diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_ctc_loss.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_ctc_loss.h new file mode 100644 index 0000000000000000000000000000000000000000..6f7a6f9e8df01408265b8b9cbb6b7a6e2c379817 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_ctc_loss.h @@ -0,0 +1,53 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) +inline ::std::tuple _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); +} + +// aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) +inline ::std::tuple _ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); +} + +// aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) +inline ::std::tuple _ctc_loss_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); +} +// aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) +inline ::std::tuple _ctc_loss_outf(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_ctc_loss_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); +} + +// aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) +inline ::std::tuple _ctc_loss_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_Tensor_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); +} +// aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) +inline ::std::tuple _ctc_loss_outf(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_ctc_loss_Tensor_out::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_embedding_bag_sparse_backward_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_embedding_bag_sparse_backward_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..db33c60b7448961492be9cd7e5159368f00b09e4 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_embedding_bag_sparse_backward_ops.h @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API _embedding_bag_sparse_backward { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, c10::SymInt, bool, int64_t, const c10::optional &, int64_t); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_embedding_bag_sparse_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor") + static at::Tensor call(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_affine_quantized.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_affine_quantized.h new file mode 100644 index 0000000000000000000000000000000000000000..4d963b42754be3087f49a91ac57d5c77bc059460 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_affine_quantized.h @@ -0,0 +1,113 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor +inline at::Tensor _empty_affine_quantized(at::IntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); +} +namespace symint { + template ::value>> + at::Tensor _empty_affine_quantized(at::IntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } +} + +// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor +inline at::Tensor _empty_affine_quantized(at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, scale, zero_point, memory_format); +} +namespace symint { + template ::value>> + at::Tensor _empty_affine_quantized(at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, scale, zero_point, memory_format); + } +} + +// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor +inline at::Tensor _empty_affine_quantized_symint(c10::SymIntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::call(size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); +} +namespace symint { + template ::value>> + at::Tensor _empty_affine_quantized(c10::SymIntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::call(size, optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } +} + +// aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor +inline at::Tensor _empty_affine_quantized_symint(c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::call(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); +} +namespace symint { + template ::value>> + at::Tensor _empty_affine_quantized(c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::call(size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); + } +} + +// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _empty_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); +} +namespace symint { + template ::value>> + at::Tensor & _empty_affine_quantized_out(at::Tensor & out, at::IntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); + } +} + +// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _empty_affine_quantized_outf(at::IntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); +} +namespace symint { + template ::value>> + at::Tensor & _empty_affine_quantized_outf(at::IntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::call(c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); + } +} + +// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _empty_affine_quantized_symint_out(at::Tensor & out, c10::SymIntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out); +} +namespace symint { + template ::value>> + at::Tensor & _empty_affine_quantized_out(at::Tensor & out, c10::SymIntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out); + } +} + +// aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _empty_affine_quantized_symint_outf(c10::SymIntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out); +} +namespace symint { + template ::value>> + at::Tensor & _empty_affine_quantized_outf(c10::SymIntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::call(size, scale, zero_point, memory_format, out); + } +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_erf_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_erf_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..1da6b839e7a5ff54aa1e92aa11c91f9e067c26c6 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_erf_compositeexplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API void _foreach_erf_out(at::TensorList out, at::TensorList self); +TORCH_API void _foreach_erf_outf(at::TensorList self, at::TensorList out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_lgamma_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_lgamma_native.h new file mode 100644 index 0000000000000000000000000000000000000000..5f03c4ef94d7002fdc73406e1d96b710e646f810 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_lgamma_native.h @@ -0,0 +1,25 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API void _foreach_lgamma_out(at::TensorList self, at::TensorList out); +TORCH_API ::std::vector foreach_tensor_lgamma_slow(at::TensorList self); +TORCH_API void foreach_tensor_lgamma_slow_(at::TensorList self); +TORCH_API ::std::vector foreach_tensor_lgamma_cuda(at::TensorList self); +TORCH_API void foreach_tensor_lgamma_cuda_(at::TensorList self); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_is_all_true_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_is_all_true_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..1c4237d61256b1f4e22b4423483c40d8cefecc02 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_is_all_true_ops.h @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API _is_all_true { + using schema = at::Tensor (const at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_is_all_true") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_is_all_true(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_masked_softmax_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_masked_softmax_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..41a7ab61a19e3f7ca833b8cc82f9a29a7b1d7d8c --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_masked_softmax_cpu_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor _masked_softmax(const at::Tensor & self, const at::Tensor & mask, c10::optional dim=c10::nullopt, c10::optional mask_type=c10::nullopt); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_size_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_size_native.h new file mode 100644 index 0000000000000000000000000000000000000000..a74ed53df883269012f4e91419dbf693f52c3c9f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_size_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor & _nested_tensor_size_out(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor _nested_tensor_size(const at::Tensor & self); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection.h new file mode 100644 index 0000000000000000000000000000000000000000..5fc8235cdffc1e38db5df6140b9fd3d374c6124d --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection.h @@ -0,0 +1,34 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _sparse_mask_projection_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches=false) { + return at::_ops::_sparse_mask_projection_out::call(self, mask, accumulate_matches, out); +} +// aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _sparse_mask_projection_outf(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches, at::Tensor & out) { + return at::_ops::_sparse_mask_projection_out::call(self, mask, accumulate_matches, out); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..062935492062c03a3da11c20c19d33469b6a2dfc --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_sparse_mask_projection_compositeexplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor & _sparse_mask_projection_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches=false); +TORCH_API at::Tensor & _sparse_mask_projection_outf(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_backward_impl_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_backward_impl_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..aaec544c26f209fa0f60ce88b6585df97d259144 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_backward_impl_compositeexplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API ::std::tuple _thnn_fused_lstm_cell_backward_impl_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias); +TORCH_API ::std::tuple _thnn_fused_lstm_cell_backward_impl_outf(const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..c64f6b554409de8fe5b7fa2772ac3d6e0b3c05c8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_thnn_fused_lstm_cell_compositeexplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API ::std::tuple _thnn_fused_lstm_cell_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias={}, const c10::optional & hidden_bias={}); +TORCH_API ::std::tuple _thnn_fused_lstm_cell_outf(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias, const c10::optional & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_transform_bias_rescale_qkv_cuda_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_transform_bias_rescale_qkv_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..4dac5055f681200f192218bff42ec839bc5b6c7f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_transform_bias_rescale_qkv_cuda_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API ::std::tuple _transform_bias_rescale_qkv(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads); + +} // namespace cuda +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_triton_scaled_dot_attention_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_triton_scaled_dot_attention_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..9aa0586bfb8391edbcc52a2d151f5fc2c9fada4d --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/_triton_scaled_dot_attention_compositeexplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor & _triton_scaled_dot_attention_out(at::Tensor & out, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p=0.0); +TORCH_API at::Tensor & _triton_scaled_dot_attention_outf(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/acosh_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/acosh_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..ae4983cc9b3e8d29b47009639e4b2e99d9278333 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/acosh_cpu_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor acosh(const at::Tensor & self); +TORCH_API at::Tensor & acosh_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & acosh_outf(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor & acosh_(at::Tensor & self); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_avg_pool3d_backward_cuda_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_avg_pool3d_backward_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..19743d58631af08944eef29a372472e2a180a648 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_avg_pool3d_backward_cuda_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor & adaptive_avg_pool3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self); +TORCH_API at::Tensor & adaptive_avg_pool3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input); + +} // namespace cuda +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_compositeimplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..05fe922a69a6c7a24ff0587d77b0c982d70aca61 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_compositeimplicitautograd_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API ::std::tuple adaptive_max_pool1d(const at::Tensor & self, at::IntArrayRef output_size); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..007d4c1b90af81317186dd13bedcc413d6367c69 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/adaptive_max_pool1d_ops.h @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API adaptive_max_pool1d { + using schema = ::std::tuple (const at::Tensor &, at::IntArrayRef); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::adaptive_max_pool1d") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor)") + static ::std::tuple call(const at::Tensor & self, at::IntArrayRef output_size); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/align_as.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/align_as.h new file mode 100644 index 0000000000000000000000000000000000000000..14ef7fff8c283bdfc5834492e66101adfe64ed23 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/align_as.h @@ -0,0 +1,26 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_meta_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_meta_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..453b8d4031aed73fad2e503ef9edec8a9ce39a4a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_meta_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace meta { + +TORCH_API at::Tensor amin(const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false); +TORCH_API at::Tensor & amin_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false); +TORCH_API at::Tensor & amin_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); + +} // namespace meta +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_native.h new file mode 100644 index 0000000000000000000000000000000000000000..1f4c90e3063b8855836d18776a514be2cd99f738 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/amin_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +struct TORCH_API structured_amin_out : public at::meta::structured_amin { +void impl(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, const at::Tensor & out); +}; +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/atan2_cuda_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/atan2_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..13cbec1d1cca1273d8aa4db730f0a37a4100ca40 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/atan2_cuda_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor atan2(const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & atan2_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & atan2_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); +TORCH_API at::Tensor & atan2_(at::Tensor & self, const at::Tensor & other); + +} // namespace cuda +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/batch_norm_update_stats_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/batch_norm_update_stats_native.h new file mode 100644 index 0000000000000000000000000000000000000000..0e852c7a20d1c2589fb8f53615080279e7ec581c --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/batch_norm_update_stats_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API ::std::tuple batch_norm_update_stats_out(const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum, at::Tensor & out0, at::Tensor & out1); +TORCH_API ::std::tuple batch_norm_update_stats_cpu(const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum); +TORCH_API ::std::tuple batch_norm_update_stats_cuda(const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/col_indices_copy.