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# mypy: allow-untyped-defs
import re
from typing import Callable

import torch
from torch import Tensor


__all__: list[str] = []


class _CodeParser:
    def __init__(self, code_string: str):
        optional_ws = r"\s*"
        required_ws = r"\s+"
        template_params = r"(?P<template_params>\<.+\>)"
        return_type = r"(?P<return_type>\w+)"
        function_name = r"(?P<function_name>\w+)"
        function_params = r"(?P<function_params>\(.+\))"
        function_body = r"(?P<function_body>\{.+\})"

        pattern = (
            optional_ws
            + "template"
            + optional_ws
            + template_params
            + optional_ws
            + return_type
            + required_ws
            + function_name
            + optional_ws
            + function_params
            + optional_ws
            + function_body
            + optional_ws
        )

        result = re.match(
            pattern, code_string, re.DOTALL
        )  # DOTALL for matching multiline

        if result is None:
            raise Exception(  # noqa: TRY002
                f"Couldn't parse code, please check correctness:\n {code_string}"
            )

        self.template_params = result["template_params"]
        self.return_type = result["return_type"]
        self.function_name = result["function_name"]
        self.function_params = result["function_params"]
        self.function_body = result["function_body"]


class _JittedFunction:
    def __init__(
        self, code_string: str, return_by_ref: bool, num_outputs: int, **kwargs
    ):
        self.code_string = code_string

        assert return_by_ref or num_outputs == 1, (
            "Return by value only works for single output. "
        )
        self.return_by_ref = return_by_ref
        self.num_outputs = num_outputs

        parsed_code = _CodeParser(code_string)
        self.kernel_name = parsed_code.function_name

        self.kwargs_dict = kwargs
        self.is_cuda_available = torch.cuda.is_available()

    def __call__(self, *tensors: Tensor, **kwargs):
        # Jiterator follow torch.cuda's lazy initialization behavior
        # Defer checking cuda's availability at the function invocation time
        assert self.is_cuda_available, (
            "Jiterator is only supported on CUDA and ROCm GPUs, none are available."
        )

        assert len(tensors) <= 8, "jiterator only supports up to 8 tensor inputs."

        expanded_kwargs = self.kwargs_dict.copy()
        for key, value in kwargs.items():
            if key in self.kwargs_dict:
                expanded_kwargs[key] = value
            else:
                raise KeyError(f"{key} is not declared in function definition")

        return torch._C._cuda_jiterator_compile_and_launch_kernel(
            self.code_string,
            self.kernel_name,
            self.return_by_ref,
            self.num_outputs,
            tensors,
            expanded_kwargs,
        )


def _create_jit_fn(code_string: str, **kwargs) -> Callable:
    """
    Create a jiterator-generated cuda kernel for an elementwise op.
    The code string has to be a valid CUDA function that describes the computation for a single element. The code
    string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
    into elementwise kernel template, and compiled on the fly. Compiled kernel will be cached in memory, as well as
    local temp dir.
    Jiterator-generated kernels accepts noncontiguous tensors, and supports broadcasting and type promotion.
    Args:
        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return by value.
        kwargs (Dict, optional): Keyword arguments for generated function
    Example::
        code_string = "template <typename T> T my_kernel(T x, T y, T alpha) { return -x + alpha * y; }"
        jitted_fn = create_jit_fn(code_string, alpha=1.0)
        a = torch.rand(3, device="cuda")
        b = torch.rand(3, device="cuda")
        # invoke jitted function like a regular python function
        result = jitted_fn(a, b, alpha=3.14)
    code_string also allows multiple function definitions, and the last function will be treated as the entry function.
    Example::
        code_string = (
            "template <typename T> T util_fn(T x, T y) { return ::sin(x) + ::cos(y); }"
        )
        code_string += "template <typename T> T my_kernel(T x, T y, T val) { return ::min(val, util_fn(x, y)); }"
        jitted_fn = create_jit_fn(code_string, val=0.0)
        a = torch.rand(3, device="cuda")
        b = torch.rand(3, device="cuda")
        # invoke jitted function like a regular python function
        result = jitted_fn(a, b)  # using default val=0.0
    Jiterator can be used together with python registration to override an operator's cuda kernel.
    Following example is overriding gelu's cuda kernel with relu.
    Example::
        code_string = "template <typename T> T my_gelu(T a) { return a > 0 ? a : 0; }"
        my_gelu = create_jit_fn(code_string)
        my_lib = torch.library.Library("aten", "IMPL")
        my_lib.impl("aten::gelu", my_gelu, "CUDA")
        # torch.nn.GELU and torch.nn.function.gelu are now overridden
        a = torch.rand(3, device="cuda")
        torch.allclose(torch.nn.functional.gelu(a), torch.nn.functional.relu(a))
    .. warning::
        This API is in beta and may change in future releases.
    .. warning::
        This API only supports up to 8 inputs and 1 output
    .. warning::
        All input tensors must live in CUDA device
    """
    return _JittedFunction(code_string, return_by_ref=False, num_outputs=1, **kwargs)


