09it/s]

 84%|████████▍ | 143392768/170498071 [27:05<05:21, 84383.80it/s]

 84%|████████▍ | 143425536/170498071 [27:06<05:13, 86287.36it/s]

 84%|████████▍ | 143458304/170498071 [27:06<05:08, 87528.54it/s]

 84%|████████▍ | 143491072/170498071 [27:06<05:05, 88493.59it/s]

 84%|████████▍ | 143523840/170498071 [27:07<05:02, 89103.93it/s]

 84%|████████▍ | 143556608/170498071 [27:07<04:59, 90033.14it/s]

 84%|████████▍ | 143589376/170498071 [27:07<04:57, 90537.38it/s]

 84%|████████▍ | 143622144/170498071 [27:08<04:55, 90810.24it/s]

 84%|████████▍ | 143654912/170498071 [27:08<04:55, 90728.78it/s]

 84%|████████▍ | 143687680/170498071 [27:09<04:54, 90980.98it/s]

 84%|████████▍ | 143720448/170498071 [27:09<05:16, 84490.62it/s]

 84%|████████▍ | 143753216/170498071 [27:09<05:10, 86240.19it/s]

 84%|████████▍ | 143785984/170498071 [27:10<05:04, 87648.56it/s]

 84%|████████▍ | 143818752/170498071 [27:10<04:59, 89000.58it/s]

 84%|████████▍ | 143851520/170498071 [27:10<04:55, 90129.05it/s]

 84%|████████▍ | 143884288/170498071 [27:11<04:52, 90990.60it/s]

 84%|████████▍ | 143917056/170498071 [27:11<04:49, 91872.34it/s]

 84%|████████▍ | 143949824/170498071 [27:11<04:46, 92743.45it/s]

 84%|████████▍ | 143982592/170498071 [27:12<04:44, 93149.03it/s]

 84%|████████▍ | 144015360/170498071 [27:12<04:41, 94121.91it/s]

 84%|████████▍ | 144048128/170498071 [27:13<05:00, 88000.14it/s]

 85%|████████▍ | 144080896/170498071 [27:13<04:52, 90388.07it/s]

 85%|████████▍ | 144113664/170498071 [27:13<04:46, 92107.44it/s]

 85%|████████▍ | 144146432/170498071 [27:14<04:41, 93667.17it/s]

 85%|████████▍ | 144179200/170498071 [27:14<04:38, 94451.32it/s]

 85%|████████▍ | 144211968/170498071 [27:14<04:36, 95072.21it/s]

 85%|████████▍ | 144244736/170498071 [27:15<04:34, 95540.92it/s]

 85%|████████▍ | 144277504/170498071 [27:15<04:33, 95948.55it/s]

 85%|████████▍ | 144310272/170498071 [27:15<04:32, 95982.70it/s]

 85%|████████▍ | 144343040/170498071 [27:16<04:32, 96000.47it/s]

 85%|████████▍ | 144375808/170498071 [27:16<04:31, 96160.64it/s]

 85%|████████▍ | 144408576/170498071 [27:16<04:51, 89635.16it/s]

 85%|████████▍ | 144441344/170498071 [27:17<04:43, 91791.90it/s]

 85%|████████▍ | 144474112/170498071 [27:17<04:39, 93231.70it/s]

 85%|████████▍ | 144506880/170498071 [27:17<04:35, 94181.42it/s]

 85%|████████▍ | 144539648/170498071 [27:18<04:33, 94948.39it/s]

 85%|████████▍ | 144572416/170498071 [27:18<04:31, 95477.17it/s]

 85%|████████▍ | 144605184/170498071 [27:18<04:30, 95786.84it/s]

 85%|████████▍ | 144637952/170498071 [27:19<04:28, 96240.75it/s]

 85%|████████▍ | 144670720/170498071 [27:19<04:27, 96665.90it/s]

 85%|████████▍ | 144703488/170498071 [27:19<04:26, 96702.29it/s]

 85%|████████▍ | 144736256/170498071 [27:20<04:45, 90240.74it/s]

 85%|████████▍ | 144769024/170498071 [27:20<04:39, 91939.39it/s]

 85%|████████▍ | 144801792/170498071 [27:21<04:36, 92986.72it/s]

 85%|████████▍ | 144834560/170498071 [27:21<04:33, 93747.93it/s]

 85%|████████▍ | 144867328/170498071 [27:21<04:31, 94327.98it/s]

 85%|████████▍ | 144900096/170498071 [27:22<04:30, 94491.44it/s]

 85%|████████▌ | 144932864/170498071 [27:22<04:30, 94664.95it/s]

 85%|████████▌ | 144965632/170498071 [27:22<04:28, 94928.62it/s]

 85%|████████▌ | 144998400/170498071 [27:23<04:28, 95052.07it/s]

 85%|████████▌ | 145031168/170498071 [27:23<04:27, 95163.12it/s]

 85%|████████▌ | 145063936/170498071 [27:23<04:28, 94552.24it/s]

 85%|████████▌ | 145096704/170498071 [27:24<04:49, 87893.73it/s]

 85%|████████▌ | 145129472/170498071 [27:24<04:43, 89431.31it/s]

 85%|████████▌ | 145162240/170498071 [27:24<04:41, 90143.69it/s]

 85%|████████▌ | 145195008/170498071 [27:25<04:38, 90951.41it/s]

 85%|████████▌ | 145227776/170498071 [27:25<04:36, 91258.25it/s]

 85%|████████▌ | 145260544/170498071 [27:25<04:34, 92023.93it/s]

 85%|████████▌ | 145293312/170498071 [27:26<04:32, 92418.98it/s]

 85%|████████▌ | 145326080/170498071 [27:26<04:31, 92657.08it/s]

 85%|████████▌ | 145358848/170498071 [27:27<04:29, 93330.12it/s]

 85%|████████▌ | 145391616/170498071 [27:27<04:28, 93390.69it/s]

 85%|████████▌ | 145424384/170498071 [27:27<04:47, 87107.02it/s]

 85%|████████▌ | 145457152/170498071 [27:28<04:41, 88858.00it/s]

 85%|████████▌ | 145489920/170498071 [27:28<04:36, 90325.61it/s]

 85%|████████▌ | 145522688/170498071 [27:28<04:32, 91532.13it/s]

 85%|████████▌ | 145555456/170498071 [27:29<04:30, 92314.65it/s]

 85%|████████▌ | 145588224/170498071 [27:29<04:27, 93168.59it/s]

 85%|████████▌ | 145620992/170498071 [27:29<04:26, 93421.86it/s]

 85%|████████▌ | 145653760/170498071 [27:30<04:25, 93457.99it/s]

 85%|████████▌ | 145686528/170498071 [27:30<04:24, 93663.17it/s]

 85%|████████▌ | 145719296/170498071 [27:30<04:24, 93621.11it/s]

 85%|████████▌ | 145752064/170498071 [27:31<04:44, 87088.41it/s]

 86%|████████▌ | 145784832/170498071 [27:31<04:38, 88809.57it/s]

 86%|████████▌ | 145817600/170498071 [27:32<04:34, 89965.20it/s]

 86%|████████▌ | 145850368/170498071 [27:32<04:31, 90637.93it/s]

 86%|████████▌ | 145883136/170498071 [27:32<04:30, 91018.89it/s]

 86%|████████▌ | 145915904/170498071 [27:33<04:28, 91440.78it/s]

 86%|████████▌ | 145948672/170498071 [27:33<04:27, 91743.17it/s]

 86%|████████▌ | 145981440/170498071 [27:33<04:27, 91620.09it/s]

 86%|████████▌ | 146014208/170498071 [27:34<04:27, 91377.69it/s]

 86%|████████▌ | 146046976/170498071 [27:34<04:27, 91349.07it/s]

 86%|████████▌ | 146079744/170498071 [27:34<04:27, 91307.36it/s]

 86%|████████▌ | 146112512/170498071 [27:35<04:48, 84654.38it/s]

 86%|████████▌ | 146145280/170498071 [27:35<04:41, 86364.43it/s]

 86%|████████▌ | 146178048/170498071 [27:36<04:38, 87468.49it/s]

 86%|████████▌ | 146210816/170498071 [27:36<04:34, 88337.35it/s]

 86%|████████▌ | 146243584/170498071 [27:36<04:31, 89210.87it/s]

 86%|████████▌ | 146276352/170498071 [27:37<04:30, 89500.98it/s]

 86%|████████▌ | 146309120/170498071 [27:37<04:28, 90042.97it/s]

 86%|████████▌ | 146341888/170498071 [27:37<04:29, 89788.19it/s]

 86%|████████▌ | 146374656/170498071 [27:38<04:28, 90000.76it/s]

 86%|████████▌ | 146407424/170498071 [27:38<04:27, 90148.38it/s]

 86%|████████▌ | 146440192/170498071 [27:39<04:47, 83573.02it/s]

 86%|████████▌ | 146472960/170498071 [27:39<04:41, 85319.02it/s]

 86%|████████▌ | 146505728/170498071 [27:39<04:37, 86440.67it/s]

 86%|████████▌ | 146538496/170498071 [27:40<04:34, 87417.89it/s]

 86%|████████▌ | 146571264/170498071 [27:40<04:31, 88188.85it/s]

 86%|████████▌ | 146604032/170498071 [27:40<04:30, 88388.99it/s]

 86%|████████▌ | 146636800/170498071 [27:41<04:29, 88502.78it/s]

 86%|████████▌ | 146669568/170498071 [27:41<04:28, 88815.27it/s]

 86%|████████▌ | 146702336/170498071 [27:42<04:27, 89076.51it/s]

 86%|████████▌ | 146735104/170498071 [27:42<04:26, 89278.92it/s]

 86%|████████▌ | 146767872/170498071 [27:42<04:24, 89627.19it/s]

 86%|████████▌ | 146800640/170498071 [27:43<04:43, 83612.75it/s]

 86%|████████▌ | 146833408/170498071 [27:43<04:36, 85590.48it/s]

 86%|████████▌ | 146866176/170498071 [27:43<04:32, 86871.33it/s]

 86%|████████▌ | 146898944/170498071 [27:44<04:28, 87956.01it/s]

 86%|████████▌ | 146931712/170498071 [27:44<04:25, 88838.67it/s]

 86%|████████▌ | 146964480/170498071 [27:45<04:23, 89422.65it/s]

 86%|████████▌ | 146997248/170498071 [27:45<04:21, 89752.23it/s]

 86%|████████▌ | 147030016/170498071 [27:45<04:20, 90066.19it/s]

 86%|████████▋ | 147062784/170498071 [27:46<04:19, 90326.61it/s]

 86%|████████▋ | 147095552/170498071 [27:46<04:19, 90100.76it/s]

 86%|████████▋ | 147128320/170498071 [27:46<04:38, 84025.66it/s]

 86%|████████▋ | 147161088/170498071 [27:47<04:31, 85851.45it/s]

 86%|████████▋ | 147193856/170498071 [27:47<04:27, 87107.84it/s]

 86%|████████▋ | 147226624/170498071 [27:48<04:24, 88102.88it/s]

 86%|████████▋ | 147259392/170498071 [27:48<04:20, 89071.52it/s]

 86%|████████▋ | 147292160/170498071 [27:48<04:18, 89764.20it/s]

 86%|████████▋ | 147324928/170498071 [27:49<04:16, 90386.99it/s]

 86%|████████▋ | 147357696/170498071 [27:49<04:14, 90910.09it/s]

 86%|████████▋ | 147390464/170498071 [27:49<04:12, 91337.05it/s]

 86%|████████▋ | 147423232/170498071 [27:50<04:12, 91560.77it/s]

 86%|████████▋ | 147456000/170498071 [27:50<04:11, 91682.94it/s]

 87%|████████▋ | 147488768/170498071 [27:50<04:29, 85434.73it/s]

 87%|████████▋ | 147521536/170498071 [27:51<04:23, 87349.29it/s]

 87%|████████▋ | 147554304/170498071 [27:51<04:19, 88569.42it/s]

 87%|████████▋ | 147587072/170498071 [27:52<04:15, 89643.52it/s]

