import sys
import os
import ctypes
from ctypes import (
    c_bool,
    c_char_p,
    c_int,
    c_int8,
    c_int32,
    c_uint8,
    c_uint32,
    c_size_t,
    c_float,
    c_double,
    c_void_p,
    POINTER,
    _Pointer,  # type: ignore
    Structure,
    Array,
)
import pathlib
from typing import List, Union


# Load the library
def _load_shared_library(lib_base_name: str):
    # Construct the paths to the possible shared library names
    _base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))
    # Searching for the library in the current directory under the name "libllama" (default name
    # for llamacpp) and "llama" (default name for this repo)
    _lib_paths: List[pathlib.Path] = []
    # Determine the file extension based on the platform
    if sys.platform.startswith("linux"):
        _lib_paths += [
            _base_path / f"lib{lib_base_name}.so",
        ]
    elif sys.platform == "darwin":
        _lib_paths += [
            _base_path / f"lib{lib_base_name}.so",
            _base_path / f"lib{lib_base_name}.dylib",
        ]
    elif sys.platform == "win32":
        _lib_paths += [
            _base_path / f"{lib_base_name}.dll",
            _base_path / f"lib{lib_base_name}.dll",
        ]
    else:
        raise RuntimeError("Unsupported platform")

    if "LLAMA_CPP_LIB" in os.environ:
        lib_base_name = os.environ["LLAMA_CPP_LIB"]
        _lib = pathlib.Path(lib_base_name)
        _base_path = _lib.parent.resolve()
        _lib_paths = [_lib.resolve()]

    cdll_args = dict()  # type: ignore
    # Add the library directory to the DLL search path on Windows (if needed)
    if sys.platform == "win32" and sys.version_info >= (3, 8):
        os.add_dll_directory(str(_base_path))
        if "CUDA_PATH" in os.environ:
            os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin"))
            os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib"))
        cdll_args["winmode"] = ctypes.RTLD_GLOBAL

    # Try to load the shared library, handling potential errors
    for _lib_path in _lib_paths:
        if _lib_path.exists():
            try:
                return ctypes.CDLL(str(_lib_path), **cdll_args)
            except Exception as e:
                raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")

    raise FileNotFoundError(
        f"Shared library with base name '{lib_base_name}' not found"
    )


# Specify the base name of the shared library to load
_lib_base_name = "llama"

# Load the library
_lib = _load_shared_library(_lib_base_name)

# Misc
c_float_p = POINTER(c_float)
c_uint8_p = POINTER(c_uint8)
c_size_t_p = POINTER(c_size_t)

# llama.h bindings

GGML_USE_CUBLAS = hasattr(_lib, "ggml_init_cublas")
GGML_CUDA_MAX_DEVICES = 16
LLAMA_MAX_DEVICES = GGML_CUDA_MAX_DEVICES if GGML_USE_CUBLAS else 1

# define LLAMA_DEFAULT_SEED 0xFFFFFFFF
LLAMA_DEFAULT_SEED = 0xFFFFFFFF

# define LLAMA_MAX_RNG_STATE (64*1024)
LLAMA_MAX_RNG_STATE = 64 * 1024

# define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
LLAMA_FILE_MAGIC_GGSN = 0x6767736E

# define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
# define LLAMA_SESSION_VERSION 2
LLAMA_SESSION_VERSION = 2


# struct llama_model;
llama_model_p = c_void_p

# struct llama_context;
llama_context_p = c_void_p


# typedef int32_t llama_pos;
llama_pos = c_int32
# typedef int32_t llama_token;
llama_token = c_int32
llama_token_p = POINTER(llama_token)
# typedef int32_t llama_seq_id;
llama_seq_id = c_int32


# enum llama_vocab_type {
#     LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
#     LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
# };
LLAMA_VOCAB_TYPE_SPM = 0
LLAMA_VOCAB_TYPE_BPE = 1


# enum llama_token_type {
#     LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
#     LLAMA_TOKEN_TYPE_NORMAL       = 1,
#     LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
#     LLAMA_TOKEN_TYPE_CONTROL      = 3,
#     LLAMA_TOKEN_TYPE_USER_DEFINED = 4,
#     LLAMA_TOKEN_TYPE_UNUSED       = 5,
#     LLAMA_TOKEN_TYPE_BYTE         = 6,
# };
LLAMA_TOKEN_TYPE_UNDEFINED = 0
LLAMA_TOKEN_TYPE_NORMAL = 1
LLAMA_TOKEN_TYPE_UNKNOWN = 2
LLAMA_TOKEN_TYPE_CONTROL = 3
LLAMA_TOKEN_TYPE_USER_DEFINED = 4
LLAMA_TOKEN_TYPE_UNUSED = 5
LLAMA_TOKEN_TYPE_BYTE = 6


# // model file types
# enum llama_ftype {
#     LLAMA_FTYPE_ALL_F32              = 0,
#     LLAMA_FTYPE_MOSTLY_F16           = 1,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q4_0          = 2,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q4_1          = 3,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
#     // LLAMA_FTYPE_MOSTLY_Q4_2       = 5,  // support has been removed
#     // LLAMA_FTYPE_MOSTLY_Q4_3       = 6,  // support has been removed
#     LLAMA_FTYPE_MOSTLY_Q8_0          = 7,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q5_0          = 8,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q5_1          = 9,  // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q2_K          = 10, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q3_K_S        = 11, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q3_K_M        = 12, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q3_K_L        = 13, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q4_K_S        = 14, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q4_K_M        = 15, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17, // except 1d tensors
#     LLAMA_FTYPE_MOSTLY_Q6_K          = 18, // except 1d tensors

#     LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
# };
LLAMA_FTYPE_ALL_F32 = 0
LLAMA_FTYPE_MOSTLY_F16 = 1
LLAMA_FTYPE_MOSTLY_Q4_0 = 2
LLAMA_FTYPE_MOSTLY_Q4_1 = 3
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4
LLAMA_FTYPE_MOSTLY_Q8_0 = 7
LLAMA_FTYPE_MOSTLY_Q5_0 = 8
LLAMA_FTYPE_MOSTLY_Q5_1 = 9
LLAMA_FTYPE_MOSTLY_Q2_K = 10
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17
LLAMA_FTYPE_MOSTLY_Q6_K = 18
LLAMA_FTYPE_GUESSED = 1024

# enum llama_rope_scaling_type {
#     LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
#     LLAMA_ROPE_SCALING_NONE        = 0,
#     LLAMA_ROPE_SCALING_LINEAR      = 1,
#     LLAMA_ROPE_SCALING_YARN        = 2,
#     LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
# };
LLAMA_ROPE_SCALING_UNSPECIFIED = -1
LLAMA_ROPE_SCALING_NONE = 0
LLAMA_ROPE_SCALING_LINEAR = 1
LLAMA_ROPE_SCALING_YARN = 2
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN

# typedef struct llama_token_data {
#     llama_token id; // token id
#     float logit;    // log-odds of the token
#     float p;        // probability of the token
# } llama_token_data;
class llama_token_data(Structure):
    _fields_ = [
        ("id", llama_token),
        ("logit", c_float),
        ("p", c_float),
    ]


llama_token_data_p = POINTER(llama_token_data)


# typedef struct llama_token_data_array {
#     llama_token_data * data;
#     size_t size;
#     bool sorted;
# } llama_token_data_array;
class llama_token_data_array(Structure):
    _fields_ = [
        ("data", llama_token_data_p),
        ("size", c_size_t),
        ("sorted", c_bool),
    ]


llama_token_data_array_p = POINTER(llama_token_data_array)

# typedef void (*llama_progress_callback)(float progress, void *ctx);
llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)


# // Input data for llama_decode
# // A llama_batch object can contain input about one or many sequences
# // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
# //
# // - token  : the token ids of the input (used when embd is NULL)
# // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
# // - pos    : the positions of the respective token in the sequence
# // - seq_id : the sequence to which the respective token belongs
# // - logits : if zero, the logits for the respective token will not be output
# //
# typedef struct llama_batch {
#     int32_t n_tokens;

#     llama_token  *  token;
#     float        *  embd;
#     llama_pos    *  pos;
#     int32_t      *  n_seq_id;
#     llama_seq_id ** seq_id;
#     int8_t       *  logits;


#     // NOTE: helpers for smooth API transition - can be deprecated in the future
#     //       for future-proof code, use the above fields instead and ignore everything below
#     //
#     // pos[i] = all_pos_0 + i*all_pos_1
#     //
#     llama_pos    all_pos_0;  // used if pos == NULL
#     llama_pos    all_pos_1;  // used if pos == NULL
#     llama_seq_id all_seq_id; // used if seq_id == NULL
# } llama_batch;
class llama_batch(Structure):
    _fields_ = [
        ("n_tokens", c_int32),
        ("token", POINTER(llama_token)),
        ("embd", c_float_p),
        ("pos", POINTER(llama_pos)),
        ("n_seq_id", POINTER(c_int32)),
        ("seq_id", POINTER(POINTER(llama_seq_id))),
        ("logits", POINTER(c_int8)),
        ("all_pos_0", llama_pos),
        ("all_pos_1", llama_pos),
        ("all_seq_id", llama_seq_id),
    ]


# struct llama_model_params {
#     int32_t n_gpu_layers; // number of layers to store in VRAM
#     int32_t main_gpu;     // the GPU that is used for scratch and small tensors
#     const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)

#     // called with a progress value between 0 and 1, pass NULL to disable
#     llama_progress_callback progress_callback;
#     // context pointer passed to the progress callback
#     void * progress_callback_user_data;


#     // Keep the booleans together to avoid misalignment during copy-by-value.
#     bool vocab_only; // only load the vocabulary, no weights
#     bool use_mmap;   // use mmap if possible
#     bool use_mlock;  // force system to keep model in RAM
# };
class llama_model_params(Structure):
    _fields_ = [
        ("n_gpu_layers", c_int32),
        ("main_gpu", c_int32),
        ("tensor_split", c_float_p),
        ("progress_callback", llama_progress_callback),
        ("progress_callback_user_data", c_void_p),
        ("vocab_only", c_bool),
        ("use_mmap", c_bool),
        ("use_mlock", c_bool),
    ]


# struct llama_context_params {
#     uint32_t seed;              // RNG seed, -1 for random
#     uint32_t n_ctx;             // text context, 0 = from model
#     uint32_t n_batch;           // prompt processing maximum batch size
#     uint32_t n_threads;         // number of threads to use for generation
#     uint32_t n_threads_batch;   // number of threads to use for batch processing
#     int8_t   rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`

#     // ref: https://github.com/ggerganov/llama.cpp/pull/2054
#     float    rope_freq_base;   // RoPE base frequency, 0 = from model
#     float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
#     float    yarn_ext_factor;  // YaRN extrapolation mix factor, NaN = from model
#     float    yarn_attn_factor; // YaRN magnitude scaling factor
#     float    yarn_beta_fast;   // YaRN low correction dim
#     float    yarn_beta_slow;   // YaRN high correction dim
#     uint32_t yarn_orig_ctx;    // YaRN original context size


#     // Keep the booleans together to avoid misalignment during copy-by-value.
#     bool mul_mat_q;  // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
#     bool f16_kv;     // use fp16 for KV cache, fp32 otherwise
#     bool logits_all; // the llama_eval() call computes all logits, not just the last one
#     bool embedding;  // embedding mode only
# };
class llama_context_params(Structure):
    _fields_ = [
        ("seed", c_uint32),
        ("n_ctx", c_uint32),
        ("n_batch", c_uint32),
        ("n_threads", c_uint32),
        ("n_threads_batch", c_uint32),
        ("rope_scaling_type", c_int8),
        ("rope_freq_base", c_float),
        ("rope_freq_scale", c_float),
        ("yarn_ext_factor", c_float),
        ("yarn_attn_factor", c_float),
        ("yarn_beta_fast", c_float),
        ("yarn_beta_slow", c_float),
        ("yarn_orig_ctx", c_uint32),
        ("mul_mat_q", c_bool),
        ("f16_kv", c_bool),
        ("logits_all", c_bool),
        ("embedding", c_bool),
    ]


# // Signature for logging events
# // Note that text includes the new line character at the end for most events.
# // If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
# // if it exists.
# // It might not exist for progress report where '.' is output repeatedly.
# typedef void (*llama_log_callback)(enum llama_log_level level, const char * text, void * user_data);
llama_log_callback = ctypes.CFUNCTYPE(None, c_int, c_char_p, c_void_p)


# // model quantization parameters
# typedef struct llama_model_quantize_params {
#     int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
#     enum llama_ftype ftype;      // quantize to this llama_ftype
#     bool allow_requantize;       // allow quantizing non-f32/f16 tensors
#     bool quantize_output_tensor; // quantize output.weight
#     bool only_copy;              // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
#     bool pure;                   // disable k-quant mixtures and quantize all tensors to the same type
# } llama_model_quantize_params;
class llama_model_quantize_params(Structure):
    _fields_ = [
        ("nthread", c_int),
        ("ftype", c_int),
        ("allow_requantize", c_bool),
        ("quantize_output_tensor", c_bool),
        ("only_copy", c_bool),
    ]


# // grammar types
# struct llama_grammar;
llama_grammar_p = c_void_p

# // grammar element type
# enum llama_gretype {
#     // end of rule definition
#     LLAMA_GRETYPE_END            = 0,

#     // start of alternate definition for rule
#     LLAMA_GRETYPE_ALT            = 1,

#     // non-terminal element: reference to rule
#     LLAMA_GRETYPE_RULE_REF       = 2,

#     // terminal element: character (code point)
#     LLAMA_GRETYPE_CHAR           = 3,

#     // inverse char(s) ([^a], [^a-b] [^abc])
#     LLAMA_GRETYPE_CHAR_NOT       = 4,

#     // modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
#     // be an inclusive range ([a-z])
#     LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,

