seamless_communication/ggml/third_party_ggml.py

"""This module is the core of the ggml-python library, it exposes a low-level [ctypes](https://docs.python.org/3/library/ctypes.html)-based interface for ggml.

Structures and functions in the `ggml.ggml` module map directly to the original ggml C library and
they operate at a fairly low level.
No additional runtime checks checks are performed nor is memory management handled automatically.
You've been warned :).

With that in mind here are some useful things to keep in mind

- Functions accept both ctypes types (c_int, c_bool, c_float, etc.) and Python types (int, bool, float, etc.) as parameters.
- Functions return Python types for simple values (int, bool, float, etc.) and ctypes types for complex values ([ggml_context_p][ggml.ggml_context_p], [ggml_tensor_p][ggml.ggml_tensor_p], etc.).
- Memory management is the responsibility of the user. The user must call [ggml.ggml_free][] on the context after calling [ggml.ggml_init][].

Example

```python
import ggml
import ctypes

# Allocate a new context with 16 MB of memory
params = ggml.ggml_init_params(mem_size=16 * 1024 * 1024, mem_buffer=None)
ctx = ggml.ggml_init(params=params)

# Instantiate tensors
x = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
b = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)

# Use ggml operations to build a computational graph
x2 = ggml.ggml_mul(ctx, x, x)
f = ggml.ggml_add(ctx, ggml.ggml_mul(ctx, a, x2), b)

gf = ggml.ggml_build_forward(f)

# Set the input values
ggml.ggml_set_f32(x, 2.0)
ggml.ggml_set_f32(a, 3.0)
ggml.ggml_set_f32(b, 4.0)

# Compute the graph
ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

# Get the output value
output = ggml.ggml_get_f32_1d(f, 0)
assert output == 16.0

# Free the context
ggml.ggml_free(ctx)
```

"""
import os
import sys
import ctypes
import pathlib
import importlib.resources
import numpy as np
from typing import Union
from typing import Type
from typing import Callable
from typing import Tuple
from typing import Dict
from typing_extensions import Self
from typing import Any
from pathlib import Path
from typing import List, Optional, Sequence, Union
from typing_extensions import TypeAlias

NULL: ctypes.c_void_p = None  # ignore: type
GGML_MEM_ALIGN = 16


# Load the library
def load_shared_library(base_path: Path, lib_base_name: str):
    # Construct the paths to the possible shared library names
    # Searching for the library in the current directory under the name "libggml" (default name
    # for ggml) and "ggml" (default name for this repo)
    lib_names: List[str] = [
        f"lib{lib_base_name}.so",
        f"lib{lib_base_name}.dylib",
        f"{lib_base_name}.dll",
    ]

    path = None
    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))
        cdll_args["winmode"] = 0

    for lib_name in lib_names:
        # Try to load the shared library, handling potential errors
        path = base_path / lib_name
        if not path.exists():
            continue
        try:
            return ctypes.CDLL(str(path), **cdll_args)
        except Exception as e:
            raise RuntimeError(f"Failed to load shared library '{path}': {e}")

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


base_path = Path(__file__).parent.resolve() / "build/src"
lib_base_name = "ggml"
lib = load_shared_library(base_path, lib_base_name)

#####################################################
# GGML Utility Types
#####################################################

CFloatArray: TypeAlias = "ctypes.Array[ctypes.c_float]"
CInt64Array: TypeAlias = "ctypes.Array[ctypes.c_int64]"
CIntPointer: TypeAlias = "ctypes._Pointer[ctypes.c_int]"  # type: ignore
CCharPointer: TypeAlias = "ctypes._Pointer[ctypes.c_char]"  # type: ignore


#####################################################
# source: ggml.h
# GGML API
#####################################################


# #define GGML_FILE_MAGIC   0x67676d6c // "ggml"
GGML_FILE_MAGIC = int("0x67676d6c", 16)
# #define GGML_FILE_VERSION 1
GGML_FILE_VERSION = 1
# #define GGML_QNT_VERSION        2    // bump this on quantization format changes
GGML_QNT_VERSION = 2
# #define GGML_QNT_VERSION_FACTOR 1000 // do not change this
GGML_QNT_VERSION_FACTOR = 1000
# #define GGML_MAX_DIMS          4
GGML_MAX_DIMS = 4
# #define GGML_MAX_NODES         4096
GGML_MAX_NODES = 4096
# #define GGML_MAX_PARAMS        256
GGML_MAX_PARAMS = 256
# #define GGML_MAX_CONTEXTS      64
GGML_MAX_CONTEXTS = 64
# #define GGML_MAX_SRC           6
GGML_MAX_SRC = 6
# #define GGML_MAX_NAME          64
GGML_MAX_NAME = 64
# #define GGML_MAX_OP_PARAMS     32
GGML_MAX_OP_PARAMS = 32
# #define GGML_DEFAULT_N_THREADS 4
GGML_DEFAULT_N_THREADS = 4

# #if UINTPTR_MAX == 0XFFFFFFFF
#     #define GGML_MEMALIGN 4
# #else
#     # define GGML_MEMALIGN 16
# #endif
GGML_MEMALIGN = (
    16 if ctypes.sizeof(ctypes.c_void_p) == 4 else 32
)  # FIXME: Check if this is correct

# #define GGML_EXIT_SUCCESS 0
GGML_EXIT_SUCCESS = 0
# #define GGML_EXIT_ABORTED 1
GGML_EXIT_ABORTED = 1

# #define GGUF_MAGIC   0x46554747 // "GGUF"
GGUF_MAGIC = int("0x46554747", 16)
# #define GGUF_VERSION 2
GGUF_VERSION = 2

# #define GGUF_DEFAULT_ALIGNMENT 32
GGUF_DEFAULT_ALIGNMENT = 32

# TODO: Check if this is correct
# typedef uint16_t ggml_fp16_t;
ggml_fp16_t = ctypes.c_uint16

CFP16Array: TypeAlias = "ctypes.Array[ggml_fp16_t]"


# GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
def ggml_fp16_to_fp32(x: ggml_fp16_t) -> float:
    return lib.ggml_fp16_to_fp32(x)


lib.ggml_fp16_to_fp32.argtypes = [ggml_fp16_t]
lib.ggml_fp16_to_fp32.restype = ctypes.c_float


# GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
def ggml_fp32_to_fp16(x: ctypes.c_float) -> int:
    return lib.ggml_fp32_to_fp16(x)


lib.ggml_fp32_to_fp16.argtypes = [ctypes.c_float]
lib.ggml_fp32_to_fp16.restype = ggml_fp16_t


# GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
def ggml_fp16_to_fp32_row(
    x: CFP16Array,
    y: CFloatArray,
    n: Union[ctypes.c_int, int],
) -> None:
    return lib.ggml_fp16_to_fp32_row(x, y, n)


lib.ggml_fp16_to_fp32_row.argtypes = [
    ctypes.POINTER(ggml_fp16_t),
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_int,
]
lib.ggml_fp16_to_fp32_row.restype = None


# GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
def ggml_fp32_to_fp16_row(
    x: CFloatArray,
    y: CFP16Array,
    n: Union[ctypes.c_int, int],
) -> None:
    return lib.ggml_fp32_to_fp16_row(x, y, n)


lib.ggml_fp32_to_fp16_row.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.POINTER(ggml_fp16_t),
    ctypes.c_int,
]
lib.ggml_fp32_to_fp16_row.restype = None


# struct ggml_context;
ggml_context_p = ctypes.c_void_p
"""Opaque pointer to a ggml_context.

ggml_context structs are not accessed directly instead they must be created using [ggml_init](ggml.ggml_init) and freed using [ggml_free](ggml.ggml_free)."""


# enum ggml_type {
#     GGML_TYPE_F32  = 0,
#     GGML_TYPE_F16  = 1,
#     GGML_TYPE_Q4_0 = 2,
#     GGML_TYPE_Q4_1 = 3,
#     // GGML_TYPE_Q4_2 = 4, support has been removed
#     // GGML_TYPE_Q4_3 (5) support has been removed
#     GGML_TYPE_Q5_0 = 6,
#     GGML_TYPE_Q5_1 = 7,
#     GGML_TYPE_Q8_0 = 8,
#     GGML_TYPE_Q8_1 = 9,
#     GGML_TYPE_Q2_K = 10,
#     GGML_TYPE_Q3_K = 11,
#     GGML_TYPE_Q4_K = 12,
#     GGML_TYPE_Q5_K = 13,
#     GGML_TYPE_Q6_K = 14,
#     GGML_TYPE_Q8_K = 15,
#     GGML_TYPE_I8,
#     GGML_TYPE_I16,
#     GGML_TYPE_I32,
#     GGML_TYPE_COUNT,
# };
GGML_TYPE_F32 = 0
GGML_TYPE_F16 = 1
GGML_TYPE_Q4_0 = 2
GGML_TYPE_Q4_1 = 3
GGML_TYPE_Q5_0 = 6
GGML_TYPE_Q5_1 = 7
GGML_TYPE_Q8_0 = 8
GGML_TYPE_Q8_1 = 9
GGML_TYPE_Q2_K = 10
GGML_TYPE_Q3_K = 11
GGML_TYPE_Q4_K = 12
GGML_TYPE_Q5_K = 13
GGML_TYPE_Q6_K = 14
GGML_TYPE_Q8_K = 15
GGML_TYPE_I8 = 16
GGML_TYPE_I16 = 17
GGML_TYPE_I32 = 18
GGML_TYPE_COUNT = 19


# enum ggml_backend {
#     GGML_BACKEND_CPU = 0,
#     GGML_BACKEND_GPU = 10,
#     GGML_BACKEND_GPU_SPLIT = 20,
# };
GGML_BACKEND_CPU = 0
GGML_BACKEND_GPU = 10
GGML_BACKEND_GPU_SPLIT = 20


# // model file types
# enum ggml_ftype {
#     GGML_FTYPE_UNKNOWN     = -1,
#     GGML_FTYPE_ALL_F32     = 0,
#     GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
#     GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
#     GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
# };
GGML_FTYPE_UNKNOWN = -1
GGML_FTYPE_ALL_F32 = 0
GGML_FTYPE_MOSTLY_F16 = 1
GGML_FTYPE_MOSTLY_Q4_0 = 2
GGML_FTYPE_MOSTLY_Q4_1 = 3
GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4
GGML_FTYPE_MOSTLY_Q8_0 = 7
GGML_FTYPE_MOSTLY_Q5_0 = 8
GGML_FTYPE_MOSTLY_Q5_1 = 9
GGML_FTYPE_MOSTLY_Q2_K = 10
GGML_FTYPE_MOSTLY_Q3_K = 11
GGML_FTYPE_MOSTLY_Q4_K = 12
GGML_FTYPE_MOSTLY_Q5_K = 13
GGML_FTYPE_MOSTLY_Q6_K = 14


# // available tensor operations:
# enum ggml_op {
#     GGML_OP_NONE = 0,

#     GGML_OP_DUP,
#     GGML_OP_ADD,
#     GGML_OP_ADD1,
#     GGML_OP_ACC,
#     GGML_OP_SUB,
#     GGML_OP_MUL,
#     GGML_OP_DIV,
#     GGML_OP_SQR,
#     GGML_OP_SQRT,
#     GGML_OP_LOG,
#     GGML_OP_SUM,
#     GGML_OP_SUM_ROWS,
#     GGML_OP_MEAN,
#     GGML_OP_ARGMAX,
#     GGML_OP_REPEAT,
#     GGML_OP_REPEAT_BACK,
#     GGML_OP_CONCAT,
#     GGML_OP_SILU_BACK,
#     GGML_OP_NORM, // normalize
#     GGML_OP_RMS_NORM,
#     GGML_OP_RMS_NORM_BACK,
#     GGML_OP_GROUP_NORM,

#     GGML_OP_MUL_MAT,
#     GGML_OP_OUT_PROD,

#     GGML_OP_SCALE,
#     GGML_OP_SET,
#     GGML_OP_CPY,
#     GGML_OP_CONT,
#     GGML_OP_RESHAPE,
#     GGML_OP_VIEW,
#     GGML_OP_PERMUTE,
#     GGML_OP_TRANSPOSE,
#     GGML_OP_GET_ROWS,
#     GGML_OP_GET_ROWS_BACK,
#     GGML_OP_DIAG,
#     GGML_OP_DIAG_MASK_INF,
#     GGML_OP_DIAG_MASK_ZERO,
#     GGML_OP_SOFT_MAX,
#     GGML_OP_SOFT_MAX_BACK,
#     GGML_OP_ROPE,
#     GGML_OP_ROPE_BACK,
#     GGML_OP_ALIBI,
#     GGML_OP_CLAMP,
#     GGML_OP_CONV_1D,
#     GGML_OP_CONV_2D,
#     GGML_OP_CONV_TRANSPOSE_2D,
#     GGML_OP_POOL_1D,
#     GGML_OP_POOL_2D,

#     GGML_OP_UPSCALE, // nearest interpolate

#     GGML_OP_FLASH_ATTN,
#     GGML_OP_FLASH_FF,
#     GGML_OP_FLASH_ATTN_BACK,
#     GGML_OP_WIN_PART,
#     GGML_OP_WIN_UNPART,
#     GGML_OP_GET_REL_POS,
#     GGML_OP_ADD_REL_POS,

#     GGML_OP_UNARY,

#     GGML_OP_MAP_UNARY,
#     GGML_OP_MAP_BINARY,

#     GGML_OP_MAP_CUSTOM1_F32,
#     GGML_OP_MAP_CUSTOM2_F32,
#     GGML_OP_MAP_CUSTOM3_F32,

#     GGML_OP_MAP_CUSTOM1,
#     GGML_OP_MAP_CUSTOM2,
#     GGML_OP_MAP_CUSTOM3,

#     GGML_OP_CROSS_ENTROPY_LOSS,
#     GGML_OP_CROSS_ENTROPY_LOSS_BACK,

#     GGML_OP_COUNT,
# };
GGML_OP_NONE = 0
GGML_OP_DUP = 1
GGML_OP_ADD = 2
GGML_OP_ADD1 = 3
GGML_OP_ACC = 4
GGML_OP_SUB = 5
GGML_OP_MUL = 6
GGML_OP_DIV = 7
GGML_OP_SQR = 8
GGML_OP_SQRT = 9
GGML_OP_LOG = 10
GGML_OP_SUM = 11
GGML_OP_SUM_ROWS = 12
GGML_OP_MEAN = 13
GGML_OP_ARGMAX = 14
GGML_OP_REPEAT = 15
GGML_OP_REPEAT_BACK = 16
GGML_OP_CONCAT = 17
GGML_OP_SILU_BACK = 18
GGML_OP_NORM = 19
GGML_OP_RMS_NORM = 20
GGML_OP_RMS_NORM_BACK = 21
GGML_OP_GROUP_NORM = 22
GGML_OP_MUL_MAT = 23
GGML_OP_OUT_PROD = 24
GGML_OP_SCALE = 25
GGML_OP_SET = 26
GGML_OP_CPY = 27
GGML_OP_CONT = 28
GGML_OP_RESHAPE = 29
GGML_OP_VIEW = 30
GGML_OP_PERMUTE = 31
GGML_OP_TRANSPOSE = 32
GGML_OP_GET_ROWS = 33
GGML_OP_GET_ROWS_BACK = 34
GGML_OP_DIAG = 35
GGML_OP_DIAG_MASK_INF = 36
GGML_OP_DIAG_MASK_ZERO = 37
GGML_OP_SOFT_MAX = 38
GGML_OP_SOFT_MAX_BACK = 39
GGML_OP_ROPE = 40
GGML_OP_ROPE_BACK = 41
GGML_OP_ALIBI = 42
GGML_OP_CLAMP = 43
GGML_OP_CONV_1D = 44
GGML_OP_CONV_2D = 45
GGML_OP_CONV_TRANSPOSE_2D = 46
GGML_OP_POOL_1D = 47
GGML_OP_POOL_2D = 48
GGML_OP_UPSCALE = 49
GGML_OP_FLASH_ATTN = 50
GGML_OP_FLASH_FF = 51
GGML_OP_FLASH_ATTN_BACK = 52
GGML_OP_WIN_PART = 53
GGML_OP_WIN_UNPART = 54
GGML_OP_GET_REL_POS = 55
GGML_OP_ADD_REL_POS = 56
GGML_OP_UNARY = 57
GGML_OP_MAP_UNARY = 58
GGML_OP_MAP_BINARY = 59
GGML_OP_MAP_CUSTOM1_F32 = 60
GGML_OP_MAP_CUSTOM2_F32 = 61
GGML_OP_MAP_CUSTOM3_F32 = 62
GGML_OP_MAP_CUSTOM1 = 63
GGML_OP_MAP_CUSTOM2 = 64
GGML_OP_MAP_CUSTOM3 = 65
GGML_OP_CROSS_ENTROPY_LOSS = 66
GGML_OP_CROSS_ENTROPY_LOSS_BACK = 67
GGML_OP_COUNT = 68


# enum ggml_unary_op {
#     GGML_UNARY_OP_ABS,
#     GGML_UNARY_OP_SGN,
#     GGML_UNARY_OP_NEG,
#     GGML_UNARY_OP_STEP,
#     GGML_UNARY_OP_TANH,
#     GGML_UNARY_OP_ELU,
#     GGML_UNARY_OP_RELU,
#     GGML_UNARY_OP_GELU,
#     GGML_UNARY_OP_GELU_QUICK,
#     GGML_UNARY_OP_SILU,
# };
GGML_UNARY_OP_ABS = 0
GGML_UNARY_OP_SGN = 1
GGML_UNARY_OP_NEG = 2
GGML_UNARY_OP_STEP = 3
GGML_UNARY_OP_TANH = 4
GGML_UNARY_OP_ELU = 5
GGML_UNARY_OP_RELU = 6
GGML_UNARY_OP_GELU = 7
GGML_UNARY_OP_GELU_QUICK = 8
GGML_UNARY_OP_SILU = 9

# enum ggml_object_type {
#     GGML_OBJECT_TENSOR,
#     GGML_OBJECT_GRAPH,
#     GGML_OBJECT_WORK_BUFFER
# };
GGML_OBJECT_TENSOR = 0
GGML_OBJECT_GRAPH = 1
GGML_OBJECT_WORK_BUFFER = 2

# // ggml object
# struct ggml_object {
#     size_t offs;
#     size_t size;

#     struct ggml_object * next;

#     enum ggml_object_type type;


#     char padding[4];
# };
class ggml_object(ctypes.Structure):
    pass


ggml_object._fields_ = [
    ("offs", ctypes.c_size_t),
    ("size", ctypes.c_size_t),
    ("next", ctypes.POINTER(ggml_object)),
    ("type", ctypes.c_int),
    ("padding", ctypes.c_char * 4),
]

ggml_object_p: TypeAlias = "ctypes._Pointer[ggml_object]"  # type: ignore

GGML_OBJECT_SIZE = ctypes.sizeof(ggml_object)


# // n-dimensional tensor
# struct ggml_tensor {
#     enum ggml_type    type;
#     enum ggml_backend backend;

#     int     n_dims;
#     int64_t ne[GGML_MAX_DIMS]; // number of elements
#     size_t  nb[GGML_MAX_DIMS]; // stride in bytes:
#                                // nb[0] = sizeof(type)
#                                // nb[1] = nb[0]   * ne[0] + padding
#                                // nb[i] = nb[i-1] * ne[i-1]

