diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_fake_quantize_learnable_per_channel_affine_backward.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_fake_quantize_learnable_per_channel_affine_backward.h new file mode 100644 index 0000000000000000000000000000000000000000..b32eab27099384f71e82ff36dc2b833bcdcd1616 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_fake_quantize_learnable_per_channel_affine_backward.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::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) +inline ::std::tuple _fake_quantize_learnable_per_channel_affine_backward(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_channel_affine_backward::call(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_lu_with_info_compositeimplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_lu_with_info_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..063523ec568a15d18933b128e72fb469555ce8d2 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_lu_with_info_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 _lu_with_info(const at::Tensor & self, bool pivot=true, bool check_errors=true); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_reshape_alias_copy_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_reshape_alias_copy_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..67c888f4b27eaa752017235cbca44c44b9ac9ea1 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_reshape_alias_copy_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 _reshape_alias_copy { + using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_reshape_alias_copy") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor") + static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride); +}; + +struct TORCH_API _reshape_alias_copy_out { + using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_reshape_alias_copy") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_attention_math.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_attention_math.h new file mode 100644 index 0000000000000000000000000000000000000000..ea245d0ca8fc22dd6c53dcf3db9955bfcb677517 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_attention_math.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::_scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None) -> (Tensor, Tensor) +inline ::std::tuple _scaled_dot_product_attention_math(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask={}, double dropout_p=0.0, bool is_causal=false, const c10::optional & dropout_mask={}, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_attention_math::call(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unpack_dual_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unpack_dual_native.h new file mode 100644 index 0000000000000000000000000000000000000000..e267e6ca017444557f9470773bbd47b46f7df861 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unpack_dual_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 _unpack_dual(const at::Tensor & dual, int64_t level); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unsafe_view_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unsafe_view_native.h new file mode 100644 index 0000000000000000000000000000000000000000..cf6c3dc26b4f3ce9366e1c3b5fc4dd4f8c34a945 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/_unsafe_view_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 _unsafe_view(const at::Tensor & self, at::IntArrayRef size); +TORCH_API at::Tensor & _unsafe_view_out_symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/arccos_compositeimplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/arccos_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d26cf494650a95cacbb998b3361a49e1197b17d1 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/arccos_compositeimplicitautograd_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 compositeimplicitautograd { + +TORCH_API at::Tensor arccos(const at::Tensor & self); +TORCH_API at::Tensor & arccos_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & arccos_outf(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor & arccos_(at::Tensor & self); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/bitwise_not_compositeexplicitautogradnonfunctional_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/bitwise_not_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..6c370da3fa693bc313ddf97ffb48b696e5d36cd1 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/bitwise_not_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 bitwise_not(const at::Tensor & self); +TORCH_API at::Tensor & bitwise_not_(at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/clip_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/clip_native.h new file mode 100644 index 0000000000000000000000000000000000000000..a62d2b2ad8f0924b278c0b9acb5c3a99cca22ae1 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/clip_native.h @@ -0,0 +1,26 @@ +#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 clip(const at::Tensor & self, const c10::optional & min=c10::nullopt, const c10::optional & max=c10::nullopt); +TORCH_API at::Tensor & clip_out(const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out); +TORCH_API at::Tensor & clip_(at::Tensor & self, const c10::optional & min=c10::nullopt, const c10::optional & max=c10::nullopt); +TORCH_API at::Tensor clip(const at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}); +TORCH_API at::Tensor & clip_out(const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out); +TORCH_API at::Tensor & clip_(at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/copy_sparse_to_sparse_meta_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/copy_sparse_to_sparse_meta_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..0d265cdf8cebe5545d3be048da6dd65d9299046e --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/copy_sparse_to_sparse_meta_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 meta { + +TORCH_API at::Tensor & copy_sparse_to_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking=false); + +} // namespace meta +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_convolution_cuda_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_convolution_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..eabb449dd5fda6857fc974ae35a37b1279c448ce --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/cudnn_convolution_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 at::Tensor cudnn_convolution(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32); + +} // namespace cuda +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/dequantize_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/dequantize_native.h new file mode 100644 index 0000000000000000000000000000000000000000..8399a325c5ee5b36e3deca8fa33cf379ef0cc19c --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/dequantize_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 at::Tensor & dequantize_self_out(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor dequantize_cpu_or_cuda(const at::Tensor & self); +TORCH_API at::Tensor dequantize_quantized(const at::Tensor & self); +TORCH_API void dequantize_tensors_out(at::TensorList tensors, at::TensorList out); +TORCH_API ::std::vector dequantize_tensors_quantized_cpu(at::TensorList tensors); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_renorm_compositeexplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_renorm_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..54c202e04152bfc601f178f0f4ca369f8470b21a --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/embedding_renorm_compositeexplicitautograd_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 compositeexplicitautograd { + +TORCH_API at::Tensor embedding_renorm(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type); +TORCH_API at::Tensor & embedding_renorm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type); +TORCH_API at::Tensor & embedding_renorm_outf(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/fmin.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/fmin.h new file mode 100644 index 0000000000000000000000000000000000000000..b421e06a6b286494dc0d7e27b18fd31a6a831d1b --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/fmin.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::fmin(Tensor self, Tensor other) -> Tensor +inline at::Tensor fmin(const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmin::call(self, other); +} + +// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fmin_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmin_out::call(self, other, out); +} +// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fmin_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::fmin_out::call(self, other, out); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/grid_sampler_2d_cpu_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/grid_sampler_2d_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d0a004abcf0d0559eea81053433c3dd147578455 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/grid_sampler_2d_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 grid_sampler_2d(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners); + +} // namespace cpu +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/hypot.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/hypot.h new file mode 100644 index 0000000000000000000000000000000000000000..589c8fea89cc8bf0a5eea98958f07fc311bd875e --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/hypot.