#     // modifies a preceding LLAMA_GRETYPE_CHAR or
#     // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
#     LLAMA_GRETYPE_CHAR_ALT       = 6,
# };
LLAMA_GRETYPE_END = 0
LLAMA_GRETYPE_ALT = 1
LLAMA_GRETYPE_RULE_REF = 2
LLAMA_GRETYPE_CHAR = 3
LLAMA_GRETYPE_CHAR_NOT = 4
LLAMA_GRETYPE_CHAR_RNG_UPPER = 5
LLAMA_GRETYPE_CHAR_ALT = 6


# typedef struct llama_grammar_element {
#     enum llama_gretype type;
#     uint32_t           value; // Unicode code point or rule ID
# } llama_grammar_element;
class llama_grammar_element(Structure):
    _fields_ = [
        ("type", c_int),
        ("value", c_uint32),
    ]


llama_grammar_element_p = POINTER(llama_grammar_element)

# // performance timing information
# struct llama_timings {
#     double t_start_ms;
#     double t_end_ms;
#     double t_load_ms;
#     double t_sample_ms;
#     double t_p_eval_ms;
#     double t_eval_ms;


#     int32_t n_sample;
#     int32_t n_p_eval;
#     int32_t n_eval;
# };
class llama_timings(Structure):
    _fields_ = [
        ("t_start_ms", c_double),
        ("t_end_ms", c_double),
        ("t_load_ms", c_double),
        ("t_sample_ms", c_double),
        ("t_p_eval_ms", c_double),
        ("t_eval_ms", c_double),
        ("n_sample", c_int32),
        ("n_p_eval", c_int32),
        ("n_eval", c_int32),
    ]


# // Helpers for getting default parameters
# LLAMA_API struct llama_model_params llama_model_default_params(void);
def llama_model_default_params() -> llama_model_params:
    return _lib.llama_model_default_params()


_lib.llama_model_default_params.argtypes = []
_lib.llama_model_default_params.restype = llama_model_params


# LLAMA_API struct llama_context_params llama_context_default_params(void);
def llama_context_default_params() -> llama_context_params:
    return _lib.llama_context_default_params()


_lib.llama_context_default_params.argtypes = []
_lib.llama_context_default_params.restype = llama_context_params


# LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
def llama_model_quantize_default_params() -> llama_model_quantize_params:
    return _lib.llama_model_quantize_default_params()


_lib.llama_model_quantize_default_params.argtypes = []
_lib.llama_model_quantize_default_params.restype = llama_model_quantize_params


# // Initialize the llama + ggml backend
# // If numa is true, use NUMA optimizations
# // Call once at the start of the program
# LLAMA_API void llama_backend_init(bool numa);
def llama_backend_init(numa: Union[c_bool, bool]):
    return _lib.llama_backend_init(numa)


_lib.llama_backend_init.argtypes = [c_bool]
_lib.llama_backend_init.restype = None


# // Call once at the end of the program - currently only used for MPI
# LLAMA_API void llama_backend_free(void);
def llama_backend_free():
    return _lib.llama_backend_free()


_lib.llama_backend_free.argtypes = []
_lib.llama_backend_free.restype = None


# LLAMA_API struct llama_model * llama_load_model_from_file(
#                          const char * path_model,
#         struct llama_model_params     params);
def llama_load_model_from_file(
    path_model: bytes, params: llama_model_params
) -> llama_model_p:
    return _lib.llama_load_model_from_file(path_model, params)


_lib.llama_load_model_from_file.argtypes = [c_char_p, llama_model_params]
_lib.llama_load_model_from_file.restype = llama_model_p


# LLAMA_API void llama_free_model(struct llama_model * model);
def llama_free_model(model: llama_model_p):
    return _lib.llama_free_model(model)


_lib.llama_free_model.argtypes = [llama_model_p]
_lib.llama_free_model.restype = None


# LLAMA_API struct llama_context * llama_new_context_with_model(
#                  struct llama_model * model,
#         struct llama_context_params   params);
def llama_new_context_with_model(
    model: llama_model_p, params: llama_context_params
) -> llama_context_p:
    return _lib.llama_new_context_with_model(model, params)


_lib.llama_new_context_with_model.argtypes = [llama_model_p, llama_context_params]
_lib.llama_new_context_with_model.restype = llama_context_p


# // Frees all allocated memory
# LLAMA_API void llama_free(struct llama_context * ctx);
def llama_free(ctx: llama_context_p):
    return _lib.llama_free(ctx)


_lib.llama_free.argtypes = [llama_context_p]
_lib.llama_free.restype = None


# LLAMA_API int64_t llama_time_us(void);
def llama_time_us() -> int:
    return _lib.llama_time_us()


_lib.llama_time_us.argtypes = []
_lib.llama_time_us.restype = ctypes.c_int64


# LLAMA_API int  llama_max_devices    (void);
def llama_max_devices() -> int:
    return _lib.llama_max_devices()


_lib.llama_max_devices.argtypes = []
_lib.llama_max_devices.restype = c_int


# LLAMA_API bool llama_mmap_supported (void);
def llama_mmap_supported() -> bool:
    return _lib.llama_mmap_supported()


_lib.llama_mmap_supported.argtypes = []
_lib.llama_mmap_supported.restype = c_bool


# LLAMA_API bool llama_mlock_supported(void);
def llama_mlock_supported() -> bool:
    return _lib.llama_mlock_supported()


_lib.llama_mlock_supported.argtypes = []
_lib.llama_mlock_supported.restype = c_bool


# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
def llama_get_model(ctx: llama_context_p) -> llama_model_p:
    return _lib.llama_get_model(ctx)


_lib.llama_get_model.argtypes = [llama_context_p]
_lib.llama_get_model.restype = llama_model_p


# LLAMA_API int llama_n_ctx      (const struct llama_context * ctx);
def llama_n_ctx(ctx: llama_context_p) -> int:
    return _lib.llama_n_ctx(ctx)


_lib.llama_n_ctx.argtypes = [llama_context_p]
_lib.llama_n_ctx.restype = c_int


# LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
def llama_vocab_type(model: llama_model_p) -> int:
    return _lib.llama_vocab_type(model)


_lib.llama_vocab_type.argtypes = [llama_model_p]
_lib.llama_vocab_type.restype = c_int


# LLAMA_API int llama_n_vocab    (const struct llama_model * model);
def llama_n_vocab(model: llama_model_p) -> int:
    return _lib.llama_n_vocab(model)


_lib.llama_n_vocab.argtypes = [llama_model_p]
_lib.llama_n_vocab.restype = c_int


# LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
def llama_n_ctx_train(model: llama_model_p) -> int:
    return _lib.llama_n_ctx_train(model)


_lib.llama_n_ctx_train.argtypes = [llama_model_p]
_lib.llama_n_ctx_train.restype = c_int


# LLAMA_API int llama_n_embd     (const struct llama_model * model);
def llama_n_embd(model: llama_model_p) -> int:
    return _lib.llama_n_embd(model)