 87%|████████▋ | 147619840/170498071 [27:52<04:13, 90129.91it/s]

 87%|████████▋ | 147652608/170498071 [27:52<04:13, 90196.60it/s]

 87%|████████▋ | 147685376/170498071 [27:53<04:11, 90765.47it/s]

 87%|████████▋ | 147718144/170498071 [27:53<04:12, 90314.73it/s]

 87%|████████▋ | 147750912/170498071 [27:53<04:11, 90444.42it/s]

 87%|████████▋ | 147783680/170498071 [27:54<04:10, 90504.11it/s]

 87%|████████▋ | 147816448/170498071 [27:54<04:28, 84625.44it/s]

 87%|████████▋ | 147849216/170498071 [27:55<04:22, 86226.60it/s]

 87%|████████▋ | 147881984/170498071 [27:55<04:18, 87470.43it/s]

 87%|████████▋ | 147914752/170498071 [27:55<04:15, 88317.99it/s]

 87%|████████▋ | 147947520/170498071 [27:56<04:13, 89125.44it/s]

 87%|████████▋ | 147980288/170498071 [27:56<04:12, 89189.54it/s]

 87%|████████▋ | 148013056/170498071 [27:56<04:12, 89138.19it/s]

 87%|████████▋ | 148045824/170498071 [27:57<04:11, 89160.81it/s]

 87%|████████▋ | 148078592/170498071 [27:57<04:12, 88945.36it/s]

 87%|████████▋ | 148111360/170498071 [27:57<04:11, 89140.40it/s]

 87%|████████▋ | 148144128/170498071 [27:58<04:31, 82391.80it/s]

 87%|████████▋ | 148176896/170498071 [27:58<04:25, 84148.58it/s]

 87%|████████▋ | 148209664/170498071 [27:59<04:22, 85044.94it/s]

 87%|████████▋ | 148242432/170498071 [27:59<04:19, 85663.57it/s]

 87%|████████▋ | 148275200/170498071 [27:59<04:17, 86432.90it/s]

 87%|████████▋ | 148307968/170498071 [28:00<04:16, 86591.14it/s]

 87%|████████▋ | 148340736/170498071 [28:00<04:14, 86993.97it/s]

 87%|████████▋ | 148373504/170498071 [28:01<04:13, 87142.26it/s]

 87%|████████▋ | 148406272/170498071 [28:01<04:13, 87300.60it/s]

 87%|████████▋ | 148439040/170498071 [28:01<04:12, 87313.15it/s]

 87%|████████▋ | 148471808/170498071 [28:02<04:11, 87578.43it/s]

 87%|████████▋ | 148504576/170498071 [28:02<04:29, 81554.31it/s]

 87%|████████▋ | 148537344/170498071 [28:02<04:23, 83438.51it/s]

 87%|████████▋ | 148570112/170498071 [28:03<04:18, 84885.84it/s]

 87%|████████▋ | 148602880/170498071 [28:03<04:14, 85886.10it/s]

 87%|████████▋ | 148635648/170498071 [28:04<04:13, 86393.33it/s]

 87%|████████▋ | 148668416/170498071 [28:04<04:11, 86857.45it/s]

 87%|████████▋ | 148701184/170498071 [28:04<04:10, 87114.09it/s]

 87%|████████▋ | 148733952/170498071 [28:05<04:10, 86907.88it/s]

 87%|████████▋ | 148766720/170498071 [28:05<04:09, 87001.11it/s]

 87%|████████▋ | 148799488/170498071 [28:05<04:08, 87179.71it/s]

 87%|████████▋ | 148832256/170498071 [28:06<04:28, 80826.89it/s]

 87%|████████▋ | 148865024/170498071 [28:06<04:21, 82584.38it/s]

 87%|████████▋ | 148897792/170498071 [28:07<04:18, 83680.51it/s]

 87%|████████▋ | 148930560/170498071 [28:07<04:14, 84654.00it/s]

 87%|████████▋ | 148963328/170498071 [28:07<04:11, 85585.19it/s]

 87%|████████▋ | 148996096/170498071 [28:08<04:09, 86041.88it/s]

 87%|████████▋ | 149028864/170498071 [28:08<04:08, 86504.56it/s]

 87%|████████▋ | 149061632/170498071 [28:09<04:07, 86678.63it/s]

 87%|████████▋ | 149094400/170498071 [28:09<04:06, 86676.50it/s]

 87%|████████▋ | 149127168/170498071 [28:09<04:06, 86810.02it/s]

 87%|████████▋ | 149159936/170498071 [28:10<04:05, 86803.28it/s]

 88%|████████▊ | 149192704/170498071 [28:10<04:23, 80705.02it/s]

 88%|████████▊ | 149225472/170498071 [28:11<04:17, 82712.76it/s]

 88%|████████▊ | 149258240/170498071 [28:11<04:12, 84094.01it/s]

 88%|████████▊ | 149291008/170498071 [28:11<04:08, 85343.94it/s]

 88%|████████▊ | 149323776/170498071 [28:12<04:05, 86200.18it/s]

 88%|████████▊ | 149356544/170498071 [28:12<04:03, 86889.17it/s]

 88%|████████▊ | 149389312/170498071 [28:12<04:00, 87623.70it/s]

 88%|████████▊ | 149422080/170498071 [28:13<03:59, 88127.49it/s]

 88%|████████▊ | 149454848/170498071 [28:13<03:57, 88546.73it/s]

 88%|████████▊ | 149487616/170498071 [28:14<03:55, 89029.71it/s]

 88%|████████▊ | 149520384/170498071 [28:14<04:12, 83101.95it/s]

 88%|████████▊ | 149553152/170498071 [28:14<04:06, 85040.10it/s]

 88%|████████▊ | 149585920/170498071 [28:15<04:01, 86523.42it/s]

 88%|████████▊ | 149618688/170498071 [28:15<03:58, 87386.16it/s]

 88%|████████▊ | 149651456/170498071 [28:15<03:55, 88407.69it/s]

 88%|████████▊ | 149684224/170498071 [28:16<03:53, 88999.83it/s]

 88%|████████▊ | 149716992/170498071 [28:16<03:52, 89457.49it/s]

 88%|████████▊ | 149749760/170498071 [28:16<03:50, 89869.66it/s]

 88%|████████▊ | 149782528/170498071 [28:17<03:49, 90088.03it/s]

 88%|████████▊ | 149815296/170498071 [28:17<03:49, 90100.93it/s]

 88%|████████▊ | 149848064/170498071 [28:18<04:05, 83977.38it/s]

 88%|████████▊ | 149880832/170498071 [28:18<04:00, 85663.79it/s]

 88%|████████▊ | 149913600/170498071 [28:18<03:56, 87074.03it/s]

 88%|████████▊ | 149946368/170498071 [28:19<03:52, 88212.31it/s]

 88%|████████▊ | 149979136/170498071 [28:19<03:50, 88975.58it/s]

 88%|████████▊ | 150011904/170498071 [28:19<03:48, 89739.80it/s]

 88%|████████▊ | 150044672/170498071 [28:20<03:46, 90193.96it/s]

 88%|████████▊ | 150077440/170498071 [28:20<03:45, 90556.13it/s]

 88%|████████▊ | 150110208/170498071 [28:21<03:43, 91156.47it/s]

 88%|████████▊ | 150142976/170498071 [28:21<03:43, 91240.09it/s]

 88%|████████▊ | 150175744/170498071 [28:21<03:42, 91521.62it/s]

 88%|████████▊ | 150208512/170498071 [28:22<03:58, 85126.23it/s]

 88%|████████▊ | 150241280/170498071 [28:22<03:52, 87032.69it/s]

 88%|████████▊ | 150274048/170498071 [28:22<03:49, 88222.61it/s]

 88%|████████▊ | 150306816/170498071 [28:23<03:46, 89183.67it/s]

 88%|████████▊ | 150339584/170498071 [28:23<03:44, 89814.31it/s]

 88%|████████▊ | 150372352/170498071 [28:24<03:42, 90398.61it/s]

 88%|████████▊ | 150405120/170498071 [28:24<03:41, 90644.49it/s]

 88%|████████▊ | 150437888/170498071 [28:24<03:40, 91169.53it/s]

 88%|████████▊ | 150470656/170498071 [28:25<03:38, 91529.41it/s]

 88%|████████▊ | 150503424/170498071 [28:25<03:37, 91893.08it/s]

 88%|████████▊ | 150536192/170498071 [28:25<03:53, 85584.17it/s]

 88%|████████▊ | 150568960/170498071 [28:26<03:46, 87839.77it/s]

 88%|████████▊ | 150601728/170498071 [28:26<03:42, 89299.92it/s]

 88%|████████▊ | 150634496/170498071 [28:26<03:40, 90245.58it/s]

 88%|████████▊ | 150667264/170498071 [28:27<03:37, 91068.33it/s]

 88%|████████▊ | 150700032/170498071 [28:27<03:36, 91437.16it/s]

 88%|████████▊ | 150732800/170498071 [28:27<03:35, 91625.49it/s]

 88%|████████▊ | 150765568/170498071 [28:28<03:35, 91692.09it/s]

 88%|████████▊ | 150798336/170498071 [28:28<03:34, 91684.33it/s]

 88%|████████▊ | 150831104/170498071 [28:29<03:34, 91508.67it/s]

 88%|████████▊ | 150863872/170498071 [28:29<03:34, 91567.89it/s]

 89%|████████▊ | 150896640/170498071 [28:29<03:49, 85229.13it/s]

 89%|████████▊ | 150929408/170498071 [28:30<03:45, 86967.27it/s]

 89%|████████▊ | 150962176/170498071 [28:30<03:42, 87991.07it/s]

 89%|████████▊ | 150994944/170498071 [28:30<03:38, 89083.51it/s]

 89%|████████▊ | 151027712/170498071 [28:31<03:37, 89480.23it/s]

 89%|████████▊ | 151060480/170498071 [28:31<03:36, 89900.31it/s]

 89%|████████▊ | 151093248/170498071 [28:32<03:35, 89935.53it/s]

 89%|████████▊ | 151126016/170498071 [28:32<03:34, 90320.08it/s]

 89%|████████▊ | 151158784/170498071 [28:32<03:33, 90549.43it/s]

 89%|████████▊ | 151191552/170498071 [28:33<03:34, 90136.05it/s]

 89%|████████▊ | 151224320/170498071 [28:33<03:49, 83928.83it/s]

 89%|████████▊ | 151257088/170498071 [28:33<03:45, 85427.64it/s]

 89%|████████▊ | 151289856/170498071 [28:34<03:42, 86304.77it/s]

 89%|████████▉ | 151322624/170498071 [28:34<03:39, 87253.24it/s]

 89%|████████▉ | 151355392/170498071 [28:35<03:38, 87529.54it/s]

 89%|████████▉ | 151388160/170498071 [28:35<03:37, 87830.45it/s]

 89%|████████▉ | 151420928/170498071 [28:35<03:36, 87959.96it/s]

 89%|████████▉ | 151453696/170498071 [28:36<03:36, 88001.21it/s]

 89%|████████▉ | 151486464/170498071 [28:36<03:35, 88339.53it/s]

 89%|████████▉ | 151519232/170498071 [28:36<03:33, 88769.14it/s]

 89%|████████▉ | 151552000/170498071 [28:37<03:33, 88816.73it/s]

 89%|████████▉ | 151584768/170498071 [28:37<03:48, 82732.72it/s]

 89%|████████▉ | 151617536/170498071 [28:38<03:43, 84487.52it/s]

 89%|████████▉ | 151650304/170498071 [28:38<03:39, 85744.62it/s]

 89%|████████▉ | 151683072/170498071 [28:38<03:37, 86601.62it/s]

 89%|████████▉ | 151715840/170498071 [28:39<03:34, 87361.79it/s]

 89%|████████▉ | 151748608/170498071 [28:39<03:33, 87787.58it/s]

 89%|████████▉ | 151781376/170498071 [28:39<03:32, 87977.40it/s]

 89%|████████▉ | 151814144/170498071 [28:40<03:31, 88285.63it/s]

 89%|████████▉ | 151846912/170498071 [28:40<03:30, 88629.42it/s]