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::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & hypot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::hypot_out::call(self, other, out); +} +// aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & hypot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::hypot_out::call(self, other, out); +} + +// aten::hypot(Tensor self, Tensor other) -> Tensor +inline at::Tensor hypot(const at::Tensor & self, const at::Tensor & other) { + return at::_ops::hypot::call(self, other); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/isin.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/isin.h new file mode 100644 index 0000000000000000000000000000000000000000..068b8641c91d70525fdda2bf7e7574b85bc7d9ab --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/isin.h @@ -0,0 +1,67 @@ +#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::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Tensor_out::call(elements, test_elements, assume_unique, invert, out); +} +// aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_outf(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Tensor_Tensor_out::call(elements, test_elements, assume_unique, invert, out); +} + +// aten::isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor +inline at::Tensor isin(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Tensor::call(elements, test_elements, assume_unique, invert); +} + +// aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_out(at::Tensor & out, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Scalar_out::call(elements, test_element, assume_unique, invert, out); +} +// aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_outf(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Tensor_Scalar_out::call(elements, test_element, assume_unique, invert, out); +} + +// aten::isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor +inline at::Tensor isin(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Scalar::call(elements, test_element, assume_unique, invert); +} + +// aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_out(at::Tensor & out, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Scalar_Tensor_out::call(element, test_elements, assume_unique, invert, out); +} +// aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & isin_outf(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Scalar_Tensor_out::call(element, test_elements, assume_unique, invert, out); +} + +// aten::isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor +inline at::Tensor isin(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Scalar_Tensor::call(element, test_elements, assume_unique, invert); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log10_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log10_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..2a5a4ed2cd42bb566686587854c7db097215ff0e --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log10_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 log10 { + 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::log10") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "log10(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +struct TORCH_API log10_ { + using schema = 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::log10_") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "log10_(Tensor(a!) self) -> Tensor(a!)") + static at::Tensor & call(at::Tensor & self); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self); +}; + +struct TORCH_API log10_out { + using schema = 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::log10") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log2_compositeexplicitautogradnonfunctional_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log2_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..0c3163a4ff9fd5eb0c470e199be0f7aec2fb4f50 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/log2_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 log2(const at::Tensor & self); +TORCH_API at::Tensor & log2_(at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/lu_unpack_meta.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/lu_unpack_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..2b6e917eb0f5b7d1732057be27b08c03e7aff98a --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/lu_unpack_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_lu_unpack : public at::impl::MetaBase { + + + void meta(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots); +}; + +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/matmul_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/matmul_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..066a940d2fe6a8338d6e0ff1c52fd1c6962eb9e5 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/matmul_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 matmul { + 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::matmul") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "matmul(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 matmul_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::matmul") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "matmul.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/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/max_unpool3d_cpu_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/max_unpool3d_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..24efbc157c4a474569e90b9c1241265e267d5efd --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/max_unpool3d_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 max_unpool3d(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding); +TORCH_API at::Tensor max_unpool3d_symint(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding); +TORCH_API at::Tensor & max_unpool3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding); +TORCH_API at::Tensor & max_unpool3d_outf(const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out); +TORCH_API at::Tensor & max_unpool3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding); +TORCH_API at::Tensor & max_unpool3d_symint_outf(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_linear_compositeexplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_linear_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..cbc76a31f8ceecc7a8a187e342786bd759cde090 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/mkldnn_linear_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 & mkldnn_linear_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias={}); +TORCH_API at::Tensor & mkldnn_linear_outf(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/new_full_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/new_full_native.h new file mode 100644 index 0000000000000000000000000000000000000000..bcc33ec99ae1ea9ee1f8b99359b6f4c3bb9f01da --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/new_full_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 new_full(const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional dtype={}, c10::optional layout={}, c10::optional device={}, c10::optional pin_memory={}); +TORCH_API at::Tensor & new_full_out_symint(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/quantize_per_channel_compositeexplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/quantize_per_channel_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..31a0af12225f7005fa25632adee59ec645672429 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/quantize_per_channel_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 & quantize_per_channel_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype); +TORCH_API at::Tensor & quantize_per_channel_outf(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/relu_cuda_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/relu_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..988962be84621ddfdc769578f98acca0309baa29 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/relu_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 relu(const at::Tensor & self); +TORCH_API at::Tensor & relu_(at::Tensor & self); + +} // namespace cuda +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/repeat.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/repeat.h new file mode 100644 index 0000000000000000000000000000000000000000..cb79b80ff5f6ad27d35ce787b1619d1e07c53583 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/repeat.h @@ -0,0 +1,83 @@ +#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 { + + +namespace symint { + template ::value>> + at::Tensor repeat(const at::Tensor & self, at::IntArrayRef repeats) { + return at::_ops::repeat::call(self, c10::fromIntArrayRefSlow(repeats)); + } +} + +namespace symint { + template ::value>> + at::Tensor repeat(const at::Tensor & self, c10::SymIntArrayRef repeats) { + return at::_ops::repeat::call(self, repeats); + } +} + +// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef repeats) { + return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out); +} +namespace symint { + template ::value>> + at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef repeats) { + return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out); + } +} + +// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & repeat_outf(const at::Tensor & self, at::IntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out); +} +namespace symint { + template ::value>> + at::Tensor & repeat_outf(const at::Tensor & self, at::IntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::call(self, c10::fromIntArrayRefSlow(repeats), out); + } +} + +// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & repeat_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef repeats) { + return at::_ops::repeat_out::call(self, repeats, out); +} +namespace symint { + template ::value>> + at::Tensor & repeat_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef repeats) { + return at::_ops::repeat_out::call(self, repeats, out); + } +} + +// aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & repeat_symint_outf(const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::call(self, repeats, out); +} +namespace