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/col_indices_copy.h new file mode 100644 index 0000000000000000000000000000000000000000..fc919ed41bb7e376f36373f6b8371c03a162a0e6 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/col_indices_copy.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::col_indices_copy(Tensor self) -> Tensor +inline at::Tensor col_indices_copy(const at::Tensor & self) { + return at::_ops::col_indices_copy::call(self); +} + +// aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & col_indices_copy_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::col_indices_copy_out::call(self, out); +} +// aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & col_indices_copy_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::col_indices_copy_out::call(self, out); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/combinations_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/combinations_native.h new file mode 100644 index 0000000000000000000000000000000000000000..19a66c44ef26352d236e5a49103faf3af61ba348 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/combinations_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor combinations(const at::Tensor & self, int64_t r=2, bool with_replacement=false); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/conv_depthwise3d.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/conv_depthwise3d.h new file mode 100644 index 0000000000000000000000000000000000000000..a0b621d08151556a7373576023b553b1b8c0454b --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/conv_depthwise3d.h @@ -0,0 +1,91 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor +inline at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); +} +namespace symint { + template ::value>> + at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } +} + +// aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor +inline at::Tensor conv_depthwise3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d::call(self, weight, kernel_size, bias, stride, padding, dilation); +} +namespace symint { + template ::value>> + at::Tensor conv_depthwise3d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } +} + +// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); +} +namespace symint { + template ::value>> + at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } +} + +// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); +} +namespace symint { + template ::value>> + at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } +} + +// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & conv_depthwise3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); +} +namespace symint { + template ::value>> + at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); + } +} + +// aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & conv_depthwise3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); +} +namespace symint { + template ::value>> + at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::call(self, weight, kernel_size, bias, stride, padding, dilation, out); + } +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_is_acceptable_compositeimplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_is_acceptable_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..e0b13b069df5a602472531615f2def9c416e6c5a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_is_acceptable_compositeimplicitautograd_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API bool cudnn_is_acceptable(const at::Tensor & self); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal_scatter_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal_scatter_native.h new file mode 100644 index 0000000000000000000000000000000000000000..9cf848832b94612a4882b7205ce03aa12e0009d2 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/diagonal_scatter_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor & diagonal_scatter_out(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out); +TORCH_API at::Tensor diagonal_scatter(const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..3640878135c9193ba5960d1704b2257d2e0eabf6 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_compositeexplicitautograd_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor embedding(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +TORCH_API at::Tensor embedding_symint(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +TORCH_API at::Tensor & embedding_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +TORCH_API at::Tensor & embedding_outf(const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out); +TORCH_API at::Tensor & embedding_symint_out(at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false); +TORCH_API at::Tensor & embedding_symint_outf(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/erfinv_compositeexplicitautogradnonfunctional_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/erfinv_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..c4aea293eb0634655d184f74a54778f32b09d504 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/erfinv_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor erfinv(const at::Tensor & self); +TORCH_API at::Tensor & erfinv_(at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fake_quantize_per_channel_affine_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fake_quantize_per_channel_affine_native.h new file mode 100644 index 0000000000000000000000000000000000000000..cb1fe3d859e3f9a1c7b0abe0d041c18465a77ff1 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fake_quantize_per_channel_affine_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor fake_quantize_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fft_hfft2_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fft_hfft2_native.h new file mode 100644 index 0000000000000000000000000000000000000000..c0f2230846366811bf04c77417153cf2938929a2 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fft_hfft2_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor fft_hfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt); +TORCH_API const at::Tensor & fft_hfft2_symint_out(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, const at::Tensor & out); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmax_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmax_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..ecc15bf7ab06a46aa2e290a333d93d0d41671a92 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmax_cpu_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor fmax(const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & fmax_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & fmax_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmin_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmin_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..b669217e276ec59dd2a440841b458b1fe46d87d2 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/fmin_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API fmin { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fmin") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fmin(Tensor self, Tensor other) -> Tensor") + static at::Tensor call(const at::Tensor & self, const at::Tensor & other); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other); +}; + +struct TORCH_API fmin_out { + using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fmin") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/gelu_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/gelu_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..57605e5bbd5ac9cd49d18cdad673f260b2358ff5 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/gelu_ops.h @@ -0,0 +1,50 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API gelu_out { + using schema = at::Tensor & (const at::Tensor &, c10::string_view, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gelu") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, c10::string_view approximate, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate, at::Tensor & out); +}; + +struct TORCH_API gelu_ { + using schema = at::Tensor & (at::Tensor &, c10::string_view); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gelu_") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!)") + static at::Tensor & call(at::Tensor & self, c10::string_view approximate); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::string_view approximate); +}; + +struct TORCH_API gelu { + using schema = at::Tensor (const at::Tensor &, c10::string_view); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gelu") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gelu(Tensor self, *, str approximate='none') -> Tensor") + static at::Tensor call(const at::Tensor & self, c10::string_view approximate); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..07a16a1ec721476c56cd6ce5b97f3a86f4d707a8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_cpu_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor hypot(const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & hypot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & hypot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); +TORCH_API at::Tensor & hypot_(at::Tensor & self, const at::Tensor & other); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_native.h new file mode 100644 index 0000000000000000000000000000000000000000..cd4430789f80280332602202f35443c98df62a2c --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/hypot_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +struct TORCH_API structured_hypot_out : public at::meta::structured_hypot { +void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out); +}; +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/int_repr.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/int_repr.h new file mode 100644 index 0000000000000000000000000000000000000000..1258cdbf1dac83be6f0bd5b53ab6f7d2f53feb9b --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/int_repr.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::int_repr(Tensor self) -> Tensor +inline at::Tensor int_repr(const at::Tensor & self) { + return at::_ops::int_repr::call(self); +} + +// aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & int_repr_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::int_repr_out::call(self, out); +} +// aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & int_repr_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::int_repr_out::call(self, out); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/inverse_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/inverse_native.h new file mode 100644 index 0000000000000000000000000000000000000000..4f387787b2a8d28111d89d626b217c07be108291 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/inverse_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor inverse(const at::Tensor & self); +TORCH_API at::Tensor & inverse_out(const at::Tensor & self, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/is_leaf.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/is_leaf.h new file mode 100644 index 0000000000000000000000000000000000000000..7a2a0c37e8f4292e49eb43edcba268ac87c394e9 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/is_leaf.h @@ -0,0 +1,26 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/istft.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/istft.h new file mode 100644 index 0000000000000000000000000000000000000000..97744e4cd48ac0f5494c2c197f01d4663d407519 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/istft.h @@ -0,0 +1,30 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor +inline at::Tensor istft(const at::Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const c10::optional & window={}, bool center=true, bool normalized=false, c10::optional onesided=c10::nullopt, c10::optional length=c10::nullopt, bool return_complex=false) { + return at::_ops::istft::call(self, n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logaddexp2_meta.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logaddexp2_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..8b179792fbe945e165f982a4efc0d29db988f41b --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logaddexp2_meta.h @@ -0,0 +1,27 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_logaddexp2 : public TensorIteratorBase { + + + void meta(const at::Tensor & self, const at::Tensor & other); +}; + +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logit_backward.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logit_backward.h new file mode 100644 index 0000000000000000000000000000000000000000..73b2093682d30e1a09ec58993423866c7e9da272 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/logit_backward.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & logit_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_backward_grad_input::call(grad_output, self, eps, grad_input); +} +// aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & logit_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps, at::Tensor & grad_input) { + return at::_ops::logit_backward_grad_input::call(grad_output, self, eps, grad_input); +} + +// aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor +inline at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_backward::call(grad_output, self, eps); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/lu_solve.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/lu_solve.h new file mode 100644 index 0000000000000000000000000000000000000000..b1c5b1ea2c5fec46bb751acac52beb86db93c7d8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/lu_solve.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & lu_solve_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) { + return at::_ops::lu_solve_out::call(self, LU_data, LU_pivots, out); +} +// aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & lu_solve_outf(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out) { + return at::_ops::lu_solve_out::call(self, LU_data, LU_pivots, out); +} + +// aten::lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor +inline at::Tensor lu_solve(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) { + return at::_ops::lu_solve::call(self, LU_data, LU_pivots); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/maximum_compositeexplicitautogradnonfunctional_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/maximum_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..bc26d25fb1ba658adf0ad9aad3d5c2f5a0c1f349 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/maximum_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor maximum(const at::Tensor & self, const at::Tensor & other); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/miopen_convolution_cuda_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/miopen_convolution_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..e5e1750c6a056276c94e8918071df61e39b07731 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/miopen_convolution_cuda_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor miopen_convolution(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic); +TORCH_API at::Tensor miopen_convolution_symint(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic); + +} // namespace cuda +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mish_compositeexplicitautogradnonfunctional_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mish_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..cce89804eaebd1d8307c02ea6f4caa2d2651d47b --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mish_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor mish(const at::Tensor & self); +TORCH_API at::Tensor & mish_(at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_max_pool2d_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_max_pool2d_native.h new file mode 100644 index 0000000000000000000000000000000000000000..cb61999b69974a3a0b4bde958da544af84d851a2 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_max_pool2d_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor & mkldnn_max_pool2d_out(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out); +TORCH_API at::Tensor mkldnn_max_pool2d(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_native.h new file mode 100644 index 0000000000000000000000000000000000000000..51c913e158a8a772e3b70738364eae52f7f1eb22 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API ::std::tuple mps_convolution_backward_out_symint(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/numpy_T_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/numpy_T_native.h new file mode 100644 index 0000000000000000000000000000000000000000..be123b40e0873f77378f8e545ecffc2671729137 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/numpy_T_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor numpy_T(const at::Tensor & self); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/one_hot_native.