# cython: language_level=3

import torch
cimport cython
import ctypes
from libc.stdint cimport int64_t, uintptr_t
from libc.stdlib cimport malloc, free
from tvm import tir
from tilelang.utils.tensor import map_torch_type

cdef class CythonKernelWrapper:
    # Class attributes to store kernel configuration and library reference
    cdef:
        object dynamic_symbolic_map    # Maps dynamic dimensions to their corresponding tensor indices
        object buffer_device_map       # Maps buffer variables to their corresponding devices
        object buffer_dtype_map        # Maps buffer variables to their corresponding dtypes
        object static_shape_map        # Maps buffer variables to their corresponding static shapes
        object static_strides_map      # Maps buffer variables to their corresponding static strides
        object static_contiguous_list  # A list contains contiguous buffers
        object ptr_map                 # Maps pointer arguments to their corresponding buffer indices
        list result_idx                # Indices of output tensors in the params list
        list params                    # List of parameter specifications (includes both inputs and outputs)
        object lib                     # Reference to the compiled library containing the kernel
        # Add new cache attributes
        list param_dtypes              # Cache for parameter dtypes
        list param_shapes              # Cache for parameter shapes as native Python lists
        object get_current_device

    def __cinit__(self, result_idx, params, lib):
        # Initialize wrapper with kernel configuration
        self.result_idx = result_idx
        self.params = params
        self.lib = lib
        # Convert TVM types to native Python types during initialization
        self.param_dtypes = [param.dtype for param in params]
        # Convert TVM shape arrays to native Python lists
        self.param_shapes = []
        self.get_current_device = torch.cuda.current_device
        for param in params:
            native_shape = []
            for dim in param.shape:
                if isinstance(dim, tir.IntImm):
                    native_shape.append(int(dim))
                elif isinstance(dim, tir.Var):
                    native_shape.append(dim)  # Keep tir.Var for dynamic dimensions
                else:
                    native_shape.append(dim)
            self.param_shapes.append(native_shape)

    def set_dynamic_symbolic_map(self, dynamic_symbolic_map):
        self.dynamic_symbolic_map = dynamic_symbolic_map
        return self

    def set_buffer_dtype_map(self, buffer_dtype_map):
        self.buffer_dtype_map = buffer_dtype_map
        return self

    def set_static_shape_map(self, static_shape_map):
        self.static_shape_map = static_shape_map
        return self

    def set_static_strides_map(self, static_strides_map):
        self.static_strides_map = static_strides_map
        return self

    def set_static_contiguous_list(self, static_contiguous_list):
        self.static_contiguous_list = static_contiguous_list
        return self

    def set_ptr_map(self, ptr_map):
        self.ptr_map = ptr_map
        return self

    def set_buffer_device_map(self, buffer_device_map):
        self.buffer_device_map = buffer_device_map
        return self

    cpdef void _check_buffer_device(self, list tensor_list):
        for param, (buffer_idx, device) in self.buffer_device_map.items():
            tensor = tensor_list[buffer_idx]
            if isinstance(tensor, torch.Tensor):
                tensor_device = tensor.device
                device_type_match = device.type == tensor_device.type
                device_index_match = (
                    tensor_device.index is None or 
                    device.index is None or 
                    tensor_device.index == device.index
                )
                if not (device_type_match and device_index_match):
                    raise ValueError(
                        f"Buffer device mismatch for parameter {param}: "
                        f"expected {device}, got {tensor_device}"
                    )

    cpdef void _check_buffer_dtype(self, list tensor_list):
        for param, (buffer_idx, torch_dtype) in self.buffer_dtype_map.items():
            tensor = tensor_list[buffer_idx]
            if isinstance(tensor, torch.Tensor) and tensor.dtype != torch_dtype:
                raise ValueError(
                    f"Buffer dtype mismatch for parameter {param}: "
                    f"expected {torch_dtype}, got {tensor.dtype}"
                )

    cpdef void _check_static_shape(self, list tensor_list):
        for param, (buffer_idx, shape_list) in self.static_shape_map.items():
            tensor = tensor_list[buffer_idx]
            if not isinstance(tensor, torch.Tensor):
                # otherwise, maybe torch.data_ptr() for T.ptr inputs
                continue
            for shape_idx, expected_shape in shape_list:
                actual_shape = tensor.shape[shape_idx]
                if actual_shape != expected_shape:
                    raise ValueError(
                        f"Static shape mismatch for parameter {param}: "
                        f"expected {expected_shape} at index {shape_idx}, "
                        f"got {actual_shape}"
                    )

    cpdef void _check_static_strides(self, list tensor_list):
        for param, (buffer_idx, strides_list) in self.static_strides_map.items():
            tensor = tensor_list[buffer_idx]
            if not isinstance(tensor, torch.Tensor):
                # otherwise, maybe torch.data_ptr() for T.ptr inputs
                continue
            for stride_idx, expected_stride in strides_list:
                # Ensure the stride index is within the valid range of tensor dimensions
                # (stride_idx should be less than the number of dimensions of the tensor)
                assert stride_idx < tensor.dim(), f"Stride index {stride_idx} out of bounds for tensor with {tensor.dim()} dimensions"
                if tensor.shape[stride_idx] == 1:
                    continue
                actual_stride = tensor.stride(stride_idx)
                if actual_stride != expected_stride:
                    raise ValueError(
                        f"Static stride mismatch for parameter {param}: "
                        f"expected {expected_stride} at index {stride_idx}, "
                        f"got {actual_stride}"
                    )

    cpdef void _check_static_contiguous(self, list tensor_list):
        for buffer_idx, param in self.static_contiguous_list:
            tensor = tensor_list[buffer_idx]
            if not isinstance(tensor, torch.Tensor):
                # otherwise, maybe torch.data_ptr() for T.ptr inputs
                continue
            if not tensor.is_contiguous():
                raise ValueError(f"Expected parameter {param} to be a contiguous tensor")

    cpdef forward(self, list inputs, int64_t stream = -1, bint skip_tensor_validation = False):
        # Validate input dimensions and prepare for kernel execution
        cdef int total_params = len(self.params)
        cdef int total_inputs = len(inputs)
        cdef int total_result_idx = len(self.result_idx)
        cdef int total_dynamic_symbolics = len(self.dynamic_symbolic_map)