#     // compute data
#     enum ggml_op op;

#     // op params - allocated as int32_t for alignment
#     int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];

#     bool is_param;

#     struct ggml_tensor * grad;
#     struct ggml_tensor * src[GGML_MAX_SRC];

#     // performance
#     int     perf_runs;
#     int64_t perf_cycles;
#     int64_t perf_time_us;

#     struct ggml_tensor * view_src;
#     size_t               view_offs;

#     void * data;

#     char name[GGML_MAX_NAME];

#     void * extra; // extra things e.g. for ggml-cuda.cu


#     char padding[4];
# };
class ggml_tensor(ctypes.Structure):
    """n-dimensional tensor

    Attributes:
        type (int): ggml_type
        backend (int): ggml_backend
        n_dims (int): number of dimensions
        ne (ctypes.Array[ctypes.c_int64]): number of elements in each dimension
        nb (ctypes.Array[ctypes.c_size_t]): stride in bytes for each dimension
        op (int): ggml operation
        op_params (ctypes.Array[ctypes.c_int32]): `GGML_MAX_OP_PARAMS`-length array of operation parameters
        is_param (bool): is this a parameter tensor
        grad (ggml_tensor_p): reference to gradient tensor
        src (ctypes.Array[ggml_tensor_p]): `GGML_MAX_SRC`-length array of source tensors
        perf_runs (int): number of performance runs
        perf_cycles (int): number of cycles
        perf_time_us (int): time in microseconds
        view_src (ggml_tensor_p): pointer to tensor if this tensor is a view, None if the tensor is not a view
        view_offs (ctypes.c_size_t): offset into the data pointer of the view tensor
        data (ctypes.c_void_p): reference to raw tensor data
        name (bytes): name of tensor
        extra (ctypes.c_void_p): extra data (e.g. for CUDA)
    """

    pass


ggml_tensor._fields_ = [
    ("type", ctypes.c_int),
    ("backend", ctypes.c_int),
    ("n_dims", ctypes.c_int),
    ("ne", ctypes.c_int64 * GGML_MAX_DIMS),
    ("nb", ctypes.c_size_t * GGML_MAX_DIMS),
    ("op", ctypes.c_int),
    (
        "op_params",
        ctypes.c_int32 * (GGML_MAX_OP_PARAMS // ctypes.sizeof(ctypes.c_int32)),
    ),
    ("is_param", ctypes.c_bool),
    ("grad", ctypes.POINTER(ggml_tensor)),
    ("src", ctypes.POINTER(ggml_tensor) * GGML_MAX_SRC),
    ("perf_runs", ctypes.c_int),
    ("perf_cycles", ctypes.c_int64),
    ("perf_time_us", ctypes.c_int64),
    ("view_src", ctypes.POINTER(ggml_tensor)),
    ("view_offs", ctypes.c_size_t),
    ("data", ctypes.c_void_p),
    ("name", ctypes.c_char * GGML_MAX_NAME),
    ("extra", ctypes.c_void_p),
    ("padding", ctypes.c_char * 4),
]

GGML_TENSOR_SIZE = ctypes.sizeof(ggml_tensor)

ggml_tensor_p: TypeAlias = "ctypes._Pointer[ggml_tensor]"  # type: ignore
"""ctypes pointer to a [ggml_tensor][ggml.ggml_tensor]

Can be dereferenced to a [ggml_tensor][ggml.ggml_tensor] object using
the `.contents` attribute."""

abort_callback_t = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_void_p)

# // the compute plan that needs to be prepared for ggml_graph_compute()
# // since https://github.com/ggerganov/ggml/issues/287
# struct ggml_cplan {
#     size_t    work_size; // size of work buffer, calculated by `ggml_graph_plan()`
#     uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`

#     int n_threads;

#     // the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
#     int n_tasks[GGML_MAX_NODES];


#     // abort ggml_graph_compute when true
#     bool (*abort_callback)(void * data);
#     void * abort_callback_data;
# };
class ggml_cplan(ctypes.Structure):
    """Compute plan for a ggml computation graph

    Attributes:
        work_size (int): size of work buffer
        work_data (ctypes.POINTER(ctypes.c_uint8)): work buffer
        n_threads (int): number of threads to use when computing the graph using [ggml_graph_compute][ggml.ggml_graph_compute]
        n_tasks (ctypes.Array[ctypes.c_int]): `n_tasks` of nodes, 1:1 mapping to cgraph nodes
        abort_callback (abort_callback_t): abort callback
        abort_callback_data (ctypes.c_void_p): abort callback data
    """

    _fields_ = [
        ("work_size", ctypes.c_size_t),
        ("work_data", ctypes.POINTER(ctypes.c_uint8)),
        ("n_threads", ctypes.c_int),
        ("n_tasks", ctypes.c_int * GGML_MAX_NODES),
        (
            "abort_callback",
            abort_callback_t,
        ),
        ("abort_callback_data", ctypes.c_void_p),
    ]


GGML_CPLAN_SIZE = ctypes.sizeof(ggml_cplan)

ggml_cplan_p: TypeAlias = "ctypes._Pointer[ggml_cplan]"  # type: ignore
"""ctypes pointer to a [ggml_cplan][ggml.ggml_cplan]

Can be dereferenced to a [ggml_cplan][ggml.ggml_cplan] object using
the `.contents` attribute."""

# // next prime after GGML_MAX_NODES
# // #define GGML_GRAPH_HASHTABLE_SIZE 4099
# // next prime after GGML_MAX_NODES * 2 (nodes + leafs)
# #define GGML_GRAPH_HASHTABLE_SIZE 8273
GGML_GRAPH_HASHTABLE_SIZE = 8273

# // computation graph
# struct ggml_cgraph {
#     int n_nodes;
#     int n_leafs;

#     struct ggml_tensor * nodes[GGML_MAX_NODES];
#     struct ggml_tensor * grads[GGML_MAX_NODES];
#     struct ggml_tensor * leafs[GGML_MAX_NODES];

#     void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];


#     // performance
#     int     perf_runs;
#     int64_t perf_cycles;
#     int64_t perf_time_us;
# };
class ggml_cgraph(ctypes.Structure):
    """ggml computation graph

    Attributes:
        n_nodes (int): number of nodes
        n_leafs (int): number of leafs
        nodes (ctypes.Array[ggml_tensor_p]): `n_nodes`-length array of compute tensors
        grads (ctypes.Array[ggml_tensor_p]): `n_nodes`-length array of gradient tensors
        leafs (ctypes.Array[ggml_tensor_p]): `n_leafs`-length array of parameter tensors
        visited_hash_table (ctypes.Array[ctypes.c_void_p]): `GGML_GRAPH_HASHTABLE_SIZE`-length array of visited nodes
        perf_runs (int): number of runs
        perf_cycles (int): number of cycles
        perf_time_us (int): computation time in microseconds"""

    _fields_ = [
        ("n_nodes", ctypes.c_int),
        ("n_leafs", ctypes.c_int),
        ("nodes", ctypes.POINTER(ggml_tensor) * GGML_MAX_NODES),
        ("grads", ctypes.POINTER(ggml_tensor) * GGML_MAX_NODES),
        ("leafs", ctypes.POINTER(ggml_tensor) * GGML_MAX_NODES),
        ("visited_hash_table", ctypes.c_void_p * GGML_GRAPH_HASHTABLE_SIZE),
        ("perf_runs", ctypes.c_int),
        ("perf_cycles", ctypes.c_int64),
        ("perf_time_us", ctypes.c_int64),
    ]


ggml_cgraph_p: TypeAlias = "ctypes._Pointer[ggml_cgraph]"  # type: ignore
"""ctypes pointer to a [ggml_cgraph][ggml.ggml_cgraph]

Can be dereferenced to a [ggml_cgraph][ggml.ggml_cgraph] object using
the `.contents` attribute."""

# static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
GGML_GRAPH_SIZE = ctypes.sizeof(ggml_cgraph)


# struct ggml_scratch {
#     size_t offs;
#     size_t size;
#     void * data;
# };
class ggml_scratch(ctypes.Structure):
    _fields_ = [
        ("offs", ctypes.c_size_t),
        ("size", ctypes.c_size_t),
        ("data", ctypes.c_void_p),
    ]


# struct ggml_init_params {
#     // memory pool
#     size_t mem_size;   // bytes
#     void * mem_buffer; // if NULL, memory will be allocated internally
#     bool   no_alloc;   // don't allocate memory for the tensor data
# };
class ggml_init_params(ctypes.Structure):
    """Initialization parameters for a ggml context

    **NOTE**: Reference counting does not cross into ggml, if you allocate a memory buffer
    in python using ctypes Arrays or a numpy array, you must keep a reference to it until
    you free the ggml context otherwise you will encounter a segmentation fault.

    Attributes:
        mem_size (int): size of memory pool in bytes
        mem_buffer (ctypes.c_void_p): pointer to memory pool, if None, memory will be allocated internally
        no_alloc (bool): don't allocate memory for tensor data
    """

    _fields_ = [
        ("mem_size", ctypes.c_size_t),
        ("mem_buffer", ctypes.c_void_p),
        ("no_alloc", ctypes.c_bool),
    ]


# // compute types

# // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
# // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
# enum ggml_task_type {
#     GGML_TASK_INIT = 0,
#     GGML_TASK_COMPUTE,
#     GGML_TASK_FINALIZE,
# };
GGML_TASK_INIT = 0
GGML_TASK_COMPUTE = 1
GGML_TASK_FINALIZE = 2

# struct ggml_compute_params {
#     enum ggml_task_type type;

#     // ith = thread index, nth = number of threads
#     int ith, nth;


#     // work buffer for all threads
#     size_t wsize;
#     void * wdata;
# };
class ggml_compute_params(ctypes.Structure):
    _fields_ = [
        ("type", ctypes.c_int),
        ("ith", ctypes.c_int),
        ("nth", ctypes.c_int),
        ("wsize", ctypes.c_size_t),
        ("wdata", ctypes.c_void_p),
    ]


ggml_compute_params_p: TypeAlias = "ctypes._Pointer[ggml_compute_params]"  # type: ignore

# // misc


# GGML_API void    ggml_time_init(void); // call this once at the beginning of the program
def ggml_time_init():
    return lib.ggml_time_init()


lib.ggml_time_init.argtypes = []
lib.ggml_time_init.restype = None


# GGML_API int64_t ggml_time_ms(void);
def ggml_time_ms() -> int:
    return lib.ggml_time_ms()


lib.ggml_time_ms.argtypes = []
lib.ggml_time_ms.restype = ctypes.c_int64


# GGML_API int64_t ggml_time_us(void);
def ggml_time_us() -> int:
    return lib.ggml_time_us()


lib.ggml_time_us.argtypes = []
lib.ggml_time_us.restype = ctypes.c_int64


# GGML_API int64_t ggml_cycles(void);
def ggml_cycles() -> int:
    return lib.ggml_cycles()


lib.ggml_cycles.argtypes = []
lib.ggml_cycles.restype = ctypes.c_int64


# GGML_API int64_t ggml_cycles_per_ms(void);
def ggml_cycles_per_ms() -> int:
    return lib.ggml_cycles_per_ms()


lib.ggml_cycles_per_ms.argtypes = []
lib.ggml_cycles_per_ms.restype = ctypes.c_int64


# GGML_API void    ggml_numa_init(void); // call once for better performance on NUMA systems
def ggml_numa_init():
    return lib.ggml_numa_init()


lib.ggml_numa_init.argtypes = []
lib.ggml_numa_init.restype = None


# GGML_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
def ggml_is_numa() -> bool:
    return lib.ggml_is_numa()


lib.ggml_is_numa.argtypes = []
lib.ggml_is_numa.restype = ctypes.c_bool


# GGML_API void    ggml_print_object (const struct ggml_object * obj);
def ggml_print_object(obj: ggml_object_p):
    return lib.ggml_print_object(obj)


lib.ggml_print_object.argtypes = [ctypes.POINTER(ggml_object)]
lib.ggml_print_object.restype = None


# GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
def ggml_print_objects(ctx: ggml_context_p):
    return lib.ggml_print_objects(ctx)


lib.ggml_print_objects.argtypes = [ggml_context_p]
lib.ggml_print_objects.restype = None


# GGML_API int64_t ggml_nelements   (const struct ggml_tensor * tensor);
def ggml_nelements(
    tensor: ggml_tensor_p,
) -> int:
    """Get the number of elements in a tensor

    Parameters:
        tensor: tensor

    Returns:
        number of elements"""
    return lib.ggml_nelements(tensor)


lib.ggml_nelements.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_nelements.restype = ctypes.c_int64


# GGML_API int64_t ggml_nrows       (const struct ggml_tensor * tensor);
def ggml_nrows(
    tensor: ggml_tensor_p,
) -> int:
    """Get the number of rows in a tensor

    Parameters:
        tensor: tensor

    Returns:
        number of rows"""
    return lib.ggml_nrows(tensor)


lib.ggml_nrows.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_nrows.restype = ctypes.c_int64


# GGML_API size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
def ggml_nbytes(
    tensor: ggml_tensor_p,
) -> int:
    """Get the number of bytes required to store tensor data

    Parameters:
        tensor: tensor

    Returns:
        number of bytes"""
    return lib.ggml_nbytes(tensor)


lib.ggml_nbytes.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_nbytes.restype = ctypes.c_size_t


# GGML_API size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
def ggml_nbytes_pad(
    tensor: ggml_tensor_p,
) -> int:
    """Get the number of bytes required to store tensor data, padded to GGML_MEM_ALIGN

    Parameters:
        tensor: tensor

    Returns:
        number of bytes"""
    return lib.ggml_nbytes_pad(tensor)


lib.ggml_nbytes_pad.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_nbytes_pad.restype = ctypes.c_size_t


# GGML_API size_t  ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
def ggml_nbytes_split(
    tensor: ggml_tensor_p,
    nrows_split: Union[ctypes.c_int, int],
) -> int:
    return lib.ggml_nbytes_split(tensor, nrows_split)


lib.ggml_nbytes_split.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_int]
lib.ggml_nbytes_split.restype = ctypes.c_size_t


# GGML_API int     ggml_blck_size (enum ggml_type type);
def ggml_blck_size(type: Union[ctypes.c_int, int]) -> int:
    return lib.ggml_blck_size(type)


lib.ggml_blck_size.argtypes = [ctypes.c_int]
lib.ggml_blck_size.restype = ctypes.c_int


# GGML_API size_t  ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
def ggml_type_size(type: Union[ctypes.c_int, int]) -> int:
    return lib.ggml_type_size(type)


lib.ggml_type_size.argtypes = [ctypes.c_int]
lib.ggml_type_size.restype = ctypes.c_size_t


# GGML_API float   ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
def ggml_type_sizef(type: Union[ctypes.c_int, int]) -> float:
    return lib.ggml_type_sizef(type)


lib.ggml_type_sizef.argtypes = [ctypes.c_int]
lib.ggml_type_sizef.restype = ctypes.c_float


# GGML_API const char * ggml_type_name(enum ggml_type type);
def ggml_type_name(type: Union[ctypes.c_int, int]) -> bytes:
    return lib.ggml_type_name(type)


lib.ggml_type_name.argtypes = [ctypes.c_int]
lib.ggml_type_name.restype = ctypes.c_char_p


# GGML_API const char * ggml_op_name  (enum ggml_op   op);
def ggml_op_name(op: Union[ctypes.c_int, int]) -> bytes:
    return lib.ggml_op_name(op)


lib.ggml_op_name.argtypes = [ctypes.c_int]
lib.ggml_op_name.restype = ctypes.c_char_p


# GGML_API const char * ggml_op_symbol(enum ggml_op   op);
def ggml_op_symbol(op: Union[ctypes.c_int, int]) -> bytes:
    return lib.ggml_op_symbol(op)


lib.ggml_op_symbol.argtypes = [ctypes.c_int]
lib.ggml_op_symbol.restype = ctypes.c_char_p


# GGML_API size_t  ggml_element_size(const struct ggml_tensor * tensor);
def ggml_element_size(
    tensor: ggml_tensor_p,
) -> int:
    return lib.ggml_element_size(tensor)


lib.ggml_element_size.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_element_size.restype = ctypes.c_size_t


# GGML_API bool    ggml_is_quantized(enum ggml_type type);
def ggml_is_quantized(type: Union[ctypes.c_int, int]) -> bool:
    return lib.ggml_is_quantized(type)


lib.ggml_is_quantized.argtypes = [ctypes.c_int]
lib.ggml_is_quantized.restype = ctypes.c_bool


# // TODO: temporary until model loading of ggml examples is refactored
# GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
def ggml_ftype_to_ggml_type(ftype: Union[ctypes.c_int, int]) -> int:
    return lib.ggml_ftype_to_ggml_type(ftype)


lib.ggml_ftype_to_ggml_type.argtypes = [ctypes.c_int]
lib.ggml_ftype_to_ggml_type.restype = ctypes.c_int


# GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
def ggml_is_transposed(
    tensor: ggml_tensor_p,
) -> bool:
    """Check if a tensor is transposed

    Parameters:
        tensor: tensor

    Returns:
        True if tensor is transposed else False"""
    return lib.ggml_is_transposed(tensor)


lib.ggml_is_transposed.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_is_transposed.restype = ctypes.c_bool


# GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
def ggml_is_contiguous(
    tensor: ggml_tensor_p,
) -> bool:
    """Check if a tensor is contiguous

    Parameters:
        tensor: tensor

    Returns:
        True if tensor is contiguous else False"""
    return lib.ggml_is_contiguous(tensor)


lib.ggml_is_contiguous.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_is_contiguous.restype = ctypes.c_bool


# GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
def ggml_is_permuted(
    tensor: ggml_tensor_p,
) -> bool:
    """Check if a tensor is permuted

    Parameters:
        tensor: tensor

    Returns:
        True if tensor is permuted else False"""
    return lib.ggml_is_permuted(tensor)


lib.ggml_is_permuted.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_is_permuted.restype = ctypes.c_bool


# GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
def ggml_are_same_shape(
    t0: ggml_tensor_p,
    t1: ggml_tensor_p,
) -> bool:
    """Check if two tensors have the same shape

    Parameters:
        t0: tensor 0
        t1: tensor 1

    Returns:
        True if tensors have the same shape else False"""
    return lib.ggml_are_same_shape(t0, t1)


lib.ggml_are_same_shape.argtypes = [
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_are_same_shape.restype = ctypes.c_bool


# // use this to compute the memory overhead of a tensor
# GGML_API size_t ggml_tensor_overhead(void);
def ggml_tensor_overhead() -> int:
    """Overhead required for a tensor struct in bytes

    Returns:
        size of tensor struct in bytes"""
    return lib.ggml_tensor_overhead()


lib.ggml_tensor_overhead.argtypes = []
lib.ggml_tensor_overhead.restype = ctypes.c_size_t

# // main


# GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
def ggml_init(
    params: ggml_init_params,
) -> ggml_context_p:
    """Instantiate a new ggml context with params.

    You must call `ggml_free()` to free the context.

    Parameters:
        params: ggml init params

    Returns:
        Pointer to ggml_context"""
    return lib.ggml_init(params)


lib.ggml_init.argtypes = [ggml_init_params]
lib.ggml_init.restype = ggml_context_p


# GGML_API void                  ggml_free(struct ggml_context * ctx);
def ggml_free(ctx: ggml_context_p):
    """Free the ggml context.

    Parameters:
        ctx: ggml context"""
    return lib.ggml_free(ctx)


lib.ggml_free.argtypes = [ggml_context_p]
lib.ggml_free.restype = None


# GGML_API size_t  ggml_used_mem(const struct ggml_context * ctx);
def ggml_used_mem(ctx: ggml_context_p) -> int:
    """Return the amount of memory used by the ggml context in bytes.

