Re-order classes in llama.py

Author: abetlen

llama_cpp/llama.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import os
 import sys
 import uuid
@@ -40,46 +42,6 @@
 )
 
 
-class LlamaState:
-    def __init__(
-        self,
-        input_ids: npt.NDArray[np.intc],
-        scores: npt.NDArray[np.single],
-        n_tokens: int,
-        llama_state: bytes,
-        llama_state_size: int,
-    ):
-        self.input_ids = input_ids
-        self.scores = scores
-        self.n_tokens = n_tokens
-        self.llama_state = llama_state
-        self.llama_state_size = llama_state_size
-
-
-LogitsProcessor = Callable[
-    [npt.NDArray[np.intc], npt.NDArray[np.single]], npt.NDArray[np.single]
-]
-
-
-class LogitsProcessorList(List[LogitsProcessor]):
-    def __call__(
-        self, input_ids: npt.NDArray[np.intc], scores: npt.NDArray[np.single]
-    ) -> npt.NDArray[np.single]:
-        for processor in self:
-            scores = processor(input_ids, scores)
-        return scores
-
-
-StoppingCriteria = Callable[[npt.NDArray[np.intc], npt.NDArray[np.single]], bool]
-
-
-class StoppingCriteriaList(List[StoppingCriteria]):
-    def __call__(
-        self, input_ids: npt.NDArray[np.intc], logits: npt.NDArray[np.single]
-    ) -> bool:
-        return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
-
-
 class Llama:
     """High-level Python wrapper for a llama.cpp model."""
 
@@ -1733,3 +1695,43 @@ def decode(self, tokens: List[int]) -> str:
     @classmethod
     def from_ggml_file(cls, path: str) -> "LlamaTokenizer":
         return cls(Llama(model_path=path, vocab_only=True))
+
+
+class LlamaState:
+    def __init__(
+        self,
+        input_ids: npt.NDArray[np.intc],
+        scores: npt.NDArray[np.single],
+        n_tokens: int,
+        llama_state: bytes,
+        llama_state_size: int,
+    ):
+        self.input_ids = input_ids
+        self.scores = scores
+        self.n_tokens = n_tokens
+        self.llama_state = llama_state
+        self.llama_state_size = llama_state_size
+
+
+LogitsProcessor = Callable[
+    [npt.NDArray[np.intc], npt.NDArray[np.single]], npt.NDArray[np.single]
+]
+
+
+class LogitsProcessorList(List[LogitsProcessor]):
+    def __call__(
+        self, input_ids: npt.NDArray[np.intc], scores: npt.NDArray[np.single]
+    ) -> npt.NDArray[np.single]:
+        for processor in self:
+            scores = processor(input_ids, scores)
+        return scores
+
+
+StoppingCriteria = Callable[[npt.NDArray[np.intc], npt.NDArray[np.single]], bool]
+
+
+class StoppingCriteriaList(List[StoppingCriteria]):
+    def __call__(
+        self, input_ids: npt.NDArray[np.intc], logits: npt.NDArray[np.single]
+    ) -> bool:
+        return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])