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/one_hot_native.h new file mode 100644 index 0000000000000000000000000000000000000000..f26843c9183189eb2966f6722df11fa8b0c34617 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/one_hot_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor one_hot(const at::Tensor & self, int64_t num_classes=-1); +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/reflection_pad1d_compositeexplicitautogradnonfunctional_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/reflection_pad1d_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..b79f15be13172a8f59ddd505115fa1cafdf49339 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/reflection_pad1d_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor reflection_pad1d(const at::Tensor & self, at::IntArrayRef padding); +TORCH_API at::Tensor reflection_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..de8337e6364af2fc66c1834def206fecc3da2690 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_cpu_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor replication_pad3d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding); +TORCH_API at::Tensor replication_pad3d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); +TORCH_API at::Tensor & replication_pad3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding); +TORCH_API at::Tensor & replication_pad3d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input); +TORCH_API at::Tensor & replication_pad3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); +TORCH_API at::Tensor & replication_pad3d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/select_backward_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/select_backward_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..bd04dbd616c6f10b298381d63952ec87f7a96499 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/select_backward_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API select_backward { + using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, int64_t, c10::SymInt); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::select_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor") + static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index); +}; + +struct TORCH_API select_backward_out { + using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, int64_t, c10::SymInt, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::select_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_transpose3d_cuda_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_transpose3d_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d0d8b37d13dc942fbce7fe766857ca4d0be99d13 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/slow_conv_transpose3d_cuda_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor slow_conv_transpose3d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1); +TORCH_API at::Tensor slow_conv_transpose3d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)); +TORCH_API at::Tensor & slow_conv_transpose3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1); +TORCH_API at::Tensor & slow_conv_transpose3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out); +TORCH_API at::Tensor & slow_conv_transpose3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)); +TORCH_API at::Tensor & slow_conv_transpose3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out); + +} // namespace cuda +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_chebyshev_polynomial_t_compositeexplicitautograd_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_chebyshev_polynomial_t_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..446a4de530b1c65fabcc5f03c88dcbbbfbcfb387 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_chebyshev_polynomial_t_compositeexplicitautograd_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor special_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); +TORCH_API at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..37eb5d53d7c05ed26a683676d6fe6aa4be741937 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self); +TORCH_API at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..72d601723e994852ecb2c272013519ae272b1ff5 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace meta { + +TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self); +TORCH_API at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out); + +} // namespace meta +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_modified_bessel_k1_meta.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_modified_bessel_k1_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..c630d89f26cb41477f7938b49269e4469b9bc93a --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_modified_bessel_k1_meta.h @@ -0,0 +1,27 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_special_modified_bessel_k1 : public TensorIteratorBase { + + + void meta(const at::Tensor & self); +}; + +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..8603be519b88c663c83bd4fb7f8de5edf1dd940e --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h @@ -0,0 +1,27 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_special_shifted_chebyshev_polynomial_w : public TensorIteratorBase { + + + void meta(const at::Tensor & x, const at::Tensor & n); +}; + +} // namespace native +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..862c8ee75d249bbdd7f511b190cb07a244feb207 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other); +TORCH_API at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/square.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/square.h new file mode 100644 index 0000000000000000000000000000000000000000..86c213c28b9040ba722a27b8c42b1514da3534cb --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/square.h @@ -0,0 +1,44 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::square(Tensor self) -> Tensor +inline at::Tensor square(const at::Tensor & self) { + return at::_ops::square::call(self); +} + +// aten::square_(Tensor(a!) self) -> Tensor(a!) +inline at::Tensor & square_(at::Tensor & self) { + return at::_ops::square_::call(self); +} + +// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & square_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::square_out::call(self, out); +} +// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & square_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::square_out::call(self, out); +} + +} diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..222cbb467d5c5f2772afed9916c0a9bfd55ec994 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace meta { + +TORCH_API at::Tensor tanh(const at::Tensor & self); +TORCH_API at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor & tanh_(at::Tensor & self); + +} // namespace meta +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..72e37ccd2b5ca05e96d857585f13b3789e4be55f --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value); +TORCH_API at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value); +TORCH_API at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out); +TORCH_API at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..c219291a8b858306cf4c5a128d0c3d685a2e47f4 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API to_dense_backward { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::optional); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::to_dense_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor") + static at::Tensor call(const at::Tensor & grad, const at::Tensor & input, c10::optional masked_grad); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, c10::optional masked_grad); +}; + +}} // namespace at::_ops diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..2e63c568895f557ebdbc349c036af244251e4ddf --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt); +TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt); +TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt); +TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales, at::Tensor & out); +TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt); +TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..c45b2ab1b28fd595f4fc1740bd94a29aac78e7e8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt); +TORCH_API at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt); +TORCH_API at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt); +TORCH_API at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input); +TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt); +TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input); + +} // namespace cpu +} // namespace at diff --git a/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/values_copy.h b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/values_copy.h new file mode 100644 index 0000000000000000000000000000000000000000..ad661701478f61b4337b0f9163170d06c05269a8 --- /dev/null +++ b/evalkit_internvl/lib/python3.10/site-packages/torch/include/ATen/ops/values_copy.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::values_copy(Tensor self) -> Tensor +inline at::Tensor values_copy(const at::Tensor & self) { + return at::_ops::values_copy::call(self); +} + +// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & values_copy_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::values_copy_out::call(self, out); +} +// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & values_copy_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::values_copy_out::call(self, out); +} + +} diff --git a/evalkit_tf437/lib/python3.10/site-packages/fsspec-2024.2.0.dist-info/LICENSE b/evalkit_tf437/lib/python3.10/site-packages/fsspec-2024.2.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..67590a5e5be5a5a2dde3fe53a7512e404a896c22 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/fsspec-2024.2.0.dist-info/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2018, Martin Durant +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/evalkit_tf437/lib/python3.10/site-packages/fsspec-2024.2.0.dist-info/REQUESTED b/evalkit_tf437/lib/python3.10/site-packages/fsspec-2024.2.0.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/__init__.py b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8c3066b2e68f1883e46f696491daad967ba606bf --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/__init__.py @@ -0,0 +1 @@ +# This file is necessary to make this directory a package. diff --git a/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/decoder.py b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..7e69ca1567857d26db2e1e2512822827630fcd5e --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/decoder.py @@ -0,0 +1,2189 @@ +# +# This file is part of pyasn1 software. +# +# Copyright (c) 2005-2020, Ilya Etingof +# License: https://pyasn1.readthedocs.io/en/latest/license.html +# +import io +import os +import sys +import warnings + +from pyasn1 import debug +from pyasn1 import error +from pyasn1.codec.ber import eoo +from pyasn1.codec.streaming import asSeekableStream +from pyasn1.codec.streaming import isEndOfStream +from pyasn1.codec.streaming import peekIntoStream +from pyasn1.codec.streaming import readFromStream +from pyasn1.compat import _MISSING +from pyasn1.error import PyAsn1Error +from pyasn1.type import base +from pyasn1.type import char +from pyasn1.type import tag +from pyasn1.type import tagmap +from pyasn1.type import univ +from pyasn1.type import useful + +__all__ = ['StreamingDecoder', 'Decoder', 'decode'] + +LOG = debug.registerLoggee(__name__, flags=debug.DEBUG_DECODER) + +noValue = base.noValue + +SubstrateUnderrunError = error.SubstrateUnderrunError + + +class AbstractPayloadDecoder(object): + protoComponent = None + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + """Decode value with fixed byte length. + + The decoder is allowed to consume as many bytes as necessary. + """ + raise error.PyAsn1Error('SingleItemDecoder not implemented for %s' % (tagSet,)) # TODO: Seems more like an NotImplementedError? + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + """Decode value with undefined length. + + The decoder is allowed to consume as many bytes as necessary. + """ + raise error.PyAsn1Error('Indefinite length mode decoder not implemented for %s' % (tagSet,)) # TODO: Seems more like an NotImplementedError? + + @staticmethod + def _passAsn1Object(asn1Object, options): + if 'asn1Object' not in options: + options['asn1Object'] = asn1Object + + return options + + +class AbstractSimplePayloadDecoder(AbstractPayloadDecoder): + @staticmethod + def substrateCollector(asn1Object, substrate, length, options): + for chunk in readFromStream(substrate, length, options): + yield chunk + + def _createComponent(self, asn1Spec, tagSet, value, **options): + if options.get('native'): + return value + elif asn1Spec is None: + return self.protoComponent.clone(value, tagSet=tagSet) + elif value is noValue: + return asn1Spec + else: + return asn1Spec.clone(value) + + +class RawPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.Any('') + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if substrateFun: + asn1Object = self._createComponent(asn1Spec, tagSet, '', **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + for value in decodeFun(substrate, asn1Spec, tagSet, length, **options): + yield value + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if substrateFun: + asn1Object = self._createComponent(asn1Spec, tagSet, '', **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + while True: + for value in decodeFun( + substrate, asn1Spec, tagSet, length, + allowEoo=True, **options): + + if value is eoo.endOfOctets: + return + + yield value + + +rawPayloadDecoder = RawPayloadDecoder() + + +class IntegerPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.Integer(0) + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + + if tagSet[0].tagFormat != tag.tagFormatSimple: + raise error.PyAsn1Error('Simple tag format expected') + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if chunk: + value = int.from_bytes(bytes(chunk), 'big', signed=True) + + else: + value = 0 + + yield self._createComponent(asn1Spec, tagSet, value, **options) + + +class BooleanPayloadDecoder(IntegerPayloadDecoder): + protoComponent = univ.Boolean(0) + + def _createComponent(self, asn1Spec, tagSet, value, **options): + return IntegerPayloadDecoder._createComponent( + self, asn1Spec, tagSet, value and 1 or 0, **options) + + +class BitStringPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.BitString(()) + supportConstructedForm = True + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + + if substrateFun: + asn1Object = self._createComponent(asn1Spec, tagSet, noValue, **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + if not length: + raise error.PyAsn1Error('Empty BIT STRING substrate') + + for chunk in isEndOfStream(substrate): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if chunk: + raise error.PyAsn1Error('Empty BIT STRING substrate') + + if tagSet[0].tagFormat == tag.tagFormatSimple: # XXX what tag to check? + + for trailingBits in readFromStream(substrate, 1, options): + if isinstance(trailingBits, SubstrateUnderrunError): + yield trailingBits + + trailingBits = ord(trailingBits) + if trailingBits > 7: + raise error.PyAsn1Error( + 'Trailing bits overflow %s' % trailingBits + ) + + for chunk in readFromStream(substrate, length - 1, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + value = self.protoComponent.fromOctetString( + chunk, internalFormat=True, padding=trailingBits) + + yield self._createComponent(asn1Spec, tagSet, value, **options) + + return + + if not self.supportConstructedForm: + raise error.PyAsn1Error('Constructed encoding form prohibited ' + 'at %s' % self.__class__.