#ifndef BLACKCAT_TENSOR_FUNCTIONS
#define BLACKCAT_TENSOR_FUNCTIONS
namespace BC {

#ifdef __CUDACC__
#define __BC_gcpu__ __host__ __device__
#define BLACKCAT_GPU_ENABLED
#else
#define __BC_gcpu__
#endif

	namespace NN_Functions {

		struct Sigmoid {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				static constexpr T e = 2.71828;

				return 1 / (1 + std::pow(e, - t));
			}
		};
		struct SigmoidAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = 1 / (1 + std::pow(e, - t));
			}
		};
		struct CachedSigmoidDeriv {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				return t * (1 - t);
			}
		};
		struct CachedSigmoidDerivAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				return t *= (1 - t);
			}
		};

		struct Tanh {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				static constexpr double e = 2.71828;

				return (powf(e, t) - powf(e, -t)) /
					   (powf(e, t) + powf(e, -t));
			}
		};
		struct TanhAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = (powf(e, t) - powf(e, -t)) /
					       (powf(e, t) + powf(e, -t));
			}
		};
		struct CachedTanhDeriv {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				return 1 - powf(t, 2);
			}
		};
		struct CachedTanhDerivAssign {

			template<class T>

			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = 1 - powf(t, 2);
			}
		};

		template<template<class,class> class tensor, class T, class ml>
		auto sigmoid(tensor<T, ml>& x) {
			return x.unExpr(SigmoidAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoid(tensor<T, ml>&& x) {
			return x.unExpr(Sigmoid());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoidDeriv(tensor<T, ml>& x) {
			return x.unExpr(CachedSigmoidDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoidDeriv(tensor<T, ml>&& x) {
			return x.unExpr(CachedSigmoidDeriv());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanh(tensor<T, ml>& x) {
			return x.unExpr(TanhAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanh(tensor<T, ml>&& x) {
			return x.unExpr(Tanh());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanhDeriv(tensor<T, ml>& x) {
			return x.unExpr(CachedTanhDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanhDeriv(tensor<T, ml>&& x) {
			return x.unExpr(CachedTanhDeriv());
		}

	}

	namespace NN_Abreviated_Functions {
		using namespace NN_Functions;
		template<template<class,class> class tensor, class T, class ml>
		auto g(tensor<T, ml>& x) {
			return x.unExpr(SigmoidAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto g(tensor<T, ml>&& x) {
			return x.unExpr(Sigmoid());
		}
//		template<template<class,class> class tensor, class T, class ml>
//		auto gd(tensor<T, ml>& x) {
//			return x.unExpr(CachedSigmoidDerivAssign());
//		}
		template<template<class,class> class tensor, class T, class ml>
		auto gd(tensor<T, ml>& x) {
			return x.unExpr(CachedSigmoidDeriv());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto h(tensor<T, ml>& x) {
			return x.unExpr(TanhAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto h(tensor<T, ml>&& x) {
			return x.unExpr(Tanh());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto hd(tensor<T, ml>& x) {
			return x.unExpr(CachedTanhDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto hd(tensor<T, ml>&& x) {
			return x.unExpr(CachedTanhDeriv());
		}

	}

}
#endif

	.file	"tmpxft_0020cb17_00000000-6_BlackCat_TensorFunctions.cudafe1.cpp"
	.text
#APP

#NO_APP
	.type	_ZL26__cudaUnregisterBinaryUtilv, @function
_ZL26__cudaUnregisterBinaryUtilv:
.LFB2033:
	.cfi_startproc
	endbr64
	movq	_ZL20__cudaFatCubinHandle(%rip), %rdi
	jmp	__cudaUnregisterFatBinary@PLT
	.cfi_endproc
.LFE2033:
	.size	_ZL26__cudaUnregisterBinaryUtilv, .-_ZL26__cudaUnregisterBinaryUtilv
	.section	.text.startup,"ax",@progbits
	.type	_ZL24__sti____cudaRegisterAllv, @function
_ZL24__sti____cudaRegisterAllv:
.LFB2056:
	.cfi_startproc
	endbr64
	pushq	%rax
	.cfi_def_cfa_offset 16
	leaq	_ZL15__fatDeviceText(%rip), %rdi
	call	__cudaRegisterFatBinary@PLT
	movq	%rax, %rdi
	movq	%rax, _ZL20__cudaFatCubinHandle(%rip)
	call	__cudaRegisterFatBinaryEnd@PLT
	leaq	_ZL26__cudaUnregisterBinaryUtilv(%rip), %rdi
	popq	%rdx
	.cfi_def_cfa_offset 8
	jmp	atexit@PLT
	.cfi_endproc
.LFE2056:
	.size	_ZL24__sti____cudaRegisterAllv, .-_ZL24__sti____cudaRegisterAllv
	.section	.init_array,"aw"
	.align 8
	.quad	_ZL24__sti____cudaRegisterAllv
	.section	.nvFatBinSegment,"aw"
	.align 8
	.type	_ZL15__fatDeviceText, @object
	.size	_ZL15__fatDeviceText, 24
_ZL15__fatDeviceText:
	.long	1180844977
	.long	1
	.quad	fatbinData
	.quad	0
	.local	_ZL20__cudaFatCubinHandle
	.comm	_ZL20__cudaFatCubinHandle,8,8
	.ident	"GCC: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0"
	.section	.note.GNU-stack,"",@progbits
	.section	.note.gnu.property,"a"
	.align 8
	.long	1f - 0f
	.long	4f - 1f
	.long	5
0:
	.string	"GNU"
1:
	.align 8
	.long	0xc0000002
	.long	3f - 2f
2:
	.long	0x3
3:
	.align 8
4:

