<p>You are given a binary string <code>s</code>.</p>

<p>Return the number of <span data-keyword="substring-nonempty">substrings</span> with <strong>dominant</strong> ones.</p>

<p>A string has <strong>dominant</strong> ones if the number of ones in the string is <strong>greater than or equal to</strong> the <strong>square</strong> of the number of zeros in the string.</p>

<p>&nbsp;</p>
<p><strong class="example">Example 1:</strong></p>

<div class="example-block">
<p><strong>Input:</strong> <span class="example-io">s = &quot;00011&quot;</span></p>

<p><strong>Output:</strong> <span class="example-io">5</span></p>

<p><strong>Explanation:</strong></p>

<p>The substrings with dominant ones are shown in the table below.</p>
</div>

<table>
	<thead>
		<tr>
			<th>i</th>
			<th>j</th>
			<th>s[i..j]</th>
			<th>Number of Zeros</th>
			<th>Number of Ones</th>
		</tr>
	</thead>
	<tbody>
		<tr>
			<td>3</td>
			<td>3</td>
			<td>1</td>
			<td>0</td>
			<td>1</td>
		</tr>
		<tr>
			<td>4</td>
			<td>4</td>
			<td>1</td>
			<td>0</td>
			<td>1</td>
		</tr>
		<tr>
			<td>2</td>
			<td>3</td>
			<td>01</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td>3</td>
			<td>4</td>
			<td>11</td>
			<td>0</td>
			<td>2</td>
		</tr>
		<tr>
			<td>2</td>
			<td>4</td>
			<td>011</td>
			<td>1</td>
			<td>2</td>
		</tr>
	</tbody>
</table>

<p><strong class="example">Example 2:</strong></p>

<div class="example-block">
<p><strong>Input:</strong> <span class="example-io">s = &quot;101101&quot;</span></p>

<p><strong>Output:</strong> <span class="example-io">16</span></p>

<p><strong>Explanation:</strong></p>

<p>The substrings with <strong>non-dominant</strong> ones are shown in the table below.</p>

<p>Since there are 21 substrings total and 5 of them have non-dominant ones, it follows that there are 16 substrings with dominant ones.</p>
</div>

<table>
	<thead>
		<tr>
			<th>i</th>
			<th>j</th>
			<th>s[i..j]</th>
			<th>Number of Zeros</th>
			<th>Number of Ones</th>
		</tr>
	</thead>
	<tbody>
		<tr>
			<td>1</td>
			<td>1</td>
			<td>0</td>
			<td>1</td>
			<td>0</td>
		</tr>
		<tr>
			<td>4</td>
			<td>4</td>
			<td>0</td>
			<td>1</td>
			<td>0</td>
		</tr>
		<tr>
			<td>1</td>
			<td>4</td>
			<td>0110</td>
			<td>2</td>
			<td>2</td>
		</tr>
		<tr>
			<td>0</td>
			<td>4</td>
			<td>10110</td>
			<td>2</td>
			<td>3</td>
		</tr>
		<tr>
			<td>1</td>
			<td>5</td>
			<td>01101</td>
			<td>2</td>
			<td>3</td>
		</tr>
	</tbody>
</table>

<p>&nbsp;</p>
<p><strong>Constraints:</strong></p>

<ul>
	<li><code>1 &lt;= s.length &lt;= 4 * 10<sup>4</sup></code></li>
	<li><code>s</code> consists only of characters <code>&#39;0&#39;</code> and <code>&#39;1&#39;</code>.</li>
</ul>

class Solution:
  def numberOfSubstrings(self, s: str) -> int:
    kLen = len(s)
    kSqrtLen = int(sqrt(kLen)) + 1
   
    class Zeros(SlidingWindow.Observer):

      def __init__(self):
        self.zeros = deque()

      def enter_cell(self, ri):
        if s[ri] == '0':
          self.zeros.append(ri)

      def exit_cell(self, li):
        if s[li] == '0':
          self.zeros.popleft()

      def aggregate(self):
        return len(self.zeros)

      def rightmost_zero_idx(self):
        return self.zeros[-1] if self.zeros else -1