 89%|████████▉ | 151879680/170498071 [28:41<03:29, 88729.13it/s]

 89%|████████▉ | 151912448/170498071 [28:41<03:43, 83048.26it/s]

 89%|████████▉ | 151945216/170498071 [28:41<03:37, 85175.58it/s]

 89%|████████▉ | 151977984/170498071 [28:42<03:33, 86684.77it/s]

 89%|████████▉ | 152010752/170498071 [28:42<03:30, 87642.14it/s]

 89%|████████▉ | 152043520/170498071 [28:42<03:28, 88379.89it/s]

 89%|████████▉ | 152076288/170498071 [28:43<03:27, 88795.91it/s]

 89%|████████▉ | 152109056/170498071 [28:43<03:25, 89515.90it/s]

 89%|████████▉ | 152141824/170498071 [28:44<03:24, 89962.36it/s]

 89%|████████▉ | 152174592/170498071 [28:44<03:22, 90282.50it/s]

 89%|████████▉ | 152207360/170498071 [28:44<03:21, 90713.47it/s]

 89%|████████▉ | 152240128/170498071 [28:45<03:35, 84679.29it/s]

 89%|████████▉ | 152272896/170498071 [28:45<03:30, 86657.07it/s]

 89%|████████▉ | 152305664/170498071 [28:45<03:26, 88118.34it/s]

 89%|████████▉ | 152338432/170498071 [28:46<03:23, 89215.71it/s]

 89%|████████▉ | 152371200/170498071 [28:46<03:21, 89815.76it/s]

 89%|████████▉ | 152403968/170498071 [28:46<03:20, 90260.73it/s]

 89%|████████▉ | 152436736/170498071 [28:47<03:19, 90626.03it/s]

 89%|████████▉ | 152469504/170498071 [28:47<03:18, 90768.66it/s]

 89%|████████▉ | 152502272/170498071 [28:48<03:18, 90838.75it/s]

 89%|████████▉ | 152535040/170498071 [28:48<03:17, 90883.57it/s]

 89%|████████▉ | 152567808/170498071 [28:48<03:16, 91258.48it/s]

 90%|████████▉ | 152600576/170498071 [28:49<03:29, 85291.47it/s]

 90%|████████▉ | 152633344/170498071 [28:49<03:24, 87257.48it/s]

 90%|████████▉ | 152666112/170498071 [28:49<03:21, 88546.75it/s]

 90%|████████▉ | 152698880/170498071 [28:50<03:18, 89670.65it/s]

 90%|████████▉ | 152731648/170498071 [28:50<03:16, 90421.89it/s]

 90%|████████▉ | 152764416/170498071 [28:50<03:14, 91025.02it/s]

 90%|████████▉ | 152797184/170498071 [28:51<03:13, 91488.43it/s]

 90%|████████▉ | 152829952/170498071 [28:51<03:12, 91937.09it/s]

 90%|████████▉ | 152862720/170498071 [28:52<03:11, 92112.01it/s]

 90%|████████▉ | 152895488/170498071 [28:52<03:10, 92357.44it/s]

 90%|████████▉ | 152928256/170498071 [28:52<03:24, 86051.18it/s]

 90%|████████▉ | 152961024/170498071 [28:53<03:19, 87930.70it/s]

 90%|████████▉ | 152993792/170498071 [28:53<03:15, 89501.67it/s]

 90%|████████▉ | 153026560/170498071 [28:53<03:13, 90481.12it/s]

 90%|████████▉ | 153059328/170498071 [28:54<03:10, 91309.39it/s]

 90%|████████▉ | 153092096/170498071 [28:54<03:09, 91956.23it/s]

 90%|████████▉ | 153124864/170498071 [28:54<03:07, 92554.29it/s]

 90%|████████▉ | 153157632/170498071 [28:55<03:06, 93074.26it/s]

 90%|████████▉ | 153190400/170498071 [28:55<03:05, 93409.99it/s]

 90%|████████▉ | 153223168/170498071 [28:56<03:04, 93483.31it/s]

 90%|████████▉ | 153255936/170498071 [28:56<03:04, 93596.52it/s]

 90%|████████▉ | 153288704/170498071 [28:56<03:17, 87103.32it/s]

 90%|████████▉ | 153321472/170498071 [28:57<03:13, 88817.06it/s]

 90%|████████▉ | 153354240/170498071 [28:57<03:10, 89912.08it/s]

 90%|████████▉ | 153387008/170498071 [28:57<03:08, 90806.81it/s]

 90%|████████▉ | 153419776/170498071 [28:58<03:06, 91472.31it/s]

 90%|█████████ | 153452544/170498071 [28:58<03:05, 91899.16it/s]

 90%|█████████ | 153485312/170498071 [28:58<03:04, 92047.35it/s]

 90%|█████████ | 153518080/170498071 [28:59<03:04, 92212.14it/s]

 90%|█████████ | 153550848/170498071 [28:59<03:03, 92307.67it/s]

 90%|█████████ | 153583616/170498071 [28:59<03:03, 92306.10it/s]

 90%|█████████ | 153616384/170498071 [29:00<03:17, 85668.07it/s]

 90%|█████████ | 153649152/170498071 [29:00<03:12, 87682.15it/s]

 90%|█████████ | 153681920/170498071 [29:01<03:09, 88877.02it/s]

 90%|█████████ | 153714688/170498071 [29:01<03:06, 89931.42it/s]

 90%|█████████ | 153747456/170498071 [29:01<03:05, 90535.41it/s]

 90%|█████████ | 153780224/170498071 [29:02<03:03, 90930.91it/s]

 90%|█████████ | 153812992/170498071 [29:02<03:02, 91247.25it/s]

 90%|█████████ | 153845760/170498071 [29:02<03:01, 91659.38it/s]

 90%|█████████ | 153878528/170498071 [29:03<03:00, 91991.70it/s]

 90%|█████████ | 153911296/170498071 [29:03<03:00, 92120.66it/s]

 90%|█████████ | 153944064/170498071 [29:04<03:12, 85986.09it/s]

 90%|█████████ | 153976832/170498071 [29:04<03:07, 87929.25it/s]

 90%|█████████ | 154009600/170498071 [29:04<03:04, 89396.49it/s]

 90%|█████████ | 154042368/170498071 [29:05<03:01, 90547.79it/s]

 90%|█████████ | 154075136/170498071 [29:05<02:59, 91311.94it/s]

 90%|█████████ | 154107904/170498071 [29:05<02:58, 91940.98it/s]

 90%|█████████ | 154140672/170498071 [29:06<02:56, 92593.18it/s]

 90%|█████████ | 154173440/170498071 [29:06<02:55, 93031.91it/s]

 90%|█████████ | 154206208/170498071 [29:06<02:54, 93307.36it/s]

 90%|█████████ | 154238976/170498071 [29:07<02:53, 93455.75it/s]

 90%|█████████ | 154271744/170498071 [29:07<02:53, 93751.23it/s]

 91%|█████████ | 154304512/170498071 [29:07<03:05, 87161.18it/s]

 91%|█████████ | 154337280/170498071 [29:08<03:01, 89097.44it/s]

 91%|█████████ | 154370048/170498071 [29:08<02:58, 90362.31it/s]

 91%|█████████ | 154402816/170498071 [29:09<02:56, 91358.50it/s]

 91%|█████████ | 154435584/170498071 [29:09<02:54, 92124.59it/s]

 91%|█████████ | 154468352/170498071 [29:09<02:52, 92742.86it/s]

 91%|█████████ | 154501120/170498071 [29:10<02:52, 92927.87it/s]

 91%|█████████ | 154533888/170498071 [29:10<02:51, 93038.68it/s]

 91%|█████████ | 154566656/170498071 [29:10<02:50, 93353.75it/s]

 91%|█████████ | 154599424/170498071 [29:11<02:50, 93348.06it/s]

 91%|█████████ | 154632192/170498071 [29:11<03:02, 86936.78it/s]

 91%|█████████ | 154664960/170498071 [29:11<02:58, 88749.39it/s]

 91%|█████████ | 154697728/170498071 [29:12<02:55, 90247.20it/s]

 91%|█████████ | 154730496/170498071 [29:12<02:52, 91254.39it/s]

 91%|█████████ | 154763264/170498071 [29:12<02:51, 92007.16it/s]

 91%|█████████ | 154796032/170498071 [29:13<02:49, 92704.17it/s]

 91%|█████████ | 154828800/170498071 [29:13<02:48, 93215.04it/s]

 91%|█████████ | 154861568/170498071 [29:14<02:47, 93576.28it/s]

 91%|█████████ | 154894336/170498071 [29:14<02:45, 94009.19it/s]

 91%|█████████ | 154927104/170498071 [29:14<02:44, 94499.93it/s]

 91%|█████████ | 154959872/170498071 [29:15<02:44, 94669.03it/s]

 91%|█████████ | 154992640/170498071 [29:15<02:55, 88416.91it/s]

 91%|█████████ | 155025408/170498071 [29:15<02:51, 90389.99it/s]

 91%|█████████ | 155058176/170498071 [29:16<02:47, 91983.44it/s]

 91%|█████████ | 155090944/170498071 [29:16<02:45, 93093.51it/s]

 91%|█████████ | 155123712/170498071 [29:16<02:43, 93984.77it/s]

 91%|█████████ | 155156480/170498071 [29:17<02:42, 94332.29it/s]

 91%|█████████ | 155189248/170498071 [29:17<02:41, 94599.38it/s]

 91%|█████████ | 155222016/170498071 [29:17<02:41, 94673.76it/s]

 91%|█████████ | 155254784/170498071 [29:18<02:40, 94831.60it/s]

 91%|█████████ | 155287552/170498071 [29:18<02:40, 94756.06it/s]

 91%|█████████ | 155320320/170498071 [29:18<02:52, 88149.33it/s]

 91%|█████████ | 155353088/170498071 [29:19<02:48, 90022.32it/s]

 91%|█████████ | 155385856/170498071 [29:19<02:45, 91413.15it/s]

 91%|█████████ | 155418624/170498071 [29:20<02:43, 92449.32it/s]

 91%|█████████ | 155451392/170498071 [29:20<02:42, 92821.22it/s]

 91%|█████████ | 155484160/170498071 [29:20<02:41, 93159.36it/s]

 91%|█████████ | 155516928/170498071 [29:21<02:40, 93359.31it/s]

 91%|█████████ | 155549696/170498071 [29:21<02:39, 93769.79it/s]

 91%|█████████▏| 155582464/170498071 [29:21<02:38, 93879.13it/s]

 91%|█████████▏| 155615232/170498071 [29:22<02:38, 94088.82it/s]

 91%|█████████▏| 155648000/170498071 [29:22<02:37, 94468.56it/s]

 91%|█████████▏| 155680768/170498071 [29:22<02:48, 88103.96it/s]

 91%|█████████▏| 155713536/170498071 [29:23<02:44, 90096.34it/s]

 91%|█████████▏| 155746304/170498071 [29:23<02:40, 91692.19it/s]

 91%|█████████▏| 155779072/170498071 [29:23<02:38, 92869.67it/s]

 91%|█████████▏| 155811840/170498071 [29:24<02:36, 93844.83it/s]

 91%|█████████▏| 155844608/170498071 [29:24<02:35, 94356.36it/s]

 91%|█████████▏| 155877376/170498071 [29:24<02:34, 94812.61it/s]

 91%|█████████▏| 155910144/170498071 [29:25<02:33, 95168.34it/s]

 91%|█████████▏| 155942912/170498071 [29:25<02:32, 95452.52it/s]

 91%|█████████▏| 155975680/170498071 [29:25<02:31, 95612.19it/s]

 92%|█████████▏| 156008448/170498071 [29:26<02:42, 89034.92it/s]

 92%|█████████▏| 156041216/170498071 [29:26<02:38, 91012.03it/s]

 92%|█████████▏| 156073984/170498071 [29:27<02:36, 92440.86it/s]

 92%|█████████▏| 156106752/170498071 [29:27<02:34, 93373.67it/s]

 92%|█████████▏| 156139520/170498071 [29:27<02:32, 94158.58it/s]