def _create_multi_output_jit_fn(
    code_string: str, num_outputs: int, **kwargs
) -> Callable:
    """
    Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.
    Args:
        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return value by reference.
        num_outputs(int): number of outputs return by the kernel
        kwargs (Dict, optional): Keyword arguments for generated function
    Example::
        code_string = "template <typename T> void my_kernel(T x, T y, T alpha, T& out) { out = -x + alpha * y; }"
        jitted_fn = create_jit_fn(code_string, alpha=1.0)
        a = torch.rand(3, device="cuda")
        b = torch.rand(3, device="cuda")
        # invoke jitted function like a regular python function
        result = jitted_fn(a, b, alpha=3.14)
    .. warning::
        This API is in beta and may change in future releases.
    .. warning::
        This API only supports up to 8 inputs and 8 outputs
    """
    return _JittedFunction(
        code_string, return_by_ref=True, num_outputs=num_outputs, **kwargs
    )

	# mypy: allow-untyped-defs
	import re
	from typing import Callable

	import torch
	from torch import Tensor


	__all__: list[str] = []


	class _CodeParser:
	def __init__(self, code_string: str):
	optional_ws = r"\s*"
	required_ws = r"\s+"
	template_params = r"(?P<template_params>\<.+\>)"
	return_type = r"(?P<return_type>\w+)"
	function_name = r"(?P<function_name>\w+)"
	function_params = r"(?P<function_params>\(.+\))"
	function_body = r"(?P<function_body>\{.+\})"

	pattern = (
	optional_ws
	+ "template"
	+ optional_ws
	+ template_params
	+ optional_ws
	+ return_type
	+ required_ws
	+ function_name
	+ optional_ws
	+ function_params
	+ optional_ws
	+ function_body
	+ optional_ws
	)

	result = re.match(
	pattern, code_string, re.DOTALL
	) # DOTALL for matching multiline

	if result is None:
	raise Exception( # noqa: TRY002
	f"Couldn't parse code, please check correctness:\n {code_string}"
	)

	self.template_params = result["template_params"]
	self.return_type = result["return_type"]
	self.function_name = result["function_name"]
	self.function_params = result["function_params"]
	self.function_body = result["function_body"]


	class _JittedFunction:
	def __init__(
	self, code_string: str, return_by_ref: bool, num_outputs: int, **kwargs
	):
	self.code_string = code_string

	assert return_by_ref or num_outputs == 1, (
	"Return by value only works for single output. "
	)
	self.return_by_ref = return_by_ref
	self.num_outputs = num_outputs

	parsed_code = _CodeParser(code_string)
	self.kernel_name = parsed_code.function_name

	self.kwargs_dict = kwargs
	self.is_cuda_available = torch.cuda.is_available()

	def __call__(self, tensors: Tensor, *kwargs):
	# Jiterator follow torch.cuda's lazy initialization behavior
	# Defer checking cuda's availability at the function invocation time
	assert self.is_cuda_available, (
	"Jiterator is only supported on CUDA and ROCm GPUs, none are available."
	)

	assert len(tensors) <= 8, "jiterator only supports up to 8 tensor inputs."