symint { + template ::value>> + at::Tensor & repeat_outf(const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::call(self, repeats, out); + } +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_cuda_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..608763284f8db350b9677e6e5ebb6e401fd1cca2 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad3d_backward_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 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 cuda +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/resize_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/resize_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..c0965932b583a3920645d91f5fc7164f1cfed4aa --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/resize_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 resize_ { + using schema = const at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::optional); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::resize_") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!)") + static const at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format); + static const at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format); +}; + +struct TORCH_API resize_out { + using schema = const at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::optional, 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::resize") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)") + static const at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format, const at::Tensor & out); + static const at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format, const at::Tensor & out); +}; + +struct TORCH_API resize { + using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::optional); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::resize") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor") + static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/scatter_reduce_cuda_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/scatter_reduce_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..2add648340654ab66e33c3e5775ee3e6a080966b --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/scatter_reduce_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 scatter_reduce(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true); +TORCH_API at::Tensor & scatter_reduce_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true); +TORCH_API at::Tensor & scatter_reduce_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out); +TORCH_API at::Tensor & scatter_reduce_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true); + +} // namespace cuda +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sigmoid_cuda_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sigmoid_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..6b78740a4273630a4ce3f98bc77b04ad267b35cf --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sigmoid_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 sigmoid(const at::Tensor & self); +TORCH_API at::Tensor & sigmoid_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & sigmoid_outf(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor & sigmoid_(at::Tensor & self); + +} // namespace cuda +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sinh_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sinh_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..620dd3533f039b353694f20b084dc2079fcad2e0 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/sinh_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 sinh { + 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::sinh") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sinh(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +struct TORCH_API sinh_ { + using schema = 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::sinh_") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sinh_(Tensor(a!) self) -> Tensor(a!)") + static at::Tensor & call(at::Tensor & self); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self); +}; + +struct TORCH_API sinh_out { + using schema = 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::sinh") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_expit.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_expit.h new file mode 100644 index 0000000000000000000000000000000000000000..b74eae968bd015bbb548b2b34bdf8544561fce8c --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_expit.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::special_expit(Tensor self) -> Tensor +inline at::Tensor special_expit(const at::Tensor & self) { + return at::_ops::special_expit::call(self); +} + +// aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & special_expit_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_expit_out::call(self, out); +} +// aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & special_expit_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_expit_out::call(self, out); +} + +} diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_psi_compositeimplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_psi_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..8933a587ec92fac30742fd3a3586c83dc2aa13bd --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/special_psi_compositeimplicitautograd_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 compositeimplicitautograd { + +TORCH_API at::Tensor special_psi(const at::Tensor & self); +TORCH_API at::Tensor & special_psi_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & special_psi_outf(const at::Tensor & self, at::Tensor & out); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/split_copy_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/split_copy_native.h new file mode 100644 index 0000000000000000000000000000000000000000..d3c8fb1cfe7f70648cf268b7296fa815dbfd71bf --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/split_copy_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 void split_copy_Tensor_out(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out); +TORCH_API ::std::vector split_copy_Tensor_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/std_cpu_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/std_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..8c511ad8573ddf129674014e5461348b5cff1282 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/std_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 std(const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false); +TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false); +TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/triangular_solve_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/triangular_solve_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..824ad9b94f95cf2b4f9e204e8e28d9e6d26efafb --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/triangular_solve_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 triangular_solve_X { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, bool, bool, bool, 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::triangular_solve") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "X") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)") + static ::std::tuple call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M); +}; + +struct TORCH_API triangular_solve { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, bool, bool, bool); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::triangular_solve") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)") + static ::std::tuple call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/type_as_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/type_as_native.h new file mode 100644 index 0000000000000000000000000000000000000000..ce295606e9b731f353d52f7c3f09dfcd612c417a --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/type_as_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 type_as(const at::Tensor & self, const at::Tensor & other); +} // namespace native +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsafe_split_compositeexplicitautograd_dispatch.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsafe_split_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d153a4ac2f8b3fbaab972c6b98d258fec8900331 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsafe_split_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 ::std::vector unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0); +TORCH_API ::std::vector unsafe_split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0); +TORCH_API void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0); +TORCH_API void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out); +TORCH_API void unsafe_split_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0); +TORCH_API void unsafe_split_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsqueeze_copy_ops.