 92%|█████████▏| 156172288/170498071 [29:28<02:31, 94659.00it/s]

 92%|█████████▏| 156205056/170498071 [29:28<02:30, 94852.21it/s]

 92%|█████████▏| 156237824/170498071 [29:28<02:30, 94795.08it/s]

 92%|█████████▏| 156270592/170498071 [29:29<02:30, 94613.99it/s]

 92%|█████████▏| 156303360/170498071 [29:29<02:30, 94559.43it/s]

 92%|█████████▏| 156336128/170498071 [29:29<02:40, 87995.24it/s]

 92%|█████████▏| 156368896/170498071 [29:30<02:37, 89607.39it/s]

 92%|█████████▏| 156401664/170498071 [29:30<02:34, 91186.74it/s]

 92%|█████████▏| 156434432/170498071 [29:30<02:32, 92098.67it/s]

 92%|█████████▏| 156467200/170498071 [29:31<02:31, 92592.06it/s]

 92%|█████████▏| 156499968/170498071 [29:31<02:30, 93309.92it/s]

 92%|█████████▏| 156532736/170498071 [29:32<02:29, 93671.59it/s]

 92%|█████████▏| 156565504/170498071 [29:32<02:28, 94087.21it/s]

 92%|█████████▏| 156598272/170498071 [29:32<02:26, 94648.75it/s]

 92%|█████████▏| 156631040/170498071 [29:33<02:26, 94832.13it/s]

 92%|█████████▏| 156663808/170498071 [29:33<02:25, 95171.38it/s]

 92%|█████████▏| 156696576/170498071 [29:33<02:35, 88771.18it/s]

 92%|█████████▏| 156729344/170498071 [29:34<02:31, 90993.40it/s]

 92%|█████████▏| 156762112/170498071 [29:34<02:28, 92690.85it/s]

 92%|█████████▏| 156794880/170498071 [29:34<02:25, 94131.50it/s]

 92%|█████████▏| 156827648/170498071 [29:35<02:23, 95024.54it/s]

 92%|█████████▏| 156860416/170498071 [29:35<02:22, 95827.95it/s]

 92%|█████████▏| 156893184/170498071 [29:35<02:21, 96136.20it/s]

 92%|█████████▏| 156925952/170498071 [29:36<02:20, 96508.26it/s]

 92%|█████████▏| 156958720/170498071 [29:36<02:19, 96729.22it/s]

 92%|█████████▏| 156991488/170498071 [29:36<02:19, 96965.95it/s]

 92%|█████████▏| 157024256/170498071 [29:37<02:28, 90548.81it/s]

 92%|█████████▏| 157057024/170498071 [29:37<02:25, 92468.70it/s]

 92%|█████████▏| 157089792/170498071 [29:37<02:22, 93881.64it/s]

 92%|█████████▏| 157122560/170498071 [29:38<02:20, 94874.66it/s]

 92%|█████████▏| 157155328/170498071 [29:38<02:19, 95551.11it/s]

 92%|█████████▏| 157188096/170498071 [29:38<02:18, 96342.22it/s]

 92%|█████████▏| 157220864/170498071 [29:39<02:16, 97198.34it/s]

 92%|█████████▏| 157253632/170498071 [29:39<02:15, 97525.41it/s]

 92%|█████████▏| 157286400/170498071 [29:39<02:14, 98114.20it/s]

 92%|█████████▏| 157319168/170498071 [29:40<02:13, 98356.27it/s]

 92%|█████████▏| 157351936/170498071 [29:40<02:13, 98400.72it/s]

 92%|█████████▏| 157384704/170498071 [29:41<02:22, 91903.36it/s]

 92%|█████████▏| 157417472/170498071 [29:41<02:19, 93841.16it/s]

 92%|█████████▏| 157450240/170498071 [29:41<02:16, 95503.51it/s]

 92%|█████████▏| 157483008/170498071 [29:41<02:14, 96914.37it/s]

 92%|█████████▏| 157515776/170498071 [29:42<02:12, 97905.76it/s]

 92%|█████████▏| 157548544/170498071 [29:42<02:11, 98406.50it/s]

 92%|█████████▏| 157581312/170498071 [29:42<02:10, 98616.59it/s]

 92%|█████████▏| 157614080/170498071 [29:43<02:10, 98949.65it/s]

 92%|█████████▏| 157646848/170498071 [29:43<02:09, 99087.04it/s]

 92%|█████████▏| 157679616/170498071 [29:43<02:09, 99092.95it/s]

 93%|█████████▎| 157712384/170498071 [29:44<02:18, 92466.00it/s]

 93%|█████████▎| 157745152/170498071 [29:44<02:14, 94670.62it/s]

 93%|█████████▎| 157777920/170498071 [29:45<02:12, 96000.03it/s]

 93%|█████████▎| 157810688/170498071 [29:45<02:10, 97004.99it/s]

 93%|█████████▎| 157843456/170498071 [29:45<02:09, 97871.70it/s]

 93%|█████████▎| 157876224/170498071 [29:46<02:08, 98341.06it/s]

 93%|█████████▎| 157908992/170498071 [29:46<02:07, 98903.48it/s]

 93%|█████████▎| 157941760/170498071 [29:46<02:06, 99337.41it/s]

 93%|█████████▎| 157974528/170498071 [29:47<02:05, 99435.47it/s]

 93%|█████████▎| 158007296/170498071 [29:47<02:05, 99427.19it/s]

 93%|█████████▎| 158040064/170498071 [29:47<02:14, 92528.39it/s]

 93%|█████████▎| 158072832/170498071 [29:48<02:11, 94687.64it/s]

 93%|█████████▎| 158105600/170498071 [29:48<02:08, 96548.02it/s]

 93%|█████████▎| 158138368/170498071 [29:48<02:06, 97692.79it/s]

 93%|█████████▎| 158171136/170498071 [29:49<02:05, 98143.33it/s]

 93%|█████████▎| 158203904/170498071 [29:49<02:04, 98609.85it/s]

 93%|█████████▎| 158236672/170498071 [29:49<02:04, 98781.65it/s]

 93%|█████████▎| 158269440/170498071 [29:50<02:03, 98952.63it/s]

 93%|█████████▎| 158302208/170498071 [29:50<02:03, 98775.34it/s]

 93%|█████████▎| 158334976/170498071 [29:50<02:03, 98843.49it/s]

 93%|█████████▎| 158367744/170498071 [29:51<02:02, 98638.83it/s]

 93%|█████████▎| 158400512/170498071 [29:51<02:12, 91114.91it/s]

 93%|█████████▎| 158433280/170498071 [29:51<02:08, 94164.22it/s]

 93%|█████████▎| 158466048/170498071 [29:52<02:06, 95378.95it/s]

 93%|█████████▎| 158498816/170498071 [29:52<02:04, 96214.22it/s]

 93%|█████████▎| 158531584/170498071 [29:52<02:03, 96833.92it/s]

 93%|█████████▎| 158564352/170498071 [29:53<02:02, 97262.26it/s]

 93%|█████████▎| 158597120/170498071 [29:53<02:01, 97576.91it/s]

 93%|█████████▎| 158629888/170498071 [29:53<02:01, 97907.50it/s]

 93%|█████████▎| 158662656/170498071 [29:54<02:00, 97817.12it/s]

 93%|█████████▎| 158695424/170498071 [29:54<02:00, 97700.33it/s]

 93%|█████████▎| 158728192/170498071 [29:54<02:09, 90864.88it/s]

 93%|█████████▎| 158760960/170498071 [29:55<02:06, 92844.80it/s]

 93%|█████████▎| 158793728/170498071 [29:55<02:04, 94338.05it/s]

 93%|█████████▎| 158826496/170498071 [29:55<02:02, 95280.03it/s]

 93%|█████████▎| 158859264/170498071 [29:56<02:00, 96341.84it/s]

 93%|█████████▎| 158892032/170498071 [29:56<01:59, 96863.10it/s]

 93%|█████████▎| 158924800/170498071 [29:56<01:59, 97218.93it/s]

 93%|█████████▎| 158957568/170498071 [29:57<01:58, 97469.34it/s]

 93%|█████████▎| 158990336/170498071 [29:57<01:57, 97536.17it/s]

 93%|█████████▎| 159023104/170498071 [29:57<01:57, 97734.48it/s]

 93%|█████████▎| 159055872/170498071 [29:58<01:57, 97663.92it/s]

 93%|█████████▎| 159088640/170498071 [29:58<02:05, 90644.90it/s]

 93%|█████████▎| 159121408/170498071 [29:58<02:02, 92773.84it/s]

 93%|█████████▎| 159154176/170498071 [29:59<02:00, 94303.37it/s]

 93%|█████████▎| 159186944/170498071 [29:59<01:58, 95484.89it/s]

 93%|█████████▎| 159219712/170498071 [29:59<01:57, 96303.34it/s]

 93%|█████████▎| 159252480/170498071 [30:00<01:55, 97136.92it/s]

 93%|█████████▎| 159285248/170498071 [30:00<01:54, 97726.83it/s]

 93%|█████████▎| 159318016/170498071 [30:00<01:53, 98104.32it/s]

 93%|█████████▎| 159350784/170498071 [30:01<01:53, 98355.04it/s]

 93%|█████████▎| 159383552/170498071 [30:01<01:53, 98316.05it/s]

 94%|█████████▎| 159416320/170498071 [30:02<02:01, 91561.15it/s]

 94%|█████████▎| 159449088/170498071 [30:02<01:58, 93502.06it/s]

 94%|█████████▎| 159481856/170498071 [30:02<01:55, 94979.33it/s]

 94%|█████████▎| 159514624/170498071 [30:03<01:54, 96045.96it/s]

 94%|█████████▎| 159547392/170498071 [30:03<01:52, 96983.48it/s]

 94%|█████████▎| 159580160/170498071 [30:03<01:51, 97790.14it/s]

 94%|█████████▎| 159612928/170498071 [30:04<01:50, 98274.75it/s]

 94%|█████████▎| 159645696/170498071 [30:04<01:50, 98547.74it/s]

 94%|█████████▎| 159678464/170498071 [30:04<01:49, 98389.10it/s]

 94%|█████████▎| 159711232/170498071 [30:05<01:49, 98418.95it/s]

 94%|█████████▎| 159744000/170498071 [30:05<01:49, 98567.86it/s]

 94%|█████████▎| 159776768/170498071 [30:05<01:57, 91587.34it/s]

 94%|█████████▎| 159809536/170498071 [30:06<01:54, 93458.88it/s]

 94%|█████████▍| 159842304/170498071 [30:06<01:51, 95415.25it/s]

 94%|█████████▍| 159875072/170498071 [30:06<01:50, 96553.37it/s]

 94%|█████████▍| 159907840/170498071 [30:07<01:48, 97474.34it/s]

 94%|█████████▍| 159940608/170498071 [30:07<01:47, 98065.70it/s]

 94%|█████████▍| 159973376/170498071 [30:07<01:46, 98658.79it/s]

 94%|█████████▍| 160006144/170498071 [30:08<01:46, 98817.08it/s]

 94%|█████████▍| 160038912/170498071 [30:08<01:45, 99036.99it/s]

 94%|█████████▍| 160071680/170498071 [30:08<01:45, 99194.29it/s]

 94%|█████████▍| 160104448/170498071 [30:09<01:52, 92591.02it/s]

 94%|█████████▍| 160137216/170498071 [30:09<01:49, 94306.80it/s]

 94%|█████████▍| 160169984/170498071 [30:09<01:47, 95708.94it/s]

 94%|█████████▍| 160202752/170498071 [30:10<01:46, 96490.87it/s]

 94%|█████████▍| 160235520/170498071 [30:10<01:45, 96960.77it/s]

 94%|█████████▍| 160268288/170498071 [30:10<01:45, 97380.71it/s]

 94%|█████████▍| 160301056/170498071 [30:11<01:44, 97429.42it/s]

 94%|█████████▍| 160333824/170498071 [30:11<01:44, 97642.88it/s]

 94%|█████████▍| 160366592/170498071 [30:11<01:43, 97721.77it/s]

 94%|█████████▍| 160399360/170498071 [30:12<01:43, 97885.72it/s]