    result = 0
    obs = Zeros()
    win = AggregateBoundSlidingWindow(s, 0, kSqrtLen, obs)
    for li, ri in win.run():
      # Count windows containing some zeros (up to kSqrtLen zeros):
      for idx, j in enumerate(obs.zeros):
        i = win.observer.zeros[idx - 1] if idx > 0 else -1
        zero_count = len(obs.zeros) - idx
        one_count = ri - j + 1 - zero_count
        if one_count >= zero_count**2:
          result += j - i
        else:
          result += max(0, j - i - (zero_count**2 - one_count) )
      # Count windows composed solely of ones:
      result += ri - obs.rightmost_zero_idx()

    return result


from collections.abc import Sequence


class SlidingWindow:
  """Represents a sliding window algorithm framework.

  This class provides a structure for implementing sliding window algorithms.
  Concrete implementations should inherit from this class and override the
  should_contract_left() and is_window_good() methods.
  """

  class Observer:

    def init(self, sequence: Sequence):
      self.seq = sequence

    def start_run(self, _range):
      ...

    def finish_run(self):
      ...

    def enter_cell(self, right_idx: int):
      ...

    def exit_cell(self, left_idx: int):
      ...

  def __init__(self, sequence: Sequence, observer=None):
    self.seq = sequence
    self.observer = observer if observer is not None else self.Observer()
    self.observer.init(sequence)

  def should_contract_left(self, left_idx: int, right_idx: int) -> bool:
    raise NotImplementedError()

  def is_window_good(self, left_idx: int, right_idx: int) -> bool:
    raise NotImplementedError()

  def run(self, _range=None):
    _range = _range if _range is not None else range(len(self.seq))
    self.observer.start_run(_range)
    li = _range.start
    for ri in _range:
      self.observer.enter_cell(ri)
      while li <= ri and self.should_contract_left(li, ri):
        step = self.observer.exit_cell(li)
        li += step if step else _range.step
      if li <= ri and self.is_window_good(li, ri):
        yield li, ri
    self.observer.finish_run()


class LengthBoundSlidingWindow(SlidingWindow):

  def __init__(self, sequence, min_len: int, max_len: int, observer=None):
    super().__init__(sequence, observer)
    self.min_len = min_len
    self.max_len = max_len

  def should_contract_left(self, li: int, ri: int) -> bool:
    return ri - li + 1 > self.max_len

  def is_window_good(self, li: int, ri: int) -> bool:
    return self.min_len <= ri - li + 1 <= self.max_len


class AggregateBoundSlidingWindow(SlidingWindow):

  def __init__(self, sequence, min_value, max_value, observer=None):
    super().__init__(sequence, observer)
    self.min_value = min_value
    self.max_value = max_value

  def should_contract_left(self, li: int, ri: int) -> bool:
    return self.observer.aggregate() > self.max_value

  def is_window_good(self, li: int, ri: int) -> bool:
    return self.min_value <= self.observer.aggregate() <= self.max_value


class Count(SlidingWindow.Observer):

  def start_run(self, _range):
    from collections import Counter
    self.counter = Counter()

  def enter_cell(self, ri):
    self.counter[self.seq[ri]] += 1

  def exit_cell(self, li):
    val = self.seq[li]
    self.counter[val] -= 1
    if self.counter[val] == 0:
      del self.counter[val]


class Min(SlidingWindow.Observer):

  def start_run(self, _range):
    from collections import deque
    self._queue = deque()

  def enter_cell(self, ri: int):
    while self._queue and self.seq[self._queue[-1]] > self.seq[ri]:
      self._queue.pop()
    self._queue.append(ri)

  def exit_cell(self, li: int):
    while self._queue and self._queue[0] <= li:
      self._queue.popleft()

  def min(self):
    return self.seq[self.min_idx()]

  def min_idx(self):
    assert self._queue
    return self._queue[0]


class Sum(SlidingWindow.Observer):

  def start_run(self, _range):
    self.sum = 0

  def enter_cell(self, ri: int):
    self.sum += self.seq[ri]

  def exit_cell(self, li: int):
    self.sum -= self.seq[li]

  def aggregate(self):
    return self.sum
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