+from _collections_abc import Set as _Set, Sequence as _Sequence, Mapping as _Mapping
+from itertools import accumulate as _accumulate
+from bisect import bisect as _bisect


+           "SystemRandom", "weighted_choice"]


+    def weighted_choice(self, data, amount=1):
+        """An iterator of random elements with the chances defined by relative
+        weights.
+
+        If argument is a mapping then generates random keys with the
+        probability that is proportional to the value mapped from this key.
+
+        If argument is a sequence then generates random indices with the
+        probability that is proportional to value at this index.
+        """
+        if isinstance(data, _Mapping):
+            indices = list(data.keys())
+            weights = data.values()
+        else:
+            indices = None
+            weights = data
+        cumulative_dist = list(_accumulate(weights))
+        total_sum = cumulative_dist[-1]
+        if any(w < 0 for w in weights):
+            raise ValueError("All weights must be non-negative")
+        if not total_sum:
+            raise ValueError("At least one weight must be greater than zero")
+        del weights
+
+        # Fast path for weighted_choice()
+        u = _bisect(cumulative_dist, self.random() * total_sum)
+        if indices is not None:
+            u = indices[u]

+        k = 1.0 / total_sum
+        cumulative_dist = [k * s for s in cumulative_dist]
+        results = []

+        if indices is None:
+            for i in range(0,amount):
+                u = self.random()
+                results.append(_bisect(cumulative_dist, u))
+            return results
+        else:
+            for i in range(0,amount):
+                u = self.random()    
+                results.append(indices[_bisect(cumulative_dist, u)])
+            return results






+weighted_choice = _inst.weighted_choice