_lib.llama_n_embd.argtypes = [llama_model_p]
_lib.llama_n_embd.restype = c_int


# // Get the model's RoPE frequency scaling factor
# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
def llama_rope_freq_scale_train(model: llama_model_p) -> float:
    return _lib.llama_rope_freq_scale_train(model)


_lib.llama_rope_freq_scale_train.argtypes = [llama_model_p]
_lib.llama_rope_freq_scale_train.restype = c_float


# // Get a string describing the model type
# LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
def llama_model_desc(
    model: llama_model_p, buf: bytes, buf_size: Union[c_size_t, int]
) -> int:
    return _lib.llama_model_desc(model, buf, buf_size)


_lib.llama_model_desc.argtypes = [llama_model_p, c_char_p, c_size_t]
_lib.llama_model_desc.restype = c_int


# // Returns the total size of all the tensors in the model in bytes
# LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
def llama_model_size(model: llama_model_p) -> int:
    return _lib.llama_model_size(model)


_lib.llama_model_size.argtypes = [llama_model_p]
_lib.llama_model_size.restype = ctypes.c_uint64


# // Returns the total number of parameters in the model
# LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
def llama_model_n_params(model: llama_model_p) -> int:
    return _lib.llama_model_n_params(model)


_lib.llama_model_n_params.argtypes = [llama_model_p]
_lib.llama_model_n_params.restype = ctypes.c_uint64


# // Get a llama model tensor
# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
def llama_get_model_tensor(
    model: llama_model_p, name: Union[c_char_p, bytes]
) -> c_void_p:
    return _lib.llama_get_model_tensor(model, name)


_lib.llama_get_model_tensor.argtypes = [llama_model_p, c_char_p]
_lib.llama_get_model_tensor.restype = c_void_p


# // Returns 0 on success
# LLAMA_API int llama_model_quantize(
#         const char * fname_inp,
#         const char * fname_out,
#         const llama_model_quantize_params * params);
def llama_model_quantize(
    fname_inp: bytes,
    fname_out: bytes,
    params,  # type: POINTER(llama_model_quantize_params) # type: ignore
) -> int:
    return _lib.llama_model_quantize(fname_inp, fname_out, params)


_lib.llama_model_quantize.argtypes = [
    c_char_p,
    c_char_p,
    POINTER(llama_model_quantize_params),
]
_lib.llama_model_quantize.restype = c_int


# // Apply a LoRA adapter to a loaded model
# // path_base_model is the path to a higher quality model to use as a base for
# // the layers modified by the adapter. Can be NULL to use the current loaded model.
# // The model needs to be reloaded before applying a new adapter, otherwise the adapter
# // will be applied on top of the previous one
# // Returns 0 on success
# LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
#         struct llama_context * ctx,
#                   const char * path_lora,
#                        float   scale,
#                   const char * path_base_model,
#                          int   n_threads),
#         "use llama_model_apply_lora_from_file instead");
def llama_apply_lora_from_file(
    ctx: llama_context_p,
    path_lora: Union[c_char_p, bytes],
    scale: Union[c_float, float],
    path_base_model: Union[c_char_p, bytes],
    n_threads: Union[c_int, int],
) -> int:
    return _lib.llama_apply_lora_from_file(
        ctx, path_lora, scale, path_base_model, n_threads
    )


_lib.llama_apply_lora_from_file.argtypes = [
    llama_context_p,
    c_char_p,
    c_float,
    c_char_p,
    c_int,
]
_lib.llama_apply_lora_from_file.restype = c_int


# LLAMA_API int llama_model_apply_lora_from_file(
#         const struct llama_model * model,
#                   const char * path_lora,
#                        float   scale,
#                   const char * path_base_model,
#                          int   n_threads);
def llama_model_apply_lora_from_file(
    model: llama_model_p,
    path_lora: Union[c_char_p, bytes],
    scale: Union[c_float, float],
    path_base_model: Union[c_char_p, bytes],
    n_threads: Union[c_int, int],
) -> int:
    return _lib.llama_model_apply_lora_from_file(
        model, path_lora, scale, path_base_model, n_threads
    )


_lib.llama_model_apply_lora_from_file.argtypes = [
    llama_model_p,
    c_char_p,
    c_float,
    c_char_p,
    c_int,
]
_lib.llama_model_apply_lora_from_file.restype = c_int

# //
# // KV cache
# //


# // Returns the number of tokens in the KV cache
# LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
#         "avoid using this, it will be removed in the future, instead - count the tokens in user code");
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
    return _lib.llama_get_kv_cache_token_count(ctx)


_lib.llama_get_kv_cache_token_count.argtypes = [llama_context_p]
_lib.llama_get_kv_cache_token_count.restype = c_int


# // Clear the KV cache
# LLAMA_API void llama_kv_cache_clear(
#         struct llama_context * ctx);
def llama_kv_cache_clear(ctx: llama_context_p):
    return _lib.llama_kv_cache_clear(ctx)


_lib.llama_kv_cache_clear.argtypes = [llama_context_p]
_lib.llama_kv_cache_clear.restype = None


# // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
# // seq_id < 0 : match any sequence
# // p0 < 0     : [0,  p1]
# // p1 < 0     : [p0, inf)
# LLAMA_API void llama_kv_cache_seq_rm(
#         struct llama_context * ctx,
#                 llama_seq_id   seq_id,
#                    llama_pos   p0,
#                    llama_pos   p1);
def llama_kv_cache_seq_rm(
    ctx: llama_context_p,
    seq_id: Union[llama_seq_id, int],
    p0: Union[llama_pos, int],
    p1: Union[llama_pos, int],
):
    return _lib.llama_kv_cache_seq_rm(ctx, seq_id, p0, p1)


_lib.llama_kv_cache_seq_rm.argtypes = [
    llama_context_p,
    llama_seq_id,
    llama_pos,
    llama_pos,
]
_lib.llama_kv_cache_seq_rm.restype = None


# // Copy all tokens that belong to the specified sequence to another sequence
# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
# // p0 < 0 : [0,  p1]
# // p1 < 0 : [p0, inf)
# LLAMA_API void llama_kv_cache_seq_cp(
#         struct llama_context * ctx,
#                 llama_seq_id   seq_id_src,
#                 llama_seq_id   seq_id_dst,
#                    llama_pos   p0,
#                    llama_pos   p1);
def llama_kv_cache_seq_cp(
    ctx: llama_context_p,
    seq_id_src: Union[llama_seq_id, int],
    seq_id_dst: Union[llama_seq_id, int],
    p0: Union[llama_pos, int],
    p1: Union[llama_pos, int],
):
    return _lib.llama_kv_cache_seq_cp(ctx, seq_id_src, seq_id_dst, p0, p1)


_lib.llama_kv_cache_seq_cp.argtypes = [
    llama_context_p,
    llama_seq_id,
    llama_seq_id,
    llama_pos,
    llama_pos,
]
_lib.llama_kv_cache_seq_cp.restype = None