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsqueeze_copy_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..ee985f3e5eee10b25642e05bd885f11d7face79e --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/unsqueeze_copy_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 unsqueeze_copy { + using schema = at::Tensor (const at::Tensor &, int64_t); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::unsqueeze_copy") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "unsqueeze_copy(Tensor self, int dim) -> Tensor") + static at::Tensor call(const at::Tensor & self, int64_t dim); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim); +}; + +struct TORCH_API unsqueeze_copy_out { + using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::unsqueeze_copy") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, int64_t dim, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/var_native.h b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/var_native.h new file mode 100644 index 0000000000000000000000000000000000000000..849c6afe71f1cb47599300cf95410fe53ff88860 --- /dev/null +++ b/llava_next/lib/python3.10/site-packages/torch/include/ATen/ops/var_native.h @@ -0,0 +1,29 @@ +#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 var(const at::Tensor & self, bool unbiased=true); +TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased=true, bool keepdim=false); +TORCH_API at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out); +TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false); +TORCH_API at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out); +TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, bool unbiased=true, bool keepdim=false); +TORCH_API at::Tensor & var_out(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out); +TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false); +TORCH_API at::Tensor & var_out(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/__pycache__/CommonTokenFactory.cpython-310.pyc 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All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +from antlr4.atn.ATNState import StarLoopEntryState + +from antlr4.atn.ATNConfigSet import ATNConfigSet +from antlr4.atn.ATNState import DecisionState +from antlr4.dfa.DFAState import DFAState +from antlr4.error.Errors import IllegalStateException + + +class DFA(object): + __slots__ = ('atnStartState', 'decision', '_states', 's0', 'precedenceDfa') + + def __init__(self, atnStartState:DecisionState, decision:int=0): + # From which ATN state did we create this DFA? + self.atnStartState = atnStartState + self.decision = decision + # A set of all DFA states. Use {@link Map} so we can get old state back + # ({@link Set} only allows you to see if it's there). + self._states = dict() + self.s0 = None + # {@code true} if this DFA is for a precedence decision; otherwise, + # {@code false}. This is the backing field for {@link #isPrecedenceDfa}, + # {@link #setPrecedenceDfa}. + self.precedenceDfa = False + + if isinstance(atnStartState, StarLoopEntryState): + if atnStartState.isPrecedenceDecision: + self.precedenceDfa = True + precedenceState = DFAState(configs=ATNConfigSet()) + precedenceState.edges = [] + precedenceState.isAcceptState = False + precedenceState.requiresFullContext = False + self.s0 = precedenceState + + + # Get the start state for a specific precedence value. + # + # @param precedence The current precedence. + # @return The start state corresponding to the specified precedence, or + # {@code null} if no start state exists for the specified precedence. + # + # @throws IllegalStateException if this is not a precedence DFA. + # @see #isPrecedenceDfa() + + def getPrecedenceStartState(self, precedence:int): + if not self.precedenceDfa: + raise IllegalStateException("Only precedence DFAs may contain a precedence start state.") + + # s0.edges is never null for a precedence DFA + if precedence < 0 or precedence >= len(self.s0.edges): + return None + return self.s0.edges[precedence] + + # Set the start state for a specific precedence value. + # + # @param precedence The current precedence. + # @param startState The start state corresponding to the specified + # precedence. + # + # @throws IllegalStateException if this is not a precedence DFA. + # @see #isPrecedenceDfa() + # + def setPrecedenceStartState(self, precedence:int, startState:DFAState): + if not self.precedenceDfa: + raise IllegalStateException("Only precedence DFAs may contain a precedence start state.") + + if precedence < 0: + return + + # synchronization on s0 here is ok. when the DFA is turned into a + # precedence DFA, s0 will be initialized once and not updated again + # s0.edges is never null for a precedence DFA + if precedence >= len(self.s0.edges): + ext = [None] * (precedence + 1 - len(self.s0.edges)) + self.s0.edges.extend(ext) + self.s0.edges[precedence] = startState + # + # Sets whether this is a precedence DFA. If the specified value differs + # from the current DFA configuration, the following actions are taken; + # otherwise no changes are made to the current DFA. + # + #
    + #
  • The {@link #states} map is cleared
    • + #
  • If {@code precedenceDfa} is {@code false}, the initial state + # {@link #s0} is set to {@code null}; otherwise, it is initialized to a new + # {@link DFAState} with an empty outgoing {@link DFAState#edges} array to + # store the start states for individual precedence values.
    • + #
  • The {@link #precedenceDfa} field is updated
    • + #
+ # + # @param precedenceDfa {@code true} if this is a precedence DFA; otherwise, + # {@code false} + + def setPrecedenceDfa(self, precedenceDfa:bool): + if self.precedenceDfa != precedenceDfa: + self._states = dict() + if precedenceDfa: + precedenceState = DFAState(configs=ATNConfigSet()) + precedenceState.edges = [] + precedenceState.isAcceptState = False + precedenceState.requiresFullContext = False + self.s0 = precedenceState + else: + self.s0 = None + self.precedenceDfa = precedenceDfa + + @property + def states(self): + return self._states + + # Return a list of all states in this DFA, ordered by state number. + def sortedStates(self): + return sorted(self._states.keys(), key=lambda state: state.stateNumber) + + def __str__(self): + return self.toString(None) + + def toString(self, literalNames:list=None, symbolicNames:list=None): + if self.s0 is None: + return "" + from antlr4.dfa.DFASerializer import DFASerializer + serializer = DFASerializer(self,literalNames,symbolicNames) + return str(serializer) + + def toLexerString(self): + if self.s0 is None: + return "" + from antlr4.dfa.DFASerializer import LexerDFASerializer + serializer = LexerDFASerializer(self) + return str(serializer) diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFASerializer.py b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFASerializer.py new file mode 100644 index 0000000000000000000000000000000000000000..bca0727b76dc54909be0bf60b6d636ec8f539927 --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFASerializer.py @@ -0,0 +1,73 @@ +# +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +#/ + +# A DFA walker that knows how to dump them to serialized strings.#/ +from io import StringIO +from antlr4 import DFA +from antlr4.Utils import str_list +from antlr4.dfa.DFAState import DFAState + + +class DFASerializer(object): + __slots__ = ('dfa', 'literalNames', 'symbolicNames') + + def __init__(self, dfa:DFA, literalNames:list=None, symbolicNames:list=None): + self.dfa = dfa + self.literalNames = literalNames + self.symbolicNames = symbolicNames + + def __str__(self): + if self.dfa.s0 is None: + return None + with StringIO() as buf: + for s in self.dfa.sortedStates(): + n = 0 + if s.edges is not None: + n = len(s.edges) + for i in range(0, n): + t = s.edges[i] + if t is not None and t.stateNumber != 0x7FFFFFFF: + buf.write(self.getStateString(s)) + label = self.getEdgeLabel(i) + buf.write("-") + buf.write(label) + buf.write("->") + buf.write(self.getStateString(t)) + buf.write('\n') + output = buf.getvalue() + if len(output)==0: + return None + else: + return output + + def getEdgeLabel(self, i:int): + if i==0: + return "EOF" + if self.literalNames is not None and i<=len(self.literalNames): + return self.literalNames[i-1] + elif self.symbolicNames is not None and i<=len(self.symbolicNames): + return self.symbolicNames[i-1] + else: + return str(i-1) + + def getStateString(self, s:DFAState): + n = s.stateNumber + baseStateStr = ( ":" if s.isAcceptState else "") + "s" + str(n) + ( "^" if s.requiresFullContext else "") + if s.isAcceptState: + if s.predicates is not None: + return baseStateStr + "=>" + str_list(s.predicates) + else: + return baseStateStr + "=>" + str(s.prediction) + else: + return baseStateStr + +class LexerDFASerializer(DFASerializer): + + def __init__(self, dfa:DFA): + super().__init__(dfa, None) + + def getEdgeLabel(self, i:int): + return "'" + chr(i) + "'" diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFAState.py b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFAState.py new file mode 100644 index 0000000000000000000000000000000000000000..51955a448886ea1fa34f0f4dff7fb0976edd1975 --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/DFAState.py @@ -0,0 +1,126 @@ +# +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +#/ + +# Map a predicate to a predicted alternative.#/ +from io import StringIO +from antlr4.atn.ATNConfigSet import ATNConfigSet +from antlr4.atn.SemanticContext import SemanticContext + + +class PredPrediction(object): + __slots__ = ('alt', 'pred') + + def __init__(self, pred:SemanticContext, alt:int): + self.alt = alt + self.pred = pred + + def __str__(self): + return "(" + str(self.pred) + ", " + str(self.alt) + ")" + +# A DFA state represents a set of possible ATN configurations. +# As Aho, Sethi, Ullman p. 117 says "The DFA uses its state +# to keep track of all possible states the ATN can be in after +# reading each input symbol. That is to say, after reading +# input a1a2..an, the DFA is in a state that represents the +# subset T of the states of the ATN that are reachable from the +# ATN's start state along some path labeled a1a2..an." +# In conventional NFA→DFA conversion, therefore, the subset T +# would be a bitset representing the set of states the +# ATN could be in. We need to track the alt predicted by each +# state as well, however. More importantly, we need to maintain +# a stack of states, tracking the closure operations as they +# jump from rule to rule, emulating rule invocations (method calls). +# I have to add a stack to simulate the proper lookahead sequences for +# the underlying LL grammar from which the ATN was derived. +# +#