__name__) + + if LOG: + LOG('assembling constructed serialization') + + # All inner fragments are of the same type, treat them as octet string + substrateFun = self.substrateCollector + + bitString = self.protoComponent.fromOctetString(b'', internalFormat=True) + + current_position = substrate.tell() + + while substrate.tell() - current_position < length: + for component in decodeFun( + substrate, self.protoComponent, substrateFun=substrateFun, + **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + trailingBits = component[0] + if trailingBits > 7: + raise error.PyAsn1Error( + 'Trailing bits overflow %s' % trailingBits + ) + + bitString = self.protoComponent.fromOctetString( + component[1:], internalFormat=True, + prepend=bitString, padding=trailingBits + ) + + yield self._createComponent(asn1Spec, tagSet, bitString, **options) + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + + if substrateFun: + asn1Object = self._createComponent(asn1Spec, tagSet, noValue, **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + # All inner fragments are of the same type, treat them as octet string + substrateFun = self.substrateCollector + + bitString = self.protoComponent.fromOctetString(b'', internalFormat=True) + + while True: # loop over fragments + + for component in decodeFun( + substrate, self.protoComponent, substrateFun=substrateFun, + allowEoo=True, **options): + + if component is eoo.endOfOctets: + break + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + trailingBits = component[0] + if trailingBits > 7: + raise error.PyAsn1Error( + 'Trailing bits overflow %s' % trailingBits + ) + + bitString = self.protoComponent.fromOctetString( + component[1:], internalFormat=True, + prepend=bitString, padding=trailingBits + ) + + yield self._createComponent(asn1Spec, tagSet, bitString, **options) + + +class OctetStringPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.OctetString('') + supportConstructedForm = True + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if substrateFun: + asn1Object = self._createComponent(asn1Spec, tagSet, noValue, **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + if tagSet[0].tagFormat == tag.tagFormatSimple: # XXX what tag to check? + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + yield self._createComponent(asn1Spec, tagSet, chunk, **options) + + return + + if not self.supportConstructedForm: + raise error.PyAsn1Error('Constructed encoding form prohibited at %s' % self.__class__.__name__) + + if LOG: + LOG('assembling constructed serialization') + + # All inner fragments are of the same type, treat them as octet string + substrateFun = self.substrateCollector + + header = b'' + + original_position = substrate.tell() + # head = popSubstream(substrate, length) + while substrate.tell() - original_position < length: + for component in decodeFun( + substrate, self.protoComponent, substrateFun=substrateFun, + **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + header += component + + yield self._createComponent(asn1Spec, tagSet, header, **options) + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if substrateFun and substrateFun is not self.substrateCollector: + asn1Object = self._createComponent(asn1Spec, tagSet, noValue, **options) + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + # All inner fragments are of the same type, treat them as octet string + substrateFun = self.substrateCollector + + header = b'' + + while True: # loop over fragments + + for component in decodeFun( + substrate, self.protoComponent, substrateFun=substrateFun, + allowEoo=True, **options): + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + if component is eoo.endOfOctets: + break + + header += component + + yield self._createComponent(asn1Spec, tagSet, header, **options) + + +class NullPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.Null('') + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + + if tagSet[0].tagFormat != tag.tagFormatSimple: + raise error.PyAsn1Error('Simple tag format expected') + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + component = self._createComponent(asn1Spec, tagSet, '', **options) + + if chunk: + raise error.PyAsn1Error('Unexpected %d-octet substrate for Null' % length) + + yield component + + +class ObjectIdentifierPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.ObjectIdentifier(()) + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if tagSet[0].tagFormat != tag.tagFormatSimple: + raise error.PyAsn1Error('Simple tag format expected') + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if not chunk: + raise error.PyAsn1Error('Empty substrate') + + oid = () + index = 0 + substrateLen = len(chunk) + while index < substrateLen: + subId = chunk[index] + index += 1 + if subId < 128: + oid += (subId,) + elif subId > 128: + # Construct subid from a number of octets + nextSubId = subId + subId = 0 + while nextSubId >= 128: + subId = (subId << 7) + (nextSubId & 0x7F) + if index >= substrateLen: + raise error.SubstrateUnderrunError( + 'Short substrate for sub-OID past %s' % (oid,) + ) + nextSubId = chunk[index] + index += 1 + oid += ((subId << 7) + nextSubId,) + elif subId == 128: + # ASN.1 spec forbids leading zeros (0x80) in OID + # encoding, tolerating it opens a vulnerability. See + # https://www.esat.kuleuven.be/cosic/publications/article-1432.pdf + # page 7 + raise error.PyAsn1Error('Invalid octet 0x80 in OID encoding') + + # Decode two leading arcs + if 0 <= oid[0] <= 39: + oid = (0,) + oid + elif 40 <= oid[0] <= 79: + oid = (1, oid[0] - 40) + oid[1:] + elif oid[0] >= 80: + oid = (2, oid[0] - 80) + oid[1:] + else: + raise error.PyAsn1Error('Malformed first OID octet: %s' % chunk[0]) + + yield self._createComponent(asn1Spec, tagSet, oid, **options) + + +class RelativeOIDPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.RelativeOID(()) + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if tagSet[0].tagFormat != tag.tagFormatSimple: + raise error.PyAsn1Error('Simple tag format expected') + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if not chunk: + raise error.PyAsn1Error('Empty substrate') + + reloid = () + index = 0 + substrateLen = len(chunk) + while index < substrateLen: + subId = chunk[index] + index += 1 + if subId < 128: + reloid += (subId,) + elif subId > 128: + # Construct subid from a number of octets + nextSubId = subId + subId = 0 + while nextSubId >= 128: + subId = (subId << 7) + (nextSubId & 0x7F) + if index >= substrateLen: + raise error.SubstrateUnderrunError( + 'Short substrate for sub-OID past %s' % (reloid,) + ) + nextSubId = chunk[index] + index += 1 + reloid += ((subId << 7) + nextSubId,) + elif subId == 128: + # ASN.1 spec forbids leading zeros (0x80) in OID + # encoding, tolerating it opens a vulnerability. See + # https://www.esat.kuleuven.be/cosic/publications/article-1432.pdf + # page 7 + raise error.PyAsn1Error('Invalid octet 0x80 in RELATIVE-OID encoding') + + yield self._createComponent(asn1Spec, tagSet, reloid, **options) + + +class RealPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.Real() + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if tagSet[0].tagFormat != tag.tagFormatSimple: + raise error.PyAsn1Error('Simple tag format expected') + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if not chunk: + yield self._createComponent(asn1Spec, tagSet, 0.0, **options) + return + + fo = chunk[0] + chunk = chunk[1:] + if fo & 0x80: # binary encoding + if not chunk: + raise error.PyAsn1Error("Incomplete floating-point value") + + if LOG: + LOG('decoding binary encoded REAL') + + n = (fo & 0x03) + 1 + + if n == 4: + n = chunk[0] + chunk = chunk[1:] + + eo, chunk = chunk[:n], chunk[n:] + + if not eo or not chunk: + raise error.PyAsn1Error('Real exponent screwed') + + e = eo[0] & 0x80 and -1 or 0 + + while eo: # exponent + e <<= 8 + e |= eo[0] + eo = eo[1:] + + b = fo >> 4 & 0x03 # base bits + + if b > 2: + raise error.PyAsn1Error('Illegal Real base') + + if b == 1: # encbase = 8 + e *= 3 + + elif b == 2: # encbase = 16 + e *= 4 + p = 0 + + while chunk: # value + p <<= 8 + p |= chunk[0] + chunk = chunk[1:] + + if fo & 0x40: # sign bit + p = -p + + sf = fo >> 2 & 0x03 # scale bits + p *= 2 ** sf + value = (p, 2, e) + + elif fo & 0x40: # infinite value + if LOG: + LOG('decoding infinite REAL') + + value = fo & 0x01 and '-inf' or 'inf' + + elif fo & 0xc0 == 0: # character encoding + if not chunk: + raise error.PyAsn1Error("Incomplete floating-point value") + + if LOG: + LOG('decoding character encoded REAL') + + try: + if fo & 0x3 == 0x1: # NR1 + value = (int(chunk), 10, 0) + + elif fo & 0x3 == 0x2: # NR2 + value = float(chunk) + + elif fo & 0x3 == 0x3: # NR3 + value = float(chunk) + + else: + raise error.SubstrateUnderrunError( + 'Unknown NR (tag %s)' % fo + ) + + except ValueError: + raise error.SubstrateUnderrunError( + 'Bad character Real syntax' + ) + + else: + raise error.SubstrateUnderrunError( + 'Unknown encoding (tag %s)' % fo + ) + + yield self._createComponent(asn1Spec, tagSet, value, **options) + + +class AbstractConstructedPayloadDecoder(AbstractPayloadDecoder): + protoComponent = None + + +class ConstructedPayloadDecoderBase(AbstractConstructedPayloadDecoder): + protoRecordComponent = None + protoSequenceComponent = None + + def _getComponentTagMap(self, asn1Object, idx): + raise NotImplementedError + + def _getComponentPositionByType(self, asn1Object, tagSet, idx): + raise NotImplementedError + + def _decodeComponentsSchemaless( + self, substrate, tagSet=None, decodeFun=None, + length=None, **options): + + asn1Object = None + + components = [] + componentTypes = set() + + original_position = substrate.tell() + + while length == -1 or substrate.tell() < original_position + length: + for component in decodeFun(substrate, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + if length == -1 and component is eoo.endOfOctets: + break + + components.append(component) + componentTypes.add(component.tagSet) + + # Now we have to guess is it SEQUENCE/SET or SEQUENCE OF/SET OF + # The heuristics is: + # * 1+ components of different types -> likely SEQUENCE/SET + # * otherwise -> likely SEQUENCE OF/SET OF + if len(componentTypes) > 1: + protoComponent = self.protoRecordComponent + + else: + protoComponent = self.protoSequenceComponent + + asn1Object = protoComponent.clone( + # construct tagSet from base tag from prototype ASN.1 object + # and additional tags recovered from the substrate + tagSet=tag.TagSet(protoComponent.tagSet.baseTag, *tagSet.superTags) + ) + + if LOG: + LOG('guessed %r container type (pass `asn1Spec` to guide the ' + 'decoder)' % asn1Object) + + for idx, component in enumerate(components): + asn1Object.setComponentByPosition( + idx, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False + ) + + yield asn1Object + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if tagSet[0].tagFormat != tag.tagFormatConstructed: + raise error.PyAsn1Error('Constructed tag format expected') + + original_position = substrate.tell() + + if substrateFun: + if asn1Spec is not None: + asn1Object = asn1Spec.clone() + + elif self.protoComponent is not None: + asn1Object = self.protoComponent.clone(tagSet=tagSet) + + else: + asn1Object = self.protoRecordComponent, self.protoSequenceComponent + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + if asn1Spec is None: + for asn1Object in self._decodeComponentsSchemaless( + substrate, tagSet=tagSet, decodeFun=decodeFun, + length=length, **options): + if isinstance(asn1Object, SubstrateUnderrunError): + yield asn1Object + + if substrate.tell() < original_position + length: + if LOG: + for trailing in readFromStream(substrate, context=options): + if isinstance(trailing, SubstrateUnderrunError): + yield trailing + + LOG('Unused trailing %d octets encountered: %s' % ( + len(trailing), debug.hexdump(trailing))) + + yield asn1Object + + return + + asn1Object = asn1Spec.clone() + asn1Object.clear() + + options = self._passAsn1Object(asn1Object, options) + + if asn1Spec.typeId in (univ.Sequence.typeId, univ.Set.typeId): + + namedTypes = asn1Spec.componentType + + isSetType = asn1Spec.typeId == univ.Set.typeId + isDeterministic = not isSetType and not namedTypes.hasOptionalOrDefault + + if LOG: + LOG('decoding %sdeterministic %s type %r chosen by type ID' % ( + not isDeterministic and 'non-' or '', isSetType and 'SET' or '', + asn1Spec)) + + seenIndices = set() + idx = 0 + while substrate.tell() - original_position < length: + if not namedTypes: + componentType = None + + elif isSetType: + componentType = namedTypes.tagMapUnique + + else: + try: + if isDeterministic: + componentType = namedTypes[idx].asn1Object + + elif namedTypes[idx].isOptional or namedTypes[idx].isDefaulted: + componentType = namedTypes.getTagMapNearPosition(idx) + + else: + componentType = namedTypes[idx].asn1Object + + except IndexError: + raise error.PyAsn1Error( + 'Excessive components decoded at %r' % (asn1Spec,) + ) + + for component in decodeFun(substrate, componentType, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + if not isDeterministic and namedTypes: + if isSetType: + idx = namedTypes.getPositionByType(component.effectiveTagSet) + + elif namedTypes[idx].isOptional or namedTypes[idx].isDefaulted: + idx = namedTypes.getPositionNearType(component.effectiveTagSet, idx) + + asn1Object.setComponentByPosition( + idx, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False + ) + + seenIndices.add(idx) + idx += 1 + + if LOG: + LOG('seen component indices %s' % seenIndices) + + if namedTypes: + if not namedTypes.requiredComponents.issubset(seenIndices): + raise error.PyAsn1Error( + 'ASN.1 object %s has uninitialized ' + 'components' % asn1Object.__class__.__name__) + + if namedTypes.hasOpenTypes: + + openTypes = options.get('openTypes', {}) + + if LOG: + LOG('user-specified open types map:') + + for k, v in openTypes.items(): + LOG('%s -> %r' % (k, v)) + + if openTypes or options.get('decodeOpenTypes', False): + + for idx, namedType in enumerate(namedTypes.namedTypes): + if not namedType.openType: + continue + + if namedType.isOptional and not asn1Object.getComponentByPosition(idx).isValue: + continue + + governingValue = asn1Object.getComponentByName( + namedType.openType.name + ) + + try: + openType = openTypes[governingValue] + + except KeyError: + + if LOG: + LOG('default open types map of component ' + '"%s.%s" governed by component "%s.%s"' + ':' % (asn1Object.__class__.__name__, + namedType.name, + asn1Object.__class__.__name__, + namedType.openType.name)) + + for k, v in namedType.openType.items(): + LOG('%s -> %r' % (k, v)) + + try: + openType = namedType.openType[governingValue] + + except KeyError: + if LOG: + LOG('failed to resolve open type by governing ' + 'value %r' % (governingValue,)) + continue + + if LOG: + LOG('resolved open type %r by governing ' + 'value %r' % (openType, governingValue)) + + containerValue = asn1Object.getComponentByPosition(idx) + + if containerValue.typeId in ( + univ.SetOf.typeId, univ.SequenceOf.typeId): + + for pos, containerElement in enumerate( + containerValue): + + stream = asSeekableStream(containerValue[pos].asOctets()) + + for component in decodeFun(stream, asn1Spec=openType, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + containerValue[pos] = component + + else: + stream = asSeekableStream(asn1Object.getComponentByPosition(idx).asOctets()) + + for component in decodeFun(stream, asn1Spec=openType, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + asn1Object.setComponentByPosition(idx, component) + + else: + inconsistency = asn1Object.isInconsistent + if inconsistency: + raise error.PyAsn1Error( + f"ASN.1 object {asn1Object.__class__.