# // Removes all tokens that do not belong to the specified sequence
# LLAMA_API void llama_kv_cache_seq_keep(
#         struct llama_context * ctx,
#                 llama_seq_id   seq_id);
def llama_kv_cache_seq_keep(
    ctx: llama_context_p,
    seq_id: Union[llama_seq_id, int],
):
    return _lib.llama_kv_cache_seq_keep(ctx, seq_id)


_lib.llama_kv_cache_seq_keep.argtypes = [llama_context_p, llama_seq_id]
_lib.llama_kv_cache_seq_keep.restype = None


# // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
# // If the KV cache is RoPEd, the KV data is updated accordingly
# // p0 < 0 : [0,  p1]
# // p1 < 0 : [p0, inf)
# LLAMA_API void llama_kv_cache_seq_shift(
#         struct llama_context * ctx,
#                 llama_seq_id   seq_id,
#                    llama_pos   p0,
#                    llama_pos   p1,
#                    llama_pos   delta);
def llama_kv_cache_seq_shift(
    ctx: llama_context_p,
    seq_id: Union[llama_seq_id, int],
    p0: Union[llama_pos, int],
    p1: Union[llama_pos, int],
    delta: Union[llama_pos, int],
):
    return _lib.llama_kv_cache_seq_shift(ctx, seq_id, p0, p1, delta)


_lib.llama_kv_cache_seq_shift.argtypes = [
    llama_context_p,
    llama_seq_id,
    llama_pos,
    llama_pos,
    llama_pos,
]
_lib.llama_kv_cache_seq_shift.restype = None

# //
# // State / sessions
# //


# Returns the maximum size in bytes of the state (rng, logits, embedding
# and kv_cache) - will often be smaller after compacting tokens
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
def llama_get_state_size(ctx: llama_context_p) -> int:
    return _lib.llama_get_state_size(ctx)


_lib.llama_get_state_size.argtypes = [llama_context_p]
_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
# LLAMA_API size_t llama_copy_state_data(
#         struct llama_context * ctx,
#                      uint8_t * dst);
def llama_copy_state_data(
    ctx: llama_context_p, dst  # type: Array[c_uint8]
) -> int:
    return _lib.llama_copy_state_data(ctx, dst)


_lib.llama_copy_state_data.argtypes = [llama_context_p, c_uint8_p]
_lib.llama_copy_state_data.restype = c_size_t


# Set the state reading from the specified address
# Returns the number of bytes read
# LLAMA_API size_t llama_set_state_data(
#         struct llama_context * ctx,
#                      uint8_t * src);
def llama_set_state_data(
    ctx: llama_context_p, src  # type: Array[c_uint8]
) -> int:
    return _lib.llama_set_state_data(ctx, src)


_lib.llama_set_state_data.argtypes = [llama_context_p, c_uint8_p]
_lib.llama_set_state_data.restype = c_size_t


# Save/load session file
# LLAMA_API bool llama_load_session_file(
#         struct llama_context * ctx,
#                   const char * path_session,
#                  llama_token * tokens_out,
#                       size_t   n_token_capacity,
#                       size_t * n_token_count_out);
def llama_load_session_file(
    ctx: llama_context_p,
    path_session: bytes,
    tokens_out,  # type: Array[llama_token]
    n_token_capacity: Union[c_size_t, int],
    n_token_count_out,  # type: _Pointer[c_size_t]
) -> int:
    return _lib.llama_load_session_file(
        ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
    )


_lib.llama_load_session_file.argtypes = [
    llama_context_p,
    c_char_p,
    llama_token_p,
    c_size_t,
    c_size_t_p,
]
_lib.llama_load_session_file.restype = c_size_t


# LLAMA_API bool llama_save_session_file(
#         struct llama_context * ctx,
#                   const char * path_session,
#            const llama_token * tokens,
#                       size_t   n_token_count);
def llama_save_session_file(
    ctx: llama_context_p,
    path_session: bytes,
    tokens,  # type: Array[llama_token]
    n_token_count: Union[c_size_t, int],
) -> int:
    return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)


_lib.llama_save_session_file.argtypes = [
    llama_context_p,
    c_char_p,
    llama_token_p,
    c_size_t,
]
_lib.llama_save_session_file.restype = c_size_t

# //
# // Decoding
# //


# // Run the llama inference to obtain the logits and probabilities for the next token(s).
# // tokens + n_tokens is the provided batch of new tokens to process
# // n_past is the number of tokens to use from previous eval calls
# // Returns 0 on success
# // DEPRECATED: use llama_decode() instead
# LLAMA_API DEPRECATED(int llama_eval(
#         struct llama_context * ctx,
#                  llama_token * tokens,
#                      int32_t   n_tokens,
#                          int   n_past),
#         "use llama_decode() instead");
def llama_eval(
    ctx: llama_context_p,
    tokens,  # type: Array[llama_token]
    n_tokens: Union[c_int, int],
    n_past: Union[c_int, int],
) -> int:
    return _lib.llama_eval(ctx, tokens, n_tokens, n_past)


_lib.llama_eval.argtypes = [llama_context_p, llama_token_p, c_int, c_int]
_lib.llama_eval.restype = c_int


# // Same as llama_eval, but use float matrix input directly.
# // DEPRECATED: use llama_decode() instead
# LLAMA_API DEPRECATED(int llama_eval_embd(
#         struct llama_context * ctx,
#                        float * embd,
#                      int32_t   n_tokens,
#                          int   n_past),
#         "use llama_decode() instead");
def llama_eval_embd(
    ctx: llama_context_p,
    embd,  # type: Array[c_float]
    n_tokens: Union[c_int, int],
    n_past: Union[c_int, int],
) -> int:
    return _lib.llama_eval_embd(ctx, embd, n_tokens, n_past)


_lib.llama_eval_embd.argtypes = [llama_context_p, c_float_p, c_int, c_int]
_lib.llama_eval_embd.restype = c_int


# // Return batch for single sequence of tokens starting at pos_0
# //
# // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
# //
# LLAMA_API struct llama_batch llama_batch_get_one(
#               llama_token * tokens,
#                   int32_t   n_tokens,
#                 llama_pos   pos_0,
#              llama_seq_id   seq_id);
def llama_batch_get_one(
    tokens,  # type: Array[llama_token]
    n_tokens: Union[c_int, int],
    pos_0: Union[llama_pos, int],
    seq_id: llama_seq_id,
) -> llama_batch:
    return _lib.llama_batch_get_one(tokens, n_tokens, pos_0, seq_id)


_lib.llama_batch_get_one.argtypes = [
    llama_token_p,
    c_int,
    llama_pos,
    llama_seq_id,
]
_lib.llama_batch_get_one.restype = llama_batch


# // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
# // Each token can be assigned up to n_seq_max sequence ids
# // The batch has to be freed with llama_batch_free()
# // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
# // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
# // The rest of the llama_batch members are allocated with size n_tokens
# // All members are left uninitialized
# LLAMA_API struct llama_batch llama_batch_init(
#         int32_t n_tokens,
#         int32_t embd,
#         int32_t n_seq_max);
def llama_batch_init(
    n_tokens: Union[c_int32, int],
    embd: Union[c_int32, int],
    n_seq_max: Union[c_int32, int],
) -> llama_batch:
    return _lib.llama_batch_init(n_tokens, embd, n_seq_max)