I use a set of ATNConfig objects not simple states. An ATNConfig +# is both a state (ala normal conversion) and a RuleContext describing +# the chain of rules (if any) followed to arrive at that state.

+# +#

A DFA state may have multiple references to a particular state, +# but with different ATN contexts (with same or different alts) +# meaning that state was reached via a different set of rule invocations.

+#/ +class DFAState(object): + __slots__ = ( + 'stateNumber', 'configs', 'edges', 'isAcceptState', 'prediction', + 'lexerActionExecutor', 'requiresFullContext', 'predicates' + ) + + def __init__(self, stateNumber:int=-1, configs:ATNConfigSet=ATNConfigSet()): + self.stateNumber = stateNumber + self.configs = configs + # {@code edges[symbol]} points to target of symbol. Shift up by 1 so (-1) + # {@link Token#EOF} maps to {@code edges[0]}. + self.edges = None + self.isAcceptState = False + # if accept state, what ttype do we match or alt do we predict? + # This is set to {@link ATN#INVALID_ALT_NUMBER} when {@link #predicates}{@code !=null} or + # {@link #requiresFullContext}. + self.prediction = 0 + self.lexerActionExecutor = None + # Indicates that this state was created during SLL prediction that + # discovered a conflict between the configurations in the state. Future + # {@link ParserATNSimulator#execATN} invocations immediately jumped doing + # full context prediction if this field is true. + self.requiresFullContext = False + # During SLL parsing, this is a list of predicates associated with the + # ATN configurations of the DFA state. When we have predicates, + # {@link #requiresFullContext} is {@code false} since full context prediction evaluates predicates + # on-the-fly. If this is not null, then {@link #prediction} is + # {@link ATN#INVALID_ALT_NUMBER}. + # + #

We only use these for non-{@link #requiresFullContext} but conflicting states. That + # means we know from the context (it's $ or we don't dip into outer + # context) that it's an ambiguity not a conflict.

+ # + #

This list is computed by {@link ParserATNSimulator#predicateDFAState}.

+ self.predicates = None + + + + # Get the set of all alts mentioned by all ATN configurations in this + # DFA state. + def getAltSet(self): + if self.configs is not None: + return set(cfg.alt for cfg in self.configs) or None + return None + + def __hash__(self): + return hash(self.configs) + + # Two {@link DFAState} instances are equal if their ATN configuration sets + # are the same. This method is used to see if a state already exists. + # + #

Because the number of alternatives and number of ATN configurations are + # finite, there is a finite number of DFA states that can be processed. + # This is necessary to show that the algorithm terminates.

+ # + #

Cannot test the DFA state numbers here because in + # {@link ParserATNSimulator#addDFAState} we need to know if any other state + # exists that has this exact set of ATN configurations. The + # {@link #stateNumber} is irrelevant.

+ def __eq__(self, other): + # compare set of ATN configurations in this set with other + if self is other: + return True + elif not isinstance(other, DFAState): + return False + else: + return self.configs==other.configs + + def __str__(self): + with StringIO() as buf: + buf.write(str(self.stateNumber)) + buf.write(":") + buf.write(str(self.configs)) + if self.isAcceptState: + buf.write("=>") + if self.predicates is not None: + buf.write(str(self.predicates)) + else: + buf.write(str(self.prediction)) + return buf.getvalue() diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__init__.py b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..216c000dc5ffc8e53cc9c596e420c1e67604d1aa --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__init__.py @@ -0,0 +1 @@ +__author__ = 'ericvergnaud' diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFA.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFA.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..46208dd2c0cbe858df56fefcc125ddb5b3f267d1 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFA.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFASerializer.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFASerializer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e1fcc46c1cdaae729c94ca801a5a21b0c348154a Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFASerializer.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFAState.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFAState.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c8601669c7fcd70d1161b2b5911d218e66182864 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/DFAState.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/__init__.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..06cf2487440b7a9d3d03b13a801088cdd262dfe9 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/dfa/__pycache__/__init__.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/DiagnosticErrorListener.py b/vlmpy310/lib/python3.10/site-packages/antlr4/error/DiagnosticErrorListener.py new file mode 100644 index 0000000000000000000000000000000000000000..32ac14b63579ce7c984c2e34f2b1c80bebe328ed --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/error/DiagnosticErrorListener.py @@ -0,0 +1,107 @@ +# +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +# + + +# +# This implementation of {@link ANTLRErrorListener} can be used to identify +# certain potential correctness and performance problems in grammars. "Reports" +# are made by calling {@link Parser#notifyErrorListeners} with the appropriate +# message. +# +#
    +#
  • Ambiguities: These are cases where more than one path through the +# grammar can match the input.
    • +#
  • Weak context sensitivity: These are cases where full-context +# prediction resolved an SLL conflict to a unique alternative which equaled the +# minimum alternative of the SLL conflict.
    • +#
  • Strong (forced) context sensitivity: These are cases where the +# full-context prediction resolved an SLL conflict to a unique alternative, +# and the minimum alternative of the SLL conflict was found to not be +# a truly viable alternative. Two-stage parsing cannot be used for inputs where +# this situation occurs.
    • +#
+ +from io import StringIO +from antlr4 import Parser, DFA +from antlr4.atn.ATNConfigSet import ATNConfigSet +from antlr4.error.ErrorListener import ErrorListener + +class DiagnosticErrorListener(ErrorListener): + + def __init__(self, exactOnly:bool=True): + # whether all ambiguities or only exact ambiguities are reported. + self.exactOnly = exactOnly + + def reportAmbiguity(self, recognizer:Parser, dfa:DFA, startIndex:int, + stopIndex:int, exact:bool, ambigAlts:set, configs:ATNConfigSet): + if self.exactOnly and not exact: + return + + with StringIO() as buf: + buf.write("reportAmbiguity d=") + buf.write(self.getDecisionDescription(recognizer, dfa)) + buf.write(": ambigAlts=") + buf.write(str(self.getConflictingAlts(ambigAlts, configs))) + buf.write(", input='") + buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex)) + buf.write("'") + recognizer.notifyErrorListeners(buf.getvalue()) + + + def reportAttemptingFullContext(self, recognizer:Parser, dfa:DFA, startIndex:int, + stopIndex:int, conflictingAlts:set, configs:ATNConfigSet): + with StringIO() as buf: + buf.write("reportAttemptingFullContext d=") + buf.write(self.getDecisionDescription(recognizer, dfa)) + buf.write(", input='") + buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex)) + buf.write("'") + recognizer.notifyErrorListeners(buf.getvalue()) + + def reportContextSensitivity(self, recognizer:Parser, dfa:DFA, startIndex:int, + stopIndex:int, prediction:int, configs:ATNConfigSet): + with StringIO() as buf: + buf.write("reportContextSensitivity d=") + buf.write(self.getDecisionDescription(recognizer, dfa)) + buf.write(", input='") + buf.write(recognizer.getTokenStream().getText(startIndex, stopIndex)) + buf.write("'") + recognizer.notifyErrorListeners(buf.getvalue()) + + def getDecisionDescription(self, recognizer:Parser, dfa:DFA): + decision = dfa.decision + ruleIndex = dfa.atnStartState.ruleIndex + + ruleNames = recognizer.ruleNames + if ruleIndex < 0 or ruleIndex >= len(ruleNames): + return str(decision) + + ruleName = ruleNames[ruleIndex] + if ruleName is None or len(ruleName)==0: + return str(decision) + + return str(decision) + " (" + ruleName + ")" + + # + # Computes the set of conflicting or ambiguous alternatives from a + # configuration set, if that information was not already provided by the + # parser. + # + # @param reportedAlts The set of conflicting or ambiguous alternatives, as + # reported by the parser. + # @param configs The conflicting or ambiguous configuration set. + # @return Returns {@code reportedAlts} if it is not {@code null}, otherwise + # returns the set of alternatives represented in {@code configs}. + # + def getConflictingAlts(self, reportedAlts:set, configs:ATNConfigSet): + if reportedAlts is not None: + return reportedAlts + + result = set() + for config in configs: + result.add(config.alt) + + return result diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorListener.py b/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorListener.py new file mode 100644 index 0000000000000000000000000000000000000000..933264d431b9829f43a38d5f0f07c83bbad703a0 --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorListener.py @@ -0,0 +1,72 @@ +# +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. + +# Provides an empty default implementation of {@link ANTLRErrorListener}. The +# default implementation of each method does nothing, but can be overridden as +# necessary. + + +import sys + +class ErrorListener(object): + + def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): + pass + + def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs): + pass + + def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs): + pass + + def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs): + pass + +class ConsoleErrorListener(ErrorListener): + # + # Provides a default instance of {@link ConsoleErrorListener}. + # + INSTANCE = None + + # + # {@inheritDoc} + # + #