    Parameters:
        ctx: ggml context

    Returns:
        amount of memory used in bytes"""
    return lib.ggml_used_mem(ctx)


lib.ggml_used_mem.argtypes = [ggml_context_p]
lib.ggml_used_mem.restype = ctypes.c_size_t


# GGML_API size_t  ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
def ggml_set_scratch(ctx: ggml_context_p, scratch: ggml_scratch) -> int:
    """Set the scratch buffer for the ggml context."""
    return lib.ggml_set_scratch(ctx, scratch)


lib.ggml_set_scratch.argtypes = [ggml_context_p, ggml_scratch]
lib.ggml_set_scratch.restype = ctypes.c_size_t


# GGML_API bool    ggml_get_no_alloc(struct ggml_context * ctx);
def ggml_get_no_alloc(ctx: ggml_context_p) -> bool:
    """Return the no_alloc flag for the ggml context."""
    return lib.ggml_get_no_alloc(ctx)


lib.ggml_get_no_alloc.argtypes = [ggml_context_p]
lib.ggml_get_no_alloc.restype = ctypes.c_bool


# GGML_API void    ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
def ggml_set_no_alloc(ctx: ggml_context_p, no_alloc: Union[ctypes.c_bool, bool]):
    """Set the no_alloc flag for the ggml context."""
    return lib.ggml_set_no_alloc(ctx, no_alloc)


lib.ggml_set_no_alloc.argtypes = [ggml_context_p, ctypes.c_bool]
lib.ggml_set_no_alloc.restype = None


# GGML_API void *  ggml_get_mem_buffer     (struct ggml_context * ctx);
def ggml_get_mem_buffer(ctx: ggml_context_p) -> Optional[ctypes.c_void_p]:
    """Return the memory buffer for the ggml context."""
    return lib.ggml_get_mem_buffer(ctx)


lib.ggml_get_mem_buffer.argtypes = [ggml_context_p]
lib.ggml_get_mem_buffer.restype = ctypes.c_void_p


# GGML_API size_t  ggml_get_mem_size       (struct ggml_context * ctx);
def ggml_get_mem_size(ctx: ggml_context_p) -> int:
    """Return the size of the memory buffer for the ggml context in bytes."""
    return lib.ggml_get_mem_size(ctx)


lib.ggml_get_mem_size.argtypes = [ggml_context_p]
lib.ggml_get_mem_size.restype = ctypes.c_size_t


# GGML_API size_t  ggml_get_max_tensor_size(const struct ggml_context * ctx);
def ggml_get_max_tensor_size(ctx: ggml_context_p) -> int:
    """Return the maximum size of a tensor in bytes."""
    return lib.ggml_get_max_tensor_size(ctx)


lib.ggml_get_max_tensor_size.argtypes = [ggml_context_p]
lib.ggml_get_max_tensor_size.restype = ctypes.c_size_t


# GGML_API struct ggml_tensor * ggml_new_tensor(
#         struct ggml_context * ctx,
#         enum   ggml_type type,
#         int    n_dims,
#         const int64_t *ne);
def ggml_new_tensor(
    ctx: ggml_context_p,
    type: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    ne: CInt64Array,
) -> ggml_tensor_p:
    """Create a new tensor with the given type, number of dimensions, and number of elements in each dimension.

    Parameters:
        ctx: ggml context
        type: ggml type
        n_dims: number of dimensions
        ne (ctypes.Array[ctypes.c_int64]): number of elements in each dimension (array of length n_dims)

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_tensor(ctx, type, n_dims, ne)


lib.ggml_new_tensor.argtypes = [
    ggml_context_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_new_tensor.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_tensor_1d(
#         struct ggml_context * ctx,
#         enum   ggml_type type,
#         int64_t ne0);
def ggml_new_tensor_1d(
    ctx: ggml_context_p, type: Union[ctypes.c_int, int], ne0: Union[ctypes.c_int64, int]
) -> ggml_tensor_p:
    """Create a new 1-dimensional tensor with the given type and number of elements.

    Parameters:
        ctx: ggml context
        type: ggml type
        ne0: number of elements in dimension 0

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_tensor_1d(ctx, type, ne0)


lib.ggml_new_tensor_1d.argtypes = [ggml_context_p, ctypes.c_int, ctypes.c_int64]
lib.ggml_new_tensor_1d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_tensor_2d(
#         struct ggml_context * ctx,
#         enum   ggml_type type,
#         int64_t ne0,
#         int64_t ne1);
def ggml_new_tensor_2d(
    ctx: ggml_context_p,
    type: Union[ctypes.c_int, int],
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    """Create a new 2-dimensional tensor with the given type and number of elements in each dimension.

    Parameters:
        ctx: ggml context
        type: ggml type
        ne0: number of elements in dimension 0
        ne1: number of elements in dimension 1

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_tensor_2d(ctx, type, ne0, ne1)


lib.ggml_new_tensor_2d.argtypes = [
    ggml_context_p,
    ctypes.c_int,
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_new_tensor_2d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_tensor_3d(
#         struct ggml_context * ctx,
#         enum   ggml_type type,
#         int64_t ne0,
#         int64_t ne1,
#         int64_t ne2);
def ggml_new_tensor_3d(
    ctx: ggml_context_p,
    type: Union[ctypes.c_int, int],
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    """Create a new 3-dimensional tensor with the given type and number of elements in each dimension.

    Parameters:
        ctx: ggml context
        type: ggml type
        ne0: number of elements in dimension 0
        ne1: number of elements in dimension 1
        ne2: number of elements in dimension 2

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_tensor_3d(ctx, type, ne0, ne1, ne2)


lib.ggml_new_tensor_3d.argtypes = [
    ggml_context_p,
    ctypes.c_int,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_new_tensor_3d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_tensor_4d(
#         struct ggml_context * ctx,
#         enum   ggml_type type,
#         int64_t ne0,
#         int64_t ne1,
#         int64_t ne2,
#         int64_t ne3);
def ggml_new_tensor_4d(
    ctx: ggml_context_p,
    type: Union[ctypes.c_int, int],
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
    ne3: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    """Create a new 4-dimensional tensor with the given type and number of elements in each dimension.

    Parameters:
        ctx: ggml context
        type: ggml type
        ne0: number of elements in dimension 0
        ne1: number of elements in dimension 1
        ne2: number of elements in dimension 2

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3)


lib.ggml_new_tensor_4d.argtypes = [
    ggml_context_p,
    ctypes.c_int,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_new_tensor_4d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
def ggml_new_i32(
    ctx: ggml_context_p, value: Union[ctypes.c_int32, int]
) -> ggml_tensor_p:
    """Create a 1 element tensor with the given integer value.

    Parameters:
        ctx: ggml context
        value: integer value

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_i32(ctx, value)


lib.ggml_new_i32.argtypes = [ggml_context_p, ctypes.c_int32]
lib.ggml_new_i32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
def ggml_new_f32(
    ctx: ggml_context_p,
    value: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    """Create a 1 element tensor with the given float value.

    Parameters:
        ctx: ggml context
        value: float value

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_new_f32(ctx, value)


lib.ggml_new_f32.argtypes = [ggml_context_p, ctypes.c_float]
lib.ggml_new_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
def ggml_dup_tensor(ctx: ggml_context_p, src: ggml_tensor_p) -> ggml_tensor_p:
    """Create a new tensor with the same type and dimensions as the source tensor.

    Parameters:
        ctx: ggml context
        src: source tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_dup_tensor(ctx, src)


lib.ggml_dup_tensor.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_dup_tensor.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
def ggml_view_tensor(ctx: ggml_context_p, src: ggml_tensor_p) -> ggml_tensor_p:
    """Create a new tensor with the same type, dimensions and data as the source tensor.

    Parameters:
        ctx: ggml context
        src: source tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_view_tensor(ctx, src)


lib.ggml_view_tensor.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_view_tensor.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
def ggml_get_tensor(ctx: ggml_context_p, name: bytes) -> ggml_tensor_p:
    """Get a tensor from the ggml context by name.

    Parameters:
        ctx: ggml context
        name: name of tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_get_tensor(ctx, name)


lib.ggml_get_tensor.argtypes = [ggml_context_p, ctypes.c_char_p]
lib.ggml_get_tensor.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
def ggml_set_zero(
    tensor: ggml_tensor_p,
) -> ggml_tensor_p:
    """Zero all elements in a tensor.

    Parameters:
        tensor: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_set_zero(tensor)


lib.ggml_set_zero.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_set_zero.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
def ggml_set_i32(
    tensor: ggml_tensor_p,
    value: Union[ctypes.c_int32, int],
) -> ggml_tensor_p:
    """Set all elements in a tensor to the given integer value.

    Parameters:
        tensor: tensor
        value: integer value

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_set_i32(tensor, value)


lib.ggml_set_i32.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_int32]
lib.ggml_set_i32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
def ggml_set_f32(
    tensor: ggml_tensor_p,
    value: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    """Set all elements in a tensor to the given float value.

    Parameters:
        tensor: tensor
        value: float value

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_set_f32(tensor, value)


lib.ggml_set_f32.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_float]
lib.ggml_set_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
def ggml_get_i32_1d(
    tensor: ggml_tensor_p,
    i: Union[ctypes.c_int, int],
) -> int:
    """Get the integer value of the i-th element in a 1-dimensional tensor.

    Parameters:
        tensor: tensor
        i: index of element

    Returns:
        integer value of element at index i"""
    return lib.ggml_get_i32_1d(tensor, i)


lib.ggml_get_i32_1d.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_int]
lib.ggml_get_i32_1d.restype = ctypes.c_int32


# GGML_API void    ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
def ggml_set_i32_1d(
    tensor: ggml_tensor_p,
    i: Union[ctypes.c_int, int],
    value: Union[ctypes.c_int32, int],
):
    """Set the integer value of the i-th element in a 1-dimensional tensor.

    Parameters:
        tensor: tensor
        i: index of element
        value: integer value to set element to"""
    return lib.ggml_set_i32_1d(tensor, i, value)


lib.ggml_set_i32_1d.argtypes = [
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int32,
]
lib.ggml_set_i32_1d.restype = None


# GGML_API float   ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
def ggml_get_f32_1d(
    tensor: ggml_tensor_p,
    i: Union[ctypes.c_int, int],
) -> float:
    """Get the float value of the i-th element in a 1-dimensional tensor.

    Parameters:
        tensor: tensor

    Returns:
        float value of element at index i"""
    return lib.ggml_get_f32_1d(tensor, i)


lib.ggml_get_f32_1d.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_int]
lib.ggml_get_f32_1d.restype = ctypes.c_float


# GGML_API void    ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
def ggml_set_f32_1d(
    tensor: ggml_tensor_p,
    i: Union[ctypes.c_int, int],
    value: Union[ctypes.c_float, float],
):
    """Set the float value of the i-th element in a 1-dimensional tensor.

    Parameters:
        tensor: tensor
        i: index of element
        value: float value to set element to"""
    return lib.ggml_set_f32_1d(tensor, i, value)


lib.ggml_set_f32_1d.argtypes = [
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_float,
]
lib.ggml_set_f32_1d.restype = None


# GGML_API void *  ggml_get_data    (const struct ggml_tensor * tensor);
def ggml_get_data(
    tensor: ggml_tensor_p,
) -> Optional[ctypes.c_void_p]:
    """Get the data pointer of a tensor.

    Parameters:
        tensor: tensor

    Returns:
        Pointer to data, or None if tensor has no data"""
    return lib.ggml_get_data(tensor)


lib.ggml_get_data.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_get_data.restype = ctypes.c_void_p


# GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
def ggml_get_data_f32(
    tensor: ggml_tensor_p,
) -> Optional[CFloatArray]:
    """Get the data pointer of a tensor as a float array.

    Parameters:
        tensor: tensor

    Returns:
        (Optional[ctypes.Array[ctypes.c_float]]): array of float to data, or None if tensor has no data
    """
    return lib.ggml_get_data_f32(tensor)


lib.ggml_get_data_f32.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_get_data_f32.restype = ctypes.POINTER(ctypes.c_float)


# GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
def ggml_get_unary_op(
    tensor: ggml_tensor_p,
) -> int:
    """Get the unary operation of a tensor.

    Parameters:
        tensor: tensor

    Returns:
        unary operation"""
    return lib.ggml_get_unary_op(tensor)


lib.ggml_get_unary_op.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_get_unary_op.restype = ctypes.c_int


# GGML_API const char *         ggml_get_name(const struct ggml_tensor * tensor);
def ggml_get_name(
    tensor: ggml_tensor_p,
) -> bytes:
    """Get the name of a tensor.

    Parameters:
        tensor: tensor

    Returns:
        name of tensor"""
    return lib.ggml_get_name(tensor)


lib.ggml_get_name.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_get_name.restype = ctypes.c_char_p


# GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
def ggml_set_name(
    tensor: ggml_tensor_p,
    name: bytes,
) -> ggml_tensor_p:
    """Set the name of a tensor.

    Parameters:
        tensor: tensor
        name: name to set tensor to

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_set_name(tensor, name)


lib.ggml_set_name.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_char_p]
lib.ggml_set_name.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
def ggml_format_name(
    tensor: ggml_tensor_p,
    fmt: bytes,
    *args: Sequence[Union[bool, int, float, str]],
) -> ggml_tensor_p:
    """Format the name of a tensor using the given format c string and arguments.

    Parameters:
        tensor: tensor
        fmt: format c string
        args: arguments to format string

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_format_name(tensor, fmt, *args)


lib.ggml_format_name.argtypes = [ctypes.POINTER(ggml_tensor), ctypes.c_char_p]
lib.ggml_format_name.restype = ctypes.POINTER(ggml_tensor)

# //
# // operations on tensors with backpropagation
# //


# GGML_API struct ggml_tensor * ggml_dup(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_dup(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_dup(ctx, a)


lib.ggml_dup.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_dup.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_dup_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_dup_inplace(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_dup_inplace(ctx, a)


lib.ggml_dup_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_dup_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_add(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_add(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Add two tensors together and return the result.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_add(ctx, a, b)


lib.ggml_add.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_add_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_add_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Add two tensors together and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_add_inplace(ctx, a, b)


lib.ggml_add_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_add1(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_add1(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_add1(ctx, a, b)


lib.ggml_add1.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add1.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_add1_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_add1_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_add1_inplace(ctx, a, b)


lib.ggml_add1_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add1_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_acc(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                nb2,
#         size_t                nb3,
#         size_t                offset);
def ggml_acc(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    nb3: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_acc(ctx, a, b, nb1, nb2, nb3, offset)


lib.ggml_acc.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_acc.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_acc_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                nb2,
#         size_t                nb3,
#         size_t                offset);
def ggml_acc_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    nb3: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_acc_inplace(ctx, a, b, nb1, nb2, nb3, offset)


lib.ggml_acc_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_acc_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sub(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_sub(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Subtract two tensors and return the result.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sub(ctx, a, b)


lib.ggml_sub.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_sub.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sub_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_sub_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Subtract two tensors and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sub_inplace(ctx, a, b)


lib.ggml_sub_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_sub_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_mul(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_mul(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Element-wise multiply two tensors and return the result.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_mul(ctx, a, b)


lib.ggml_mul.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_mul.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_mul_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_mul_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Element-wise multiply two tensors and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_mul_inplace(ctx, a, b)


lib.ggml_mul_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_mul_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_div(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_div(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Element-wise divide two tensors and return the result.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_div(ctx, a, b)


lib.ggml_div.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_div.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_div_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_div_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Element-wise divide two tensors and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_div_inplace(ctx, a, b)


lib.ggml_div_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_div_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sqr(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sqr(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Square all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sqr(ctx, a)


lib.ggml_sqr.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sqr.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sqr_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sqr_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Square all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sqr_inplace(ctx, a)


lib.ggml_sqr_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sqr_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sqrt(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sqrt(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Square root all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sqrt(ctx, a)


lib.ggml_sqrt.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sqrt.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sqrt_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sqrt_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Square root all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sqrt_inplace(ctx, a)


lib.ggml_sqrt_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sqrt_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_log(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_log(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Take the natural logarithm of all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_log(ctx, a)


lib.ggml_log.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_log.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_log_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_log_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Take the natural logarithm of all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_log_inplace(ctx, a)


lib.ggml_log_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_log_inplace.restype = ctypes.POINTER(ggml_tensor)


# // return scalar
# GGML_API struct ggml_tensor * ggml_sum(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sum(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Sum all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sum(ctx, a)


lib.ggml_sum.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sum.restype = ctypes.POINTER(ggml_tensor)


# // sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]
# GGML_API struct ggml_tensor * ggml_sum_rows(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sum_rows(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Sum all elements in a tensor along the first axis and return the result.

    sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sum_rows(ctx, a)


lib.ggml_sum_rows.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sum_rows.restype = ctypes.POINTER(ggml_tensor)


# // mean along rows
# GGML_API struct ggml_tensor * ggml_mean(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_mean(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Take the mean of all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_mean(ctx, a)


lib.ggml_mean.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_mean.restype = ctypes.POINTER(ggml_tensor)


# // argmax along rows
# GGML_API struct ggml_tensor * ggml_argmax(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_argmax(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Take the argmax of all elements in a tensor and return the result.

    argmax along rows

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_argmax(ctx, a)


lib.ggml_argmax.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_argmax.restype = ctypes.POINTER(ggml_tensor)


# // if a is the same shape as b, and a is not parameter, return a
# // otherwise, return a new tensor: repeat(a) to fit in b
# GGML_API struct ggml_tensor * ggml_repeat(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_repeat(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Repeat a tensor to fit the shape of another tensor.

    If a is the same shape as b, and a is not parameter, return a

    Parameters:
        ctx: ggml context
        a: tensor to repeat
        b: tensor to fit

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_repeat(ctx, a, b)


lib.ggml_repeat.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_repeat.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_repeat_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_repeat_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_repeat_back(ctx, a, b)


lib.ggml_repeat_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_repeat_back.restype = ctypes.POINTER(ggml_tensor)


# // concat a and b on dim 2
# // used in stable-diffusion
# GGML_API struct ggml_tensor * ggml_concat(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_concat(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Concatenate two tensors along the second axis and return the result.

    Parameters:
        ctx: ggml context
        a: first tensor
        b: second tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_concat(ctx, a, b)


lib.ggml_concat.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_concat.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_abs(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_abs(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Take the absolute value of all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_abs(ctx, a)


lib.ggml_abs.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_abs.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_abs_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_abs_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Take the absolute value of all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_abs_inplace(ctx, a)


lib.ggml_abs_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_abs_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sgn(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sgn(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Get the sign of all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sgn(ctx, a)


lib.ggml_sgn.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sgn.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_sgn_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_sgn_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Get the sign of all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_sgn_inplace(ctx, a)


lib.ggml_sgn_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_sgn_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_neg(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_neg(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Negate all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_neg(ctx, a)


lib.ggml_neg.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_neg.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_neg_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_neg_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Negate all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_neg_inplace(ctx, a)


lib.ggml_neg_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_neg_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_step(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_step(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_step(ctx, a)


lib.ggml_step.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_step.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_tanh(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_tanh(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the tanh activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_tanh(ctx, a)


lib.ggml_tanh.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_tanh.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_tanh_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_tanh_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the tanh activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_tanh_inplace(ctx, a)


lib.ggml_tanh_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_tanh_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_elu(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_elu(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the ELU activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_elu(ctx, a)


lib.ggml_elu.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_elu.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_elu_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_elu_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the ELU activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_elu_inplace(ctx, a)


lib.ggml_elu_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_elu_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_relu(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_relu(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the ReLU activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_relu(ctx, a)


lib.ggml_relu.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_relu.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_relu_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_relu_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the ReLU activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_relu_inplace(ctx, a)


lib.ggml_relu_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_relu_inplace.restype = ctypes.POINTER(ggml_tensor)


# // TODO: double-check this computation is correct
# GGML_API struct ggml_tensor * ggml_gelu(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_gelu(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the Gaussian Error Linear Unit activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_gelu(ctx, a)


lib.ggml_gelu.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_gelu.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_gelu_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_gelu_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the Gaussian Error Linear Unit activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_gelu_inplace(ctx, a)


lib.ggml_gelu_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_gelu_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_gelu_quick(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_gelu_quick(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the Gaussian Error Linear Unit activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_gelu_quick(ctx, a)


lib.ggml_gelu_quick.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_gelu_quick.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_gelu_quick_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the Gaussian Error Linear Unit activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_gelu_quick_inplace(ctx, a)


lib.ggml_gelu_quick_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_gelu_quick_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_silu(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_silu(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Apply the Sigmoid Linear Unit activation function to all elements in a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_silu(ctx, a)


lib.ggml_silu.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_silu.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_silu_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_silu_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Apply the Sigmoid Linear Unit activation function to all elements in a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_silu_inplace(ctx, a)


lib.ggml_silu_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_silu_inplace.restype = ctypes.POINTER(ggml_tensor)


# // a - x
# // b - dy
# GGML_API struct ggml_tensor * ggml_silu_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_silu_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_silu_back(ctx, a, b)


lib.ggml_silu_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_silu_back.restype = ctypes.POINTER(ggml_tensor)


# // normalize along rows
# GGML_API struct ggml_tensor * ggml_norm(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a
#         float                eps);
def ggml_norm(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    eps: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    """Normalize all elements in a tensor along the first axis and return the result.

    normalize along rows.

