Use new interface in statistical

tests/fsm/test_statistical.py

@@ -1,27 +1,12 @@
 from typing import Callable, List, Optional
 
 import numpy as np
-from outlines_core.fsm.guide import RegexGuide
+from outlines_core.fsm import Guide, Index, Vocabulary
 from pytest import approx
 from scipy.stats import ks_2samp
 
 
 def test_generate_length():
-    class MockTokenizer:
-        vocabulary = {"0": 1, "1": 2, "eos": 3}
-        inverse_vocabulary = {1: "0", 2: "1", 3: ""}
-        special_tokens = {"eos"}
-        eos_token_id = 3
-
-        def length(self):
-            return len(self.vocabulary)
-
-        def convert_token_to_string(self, token):
-            return token
-
-        def decode(self, token):
-            return self.inverse_vocabulary[token]
-
     class NextToken:
         def __init__(
             self,
@@ -43,17 +28,18 @@ def __call__(
             next_t = [self.rng.choice(self.states, p=prob / np.sum(prob))]
             return tokens + next_t if tokens is not None else next_t
 
-    def generate(model, tokenizer, regex_str) -> Optional[List[int]]:
-        n_tokens = tokenizer.length()
+    def generate(model, regex_str) -> Optional[List[int]]:
+        vocabulary = Vocabulary(3, {"0": [1], "1": [2], "2": [4]})
+        index = Index(regex_str, vocabulary)
+        guide = Guide(index)
 
-        fsm = RegexGuide.from_regex(regex_str, tokenizer)
-        state: int = fsm.initial_state
+        n_tokens = len(vocabulary)
         tokens = None
-        while state != -1:
-            allowed = fsm.get_next_instruction(state).tokens
+        allowed = guide.get_start_tokens()
+        while not guide.is_finished():
             mask: List[int] = [1 if s in allowed else 0 for s in range(1, n_tokens + 1)]
             tokens = model(tokens, mask=mask)
-            state = fsm.get_next_state(state, tokens[-1])
+            allowed = guide.read_next_token(tokens[-1])
         return tokens
 
     def prob_non_markov(tokens: List[int]) -> np.array:
@@ -75,16 +61,15 @@ def prob_markov(token: List[int]) -> np.array:
 
     n_samples: int = 250
     regex_str: str = r"11[01]+|0[01]*"
-    tokenizer = MockTokenizer()
     model1 = NextToken(prob_markov, p0, states, 30127)
     model2 = NextToken(prob_non_markov, p0, states, 24601)
 
     lengths1: np.array = np.zeros((n_samples,))
     lengths2: np.array = np.zeros((n_samples,))
     for i in range(n_samples):
-        out1: List[int] = generate(model1, tokenizer, regex_str)
+        out1: List[int] = generate(model1, regex_str)
         lengths1[i] = len(out1) - 1  # take off the eos token
-        out2: List[int] = generate(model2, tokenizer, regex_str)
+        out2: List[int] = generate(model2, regex_str)
         lengths2[i] = len(out2) - 1  # take off the eos token
 
     # 2 sample KS test to check that lengths has the same distribution as