+ # This implementation prints messages to {@link System#err} containing the + # values of {@code line}, {@code charPositionInLine}, and {@code msg} using + # the following format.

+ # + # 
+    # line line:charPositionInLine msg
+    #
+ # + def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): + print("line " + str(line) + ":" + str(column) + " " + msg, file=sys.stderr) + +ConsoleErrorListener.INSTANCE = ConsoleErrorListener() + +class ProxyErrorListener(ErrorListener): + + def __init__(self, delegates): + super().__init__() + if delegates is None: + raise ReferenceError("delegates") + self.delegates = delegates + + def syntaxError(self, recognizer, offendingSymbol, line, column, msg, e): + for delegate in self.delegates: + delegate.syntaxError(recognizer, offendingSymbol, line, column, msg, e) + + def reportAmbiguity(self, recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs): + for delegate in self.delegates: + delegate.reportAmbiguity(recognizer, dfa, startIndex, stopIndex, exact, ambigAlts, configs) + + def reportAttemptingFullContext(self, recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs): + for delegate in self.delegates: + delegate.reportAttemptingFullContext(recognizer, dfa, startIndex, stopIndex, conflictingAlts, configs) + + def reportContextSensitivity(self, recognizer, dfa, startIndex, stopIndex, prediction, configs): + for delegate in self.delegates: + delegate.reportContextSensitivity(recognizer, dfa, startIndex, stopIndex, prediction, configs) diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorStrategy.py b/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorStrategy.py new file mode 100644 index 0000000000000000000000000000000000000000..0f7caadb240445e6d997ad582a51836f95cab5c5 --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/error/ErrorStrategy.py @@ -0,0 +1,709 @@ +# +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +# +import sys +from antlr4.IntervalSet import IntervalSet + +from antlr4.Token import Token +from antlr4.atn.ATNState import ATNState +from antlr4.error.Errors import RecognitionException, NoViableAltException, InputMismatchException, \ + FailedPredicateException, ParseCancellationException + +# need forward declaration +Parser = None + +class ErrorStrategy(object): + + def reset(self, recognizer:Parser): + pass + + def recoverInline(self, recognizer:Parser): + pass + + def recover(self, recognizer:Parser, e:RecognitionException): + pass + + def sync(self, recognizer:Parser): + pass + + def inErrorRecoveryMode(self, recognizer:Parser): + pass + + def reportError(self, recognizer:Parser, e:RecognitionException): + pass + + +# This is the default implementation of {@link ANTLRErrorStrategy} used for +# error reporting and recovery in ANTLR parsers. +# +class DefaultErrorStrategy(ErrorStrategy): + + def __init__(self): + super().__init__() + # Indicates whether the error strategy is currently "recovering from an + # error". This is used to suppress reporting multiple error messages while + # attempting to recover from a detected syntax error. + # + # @see #inErrorRecoveryMode + # + self.errorRecoveryMode = False + + # The index into the input stream where the last error occurred. + # This is used to prevent infinite loops where an error is found + # but no token is consumed during recovery...another error is found, + # ad nauseum. This is a failsafe mechanism to guarantee that at least + # one token/tree node is consumed for two errors. + # + self.lastErrorIndex = -1 + self.lastErrorStates = None + self.nextTokensContext = None + self.nextTokenState = 0 + + #

The default implementation simply calls {@link #endErrorCondition} to + # ensure that the handler is not in error recovery mode.

+ def reset(self, recognizer:Parser): + self.endErrorCondition(recognizer) + + # + # This method is called to enter error recovery mode when a recognition + # exception is reported. + # + # @param recognizer the parser instance + # + def beginErrorCondition(self, recognizer:Parser): + self.errorRecoveryMode = True + + def inErrorRecoveryMode(self, recognizer:Parser): + return self.errorRecoveryMode + + # + # This method is called to leave error recovery mode after recovering from + # a recognition exception. + # + # @param recognizer + # + def endErrorCondition(self, recognizer:Parser): + self.errorRecoveryMode = False + self.lastErrorStates = None + self.lastErrorIndex = -1 + + # + # {@inheritDoc} + # + #

The default implementation simply calls {@link #endErrorCondition}.

+ # + def reportMatch(self, recognizer:Parser): + self.endErrorCondition(recognizer) + + # + # {@inheritDoc} + # + #

The default implementation returns immediately if the handler is already + # in error recovery mode. Otherwise, it calls {@link #beginErrorCondition} + # and dispatches the reporting task based on the runtime type of {@code e} + # according to the following table.

+ # + #
    + #
  • {@link NoViableAltException}: Dispatches the call to + # {@link #reportNoViableAlternative}
    • + #
  • {@link InputMismatchException}: Dispatches the call to + # {@link #reportInputMismatch}
    • + #
  • {@link FailedPredicateException}: Dispatches the call to + # {@link #reportFailedPredicate}
    • + #
  • All other types: calls {@link Parser#notifyErrorListeners} to report + # the exception
    • + #
+ # + def reportError(self, recognizer:Parser, e:RecognitionException): + # if we've already reported an error and have not matched a token + # yet successfully, don't report any errors. + if self.inErrorRecoveryMode(recognizer): + return # don't report spurious errors + self.beginErrorCondition(recognizer) + if isinstance( e, NoViableAltException ): + self.reportNoViableAlternative(recognizer, e) + elif isinstance( e, InputMismatchException ): + self.reportInputMismatch(recognizer, e) + elif isinstance( e, FailedPredicateException ): + self.reportFailedPredicate(recognizer, e) + else: + print("unknown recognition error type: " + type(e).__name__) + recognizer.notifyErrorListeners(e.message, e.offendingToken, e) + + # + # {@inheritDoc} + # + #

The default implementation resynchronizes the parser by consuming tokens + # until we find one in the resynchronization set--loosely the set of tokens + # that can follow the current rule.