 94%|█████████▍| 160432128/170498071 [30:12<01:50, 91253.54it/s]

 94%|█████████▍| 160464896/170498071 [30:12<01:47, 93332.60it/s]

 94%|█████████▍| 160497664/170498071 [30:13<01:45, 94888.82it/s]

 94%|█████████▍| 160530432/170498071 [30:13<01:43, 95912.11it/s]

 94%|█████████▍| 160563200/170498071 [30:13<01:42, 96555.16it/s]

 94%|█████████▍| 160595968/170498071 [30:14<01:42, 96998.11it/s]

 94%|█████████▍| 160628736/170498071 [30:14<01:41, 97188.04it/s]

 94%|█████████▍| 160661504/170498071 [30:14<01:41, 97097.90it/s]

 94%|█████████▍| 160694272/170498071 [30:15<01:40, 97095.06it/s]

 94%|█████████▍| 160727040/170498071 [30:15<01:40, 97085.87it/s]

 94%|█████████▍| 160759808/170498071 [30:16<01:47, 90614.16it/s]

 94%|█████████▍| 160792576/170498071 [30:16<01:46, 91416.12it/s]

 94%|█████████▍| 160825344/170498071 [30:16<01:43, 93693.29it/s]

 94%|█████████▍| 160858112/170498071 [30:17<01:43, 93547.88it/s]

 94%|█████████▍| 160890880/170498071 [30:17<01:40, 95186.55it/s]

 94%|█████████▍| 160923648/170498071 [30:17<01:40, 95654.46it/s]

 94%|█████████▍| 160956416/170498071 [30:18<01:39, 96037.66it/s]

 94%|█████████▍| 160989184/170498071 [30:18<01:38, 96399.03it/s]

 94%|█████████▍| 161021952/170498071 [30:18<01:38, 96493.31it/s]

 94%|█████████▍| 161054720/170498071 [30:19<01:37, 96468.64it/s]

 94%|█████████▍| 161087488/170498071 [30:19<01:38, 95732.73it/s]

 94%|█████████▍| 161120256/170498071 [30:19<01:43, 90349.04it/s]

 95%|█████████▍| 161153024/170498071 [30:20<01:40, 92708.95it/s]

 95%|█████████▍| 161185792/170498071 [30:20<01:39, 93852.57it/s]

 95%|█████████▍| 161218560/170498071 [30:20<01:37, 94754.67it/s]

 95%|█████████▍| 161251328/170498071 [30:21<01:37, 95245.12it/s]

 95%|█████████▍| 161284096/170498071 [30:21<01:36, 95690.06it/s]

 95%|█████████▍| 161316864/170498071 [30:21<01:35, 96127.32it/s]

 95%|█████████▍| 161349632/170498071 [30:22<01:34, 96417.58it/s]

 95%|█████████▍| 161382400/170498071 [30:22<01:34, 96603.72it/s]

 95%|█████████▍| 161415168/170498071 [30:22<01:33, 96947.60it/s]

 95%|█████████▍| 161447936/170498071 [30:23<01:33, 97186.59it/s]

 95%|█████████▍| 161480704/170498071 [30:23<01:39, 90637.83it/s]

 95%|█████████▍| 161513472/170498071 [30:23<01:36, 92831.78it/s]

 95%|█████████▍| 161546240/170498071 [30:24<01:34, 94303.59it/s]

 95%|█████████▍| 161579008/170498071 [30:24<01:34, 94658.93it/s]

 95%|█████████▍| 161611776/170498071 [30:24<01:32, 95627.47it/s]

 95%|█████████▍| 161644544/170498071 [30:25<01:31, 97174.63it/s]

 95%|█████████▍| 161677312/170498071 [30:25<01:30, 97268.51it/s]

 95%|█████████▍| 161710080/170498071 [30:25<01:30, 97406.70it/s]

 95%|█████████▍| 161742848/170498071 [30:26<01:29, 97552.04it/s]

 95%|█████████▍| 161775616/170498071 [30:26<01:29, 97665.12it/s]

 95%|█████████▍| 161808384/170498071 [30:27<01:35, 91094.71it/s]

 95%|█████████▍| 161841152/170498071 [30:27<01:33, 93013.63it/s]

 95%|█████████▍| 161873920/170498071 [30:27<01:31, 94629.90it/s]

 95%|█████████▍| 161906688/170498071 [30:28<01:30, 95301.34it/s]

 95%|█████████▍| 161939456/170498071 [30:28<01:29, 96042.85it/s]

 95%|█████████▍| 161972224/170498071 [30:28<01:28, 96431.89it/s]

 95%|█████████▌| 162004992/170498071 [30:29<01:27, 96911.57it/s]

 95%|█████████▌| 162037760/170498071 [30:29<01:27, 97199.95it/s]

 95%|█████████▌| 162070528/170498071 [30:29<01:26, 97498.79it/s]

 95%|█████████▌| 162103296/170498071 [30:30<01:26, 97482.90it/s]

 95%|█████████▌| 162136064/170498071 [30:30<01:32, 90600.19it/s]

 95%|█████████▌| 162168832/170498071 [30:30<01:29, 92652.68it/s]

 95%|█████████▌| 162201600/170498071 [30:31<01:28, 93908.93it/s]

 95%|█████████▌| 162234368/170498071 [30:31<01:27, 94919.71it/s]

 95%|█████████▌| 162267136/170498071 [30:31<01:26, 95652.74it/s]

 95%|█████████▌| 162299904/170498071 [30:32<01:25, 96411.11it/s]

 95%|█████████▌| 162332672/170498071 [30:32<01:24, 97097.08it/s]

 95%|█████████▌| 162365440/170498071 [30:32<01:29, 90584.03it/s]

 95%|█████████▌| 162398208/170498071 [30:33<01:22, 98631.84it/s]

 95%|█████████▌| 162430976/170498071 [30:33<01:21, 99119.73it/s]

 95%|█████████▌| 162463744/170498071 [30:33<01:21, 98334.89it/s]

 95%|█████████▌| 162496512/170498071 [30:34<01:28, 90194.87it/s]

 95%|█████████▌| 162529280/170498071 [30:34<01:26, 91617.99it/s]

 95%|█████████▌| 162562048/170498071 [30:34<01:25, 92372.90it/s]

 95%|█████████▌| 162594816/170498071 [30:35<01:25, 92899.47it/s]

 95%|█████████▌| 162627584/170498071 [30:35<01:24, 93298.82it/s]

 95%|█████████▌| 162660352/170498071 [30:36<01:23, 93596.63it/s]

 95%|█████████▌| 162693120/170498071 [30:36<01:23, 93801.66it/s]

 95%|█████████▌| 162725888/170498071 [30:36<01:22, 94068.36it/s]

 95%|█████████▌| 162758656/170498071 [30:37<01:22, 94073.48it/s]

 95%|█████████▌| 162791424/170498071 [30:37<01:22, 93866.15it/s]

 95%|█████████▌| 162824192/170498071 [30:37<01:27, 87424.49it/s]

 96%|█████████▌| 162856960/170498071 [30:38<01:25, 89116.01it/s]

 96%|█████████▌| 162889728/170498071 [30:38<01:24, 90313.55it/s]

 96%|█████████▌| 162922496/170498071 [30:38<01:23, 91110.52it/s]

 96%|█████████▌| 162955264/170498071 [30:39<01:22, 91846.31it/s]

 96%|█████████▌| 162988032/170498071 [30:39<01:21, 92224.79it/s]

 96%|█████████▌| 163020800/170498071 [30:39<01:20, 92572.73it/s]

 96%|█████████▌| 163053568/170498071 [30:40<01:20, 92567.66it/s]

 96%|█████████▌| 163086336/170498071 [30:40<01:19, 92689.18it/s]

 96%|█████████▌| 163119104/170498071 [30:40<01:19, 92668.22it/s]

 96%|█████████▌| 163151872/170498071 [30:41<01:19, 92771.32it/s]

 96%|█████████▌| 163184640/170498071 [30:41<01:24, 86294.75it/s]

 96%|█████████▌| 163217408/170498071 [30:42<01:22, 88444.18it/s]

 96%|█████████▌| 163250176/170498071 [30:42<01:21, 89435.12it/s]

 96%|█████████▌| 163282944/170498071 [30:42<01:18, 91595.60it/s]

 96%|█████████▌| 163315712/170498071 [30:43<01:17, 92530.24it/s]

 96%|█████████▌| 163348480/170498071 [30:43<01:16, 93107.48it/s]

 96%|█████████▌| 163381248/170498071 [30:43<01:16, 93559.40it/s]

 96%|█████████▌| 163414016/170498071 [30:44<01:15, 93794.70it/s]

 96%|█████████▌| 163446784/170498071 [30:44<01:15, 93973.82it/s]

 96%|█████████▌| 163479552/170498071 [30:44<01:14, 94095.36it/s]

 96%|█████████▌| 163512320/170498071 [30:45<01:19, 87587.19it/s]

 96%|█████████▌| 163545088/170498071 [30:45<01:17, 89431.05it/s]

 96%|█████████▌| 163577856/170498071 [30:46<01:16, 90923.88it/s]

 96%|█████████▌| 163610624/170498071 [30:46<01:14, 92043.38it/s]

 96%|█████████▌| 163643392/170498071 [30:46<01:13, 93138.32it/s]

 96%|█████████▌| 163676160/170498071 [30:47<01:12, 93721.22it/s]

 96%|█████████▌| 163708928/170498071 [30:47<01:12, 94193.29it/s]

 96%|█████████▌| 163741696/170498071 [30:47<01:11, 94516.02it/s]

 96%|█████████▌| 163774464/170498071 [30:48<01:10, 94871.69it/s]

 96%|█████████▌| 163807232/170498071 [30:48<01:10, 95010.45it/s]

 96%|█████████▌| 163840000/170498071 [30:48<01:09, 95123.35it/s]

 96%|█████████▌| 163872768/170498071 [30:49<01:14, 88544.78it/s]

 96%|█████████▌| 163905536/170498071 [30:49<01:12, 90557.55it/s]

 96%|█████████▌| 163938304/170498071 [30:49<01:11, 92121.29it/s]

 96%|█████████▌| 163971072/170498071 [30:50<01:10, 92948.76it/s]

 96%|█████████▌| 164003840/170498071 [30:50<01:09, 93577.85it/s]

 96%|█████████▌| 164036608/170498071 [30:50<01:08, 94004.01it/s]

 96%|█████████▌| 164069376/170498071 [30:51<01:08, 94177.99it/s]

 96%|█████████▌| 164102144/170498071 [30:51<01:07, 94069.97it/s]

 96%|█████████▋| 164134912/170498071 [30:51<01:07, 94093.49it/s]

 96%|█████████▋| 164167680/170498071 [30:52<01:07, 94141.20it/s]

 96%|█████████▋| 164200448/170498071 [30:52<01:12, 87393.98it/s]

 96%|█████████▋| 164233216/170498071 [30:53<01:09, 89555.45it/s]

 96%|█████████▋| 164265984/170498071 [30:53<01:08, 90998.90it/s]

 96%|█████████▋| 164298752/170498071 [30:53<01:07, 92243.59it/s]

 96%|█████████▋| 164331520/170498071 [30:54<01:06, 92849.47it/s]

 96%|█████████▋| 164364288/170498071 [30:54<01:05, 93631.26it/s]

 96%|█████████▋| 164397056/170498071 [30:54<01:04, 94193.52it/s]

 96%|█████████▋| 164429824/170498071 [30:55<01:04, 93461.13it/s]

 96%|█████████▋| 164462592/170498071 [30:55<01:04, 93998.44it/s]

 96%|█████████▋| 164495360/170498071 [30:55<01:02, 95351.15it/s]

 96%|█████████▋| 164528128/170498071 [30:56<01:07, 88386.35it/s]

 97%|█████████▋| 164560896/170498071 [30:56<01:05, 89968.93it/s]

 97%|█████████▋| 164593664/170498071 [30:57<01:04, 91213.45it/s]

 97%|█████████▋| 164626432/170498071 [30:57<01:03, 92285.30it/s]