    Parameters:
        ctx: ggml context
        a: tensor
        eps: minimum value to avoid division by zero

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_norm(ctx, a, eps)


lib.ggml_norm.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor), ctypes.c_float]
lib.ggml_norm.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_norm_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a
#         float                eps);
def ggml_norm_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    eps: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    """Normalize all elements in a tensor along the first axis and store the result in the first tensor.

    normalize along rows.

    Parameters:
        ctx: ggml context
        a: tensor
        eps: minimum value to avoid division by zero

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_norm_inplace(ctx, a, eps)


lib.ggml_norm_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_float,
]
lib.ggml_norm_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_rms_norm(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         float                 eps);
def ggml_rms_norm(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    eps: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    """Compute the RMS norm of a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor
        eps: float

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_rms_norm(ctx, a, eps)


lib.ggml_rms_norm.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_float,
]
lib.ggml_rms_norm.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         float                 eps);
def ggml_rms_norm_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    eps: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_rms_norm_inplace(ctx, a, eps)


lib.ggml_rms_norm_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_float,
]
lib.ggml_rms_norm_inplace.restype = ctypes.POINTER(ggml_tensor)


# // group normalize along ne0*ne1*n_groups
# // used in stable-diffusion
# // TODO: eps is hardcoded to 1e-6 for now
# GGML_API struct ggml_tensor * ggml_group_norm(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_groups);
def ggml_group_norm(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_groups: int,
) -> ggml_tensor_p:
    """Group normalize a tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor
        n_groups: int

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_group_norm(ctx, a, n_groups)


lib.ggml_group_norm.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_group_norm.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_group_norm_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_groups);
def ggml_group_norm_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_groups: int,
) -> ggml_tensor_p:
    """Group normalize a tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor
        n_groups: int

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_group_norm_inplace(ctx, a, n_groups)


lib.ggml_group_norm_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_group_norm_inplace.restype = ctypes.POINTER(ggml_tensor)


# // a - x
# // b - dy
# GGML_API struct ggml_tensor * ggml_rms_norm_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b
#         float                 eps);
def ggml_rms_norm_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    eps: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_rms_norm_back(ctx, a, b, eps)


lib.ggml_rms_norm_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_float,
]
lib.ggml_rms_norm_back.restype = ctypes.POINTER(ggml_tensor)


# // A: m rows, n columns
# // B: p rows, n columns  (i.e. we transpose it internally)
# // result is m columns, p rows
# GGML_API struct ggml_tensor * ggml_mul_mat(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_mul_mat(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Multiply two matrices and return the result.

    A: m rows, n columns
    B: p rows, n columns  (i.e. we transpose it internally)
    result is m columns, p rows

    Parameters:
        ctx: ggml context
        a: tensor
        b: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_mul_mat(ctx, a, b)


lib.ggml_mul_mat.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_mul_mat.restype = ctypes.POINTER(ggml_tensor)


# // A: m columns, n rows,
# // B: p columns, n rows,
# // result is m columns, p rows
# GGML_API struct ggml_tensor * ggml_out_prod(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_out_prod(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Compute the outer product of two matrices and return the result.

    A: m columns, n rows,
    B: p columns, n rows,
    result is m columns, p rows

    Parameters:
        ctx: ggml context
        a: tensor
        b: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_out_prod(ctx, a, b)


lib.ggml_out_prod.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_out_prod.restype = ctypes.POINTER(ggml_tensor)

# //
# // operations on tensors without backpropagation
# //


# GGML_API struct ggml_tensor * ggml_scale(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_scale(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Scale a tensor by another tensor and return the result.

    Parameters:
        ctx: ggml context
        a: tensor
        b: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_scale(ctx, a, b)


lib.ggml_scale.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_scale.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_scale_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_scale_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Scale a tensor by another tensor and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_scale_inplace(ctx, a, b)


lib.ggml_scale_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_scale_inplace.restype = ctypes.POINTER(ggml_tensor)


# // b -> view(a,offset,nb1,nb2,3), return modified a
# GGML_API struct ggml_tensor * ggml_set(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                nb2,
#         size_t                nb3,
#         size_t                offset);
def ggml_set(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    nb3: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set(ctx, a, b, nb1, nb2, nb3, offset)


lib.ggml_set.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_set.restype = ctypes.POINTER(ggml_tensor)


# // b -> view(a,offset,nb1,nb2,3), return view(a)
# GGML_API struct ggml_tensor * ggml_set_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                nb2,
#         size_t                nb3,
#         size_t                offset);
def ggml_set_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    nb3: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set_inplace(ctx, a, b, nb1, nb2, nb3, offset)


lib.ggml_set_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_set_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_set_1d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                offset);
def ggml_set_1d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set_1d(ctx, a, b, offset)


lib.ggml_set_1d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
]
lib.ggml_set_1d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_set_1d_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                offset);
def ggml_set_1d_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set_1d_inplace(ctx, a, b, offset)


lib.ggml_set_1d_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
]
lib.ggml_set_1d_inplace.restype = ctypes.POINTER(ggml_tensor)


# // b -> view(a,offset,nb1,nb2,3), return modified a
# GGML_API struct ggml_tensor * ggml_set_2d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                offset);
def ggml_set_2d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set_2d(ctx, a, b, nb1, offset)


lib.ggml_set_2d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_set_2d.restype = ctypes.POINTER(ggml_tensor)


# // b -> view(a,offset,nb1,nb2,3), return view(a)
# GGML_API struct ggml_tensor * ggml_set_2d_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         size_t                nb1,
#         size_t                offset);
def ggml_set_2d_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    nb1: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_set_2d_inplace(ctx, a, b, nb1, offset)


lib.ggml_set_2d_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_set_2d_inplace.restype = ctypes.POINTER(ggml_tensor)


# // a -> b, return view(b)
# GGML_API struct ggml_tensor * ggml_cpy(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_cpy(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_cpy(ctx, a, b)


lib.ggml_cpy.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_cpy.restype = ctypes.POINTER(ggml_tensor)


# // a -> b, in-place, return view(b)
# GGML_API struct ggml_tensor * ggml_cpy_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_cpy_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_cpy_inplace(ctx, a, b)


lib.ggml_cpy_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_cpy_inplace.restype = ctypes.POINTER(ggml_tensor)


# // make contiguous
# GGML_API struct ggml_tensor * ggml_cont(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_cont(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Make a tensor contiguous and return the result.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_cont(ctx, a)


lib.ggml_cont.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_cont.restype = ctypes.POINTER(ggml_tensor)


# // make contiguous, in-place
# GGML_API struct ggml_tensor * ggml_cont_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_cont_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
) -> ggml_tensor_p:
    """Make a tensor contiguous and store the result in the first tensor.

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_cont_inplace(ctx, a)


lib.ggml_cont_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_cont_inplace.restype = ctypes.POINTER(ggml_tensor)


# // return view(a), b specifies the new shape
# // TODO: when we start computing gradient, make a copy instead of view
# GGML_API struct ggml_tensor * ggml_reshape(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_reshape(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_reshape(ctx, a, b)


lib.ggml_reshape.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_reshape.restype = ctypes.POINTER(ggml_tensor)


# // return view(a)
# // TODO: when we start computing gradient, make a copy instead of view
# GGML_API struct ggml_tensor * ggml_reshape_1d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0);
def ggml_reshape_1d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    return lib.ggml_reshape_1d(ctx, a, ne0)


lib.ggml_reshape_1d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
]
lib.ggml_reshape_1d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_reshape_2d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1);
def ggml_reshape_2d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    return lib.ggml_reshape_2d(ctx, a, ne0, ne1)


lib.ggml_reshape_2d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_reshape_2d.restype = ctypes.POINTER(ggml_tensor)


# // return view(a)
# // TODO: when we start computing gradient, make a copy instead of view
# GGML_API struct ggml_tensor * ggml_reshape_3d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1,
#         int64_t               ne2);
def ggml_reshape_3d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    return lib.ggml_reshape_3d(ctx, a, ne0, ne1, ne2)


lib.ggml_reshape_3d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_reshape_3d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_reshape_4d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1,
#         int64_t               ne2,
#         int64_t               ne3);
def ggml_reshape_4d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
    ne3: Union[ctypes.c_int64, int],
) -> ggml_tensor_p:
    return lib.ggml_reshape_4d(ctx, a, ne0, ne1, ne2, ne3)


lib.ggml_reshape_4d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
]
lib.ggml_reshape_4d.restype = ctypes.POINTER(ggml_tensor)


# // offset in bytes
# GGML_API struct ggml_tensor * ggml_view_1d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         size_t                offset);
def ggml_view_1d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_view_1d(ctx, a, ne0, offset)


lib.ggml_view_1d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_size_t,
]
lib.ggml_view_1d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_view_2d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1,
#         size_t                nb1, // row stride in bytes
#         size_t                offset);
def ggml_view_2d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    nb1: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_view_2d(ctx, a, ne0, ne1, nb1, offset)


lib.ggml_view_2d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_view_2d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_view_3d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1,
#         int64_t               ne2,
#         size_t                nb1, // row   stride in bytes
#         size_t                nb2, // slice stride in bytes
#         size_t                offset);
def ggml_view_3d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_view_3d(ctx, a, ne0, ne1, ne2, nb1, nb2, offset)


lib.ggml_view_3d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_view_3d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_view_4d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int64_t               ne0,
#         int64_t               ne1,
#         int64_t               ne2,
#         int64_t               ne3,
#         size_t                nb1, // row   stride in bytes
#         size_t                nb2, // slice stride in bytes
#         size_t                nb3,
#         size_t                offset);
def ggml_view_4d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    ne0: Union[ctypes.c_int64, int],
    ne1: Union[ctypes.c_int64, int],
    ne2: Union[ctypes.c_int64, int],
    ne3: Union[ctypes.c_int64, int],
    nb1: Union[ctypes.c_size_t, int],
    nb2: Union[ctypes.c_size_t, int],
    nb3: Union[ctypes.c_size_t, int],
    offset: Union[ctypes.c_size_t, int],
) -> ggml_tensor_p:
    return lib.ggml_view_4d(ctx, a, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset)


lib.ggml_view_4d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_int64,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
    ctypes.c_size_t,
]
lib.ggml_view_4d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_permute(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   axis0,
#         int                   axis1,
#         int                   axis2,
#         int                   axis3);
def ggml_permute(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    axis0: Union[ctypes.c_int, int],
    axis1: Union[ctypes.c_int, int],
    axis2: Union[ctypes.c_int, int],
    axis3: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_permute(ctx, a, axis0, axis1, axis2, axis3)


lib.ggml_permute.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_permute.restype = ctypes.POINTER(ggml_tensor)


# // alias for ggml_permute(ctx, a, 1, 0, 2, 3)
# GGML_API struct ggml_tensor * ggml_transpose(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_transpose(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    """Transpose *the first two dimensions* of a tensor and return the result.

    alias for `ggml_permute(ctx, a, 1, 0, 2, 3)`

    Parameters:
        ctx: ggml context
        a: tensor

    Returns:
        Pointer to ggml_tensor"""
    return lib.ggml_transpose(ctx, a)


lib.ggml_transpose.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_transpose.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_get_rows(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_get_rows(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_get_rows(ctx, a, b)


lib.ggml_get_rows.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_get_rows.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_get_rows_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         struct ggml_tensor  * c);
def ggml_get_rows_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_get_rows_back(ctx, a, b, c)


lib.ggml_get_rows_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_get_rows_back.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_diag(
#     struct ggml_context     * ctx,
#     struct ggml_tensor      * a);
def ggml_diag(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_diag(ctx, a)


lib.ggml_diag.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_diag.restype = ctypes.POINTER(ggml_tensor)


# // set elements above the diagonal to -INF
# GGML_API struct ggml_tensor * ggml_diag_mask_inf(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past);
def ggml_diag_mask_inf(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_diag_mask_inf(ctx, a, n_past)


lib.ggml_diag_mask_inf.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_diag_mask_inf.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past);
def ggml_diag_mask_inf_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_diag_mask_inf_inplace(ctx, a, n_past)


lib.ggml_diag_mask_inf_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_diag_mask_inf_inplace.restype = ctypes.POINTER(ggml_tensor)


# // set elements above the diagonal to 0
# GGML_API struct ggml_tensor * ggml_diag_mask_zero(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past);
def ggml_diag_mask_zero(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_diag_mask_zero(ctx, a, n_past)


lib.ggml_diag_mask_zero.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_diag_mask_zero.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past);
def ggml_diag_mask_zero_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_diag_mask_zero_inplace(ctx, a, n_past)


lib.ggml_diag_mask_zero_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_diag_mask_zero_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_soft_max(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_soft_max(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_soft_max(ctx, a)


lib.ggml_soft_max.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_soft_max.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_soft_max_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a);
def ggml_soft_max_inplace(ctx: ggml_context_p, a: ggml_tensor_p) -> ggml_tensor_p:
    return lib.ggml_soft_max_inplace(ctx, a)


lib.ggml_soft_max_inplace.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_soft_max_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_soft_max_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_soft_max_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_soft_max_back(ctx, a, b)


lib.ggml_soft_max_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_soft_max_back.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_soft_max_back_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_soft_max_back_inplace(ctx, a, b)


lib.ggml_soft_max_back_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_soft_max_back_inplace.restype = ctypes.POINTER(ggml_tensor)


# // rotary position embedding
# // if mode & 1 == 1, skip n_past elements
# // if mode & 2 == 1, GPT-NeoX style
# // if mode & 4 == 1, ChatGLM style
# // TODO: avoid creating a new tensor every time
# GGML_API struct ggml_tensor * ggml_rope(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         int                   mode,
#         int                   n_ctx);
def ggml_rope(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    mode: Union[ctypes.c_int, int],
    n_ctx: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_rope(ctx, a, n_past, n_dims, mode, n_ctx)


lib.ggml_rope.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_rope.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_rope_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         int                   mode,
#         int                   n_ctx);
def ggml_rope_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    mode: Union[ctypes.c_int, int],
    n_ctx: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_rope_inplace(ctx, a, n_past, n_dims, mode, n_ctx)


lib.ggml_rope_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_rope_inplace.restype = ctypes.POINTER(ggml_tensor)


# // custom RoPE
# GGML_API struct ggml_tensor * ggml_rope_custom(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         int                   mode,
#         int                   n_ctx,
#         float                 freq_base,
#         float                 freq_scale);
def ggml_rope_custom(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    mode: Union[ctypes.c_int, int],
    n_ctx: Union[ctypes.c_int, int],
    freq_base: Union[ctypes.c_float, float],
    freq_scale: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_rope_custom(
        ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale
    )


lib.ggml_rope_custom.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_float,
    ctypes.c_float,
]
lib.ggml_rope_custom.restype = ctypes.POINTER(ggml_tensor)


# // in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         int                   mode,
#         int                   n_ctx,
#         float                 freq_base,
#         float                 freq_scale);
def ggml_rope_custom_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    mode: Union[ctypes.c_int, int],
    n_ctx: Union[ctypes.c_int, int],
    freq_base: Union[ctypes.c_float, float],
    freq_scale: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_rope_custom_inplace(
        ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale
    )


lib.ggml_rope_custom_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_float,
    ctypes.c_float,
]
lib.ggml_rope_custom_inplace.restype = ctypes.POINTER(ggml_tensor)


# // xPos RoPE, in-place, returns view(a)
# GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         float                 base,
#         bool                  down);
def ggml_rope_xpos_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    base: Union[ctypes.c_float, float],
    down: Union[ctypes.c_bool, bool],
) -> ggml_tensor_p:
    return lib.ggml_rope_xpos_inplace(ctx, a, n_past, n_dims, base, down)


lib.ggml_rope_xpos_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_float,
    ctypes.c_bool,
]
lib.ggml_rope_xpos_inplace.restype = ctypes.POINTER(ggml_tensor)


# // rotary position embedding backward, i.e compute dx from dy
# // a - dy
# GGML_API struct ggml_tensor * ggml_rope_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_dims,
#         int                   mode,
#         int                   n_ctx,
#         float                 freq_base,
#         float                 freq_scale,
#         float                 xpos_base,
#         bool                  xpos_down);
def ggml_rope_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_dims: Union[ctypes.c_int, int],
    mode: Union[ctypes.c_int, int],
    n_ctx: Union[ctypes.c_int, int],
    freq_base: Union[ctypes.c_float, float],
    freq_scale: Union[ctypes.c_float, float],
    xpos_base: Union[ctypes.c_float, float],
    xpos_down: Union[ctypes.c_bool, bool],
) -> ggml_tensor_p:
    return lib.ggml_rope_back(
        ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, xpos_base, xpos_down
    )


lib.ggml_rope_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_float,
    ctypes.c_float,
    ctypes.c_float,
    ctypes.c_bool,
]
lib.ggml_rope_back.restype = ctypes.POINTER(ggml_tensor)


# // alibi position embedding
# // in-place, returns view(a)
# struct ggml_tensor * ggml_alibi(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   n_past,
#         int                   n_head,
#         float                 bias_max);
def ggml_alibi(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    n_past: Union[ctypes.c_int, int],
    n_head: Union[ctypes.c_int, int],
    bias_max: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_alibi(ctx, a, n_past, n_head, bias_max)


lib.ggml_alibi.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_float,
]
lib.ggml_alibi.restype = ctypes.POINTER(ggml_tensor)


# // clamp
# // in-place, returns view(a)
# struct ggml_tensor * ggml_clamp(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         float                 min,
#         float                 max);
def ggml_clamp(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    min: Union[ctypes.c_float, float],
    max: Union[ctypes.c_float, float],
) -> ggml_tensor_p:
    return lib.ggml_clamp(ctx, a, min, max)


lib.ggml_clamp.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_float,
    ctypes.c_float,
]
lib.ggml_clamp.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_conv_1d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         int                   s0,  // stride
#         int                   p0,  // padding
#         int                   d0); // dilation
def ggml_conv_1d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    s0: Union[ctypes.c_int, int],
    p0: Union[ctypes.c_int, int],
    d0: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """Convolution 1D

    Parameters:
        a: input tensor
        b: filter tensor
        s0: stride
        p0: padding
        d0: dilation

    Returns:
        output tensor"""
    return lib.ggml_conv_1d(ctx, a, b, s0, p0, d0)


lib.ggml_conv_1d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_conv_1d.restype = ctypes.POINTER(ggml_tensor)


# // conv_1d with padding = half
# // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
# GGML_API struct ggml_tensor* ggml_conv_1d_ph(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         int                   s,
#         int                   d);
def ggml_conv_1d_ph(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    s: Union[ctypes.c_int, int],
    d: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """Convolution 1D with padding = half

    Parameters:
        a: input tensor
        b: filter tensor
        s: stride
        d: dilation

    Returns:
        output tensor"""
    return lib.ggml_conv_1d_ph(ctx, a, b, s, d)


lib.ggml_conv_1d_ph.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_conv_1d_ph.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_conv_2d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         int                   s0,
#         int                   s1,
#         int                   p0,
#         int                   p1,
#         int                   d0,
#         int                   d1);
def ggml_conv_2d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    s0: Union[ctypes.c_int, int],
    s1: Union[ctypes.c_int, int],
    p0: Union[ctypes.c_int, int],
    p1: Union[ctypes.c_int, int],
    d0: Union[ctypes.c_int, int],
    d1: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """Convolution 2D

    Parameters:
        a: input tensor
        b: filter tensor
        s0: stride
        s1: stride
        p0: padding
        p1: padding
        d0: dilation
        d1: dilation

    Returns:
        output tensor"""
    return lib.ggml_conv_2d(ctx, a, b, s0, s1, p0, p1, d0, d1)


lib.ggml_conv_2d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_conv_2d.restype = ctypes.POINTER(ggml_tensor)


# // kernel size is a->ne[0] x a->ne[1]
# // stride is equal to kernel size
# // padding is zero
# // example:
# // a:     16   16    3  768
# // b:   1024 1024    3    1
# // res:   64   64  768    1
# // used in sam
# GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_conv_2d_sk_p0(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Convolution 2D

