Fix: runtime type errors

llama_cpp/llama_cpp.py

@@ -258,7 +258,9 @@ _lib.llama_get_state_size.restype = c_size_t
 # Copies the state to the specified destination address.
 # Destination needs to have allocated enough memory.
 # Returns the number of bytes copied
-def llama_copy_state_data(ctx: llama_context_p, dest: Array[c_uint8]) -> c_size_t:
+def llama_copy_state_data(
+    ctx: llama_context_p, dest  # type: Array[c_uint8]
+) -> c_size_t:
     return _lib.llama_copy_state_data(ctx, dest)
 
 
@@ -282,9 +284,9 @@ _lib.llama_set_state_data.restype = c_size_t
 def llama_load_session_file(
     ctx: llama_context_p,
     path_session: bytes,
-    tokens_out: Array[llama_token],
+    tokens_out,  # type: Array[llama_token]
     n_token_capacity: c_size_t,
-    n_token_count_out: _Pointer[c_size_t],
+    n_token_count_out,  # type: _Pointer[c_size_t]
 ) -> c_size_t:
     return _lib.llama_load_session_file(
         ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
@@ -304,7 +306,7 @@ _lib.llama_load_session_file.restype = c_size_t
 def llama_save_session_file(
     ctx: llama_context_p,
     path_session: bytes,
-    tokens: Array[llama_token],
+    tokens,  # type: Array[llama_token]
     n_token_count: c_size_t,
 ) -> c_size_t:
     return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
@@ -325,7 +327,7 @@ _lib.llama_save_session_file.restype = c_size_t
 # Returns 0 on success
 def llama_eval(
     ctx: llama_context_p,
-    tokens: Array[llama_token],
+    tokens,  # type: Array[llama_token]
     n_tokens: c_int,
     n_past: c_int,
     n_threads: c_int,
@@ -345,7 +347,7 @@ _lib.llama_eval.restype = c_int
 def llama_tokenize(
     ctx: llama_context_p,
     text: bytes,
-    tokens: Array[llama_token],
+    tokens,  # type: Array[llama_token]
     n_max_tokens: c_int,
     add_bos: c_bool,
 ) -> c_int:
@@ -444,8 +446,8 @@ _lib.llama_token_nl.restype = llama_token
 # @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
 def llama_sample_repetition_penalty(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
-    last_tokens_data: Array[llama_token],
+    candidates,  # type: _Pointer[llama_token_data_array]
+    last_tokens_data,  # type: Array[llama_token]
     last_tokens_size: c_int,
     penalty: c_float,
 ):
@@ -467,8 +469,8 @@ _lib.llama_sample_repetition_penalty.restype = None
 # @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
 def llama_sample_frequency_and_presence_penalties(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
-    last_tokens_data: Array[llama_token],
+    candidates,  # type: _Pointer[llama_token_data_array]
+    last_tokens_data,  # type: Array[llama_token]
     last_tokens_size: c_int,
     alpha_frequency: c_float,
     alpha_presence: c_float,
@@ -495,7 +497,9 @@ _lib.llama_sample_frequency_and_presence_penalties.restype = None
 
 
 # @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-def llama_sample_softmax(ctx: llama_context_p, candidates: _Pointer[llama_token_data]):
+def llama_sample_softmax(
+    ctx: llama_context_p, candidates  # type: _Pointer[llama_token_data]
+):
     return _lib.llama_sample_softmax(ctx, candidates)
 
 
@@ -509,7 +513,7 @@ _lib.llama_sample_softmax.restype = None
 # @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
 def llama_sample_top_k(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     k: c_int,
     min_keep: c_size_t = c_size_t(1),
 ):
@@ -528,7 +532,7 @@ _lib.llama_sample_top_k.restype = None
 # @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
 def llama_sample_top_p(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     p: c_float,
     min_keep: c_size_t = c_size_t(1),
 ):
@@ -547,7 +551,7 @@ _lib.llama_sample_top_p.restype = None
 # @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
 def llama_sample_tail_free(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     z: c_float,
     min_keep: c_size_t = c_size_t(1),
 ):
@@ -566,7 +570,7 @@ _lib.llama_sample_tail_free.restype = None
 # @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
 def llama_sample_typical(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     p: c_float,
     min_keep: c_size_t = c_size_t(1),
 ):
@@ -583,7 +587,9 @@ _lib.llama_sample_typical.restype = None
 
 
 def llama_sample_temperature(
-    ctx: llama_context_p, candidates: _Pointer[llama_token_data_array], temp: c_float
+    ctx: llama_context_p,
+    candidates,  # type: _Pointer[llama_token_data_array]
+    temp: c_float,
 ):
     return _lib.llama_sample_temperature(ctx, candidates, temp)
 
@@ -604,11 +610,11 @@ _lib.llama_sample_temperature.restype = None
 # @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
 def llama_sample_token_mirostat(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     tau: c_float,
     eta: c_float,
     m: c_int,
-    mu: _Pointer[c_float],
+    mu,  # type: _Pointer[c_float]
 ) -> llama_token:
     return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
 
@@ -631,10 +637,10 @@ _lib.llama_sample_token_mirostat.restype = llama_token
 # @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
 def llama_sample_token_mirostat_v2(
     ctx: llama_context_p,
-    candidates: _Pointer[llama_token_data_array],
+    candidates,  # type: _Pointer[llama_token_data_array]
     tau: c_float,
     eta: c_float,
-    mu: _Pointer[c_float],
+    mu,  # type: _Pointer[c_float]
 ) -> llama_token:
     return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
 
@@ -651,7 +657,8 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
 
 # @details Selects the token with the highest probability.
 def llama_sample_token_greedy(
-    ctx: llama_context_p, candidates: _Pointer[llama_token_data_array]
+    ctx: llama_context_p,
+    candidates,  # type: _Pointer[llama_token_data_array]
 ) -> llama_token:
     return _lib.llama_sample_token_greedy(ctx, candidates)
 
@@ -665,7 +672,8 @@ _lib.llama_sample_token_greedy.restype = llama_token
 
 # @details Randomly selects a token from the candidates based on their probabilities.
 def llama_sample_token(
-    ctx: llama_context_p, candidates: _Pointer[llama_token_data_array]
+    ctx: llama_context_p,
+    candidates,  # type: _Pointer[llama_token_data_array]
 ) -> llama_token:
     return _lib.llama_sample_token(ctx, candidates)