 97%|█████████▋| 164659200/170498071 [30:57<01:02, 92818.55it/s]

 97%|█████████▋| 164691968/170498071 [30:58<01:02, 93221.77it/s]

 97%|█████████▋| 164724736/170498071 [30:58<01:01, 93576.71it/s]

 97%|█████████▋| 164757504/170498071 [30:58<01:01, 93662.29it/s]

 97%|█████████▋| 164790272/170498071 [30:59<01:00, 93840.87it/s]

 97%|█████████▋| 164823040/170498071 [30:59<01:00, 94001.01it/s]

 97%|█████████▋| 164855808/170498071 [30:59<00:59, 94072.97it/s]

 97%|█████████▋| 164888576/170498071 [31:00<01:04, 87383.53it/s]

 97%|█████████▋| 164921344/170498071 [31:00<01:02, 89244.36it/s]

 97%|█████████▋| 164954112/170498071 [31:00<01:01, 90695.23it/s]

 97%|█████████▋| 164986880/170498071 [31:01<01:00, 91665.11it/s]

 97%|█████████▋| 165019648/170498071 [31:01<00:59, 92795.24it/s]

 97%|█████████▋| 165052416/170498071 [31:01<00:58, 92644.99it/s]

 97%|█████████▋| 165085184/170498071 [31:02<00:57, 94444.40it/s]

 97%|█████████▋| 165117952/170498071 [31:02<00:56, 94876.43it/s]

 97%|█████████▋| 165150720/170498071 [31:02<00:56, 94984.01it/s]

 97%|█████████▋| 165183488/170498071 [31:03<00:55, 95134.67it/s]

 97%|█████████▋| 165216256/170498071 [31:03<00:59, 89108.46it/s]

 97%|█████████▋| 165249024/170498071 [31:04<00:57, 91290.19it/s]

 97%|█████████▋| 165281792/170498071 [31:04<00:56, 92914.76it/s]

 97%|█████████▋| 165314560/170498071 [31:04<00:55, 94187.50it/s]

 97%|█████████▋| 165347328/170498071 [31:05<00:54, 94940.99it/s]

 97%|█████████▋| 165380096/170498071 [31:05<00:53, 95520.44it/s]

 97%|█████████▋| 165412864/170498071 [31:05<00:53, 95722.62it/s]

 97%|█████████▋| 165445632/170498071 [31:06<00:52, 95936.77it/s]

 97%|█████████▋| 165478400/170498071 [31:06<00:52, 95980.35it/s]

 97%|█████████▋| 165511168/170498071 [31:06<00:51, 96153.94it/s]

 97%|█████████▋| 165543936/170498071 [31:07<00:51, 96285.45it/s]

 97%|█████████▋| 165576704/170498071 [31:07<00:54, 89600.73it/s]

 97%|█████████▋| 165609472/170498071 [31:07<00:53, 91439.41it/s]

 97%|█████████▋| 165642240/170498071 [31:08<00:52, 92661.59it/s]

 97%|█████████▋| 165675008/170498071 [31:08<00:51, 93594.38it/s]

 97%|█████████▋| 165707776/170498071 [31:08<00:50, 94227.51it/s]

 97%|█████████▋| 165740544/170498071 [31:09<00:50, 94536.83it/s]

 97%|█████████▋| 165773312/170498071 [31:09<00:49, 94845.67it/s]

 97%|█████████▋| 165806080/170498071 [31:09<00:49, 95389.52it/s]

 97%|█████████▋| 165838848/170498071 [31:10<00:48, 95767.35it/s]

 97%|█████████▋| 165871616/170498071 [31:10<00:48, 96051.72it/s]

 97%|█████████▋| 165904384/170498071 [31:11<00:51, 89504.00it/s]

 97%|█████████▋| 165937152/170498071 [31:11<00:49, 91495.53it/s]

 97%|█████████▋| 165969920/170498071 [31:11<00:48, 92882.18it/s]

 97%|█████████▋| 166002688/170498071 [31:12<00:47, 93834.53it/s]

 97%|█████████▋| 166035456/170498071 [31:12<00:47, 94575.98it/s]

 97%|█████████▋| 166068224/170498071 [31:12<00:46, 95012.69it/s]

 97%|█████████▋| 166100992/170498071 [31:13<00:46, 95374.28it/s]

 97%|█████████▋| 166133760/170498071 [31:13<00:45, 95430.57it/s]

 97%|█████████▋| 166166528/170498071 [31:13<00:45, 95618.16it/s]

 97%|█████████▋| 166199296/170498071 [31:14<00:44, 95983.69it/s]

 97%|█████████▋| 166232064/170498071 [31:14<00:47, 89474.64it/s]

 98%|█████████▊| 166264832/170498071 [31:14<00:46, 91333.03it/s]

 98%|█████████▊| 166297600/170498071 [31:15<00:45, 92864.51it/s]

 98%|█████████▊| 166330368/170498071 [31:15<00:44, 93884.81it/s]

 98%|█████████▊| 166363136/170498071 [31:15<00:43, 94596.12it/s]

 98%|█████████▊| 166395904/170498071 [31:16<00:43, 94902.41it/s]

 98%|█████████▊| 166428672/170498071 [31:16<00:42, 95298.25it/s]

 98%|█████████▊| 166461440/170498071 [31:16<00:42, 95683.21it/s]

 98%|█████████▊| 166494208/170498071 [31:17<00:44, 90222.81it/s]

 98%|█████████▊| 166526976/170498071 [31:17<00:41, 96638.33it/s]

 98%|█████████▊| 166559744/170498071 [31:17<00:40, 97707.67it/s]

 98%|█████████▊| 166592512/170498071 [31:18<00:43, 90302.52it/s]

 98%|█████████▊| 166625280/170498071 [31:18<00:42, 92024.31it/s]

 98%|█████████▊| 166658048/170498071 [31:19<00:41, 92938.45it/s]

 98%|█████████▊| 166690816/170498071 [31:19<00:40, 93632.96it/s]

 98%|█████████▊| 166723584/170498071 [31:19<00:40, 94233.57it/s]

 98%|█████████▊| 166756352/170498071 [31:20<00:39, 94776.07it/s]

 98%|█████████▊| 166789120/170498071 [31:20<00:38, 95189.48it/s]

 98%|█████████▊| 166821888/170498071 [31:20<00:38, 95638.90it/s]

 98%|█████████▊| 166854656/170498071 [31:21<00:38, 95844.55it/s]

 98%|█████████▊| 166887424/170498071 [31:21<00:37, 96065.12it/s]

 98%|█████████▊| 166920192/170498071 [31:21<00:40, 89436.01it/s]

 98%|█████████▊| 166952960/170498071 [31:22<00:38, 91241.38it/s]

 98%|█████████▊| 166985728/170498071 [31:22<00:38, 92376.35it/s]

 98%|█████████▊| 167018496/170498071 [31:22<00:37, 93335.69it/s]

 98%|█████████▊| 167051264/170498071 [31:23<00:36, 94058.66it/s]

 98%|█████████▊| 167084032/170498071 [31:23<00:36, 94712.90it/s]

 98%|█████████▊| 167116800/170498071 [31:23<00:35, 95015.13it/s]

 98%|█████████▊| 167149568/170498071 [31:24<00:35, 95261.51it/s]

 98%|█████████▊| 167182336/170498071 [31:24<00:34, 95525.30it/s]

 98%|█████████▊| 167215104/170498071 [31:24<00:34, 95725.08it/s]

 98%|█████████▊| 167247872/170498071 [31:25<00:33, 95947.66it/s]

 98%|█████████▊| 167280640/170498071 [31:25<00:35, 89456.57it/s]

 98%|█████████▊| 167313408/170498071 [31:26<00:34, 91445.05it/s]

 98%|█████████▊| 167346176/170498071 [31:26<00:33, 92881.46it/s]

 98%|█████████▊| 167378944/170498071 [31:26<00:33, 93891.40it/s]

 98%|█████████▊| 167411712/170498071 [31:27<00:32, 94630.75it/s]

 98%|█████████▊| 167444480/170498071 [31:27<00:32, 94816.20it/s]

 98%|█████████▊| 167477248/170498071 [31:27<00:31, 95217.12it/s]

 98%|█████████▊| 167510016/170498071 [31:28<00:31, 95156.40it/s]

 98%|█████████▊| 167542784/170498071 [31:28<00:31, 94929.49it/s]

 98%|█████████▊| 167575552/170498071 [31:28<00:30, 95094.33it/s]

 98%|█████████▊| 167608320/170498071 [31:29<00:32, 88802.75it/s]

 98%|█████████▊| 167641088/170498071 [31:29<00:31, 90889.81it/s]

 98%|█████████▊| 167673856/170498071 [31:29<00:30, 92526.51it/s]

 98%|█████████▊| 167706624/170498071 [31:30<00:29, 93531.10it/s]

 98%|█████████▊| 167739392/170498071 [31:30<00:29, 94274.25it/s]

 98%|█████████▊| 167772160/170498071 [31:30<00:28, 94929.74it/s]

 98%|█████████▊| 167804928/170498071 [31:31<00:28, 94897.19it/s]

 98%|█████████▊| 167837696/170498071 [31:31<00:28, 94928.01it/s]

 98%|█████████▊| 167870464/170498071 [31:31<00:27, 94929.17it/s]

 98%|█████████▊| 167903232/170498071 [31:32<00:27, 95497.45it/s]

 98%|█████████▊| 167936000/170498071 [31:32<00:26, 95792.03it/s]

 99%|█████████▊| 167968768/170498071 [31:33<00:28, 89334.53it/s]

 99%|█████████▊| 168001536/170498071 [31:33<00:27, 91502.78it/s]

 99%|█████████▊| 168034304/170498071 [31:33<00:26, 92939.78it/s]

 99%|█████████▊| 168067072/170498071 [31:34<00:25, 94331.06it/s]

 99%|█████████▊| 168099840/170498071 [31:34<00:25, 95105.06it/s]

 99%|█████████▊| 168132608/170498071 [31:34<00:24, 95444.92it/s]

 99%|█████████▊| 168165376/170498071 [31:35<00:24, 95780.55it/s]

 99%|█████████▊| 168198144/170498071 [31:35<00:23, 96299.02it/s]

 99%|█████████▊| 168230912/170498071 [31:35<00:23, 96797.32it/s]

 99%|█████████▊| 168263680/170498071 [31:36<00:24, 90434.51it/s]

 99%|█████████▊| 168296448/170498071 [31:36<00:24, 91181.41it/s]

 99%|█████████▊| 168329216/170498071 [31:36<00:23, 94105.91it/s]

 99%|█████████▊| 168361984/170498071 [31:37<00:22, 94903.49it/s]

 99%|█████████▉| 168394752/170498071 [31:37<00:21, 95892.58it/s]

 99%|█████████▉| 168427520/170498071 [31:37<00:21, 96504.64it/s]

 99%|█████████▉| 168460288/170498071 [31:38<00:21, 96977.67it/s]

 99%|█████████▉| 168493056/170498071 [31:38<00:20, 97019.06it/s]

 99%|█████████▉| 168525824/170498071 [31:38<00:20, 96959.35it/s]

 99%|█████████▉| 168558592/170498071 [31:39<00:19, 97147.45it/s]

 99%|█████████▉| 168591360/170498071 [31:39<00:19, 96877.55it/s]

 99%|█████████▉| 168624128/170498071 [31:39<00:20, 90112.27it/s]

 99%|█████████▉| 168656896/170498071 [31:40<00:20, 91886.09it/s]

 99%|█████████▉| 168689664/170498071 [31:40<00:19, 92390.69it/s]

 99%|█████████▉| 168722432/170498071 [31:40<00:18, 94775.11it/s]

 99%|█████████▉| 168755200/170498071 [31:41<00:18, 95455.60it/s]

 99%|█████████▉| 168787968/170498071 [31:41<00:18, 94782.16it/s]

 99%|█████████▉| 168820736/170498071 [31:41<00:17, 96529.00it/s]

 99%|█████████▉| 168853504/170498071 [31:42<00:16, 96746.94it/s]

 99%|█████████▉| 168886272/170498071 [31:42<00:16, 97221.13it/s]