#ifndef BLACKCAT_TENSOR_FUNCTIONS
#define BLACKCAT_TENSOR_FUNCTIONS
namespace BC {

#ifdef __HIPCC__
#define __BC_gcpu__ __host__ __device__
#define BLACKCAT_GPU_ENABLED
#else
#define __BC_gcpu__
#endif

	namespace NN_Functions {

		struct Sigmoid {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				static constexpr T e = 2.71828;

				return 1 / (1 + std::pow(e, - t));
			}
		};
		struct SigmoidAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = 1 / (1 + std::pow(e, - t));
			}
		};
		struct CachedSigmoidDeriv {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				return t * (1 - t);
			}
		};
		struct CachedSigmoidDerivAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				return t *= (1 - t);
			}
		};

		struct Tanh {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				static constexpr double e = 2.71828;

				return (powf(e, t) - powf(e, -t)) /
					   (powf(e, t) + powf(e, -t));
			}
		};
		struct TanhAssign {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = (powf(e, t) - powf(e, -t)) /
					       (powf(e, t) + powf(e, -t));
			}
		};
		struct CachedTanhDeriv {

			template<class T>
			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T t) const {
				return 1 - powf(t, 2);
			}
		};
		struct CachedTanhDerivAssign {

			template<class T>

			__BC_gcpu__ inline __attribute__((always_inline))  T operator () (T& t) const {
				static constexpr T e = 2.71828;

				return t = 1 - powf(t, 2);
			}
		};

		template<template<class,class> class tensor, class T, class ml>
		auto sigmoid(tensor<T, ml>& x) {
			return x.unExpr(SigmoidAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoid(tensor<T, ml>&& x) {
			return x.unExpr(Sigmoid());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoidDeriv(tensor<T, ml>& x) {
			return x.unExpr(CachedSigmoidDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto sigmoidDeriv(tensor<T, ml>&& x) {
			return x.unExpr(CachedSigmoidDeriv());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanh(tensor<T, ml>& x) {
			return x.unExpr(TanhAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanh(tensor<T, ml>&& x) {
			return x.unExpr(Tanh());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanhDeriv(tensor<T, ml>& x) {
			return x.unExpr(CachedTanhDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto tanhDeriv(tensor<T, ml>&& x) {
			return x.unExpr(CachedTanhDeriv());
		}

	}

	namespace NN_Abreviated_Functions {
		using namespace NN_Functions;
		template<template<class,class> class tensor, class T, class ml>
		auto g(tensor<T, ml>& x) {
			return x.unExpr(SigmoidAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto g(tensor<T, ml>&& x) {
			return x.unExpr(Sigmoid());
		}
//		template<template<class,class> class tensor, class T, class ml>
//		auto gd(tensor<T, ml>& x) {
//			return x.unExpr(CachedSigmoidDerivAssign());
//		}
		template<template<class,class> class tensor, class T, class ml>
		auto gd(tensor<T, ml>& x) {
			return x.unExpr(CachedSigmoidDeriv());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto h(tensor<T, ml>& x) {
			return x.unExpr(TanhAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto h(tensor<T, ml>&& x) {
			return x.unExpr(Tanh());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto hd(tensor<T, ml>& x) {
			return x.unExpr(CachedTanhDerivAssign());
		}
		template<template<class,class> class tensor, class T, class ml>
		auto hd(tensor<T, ml>&& x) {
			return x.unExpr(CachedTanhDeriv());
		}

	}

}
#endif

	.text
	.file	"BlackCat_TensorFunctions.hip"
	.type	__hip_cuid_,@object             # @__hip_cuid_
	.bss
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               # 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_

	.text
	.p2alignl 7, 3214868480
	.fill 96, 4, 3214868480
	.type	__hip_cuid_,@object             ; @__hip_cuid_
	.section	.bss,"aw",@nobits
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               ; 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_
	.amdgcn_target "amdgcn-amd-amdhsa--gfx1100"
	.amdgpu_metadata
---
amdhsa.kernels:  []
amdhsa.target:   amdgcn-amd-amdhsa--gfx1100
amdhsa.version:
  - 1
  - 2
...

	.end_amdgpu_metadata