__name__} is inconsistent") + + else: + componentType = asn1Spec.componentType + + if LOG: + LOG('decoding type %r chosen by given `asn1Spec`' % componentType) + + idx = 0 + + while substrate.tell() - original_position < length: + for component in decodeFun(substrate, componentType, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + asn1Object.setComponentByPosition( + idx, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False + ) + + idx += 1 + + yield asn1Object + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if tagSet[0].tagFormat != tag.tagFormatConstructed: + raise error.PyAsn1Error('Constructed tag format expected') + + if substrateFun is not None: + if asn1Spec is not None: + asn1Object = asn1Spec.clone() + + elif self.protoComponent is not None: + asn1Object = self.protoComponent.clone(tagSet=tagSet) + + else: + asn1Object = self.protoRecordComponent, self.protoSequenceComponent + + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + if asn1Spec is None: + for asn1Object in self._decodeComponentsSchemaless( + substrate, tagSet=tagSet, decodeFun=decodeFun, + length=length, **dict(options, allowEoo=True)): + if isinstance(asn1Object, SubstrateUnderrunError): + yield asn1Object + + yield asn1Object + + return + + asn1Object = asn1Spec.clone() + asn1Object.clear() + + options = self._passAsn1Object(asn1Object, options) + + if asn1Spec.typeId in (univ.Sequence.typeId, univ.Set.typeId): + + namedTypes = asn1Object.componentType + + isSetType = asn1Object.typeId == univ.Set.typeId + isDeterministic = not isSetType and not namedTypes.hasOptionalOrDefault + + if LOG: + LOG('decoding %sdeterministic %s type %r chosen by type ID' % ( + not isDeterministic and 'non-' or '', isSetType and 'SET' or '', + asn1Spec)) + + seenIndices = set() + + idx = 0 + + while True: # loop over components + if len(namedTypes) <= idx: + asn1Spec = None + + elif isSetType: + asn1Spec = namedTypes.tagMapUnique + + else: + try: + if isDeterministic: + asn1Spec = namedTypes[idx].asn1Object + + elif namedTypes[idx].isOptional or namedTypes[idx].isDefaulted: + asn1Spec = namedTypes.getTagMapNearPosition(idx) + + else: + asn1Spec = namedTypes[idx].asn1Object + + except IndexError: + raise error.PyAsn1Error( + 'Excessive components decoded at %r' % (asn1Object,) + ) + + for component in decodeFun(substrate, asn1Spec, allowEoo=True, **options): + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + if component is eoo.endOfOctets: + break + + if not isDeterministic and namedTypes: + if isSetType: + idx = namedTypes.getPositionByType(component.effectiveTagSet) + + elif namedTypes[idx].isOptional or namedTypes[idx].isDefaulted: + idx = namedTypes.getPositionNearType(component.effectiveTagSet, idx) + + asn1Object.setComponentByPosition( + idx, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False + ) + + seenIndices.add(idx) + idx += 1 + + if LOG: + LOG('seen component indices %s' % seenIndices) + + if namedTypes: + if not namedTypes.requiredComponents.issubset(seenIndices): + raise error.PyAsn1Error( + 'ASN.1 object %s has uninitialized ' + 'components' % asn1Object.__class__.__name__) + + if namedTypes.hasOpenTypes: + + openTypes = options.get('openTypes', {}) + + if LOG: + LOG('user-specified open types map:') + + for k, v in openTypes.items(): + LOG('%s -> %r' % (k, v)) + + if openTypes or options.get('decodeOpenTypes', False): + + for idx, namedType in enumerate(namedTypes.namedTypes): + if not namedType.openType: + continue + + if namedType.isOptional and not asn1Object.getComponentByPosition(idx).isValue: + continue + + governingValue = asn1Object.getComponentByName( + namedType.openType.name + ) + + try: + openType = openTypes[governingValue] + + except KeyError: + + if LOG: + LOG('default open types map of component ' + '"%s.%s" governed by component "%s.%s"' + ':' % (asn1Object.__class__.__name__, + namedType.name, + asn1Object.__class__.__name__, + namedType.openType.name)) + + for k, v in namedType.openType.items(): + LOG('%s -> %r' % (k, v)) + + try: + openType = namedType.openType[governingValue] + + except KeyError: + if LOG: + LOG('failed to resolve open type by governing ' + 'value %r' % (governingValue,)) + continue + + if LOG: + LOG('resolved open type %r by governing ' + 'value %r' % (openType, governingValue)) + + containerValue = asn1Object.getComponentByPosition(idx) + + if containerValue.typeId in ( + univ.SetOf.typeId, univ.SequenceOf.typeId): + + for pos, containerElement in enumerate( + containerValue): + + stream = asSeekableStream(containerValue[pos].asOctets()) + + for component in decodeFun(stream, asn1Spec=openType, + **dict(options, allowEoo=True)): + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + containerValue[pos] = component + + else: + stream = asSeekableStream(asn1Object.getComponentByPosition(idx).asOctets()) + for component in decodeFun(stream, asn1Spec=openType, + **dict(options, allowEoo=True)): + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + asn1Object.setComponentByPosition(idx, component) + + else: + inconsistency = asn1Object.isInconsistent + if inconsistency: + raise error.PyAsn1Error( + f"ASN.1 object {asn1Object.__class__.__name__} is inconsistent") + + else: + componentType = asn1Spec.componentType + + if LOG: + LOG('decoding type %r chosen by given `asn1Spec`' % componentType) + + idx = 0 + + while True: + + for component in decodeFun( + substrate, componentType, allowEoo=True, **options): + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + if component is eoo.endOfOctets: + break + + asn1Object.setComponentByPosition( + idx, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False + ) + + idx += 1 + + yield asn1Object + + +class SequenceOrSequenceOfPayloadDecoder(ConstructedPayloadDecoderBase): + protoRecordComponent = univ.Sequence() + protoSequenceComponent = univ.SequenceOf() + + +class SequencePayloadDecoder(SequenceOrSequenceOfPayloadDecoder): + protoComponent = univ.Sequence() + + +class SequenceOfPayloadDecoder(SequenceOrSequenceOfPayloadDecoder): + protoComponent = univ.SequenceOf() + + +class SetOrSetOfPayloadDecoder(ConstructedPayloadDecoderBase): + protoRecordComponent = univ.Set() + protoSequenceComponent = univ.SetOf() + + +class SetPayloadDecoder(SetOrSetOfPayloadDecoder): + protoComponent = univ.Set() + + +class SetOfPayloadDecoder(SetOrSetOfPayloadDecoder): + protoComponent = univ.SetOf() + + +class ChoicePayloadDecoder(ConstructedPayloadDecoderBase): + protoComponent = univ.Choice() + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if asn1Spec is None: + asn1Object = self.protoComponent.clone(tagSet=tagSet) + + else: + asn1Object = asn1Spec.clone() + + if substrateFun: + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + options = self._passAsn1Object(asn1Object, options) + + if asn1Object.tagSet == tagSet: + if LOG: + LOG('decoding %s as explicitly tagged CHOICE' % (tagSet,)) + + for component in decodeFun( + substrate, asn1Object.componentTagMap, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + else: + if LOG: + LOG('decoding %s as untagged CHOICE' % (tagSet,)) + + for component in decodeFun( + substrate, asn1Object.componentTagMap, tagSet, length, + state, **options): + if isinstance(component, SubstrateUnderrunError): + yield component + + effectiveTagSet = component.effectiveTagSet + + if LOG: + LOG('decoded component %s, effective tag set %s' % (component, effectiveTagSet)) + + asn1Object.setComponentByType( + effectiveTagSet, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False, + innerFlag=False + ) + + yield asn1Object + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if asn1Spec is None: + asn1Object = self.protoComponent.clone(tagSet=tagSet) + + else: + asn1Object = asn1Spec.clone() + + if substrateFun: + for chunk in substrateFun(asn1Object, substrate, length, options): + yield chunk + + return + + options = self._passAsn1Object(asn1Object, options) + + isTagged = asn1Object.tagSet == tagSet + + if LOG: + LOG('decoding %s as %stagged CHOICE' % ( + tagSet, isTagged and 'explicitly ' or 'un')) + + while True: + + if isTagged: + iterator = decodeFun( + substrate, asn1Object.componentType.tagMapUnique, + **dict(options, allowEoo=True)) + + else: + iterator = decodeFun( + substrate, asn1Object.componentType.tagMapUnique, + tagSet, length, state, **dict(options, allowEoo=True)) + + for component in iterator: + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + effectiveTagSet = component.effectiveTagSet + + if LOG: + LOG('decoded component %s, effective tag set ' + '%s' % (component, effectiveTagSet)) + + asn1Object.setComponentByType( + effectiveTagSet, component, + verifyConstraints=False, + matchTags=False, matchConstraints=False, + innerFlag=False + ) + + if not isTagged: + break + + if not isTagged or component is eoo.endOfOctets: + break + + yield asn1Object + + +class AnyPayloadDecoder(AbstractSimplePayloadDecoder): + protoComponent = univ.Any() + + def valueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if asn1Spec is None: + isUntagged = True + + elif asn1Spec.__class__ is tagmap.TagMap: + isUntagged = tagSet not in asn1Spec.tagMap + + else: + isUntagged = tagSet != asn1Spec.tagSet + + if isUntagged: + fullPosition = substrate.markedPosition + currentPosition = substrate.tell() + + substrate.seek(fullPosition, os.SEEK_SET) + length += currentPosition - fullPosition + + if LOG: + for chunk in peekIntoStream(substrate, length): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + LOG('decoding as untagged ANY, substrate ' + '%s' % debug.hexdump(chunk)) + + if substrateFun: + for chunk in substrateFun( + self._createComponent(asn1Spec, tagSet, noValue, **options), + substrate, length, options): + yield chunk + + return + + for chunk in readFromStream(substrate, length, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + yield self._createComponent(asn1Spec, tagSet, chunk, **options) + + def indefLenValueDecoder(self, substrate, asn1Spec, + tagSet=None, length=None, state=None, + decodeFun=None, substrateFun=None, + **options): + if asn1Spec is None: + isTagged = False + + elif asn1Spec.__class__ is tagmap.TagMap: + isTagged = tagSet in asn1Spec.tagMap + + else: + isTagged = tagSet == asn1Spec.tagSet + + if isTagged: + # tagged Any type -- consume header substrate + chunk = b'' + + if LOG: + LOG('decoding as tagged ANY') + + else: + # TODO: Seems not to be tested + fullPosition = substrate.markedPosition + currentPosition = substrate.tell() + + substrate.seek(fullPosition, os.SEEK_SET) + for chunk in readFromStream(substrate, currentPosition - fullPosition, options): + if isinstance(chunk, SubstrateUnderrunError): + yield chunk + + if LOG: + LOG('decoding as untagged ANY, header substrate %s' % debug.hexdump(chunk)) + + # Any components do not inherit initial tag + asn1Spec = self.protoComponent + + if substrateFun and substrateFun is not self.substrateCollector: + asn1Object = self._createComponent( + asn1Spec, tagSet, noValue, **options) + + for chunk in substrateFun( + asn1Object, chunk + substrate, length + len(chunk), options): + yield chunk + + return + + if LOG: + LOG('assembling constructed serialization') + + # All inner fragments are of the same type, treat them as octet string + substrateFun = self.substrateCollector + + while True: # loop over fragments + + for component in decodeFun( + substrate, asn1Spec, substrateFun=substrateFun, + allowEoo=True, **options): + + if isinstance(component, SubstrateUnderrunError): + yield component + + if component is eoo.endOfOctets: + break + + if component is eoo.endOfOctets: + break + + chunk += component + + if substrateFun: + yield chunk # TODO: Weird + + else: + yield self._createComponent(asn1Spec, tagSet, chunk, **options) + + +# character string types +class UTF8StringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.UTF8String() + + +class NumericStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.NumericString() + + +class PrintableStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.PrintableString() + + +class TeletexStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.TeletexString() + + +class VideotexStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.VideotexString() + + +class IA5StringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.IA5String() + + +class GraphicStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.GraphicString() + + +class VisibleStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.VisibleString() + + +class GeneralStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.GeneralString() + + +class UniversalStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.UniversalString() + + +class BMPStringPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = char.BMPString() + + +# "useful" types +class ObjectDescriptorPayloadDecoder(OctetStringPayloadDecoder): + protoComponent = useful.ObjectDescriptor() + + +class GeneralizedTimePayloadDecoder(OctetStringPayloadDecoder): + protoComponent = useful.GeneralizedTime() + + +class UTCTimePayloadDecoder(OctetStringPayloadDecoder): + protoComponent = useful.UTCTime() + + +TAG_MAP = { + univ.Integer.tagSet: IntegerPayloadDecoder(), + univ.Boolean.tagSet: BooleanPayloadDecoder(), + univ.BitString.tagSet: BitStringPayloadDecoder(), + univ.OctetString.tagSet: OctetStringPayloadDecoder(), + univ.Null.tagSet: NullPayloadDecoder(), + univ.ObjectIdentifier.tagSet: ObjectIdentifierPayloadDecoder(), + univ.RelativeOID.tagSet: RelativeOIDPayloadDecoder(), + univ.Enumerated.tagSet: IntegerPayloadDecoder(), + univ.Real.tagSet: RealPayloadDecoder(), + univ.Sequence.tagSet: SequenceOrSequenceOfPayloadDecoder(), # conflicts with SequenceOf + univ.Set.tagSet: SetOrSetOfPayloadDecoder(), # conflicts with SetOf + univ.Choice.tagSet: ChoicePayloadDecoder(), # conflicts with Any + # character string types + char.UTF8String.tagSet: UTF8StringPayloadDecoder(), + char.NumericString.tagSet: NumericStringPayloadDecoder(), + char.PrintableString.tagSet: PrintableStringPayloadDecoder(), + char.TeletexString.tagSet: TeletexStringPayloadDecoder(), + char.VideotexString.tagSet: VideotexStringPayloadDecoder(), + char.IA5String.tagSet: IA5StringPayloadDecoder(), + char.GraphicString.tagSet: GraphicStringPayloadDecoder(), + char.VisibleString.tagSet: VisibleStringPayloadDecoder(), + char.GeneralString.tagSet: GeneralStringPayloadDecoder(), + char.UniversalString.tagSet: UniversalStringPayloadDecoder(), + char.BMPString.tagSet: BMPStringPayloadDecoder(), + # useful types + useful.ObjectDescriptor.tagSet: ObjectDescriptorPayloadDecoder(), + useful.GeneralizedTime.tagSet: GeneralizedTimePayloadDecoder(), + useful.UTCTime.tagSet: UTCTimePayloadDecoder() +} + +# Type-to-codec map for ambiguous ASN.1 types +TYPE_MAP = { + univ.Set.typeId: SetPayloadDecoder(), + univ.SetOf.typeId: SetOfPayloadDecoder(), + univ.Sequence.typeId: SequencePayloadDecoder(), + univ.SequenceOf.typeId: SequenceOfPayloadDecoder(), + univ.Choice.typeId: ChoicePayloadDecoder(), + univ.Any.typeId: AnyPayloadDecoder() +} + +# Put in non-ambiguous types for faster codec lookup +for typeDecoder in TAG_MAP.values(): + if typeDecoder.protoComponent is not None: + typeId = typeDecoder.protoComponent.