        # Ensure the number of inputs matches expected parameter count
        if total_params != total_inputs + total_result_idx:
            raise ValueError(
                f"Expected {len(self.params)} inputs, got {len(inputs) + len(self.result_idx)} with {len(inputs)} inputs and {len(self.result_idx)} outputs"
            )

        # Use current CUDA stream if none specified
        if stream == -1: 
            if torch.cuda.is_available():
                try:
                    stream = torch._C._cuda_getCurrentRawStream(torch.cuda.current_device())
                except ImportError:
                    stream = torch.cuda.current_stream().cuda_stream
            else:
                stream = 0

        cdef int ins_idx = 0
        cdef list tensor_list = []

        # Prepare input and output tensors
        for i in range(len(self.params)):
            if i in self.result_idx:
                dtype = self.param_dtypes[i]
                shape = []
                # Now working with native Python list, no FFI calls needed
                for s in self.param_shapes[i]:
                    if isinstance(s, tir.Var):
                        for key in self.dynamic_symbolic_map:
                            if(str(s) == str(key)):
                                ref_id, ref_tensor_idx, ref_shape_idx = self.dynamic_symbolic_map[key]
                                shape.append(tensor_list[ref_tensor_idx].shape[ref_shape_idx])
                    else:  # Already converted to Python int during initialization
                        shape.append(s)
                device = inputs[0].device if len(inputs) > 0 else torch.cuda.current_device()
                if len(shape) == 0:
                    param_name = self.params[i].name if hasattr(self.params[i], 'name') else f'parameter_{i}'
                    raise ValueError(
                        f"Cannot create output tensor (name={param_name}) - 0-dimensional tensors are not supported. "
                        f"Expected shape: {shape}"
                    )
                tensor = torch.empty(*shape, dtype=dtype, device=device)
            else:
                tensor = inputs[ins_idx]
                ins_idx += 1
            # TODO(chenggang): remove this check or rewrite by ourselves?
            '''
            if isinstance(tensor, torch.Tensor) and tensor._base is not None and not tensor.is_contiguous():
                base_tensor = tensor._base.as_strided(tensor._base.shape, tensor.stride())
                if torch._debug_has_internal_overlap(base_tensor):
                    raise ValueError(f"Cannot use an overlapping tensor"
                                     f"(shape={tensor.shape}, strides={tensor.stride()}, "
                                     f"overlap={torch._debug_has_internal_overlap(base_tensor)}) as the kernel input")
            '''
            tensor_list.append(tensor)

        # Convert tensor pointers to C void pointers for kernel call
        cdef dict dtype_to_ctype = {
            torch.float16: ctypes.c_float,
            torch.float32: ctypes.c_float,
            torch.float64: ctypes.c_double,
            torch.int8: ctypes.c_int8,
            torch.int16: ctypes.c_int16,
            torch.int32: ctypes.c_int32,
            torch.int64: ctypes.c_int64,
            torch.bool: ctypes.c_bool,
        }
        
        call_args = []
        for i, tensor in enumerate(tensor_list):
            if isinstance(tensor, torch.Tensor):
                call_args.append(ctypes.c_void_p(tensor.data_ptr()))
            elif isinstance(tensor, (int, float, bool)):
                if i in self.ptr_map:
                    call_args.append(ctypes.c_void_p(tensor))
                else:
                    dtype = self.param_dtypes[i]
                    if dtype not in dtype_to_ctype:
                        raise ValueError(f"Unsupported tensor dtype: {dtype}")
                    call_args.append(dtype_to_ctype[dtype](tensor))
            else:
                raise ValueError(f"Unsupported tensor type: {type(tensor)}")

        # Check buffer device
        if not skip_tensor_validation:
            self._check_buffer_device(tensor_list)
            self._check_buffer_dtype(tensor_list)
            self._check_static_shape(tensor_list)
            self._check_static_strides(tensor_list)
            self._check_static_contiguous(tensor_list)

        # Add dynamic dimension values to kernel arguments
        for _, (ref_id, buffer_idx, shape_idx) in self.dynamic_symbolic_map.items():
            if ref_id == 0:
                call_args.append(tensor_list[buffer_idx].shape[shape_idx])
            else:
                call_args.append(tensor_list[buffer_idx].stride(shape_idx))

        # Add CUDA stream to kernel arguments
        call_args.append(ctypes.c_void_p(stream))

        # Execute the kernel
        result = self.lib.call(*call_args)
        if result != 0:
            error_msg = self.lib.get_last_error().decode('utf-8')
            raise RuntimeError(f"Kernel call failed: {error_msg}")

        # Return output tensor(s)
        if len(self.result_idx) == 1:
            return tensor_list[self.result_idx[0]]
        else:
            return [tensor_list[i] for i in self.result_idx]
    