 99%|█████████▉| 168919040/170498071 [31:43<00:16, 97367.33it/s]

 99%|█████████▉| 168951808/170498071 [31:43<00:15, 97364.75it/s]

 99%|█████████▉| 168984576/170498071 [31:43<00:16, 90983.36it/s]

 99%|█████████▉| 169017344/170498071 [31:44<00:15, 93069.00it/s]

 99%|█████████▉| 169050112/170498071 [31:44<00:15, 94594.95it/s]

 99%|█████████▉| 169082880/170498071 [31:44<00:14, 95557.65it/s]

 99%|█████████▉| 169115648/170498071 [31:45<00:14, 96258.24it/s]

 99%|█████████▉| 169148416/170498071 [31:45<00:13, 96752.78it/s]

 99%|█████████▉| 169181184/170498071 [31:45<00:13, 97019.88it/s]

 99%|█████████▉| 169213952/170498071 [31:46<00:13, 97223.16it/s]

 99%|█████████▉| 169246720/170498071 [31:46<00:12, 97355.83it/s]

 99%|█████████▉| 169279488/170498071 [31:46<00:12, 97712.98it/s]

 99%|█████████▉| 169312256/170498071 [31:47<00:13, 88348.81it/s]

 99%|█████████▉| 169345024/170498071 [31:47<00:12, 93144.56it/s]

 99%|█████████▉| 169377792/170498071 [31:47<00:11, 95770.08it/s]

 99%|█████████▉| 169410560/170498071 [31:48<00:11, 96516.59it/s]

 99%|█████████▉| 169443328/170498071 [31:48<00:10, 97183.39it/s]

 99%|█████████▉| 169476096/170498071 [31:48<00:10, 97179.67it/s]

 99%|█████████▉| 169508864/170498071 [31:49<00:10, 97307.02it/s]

 99%|█████████▉| 169541632/170498071 [31:49<00:09, 97501.21it/s]

 99%|█████████▉| 169574400/170498071 [31:49<00:09, 97369.95it/s]

 99%|█████████▉| 169607168/170498071 [31:50<00:09, 97150.03it/s]

 99%|█████████▉| 169639936/170498071 [31:50<00:08, 96832.41it/s]

100%|█████████▉| 169672704/170498071 [31:50<00:09, 90068.32it/s]

100%|█████████▉| 169705472/170498071 [31:51<00:08, 91856.91it/s]

100%|█████████▉| 169738240/170498071 [31:51<00:08, 93261.56it/s]

100%|█████████▉| 169771008/170498071 [31:51<00:07, 94146.03it/s]

100%|█████████▉| 169803776/170498071 [31:52<00:07, 94838.28it/s]

100%|█████████▉| 169836544/170498071 [31:52<00:06, 95036.22it/s]

100%|█████████▉| 169869312/170498071 [31:53<00:06, 95135.95it/s]

100%|█████████▉| 169902080/170498071 [31:53<00:06, 95227.10it/s]

100%|█████████▉| 169934848/170498071 [31:53<00:05, 95236.34it/s]

100%|█████████▉| 169967616/170498071 [31:54<00:05, 95153.17it/s]

100%|█████████▉| 170000384/170498071 [31:54<00:05, 83067.08it/s]

100%|█████████▉| 170033152/170498071 [31:54<00:05, 91076.13it/s]

100%|█████████▉| 170065920/170498071 [31:55<00:04, 93176.92it/s]

100%|█████████▉| 170098688/170498071 [31:55<00:04, 93411.61it/s]

100%|█████████▉| 170131456/170498071 [31:55<00:03, 93756.92it/s]

100%|█████████▉| 170164224/170498071 [31:56<00:03, 93905.94it/s]

100%|█████████▉| 170196992/170498071 [31:56<00:03, 93888.30it/s]

100%|█████████▉| 170229760/170498071 [31:56<00:02, 94059.45it/s]

100%|█████████▉| 170262528/170498071 [31:57<00:02, 94408.57it/s]

100%|█████████▉| 170295296/170498071 [31:57<00:02, 94580.34it/s]

100%|█████████▉| 170328064/170498071 [31:58<00:01, 88204.16it/s]

100%|█████████▉| 170360832/170498071 [31:58<00:01, 90133.95it/s]

100%|█████████▉| 170393600/170498071 [31:58<00:01, 91845.69it/s]

100%|█████████▉| 170426368/170498071 [31:59<00:00, 92874.86it/s]

100%|█████████▉| 170459136/170498071 [31:59<00:00, 93695.87it/s]

100%|█████████▉| 170491904/170498071 [31:59<00:00, 94168.01it/s]

100%|██████████| 170498071/170498071 [31:59<00:00, 88812.76it/s]

Extracting ./data/cifar-10-python.tar.gz to ./data

Files already downloaded and verified

Let us take a look at the original (latent) images and their scaled versions.

Visualize Images¶

# @title Visualize Images

set_seed(seed=42, seed_torch=True)

num_images_show = 8
cifar_demo_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        (0.49139968, 0.48215827, 0.44653124),
        (0.24703233, 0.24348505, 0.26158768))
    ])

# Demo datasets
cifar10_demo_trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=cifar_demo_transform)
cifar3_demo_trainset = CIFAR3(cifar10_demo_trainset, cifar3_classes)
cifar3scaled_demo_trainset = CIFAR3(cifar10_demo_trainset, cifar3_classes, scale=True)

# functions to show an image
def imshow(img, title):
    with plt.xkcd():
        img = (img * 0.25) + 0.5     # unnormalize
        npimg = img.numpy()
        plt.title(title)
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
        plt.xticks([])
        plt.yticks([])
        plt.show()

# get some random training images
random_idxs = np.random.choice(range(len(cifar3_demo_trainset)), num_images_show)
cifar3_imgs = None
cifar3scaled_imgs = None
cifar3_labels_ = []

for ridx in random_idxs:
    c4img, lbl_ = cifar3_demo_trainset[ridx]
    c4simg, slbl_ = cifar3scaled_demo_trainset[ridx]
    assert lbl_ == slbl_, 'Labels do not match'
    cifar3_labels_.append(lbl_)
    cifar3_imgs = c4img.unsqueeze(0) if (cifar3_imgs is None) else torch.cat((cifar3_imgs, c4img.unsqueeze(0)))
    cifar3scaled_imgs = c4simg.unsqueeze(0) if (cifar3scaled_imgs is None) else torch.cat((cifar3scaled_imgs, c4simg.unsqueeze(0)))

# print labels
print('Labels = ' + ', '.join(f'{cifar10_classes[list(cifar3_classes.keys())[cifar3_labels_[j]]]:5s}' for j in range(num_images_show)))
# show images
imshow(torchvision.utils.make_grid(cifar3_imgs), 'CIFAR3')
# print scaling factor
print('Scaling factors = ' + ', '.join([str(round(i.item(), 3) )for i in cifar3scaled_demo_trainset.scale_values[random_idxs]]))
# show scaled images
imshow(torchvision.utils.make_grid(cifar3scaled_imgs), 'CIFAR3 Scaled')

Files already downloaded and verified

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Labels = car  , plane, car  , car  , cat  , cat  , car  , car

Scaling factors = 0.14, 0.171, 0.224, 0.148, 0.221, 0.179, 0.099, 0.155

Here, we define the CNN model with an optional parameter for adding the LayerNorm layer.

Define CIFARNet model¶

# @title Define CIFARNet model

set_seed(seed=42, seed_torch=True)

class CIFARNet(nn.Module):
    def __init__(self, layer_norm=False):
        super().__init__()
        self.features = nn.Sequential(OrderedDict([
            ('conv1', nn.Conv2d(3, 16, 5, padding='same')),
            ('norm1', nn.LayerNorm((16, 8, 8))),
            ('relu1', nn.ReLU()),
            ('maxpool1', nn.MaxPool2d(3, padding=1,  stride=2)),
            ('conv2', nn.Conv2d(16, 32, 3, padding='same')),
            ('norm2', nn.LayerNorm((32, 4, 4))),
            ('relu2', nn.ReLU()),
            ('avgpool2', nn.AvgPool2d(3, padding=1,  stride=2))
        ]))
        if not layer_norm:
            del self.features.norm1
            del self.features.norm2
        self.classifier = nn.Sequential(OrderedDict([
            ('fc1', nn.Linear(128, 64)),
            ('fc2', nn.Linear(64, 3)),
        ]))
        # Initialize weights
        nn.init.normal_(self.features.conv1.weight, mean=0.0, std=1e-4)
        nn.init.normal_(self.features.conv2.weight, mean=0.0, std=1e-4)
        nn.init.normal_(self.classifier.fc1.weight, mean=0.0, std=1e-1)
        nn.init.normal_(self.classifier.fc2.weight, mean=0.0, std=1e-1)

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = self.classifier(x)
        return x

It will take around 3 minutes to complete training on different types of models.

Training & Evaluating the models¶

# @title Training & Evaluating the models

# Training
n_epochs = 10
learning_rate = 5e-2
momentum = 0.9

# Unscaled CIFAR3
cifar3_net = CIFARNet(layer_norm=False).to(DEVICE)
losses_iter, losses_epoch = train_cnns(cifar3_net, cifar3_trainloader, \
                                  learning_rate, momentum, n_epochs)
# With LayerNorm
cifar3_net_LN = CIFARNet(layer_norm=True).to(DEVICE)
losses_iter_LN, losses_epoch_LN = train_cnns(cifar3_net_LN, cifar3_trainloader, \
                                  learning_rate, momentum, n_epochs)

# Scaled CIFAR3
cifar3scaled_net = CIFARNet(layer_norm=False).to(DEVICE)
losses_iter_scaled, losses_epoch_scaled = train_cnns(cifar3scaled_net, cifar3scaled_trainloader, \
                                  learning_rate, momentum, n_epochs)
# With LayerNorm
cifar3scaled_net_LN = CIFARNet(layer_norm=True).to(DEVICE)
losses_iter_scaled_LN, losses_epoch_scaled_LN = train_cnns(cifar3scaled_net_LN, cifar3scaled_trainloader, \
                                  learning_rate, momentum, n_epochs)

with plt.xkcd():
    # Plot training losses
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))

    # Plot 1 - Unscaled CIFAR3
    # Plot loss per epoch
    ax[0].plot(range(1, len(losses_epoch)+1), np.log(losses_epoch), '-b', label='Without LN')
    ax[0].plot(range(1, len(losses_epoch_LN)+1), np.log(losses_epoch_LN), '-r', label='With LN')
    ax[0].legend()
    ax[0].set_xlabel('Epochs')
    ax[0].set_ylabel('Log Cross Entropy Loss')
    ax[0].set_title('Unscaled CIFAR3: Training Loss')

    # Plot 2 - Scaled CIFAR3
    # Plot loss per epoch
    ax[1].plot(range(1, len(losses_epoch_scaled)+1), np.log(losses_epoch_scaled), '-b', label='Without LN')
    ax[1].plot(range(1, len(losses_epoch_scaled_LN)+1), np.log(losses_epoch_scaled_LN), '-r', label='With LN')
    ax[1].legend()
    ax[1].set_xlabel('Epochs')
    ax[1].set_ylabel('Log Cross Entropy Loss')
    ax[1].set_title('Scaled CIFAR3: Training Loss')

    plt.tight_layout()
    plt.show()

# Training evaluation
training_loss, training_accuracy = evaluate_cnns(cifar3_net, cifar3_trainloader)
training_loss_LN, training_accuracy_LN = evaluate_cnns(cifar3_net_LN, cifar3_trainloader)
training_loss_scaled, training_accuracy_scaled = evaluate_cnns(cifar3scaled_net, cifar3scaled_trainloader)
training_loss_scaled_LN, training_accuracy_scaled_LN = evaluate_cnns(cifar3scaled_net_LN, cifar3scaled_trainloader)