+ # + def recover(self, recognizer:Parser, e:RecognitionException): + if self.lastErrorIndex==recognizer.getInputStream().index \ + and self.lastErrorStates is not None \ + and recognizer.state in self.lastErrorStates: + # uh oh, another error at same token index and previously-visited + # state in ATN; must be a case where LT(1) is in the recovery + # token set so nothing got consumed. Consume a single token + # at least to prevent an infinite loop; this is a failsafe. + recognizer.consume() + + self.lastErrorIndex = recognizer._input.index + if self.lastErrorStates is None: + self.lastErrorStates = [] + self.lastErrorStates.append(recognizer.state) + followSet = self.getErrorRecoverySet(recognizer) + self.consumeUntil(recognizer, followSet) + + # The default implementation of {@link ANTLRErrorStrategy#sync} makes sure + # that the current lookahead symbol is consistent with what were expecting + # at this point in the ATN. You can call this anytime but ANTLR only + # generates code to check before subrules/loops and each iteration. + # + #

Implements Jim Idle's magic sync mechanism in closures and optional + # subrules. E.g.,

+ # + # 
+    # a : sync ( stuff sync )* ;
+    # sync : {consume to what can follow sync} ;
+    #
+ # + # At the start of a sub rule upon error, {@link #sync} performs single + # token deletion, if possible. If it can't do that, it bails on the current + # rule and uses the default error recovery, which consumes until the + # resynchronization set of the current rule. + # + #

If the sub rule is optional ({@code (...)?}, {@code (...)*}, or block + # with an empty alternative), then the expected set includes what follows + # the subrule.

+ # + #

During loop iteration, it consumes until it sees a token that can start a + # sub rule or what follows loop. Yes, that is pretty aggressive. We opt to + # stay in the loop as long as possible.

+ # + #

ORIGINS

+ # + #

Previous versions of ANTLR did a poor job of their recovery within loops. + # A single mismatch token or missing token would force the parser to bail + # out of the entire rules surrounding the loop. So, for rule

+ # + # 
+    # classDef : 'class' ID '{' member* '}'
+    #
+ # + # input with an extra token between members would force the parser to + # consume until it found the next class definition rather than the next + # member definition of the current class. + # + #

This functionality cost a little bit of effort because the parser has to + # compare token set at the start of the loop and at each iteration. If for + # some reason speed is suffering for you, you can turn off this + # functionality by simply overriding this method as a blank { }.

+ # + def sync(self, recognizer:Parser): + # If already recovering, don't try to sync + if self.inErrorRecoveryMode(recognizer): + return + + s = recognizer._interp.atn.states[recognizer.state] + la = recognizer.getTokenStream().LA(1) + # try cheaper subset first; might get lucky. seems to shave a wee bit off + nextTokens = recognizer.atn.nextTokens(s) + if la in nextTokens: + self.nextTokensContext = None + self.nextTokenState = ATNState.INVALID_STATE_NUMBER + return + elif Token.EPSILON in nextTokens: + if self.nextTokensContext is None: + # It's possible the next token won't match information tracked + # by sync is restricted for performance. + self.nextTokensContext = recognizer._ctx + self.nextTokensState = recognizer._stateNumber + return + + if s.stateType in [ATNState.BLOCK_START, ATNState.STAR_BLOCK_START, + ATNState.PLUS_BLOCK_START, ATNState.STAR_LOOP_ENTRY]: + # report error and recover if possible + if self.singleTokenDeletion(recognizer)is not None: + return + else: + raise InputMismatchException(recognizer) + + elif s.stateType in [ATNState.PLUS_LOOP_BACK, ATNState.STAR_LOOP_BACK]: + self.reportUnwantedToken(recognizer) + expecting = recognizer.getExpectedTokens() + whatFollowsLoopIterationOrRule = expecting.addSet(self.getErrorRecoverySet(recognizer)) + self.consumeUntil(recognizer, whatFollowsLoopIterationOrRule) + + else: + # do nothing if we can't identify the exact kind of ATN state + pass + + # This is called by {@link #reportError} when the exception is a + # {@link NoViableAltException}. + # + # @see #reportError + # + # @param recognizer the parser instance + # @param e the recognition exception + # + def reportNoViableAlternative(self, recognizer:Parser, e:NoViableAltException): + tokens = recognizer.getTokenStream() + if tokens is not None: + if e.startToken.type==Token.EOF: + input = "" + else: + input = tokens.getText(e.startToken, e.offendingToken) + else: + input = "" + msg = "no viable alternative at input " + self.escapeWSAndQuote(input) + recognizer.notifyErrorListeners(msg, e.offendingToken, e) + + # + # This is called by {@link #reportError} when the exception is an + # {@link InputMismatchException}. + # + # @see #reportError + # + # @param recognizer the parser instance + # @param e the recognition exception + # + def reportInputMismatch(self, recognizer:Parser, e:InputMismatchException): + msg = "mismatched input " + self.getTokenErrorDisplay(e.offendingToken) \ + + " expecting " + e.getExpectedTokens().toString(recognizer.literalNames, recognizer.symbolicNames) + recognizer.notifyErrorListeners(msg, e.offendingToken, e) + + # + # This is called by {@link #reportError} when the exception is a + # {@link FailedPredicateException}. + # + # @see #reportError + # + # @param recognizer the parser instance + # @param e the recognition exception + # + def reportFailedPredicate(self, recognizer, e): + ruleName = recognizer.ruleNames[recognizer._ctx.getRuleIndex()] + msg = "rule " + ruleName + " " + e.message + recognizer.notifyErrorListeners(msg, e.offendingToken, e) + + # This method is called to report a syntax error which requires the removal + # of a token from the input stream. At the time this method is called, the + # erroneous symbol is current {@code LT(1)} symbol and has not yet been + # removed from the input stream. When this method returns, + # {@code recognizer} is in error recovery mode. + # + #

This method is called when {@link #singleTokenDeletion} identifies + # single-token deletion as a viable recovery strategy for a mismatched + # input error.

+ # + #

The default implementation simply returns if the handler is already in + # error recovery mode. Otherwise, it calls {@link #beginErrorCondition} to + # enter error recovery mode, followed by calling + # {@link Parser#notifyErrorListeners}.