_lib.llama_batch_init.argtypes = [c_int32, c_int32, c_int32]
_lib.llama_batch_init.restype = llama_batch


# // Frees a batch of tokens allocated with llama_batch_init()
# LLAMA_API void llama_batch_free(struct llama_batch batch);
def llama_batch_free(batch: llama_batch):
    return _lib.llama_batch_free(batch)


_lib.llama_batch_free.argtypes = [llama_batch]
_lib.llama_batch_free.restype = None


# // Positive return values does not mean a fatal error, but rather a warning.
# //   0 - success
# //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
# // < 0 - error
# LLAMA_API int llama_decode(
#         struct llama_context * ctx,
#           struct llama_batch   batch);
def llama_decode(ctx: llama_context_p, batch: llama_batch) -> int:
    return _lib.llama_decode(ctx, batch)


_lib.llama_decode.argtypes = [llama_context_p, llama_batch]
_lib.llama_decode.restype = c_int


# // Set the number of threads used for decoding
# // n_threads is the number of threads used for generation (single token)
# // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
# LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
def llama_set_n_threads(
    ctx: llama_context_p,
    n_threads: Union[c_uint32, int],
    n_threads_batch: Union[c_uint32, int],
):
    return _lib.llama_set_n_threads(ctx, n_threads, n_threads_batch)


_lib.llama_set_n_threads.argtypes = [llama_context_p, c_uint32, c_uint32]
_lib.llama_set_n_threads.restype = None


# // Token logits obtained from the last call to llama_eval()
# // The logits for the last token are stored in the last row
# // Logits for which llama_batch.logits[i] == 0 are undefined
# // Rows: n_tokens provided with llama_batch
# // Cols: n_vocab
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
def llama_get_logits(
    ctx: llama_context_p,
):  # type: (...) -> Array[float] # type: ignore
    return _lib.llama_get_logits(ctx)


_lib.llama_get_logits.argtypes = [llama_context_p]
_lib.llama_get_logits.restype = c_float_p


# // Logits for the ith token. Equivalent to:
# // llama_get_logits(ctx) + i*n_vocab
# LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
def llama_get_logits_ith(
    ctx: llama_context_p, i: Union[c_int32, int]
):  # type: (...) -> Array[float] # type: ignore
    return _lib.llama_get_logits_ith(ctx, i)


_lib.llama_get_logits_ith.argtypes = [llama_context_p, c_int32]
_lib.llama_get_logits_ith.restype = c_float_p


# Get the embeddings for the input
# shape: [n_embd] (1-dimensional)
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
def llama_get_embeddings(
    ctx: llama_context_p,
):  # type: (...) -> Array[float] # type: ignore
    return _lib.llama_get_embeddings(ctx)


_lib.llama_get_embeddings.argtypes = [llama_context_p]
_lib.llama_get_embeddings.restype = c_float_p


# //
# // Vocab
# //


# LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
def llama_token_get_text(model: llama_model_p, token: Union[llama_token, int]) -> bytes:
    return _lib.llama_token_get_text(model, token)


_lib.llama_token_get_text.argtypes = [llama_model_p, llama_token]
_lib.llama_token_get_text.restype = c_char_p


# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
def llama_token_get_score(model: llama_model_p, token: Union[llama_token, int]) -> float:
    return _lib.llama_token_get_score(model, token)


_lib.llama_token_get_score.argtypes = [llama_model_p, llama_token]
_lib.llama_token_get_score.restype = c_float


# LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
def llama_token_get_type(model: llama_model_p, token: Union[llama_token, int]) -> int:
    return _lib.llama_token_get_type(model, token)


_lib.llama_token_get_type.argtypes = [llama_model_p, llama_token]
_lib.llama_token_get_type.restype = ctypes.c_int


# // Special tokens


# LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
def llama_token_bos(model: llama_model_p) -> int:
    return _lib.llama_token_bos(model)


_lib.llama_token_bos.argtypes = [llama_model_p]
_lib.llama_token_bos.restype = llama_token


# LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
def llama_token_eos(model: llama_model_p) -> int:
    return _lib.llama_token_eos(model)


_lib.llama_token_eos.argtypes = [llama_model_p]
_lib.llama_token_eos.restype = llama_token


# LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
def llama_token_nl(model: llama_model_p) -> int:
    return _lib.llama_token_nl(model)


_lib.llama_token_nl.argtypes = [llama_model_p]
_lib.llama_token_nl.restype = llama_token


# // codellama infill tokens
# LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
def llama_token_prefix(model: llama_model_p) -> int:
    return _lib.llama_token_prefix(model)


_lib.llama_token_prefix.argtypes = [llama_model_p]
_lib.llama_token_prefix.restype = llama_token


# LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
def llama_token_middle(model: llama_model_p) -> int:
    return _lib.llama_token_middle(model)


_lib.llama_token_middle.argtypes = [llama_model_p]
_lib.llama_token_middle.restype = llama_token


# LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
def llama_token_suffix(model: llama_model_p) -> int:
    return _lib.llama_token_suffix(model)


_lib.llama_token_suffix.argtypes = [llama_model_p]
_lib.llama_token_suffix.restype = llama_token


# LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle
def llama_token_eot(model: llama_model_p) -> int:
    return _lib.llama_token_eot(model)


_lib.llama_token_eot.argtypes = [llama_model_p]
_lib.llama_token_eot.restype = llama_token


# //
# // Tokenization
# //


# /// @details Convert the provided text into tokens.
# /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
# /// @return Returns the number of tokens on success, no more than n_max_tokens
# /// @return Returns a negative number on failure - the number of tokens that would have been returned
# /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
# ///                Does not insert a leading space.
# LLAMA_API int llama_tokenize(
#     const struct llama_model * model,
#                   const char * text,
#                          int   text_len,
#                  llama_token * tokens,
#                          int   n_max_tokens,
#                         bool   add_bos,
#                         bool   special);
def llama_tokenize(
    model: llama_model_p,
    text: bytes,
    text_len: Union[c_int, int],
    tokens,  # type: Array[llama_token]
    n_max_tokens: Union[c_int, int],
    add_bos: Union[c_bool, bool],
    special: Union[c_bool, bool],
) -> int:
    return _lib.llama_tokenize(
        model, text, text_len, tokens, n_max_tokens, add_bos, special
    )


_lib.llama_tokenize.argtypes = [
    llama_model_p,
    c_char_p,
    c_int,
    llama_token_p,
    c_int,
    c_bool,
    c_bool,
]
_lib.llama_tokenize.restype = c_int


# // Token Id -> Piece.
# // Uses the vocabulary in the provided context.
# // Does not write null terminator to the buffer.
# // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
# LLAMA_API int llama_token_to_piece(
#           const struct llama_model * model,
#                        llama_token   token,
#                               char * buf,
#                               int    length);
def llama_token_to_piece(
    model: llama_model_p,
    token: Union[llama_token, int],
    buf: Union[c_char_p, bytes],
    length: Union[c_int, int],
) -> int:
    return _lib.llama_token_to_piece(model, token, buf, length)


