hunk.code_window:         # *greater than* the boundary value instead.
           key_fn = key if key else lambda x: x
           comp_fn = lambda x, b: key_fn(x) > b \
               if descending else lambda x, b: key_fn(x) < b  # noqa E731
           boundary_indices = [
               len([1 for x in items if comp_fn(x, b)]) for b in boundaries
           ]
           ret = []
           prev_i = 0
           for i in boundary_indices:
               ret.append(items[prev_i:i])
               prev_i = i
  
hunk.labels: keep keep keep keep replace replace replace keep keep keep keep keep
hunk.after_edit:         # Compute the boundary indices in O(n) time via scan.
           boundary_indices = []
           remaining = boundaries.copy()
           for i, x in enumerate(items):
               while remaining and not comp_fn(x, remaining[0]):
                   remaining.pop(0)
                   boundary_indices.append(i)
           for _ in remaining:
               boundary_indices.append(len(items))
           assert len(boundary_indices) == len(boundaries)
  
hunk.file_path: python/ray/data/impl/simple_block.py
hunk.type: replace
file: from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.schedules.schedule import Schedule
  
  tf1, tf, tfv = try_import_tf()
  
  
  def _linear_interpolation(left, right, alpha):
      return left + alpha * (right - left)
  
  
  class PiecewiseSchedule(Schedule):
      def __init__(self,
                   endpoints,
                   framework,
                   interpolation=_linear_interpolation,
                   outside_value=None):
          """
          Args:
              endpoints (List[Tuple[int,float]]): A list of tuples
                  `(t, value)` such that the output
                  is an interpolation (given by the `interpolation` callable)
                  between two values.
                  E.g.
                  t=400 and endpoints=[(0, 20.0),(500, 30.0)]
                  output=20.0 + 0.8 * (30.0 - 20.0) = 28.0
                  NOTE: All the values for time must be sorted in an increasing
                  order.
  
              interpolation (callable): A function that takes the left-value,
                  the right-value and an alpha interpolation parameter
                  (0.0=only left value, 1.0=only right value), which is the
                  fraction of distance from left endpoint to right endpoint.
  
              outside_value (Optional[float]): If t in call to `value` is
                  outside of all the intervals in `endpoints` this value is
                  returned. If None then an AssertionError is raised when outside
                  value is requested.
          """
          super().__init__(framework=framework)
  
          idxes = [e[0] for e in endpoints]
          assert idxes == sorted(idxes)
          self.interpolation = interpolation
          self.outside_value = outside_value
          self.endpoints = [(int(e[0]), float(e[1])) for e in endpoints]
  
      @override(Schedule)
      def _value(self, t):
          # Find t in our list of endpoints.
          for (l_t, l), (r_t, r) in zip(self.endpoints[:-1], self.endpoints[1:]):
              # When found, return an interpolation (default: linear).
              if l_t <= t < r_t:
                  alpha = float(t - l_t) / (r_t - l_t)
                  return self.interpolation(l, r, alpha)
  
          # t does not belong to any of the pieces, return `self.outside_value`.
          assert self.outside_value is not None
          return self.outside_value
  
      @override(Schedule)
      def _tf_value_op(self, t):
          assert self.outside_value is not None, \
              "tf-version of PiecewiseSchedule requires `outside_value` to be " \
              "provided!"
  
          endpoints = tf.cast(
              tf.stack([e[0] for e in self.endpoints] + [-1]), tf.int64)
  
          # Create all possible interpolation results.
          results_list = []
          for (l_t, l), (r_t, r) in zip(self.endpoints[:-1], self.endpoints[1:]):
              alpha = tf.cast(t - l_t, tf.float32) / \
                      tf.cast(r_t - l_t, tf.float32)
              results_list.append(self.interpolation(l, r, alpha))
          # If t does not belong to any of the pieces, return `outside_value`.
          results_list.append(self.outside_value)
          results_list = tf.stack(results_list)
  
          # Return correct results tensor depending on where we find t.
          def _cond(i, x):
              x = tf.cast(x, tf.int64)
              return tf.logical_not(
                  tf.logical_or(
                      tf.equal(endpoints[i + 1], -1),
                      tf.logical_and(endpoints[i] <= x, x < endpoints[i + 1])))
  
          def _body(i, x):
              return (i + 1, t)
  
          idx_and_t = tf.while_loop(_cond, _body,
                                    [tf.constant(0, dtype=tf.int64), t])
          return results_list[idx_and_t[0]]
  