__class__.typeId + if typeId is not None and typeId not in TYPE_MAP: + TYPE_MAP[typeId] = typeDecoder + + +(stDecodeTag, + stDecodeLength, + stGetValueDecoder, + stGetValueDecoderByAsn1Spec, + stGetValueDecoderByTag, + stTryAsExplicitTag, + stDecodeValue, + stDumpRawValue, + stErrorCondition, + stStop) = [x for x in range(10)] + + +EOO_SENTINEL = bytes((0, 0)) + + +class SingleItemDecoder(object): + defaultErrorState = stErrorCondition + #defaultErrorState = stDumpRawValue + defaultRawDecoder = AnyPayloadDecoder() + + supportIndefLength = True + + TAG_MAP = TAG_MAP + TYPE_MAP = TYPE_MAP + + def __init__(self, tagMap=_MISSING, typeMap=_MISSING, **ignored): + self._tagMap = tagMap if tagMap is not _MISSING else self.TAG_MAP + self._typeMap = typeMap if typeMap is not _MISSING else self.TYPE_MAP + + # Tag & TagSet objects caches + self._tagCache = {} + self._tagSetCache = {} + + def __call__(self, substrate, asn1Spec=None, + tagSet=None, length=None, state=stDecodeTag, + decodeFun=None, substrateFun=None, + **options): + + allowEoo = options.pop('allowEoo', False) + + if LOG: + LOG('decoder called at scope %s with state %d, working with up ' + 'to %s octets of substrate: ' + '%s' % (debug.scope, state, length, substrate)) + + # Look for end-of-octets sentinel + if allowEoo and self.supportIndefLength: + + for eoo_candidate in readFromStream(substrate, 2, options): + if isinstance(eoo_candidate, SubstrateUnderrunError): + yield eoo_candidate + + if eoo_candidate == EOO_SENTINEL: + if LOG: + LOG('end-of-octets sentinel found') + yield eoo.endOfOctets + return + + else: + substrate.seek(-2, os.SEEK_CUR) + + tagMap = self._tagMap + typeMap = self._typeMap + tagCache = self._tagCache + tagSetCache = self._tagSetCache + + value = noValue + + substrate.markedPosition = substrate.tell() + + while state is not stStop: + + if state is stDecodeTag: + # Decode tag + isShortTag = True + + for firstByte in readFromStream(substrate, 1, options): + if isinstance(firstByte, SubstrateUnderrunError): + yield firstByte + + firstOctet = ord(firstByte) + + try: + lastTag = tagCache[firstOctet] + + except KeyError: + integerTag = firstOctet + tagClass = integerTag & 0xC0 + tagFormat = integerTag & 0x20 + tagId = integerTag & 0x1F + + if tagId == 0x1F: + isShortTag = False + lengthOctetIdx = 0 + tagId = 0 + + while True: + for integerByte in readFromStream(substrate, 1, options): + if isinstance(integerByte, SubstrateUnderrunError): + yield integerByte + + if not integerByte: + raise error.SubstrateUnderrunError( + 'Short octet stream on long tag decoding' + ) + + integerTag = ord(integerByte) + lengthOctetIdx += 1 + tagId <<= 7 + tagId |= (integerTag & 0x7F) + + if not integerTag & 0x80: + break + + lastTag = tag.Tag( + tagClass=tagClass, tagFormat=tagFormat, tagId=tagId + ) + + if isShortTag: + # cache short tags + tagCache[firstOctet] = lastTag + + if tagSet is None: + if isShortTag: + try: + tagSet = tagSetCache[firstOctet] + + except KeyError: + # base tag not recovered + tagSet = tag.TagSet((), lastTag) + tagSetCache[firstOctet] = tagSet + else: + tagSet = tag.TagSet((), lastTag) + + else: + tagSet = lastTag + tagSet + + state = stDecodeLength + + if LOG: + LOG('tag decoded into %s, decoding length' % tagSet) + + if state is stDecodeLength: + # Decode length + for firstOctet in readFromStream(substrate, 1, options): + if isinstance(firstOctet, SubstrateUnderrunError): + yield firstOctet + + firstOctet = ord(firstOctet) + + if firstOctet < 128: + length = firstOctet + + elif firstOctet > 128: + size = firstOctet & 0x7F + # encoded in size bytes + for encodedLength in readFromStream(substrate, size, options): + if isinstance(encodedLength, SubstrateUnderrunError): + yield encodedLength + encodedLength = list(encodedLength) + # missing check on maximum size, which shouldn't be a + # problem, we can handle more than is possible + if len(encodedLength) != size: + raise error.SubstrateUnderrunError( + '%s<%s at %s' % (size, len(encodedLength), tagSet) + ) + + length = 0 + for lengthOctet in encodedLength: + length <<= 8 + length |= lengthOctet + size += 1 + + else: # 128 means indefinite + length = -1 + + if length == -1 and not self.supportIndefLength: + raise error.PyAsn1Error('Indefinite length encoding not supported by this codec') + + state = stGetValueDecoder + + if LOG: + LOG('value length decoded into %d' % length) + + if state is stGetValueDecoder: + if asn1Spec is None: + state = stGetValueDecoderByTag + + else: + state = stGetValueDecoderByAsn1Spec + # + # There're two ways of creating subtypes in ASN.1 what influences + # decoder operation. These methods are: + # 1) Either base types used in or no IMPLICIT tagging has been + # applied on subtyping. + # 2) Subtype syntax drops base type information (by means of + # IMPLICIT tagging. + # The first case allows for complete tag recovery from substrate + # while the second one requires original ASN.1 type spec for + # decoding. + # + # In either case a set of tags (tagSet) is coming from substrate + # in an incremental, tag-by-tag fashion (this is the case of + # EXPLICIT tag which is most basic). Outermost tag comes first + # from the wire. + # + if state is stGetValueDecoderByTag: + try: + concreteDecoder = tagMap[tagSet] + + except KeyError: + concreteDecoder = None + + if concreteDecoder: + state = stDecodeValue + + else: + try: + concreteDecoder = tagMap[tagSet[:1]] + + except KeyError: + concreteDecoder = None + + if concreteDecoder: + state = stDecodeValue + else: + state = stTryAsExplicitTag + + if LOG: + LOG('codec %s chosen by a built-in type, decoding %s' % (concreteDecoder and concreteDecoder.__class__.__name__ or "", state is stDecodeValue and 'value' or 'as explicit tag')) + debug.scope.push(concreteDecoder is None and '?' or concreteDecoder.protoComponent.__class__.__name__) + + if state is stGetValueDecoderByAsn1Spec: + + if asn1Spec.__class__ is tagmap.TagMap: + try: + chosenSpec = asn1Spec[tagSet] + + except KeyError: + chosenSpec = None + + if LOG: + LOG('candidate ASN.1 spec is a map of:') + + for firstOctet, v in asn1Spec.presentTypes.items(): + LOG(' %s -> %s' % (firstOctet, v.__class__.__name__)) + + if asn1Spec.skipTypes: + LOG('but neither of: ') + for firstOctet, v in asn1Spec.skipTypes.items(): + LOG(' %s -> %s' % (firstOctet, v.__class__.__name__)) + LOG('new candidate ASN.1 spec is %s, chosen by %s' % (chosenSpec is None and '' or chosenSpec.prettyPrintType(), tagSet)) + + elif tagSet == asn1Spec.tagSet or tagSet in asn1Spec.tagMap: + chosenSpec = asn1Spec + if LOG: + LOG('candidate ASN.1 spec is %s' % asn1Spec.__class__.__name__) + + else: + chosenSpec = None + + if chosenSpec is not None: + try: + # ambiguous type or just faster codec lookup + concreteDecoder = typeMap[chosenSpec.typeId] + + if LOG: + LOG('value decoder chosen for an ambiguous type by type ID %s' % (chosenSpec.typeId,)) + + except KeyError: + # use base type for codec lookup to recover untagged types + baseTagSet = tag.TagSet(chosenSpec.tagSet.baseTag, chosenSpec.tagSet.baseTag) + try: + # base type or tagged subtype + concreteDecoder = tagMap[baseTagSet] + + if LOG: + LOG('value decoder chosen by base %s' % (baseTagSet,)) + + except KeyError: + concreteDecoder = None + + if concreteDecoder: + asn1Spec = chosenSpec + state = stDecodeValue + + else: + state = stTryAsExplicitTag + + else: + concreteDecoder = None + state = stTryAsExplicitTag + + if LOG: + LOG('codec %s chosen by ASN.1 spec, decoding %s' % (state is stDecodeValue and concreteDecoder.__class__.__name__ or "", state is stDecodeValue and 'value' or 'as explicit tag')) + debug.scope.push(chosenSpec is None and '?' or chosenSpec.__class__.__name__) + + if state is stDecodeValue: + if not options.get('recursiveFlag', True) and not substrateFun: # deprecate this + def substrateFun(asn1Object, _substrate, _length, _options): + """Legacy hack to keep the recursiveFlag=False option supported. + + The decode(..., substrateFun=userCallback) option was introduced in 0.1.4 as a generalization + of the old recursiveFlag=False option. Users should pass their callback instead of using + recursiveFlag. + """ + yield asn1Object + + original_position = substrate.tell() + + if length == -1: # indef length + for value in concreteDecoder.indefLenValueDecoder( + substrate, asn1Spec, + tagSet, length, stGetValueDecoder, + self, substrateFun, **options): + if isinstance(value, SubstrateUnderrunError): + yield value + + else: + for value in concreteDecoder.valueDecoder( + substrate, asn1Spec, + tagSet, length, stGetValueDecoder, + self, substrateFun, **options): + if isinstance(value, SubstrateUnderrunError): + yield value + + bytesRead = substrate.tell() - original_position + if not substrateFun and bytesRead != length: + raise PyAsn1Error( + "Read %s bytes instead of expected %s." % (bytesRead, length)) + elif substrateFun and bytesRead > length: + # custom substrateFun may be used for partial decoding, reading less is expected there + raise PyAsn1Error( + "Read %s bytes are more than expected %s." % (bytesRead, length)) + + if LOG: + LOG('codec %s yields type %s, value:\n%s\n...' % ( + concreteDecoder.__class__.__name__, value.__class__.__name__, + isinstance(value, base.Asn1Item) and value.prettyPrint() or value)) + + state = stStop + break + + if state is stTryAsExplicitTag: + if (tagSet and + tagSet[0].tagFormat == tag.tagFormatConstructed and + tagSet[0].tagClass != tag.tagClassUniversal): + # Assume explicit tagging + concreteDecoder = rawPayloadDecoder + state = stDecodeValue + + else: + concreteDecoder = None + state = self.defaultErrorState + + if LOG: + LOG('codec %s chosen, decoding %s' % (concreteDecoder and concreteDecoder.__class__.__name__ or "", state is stDecodeValue and 'value' or 'as failure')) + + if state is stDumpRawValue: + concreteDecoder = self.defaultRawDecoder + + if LOG: + LOG('codec %s chosen, decoding value' % concreteDecoder.__class__.__name__) + + state = stDecodeValue + + if state is stErrorCondition: + raise error.PyAsn1Error( + '%s not in asn1Spec: %r' % (tagSet, asn1Spec) + ) + + if LOG: + debug.scope.pop() + LOG('decoder left scope %s, call completed' % debug.scope) + + yield value + + +class StreamingDecoder(object): + """Create an iterator that turns BER/CER/DER byte stream into ASN.1 objects. + + On each iteration, consume whatever BER/CER/DER serialization is + available in the `substrate` stream-like object and turns it into + one or more, possibly nested, ASN.1 objects. + + Parameters + ---------- + substrate: :py:class:`file`, :py:class:`io.BytesIO` + BER/CER/DER serialization in form of a byte stream + + Keyword Args + ------------ + asn1Spec: :py:class:`~pyasn1.type.base.PyAsn1Item` + A pyasn1 type object to act as a template guiding the decoder. + Depending on the ASN.1 structure being decoded, `asn1Spec` may + or may not be required. One of the reasons why `asn1Spec` may + me required is that ASN.1 structure is encoded in the *IMPLICIT* + tagging mode. + + Yields + ------ + : :py:class:`~pyasn1.type.base.PyAsn1Item`, :py:class:`~pyasn1.error.SubstrateUnderrunError` + Decoded ASN.1 object (possibly, nested) or + :py:class:`~pyasn1.error.SubstrateUnderrunError` object indicating + insufficient BER/CER/DER serialization on input to fully recover ASN.1 + objects from it. + + In the latter case the caller is advised to ensure some more data in + the input stream, then call the iterator again. The decoder will resume + the decoding process using the newly arrived data. + + The `context` property of :py:class:`~pyasn1.error.SubstrateUnderrunError` + object might hold a reference to the partially populated ASN.1 object + being reconstructed. + + Raises + ------ + ~pyasn1.error.PyAsn1Error, ~pyasn1.error.EndOfStreamError + `PyAsn1Error` on deserialization error, `EndOfStreamError` on + premature stream closure. + + Examples + -------- + Decode BER serialisation without ASN.1 schema + + .. code-block:: pycon + + >>> stream = io.BytesIO( + ... b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03') + >>> + >>> for asn1Object in StreamingDecoder(stream): + ... print(asn1Object) + >>> + SequenceOf: + 1 2 3 + + Decode BER serialisation with ASN.1 schema + + .. code-block:: pycon + + >>> stream = io.BytesIO( + ... b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03') + >>> + >>> schema = SequenceOf(componentType=Integer()) + >>> + >>> decoder = StreamingDecoder(stream, asn1Spec=schema) + >>> for asn1Object in decoder: + ... print(asn1Object) + >>> + SequenceOf: + 1 2 3 + """ + + SINGLE_ITEM_DECODER = SingleItemDecoder + + def __init__(self, substrate, asn1Spec=None, **options): + self._singleItemDecoder = self.SINGLE_ITEM_DECODER(**options) + self._substrate = asSeekableStream(substrate) + self._asn1Spec = asn1Spec + self._options = options + + def __iter__(self): + while True: + for asn1Object in self._singleItemDecoder( + self._substrate, self._asn1Spec, **self._options): + yield asn1Object + + for chunk in isEndOfStream(self._substrate): + if isinstance(chunk, SubstrateUnderrunError): + yield + + break + + if chunk: + break + + +class Decoder(object): + """Create a BER decoder object. + + Parse BER/CER/DER octet-stream into one, possibly nested, ASN.1 object. + """ + STREAMING_DECODER = StreamingDecoder + + @classmethod + def __call__(cls, substrate, asn1Spec=None, **options): + """Turns BER/CER/DER octet stream into an ASN.1 object. + + Takes BER/CER/DER octet-stream in form of :py:class:`bytes` + and decode it into an ASN.1 object + (e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative) which + may be a scalar or an arbitrary nested structure. + + Parameters + ---------- + substrate: :py:class:`bytes` + BER/CER/DER octet-stream to parse + + Keyword Args + ------------ + asn1Spec: :py:class:`~pyasn1.type.base.PyAsn1Item` + A pyasn1 type object (:py:class:`~pyasn1.type.base.PyAsn1Item` + derivative) to act as a template guiding the decoder. + Depending on the ASN.1 structure being decoded, `asn1Spec` may or + may not be required. Most common reason for it to require is that + ASN.1 structure is encoded in *IMPLICIT* tagging mode. + + substrateFun: :py:class:`Union[ + Callable[[pyasn1.type.base.PyAsn1Item, bytes, int], + Tuple[pyasn1.type.base.PyAsn1Item, bytes]], + Callable[[pyasn1.type.base.PyAsn1Item, io.BytesIO, int, dict], + Generator[Union[pyasn1.type.base.PyAsn1Item, + pyasn1.error.SubstrateUnderrunError], + None, None]] + ]` + User callback meant to generalize special use cases like non-recursive or + partial decoding. A 3-arg non-streaming variant is supported for backwards + compatiblilty in addition to the newer 4-arg streaming variant. + The callback will receive the uninitialized object recovered from substrate + as 1st argument, the uninterpreted payload as 2nd argument, and the length + of the uninterpreted payload as 3rd argument. The streaming variant will + additionally receive the decode(..., **options) kwargs as 4th argument. + The non-streaming variant shall return an object that will be propagated + as decode() return value as 1st item, and the remainig payload for further + decode passes as 2nd item. + The streaming variant shall yield an object that will be propagated as + decode() return value, and leave the remaining payload in the stream. + + Returns + ------- + : :py:class:`tuple` + A tuple of :py:class:`~pyasn1.type.base.PyAsn1Item` object + recovered from BER/CER/DER substrate and the unprocessed trailing + portion of the `substrate` (may be empty) + + Raises + ------ + : :py:class:`~pyasn1.error.PyAsn1Error` + :py:class:`~pyasn1.error.SubstrateUnderrunError` on insufficient + input or :py:class:`~pyasn1.error.PyAsn1Error` on decoding error. + + Examples + -------- + Decode BER/CER/DER serialisation without ASN.1 schema + + .. code-block:: pycon + + >>> s, unprocessed = decode(b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03') + >>> str(s) + SequenceOf: + 1 2 3 + + Decode BER/CER/DER serialisation with ASN.1 schema + + .. code-block:: pycon + + >>> seq = SequenceOf(componentType=Integer()) + >>> s, unprocessed = decode( + b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03', asn1Spec=seq) + >>> str(s) + SequenceOf: + 1 2 3 + + """ + substrate = asSeekableStream(substrate) + + if "substrateFun" in options: + origSubstrateFun = options["substrateFun"] + + def substrateFunWrapper(asn1Object, substrate, length, options=None): + """Support both 0.4 and 0.5 style APIs. + + substrateFun API has changed in 0.5 for use with streaming decoders. To stay backwards compatible, + we first try if we received a streaming user callback. If that fails,we assume we've received a + non-streaming v0.4 user callback and convert it for streaming on the fly + """ + try: + substrate_gen = origSubstrateFun(asn1Object, substrate, length, options) + except TypeError as _value: + if _value.__traceback__.tb_next: + # Traceback depth > 1 means TypeError from inside user provided function + raise + # invariant maintained at Decoder.