	expanded_kwargs = self.kwargs_dict.copy()
	for key, value in kwargs.items():
	if key in self.kwargs_dict:
	expanded_kwargs[key] = value
	else:
	raise KeyError(f"{key} is not declared in function definition")

	return torch._C._cuda_jiterator_compile_and_launch_kernel(
	self.code_string,
	self.kernel_name,
	self.return_by_ref,
	self.num_outputs,
	tensors,
	expanded_kwargs,
	)


	def _create_jit_fn(code_string: str, **kwargs) -> Callable:
	"""
	Create a jiterator-generated cuda kernel for an elementwise op.

	The code string has to be a valid CUDA function that describes the computation for a single element. The code
	string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
	into elementwise kernel template, and compiled on the fly. Compiled kernel will be cached in memory, as well as
	local temp dir.

	Jiterator-generated kernels accepts noncontiguous tensors, and supports broadcasting and type promotion.

	Args:
	code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return by value.
	kwargs (Dict, optional): Keyword arguments for generated function

	Example::

	code_string = "template <typename T> T my_kernel(T x, T y, T alpha) { return -x + alpha * y; }"
	jitted_fn = create_jit_fn(code_string, alpha=1.0)
	a = torch.rand(3, device="cuda")
	b = torch.rand(3, device="cuda")
	# invoke jitted function like a regular python function
	result = jitted_fn(a, b, alpha=3.14)

	code_string also allows multiple function definitions, and the last function will be treated as the entry function.

	Example::

	code_string = (
	"template <typename T> T util_fn(T x, T y) { return ::sin(x) + ::cos(y); }"
	)
	code_string += "template <typename T> T my_kernel(T x, T y, T val) { return ::min(val, util_fn(x, y)); }"
	jitted_fn = create_jit_fn(code_string, val=0.0)
	a = torch.rand(3, device="cuda")
	b = torch.rand(3, device="cuda")
	# invoke jitted function like a regular python function
	result = jitted_fn(a, b) # using default val=0.0

	Jiterator can be used together with python registration to override an operator's cuda kernel.
	Following example is overriding gelu's cuda kernel with relu.

	Example::

	code_string = "template <typename T> T my_gelu(T a) { return a > 0 ? a : 0; }"
	my_gelu = create_jit_fn(code_string)
	my_lib = torch.library.Library("aten", "IMPL")
	my_lib.impl("aten::gelu", my_gelu, "CUDA")
	# torch.nn.GELU and torch.nn.function.gelu are now overridden
	a = torch.rand(3, device="cuda")
	torch.allclose(torch.nn.functional.gelu(a), torch.nn.functional.relu(a))

	.. warning::
	This API is in beta and may change in future releases.

	.. warning::
	This API only supports up to 8 inputs and 1 output

	.. warning::
	All input tensors must live in CUDA device
	"""
	return _JittedFunction(code_string, return_by_ref=False, num_outputs=1, **kwargs)


	def _create_multi_output_jit_fn(
	code_string: str, num_outputs: int, **kwargs
	) -> Callable:
	"""
	Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.

	Args:
	code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return value by reference.
	num_outputs(int): number of outputs return by the kernel
	kwargs (Dict, optional): Keyword arguments for generated function

	Example::

	code_string = "template <typename T> void my_kernel(T x, T y, T alpha, T& out) { out = -x + alpha * y; }"
	jitted_fn = create_jit_fn(code_string, alpha=1.0)
	a = torch.rand(3, device="cuda")
	b = torch.rand(3, device="cuda")
	# invoke jitted function like a regular python function
	result = jitted_fn(a, b, alpha=3.14)

	.. warning::
	This API is in beta and may change in future releases.

	.. warning::
	This API only supports up to 8 inputs and 8 outputs
	"""
	return _JittedFunction(
	code_string, return_by_ref=True, num_outputs=num_outputs, **kwargs
	)