    Parameters:
        a: input tensor
        b: filter tensor

    Returns:
        output tensor"""
    return lib.ggml_conv_2d_sk_p0(ctx, a, b)


lib.ggml_conv_2d_sk_p0.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_conv_2d_sk_p0.restype = ctypes.POINTER(ggml_tensor)


# // kernel size is a->ne[0] x a->ne[1]
# // stride is 1
# // padding is half
# // example:
# // a:      3    3    256  256
# // b:     64   64    256    1
# // res:   64   64    256    1
# // used in sam
# GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b);
def ggml_conv_2d_s1_ph(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    """Convolution 2D with stride = 1 and padding = half

    Parameters:
        a: input tensor
        b: filter tensor

    Returns:
        output tensor"""
    return lib.ggml_conv_2d_s1_ph(ctx, a, b)


lib.ggml_conv_2d_s1_ph.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_conv_2d_s1_ph.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b,
#         int                   stride);
def ggml_conv_transpose_2d_p0(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    stride: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """Convolution Transpose 2D with padding = zero

    Parameters:
        a: input tensor
        b: filter tensor
        stride: stride

    Returns:
        output tensor"""
    return lib.ggml_conv_transpose_2d_p0(ctx, a, b, stride)


lib.ggml_conv_transpose_2d_p0.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_conv_transpose_2d_p0.restype = ctypes.POINTER(ggml_tensor)

# enum ggml_op_pool {
#     GGML_OP_POOL_MAX,
#     GGML_OP_POOL_AVG,
#     GGML_OP_POOL_COUNT,
# };
GGML_OP_POOL_MAX = 0
GGML_OP_POOL_AVG = 1
GGML_OP_POOL_COUNT = 2


# GGML_API struct ggml_tensor * ggml_pool_1d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         enum ggml_op_pool     op,
#         int                   k0, // kernel size
#         int                   s0, // stride
#         int                   p0); // padding
def ggml_pool_1d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    op: Union[ctypes.c_int, int],
    k0: Union[ctypes.c_int, int],
    s0: Union[ctypes.c_int, int],
    p0: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """1D Pooling

    Parameters:
        a: input tensor
        op: pooling operation
        k0: kernel size
        s0: stride
        p0: padding

    Returns:
        output tensor"""
    return lib.ggml_pool_1d(ctx, a, op, k0, s0, p0)


lib.ggml_pool_1d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_pool_1d.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_pool_2d(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         enum ggml_op_pool     op,
#         int                   k0,
#         int                   k1,
#         int                   s0,
#         int                   s1,
#         int                   p0,
#         int                   p1);
def ggml_pool_2d(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    op: Union[ctypes.c_int, int],
    k0: Union[ctypes.c_int, int],
    k1: Union[ctypes.c_int, int],
    s0: Union[ctypes.c_int, int],
    s1: Union[ctypes.c_int, int],
    p0: Union[ctypes.c_int, int],
    p1: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """2D Pooling

    Parameters:
        a: input tensor
        op: pooling operation
        k0: kernel size
        k1: kernel size
        s0: stride
        s1: stride
        p0: padding
        p1: padding

    Returns:
        output tensor"""
    return lib.ggml_pool_2d(ctx, a, op, k0, k1, s0, s1, p0, p1)


lib.ggml_pool_2d.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_pool_2d.restype = ctypes.POINTER(ggml_tensor)


# // nearest interpolate
# // used in stable-diffusion
# GGML_API struct ggml_tensor * ggml_upscale(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   scale_factor);
def ggml_upscale(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    scale_factor: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    """Upscale

    Parameters:
        a: input tensor
        scale_factor: scale factor

    Returns:
        output tensor"""
    return lib.ggml_upscale(ctx, a, scale_factor)


lib.ggml_upscale.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_upscale.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_flash_attn(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * q,
#         struct ggml_tensor  * k,
#         struct ggml_tensor  * v,
#         bool                  masked);
def ggml_flash_attn(
    ctx: ggml_context_p,
    q: ggml_tensor_p,
    k: ggml_tensor_p,
    v: ggml_tensor_p,
    masked: Union[ctypes.c_bool, bool],
) -> ggml_tensor_p:
    return lib.ggml_flash_attn(ctx, q, k, v, masked)


lib.ggml_flash_attn.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_bool,
]
lib.ggml_flash_attn.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_flash_attn_back(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * q,
#         struct ggml_tensor  * k,
#         struct ggml_tensor  * v,
#         struct ggml_tensor  * d,
#         bool                  masked);
def ggml_flash_attn_back(
    ctx: ggml_context_p,
    q: ggml_tensor_p,
    k: ggml_tensor_p,
    v: ggml_tensor_p,
    d: ggml_tensor_p,
    masked: Union[ctypes.c_bool, bool],
) -> ggml_tensor_p:
    return lib.ggml_flash_attn_back(ctx, q, k, v, d, masked)


lib.ggml_flash_attn_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_bool,
]
lib.ggml_flash_attn_back.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_flash_ff(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * b0,
#         struct ggml_tensor  * b1,
#         struct ggml_tensor  * c0,
#         struct ggml_tensor  * c1);
def ggml_flash_ff(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b0: ggml_tensor_p,
    b1: ggml_tensor_p,
    c0: ggml_tensor_p,
    c1: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_flash_ff(ctx, a, b0, b1, c0, c1)


lib.ggml_flash_ff.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_flash_ff.restype = ctypes.POINTER(ggml_tensor)


# // partition into non-overlapping windows with padding if needed
# // example:
# // a:   768   64   64    1
# // w:    14
# // res: 768   14   14    25
# // used in sam
# GGML_API struct ggml_tensor * ggml_win_part(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   w);
def ggml_win_part(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    w: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_win_part(ctx, a, w)


lib.ggml_win_part.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_win_part.restype = ctypes.POINTER(ggml_tensor)


# // reverse of ggml_win_part
# // used in sam
# GGML_API struct ggml_tensor * ggml_win_unpart(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   w0,
#         int                   h0,
#         int                   w);
def ggml_win_unpart(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    w0: Union[ctypes.c_int, int],
    h0: Union[ctypes.c_int, int],
    w: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_win_unpart(ctx, a, w0, h0, w)


lib.ggml_win_unpart.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_win_unpart.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_unary(
#         struct ggml_context * ctx,
#             struct ggml_tensor * a,
#             enum ggml_unary_op op);
def ggml_unary(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    op: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_unary(ctx, a, op)


lib.ggml_unary.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_unary.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_unary_inplace(
#     struct ggml_context * ctx,
#     struct ggml_tensor  * a,
#     enum ggml_unary_op op);
def ggml_unary_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    op: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_unary_inplace(ctx, a, op)


lib.ggml_unary_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
]
lib.ggml_unary_inplace.restype = ctypes.POINTER(ggml_tensor)


# // used in sam
# GGML_API struct ggml_tensor * ggml_get_rel_pos(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         int                   qh,
#         int                   kh);
def ggml_get_rel_pos(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    qh: Union[ctypes.c_int, int],
    kh: Union[ctypes.c_int, int],
) -> ggml_tensor_p:
    return lib.ggml_get_rel_pos(ctx, a, qh, kh)


lib.ggml_get_rel_pos.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
]
lib.ggml_get_rel_pos.restype = ctypes.POINTER(ggml_tensor)


# // used in sam
# GGML_API struct ggml_tensor * ggml_add_rel_pos(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * pw,
#         struct ggml_tensor  * ph);
def ggml_add_rel_pos(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    pw: ggml_tensor_p,
    ph: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_add_rel_pos(ctx, a, pw, ph)


lib.ggml_add_rel_pos.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add_rel_pos.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * a,
#         struct ggml_tensor  * pw,
#         struct ggml_tensor  * ph);
def ggml_add_rel_pos_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    pw: ggml_tensor_p,
    ph: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_add_rel_pos_inplace(ctx, a, pw, ph)


lib.ggml_add_rel_pos_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_add_rel_pos_inplace.restype = ctypes.POINTER(ggml_tensor)

# // custom operators (DEPRECATED)

# typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
ggml_unary_op_f32_t = ctypes.CFUNCTYPE(
    None, ctypes.c_int, ctypes.POINTER(ctypes.c_float), ctypes.POINTER(ctypes.c_float)
)

# typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
ggml_binary_op_f32_t = ctypes.CFUNCTYPE(
    None,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_float),
    ctypes.POINTER(ctypes.c_float),
    ctypes.POINTER(ctypes.c_float),
)

# typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
ggml_custom1_op_f32_t = ctypes.CFUNCTYPE(
    None, ctypes.POINTER(ggml_tensor), ctypes.POINTER(ggml_tensor)
)
"""Unary operator function type"""

# typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
ggml_custom2_op_f32_t = ctypes.CFUNCTYPE(
    None,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
)
"""Binary operator function type"""

# typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
ggml_custom3_op_f32_t = ctypes.CFUNCTYPE(
    None,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
)
"""Ternary operator function type"""


# GGML_API struct ggml_tensor * ggml_map_unary_f32(
#         struct ggml_context        * ctx,
#         struct ggml_tensor         * a,
#                ggml_unary_op_f32_t   fun);
def ggml_map_unary_f32(
    ctx: ggml_context_p, a: ggml_tensor_p, fun: "ctypes._FuncPointer"  # type: ignore
) -> ggml_tensor_p:
    return lib.ggml_map_unary_f32(ctx, a, fun)


lib.ggml_map_unary_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_unary_op_f32_t,
]
lib.ggml_map_unary_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
#         struct ggml_context        * ctx,
#         struct ggml_tensor         * a,
#                 ggml_unary_op_f32_t   fun);
def ggml_map_unary_inplace_f32(
    ctx: ggml_context_p, a: ggml_tensor_p, fun: "ctypes._FuncPointer"  # type: ignore
) -> ggml_tensor_p:
    return lib.ggml_map_unary_inplace_f32(ctx, a, fun)


lib.ggml_map_unary_inplace_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_unary_op_f32_t,
]
lib.ggml_map_unary_inplace_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_binary_f32(
#         struct ggml_context         * ctx,
#         struct ggml_tensor          * a,
#         struct ggml_tensor          * b,
#                ggml_binary_op_f32_t   fun);
def ggml_map_binary_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    return lib.ggml_map_binary_f32(ctx, a, b, fun)


lib.ggml_map_binary_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_binary_op_f32_t,
]
lib.ggml_map_binary_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
#         struct ggml_context         * ctx,
#         struct ggml_tensor          * a,
#         struct ggml_tensor          * b,
#                 ggml_binary_op_f32_t   fun);
def ggml_map_binary_inplace_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    return lib.ggml_map_binary_inplace_f32(ctx, a, b, fun)


lib.ggml_map_binary_inplace_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_binary_op_f32_t,
]
lib.ggml_map_binary_inplace_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom1_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#                 ggml_custom1_op_f32_t   fun);
def ggml_map_custom1_f32(
    ctx: ggml_context_p, a: ggml_tensor_p, fun: "ctypes._FuncPointer"  # type: ignore
) -> ggml_tensor_p:
    """Custom unary operator on a tensor.

    Example:
        ```python
        import ggml

        @ggml.ggml_custom1_op_f32_t
        def custom_op(b: ggml.tensor_p, a: ggml.tensor_p):
            # do something with a and copy to b
            return

        ...

        b = ggml.ggml_map_custom1_f32(ctx, a, custom_op)
        ```

    Parameters:
        a: input tensor
        fun (ggml.ggml_custom1_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom1_f32(ctx, a, fun)


lib.ggml_map_custom1_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_custom1_op_f32_t,
]
lib.ggml_map_custom1_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#                 ggml_custom1_op_f32_t   fun);
def ggml_map_custom1_inplace_f32(
    ctx: ggml_context_p, a: ggml_tensor_p, fun: "ctypes._CFuncPtr"  # type: ignore
) -> ggml_tensor_p:
    """Custom unary operator on a tensor inplace.

    Parameters:
        a: input tensor
        fun (ggml.ggml_custom1_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom1_inplace_f32(ctx, a, fun)


lib.ggml_map_custom1_inplace_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_custom1_op_f32_t,
]
lib.ggml_map_custom1_inplace_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom2_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#         struct ggml_tensor           * b,
#                 ggml_custom2_op_f32_t   fun);
def ggml_map_custom2_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    """Custom binary operator on two tensors.

    Parameters:
        a: input tensor
        b: input tensor
        fun (ggml.ggml_custom2_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom2_f32(ctx, a, b, fun)


lib.ggml_map_custom2_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom2_op_f32_t,
]
lib.ggml_map_custom2_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#         struct ggml_tensor           * b,
#                 ggml_custom2_op_f32_t   fun);
def ggml_map_custom2_inplace_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    """Custom binary operator on two tensors inplace.

    Parameters:
        a: input tensor
        b: input tensor
        fun (ggml.ggml_custom2_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom2_inplace_f32(ctx, a, b, fun)


lib.ggml_map_custom2_inplace_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom2_op_f32_t,
]
lib.ggml_map_custom2_inplace_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom3_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#         struct ggml_tensor           * b,
#         struct ggml_tensor           * c,
#                 ggml_custom3_op_f32_t   fun);
def ggml_map_custom3_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    """Custom ternary operator on three tensors.

    Parameters:
        a: input tensor
        b: input tensor
        c: input tensor
        fun (ggml.ggml_custom3_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom3_f32(ctx, a, b, c, fun)


lib.ggml_map_custom3_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom3_op_f32_t,
]
lib.ggml_map_custom3_f32.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
#         struct ggml_context          * ctx,
#         struct ggml_tensor           * a,
#         struct ggml_tensor           * b,
#         struct ggml_tensor           * c,
#                 ggml_custom3_op_f32_t   fun);
def ggml_map_custom3_inplace_f32(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
) -> ggml_tensor_p:
    """Custom ternary operator on three tensors inplace.

    Parameters:
        a: input tensor
        b: input tensor
        c: input tensor
        fun (ggml.ggml_custom3_op_f32_t): function to apply to each element

    Returns:
        output tensor"""
    return lib.ggml_map_custom3_inplace_f32(ctx, a, b, c, fun)


lib.ggml_map_custom3_inplace_f32.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom3_op_f32_t,
]
lib.ggml_map_custom3_inplace_f32.restype = ctypes.POINTER(ggml_tensor)

# // custom operators v2

# typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
ggml_custom1_op_t = ctypes.CFUNCTYPE(
    None,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_void_p,
)
"""Custom unary operator on a tensor."""

# typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
ggml_custom2_op_t = ctypes.CFUNCTYPE(
    None,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_void_p,
)
"""Custom binary operator on two tensors."""

# typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
ggml_custom3_op_t = ctypes.CFUNCTYPE(
    None,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.c_int,
    ctypes.c_int,
    ctypes.c_void_p,
)
"""Custom ternary operator on three tensors."""

# #define GGML_N_TASKS_MAX -1
GGML_N_TASKS_MAX = -1


# GGML_API struct ggml_tensor * ggml_map_custom1(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         ggml_custom1_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom1(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom1(ctx, a, fun, n_tasks, userdata)


lib.ggml_map_custom1.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_custom1_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom1.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         ggml_custom1_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom1_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom1_inplace(ctx, a, fun, n_tasks, userdata)


lib.ggml_map_custom1_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ggml_custom1_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom1_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom2(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         struct ggml_tensor    * b,
#         ggml_custom2_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom2(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom2(ctx, a, b, fun, n_tasks, userdata)


lib.ggml_map_custom2.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom2_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom2.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         struct ggml_tensor    * b,
#         ggml_custom2_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom2_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom2_inplace(ctx, a, b, fun, n_tasks, userdata)


lib.ggml_map_custom2_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom2_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom2_inplace.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom3(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         struct ggml_tensor    * b,
#         struct ggml_tensor    * c,
#         ggml_custom3_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom3(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom3(ctx, a, b, c, fun, n_tasks, userdata)


lib.ggml_map_custom3.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom3_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom3.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
#         struct ggml_context   * ctx,
#         struct ggml_tensor    * a,
#         struct ggml_tensor    * b,
#         struct ggml_tensor    * c,
#         ggml_custom3_op_t       fun,
#         int                     n_tasks,
#         void                  * userdata);
def ggml_map_custom3_inplace(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
    fun: "ctypes._FuncPointer",  # type: ignore
    n_tasks: Union[ctypes.c_int, int],
    userdata: Optional[ctypes.c_void_p],
) -> ggml_tensor_p:
    return lib.ggml_map_custom3_inplace(ctx, a, b, c, fun, n_tasks, userdata)


lib.ggml_map_custom3_inplace.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ggml_custom3_op_t,
    ctypes.c_int,
    ctypes.c_void_p,
]
lib.ggml_map_custom3_inplace.restype = ctypes.POINTER(ggml_tensor)

# // loss function


# GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
#         struct ggml_context         * ctx,
#         struct ggml_tensor          * a,
#         struct ggml_tensor          * b);
def ggml_cross_entropy_loss(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_cross_entropy_loss(ctx, a, b)


lib.ggml_cross_entropy_loss.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_cross_entropy_loss.restype = ctypes.POINTER(ggml_tensor)


# GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
#         struct ggml_context         * ctx,
#         struct ggml_tensor          * a,
#         struct ggml_tensor          * b,
#         struct ggml_tensor          * c);
def ggml_cross_entropy_loss_back(
    ctx: ggml_context_p,
    a: ggml_tensor_p,
    b: ggml_tensor_p,
    c: ggml_tensor_p,
) -> ggml_tensor_p:
    return lib.ggml_cross_entropy_loss_back(ctx, a, b, c)


lib.ggml_cross_entropy_loss_back.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_cross_entropy_loss_back.restype = ctypes.POINTER(ggml_tensor)

# //
# // automatic differentiation
# //


# GGML_API void ggml_set_param(
#         struct ggml_context * ctx,
#         struct ggml_tensor  * tensor);
def ggml_set_param(ctx: ggml_context_p, tensor: ggml_tensor_p):
    return lib.ggml_set_param(ctx, tensor)


lib.ggml_set_param.argtypes = [ggml_context_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_set_param.restype = None


# GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
def ggml_build_forward_expand(
    cgraph: ggml_cgraph_p,
    tensor: ggml_tensor_p,
):
    """Add a tensor to the forward computation graph. This is used to
    compute and save the value of the tensor.

    Parameters:
        cgraph: The graph.
        tensor: The tensor."""
    return lib.ggml_build_forward_expand(cgraph, tensor)


lib.ggml_build_forward_expand.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_build_forward_expand.restype = None


# GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
def ggml_build_backward_expand(
    ctx: ggml_context_p,
    gf: ggml_cgraph_p,
    gb: ggml_cgraph_p,
    keep: Union[ctypes.c_bool, bool],
):
    """Add a tensor to the backward computation graph. This is used to
    compute the gradient of the tensor.

    Parameters:
        ctx: The context.
        gf: The forward graph.
        gb: The backward graph.
        keep: Whether to keep the tensor."""
    return lib.ggml_build_backward_expand(ctx, gf, gb, keep)


lib.ggml_build_backward_expand.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_cgraph),
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_bool,
]
lib.ggml_build_backward_expand.restype = None


# GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
def ggml_build_forward(
    tensor: ggml_tensor_p,
) -> ggml_cgraph:
    """Build the forward computation graph.

    Parameters:
        tensor: The tensor.

    Returns:
        The graph."""
    return lib.ggml_build_forward(tensor)


lib.ggml_build_forward.argtypes = [ctypes.POINTER(ggml_tensor)]
lib.ggml_build_forward.restype = ggml_cgraph


# GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
def ggml_build_backward(
    ctx: ggml_context_p,
    gf: ggml_cgraph_p,
    keep: Union[ctypes.c_bool, bool],
) -> ggml_cgraph:
    return lib.ggml_build_backward(ctx, gf, keep)


lib.ggml_build_backward.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_bool,
]
lib.ggml_build_backward.restype = ggml_cgraph


# // graph allocation in a context
# GGML_API struct ggml_cgraph * ggml_new_graph        (struct ggml_context * ctx);
def ggml_new_graph(
    ctx: ggml_context_p,
) -> ggml_cgraph:
    """Create a new graph.