# Evaluation
test_loss, test_accuracy = evaluate_cnns(cifar3_net, cifar3_testloader)
test_loss_LN, test_accuracy_LN = evaluate_cnns(cifar3_net_LN, cifar3_testloader)
test_loss_scaled, test_accuracy_scaled = evaluate_cnns(cifar3scaled_net, cifar3scaled_testloader)
test_loss_scaled_LN, test_accuracy_scaled_LN = evaluate_cnns(cifar3scaled_net_LN, cifar3scaled_testloader)

with plt.xkcd():
    # Plot
    fig, axs = plt.subplots(1, 2, figsize=(10, 5))

    training_cm = [[training_accuracy, training_accuracy_LN], [training_accuracy_scaled, training_accuracy_scaled_LN]]
    testing_cm = [[test_accuracy, test_accuracy_LN], [test_accuracy_scaled, test_accuracy_scaled_LN]]
    all_cm = np.array(training_cm+training_cm).flatten()
    vmin = np.min(all_cm)
    vmax = np.max(all_cm)

    training_disp = ConfusionMatrixDisplay(confusion_matrix=np.array(training_cm),
                                   display_labels=['Unscaled CIFAR3', 'Scaled CIFAR3'])
    training_disp.plot(cmap=plt.cm.Greys, ax=axs[0])
    axs[0].images[-1].set_clim(vmin, vmax)
    axs[0].set_yticks([0, 1], ['Unscaled CIFAR3', 'Scaled CIFAR3'], rotation=90)
    axs[0].set_xticks([0, 1], ['without LayerNorm', 'with LayerNorm'])
    axs[0].set_xlabel('Model')
    axs[0].set_ylabel('Dataset')
    axs[0].set_title('Training Accuracy')

    testing_disp = ConfusionMatrixDisplay(confusion_matrix=np.array(testing_cm),
                                   display_labels=['Unscaled CIFAR3', 'Scaled CIFAR3'])
    testing_disp.plot(cmap=plt.cm.Greys, ax=axs[1])
    axs[1].images[-1].set_clim(vmin, vmax)
    axs[1].set_yticks([0, 1], ['Unscaled CIFAR3', 'Scaled CIFAR3'], rotation=90)
    axs[1].set_xticks([0, 1], ['without LayerNorm', 'with LayerNorm'])
    axs[1].set_xlabel('Model')
    axs[1].set_ylabel('Dataset')
    axs[1].set_title('Testing Accuracy')
    plt.tight_layout()
    # Removing shadows from text inside confusion matrix
    for txt in training_disp.text_.flatten():
      txt.set_path_effects([path_effects.Normal()])
    for txt in testing_disp.text_.flatten():
      txt.set_path_effects([path_effects.Normal()])
    plt.show()

  0%|          | 0/10 [00:00<?, ?it/s]

 10%|█         | 1/10 [00:04<00:43,  4.79s/it]

 20%|██        | 2/10 [00:10<00:40,  5.04s/it]

 30%|███       | 3/10 [00:15<00:36,  5.26s/it]

 40%|████      | 4/10 [00:21<00:33,  5.62s/it]

 50%|█████     | 5/10 [00:27<00:27,  5.58s/it]

 60%|██████    | 6/10 [00:32<00:21,  5.49s/it]

 70%|███████   | 7/10 [00:39<00:18,  6.04s/it]

 80%|████████  | 8/10 [00:44<00:11,  5.69s/it]

 90%|█████████ | 9/10 [00:51<00:06,  6.06s/it]

100%|██████████| 10/10 [00:56<00:00,  5.84s/it]

100%|██████████| 10/10 [00:56<00:00,  5.69s/it]

  0%|          | 0/10 [00:00<?, ?it/s]

 10%|█         | 1/10 [00:04<00:42,  4.69s/it]

 20%|██        | 2/10 [00:10<00:42,  5.27s/it]

 30%|███       | 3/10 [00:16<00:40,  5.83s/it]

 40%|████      | 4/10 [00:22<00:35,  5.93s/it]

 50%|█████     | 5/10 [00:30<00:31,  6.36s/it]

 60%|██████    | 6/10 [00:35<00:23,  5.99s/it]

 70%|███████   | 7/10 [00:40<00:17,  5.78s/it]

 80%|████████  | 8/10 [00:48<00:12,  6.27s/it]

 90%|█████████ | 9/10 [00:54<00:06,  6.29s/it]

100%|██████████| 10/10 [00:59<00:00,  5.87s/it]

100%|██████████| 10/10 [00:59<00:00,  5.93s/it]

  0%|          | 0/10 [00:00<?, ?it/s]

 10%|█         | 1/10 [00:05<00:49,  5.53s/it]

 20%|██        | 2/10 [00:10<00:43,  5.43s/it]

 30%|███       | 3/10 [00:16<00:37,  5.38s/it]

 40%|████      | 4/10 [00:21<00:32,  5.34s/it]

 50%|█████     | 5/10 [00:27<00:28,  5.74s/it]

 60%|██████    | 6/10 [00:32<00:21,  5.43s/it]

 70%|███████   | 7/10 [00:38<00:16,  5.59s/it]

 80%|████████  | 8/10 [00:44<00:11,  5.53s/it]

 90%|█████████ | 9/10 [00:50<00:05,  5.73s/it]

100%|██████████| 10/10 [00:56<00:00,  5.96s/it]

100%|██████████| 10/10 [00:56<00:00,  5.67s/it]

  0%|          | 0/10 [00:00<?, ?it/s]

 10%|█         | 1/10 [00:06<00:57,  6.40s/it]

 20%|██        | 2/10 [00:11<00:47,  5.90s/it]

 30%|███       | 3/10 [00:17<00:40,  5.82s/it]

 40%|████      | 4/10 [00:22<00:31,  5.31s/it]

 50%|█████     | 5/10 [00:28<00:28,  5.79s/it]

 60%|██████    | 6/10 [00:35<00:24,  6.14s/it]

 70%|███████   | 7/10 [00:42<00:19,  6.36s/it]

 80%|████████  | 8/10 [00:48<00:12,  6.24s/it]

 90%|█████████ | 9/10 [00:53<00:05,  5.94s/it]

100%|██████████| 10/10 [00:59<00:00,  5.93s/it]

100%|██████████| 10/10 [00:59<00:00,  5.97s/it]

Let us also check whether normalization improves generalization with different scaling parameters.

Test Generalization¶

# @title Test Generalization

n_tests = 5

cifar3_scaling_performances = {}
cifar3_scaling_performances_LN = {}

# scaling_limits_tests = [[0.3, 0.5], [0.5, 0.75], [0.75, 1]]
scaling_limits_tests = [[0.4, 0.6], [0.8, 1]]

for test_idx in range(n_tests):
  cifar3_scaled_testloaders = {
    '0.05-0.25 (training range)': cifar3scaled_testloader
  }

  for sc_ in scaling_limits_tests:
    sc_str = '-'.join([str(i) for i in sc_])
    testset_ = CIFAR3(cifar10_testset, cifar3_classes, scale=True, scaling_limits=sc_)
    testloader_ = torch.utils.data.DataLoader(testset_, batch_size=batch_size,
                                          shuffle=False)
    cifar3_scaled_testloaders[sc_str] = testloader_

  for scaling_lims, scaling_testloader in cifar3_scaled_testloaders.items():
    _, noLN_accuracy = evaluate_cnns(cifar3scaled_net, scaling_testloader)
    _, LN_accuracy = evaluate_cnns(cifar3scaled_net_LN, scaling_testloader)
    if scaling_lims in cifar3_scaling_performances.keys():
      cifar3_scaling_performances[scaling_lims] += [noLN_accuracy]
      cifar3_scaling_performances_LN[scaling_lims] += [LN_accuracy]
    else:
      cifar3_scaling_performances[scaling_lims] = [noLN_accuracy]
      cifar3_scaling_performances_LN[scaling_lims] = [LN_accuracy]

with plt.xkcd():
    plt.figure(figsize=(5, 5))
    mean_cifar3_scaling_performances = [np.mean(i) for i in cifar3_scaling_performances.values()]
    std_cifar3_scaling_performances = [np.std(i) for i in cifar3_scaling_performances.values()]
    plt.plot(range(1, len(cifar3_scaled_testloaders)+1), mean_cifar3_scaling_performances, \
             '-.b', label='Without LN')
    plt.errorbar(range(1, len(cifar3_scaled_testloaders)+1), mean_cifar3_scaling_performances, \
             yerr=std_cifar3_scaling_performances, color='b', capsize=5, capthick=2)
    mean_cifar3_scaling_performances_LN = [np.mean(i) for i in cifar3_scaling_performances_LN.values()]
    std_cifar3_scaling_performances_LN = [np.std(i) for i in cifar3_scaling_performances_LN.values()]
    plt.plot(range(1, len(cifar3_scaled_testloaders)+1), mean_cifar3_scaling_performances_LN, \
             '-.r', label='With LN')
    plt.errorbar(range(1, len(cifar3_scaled_testloaders)+1), mean_cifar3_scaling_performances_LN, \
             yerr=std_cifar3_scaling_performances_LN, color='r', capsize=5, capthick=2)
    plt.xticks(range(1, len(cifar3_scaled_testloaders)+1), labels=cifar3_scaled_testloaders.keys())
    plt.xlabel('CIFAR3 scaling range')
    plt.ylabel('Test accuracy')
    plt.title('Testing Generalization for scaled CIFARNet')
    plt.legend()
    plt.show()

By adding a normalization layer: (i) the training process converges quicker, (ii) we achieve better test accuracy and (iii) we achieve better out-of-distribution generalization accuracy in the image recognition tasks.

Video 8: Section summary

Youtube

Bilibili

Summary¶

Estimated timing of tutorial: 50 minutes

In this tutorial, we observed that normalization as an inductive bias is useful. We have implemented the normalization function and explored the examples. Finally, we discovered the benefits of using normalization.

Video 9: Tutorial summary

Youtube

Bilibili

The Big Picture¶

In this tutorial, we observed that normalization as an inductive bias is useful. We first showed that models struggle to learn the normalization function and it makes sense computationally to implement this operation as a dedicated microcircuit, our topic of the day! We further showed the effect that normalization has on efficient model training and how it enables out-of-distribution accuracy by stabilizing the training process and recovering images that had been corrupted by scaling.

We ended by looking at how normalization processes occur in brains and we prompt you to think whether any of the computational advantages in neural networks might perform similar functions in the brain. Are there things we can learn and apply to studying brains? Perhaps further work in normalization in neuroscience might end up introducing variations of brain-inspired normalization into neural networks, which could help the model learn even better!

Tutorial 2: Normalization¶

Tutorial Objectives¶

Setup¶

Install and import feedback gadget¶

Imports¶

Figure settings¶

Helper functions¶

Set random seed¶

Set device (GPU or CPU)¶

Section 1: Can ReLUs implement normalization?¶

Generate y=1x+ϵy=\frac{1}{x+\epsilon}y=x+ϵ1​ train and test dataloaders¶

Coding Exercise 1: ReLUNet¶

Training & Evaluating model¶

Plot Training Loss Dynamics¶

Vizualize ReLUs¶

Plot Weight Dynamics¶

Coding Exercise 1 Discussion¶

Coding Exercise 2: Test other non-linear activation functions¶

Train & Evaluate¶

Section 2: Benefits of normalization¶

Subsection 2.1: Explore normalization¶

Coding Exercise 2.1: Implement normalization¶

Interactive Demo 2.1¶

Effect of smoothing factor (σ\sigmaσ), p-norm (ppp) and scaling factor (ggg)¶

Subsection 2.2: Estimating latent properties¶

Interactive Demo 2.2.1¶

Plot correlation of estimated reflectance with true reflectance¶

Interactive Demo 2.2.2¶

Effect of smoothing factor (σ\sigmaσ), p-norm (ppp) and scaling factor (ggg)¶

Subsection 2.3: Layer Normalization¶

Get CIFAR3 scaled and unscaled dataloader¶

Visualize Images¶

Define CIFARNet model¶

Training & Evaluating the models¶

Test Generalization¶

Summary¶

The Big Picture¶

Generate $y=\frac{1}{x+\epsilon}$ train and test dataloaders¶

Effect of smoothing factor ( $\sigma$ ), p-norm ( $p$ ) and scaling factor ( $g$ )¶

Effect of smoothing factor ( $\sigma$ ), p-norm ( $p$ ) and scaling factor ( $g$ )¶