__call__ entry + assert isinstance(substrate, io.BytesIO) # nosec assert_used + substrate_gen = Decoder._callSubstrateFunV4asV5(origSubstrateFun, asn1Object, substrate, length) + for value in substrate_gen: + yield value + + options["substrateFun"] = substrateFunWrapper + + streamingDecoder = cls.STREAMING_DECODER( + substrate, asn1Spec, **options) + + for asn1Object in streamingDecoder: + if isinstance(asn1Object, SubstrateUnderrunError): + raise error.SubstrateUnderrunError('Short substrate on input') + + try: + tail = next(readFromStream(substrate)) + + except error.EndOfStreamError: + tail = b'' + + return asn1Object, tail + + @staticmethod + def _callSubstrateFunV4asV5(substrateFunV4, asn1Object, substrate, length): + substrate_bytes = substrate.read() + if length == -1: + length = len(substrate_bytes) + value, nextSubstrate = substrateFunV4(asn1Object, substrate_bytes, length) + nbytes = substrate.write(nextSubstrate) + substrate.truncate() + substrate.seek(-nbytes, os.SEEK_CUR) + yield value + +#: Turns BER octet stream into an ASN.1 object. +#: +#: Takes BER octet-stream and decode it into an ASN.1 object +#: (e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative) which +#: may be a scalar or an arbitrary nested structure. +#: +#: Parameters +#: ---------- +#: substrate: :py:class:`bytes` +#: BER octet-stream +#: +#: Keyword Args +#: ------------ +#: asn1Spec: any pyasn1 type object e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative +#: A pyasn1 type object to act as a template guiding the decoder. Depending on the ASN.1 structure +#: being decoded, *asn1Spec* may or may not be required. Most common reason for +#: it to require is that ASN.1 structure is encoded in *IMPLICIT* tagging mode. +#: +#: Returns +#: ------- +#: : :py:class:`tuple` +#: A tuple of pyasn1 object recovered from BER substrate (:py:class:`~pyasn1.type.base.PyAsn1Item` derivative) +#: and the unprocessed trailing portion of the *substrate* (may be empty) +#: +#: Raises +#: ------ +#: ~pyasn1.error.PyAsn1Error, ~pyasn1.error.SubstrateUnderrunError +#: On decoding errors +#: +#: Notes +#: ----- +#: This function is deprecated. Please use :py:class:`Decoder` or +#: :py:class:`StreamingDecoder` class instance. +#: +#: Examples +#: -------- +#: Decode BER serialisation without ASN.1 schema +#: +#: .. code-block:: pycon +#: +#: >>> s, _ = decode(b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03') +#: >>> str(s) +#: SequenceOf: +#: 1 2 3 +#: +#: Decode BER serialisation with ASN.1 schema +#: +#: .. code-block:: pycon +#: +#: >>> seq = SequenceOf(componentType=Integer()) +#: >>> s, _ = decode(b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03', asn1Spec=seq) +#: >>> str(s) +#: SequenceOf: +#: 1 2 3 +#: +decode = Decoder() + +def __getattr__(attr: str): + if newAttr := {"tagMap": "TAG_MAP", "typeMap": "TYPE_MAP"}.get(attr): + warnings.warn(f"{attr} is deprecated. Please use {newAttr} instead.", DeprecationWarning) + return globals()[newAttr] + raise AttributeError(attr) diff --git a/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/encoder.py b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d16fb1f88b098f652fed706e746d8b09bdcc9609 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/encoder.py @@ -0,0 +1,954 @@ +# +# This file is part of pyasn1 software. +# +# Copyright (c) 2005-2020, Ilya Etingof +# License: https://pyasn1.readthedocs.io/en/latest/license.html +# +import sys +import warnings + +from pyasn1 import debug +from pyasn1 import error +from pyasn1.codec.ber import eoo +from pyasn1.compat import _MISSING +from pyasn1.compat.integer import to_bytes +from pyasn1.type import char +from pyasn1.type import tag +from pyasn1.type import univ +from pyasn1.type import useful + +__all__ = ['Encoder', 'encode'] + +LOG = debug.registerLoggee(__name__, flags=debug.DEBUG_ENCODER) + + +class AbstractItemEncoder(object): + supportIndefLenMode = True + + # An outcome of otherwise legit call `encodeFun(eoo.endOfOctets)` + eooIntegerSubstrate = (0, 0) + eooOctetsSubstrate = bytes(eooIntegerSubstrate) + + # noinspection PyMethodMayBeStatic + def encodeTag(self, singleTag, isConstructed): + tagClass, tagFormat, tagId = singleTag + encodedTag = tagClass | tagFormat + if isConstructed: + encodedTag |= tag.tagFormatConstructed + + if tagId < 31: + return encodedTag | tagId, + + else: + substrate = tagId & 0x7f, + + tagId >>= 7 + + while tagId: + substrate = (0x80 | (tagId & 0x7f),) + substrate + tagId >>= 7 + + return (encodedTag | 0x1F,) + substrate + + def encodeLength(self, length, defMode): + if not defMode and self.supportIndefLenMode: + return (0x80,) + + if length < 0x80: + return length, + + else: + substrate = () + while length: + substrate = (length & 0xff,) + substrate + length >>= 8 + + substrateLen = len(substrate) + + if substrateLen > 126: + raise error.PyAsn1Error('Length octets overflow (%d)' % substrateLen) + + return (0x80 | substrateLen,) + substrate + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + raise error.PyAsn1Error('Not implemented') + + def encode(self, value, asn1Spec=None, encodeFun=None, **options): + + if asn1Spec is None: + tagSet = value.tagSet + else: + tagSet = asn1Spec.tagSet + + # untagged item? + if not tagSet: + substrate, isConstructed, isOctets = self.encodeValue( + value, asn1Spec, encodeFun, **options + ) + return substrate + + defMode = options.get('defMode', True) + + substrate = b'' + + for idx, singleTag in enumerate(tagSet.superTags): + + defModeOverride = defMode + + # base tag? + if not idx: + try: + substrate, isConstructed, isOctets = self.encodeValue( + value, asn1Spec, encodeFun, **options + ) + + except error.PyAsn1Error as exc: + raise error.PyAsn1Error( + 'Error encoding %r: %s' % (value, exc)) + + if LOG: + LOG('encoded %svalue %s into %s' % ( + isConstructed and 'constructed ' or '', value, substrate + )) + + if not substrate and isConstructed and options.get('ifNotEmpty', False): + return substrate + + if not isConstructed: + defModeOverride = True + + if LOG: + LOG('overridden encoding mode into definitive for primitive type') + + header = self.encodeTag(singleTag, isConstructed) + + if LOG: + LOG('encoded %stag %s into %s' % ( + isConstructed and 'constructed ' or '', + singleTag, debug.hexdump(bytes(header)))) + + header += self.encodeLength(len(substrate), defModeOverride) + + if LOG: + LOG('encoded %s octets (tag + payload) into %s' % ( + len(substrate), debug.hexdump(bytes(header)))) + + if isOctets: + substrate = bytes(header) + substrate + + if not defModeOverride: + substrate += self.eooOctetsSubstrate + + else: + substrate = header + substrate + + if not defModeOverride: + substrate += self.eooIntegerSubstrate + + if not isOctets: + substrate = bytes(substrate) + + return substrate + + +class EndOfOctetsEncoder(AbstractItemEncoder): + def encodeValue(self, value, asn1Spec, encodeFun, **options): + return b'', False, True + + +class BooleanEncoder(AbstractItemEncoder): + supportIndefLenMode = False + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + return value and (1,) or (0,), False, False + + +class IntegerEncoder(AbstractItemEncoder): + supportIndefLenMode = False + supportCompactZero = False + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if value == 0: + if LOG: + LOG('encoding %spayload for zero INTEGER' % ( + self.supportCompactZero and 'no ' or '' + )) + + # de-facto way to encode zero + if self.supportCompactZero: + return (), False, False + else: + return (0,), False, False + + return to_bytes(int(value), signed=True), False, True + + +class BitStringEncoder(AbstractItemEncoder): + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is not None: + # TODO: try to avoid ASN.1 schema instantiation + value = asn1Spec.clone(value) + + valueLength = len(value) + if valueLength % 8: + alignedValue = value << (8 - valueLength % 8) + else: + alignedValue = value + + maxChunkSize = options.get('maxChunkSize', 0) + if not maxChunkSize or len(alignedValue) <= maxChunkSize * 8: + substrate = alignedValue.asOctets() + return bytes((len(substrate) * 8 - valueLength,)) + substrate, False, True + + if LOG: + LOG('encoding into up to %s-octet chunks' % maxChunkSize) + + baseTag = value.tagSet.baseTag + + # strip off explicit tags + if baseTag: + tagSet = tag.TagSet(baseTag, baseTag) + + else: + tagSet = tag.TagSet() + + alignedValue = alignedValue.clone(tagSet=tagSet) + + stop = 0 + substrate = b'' + while stop < valueLength: + start = stop + stop = min(start + maxChunkSize * 8, valueLength) + substrate += encodeFun(alignedValue[start:stop], asn1Spec, **options) + + return substrate, True, True + + +class OctetStringEncoder(AbstractItemEncoder): + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + + if asn1Spec is None: + substrate = value.asOctets() + + elif not isinstance(value, bytes): + substrate = asn1Spec.clone(value).asOctets() + + else: + substrate = value + + maxChunkSize = options.get('maxChunkSize', 0) + + if not maxChunkSize or len(substrate) <= maxChunkSize: + return substrate, False, True + + if LOG: + LOG('encoding into up to %s-octet chunks' % maxChunkSize) + + # strip off explicit tags for inner chunks + + if asn1Spec is None: + baseTag = value.tagSet.baseTag + + # strip off explicit tags + if baseTag: + tagSet = tag.TagSet(baseTag, baseTag) + + else: + tagSet = tag.TagSet() + + asn1Spec = value.clone(tagSet=tagSet) + + elif not isinstance(value, bytes): + baseTag = asn1Spec.tagSet.baseTag + + # strip off explicit tags + if baseTag: + tagSet = tag.TagSet(baseTag, baseTag) + + else: + tagSet = tag.TagSet() + + asn1Spec = asn1Spec.clone(tagSet=tagSet) + + pos = 0 + substrate = b'' + + while True: + chunk = value[pos:pos + maxChunkSize] + if not chunk: + break + + substrate += encodeFun(chunk, asn1Spec, **options) + pos += maxChunkSize + + return substrate, True, True + + +class NullEncoder(AbstractItemEncoder): + supportIndefLenMode = False + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + return b'', False, True + + +class ObjectIdentifierEncoder(AbstractItemEncoder): + supportIndefLenMode = False + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is not None: + value = asn1Spec.clone(value) + + oid = value.asTuple() + + # Build the first pair + try: + first = oid[0] + second = oid[1] + + except IndexError: + raise error.PyAsn1Error('Short OID %s' % (value,)) + + if 0 <= second <= 39: + if first == 1: + oid = (second + 40,) + oid[2:] + elif first == 0: + oid = (second,) + oid[2:] + elif first == 2: + oid = (second + 80,) + oid[2:] + else: + raise error.PyAsn1Error('Impossible first/second arcs at %s' % (value,)) + + elif first == 2: + oid = (second + 80,) + oid[2:] + + else: + raise error.PyAsn1Error('Impossible first/second arcs at %s' % (value,)) + + octets = () + + # Cycle through subIds + for subOid in oid: + if 0 <= subOid <= 127: + # Optimize for the common case + octets += (subOid,) + + elif subOid > 127: + # Pack large Sub-Object IDs + res = (subOid & 0x7f,) + subOid >>= 7 + + while subOid: + res = (0x80 | (subOid & 0x7f),) + res + subOid >>= 7 + + # Add packed Sub-Object ID to resulted Object ID + octets += res + + else: + raise error.PyAsn1Error('Negative OID arc %s at %s' % (subOid, value)) + + return octets, False, False + + +class RelativeOIDEncoder(AbstractItemEncoder): + supportIndefLenMode = False + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is not None: + value = asn1Spec.clone(value) + + octets = () + + # Cycle through subIds + for subOid in value.asTuple(): + if 0 <= subOid <= 127: + # Optimize for the common case + octets += (subOid,) + + elif subOid > 127: + # Pack large Sub-Object IDs + res = (subOid & 0x7f,) + subOid >>= 7 + + while subOid: + res = (0x80 | (subOid & 0x7f),) + res + subOid >>= 7 + + # Add packed Sub-Object ID to resulted RELATIVE-OID + octets += res + + else: + raise error.PyAsn1Error('Negative RELATIVE-OID arc %s at %s' % (subOid, value)) + + return octets, False, False + + +class RealEncoder(AbstractItemEncoder): + supportIndefLenMode = False + binEncBase = 2 # set to None to choose encoding base automatically + + @staticmethod + def _dropFloatingPoint(m, encbase, e): + ms, es = 1, 1 + if m < 0: + ms = -1 # mantissa sign + + if e < 0: + es = -1 # exponent sign + + m *= ms + + if encbase == 8: + m *= 2 ** (abs(e) % 3 * es) + e = abs(e) // 3 * es + + elif encbase == 16: + m *= 2 ** (abs(e) % 4 * es) + e = abs(e) // 4 * es + + while True: + if int(m) != m: + m *= encbase + e -= 1 + continue + break + + return ms, int(m), encbase, e + + def _chooseEncBase(self, value): + m, b, e = value + encBase = [2, 8, 16] + if value.binEncBase in encBase: + return self._dropFloatingPoint(m, value.binEncBase, e) + + elif self.binEncBase in encBase: + return self._dropFloatingPoint(m, self.binEncBase, e) + + # auto choosing base 2/8/16 + mantissa = [m, m, m] + exponent = [e, e, e] + sign = 1 + encbase = 2 + e = float('inf') + + for i in range(3): + (sign, + mantissa[i], + encBase[i], + exponent[i]) = self._dropFloatingPoint(mantissa[i], encBase[i], exponent[i]) + + if abs(exponent[i]) < abs(e) or (abs(exponent[i]) == abs(e) and mantissa[i] < m): + e = exponent[i] + m = int(mantissa[i]) + encbase = encBase[i] + + if LOG: + LOG('automatically chosen REAL encoding base %s, sign %s, mantissa %s, ' + 'exponent %s' % (encbase, sign, m, e)) + + return sign, m, encbase, e + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is not None: + value = asn1Spec.clone(value) + + if value.isPlusInf: + return (0x40,), False, False + + if value.isMinusInf: + return (0x41,), False, False + + m, b, e = value + + if not m: + return b'', False, True + + if b == 10: + if LOG: + LOG('encoding REAL into character form') + + return b'\x03%dE%s%d' % (m, e == 0 and b'+' or b'', e), False, True + + elif b == 2: + fo = 0x80 # binary encoding + ms, m, encbase, e = self._chooseEncBase(value) + + if ms < 0: # mantissa sign + fo |= 0x40 # sign bit + + # exponent & mantissa normalization + if encbase == 2: + while m & 0x1 == 0: + m >>= 1 + e += 1 + + elif encbase == 8: + while m & 0x7 == 0: + m >>= 3 + e += 1 + fo |= 0x10 + + else: # encbase = 16 + while m & 0xf == 0: + m >>= 4 + e += 1 + fo |= 0x20 + + sf = 0 # scale factor + + while m & 0x1 == 0: + m >>= 1 + sf += 1 + + if sf > 3: + raise error.PyAsn1Error('Scale factor overflow') # bug if raised + + fo |= sf << 2 + eo = b'' + if e == 0 or e == -1: + eo = bytes((e & 0xff,)) + + else: + while e not in (0, -1): + eo = bytes((e & 0xff,)) + eo + e >>= 8 + + if e == 0 and eo and eo[0] & 0x80: + eo = bytes((0,)) + eo + + if e == -1 and eo and not (eo[0] & 0x80): + eo = bytes((0xff,)) + eo + + n = len(eo) + if n > 0xff: + raise error.PyAsn1Error('Real exponent overflow') + + if n == 1: + pass + + elif n == 2: + fo |= 1 + + elif n == 3: + fo |= 2 + + else: + fo |= 3 + eo = bytes((n & 0xff,)) + eo + + po = b'' + + while m: + po = bytes((m & 0xff,)) + po + m >>= 8 + + substrate = bytes((fo,)) + eo + po + + return substrate, False, True + + else: + raise error.PyAsn1Error('Prohibited Real base %s' % b) + + +class SequenceEncoder(AbstractItemEncoder): + omitEmptyOptionals = False + + # TODO: handling three flavors of input is too much -- split over codecs + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + + substrate = b'' + + omitEmptyOptionals = options.get( + 'omitEmptyOptionals', self.omitEmptyOptionals) + + if LOG: + LOG('%sencoding empty OPTIONAL components' % ( + omitEmptyOptionals and 'not ' or '')) + + if asn1Spec is None: + # instance of ASN.1 schema + inconsistency = value.isInconsistent + if inconsistency: + raise error.PyAsn1Error( + f"ASN.1 object {value.__class__.__name__} is inconsistent") + + namedTypes = value.componentType + + for idx, component in enumerate(value.values()): + if namedTypes: + namedType = namedTypes[idx] + + if namedType.isOptional and not component.isValue: + if LOG: + LOG('not encoding OPTIONAL component %r' % (namedType,)) + continue + + if namedType.isDefaulted and component == namedType.asn1Object: + if LOG: + LOG('not encoding DEFAULT component %r' % (namedType,)) + continue + + if omitEmptyOptionals: + options.update(ifNotEmpty=namedType.isOptional) + + # wrap open type blob if needed + if namedTypes and namedType.openType: + + wrapType = namedType.asn1Object + + if wrapType.typeId in ( + univ.SetOf.typeId, univ.SequenceOf.typeId): + + substrate += encodeFun( + component, asn1Spec, + **dict(options, wrapType=wrapType.componentType)) + + else: + chunk = encodeFun(component, asn1Spec, **options) + + if wrapType.isSameTypeWith(component): + substrate += chunk + + else: + substrate += encodeFun(chunk, wrapType, **options) + + if LOG: + LOG('wrapped with wrap type %r' % (wrapType,)) + + else: + substrate += encodeFun(component, asn1Spec, **options) + + else: + # bare Python value + ASN.1 schema + for idx, namedType in enumerate(asn1Spec.componentType.namedTypes): + + try: + component = value[namedType.name] + + except KeyError: + raise error.PyAsn1Error('Component name "%s" not found in %r' % ( + namedType.name, value)) + + if namedType.isOptional and namedType.name not in value: + if LOG: + LOG('not encoding OPTIONAL component %r' % (namedType,)) + continue + + if namedType.isDefaulted and component == namedType.asn1Object: + if LOG: + LOG('not encoding DEFAULT component %r' % (namedType,)) + continue + + if omitEmptyOptionals: + options.update(ifNotEmpty=namedType.isOptional) + + componentSpec = namedType.asn1Object + + # wrap open type blob if needed + if namedType.openType: + + if componentSpec.typeId in ( + univ.SetOf.typeId, univ.SequenceOf.typeId): + + substrate += encodeFun( + component, componentSpec, + **dict(options, wrapType=componentSpec.componentType)) + + else: + chunk = encodeFun(component, componentSpec, **options) + + if componentSpec.isSameTypeWith(component): + substrate += chunk + + else: + substrate += encodeFun(chunk, componentSpec, **options) + + if LOG: + LOG('wrapped with wrap type %r' % (componentSpec,)) + + else: + substrate += encodeFun(component, componentSpec, **options) + + return substrate, True, True + + +class SequenceOfEncoder(AbstractItemEncoder): + def _encodeComponents(self, value, asn1Spec, encodeFun, **options): + + if asn1Spec is None: + inconsistency = value.isInconsistent + if inconsistency: + raise error.PyAsn1Error( + f"ASN.1 object {value.