_lib.llama_token_to_piece.argtypes = [llama_model_p, llama_token, c_char_p, c_int]
_lib.llama_token_to_piece.restype = c_int


# //
# // Grammar
# //


# LLAMA_API struct llama_grammar * llama_grammar_init(
#         const llama_grammar_element ** rules,
#                                 size_t    n_rules,
#                                 size_t    start_rule_index);
def llama_grammar_init(
    rules,  # type: Array[llama_grammar_element_p] # type: ignore
    n_rules: Union[c_size_t, int],
    start_rule_index: Union[c_size_t, int],
) -> llama_grammar_p:
    return _lib.llama_grammar_init(rules, n_rules, start_rule_index)


_lib.llama_grammar_init.argtypes = [
    POINTER(llama_grammar_element_p),
    c_size_t,
    c_size_t,
]
_lib.llama_grammar_init.restype = llama_grammar_p


# LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
def llama_grammar_free(grammar: llama_grammar_p):
    return _lib.llama_grammar_free(grammar)


_lib.llama_grammar_free.argtypes = [llama_grammar_p]
_lib.llama_grammar_free.restype = None


# LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
def llama_grammar_copy(grammar: llama_grammar_p) -> llama_grammar_p:
    return _lib.llama_grammar_copy(grammar)


_lib.llama_grammar_copy.argtypes = [llama_grammar_p]
_lib.llama_grammar_copy.restype = llama_grammar_p

# //
# // Sampling functions
# //


# // Sets the current rng seed.
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
def llama_set_rng_seed(ctx: llama_context_p, seed: Union[c_uint32, int]):
    return _lib.llama_set_rng_seed(ctx, seed)


_lib.llama_set_rng_seed.argtypes = [llama_context_p, c_uint32]
_lib.llama_set_rng_seed.restype = None


# /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
# /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
# LLAMA_API void llama_sample_repetition_penalties(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#            const llama_token * last_tokens,
#                       size_t   penalty_last_n,
#                        float   penalty_repeat,
#                        float   penalty_freq,
#                        float   penalty_present);
def llama_sample_repetition_penalties(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    last_tokens_data,  # type: Array[llama_token]
    penalty_last_n: Union[c_size_t, int],
    penalty_repeat: Union[c_float, float],
    penalty_freq: Union[c_float, float],
    penalty_present: Union[c_float, float],
):
    return _lib.llama_sample_repetition_penalties(
        ctx,
        candidates,
        last_tokens_data,
        penalty_last_n,
        penalty_repeat,
        penalty_freq,
        penalty_present,
    )


_lib.llama_sample_repetition_penalties.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    llama_token_p,
    c_size_t,
    c_float,
    c_float,
    c_float,
]
_lib.llama_sample_repetition_penalties.restype = None


# /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
# /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
# /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
# LLAMA_API void llama_sample_classifier_free_guidance(
#             struct llama_context * ctx,
#         llama_token_data_array * candidates,
#             struct llama_context * guidance_ctx,
#                             float   scale);
def llama_sample_classifier_free_guidance(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    guidance_ctx: llama_context_p,
    scale: Union[c_float, float],
):
    return _lib.llama_sample_classifier_free_guidance(
        ctx, candidates, guidance_ctx, scale
    )


_lib.llama_sample_classifier_free_guidance.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    llama_context_p,
    c_float,
]
_lib.llama_sample_classifier_free_guidance.restype = None


# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
# LLAMA_API void llama_sample_softmax(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates);
def llama_sample_softmax(
    ctx: llama_context_p, candidates  # type: _Pointer[llama_token_data]
):
    return _lib.llama_sample_softmax(ctx, candidates)


_lib.llama_sample_softmax.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
]
_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
# LLAMA_API void llama_sample_top_k(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                          int   k,
#                       size_t   min_keep);
def llama_sample_top_k(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    k: Union[c_int, int],
    min_keep: Union[c_size_t, int],
):
    return _lib.llama_sample_top_k(ctx, candidates, k, min_keep)


_lib.llama_sample_top_k.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_int,
    c_size_t,
]
_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
# LLAMA_API void llama_sample_top_p(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   p,
#                       size_t   min_keep);
def llama_sample_top_p(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    p: Union[c_float, float],
    min_keep: Union[c_size_t, int],
):
    return _lib.llama_sample_top_p(ctx, candidates, p, min_keep)


_lib.llama_sample_top_p.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_size_t,
]
_lib.llama_sample_top_p.restype = None


# /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
# LLAMA_API void llama_sample_min_p(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   p,
#                       size_t   min_keep);
def llama_sample_min_p(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    p: Union[c_float, float],
    min_keep: Union[c_size_t, int],
):
    return _lib.llama_sample_min_p(ctx, candidates, p, min_keep)


_lib.llama_sample_min_p.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_size_t,
]
_lib.llama_sample_min_p.restype = None


# /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
# LLAMA_API void llama_sample_tail_free(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   z,
#                       size_t   min_keep);
def llama_sample_tail_free(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    z: Union[c_float, float],
    min_keep: Union[c_size_t, int],
):
    return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep)


_lib.llama_sample_tail_free.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_size_t,
]
_lib.llama_sample_tail_free.restype = None


# /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
# LLAMA_API void llama_sample_typical(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   p,
#                       size_t   min_keep);
def llama_sample_typical(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    p: Union[c_float, float],
    min_keep: Union[c_size_t, int],
):
    return _lib.llama_sample_typical(ctx, candidates, p, min_keep)


_lib.llama_sample_typical.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_size_t,
]
_lib.llama_sample_typical.restype = None


# LLAMA_API void llama_sample_temp(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   temp);
def llama_sample_temp(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    temp: Union[c_float, float],
):
    return _lib.llama_sample_temp(ctx, candidates, temp)


_lib.llama_sample_temp.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
]
_lib.llama_sample_temp.restype = None


# LLAMA_API DEPRECATED(void llama_sample_temperature(
#             struct llama_context * ctx,
#           llama_token_data_array * candidates,
#                            float   temp),
#         "use llama_sample_temp instead");
def llama_sample_temperature(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    temp: Union[c_float, float],
):
    return _lib.llama_sample_temperature(ctx, candidates, temp)


_lib.llama_sample_temperature.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
]
_lib.llama_sample_temperature.restype = None


# /// @details Apply constraints from grammar
# LLAMA_API void llama_sample_grammar(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#   const struct llama_grammar * grammar);
def llama_sample_grammar(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    grammar,  # type: llama_grammar_p
):
    return _lib.llama_sample_grammar(ctx, candidates, grammar)


_lib.llama_sample_grammar.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    llama_grammar_p,
]
_lib.llama_sample_grammar.restype = None


# /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
# /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
# /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
# /// @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.
# LLAMA_API llama_token llama_sample_token_mirostat(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   tau,
#                        float   eta,
#                          int   m,
#                        float * mu);
def llama_sample_token_mirostat(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    tau: Union[c_float, float],
    eta: Union[c_float, float],
    m: Union[c_int, int],
    mu,  # type: _Pointer[c_float]
) -> int:
    return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)