    Parameters:
        ctx: The context.

    Returns:
        The graph."""
    return lib.ggml_new_graph(ctx)


lib.ggml_new_graph.argtypes = [ggml_context_p]
lib.ggml_new_graph.restype = ggml_cgraph


# GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
def ggml_build_forward_ctx(
    ctx: ggml_context_p,
    tensor: ggml_tensor_p,
) -> ggml_cgraph:
    """Build the forward computation graph in a context.

    Parameters:
        ctx: The context.
        tensor: The tensor.

    Returns:
        The graph."""
    return lib.ggml_build_forward_ctx(ctx, tensor)


lib.ggml_build_forward_ctx.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_build_forward_ctx.restype = ggml_cgraph


# GGML_API size_t ggml_graph_overhead(void);
def ggml_graph_overhead() -> int:
    """Get the overhead of the graph."""
    return lib.ggml_graph_overhead()


lib.ggml_graph_overhead.argtypes = []
lib.ggml_graph_overhead.restype = ctypes.c_size_t


# // ggml_graph_plan() has to be called before ggml_graph_compute()
# // when plan.work_size > 0, caller must allocate memory for plan.work_data
# GGML_API struct ggml_cplan ggml_graph_plan   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
def ggml_graph_plan(
    cgraph: ggml_cgraph_p,
    n_threads: Union[ctypes.c_int, int] = GGML_DEFAULT_N_THREADS,
) -> ggml_cplan:
    """Plan the computation graph.

    Parameters:
        cgraph: The graph.
        n_threads: The number of threads to use.

    Returns:
        The plan."""
    return lib.ggml_graph_plan(cgraph, n_threads)


lib.ggml_graph_plan.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_int,
]
lib.ggml_graph_plan.restype = ggml_cplan


# GGML_API               int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
def ggml_graph_compute(
    cgraph: ggml_cgraph_p,
    cplan: ggml_cplan_p,
) -> int:
    """Compute the graph.

    Parameters:
        cgraph: The graph.
        cplan: The plan."""
    return lib.ggml_graph_compute(cgraph, cplan)


lib.ggml_graph_compute.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.POINTER(ggml_cplan),
]
lib.ggml_graph_compute.restype = ctypes.c_int


# GGML_API              void ggml_graph_reset  (struct ggml_cgraph * cgraph);
def ggml_graph_reset(
    cgraph: ggml_cgraph_p,
):
    """Reset the graph.

    Parameters:
        cgraph: The graph."""
    return lib.ggml_graph_reset(cgraph)


# // same as ggml_graph_compute() but the work data is allocated as a part of the context
# // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
# GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
def ggml_graph_compute_with_ctx(
    ctx: ggml_context_p,
    cgraph: ggml_cgraph_p,
    n_threads: Union[ctypes.c_int, int],
):
    """Compute the graph with a context.

    Parameters:
        ctx: The context.
        cgraph: The graph.
        n_threads: The number of threads to use."""
    return lib.ggml_graph_compute_with_ctx(ctx, cgraph, n_threads)


lib.ggml_graph_compute_with_ctx.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_int,
]
lib.ggml_graph_compute_with_ctx.restype = None


# GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
def ggml_graph_get_tensor(
    cgraph: ggml_cgraph_p,
    name: bytes,
) -> ggml_tensor_p:
    """Get a tensor from the graph by name.

    Parameters:
        cgraph: The graph.
        name: The name of the tensor.

    Returns:
        The tensor."""
    return lib.ggml_graph_get_tensor(cgraph, name)


lib.ggml_graph_get_tensor.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_char_p,
]
lib.ggml_graph_get_tensor.restype = ctypes.POINTER(ggml_tensor)


# GGML_API void               ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
def ggml_graph_export(
    cgraph: ggml_cgraph_p,
    fname: bytes,
):
    return lib.ggml_graph_export(cgraph, fname)


lib.ggml_graph_export.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_char_p,
]
lib.ggml_graph_export.restype = None


# GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
def ggml_graph_import(
    fname: bytes,
    ctx_data: "ctypes._Pointer[ggml_context_p]",  # type: ignore
    ctx_eval: "ctypes._Pointer[ggml_context_p]",  # type: ignore
) -> ggml_cgraph:
    return lib.ggml_graph_import(fname, ctx_data, ctx_eval)


lib.ggml_graph_import.argtypes = [
    ctypes.c_char_p,
    ctypes.POINTER(ggml_context_p),
    ctypes.POINTER(ggml_context_p),
]
lib.ggml_graph_import.restype = ggml_cgraph


# // print info and performance information for the graph
# GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
def ggml_graph_print(
    cgraph: ggml_cgraph_p,
):
    return lib.ggml_graph_print(cgraph)


lib.ggml_graph_print.argtypes = [ctypes.POINTER(ggml_cgraph)]
lib.ggml_graph_print.restype = None


# // dump the graph into a file using the dot format
# GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
def ggml_graph_dump_dot(
    gb: ggml_cgraph_p,
    gf: ggml_cgraph_p,
    filename: bytes,
):
    return lib.ggml_graph_dump_dot(gb, gf, filename)


lib.ggml_graph_dump_dot.argtypes = [
    ctypes.POINTER(ggml_cgraph),
    ctypes.POINTER(ggml_cgraph),
    ctypes.c_char_p,
]
lib.ggml_graph_dump_dot.restype = None


# //
# // optimization
# //

# // optimization methods
# enum ggml_opt_type {
#     GGML_OPT_ADAM,
#     GGML_OPT_LBFGS,
# };
GGML_OPT_ADAM = 0
GGML_OPT_LBFGS = 1

# // linesearch methods
# enum ggml_linesearch {
#     GGML_LINESEARCH_DEFAULT = 1,

#     GGML_LINESEARCH_BACKTRACKING_ARMIJO       = 0,
#     GGML_LINESEARCH_BACKTRACKING_WOLFE        = 1,
#     GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
# };
GGML_LINESEARCH_DEFAULT = 1
GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0
GGML_LINESEARCH_BACKTRACKING_WOLFE = 1
GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2

# // optimization return values
# enum ggml_opt_result {
#     GGML_OPT_OK = 0,
#     GGML_OPT_DID_NOT_CONVERGE,
#     GGML_OPT_NO_CONTEXT,
#     GGML_OPT_INVALID_WOLFE,
#     GGML_OPT_FAIL,

#     GGML_LINESEARCH_FAIL = -128,
#     GGML_LINESEARCH_MINIMUM_STEP,
#     GGML_LINESEARCH_MAXIMUM_STEP,
#     GGML_LINESEARCH_MAXIMUM_ITERATIONS,
#     GGML_LINESEARCH_INVALID_PARAMETERS,
# };
GGML_OPT_OK = 0
GGML_OPT_DID_NOT_CONVERGE = 1
GGML_OPT_NO_CONTEXT = 2
GGML_OPT_INVALID_WOLFE = 3
GGML_OPT_FAIL = 4
GGML_LINESEARCH_FAIL = -128
GGML_LINESEARCH_MINIMUM_STEP = -127
GGML_LINESEARCH_MAXIMUM_STEP = -126
GGML_LINESEARCH_MAXIMUM_ITERATIONS = -125
GGML_LINESEARCH_INVALID_PARAMETERS = -124

# typedef void (*ggml_opt_callback)(void * data, float * sched);
ggml_opt_callback = ctypes.CFUNCTYPE(
    None,
    ctypes.c_void_p,
    ctypes.POINTER(ctypes.c_float),
)

# // optimization parameters
# //
# //   see ggml.c (ggml_opt_default_params) for default values
# //
# struct ggml_opt_params {
#     enum ggml_opt_type type;

#     int n_threads;

#     // delta-based convergence test
#     //
#     //   if past == 0 - disabled
#     //   if past > 0:
#     //     stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
#     //
#     int past;
#     float delta;

#     // maximum number of iterations without improvement
#     //
#     //   if 0 - disabled
#     //   if > 0:
#     //     assume convergence if no cost improvement in this number of iterations
#     //
#     int max_no_improvement;

#     bool print_forward_graph;
#     bool print_backward_graph;

#     // ADAM parameters
#     struct {
#         int n_iter;

#         float sched; // schedule multiplier (fixed, decay or warmup)
#         float decay; // weight decay for AdamW, use 0.0f to disable
#         int   decay_min_ndim; // minimum number of tensor dimension to apply weight decay
#         float alpha; // learning rate
#         float beta1;
#         float beta2;
#         float eps;   // epsilon for numerical stability
#         float eps_f; // epsilon for convergence test
#         float eps_g; // epsilon for convergence test
#         float gclip; // gradient clipping
#     } adam;

#     // LBFGS parameters
#     struct {
#         int m; // number of corrections to approximate the inv. Hessian
#         int n_iter;
#         int max_linesearch;

#         float eps;      // convergence tolerance
#         float ftol;     // line search tolerance
#         float wolfe;
#         float min_step;
#         float max_step;

#         enum ggml_linesearch linesearch;
#     } lbfgs;
# };


class ggml_opt_params_adam(ctypes.Structure):
    _fields_ = [
        ("n_iter", ctypes.c_int),
        ("sched", ctypes.c_float),
        ("decay", ctypes.c_float),
        ("decay_min_ndim", ctypes.c_int),
        ("alpha", ctypes.c_float),
        ("beta1", ctypes.c_float),
        ("beta2", ctypes.c_float),
        ("eps", ctypes.c_float),
        ("eps_f", ctypes.c_float),
        ("eps_g", ctypes.c_float),
        ("gclip", ctypes.c_float),
    ]


class ggml_opt_params_lbfgs(ctypes.Structure):
    _fields_ = [
        ("m", ctypes.c_int),
        ("n_iter", ctypes.c_int),
        ("max_linesearch", ctypes.c_int),
        ("eps", ctypes.c_float),
        ("ftol", ctypes.c_float),
        ("wolfe", ctypes.c_float),
        ("min_step", ctypes.c_float),
        ("max_step", ctypes.c_float),
        ("linesearch", ctypes.c_int),
    ]


class ggml_opt_params(ctypes.Structure):
    _fields_ = [
        ("type", ctypes.c_int),
        ("n_threads", ctypes.c_int),
        ("past", ctypes.c_int),
        ("delta", ctypes.c_float),
        ("max_no_improvement", ctypes.c_int),
        ("print_forward_graph", ctypes.c_bool),
        ("print_backward_graph", ctypes.c_bool),
        ("adam", ggml_opt_params_adam),
        ("lbfgs", ggml_opt_params_lbfgs),
    ]


# struct ggml_opt_context {
#     struct ggml_context * ctx;
#     struct ggml_opt_params params;

#     int iter;
#     int64_t nx; // number of parameter elements

#     bool just_initialized;

#     float loss_before;
#     float loss_after;

#     struct {
#         struct ggml_tensor * m;  // first moment
#         struct ggml_tensor * v;  // second moment
#         struct ggml_tensor * pf; // past function values
#         float fx_best;
#         float fx_prev;
#         int n_no_improvement;
#     } adam;

#     struct {
#         struct ggml_tensor * x;    // current parameters
#         struct ggml_tensor * xp;   // previous parameters
#         struct ggml_tensor * g;    // current gradient
#         struct ggml_tensor * gp;   // previous gradient
#         struct ggml_tensor * d;    // search direction
#         struct ggml_tensor * pf;   // past function values
#         struct ggml_tensor * lmal; // the L-BFGS memory alpha
#         struct ggml_tensor * lmys; // the L-BFGS memory ys
#         struct ggml_tensor * lms;  // the L-BFGS memory s
#         struct ggml_tensor * lmy;  // the L-BFGS memory y
#         float fx_best;
#         float step;
#         int j;
#         int k;
#         int end;
#         int n_no_improvement;
#     } lbfgs;
# };


class ggml_opt_context_adam(ctypes.Structure):
    _fields_ = [
        ("m", ctypes.POINTER(ggml_tensor)),
        ("v", ctypes.POINTER(ggml_tensor)),
        ("pf", ctypes.POINTER(ggml_tensor)),
        ("fx_best", ctypes.c_float),
        ("fx_prev", ctypes.c_float),
        ("n_no_improvement", ctypes.c_int),
    ]


class ggml_opt_context_lbfgs(ctypes.Structure):
    _fields_ = [
        ("x", ctypes.POINTER(ggml_tensor)),
        ("xp", ctypes.POINTER(ggml_tensor)),
        ("g", ctypes.POINTER(ggml_tensor)),
        ("gp", ctypes.POINTER(ggml_tensor)),
        ("d", ctypes.POINTER(ggml_tensor)),
        ("pf", ctypes.POINTER(ggml_tensor)),
        ("lmal", ctypes.POINTER(ggml_tensor)),
        ("lmys", ctypes.POINTER(ggml_tensor)),
        ("lms", ctypes.POINTER(ggml_tensor)),
        ("lmy", ctypes.POINTER(ggml_tensor)),
        ("fx_best", ctypes.c_float),
        ("step", ctypes.c_float),
        ("j", ctypes.c_int),
        ("k", ctypes.c_int),
        ("end", ctypes.c_int),
        ("n_no_improvement", ctypes.c_int),
    ]


class ggml_opt_context(ctypes.Structure):
    _fields_ = [
        ("ctx", ggml_context_p),
        ("params", ggml_opt_params),
        ("iter", ctypes.c_int),
        ("nx", ctypes.c_int64),
        ("just_initialized", ctypes.c_bool),
        ("loss_before", ctypes.c_float),
        ("loss_after", ctypes.c_float),
        ("adam", ggml_opt_context_adam),
        ("lbfgs", ggml_opt_context_lbfgs),
    ]


ggml_opt_context_p = ctypes.POINTER(ggml_opt_context)


# GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
def ggml_opt_default_params(type: Union[ctypes.c_int, bool]) -> ggml_opt_params:
    return lib.ggml_opt_default_params(type)


lib.ggml_opt_default_params.argtypes = [ctypes.c_int]
lib.ggml_opt_default_params.restype = ggml_opt_params


# // optimize the function defined by the tensor f
# GGML_API enum ggml_opt_result ggml_opt(
#         struct ggml_context * ctx,
#         struct ggml_opt_params params,
#         struct ggml_tensor * f);
def ggml_opt(
    ctx: ggml_context_p,
    params: ggml_opt_params,
    f: ggml_tensor_p,
) -> int:
    return lib.ggml_opt(ctx, params, f)


lib.ggml_opt.argtypes = [ggml_context_p, ggml_opt_params, ctypes.POINTER(ggml_tensor)]
lib.ggml_opt.restype = ctypes.c_int


# // initialize optimizer context
# GGML_API void ggml_opt_init(
#         struct ggml_context     * ctx,
#         struct ggml_opt_context * opt,
#         struct ggml_opt_params    params,
#         int64_t                   nx);
def ggml_opt_init(
    ctx: ggml_context_p,
    opt: "ctypes._Pointer[ggml_opt_context]",  # type: ignore
    params: ggml_opt_params,
    nx: Union[ctypes.c_int64, int],
):
    return lib.ggml_opt_init(ctx, opt, params, nx)


lib.ggml_opt_init.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_opt_context),
    ggml_opt_params,
    ctypes.c_int64,
]
lib.ggml_opt_init.restype = None


# // continue optimizing the function defined by the tensor f
# GGML_API enum ggml_opt_result ggml_opt_resume(
#         struct ggml_context * ctx,
#         struct ggml_opt_context * opt,
#         struct ggml_tensor * f);
def ggml_opt_resume(
    ctx: ggml_context_p,
    opt: "ctypes._Pointer[ggml_opt_context]",  # type: ignore
    f: ggml_tensor_p,
) -> int:
    return lib.ggml_opt_resume(ctx, opt, f)


lib.ggml_opt_resume.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_opt_context),
    ctypes.POINTER(ggml_tensor),
]
lib.ggml_opt_resume.restype = ctypes.c_int

# // continue optimizing the function defined by the tensor f
# GGML_API enum ggml_opt_result ggml_opt_resume_g(
#         struct ggml_context * ctx,
#         struct ggml_opt_context * opt,
#         struct ggml_tensor * f,
#         struct ggml_cgraph * gf,
#         struct ggml_cgraph * gb,
#         ggml_opt_callback callback,
#         void * callback_data);


# // continue optimizing the function defined by the tensor f
# GGML_API enum ggml_opt_result ggml_opt_resume_g(
#         struct ggml_context * ctx,
#         struct ggml_opt_context * opt,
#         struct ggml_tensor * f,
#         struct ggml_cgraph * gf,
#         struct ggml_cgraph * gb);
def ggml_opt_resume_g(
    ctx: ggml_context_p,
    opt: "ctypes._Pointer[ggml_opt_context]",  # type: ignore
    f: ggml_tensor_p,
    gf: ggml_cgraph_p,
    gb: ggml_cgraph_p,
    callback: ggml_opt_callback = None,
    callback_data: ctypes.c_void_p = None,
) -> int:
    return lib.ggml_opt_resume_g(ctx, opt, f, gf, gb, callback, callback_data)


lib.ggml_opt_resume_g.argtypes = [
    ggml_context_p,
    ctypes.POINTER(ggml_opt_context),
    ctypes.POINTER(ggml_tensor),
    ctypes.POINTER(ggml_cgraph),
    ctypes.POINTER(ggml_cgraph),
    ggml_opt_callback,
    ctypes.c_void_p,
]
lib.ggml_opt_resume_g.restype = ctypes.c_int

# //
# // quantization
# //


# GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
def ggml_quantize_q4_0(
    src: CFloatArray,
    dst: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
    k: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_q4_0(src, dst, n, k, hist)


lib.ggml_quantize_q4_0.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_q4_0.restype = ctypes.c_size_t


# GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
def ggml_quantize_q4_1(
    src: CFloatArray,
    dst: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
    k: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_q4_1(src, dst, n, k, hist)


lib.ggml_quantize_q4_1.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_q4_1.restype = ctypes.c_size_t


# GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
def ggml_quantize_q5_0(
    src: CFloatArray,
    dst: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
    k: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_q5_0(src, dst, n, k, hist)


lib.ggml_quantize_q5_0.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_q5_0.restype = ctypes.c_size_t


# GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
def ggml_quantize_q5_1(
    src: CFloatArray,
    dst: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
    k: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_q5_1(src, dst, n, k, hist)


lib.ggml_quantize_q5_1.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_q5_1.restype = ctypes.c_size_t


# GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
def ggml_quantize_q8_0(
    src: CFloatArray,
    dst: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
    k: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_q8_0(src, dst, n, k, hist)


lib.ggml_quantize_q8_0.argtypes = [
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_q8_0.restype = ctypes.c_size_t


# GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
def ggml_quantize_chunk(
    type: Union[ctypes.c_int, int],
    src: CFloatArray,
    dst: ctypes.c_void_p,
    start: Union[ctypes.c_int, int],
    n: Union[ctypes.c_int, int],
    hist: CInt64Array,
) -> int:
    return lib.ggml_quantize_chunk(type, src, dst, start, n, hist)


lib.ggml_quantize_chunk.argtypes = [
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_float),
    ctypes.c_void_p,
    ctypes.c_int,
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_int64),
]
lib.ggml_quantize_chunk.restype = ctypes.c_size_t

# //
# // gguf
# //

# enum gguf_type {
#     GGUF_TYPE_UINT8   = 0,
#     GGUF_TYPE_INT8    = 1,
#     GGUF_TYPE_UINT16  = 2,
#     GGUF_TYPE_INT16   = 3,
#     GGUF_TYPE_UINT32  = 4,
#     GGUF_TYPE_INT32   = 5,
#     GGUF_TYPE_FLOAT32 = 6,
#     GGUF_TYPE_BOOL    = 7,
#     GGUF_TYPE_STRING  = 8,
#     GGUF_TYPE_ARRAY   = 9,
#     GGUF_TYPE_UINT64  = 10,
#     GGUF_TYPE_INT64   = 11,
#     GGUF_TYPE_FLOAT64 = 12,
#     GGUF_TYPE_COUNT,       // marks the end of the enum
# };
GGUF_TYPE_UINT8 = 0
GGUF_TYPE_INT8 = 1
GGUF_TYPE_UINT16 = 2
GGUF_TYPE_INT16 = 3
GGUF_TYPE_UINT32 = 4
GGUF_TYPE_INT32 = 5
GGUF_TYPE_FLOAT32 = 6
GGUF_TYPE_BOOL = 7
GGUF_TYPE_STRING = 8
GGUF_TYPE_ARRAY = 9
GGUF_TYPE_COUNT = 10