__class__.__name__} is inconsistent") + + else: + asn1Spec = asn1Spec.componentType + + chunks = [] + + wrapType = options.pop('wrapType', None) + + for idx, component in enumerate(value): + chunk = encodeFun(component, asn1Spec, **options) + + if (wrapType is not None and + not wrapType.isSameTypeWith(component)): + # wrap encoded value with wrapper container (e.g. ANY) + chunk = encodeFun(chunk, wrapType, **options) + + if LOG: + LOG('wrapped with wrap type %r' % (wrapType,)) + + chunks.append(chunk) + + return chunks + + def encodeValue(self, value, asn1Spec, encodeFun, **options): + chunks = self._encodeComponents( + value, asn1Spec, encodeFun, **options) + + return b''.join(chunks), True, True + + +class ChoiceEncoder(AbstractItemEncoder): + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is None: + component = value.getComponent() + else: + names = [namedType.name for namedType in asn1Spec.componentType.namedTypes + if namedType.name in value] + if len(names) != 1: + raise error.PyAsn1Error('%s components for Choice at %r' % (len(names) and 'Multiple ' or 'None ', value)) + + name = names[0] + + component = value[name] + asn1Spec = asn1Spec[name] + + return encodeFun(component, asn1Spec, **options), True, True + + +class AnyEncoder(OctetStringEncoder): + def encodeValue(self, value, asn1Spec, encodeFun, **options): + if asn1Spec is None: + value = value.asOctets() + elif not isinstance(value, bytes): + value = asn1Spec.clone(value).asOctets() + + return value, not options.get('defMode', True), True + + +TAG_MAP = { + eoo.endOfOctets.tagSet: EndOfOctetsEncoder(), + univ.Boolean.tagSet: BooleanEncoder(), + univ.Integer.tagSet: IntegerEncoder(), + univ.BitString.tagSet: BitStringEncoder(), + univ.OctetString.tagSet: OctetStringEncoder(), + univ.Null.tagSet: NullEncoder(), + univ.ObjectIdentifier.tagSet: ObjectIdentifierEncoder(), + univ.RelativeOID.tagSet: RelativeOIDEncoder(), + univ.Enumerated.tagSet: IntegerEncoder(), + univ.Real.tagSet: RealEncoder(), + # Sequence & Set have same tags as SequenceOf & SetOf + univ.SequenceOf.tagSet: SequenceOfEncoder(), + univ.SetOf.tagSet: SequenceOfEncoder(), + univ.Choice.tagSet: ChoiceEncoder(), + # character string types + char.UTF8String.tagSet: OctetStringEncoder(), + char.NumericString.tagSet: OctetStringEncoder(), + char.PrintableString.tagSet: OctetStringEncoder(), + char.TeletexString.tagSet: OctetStringEncoder(), + char.VideotexString.tagSet: OctetStringEncoder(), + char.IA5String.tagSet: OctetStringEncoder(), + char.GraphicString.tagSet: OctetStringEncoder(), + char.VisibleString.tagSet: OctetStringEncoder(), + char.GeneralString.tagSet: OctetStringEncoder(), + char.UniversalString.tagSet: OctetStringEncoder(), + char.BMPString.tagSet: OctetStringEncoder(), + # useful types + useful.ObjectDescriptor.tagSet: OctetStringEncoder(), + useful.GeneralizedTime.tagSet: OctetStringEncoder(), + useful.UTCTime.tagSet: OctetStringEncoder() +} + +# Put in ambiguous & non-ambiguous types for faster codec lookup +TYPE_MAP = { + univ.Boolean.typeId: BooleanEncoder(), + univ.Integer.typeId: IntegerEncoder(), + univ.BitString.typeId: BitStringEncoder(), + univ.OctetString.typeId: OctetStringEncoder(), + univ.Null.typeId: NullEncoder(), + univ.ObjectIdentifier.typeId: ObjectIdentifierEncoder(), + univ.RelativeOID.typeId: RelativeOIDEncoder(), + univ.Enumerated.typeId: IntegerEncoder(), + univ.Real.typeId: RealEncoder(), + # Sequence & Set have same tags as SequenceOf & SetOf + univ.Set.typeId: SequenceEncoder(), + univ.SetOf.typeId: SequenceOfEncoder(), + univ.Sequence.typeId: SequenceEncoder(), + univ.SequenceOf.typeId: SequenceOfEncoder(), + univ.Choice.typeId: ChoiceEncoder(), + univ.Any.typeId: AnyEncoder(), + # character string types + char.UTF8String.typeId: OctetStringEncoder(), + char.NumericString.typeId: OctetStringEncoder(), + char.PrintableString.typeId: OctetStringEncoder(), + char.TeletexString.typeId: OctetStringEncoder(), + char.VideotexString.typeId: OctetStringEncoder(), + char.IA5String.typeId: OctetStringEncoder(), + char.GraphicString.typeId: OctetStringEncoder(), + char.VisibleString.typeId: OctetStringEncoder(), + char.GeneralString.typeId: OctetStringEncoder(), + char.UniversalString.typeId: OctetStringEncoder(), + char.BMPString.typeId: OctetStringEncoder(), + # useful types + useful.ObjectDescriptor.typeId: OctetStringEncoder(), + useful.GeneralizedTime.typeId: OctetStringEncoder(), + useful.UTCTime.typeId: OctetStringEncoder() +} + + +class SingleItemEncoder(object): + fixedDefLengthMode = None + fixedChunkSize = None + + TAG_MAP = TAG_MAP + TYPE_MAP = TYPE_MAP + + def __init__(self, tagMap=_MISSING, typeMap=_MISSING, **ignored): + self._tagMap = tagMap if tagMap is not _MISSING else self.TAG_MAP + self._typeMap = typeMap if typeMap is not _MISSING else self.TYPE_MAP + + def __call__(self, value, asn1Spec=None, **options): + try: + if asn1Spec is None: + typeId = value.typeId + else: + typeId = asn1Spec.typeId + + except AttributeError: + raise error.PyAsn1Error('Value %r is not ASN.1 type instance ' + 'and "asn1Spec" not given' % (value,)) + + if LOG: + LOG('encoder called in %sdef mode, chunk size %s for type %s, ' + 'value:\n%s' % (not options.get('defMode', True) and 'in' or '', + options.get('maxChunkSize', 0), + asn1Spec is None and value.prettyPrintType() or + asn1Spec.prettyPrintType(), value)) + + if self.fixedDefLengthMode is not None: + options.update(defMode=self.fixedDefLengthMode) + + if self.fixedChunkSize is not None: + options.update(maxChunkSize=self.fixedChunkSize) + + try: + concreteEncoder = self._typeMap[typeId] + + if LOG: + LOG('using value codec %s chosen by type ID ' + '%s' % (concreteEncoder.__class__.__name__, typeId)) + + except KeyError: + if asn1Spec is None: + tagSet = value.tagSet + else: + tagSet = asn1Spec.tagSet + + # use base type for codec lookup to recover untagged types + baseTagSet = tag.TagSet(tagSet.baseTag, tagSet.baseTag) + + try: + concreteEncoder = self._tagMap[baseTagSet] + + except KeyError: + raise error.PyAsn1Error('No encoder for %r (%s)' % (value, tagSet)) + + if LOG: + LOG('using value codec %s chosen by tagSet ' + '%s' % (concreteEncoder.__class__.__name__, tagSet)) + + substrate = concreteEncoder.encode(value, asn1Spec, self, **options) + + if LOG: + LOG('codec %s built %s octets of substrate: %s\nencoder ' + 'completed' % (concreteEncoder, len(substrate), + debug.hexdump(substrate))) + + return substrate + + +class Encoder(object): + SINGLE_ITEM_ENCODER = SingleItemEncoder + + def __init__(self, tagMap=_MISSING, typeMap=_MISSING, **options): + self._singleItemEncoder = self.SINGLE_ITEM_ENCODER( + tagMap=tagMap, typeMap=typeMap, **options + ) + + def __call__(self, pyObject, asn1Spec=None, **options): + return self._singleItemEncoder( + pyObject, asn1Spec=asn1Spec, **options) + + +#: Turns ASN.1 object into BER octet stream. +#: +#: Takes any ASN.1 object (e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative) +#: walks all its components recursively and produces a BER octet stream. +#: +#: Parameters +#: ---------- +#: value: either a Python or pyasn1 object (e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative) +#: A Python or pyasn1 object to encode. If Python object is given, `asnSpec` +#: parameter is required to guide the encoding process. +#: +#: Keyword Args +#: ------------ +#: asn1Spec: +#: Optional ASN.1 schema or value object e.g. :py:class:`~pyasn1.type.base.PyAsn1Item` derivative +#: +#: defMode: :py:class:`bool` +#: If :obj:`False`, produces indefinite length encoding +#: +#: maxChunkSize: :py:class:`int` +#: Maximum chunk size in chunked encoding mode (0 denotes unlimited chunk size) +#: +#: Returns +#: ------- +#: : :py:class:`bytes` +#: Given ASN.1 object encoded into BER octetstream +#: +#: Raises +#: ------ +#: ~pyasn1.error.PyAsn1Error +#: On encoding errors +#: +#: Examples +#: -------- +#: Encode Python value into BER with ASN.1 schema +#: +#: .. code-block:: pycon +#: +#: >>> seq = SequenceOf(componentType=Integer()) +#: >>> encode([1, 2, 3], asn1Spec=seq) +#: b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03' +#: +#: Encode ASN.1 value object into BER +#: +#: .. code-block:: pycon +#: +#: >>> seq = SequenceOf(componentType=Integer()) +#: >>> seq.extend([1, 2, 3]) +#: >>> encode(seq) +#: b'0\t\x02\x01\x01\x02\x01\x02\x02\x01\x03' +#: +encode = Encoder() + +def __getattr__(attr: str): + if newAttr := {"tagMap": "TAG_MAP", "typeMap": "TYPE_MAP"}.get(attr): + warnings.warn(f"{attr} is deprecated. Please use {newAttr} instead.", DeprecationWarning) + return globals()[newAttr] + raise AttributeError(attr) diff --git a/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/eoo.py b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/eoo.py new file mode 100644 index 0000000000000000000000000000000000000000..8c91a3d285d30fc06838e70478eeea8c64c9c2bc --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/ber/eoo.py @@ -0,0 +1,28 @@ +# +# This file is part of pyasn1 software. +# +# Copyright (c) 2005-2020, Ilya Etingof +# License: https://pyasn1.readthedocs.io/en/latest/license.html +# +from pyasn1.type import base +from pyasn1.type import tag + +__all__ = ['endOfOctets'] + + +class EndOfOctets(base.SimpleAsn1Type): + defaultValue = 0 + tagSet = tag.initTagSet( + tag.Tag(tag.tagClassUniversal, tag.tagFormatSimple, 0x00) + ) + + _instance = None + + def __new__(cls, *args, **kwargs): + if cls._instance is None: + cls._instance = object.__new__(cls, *args, **kwargs) + + return cls._instance + + +endOfOctets = EndOfOctets() diff --git a/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/streaming.py b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/streaming.py new file mode 100644 index 0000000000000000000000000000000000000000..c35f24899bca4defd54ec0744bf46a8f831f69a7 --- /dev/null +++ b/evalkit_tf437/lib/python3.10/site-packages/pyasn1/codec/streaming.py @@ -0,0 +1,234 @@ +# +# This file is part of pyasn1 software. +# +# Copyright (c) 2005-2019, Ilya Etingof +# License: https://pyasn1.readthedocs.io/en/latest/license.html +# +import io +import os + +from pyasn1 import error +from pyasn1.type import univ + +class CachingStreamWrapper(io.IOBase): + """Wrapper around non-seekable streams. + + Note that the implementation is tied to the decoder, + not checking for dangerous arguments for the sake + of performance. + + The read bytes are kept in an internal cache until + setting _markedPosition which may reset the cache. + """ + def __init__(self, raw): + self._raw = raw + self._cache = io.BytesIO() + self._markedPosition = 0 + + def peek(self, n): + result = self.read(n) + self._cache.seek(-len(result), os.SEEK_CUR) + return result + + def seekable(self): + return True + + def seek(self, n=-1, whence=os.SEEK_SET): + # Note that this not safe for seeking forward. + return self._cache.seek(n, whence) + + def read(self, n=-1): + read_from_cache = self._cache.read(n) + if n != -1: + n -= len(read_from_cache) + if not n: # 0 bytes left to read + return read_from_cache + + read_from_raw = self._raw.read(n) + + self._cache.write(read_from_raw) + + return read_from_cache + read_from_raw + + @property + def markedPosition(self): + """Position where the currently processed element starts. + + This is used for back-tracking in SingleItemDecoder.__call__ + and (indefLen)ValueDecoder and should not be used for other purposes. + The client is not supposed to ever seek before this position. + """ + return self._markedPosition + + @markedPosition.setter + def markedPosition(self, value): + # By setting the value, we ensure we won't seek back before it. + # `value` should be the same as the current position + # We don't check for this for performance reasons. + self._markedPosition = value + + # Whenever we set _marked_position, we know for sure + # that we will not return back, and thus it is + # safe to drop all cached data. + if self._cache.tell() > io.DEFAULT_BUFFER_SIZE: + self._cache = io.BytesIO(self._cache.read()) + self._markedPosition = 0 + + def tell(self): + return self._cache.tell() + + +def asSeekableStream(substrate): + """Convert object to seekable byte-stream. + + Parameters + ---------- + substrate: :py:class:`bytes` or :py:class:`io.IOBase` or :py:class:`univ.OctetString` + + Returns + ------- + : :py:class:`io.IOBase` + + Raises + ------ + : :py:class:`~pyasn1.error.PyAsn1Error` + If the supplied substrate cannot be converted to a seekable stream. + """ + if isinstance(substrate, io.BytesIO): + return substrate + + elif isinstance(substrate, bytes): + return io.BytesIO(substrate) + + elif isinstance(substrate, univ.OctetString): + return io.BytesIO(substrate.asOctets()) + + try: + if substrate.seekable(): # Will fail for most invalid types + return substrate + else: + return CachingStreamWrapper(substrate) + + except AttributeError: + raise error.UnsupportedSubstrateError( + "Cannot convert " + substrate.__class__.__name__ + + " to a seekable bit stream.") + + +def isEndOfStream(substrate): + """Check whether we have reached the end of a stream. + + Although it is more effective to read and catch exceptions, this + function + + Parameters + ---------- + substrate: :py:class:`IOBase` + Stream to check + + Returns + ------- + : :py:class:`bool` + """ + if isinstance(substrate, io.BytesIO): + cp = substrate.tell() + substrate.seek(0, os.SEEK_END) + result = substrate.tell() == cp + substrate.seek(cp, os.SEEK_SET) + yield result + + else: + received = substrate.read(1) + if received is None: + yield + + if received: + substrate.seek(-1, os.SEEK_CUR) + + yield not received + + +def peekIntoStream(substrate, size=-1): + """Peek into stream. + + Parameters + ---------- + substrate: :py:class:`IOBase` + Stream to read from. + + size: :py:class:`int` + How many bytes to peek (-1 = all available) + + Returns + ------- + : :py:class:`bytes` or :py:class:`str` + The return type depends on Python major version + """ + if hasattr(substrate, "peek"): + received = substrate.peek(size) + if received is None: + yield + + while len(received) < size: + yield + + yield received + + else: + current_position = substrate.tell() + try: + for chunk in readFromStream(substrate, size): + yield chunk + + finally: + substrate.seek(current_position) + + +def readFromStream(substrate, size=-1, context=None): + """Read from the stream. + + Parameters + ---------- + substrate: :py:class:`IOBase` + Stream to read from. + + Keyword parameters + ------------------ + size: :py:class:`int` + How many bytes to read (-1 = all available) + + context: :py:class:`dict` + Opaque caller context will be attached to exception objects created + by this function. + + Yields + ------ + : :py:class:`bytes` or :py:class:`str` or :py:class:`SubstrateUnderrunError` + Read data or :py:class:`~pyasn1.error.SubstrateUnderrunError` + object if no `size` bytes is readily available in the stream. The + data type depends on Python major version + + Raises + ------ + : :py:class:`~pyasn1.error.EndOfStreamError` + Input stream is exhausted + """ + while True: + # this will block unless stream is non-blocking + received = substrate.read(size) + if received is None: # non-blocking stream can do this + yield error.SubstrateUnderrunError(context=context) + + elif not received and size != 0: # end-of-stream + raise error.EndOfStreamError(context=context) + + elif len(received) < size: + substrate.seek(-len(received), os.SEEK_CUR) + + # behave like a non-blocking stream + yield error.SubstrateUnderrunError(context=context) + + else: + break + + yield received