_lib.llama_sample_token_mirostat.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_float,
    c_int,
    c_float_p,
]
_lib.llama_sample_token_mirostat.restype = llama_token


# /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
# /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
# /// @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.
# LLAMA_API llama_token llama_sample_token_mirostat_v2(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates,
#                        float   tau,
#                        float   eta,
#                        float * mu);
def llama_sample_token_mirostat_v2(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
    tau: Union[c_float, float],
    eta: Union[c_float, float],
    mu,  # type: _Pointer[c_float]
) -> int:
    return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)


_lib.llama_sample_token_mirostat_v2.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
    c_float,
    c_float,
    c_float_p,
]
_lib.llama_sample_token_mirostat_v2.restype = llama_token


# /// @details Selects the token with the highest probability.
# ///          Does not compute the token probabilities. Use llama_sample_softmax() instead.
# LLAMA_API llama_token llama_sample_token_greedy(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates);
def llama_sample_token_greedy(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
) -> int:
    return _lib.llama_sample_token_greedy(ctx, candidates)


_lib.llama_sample_token_greedy.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
]
_lib.llama_sample_token_greedy.restype = llama_token


# /// @details Randomly selects a token from the candidates based on their probabilities.
# LLAMA_API llama_token llama_sample_token(
#         struct llama_context * ctx,
#       llama_token_data_array * candidates);
def llama_sample_token(
    ctx: llama_context_p,
    candidates,  # type: _Pointer[llama_token_data_array]
) -> int:
    return _lib.llama_sample_token(ctx, candidates)


_lib.llama_sample_token.argtypes = [
    llama_context_p,
    llama_token_data_array_p,
]
_lib.llama_sample_token.restype = llama_token


# /// @details Accepts the sampled token into the grammar
# LLAMA_API void llama_grammar_accept_token(
#         struct llama_context * ctx,
#         struct llama_grammar * grammar,
#                  llama_token   token);
def llama_grammar_accept_token(
    ctx: llama_context_p,
    grammar: llama_grammar_p,
    token: Union[llama_token, int],
) -> None:
    _lib.llama_grammar_accept_token(ctx, grammar, token)


_lib.llama_grammar_accept_token.argtypes = [
    llama_context_p,
    llama_grammar_p,
    llama_token,
]
_lib.llama_grammar_accept_token.restype = None


# //
# // Beam search
# //

# struct llama_beam_view {
#     const llama_token * tokens;


#     size_t n_tokens;
#     float  p;        // Cumulative beam probability (renormalized relative to all beams)
#     bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
# };
class llama_beam_view(ctypes.Structure):
    _fields_ = [
        ("tokens", llama_token_p),
        ("n_tokens", c_size_t),
        ("p", c_float),
        ("eob", c_bool),
    ]


# // Passed to beam_search_callback function.
# // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
# // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
# // These pointers are valid only during the synchronous callback, so should not be saved.
# struct llama_beams_state {
#     struct llama_beam_view * beam_views;
#     size_t n_beams;               // Number of elements in beam_views[].
#     size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
#     bool   last_call;             // True iff this is the last callback invocation.
# };
class llama_beams_state(ctypes.Structure):
    _fields_ = [
        ("beam_views", POINTER(llama_beam_view)),
        ("n_beams", c_size_t),
        ("common_prefix_length", c_size_t),
        ("last_call", c_bool),
    ]


# // Type of pointer to the beam_search_callback function.
# // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
# // passed back to beam_search_callback. This avoids having to use global variables in the callback.
# typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
llama_beam_search_callback_fn_t = ctypes.CFUNCTYPE(None, c_void_p, llama_beams_state)


# /// @details Deterministically returns entire sentence constructed by a beam search.
# /// @param ctx Pointer to the llama_context.
# /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
# /// @param callback_data A pointer that is simply passed back to callback.
# /// @param n_beams Number of beams to use.
# /// @param n_past Number of tokens already evaluated.
# /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
# /// @param n_threads Number of threads as passed to llama_eval().
# LLAMA_API void llama_beam_search(
#                struct llama_context * ctx,
#     llama_beam_search_callback_fn_t   callback,
#                                void * callback_data,
#                              size_t   n_beams,
#                                 int   n_past,
#                                 int   n_predict);
def llama_beam_search(
    ctx: llama_context_p,
    callback: "ctypes._CFuncPtr[None, c_void_p, llama_beams_state]",  # type: ignore
    callback_data: c_void_p,
    n_beams: Union[c_size_t, int],
    n_past: Union[c_int, int],
    n_predict: Union[c_int, int],
):
    return _lib.llama_beam_search(
        ctx, callback, callback_data, n_beams, n_past, n_predict
    )


_lib.llama_beam_search.argtypes = [
    llama_context_p,
    llama_beam_search_callback_fn_t,
    c_void_p,
    c_size_t,
    c_int,
    c_int,
]
_lib.llama_beam_search.restype = None


# Performance information


# LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
def llama_get_timings(ctx: llama_context_p) -> llama_timings:
    return _lib.llama_get_timings(ctx)


_lib.llama_get_timings.argtypes = [llama_context_p]
_lib.llama_get_timings.restype = llama_timings


# LLAMA_API void llama_print_timings(struct llama_context * ctx);
def llama_print_timings(ctx: llama_context_p):
    _lib.llama_print_timings(ctx)


_lib.llama_print_timings.argtypes = [llama_context_p]
_lib.llama_print_timings.restype = None


# LLAMA_API void llama_reset_timings(struct llama_context * ctx);
def llama_reset_timings(ctx: llama_context_p):
    _lib.llama_reset_timings(ctx)


_lib.llama_reset_timings.argtypes = [llama_context_p]
_lib.llama_reset_timings.restype = None


# Print system information
# LLAMA_API const char * llama_print_system_info(void);
def llama_print_system_info() -> bytes:
    return _lib.llama_print_system_info()


_lib.llama_print_system_info.argtypes = []
_lib.llama_print_system_info.restype = c_char_p


# NOTE: THIS IS CURRENTLY BROKEN AS ggml_log_callback IS NOT EXPOSED IN LLAMA.H
# // Set callback for all future logging events.
# // If this is not called, or NULL is supplied, everything is output on stderr.
# LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
def llama_log_set(
    log_callback: "ctypes._FuncPointer", user_data: c_void_p  # type: ignore
):
    return _lib.llama_log_set(log_callback, user_data)


_lib.llama_log_set.argtypes = [llama_log_callback, c_void_p]
_lib.llama_log_set.restype = None


# LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
def llama_dump_timing_info_yaml(stream: ctypes.c_void_p, ctx: llama_context_p):
    return _lib.llama_dump_timing_info_yaml(stream, ctx)


_lib.llama_dump_timing_info_yaml.argtypes = [ctypes.c_void_p, llama_context_p]
_lib.llama_dump_timing_info_yaml.restype = None