# struct gguf_context;
gguf_context_p = ctypes.c_void_p

# struct gguf_init_params {
#     bool no_alloc;


#     // if not NULL, create a ggml_context and allocate the tensor data in it
#     struct ggml_context ** ctx;
# };
class gguf_init_params(ctypes.Structure):
    _fields_ = [
        ("no_alloc", ctypes.c_bool),
        ("ctx", ctypes.POINTER(ggml_context_p)),
    ]


# GGML_API struct gguf_context * gguf_init_empty(void);
def gguf_init_empty() -> gguf_context_p:
    return lib.gguf_init_empty()


lib.gguf_init_empty.argtypes = []
lib.gguf_init_empty.restype = gguf_context_p


# GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
def gguf_init_from_file(
    fname: bytes,
    params: gguf_init_params,
) -> gguf_context_p:
    return lib.gguf_init_from_file(fname, params)


lib.gguf_init_from_file.argtypes = [
    ctypes.c_char_p,
    gguf_init_params,
]
lib.gguf_init_from_file.restype = gguf_context_p

# //GGML_API struct gguf_context * gguf_init_from_buffer(..);


# GGML_API void gguf_free(struct gguf_context * ctx);
def gguf_free(
    ctx: gguf_context_p,
):
    return lib.gguf_free(ctx)


lib.gguf_free.argtypes = [
    gguf_context_p,
]
lib.gguf_free.restype = None


# GGML_API const char * gguf_type_name(enum gguf_type type);
def gguf_type_name(
    type: Union[ctypes.c_int, int],
) -> bytes:
    return lib.gguf_type_name(type)


lib.gguf_type_name.argtypes = [
    ctypes.c_int,
]
lib.gguf_type_name.restype = ctypes.c_char_p


# GGML_API int    gguf_get_version    (const struct gguf_context * ctx);
def gguf_get_version(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_version(ctx)


lib.gguf_get_version.argtypes = [
    gguf_context_p,
]
lib.gguf_get_version.restype = ctypes.c_int


# GGML_API size_t gguf_get_alignment  (const struct gguf_context * ctx);
def gguf_get_alignment(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_alignment(ctx)


lib.gguf_get_alignment.argtypes = [
    gguf_context_p,
]
lib.gguf_get_alignment.restype = ctypes.c_size_t


# GGML_API size_t gguf_get_data_offset(const struct gguf_context * ctx);
def gguf_get_data_offset(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_data_offset(ctx)


lib.gguf_get_data_offset.argtypes = [
    gguf_context_p,
]
lib.gguf_get_data_offset.restype = ctypes.c_size_t


# GGML_API void * gguf_get_data       (const struct gguf_context * ctx);
def gguf_get_data(
    ctx: gguf_context_p,
) -> ctypes.c_void_p:
    return lib.gguf_get_data(ctx)


lib.gguf_get_data.argtypes = [
    gguf_context_p,
]
lib.gguf_get_data.restype = ctypes.c_void_p


# GGML_API int          gguf_get_n_kv(const struct gguf_context * ctx);
def gguf_get_n_kv(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_n_kv(ctx)


lib.gguf_get_n_kv.argtypes = [
    gguf_context_p,
]
lib.gguf_get_n_kv.restype = ctypes.c_int


# GGML_API int          gguf_find_key(const struct gguf_context * ctx, const char * key);
def gguf_find_key(
    ctx: gguf_context_p,
    key: bytes,
) -> int:
    return lib.gguf_find_key(ctx, key)


lib.gguf_find_key.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
]
lib.gguf_find_key.restype = ctypes.c_int


# GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int i);
def gguf_get_key(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> bytes:
    return lib.gguf_get_key(ctx, i)


lib.gguf_get_key.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_key.restype = ctypes.c_char_p


# GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int i);
def gguf_get_kv_type(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_kv_type(ctx, i)


lib.gguf_get_kv_type.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_kv_type.restype = ctypes.c_int


# GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int i);
def gguf_get_arr_type(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_arr_type(ctx, i)


lib.gguf_get_arr_type.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_arr_type.restype = ctypes.c_int


# // results are undefined if the wrong type is used for the key
# GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int i);
def gguf_get_val_u8(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_u8(ctx, i)


lib.gguf_get_val_u8.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_u8.restype = ctypes.c_uint8


# GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int i);
def gguf_get_val_i8(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_i8(ctx, i)


lib.gguf_get_val_i8.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_i8.restype = ctypes.c_int8


# GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int i);
def gguf_get_val_u16(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_u16(ctx, i)


lib.gguf_get_val_u16.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_u16.restype = ctypes.c_uint16


# GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int i);
def gguf_get_val_i16(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_i16(ctx, i)


lib.gguf_get_val_i16.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_i16.restype = ctypes.c_int16


# GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int i);
def gguf_get_val_u32(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_u32(ctx, i)


lib.gguf_get_val_u32.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_u32.restype = ctypes.c_uint32


# GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int i);
def gguf_get_val_i32(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_i32(ctx, i)


lib.gguf_get_val_i32.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_i32.restype = ctypes.c_int32


# GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int i);
def gguf_get_val_f32(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> float:
    return lib.gguf_get_val_f32(ctx, i)


lib.gguf_get_val_f32.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_f32.restype = ctypes.c_float


# GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int i);
def gguf_get_val_u64(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_u64(ctx, i)


lib.gguf_get_val_u64.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_u64.restype = ctypes.c_uint64


# GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int i);
def gguf_get_val_i64(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_val_i64(ctx, i)


lib.gguf_get_val_i64.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_i64.restype = ctypes.c_int64


# GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int i);
def gguf_get_val_f64(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> float:
    return lib.gguf_get_val_f64(ctx, i)


lib.gguf_get_val_f64.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_f64.restype = ctypes.c_double


# GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int i);
def gguf_get_val_bool(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> bool:
    return lib.gguf_get_val_bool(ctx, i)


lib.gguf_get_val_bool.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_bool.restype = ctypes.c_bool


# GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int i);
def gguf_get_val_str(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> bytes:
    return lib.gguf_get_val_str(ctx, i)


lib.gguf_get_val_str.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_val_str.restype = ctypes.c_char_p


# GGML_API int          gguf_get_arr_n   (const struct gguf_context * ctx, int i);
def gguf_get_arr_n(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_arr_n(ctx, i)


lib.gguf_get_arr_n.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_arr_n.restype = ctypes.c_int


# GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int i);
def gguf_get_arr_data(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> ctypes.c_void_p:
    return lib.gguf_get_arr_data(ctx, i)


lib.gguf_get_arr_data.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_arr_data.restype = ctypes.c_void_p


# GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
def gguf_get_arr_str(
    ctx: gguf_context_p,
    key_id: Union[ctypes.c_int, int],
    i: Union[ctypes.c_int, int],
) -> bytes:
    return lib.gguf_get_arr_str(ctx, key_id, i)


lib.gguf_get_arr_str.argtypes = [
    gguf_context_p,
    ctypes.c_int,
    ctypes.c_int,
]
lib.gguf_get_arr_str.restype = ctypes.c_char_p


# GGML_API int    gguf_get_n_tensors    (const struct gguf_context * ctx);
def gguf_get_n_tensors(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_n_tensors(ctx)


lib.gguf_get_n_tensors.argtypes = [
    gguf_context_p,
]
lib.gguf_get_n_tensors.restype = ctypes.c_int


# GGML_API int    gguf_find_tensor      (const struct gguf_context * ctx, const char * name);
def gguf_find_tensor(
    ctx: gguf_context_p,
    name: bytes,
) -> int:
    return lib.gguf_find_tensor(ctx, name)


lib.gguf_find_tensor.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
]
lib.gguf_find_tensor.restype = ctypes.c_int


# GGML_API size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int i);
def gguf_get_tensor_offset(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> int:
    return lib.gguf_get_tensor_offset(ctx, i)


lib.gguf_get_tensor_offset.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_tensor_offset.restype = ctypes.c_size_t


# GGML_API char * gguf_get_tensor_name  (const struct gguf_context * ctx, int i);
def gguf_get_tensor_name(
    ctx: gguf_context_p,
    i: Union[ctypes.c_int, int],
) -> bytes:
    return lib.gguf_get_tensor_name(ctx, i)


lib.gguf_get_tensor_name.argtypes = [
    gguf_context_p,
    ctypes.c_int,
]
lib.gguf_get_tensor_name.restype = ctypes.c_char_p


# // overrides existing values or adds a new one
# GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t  val);
def gguf_set_val_u8(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_uint8, int],
):
    return lib.gguf_set_val_u8(ctx, key, val)


lib.gguf_set_val_u8.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_uint8,
]
lib.gguf_set_val_u8.restype = None


# GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t   val);
def gguf_set_val_i8(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_int8, int],
):
    return lib.gguf_set_val_i8(ctx, key, val)


lib.gguf_set_val_i8.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int8,
]
lib.gguf_set_val_i8.restype = None


# GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
def gguf_set_val_u16(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_uint16, int],
):
    return lib.gguf_set_val_u16(ctx, key, val)


lib.gguf_set_val_u16.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_uint16,
]
lib.gguf_set_val_u16.restype = None


# GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t  val);
def gguf_set_val_i16(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_int16, int],
):
    return lib.gguf_set_val_i16(ctx, key, val)


lib.gguf_set_val_i16.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int16,
]
lib.gguf_set_val_i16.restype = None


# GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
def gguf_set_val_u32(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_uint32, int],
):
    return lib.gguf_set_val_u32(ctx, key, val)


lib.gguf_set_val_u32.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_uint32,
]
lib.gguf_set_val_u32.restype = None


# GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t  val);
def gguf_set_val_i32(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_int32, int],
):
    return lib.gguf_set_val_i32(ctx, key, val)


lib.gguf_set_val_i32.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int32,
]
lib.gguf_set_val_i32.restype = None


# GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float    val);
def gguf_set_val_f32(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_float, float],
):
    return lib.gguf_set_val_f32(ctx, key, val)


lib.gguf_set_val_f32.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_float,
]
lib.gguf_set_val_f32.restype = None


# GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
def gguf_set_val_u64(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_uint64, int],
):
    return lib.gguf_set_val_u64(ctx, key, val)


lib.gguf_set_val_u64.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_uint64,
]
lib.gguf_set_val_u64.restype = None


# GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t  val);
def gguf_set_val_i64(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_int64, int],
):
    return lib.gguf_set_val_i64(ctx, key, val)


lib.gguf_set_val_i64.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int64,
]
lib.gguf_set_val_i64.restype = None


# GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double   val);
def gguf_set_val_f64(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_double, float],
):
    return lib.gguf_set_val_f64(ctx, key, val)


lib.gguf_set_val_f64.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_double,
]
lib.gguf_set_val_f64.restype = None


# GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool     val);
def gguf_set_val_bool(
    ctx: gguf_context_p,
    key: bytes,
    val: Union[ctypes.c_bool, bool],
):
    return lib.gguf_set_val_bool(ctx, key, val)


lib.gguf_set_val_bool.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_bool,
]
lib.gguf_set_val_bool.restype = None


# GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
def gguf_set_val_str(
    ctx: gguf_context_p,
    key: bytes,
    val: bytes,
):
    return lib.gguf_set_val_str(ctx, key, val)


lib.gguf_set_val_str.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_char_p,
]
lib.gguf_set_val_str.restype = None


# GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
def gguf_set_arr_data(
    ctx: gguf_context_p,
    key: bytes,
    type: Union[ctypes.c_int, int],
    data: ctypes.c_void_p,
    n: Union[ctypes.c_int, int],
):
    return lib.gguf_set_arr_data(ctx, key, type, data, n)


lib.gguf_set_arr_data.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int,
    ctypes.c_void_p,
    ctypes.c_int,
]
lib.gguf_set_arr_data.restype = None


# GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
def gguf_set_arr_str(
    ctx: gguf_context_p,
    key: bytes,
    data: CCharPointer,
    n: Union[ctypes.c_int, int],
):
    return lib.gguf_set_arr_str(ctx, key, data, n)


lib.gguf_set_arr_str.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.POINTER(ctypes.c_char_p),
    ctypes.c_int,
]
lib.gguf_set_arr_str.restype = None


# // set or add KV pairs from another context
# GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
def gguf_set_kv(
    ctx: gguf_context_p,
    src: gguf_context_p,
):
    return lib.gguf_set_kv(ctx, src)


lib.gguf_set_kv.argtypes = [
    gguf_context_p,
    gguf_context_p,
]
lib.gguf_set_kv.restype = None


# // manage tensor info
# GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
def gguf_add_tensor(
    ctx: gguf_context_p,
    tensor: ggml_tensor_p,
):
    return lib.gguf_add_tensor(ctx, tensor)


lib.gguf_add_tensor.argtypes = [
    gguf_context_p,
    ctypes.POINTER(ggml_tensor),
]
lib.gguf_add_tensor.restype = None


# GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
def gguf_set_tensor_type(
    ctx: gguf_context_p,
    name: bytes,
    type: Union[ctypes.c_int, int],
):
    return lib.gguf_set_tensor_type(ctx, name, type)


lib.gguf_set_tensor_type.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_int,
]
lib.gguf_set_tensor_type.restype = None


# GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
def gguf_set_tensor_data(
    ctx: gguf_context_p,
    name: bytes,
    data: ctypes.c_void_p,
    size: Union[ctypes.c_size_t, int],
):
    return lib.gguf_set_tensor_data(ctx, name, data, size)


lib.gguf_set_tensor_data.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_void_p,
    ctypes.c_size_t,
]
lib.gguf_set_tensor_data.restype = None

# // writing gguf files can be done in 2 ways:
# //
# // - write the entire gguf_context to a binary file in a single pass:
# //
# //   gguf_write_to_file(ctx, fname);
# //
# // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
# //
# //   FILE * f = fopen(fname, "wb");
# //   fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
# //   fwrite(f, ...);
# //   void * data = gguf_meta_get_meta_data(ctx);
# //   fseek(f, 0, SEEK_SET);
# //   fwrite(f, data, gguf_get_meta_size(ctx));
# //   free(data);
# //   fclose(f);
# //


# // write the entire context to a binary file
# GGML_API void gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
def gguf_write_to_file(
    ctx: gguf_context_p,
    fname: bytes,
    only_meta: Union[ctypes.c_bool, bool],
):
    return lib.gguf_write_to_file(ctx, fname, only_meta)


lib.gguf_write_to_file.argtypes = [
    gguf_context_p,
    ctypes.c_char_p,
    ctypes.c_bool,
]
lib.gguf_write_to_file.restype = None


# // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
# GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
def gguf_get_meta_size(
    ctx: gguf_context_p,
) -> int:
    return lib.gguf_get_meta_size(ctx)


lib.gguf_get_meta_size.argtypes = [
    gguf_context_p,
]
lib.gguf_get_meta_size.restype = ctypes.c_size_t


# GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
def gguf_get_meta_data(
    ctx: gguf_context_p,
    data: ctypes.c_void_p,
):
    return lib.gguf_get_meta_data(ctx, data)


lib.gguf_get_meta_data.argtypes = [
    gguf_context_p,
    ctypes.c_void_p,
]
lib.gguf_get_meta_data.restype = None


# //
# // system info
# //


# GGML_API int ggml_cpu_has_avx        (void);
def ggml_cpu_has_avx() -> int:
    return lib.ggml_cpu_has_avx()


lib.ggml_cpu_has_avx.argtypes = []
lib.ggml_cpu_has_avx.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_avx2       (void);
def ggml_cpu_has_avx2() -> int:
    return lib.ggml_cpu_has_avx2()


lib.ggml_cpu_has_avx2.argtypes = []
lib.ggml_cpu_has_avx2.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_avx512     (void);
def ggml_cpu_has_avx512() -> int:
    return lib.ggml_cpu_has_avx512()


lib.ggml_cpu_has_avx512.argtypes = []
lib.ggml_cpu_has_avx512.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_avx512_vbmi(void);
def ggml_cpu_has_avx512_vbmi() -> int:
    return lib.ggml_cpu_has_avx512_vbmi()


lib.ggml_cpu_has_avx512_vbmi.argtypes = []
lib.ggml_cpu_has_avx512_vbmi.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_avx512_vnni(void);
def ggml_cpu_has_avx512_vnni() -> int:
    return lib.ggml_cpu_has_avx512_vnni()


lib.ggml_cpu_has_avx512_vnni.argtypes = []
lib.ggml_cpu_has_avx512_vnni.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_fma        (void);
def ggml_cpu_has_fma() -> int:
    return lib.ggml_cpu_has_fma()


lib.ggml_cpu_has_fma.argtypes = []
lib.ggml_cpu_has_fma.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_neon       (void);
def ggml_cpu_has_neon() -> int:
    return lib.ggml_cpu_has_neon()


lib.ggml_cpu_has_neon.argtypes = []
lib.ggml_cpu_has_neon.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_arm_fma    (void);
def ggml_cpu_has_arm_fma() -> int:
    return lib.ggml_cpu_has_arm_fma()


lib.ggml_cpu_has_arm_fma.argtypes = []
lib.ggml_cpu_has_arm_fma.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_f16c       (void);
def ggml_cpu_has_f16c() -> int:
    return lib.ggml_cpu_has_f16c()


lib.ggml_cpu_has_f16c.argtypes = []
lib.ggml_cpu_has_f16c.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_fp16_va    (void);
def ggml_cpu_has_fp16_va() -> int:
    return lib.ggml_cpu_has_fp16_va()


lib.ggml_cpu_has_fp16_va.argtypes = []
lib.ggml_cpu_has_fp16_va.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_wasm_simd  (void);
def ggml_cpu_has_wasm_simd() -> int:
    return lib.ggml_cpu_has_wasm_simd()


lib.ggml_cpu_has_wasm_simd.argtypes = []
lib.ggml_cpu_has_wasm_simd.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_blas       (void);
def ggml_cpu_has_blas() -> int:
    return lib.ggml_cpu_has_blas()


lib.ggml_cpu_has_blas.argtypes = []
lib.ggml_cpu_has_blas.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_cublas     (void);
def ggml_cpu_has_cublas() -> int:
    return lib.ggml_cpu_has_cublas()


lib.ggml_cpu_has_cublas.argtypes = []
lib.ggml_cpu_has_cublas.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_clblast    (void);
def ggml_cpu_has_clblast() -> int:
    return lib.ggml_cpu_has_clblast()


lib.ggml_cpu_has_clblast.argtypes = []
lib.ggml_cpu_has_clblast.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_gpublas    (void);
def ggml_cpu_has_gpublas() -> int:
    return lib.ggml_cpu_has_gpublas()


lib.ggml_cpu_has_gpublas.argtypes = []
lib.ggml_cpu_has_gpublas.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_sse3       (void);
def ggml_cpu_has_sse3() -> int:
    return lib.ggml_cpu_has_sse3()


lib.ggml_cpu_has_sse3.argtypes = []
lib.ggml_cpu_has_sse3.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_ssse3      (void);
def ggml_cpu_has_ssse3() -> int:
    return lib.ggml_cpu_has_ssse3()


lib.ggml_cpu_has_ssse3.argtypes = []
lib.ggml_cpu_has_ssse3.restype = ctypes.c_int


# GGML_API int ggml_cpu_has_vsx        (void);
def ggml_cpu_has_vsx() -> int:
    return lib.ggml_cpu_has_vsx()


lib.ggml_cpu_has_vsx.argtypes = []
lib.ggml_cpu_has_vsx.restype = ctypes.c_int


# //
# // Internal types and functions exposed for tests and benchmarks
# //

# typedef void (*ggml_to_float_t)(const void * x, float * y, int k);
ggml_to_float_t = ctypes.CFUNCTYPE(
    None, ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int
)

# typedef void (*ggml_from_float_t)(const float * x, void * y, int k);
ggml_from_float_t = ctypes.CFUNCTYPE(
    None, ctypes.POINTER(ctypes.c_float), ctypes.c_void_p, ctypes.c_int
)

# typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y);
ggml_vec_dot_t = ctypes.CFUNCTYPE(
    None, ctypes.c_int, ctypes.POINTER(ctypes.c_float), ctypes.c_void_p, ctypes.c_void_p
)


# typedef struct {
#     const char      * type_name;
#     int               blck_size;
#     size_t            type_size;
#     bool              is_quantized;
#     ggml_to_float_t   to_float;
#     ggml_from_float_t from_float;
#     ggml_from_float_t from_float_reference;
#     ggml_vec_dot_t    vec_dot;
#     enum ggml_type    vec_dot_type;
# } ggml_type_traits_t;
class ggml_type_traits_t(ctypes.Structure):
    _fields_ = [
        ("type_name", ctypes.c_char_p),
        ("blck_size", ctypes.c_int),
        ("type_size", ctypes.c_size_t),
        ("is_quantized", ctypes.c_bool),
        ("to_float", ggml_to_float_t),
        ("from_float", ggml_from_float_t),
        ("from_float_reference", ggml_from_float_t),
        ("vec_dot", ggml_vec_dot_t),
        ("vec_dot_type", ctypes.c_int),
    ]


# ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
def ggml_internal_get_type_traits(type: Union[ctypes.c_int, int]) -> ggml_type_traits_t:
    return lib.ggml_internal_get_type_traits(type)


lib.ggml_internal_get_type_traits.argtypes = [ctypes.c_int]
lib.ggml_internal_get_type_traits.restype = ggml_type_traits_t


#####################################################
# GGML ALLOC API
# source: ggml-alloc.h
#####################################################

ggml_allocr_p = ctypes.c_void_p


# GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
def ggml_allocr_new(
    data: ctypes.c_void_p,
    size: Union[ctypes.c_size_t, int],
    alignment: Union[ctypes.c_size_t, int],
) -> ggml_allocr_p:
    return lib.ggml_allocr_new(data, size, alignment)


lib.ggml_allocr_new.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_size_t]
lib.ggml_allocr_new.restype = ggml_allocr_p


# GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
def ggml_allocr_new_measure(
    alignment: Union[ctypes.c_size_t, int],
) -> ggml_allocr_p:
    return lib.ggml_allocr_new_measure(alignment)


lib.ggml_allocr_new_measure.argtypes = [ctypes.c_size_t]
lib.ggml_allocr_new_measure.restype = ggml_allocr_p


# // tell the allocator to parse nodes following the order described in the list
# // you should call this if your graph are optimized to execute out-of-order
# GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
def ggml_allocr_set_parse_seq(
    alloc: ggml_allocr_p,
    list: CIntPointer,
    n: Union[ctypes.c_int, int],
):
    return lib.ggml_allocr_set_parse_seq(alloc, list, n)


lib.ggml_allocr_set_parse_seq.argtypes = [
    ggml_allocr_p,
    ctypes.POINTER(ctypes.c_int),
    ctypes.c_int,
]
lib.ggml_allocr_set_parse_seq.restype = None


# GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
def ggml_allocr_free(
    alloc: ggml_allocr_p,
):
    return lib.ggml_allocr_free(alloc)


lib.ggml_allocr_free.argtypes = [ggml_allocr_p]
lib.ggml_allocr_free.restype = None


# GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);
def ggml_allocr_is_measure(
    alloc: ggml_allocr_p,
) -> bool:
    return lib.ggml_allocr_is_measure(alloc)


lib.ggml_allocr_is_measure.argtypes = [ggml_allocr_p]
lib.ggml_allocr_is_measure.restype = ctypes.c_bool


# GGML_API void   ggml_allocr_reset(struct ggml_allocr * alloc);
def ggml_allocr_reset(
    alloc: ggml_allocr_p,
):
    return lib.ggml_allocr_reset(alloc)


lib.ggml_allocr_reset.argtypes = [ggml_allocr_p]
lib.ggml_allocr_reset.restype = None


# GGML_API void   ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
def ggml_allocr_alloc(
    alloc: ggml_allocr_p,
    tensor: ggml_tensor_p,
):
    return lib.ggml_allocr_alloc(alloc, tensor)


lib.ggml_allocr_alloc.argtypes = [ggml_allocr_p, ctypes.POINTER(ggml_tensor)]
lib.ggml_allocr_alloc.restype = None


# GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
def ggml_allocr_alloc_graph(
    alloc: ggml_allocr_p,
    graph: ggml_cgraph_p,
) -> int:
    return lib.ggml_allocr_alloc_graph(alloc, graph)


lib.ggml_allocr_alloc_graph.argtypes = [ggml_allocr_p, ctypes.POINTER(ggml_cgraph)]
lib.ggml_allocr_alloc_graph.restype = ctypes.c_size_t


#####################################################
# GGML CUDA API
# source: ggml-cuda.h
#####################################################


GGML_USE_CUBLAS = hasattr(lib, "ggml_init_cublas")


GGML_CUDA_MAX_DEVICES = 16


# GGML_API void   ggml_init_cublas(void);
def ggml_init_cublas():
    return lib.ggml_init_cublas()


if GGML_USE_CUBLAS:
    lib.ggml_init_cublas.argtypes = []
    lib.ggml_init_cublas.restype = None


# void * ggml_cuda_host_malloc(size_t size);
def ggml_cuda_host_malloc(
    size: Union[ctypes.c_size_t, int],
) -> Optional[ctypes.c_void_p]:
    return lib.ggml_cuda_host_malloc(size)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_host_malloc.argtypes = [ctypes.c_size_t]
    lib.ggml_cuda_host_malloc.restype = ctypes.c_void_p


# void   ggml_cuda_host_free(void * ptr);
def ggml_cuda_host_free(
    ptr: ctypes.c_void_p,
):
    return lib.ggml_cuda_host_free(ptr)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_host_free.argtypes = [ctypes.c_void_p]
    lib.ggml_cuda_host_free.restype = None


# GGML_API bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
def ggml_cuda_can_mul_mat(
    src0: ggml_tensor_p,
    src1: ggml_tensor_p,
    dst: ggml_tensor_p,
) -> bool:
    return lib.ggml_cuda_can_mul_mat(src0, src1, dst)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_can_mul_mat.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_can_mul_mat.restype = ctypes.c_bool


# GGML_API void   ggml_cuda_set_tensor_split(const float * tensor_split);
def ggml_cuda_set_tensor_split(
    tensor_split: CFloatArray,
):
    return lib.ggml_cuda_set_tensor_split(tensor_split)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_set_tensor_split.argtypes = [ctypes.POINTER(ctypes.c_float)]
    lib.ggml_cuda_set_tensor_split.restype = None


# void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
def ggml_cuda_transform_tensor(
    data: ctypes.c_void_p,
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_transform_tensor(data, tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_transform_tensor.argtypes = [
        ctypes.c_void_p,
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_transform_tensor.restype = None


# void   ggml_cuda_free_data(struct ggml_tensor * tensor);
def ggml_cuda_free_data(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_free_data(tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_free_data.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_free_data.restype = None


# void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
def ggml_cuda_assign_buffers(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_assign_buffers(tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_assign_buffers.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_assign_buffers.restype = None


# void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
def ggml_cuda_assign_buffers_no_scratch(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_assign_buffers_no_scratch(tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_assign_buffers_no_scratch.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_assign_buffers_no_scratch.restype = None


# GGML_API void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
def ggml_cuda_assign_buffers_force_inplace(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_assign_buffers_force_inplace(tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_assign_buffers_force_inplace.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_assign_buffers_force_inplace.restype = None


# GGML_API void   ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
def ggml_cuda_assign_buffers_no_alloc(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cuda_assign_buffers_no_alloc(tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_assign_buffers_no_alloc.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_assign_buffers_no_alloc.restype = None


# GGML_API void   ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
def ggml_cuda_assign_scratch_offset(
    tensor: ggml_tensor_p,
    offset: Union[ctypes.c_size_t, int],
):
    return lib.ggml_cuda_assign_scratch_offset(tensor, offset)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_assign_scratch_offset.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.c_size_t,
    ]
    lib.ggml_cuda_assign_scratch_offset.restype = None


# void   ggml_cuda_set_main_device(int main_device);
def ggml_cuda_set_main_device(
    main_device: Union[ctypes.c_int, int],
):
    return lib.ggml_cuda_set_main_device(main_device)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_set_main_device.argtypes = [
        ctypes.c_int,
    ]
    lib.ggml_cuda_set_main_device.restype = None


# GGML_API void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
def ggml_cuda_set_mul_mat_q(
    mul_mat_q: Union[ctypes.c_bool, bool],
):
    return lib.ggml_cuda_set_mul_mat_q(mul_mat_q)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_set_mul_mat_q.argtypes = [
        ctypes.c_bool,
    ]
    lib.ggml_cuda_set_mul_mat_q.restype = None


# void   ggml_cuda_set_scratch_size(size_t scratch_size);
def ggml_cuda_set_scratch_size(
    scratch_size: Union[ctypes.c_size_t, int],
):
    return lib.ggml_cuda_set_scratch_size(scratch_size)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_set_scratch_size.argtypes = [
        ctypes.c_size_t,
    ]
    lib.ggml_cuda_set_scratch_size.restype = None


# void   ggml_cuda_free_scratch(void);
def ggml_cuda_free_scratch():
    return lib.ggml_cuda_free_scratch()


if GGML_USE_CUBLAS:
    lib.ggml_cuda_free_scratch.argtypes = []
    lib.ggml_cuda_free_scratch.restype = None


# GGML_API bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
def ggml_cuda_compute_forward(
    params: ggml_compute_params_p,
    tensor: ggml_tensor_p,
) -> bool:
    return lib.ggml_cuda_compute_forward(params, tensor)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_compute_forward.argtypes = [
        ctypes.POINTER(ggml_compute_params),
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cuda_compute_forward.restype = ctypes.c_bool


# GGML_API int    ggml_cuda_get_device_count(void);
def ggml_cuda_get_device_count() -> int:
    return lib.ggml_cuda_get_device_count()


if GGML_USE_CUBLAS:
    lib.ggml_cuda_get_device_count.argtypes = []
    lib.ggml_cuda_get_device_count.restype = ctypes.c_int


# GGML_API void   ggml_cuda_get_device_description(int device, char * description, size_t description_size);
def ggml_cuda_get_device_description(
    device: Union[ctypes.c_int, int],
    description: bytes,
    description_size: Union[ctypes.c_size_t, int],
):
    return lib.ggml_cuda_get_device_description(device, description, description_size)


if GGML_USE_CUBLAS:
    lib.ggml_cuda_get_device_description.argtypes = [
        ctypes.c_int,
        ctypes.c_char_p,
        ctypes.c_size_t,
    ]
    lib.ggml_cuda_get_device_description.restype = None

#####################################################
# GGML METAL API
# source: ggml-metal.h
#####################################################


GGML_USE_METAL = hasattr(lib, "ggml_metal_init")


# // max memory buffers that can be mapped to the device
# #define GGML_METAL_MAX_BUFFERS 16
GGML_METAL_MAX_BUFFERS = 16
# #define GGML_METAL_MAX_COMMAND_BUFFERS 32
GGML_METAL_MAX_COMMAND_BUFFERS = 32

# struct ggml_metal_context;
ggml_metal_context_p = ctypes.c_void_p


# struct ggml_metal_context * ggml_metal_init(int n_cb);
def ggml_metal_init(
    n_cb: Union[ctypes.c_int, int],
) -> ggml_metal_context_p:
    return lib.ggml_metal_init(n_cb)


if GGML_USE_METAL:
    lib.ggml_metal_init.argtypes = [ctypes.c_int]
    lib.ggml_metal_init.restype = ggml_metal_context_p


# void ggml_metal_free(struct ggml_metal_context * ctx);
def ggml_metal_free(
    ctx: ggml_metal_context_p,
):
    return lib.ggml_metal_free(ctx)


if GGML_USE_METAL:
    lib.ggml_metal_free.argtypes = [ggml_metal_context_p]
    lib.ggml_metal_free.restype = None


# // set the number of command buffers to use
# void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
def ggml_metal_set_n_cb(
    ctx: ggml_metal_context_p,
    n_cb: Union[ctypes.c_int, int],
):
    return lib.ggml_metal_set_n_cb(ctx, n_cb)


if GGML_USE_METAL:
    lib.ggml_metal_set_n_cb.argtypes = [ggml_metal_context_p, ctypes.c_int]
    lib.ggml_metal_set_n_cb.restype = None


# // creates a mapping between a host memory buffer and a device memory buffer
# // - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
# // - the mapping is used during computation to determine the arguments of the compute kernels
# // - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
# // - max_size specifies the maximum size of a tensor and is used to create shared views such
# //   that it is guaranteed that the tensor will fit in at least one of the views
# //
# bool ggml_metal_add_buffer(
#         struct ggml_metal_context * ctx,
#                        const char * name,
#                              void * data,
#                            size_t   size,
#                            size_t   max_size);
def ggml_metal_add_buffer(
    ctx: ggml_metal_context_p,
    name: bytes,
    data: ctypes.c_void_p,
    size: Union[ctypes.c_size_t, int],
    max_size: Union[ctypes.c_size_t, int],
) -> bool:
    return lib.ggml_metal_add_buffer(ctx, name, data, size, max_size)


if GGML_USE_METAL:
    lib.ggml_metal_add_buffer.argtypes = [
        ggml_metal_context_p,
        ctypes.c_char_p,
        ctypes.c_void_p,
        ctypes.c_size_t,
        ctypes.c_size_t,
    ]
    lib.ggml_metal_add_buffer.restype = ctypes.c_bool


# // set data from host memory into the device
# void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
def ggml_metal_set_tensor(
    ctx: ggml_metal_context_p,
    t: ggml_tensor_p,
):
    return lib.ggml_metal_set_tensor(ctx, t)


if GGML_USE_METAL:
    lib.ggml_metal_set_tensor.argtypes = [
        ggml_metal_context_p,
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_metal_set_tensor.restype = None


# // get data from the device into host memory
# void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
def ggml_metal_get_tensor(
    ctx: ggml_metal_context_p,
    t: ggml_tensor_p,
):
    return lib.ggml_metal_get_tensor(ctx, t)


if GGML_USE_METAL:
    lib.ggml_metal_get_tensor.argtypes = [
        ggml_metal_context_p,
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_metal_get_tensor.restype = None


# // try to find operations that can be run concurrently in the graph
# // you should run it again if the topology of your graph changes
# void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
def ggml_metal_graph_find_concurrency(
    ctx: ggml_metal_context_p,
    gf: ggml_cgraph_p,
    check_mem: Union[ctypes.c_bool, bool],
):
    return lib.ggml_metal_graph_find_concurrency(ctx, gf, check_mem)


if GGML_USE_METAL:
    lib.ggml_metal_graph_find_concurrency.argtypes = [
        ggml_metal_context_p,
        ctypes.POINTER(ggml_cgraph),
        ctypes.c_bool,
    ]
    lib.ggml_metal_graph_find_concurrency.restype = None


# // if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
# int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
def ggml_metal_if_optimized(
    ctx: ggml_metal_context_p,
) -> int:
    return lib.ggml_metal_if_optimized(ctx)


if GGML_USE_METAL:
    lib.ggml_metal_if_optimized.argtypes = [
        ggml_metal_context_p,
    ]
    lib.ggml_metal_if_optimized.restype = ctypes.c_int


# // output the concur_list for ggml_alloc
# int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
def ggml_metal_get_concur_list(
    ctx: ggml_metal_context_p,
) -> CIntPointer:
    return lib.ggml_metal_get_concur_list(ctx)


if GGML_USE_METAL:
    lib.ggml_metal_get_concur_list.argtypes = [
        ggml_metal_context_p,
    ]
    lib.ggml_metal_get_concur_list.restype = ctypes.POINTER(ctypes.c_int)


# // same as ggml_graph_compute but uses Metal
# // creates gf->n_threads command buffers in parallel
# void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
def ggml_metal_graph_compute(
    ctx: ggml_metal_context_p,
    gf: ggml_cgraph_p,
):
    return lib.ggml_metal_graph_compute(ctx, gf)


if GGML_USE_METAL:
    lib.ggml_metal_graph_compute.argtypes = [
        ggml_metal_context_p,
        ctypes.POINTER(ggml_cgraph),
    ]
    lib.ggml_metal_graph_compute.restype = None


#####################################################
# GGML OPENCL API
# source: ggml-opencl.h
#####################################################


GGML_USE_CLBLAST = hasattr(lib, "ggml_cl_init")


# void ggml_cl_init(void);
def ggml_cl_init():
    return lib.ggml_cl_init()


if GGML_USE_CLBLAST:
    lib.ggml_cl_init.argtypes = []
    lib.ggml_cl_init.restype = None


# void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
def ggml_cl_mul(
    src0: ggml_tensor_p,
    src1: ggml_tensor_p,
    dst: ggml_tensor_p,
):
    return lib.ggml_cl_mul(src0, src1, dst)


if GGML_USE_CLBLAST:
    lib.ggml_cl_mul.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cl_mul.restype = None


# bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
def ggml_cl_can_mul_mat(
    src0: ggml_tensor_p,
    src1: ggml_tensor_p,
    dst: ggml_tensor_p,
) -> bool:
    return lib.ggml_cl_can_mul_mat(src0, src1, dst)


if GGML_USE_CLBLAST:
    lib.ggml_cl_can_mul_mat.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cl_can_mul_mat.restype = ctypes.c_bool


# size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
def ggml_cl_mul_mat_get_wsize(
    src0: ggml_tensor_p,
    src1: ggml_tensor_p,
    dst: ggml_tensor_p,
) -> int:
    return lib.ggml_cl_mul_mat_get_wsize(src0, src1, dst)


if GGML_USE_CLBLAST:
    lib.ggml_cl_mul_mat_get_wsize.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cl_mul_mat_get_wsize.restype = ctypes.c_size_t


# void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
def ggml_cl_mul_mat(
    src0: ggml_tensor_p,
    src1: ggml_tensor_p,
    dst: ggml_tensor_p,
    wdata: ctypes.c_void_p,
    wsize: Union[ctypes.c_size_t, int],
):
    return lib.ggml_cl_mul_mat(src0, src1, dst, wdata, wsize)


if GGML_USE_CLBLAST:
    lib.ggml_cl_mul_mat.argtypes = [
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.POINTER(ggml_tensor),
        ctypes.c_void_p,
        ctypes.c_size_t,
    ]
    lib.ggml_cl_mul_mat.restype = None


# void * ggml_cl_host_malloc(size_t size);
def ggml_cl_host_malloc(
    size: Union[ctypes.c_size_t, int],
) -> Optional[ctypes.c_void_p]:
    return lib.ggml_cl_host_malloc(size)


if GGML_USE_CLBLAST:
    lib.ggml_cl_host_malloc.argtypes = [
        ctypes.c_size_t,
    ]
    lib.ggml_cl_host_malloc.restype = ctypes.c_void_p


# void   ggml_cl_host_free(void * ptr);
def ggml_cl_host_free(
    ptr: ctypes.c_void_p,
):
    return lib.ggml_cl_host_free(ptr)


if GGML_USE_CLBLAST:
    lib.ggml_cl_host_free.argtypes = [
        ctypes.c_void_p,
    ]
    lib.ggml_cl_host_free.restype = None


# void ggml_cl_free_data(const struct ggml_tensor* tensor);
def ggml_cl_free_data(
    tensor: ggml_tensor_p,
):
    return lib.ggml_cl_free_data(tensor)


if GGML_USE_CLBLAST:
    lib.ggml_cl_free_data.argtypes = [
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cl_free_data.restype = None


# void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
def ggml_cl_transform_tensor(
    data: ctypes.c_void_p,
    tensor: ggml_tensor_p,
):
    return lib.ggml_cl_transform_tensor(data, tensor)


if GGML_USE_CLBLAST:
    lib.ggml_cl_transform_tensor.argtypes = [
        ctypes.c_void_p,
        ctypes.POINTER(ggml_tensor),
    ]
    lib.ggml_cl_transform_tensor.restype = None