+ # + # @param recognizer the parser instance + # + def reportUnwantedToken(self, recognizer:Parser): + if self.inErrorRecoveryMode(recognizer): + return + + self.beginErrorCondition(recognizer) + t = recognizer.getCurrentToken() + tokenName = self.getTokenErrorDisplay(t) + expecting = self.getExpectedTokens(recognizer) + msg = "extraneous input " + tokenName + " expecting " \ + + expecting.toString(recognizer.literalNames, recognizer.symbolicNames) + recognizer.notifyErrorListeners(msg, t, None) + + # This method is called to report a syntax error which requires the + # insertion of a missing token into the input stream. At the time this + # method is called, the missing token has not yet been inserted. When this + # method returns, {@code recognizer} is in error recovery mode. + # + #

This method is called when {@link #singleTokenInsertion} identifies + # single-token insertion as a viable recovery strategy for a mismatched + # input error.

+ # + #

The default implementation simply returns if the handler is already in + # error recovery mode. Otherwise, it calls {@link #beginErrorCondition} to + # enter error recovery mode, followed by calling + # {@link Parser#notifyErrorListeners}.

+ # + # @param recognizer the parser instance + # + def reportMissingToken(self, recognizer:Parser): + if self.inErrorRecoveryMode(recognizer): + return + self.beginErrorCondition(recognizer) + t = recognizer.getCurrentToken() + expecting = self.getExpectedTokens(recognizer) + msg = "missing " + expecting.toString(recognizer.literalNames, recognizer.symbolicNames) \ + + " at " + self.getTokenErrorDisplay(t) + recognizer.notifyErrorListeners(msg, t, None) + + #

The default implementation attempts to recover from the mismatched input + # by using single token insertion and deletion as described below. If the + # recovery attempt fails, this method throws an + # {@link InputMismatchException}.

+ # + #

EXTRA TOKEN (single token deletion)

+ # + #

{@code LA(1)} is not what we are looking for. If {@code LA(2)} has the + # right token, however, then assume {@code LA(1)} is some extra spurious + # token and delete it. Then consume and return the next token (which was + # the {@code LA(2)} token) as the successful result of the match operation.

+ # + #

This recovery strategy is implemented by {@link #singleTokenDeletion}.

+ # + #

MISSING TOKEN (single token insertion)

+ # + #

If current token (at {@code LA(1)}) is consistent with what could come + # after the expected {@code LA(1)} token, then assume the token is missing + # and use the parser's {@link TokenFactory} to create it on the fly. The + # "insertion" is performed by returning the created token as the successful + # result of the match operation.

+ # + #

This recovery strategy is implemented by {@link #singleTokenInsertion}.

+ # + #

EXAMPLE

+ # + #

For example, Input {@code i=(3;} is clearly missing the {@code ')'}. When + # the parser returns from the nested call to {@code expr}, it will have + # call chain:

+ # + # 
+    # stat → expr → atom
+    #
+ # + # and it will be trying to match the {@code ')'} at this point in the + # derivation: + # + # 
+    # => ID '=' '(' INT ')' ('+' atom)* ';'
+    #                    ^
+    #
+ # + # The attempt to match {@code ')'} will fail when it sees {@code ';'} and + # call {@link #recoverInline}. To recover, it sees that {@code LA(1)==';'} + # is in the set of tokens that can follow the {@code ')'} token reference + # in rule {@code atom}. It can assume that you forgot the {@code ')'}. + # + def recoverInline(self, recognizer:Parser): + # SINGLE TOKEN DELETION + matchedSymbol = self.singleTokenDeletion(recognizer) + if matchedSymbol is not None: + # we have deleted the extra token. + # now, move past ttype token as if all were ok + recognizer.consume() + return matchedSymbol + + # SINGLE TOKEN INSERTION + if self.singleTokenInsertion(recognizer): + return self.getMissingSymbol(recognizer) + + # even that didn't work; must throw the exception + raise InputMismatchException(recognizer) + + # + # This method implements the single-token insertion inline error recovery + # strategy. It is called by {@link #recoverInline} if the single-token + # deletion strategy fails to recover from the mismatched input. If this + # method returns {@code true}, {@code recognizer} will be in error recovery + # mode. + # + #

This method determines whether or not single-token insertion is viable by + # checking if the {@code LA(1)} input symbol could be successfully matched + # if it were instead the {@code LA(2)} symbol. If this method returns + # {@code true}, the caller is responsible for creating and inserting a + # token with the correct type to produce this behavior.

+ # + # @param recognizer the parser instance + # @return {@code true} if single-token insertion is a viable recovery + # strategy for the current mismatched input, otherwise {@code false} + # + def singleTokenInsertion(self, recognizer:Parser): + currentSymbolType = recognizer.getTokenStream().LA(1) + # if current token is consistent with what could come after current + # ATN state, then we know we're missing a token; error recovery + # is free to conjure up and insert the missing token + atn = recognizer._interp.atn + currentState = atn.states[recognizer.state] + next = currentState.transitions[0].target + expectingAtLL2 = atn.nextTokens(next, recognizer._ctx) + if currentSymbolType in expectingAtLL2: + self.reportMissingToken(recognizer) + return True + else: + return False + + # This method implements the single-token deletion inline error recovery + # strategy. It is called by {@link #recoverInline} to attempt to recover + # from mismatched input. If this method returns null, the parser and error + # handler state will not have changed. If this method returns non-null, + # {@code recognizer} will not be in error recovery mode since the + # returned token was a successful match. + # + #

If the single-token deletion is successful, this method calls + # {@link #reportUnwantedToken} to report the error, followed by + # {@link Parser#consume} to actually "delete" the extraneous token. Then, + # before returning {@link #reportMatch} is called to signal a successful + # match.

+ # + # @param recognizer the parser instance + # @return the successfully matched {@link Token} instance if single-token + # deletion successfully recovers from the mismatched input, otherwise + # {@code null} + # + def singleTokenDeletion(self, recognizer:Parser): + nextTokenType = recognizer.getTokenStream().LA(2) + expecting = self.getExpectedTokens(recognizer) + if nextTokenType in expecting: + self.reportUnwantedToken(recognizer) + # print("recoverFromMismatchedToken deleting " \ + # + str(recognizer.getTokenStream().LT(1)) \ + # + " since " + str(recognizer.getTokenStream().LT(2)) \ + # + " is what we want", file=sys.stderr) + recognizer.consume() # simply delete extra token + # we want to return the token we're actually matching + matchedSymbol = recognizer.getCurrentToken() + self.reportMatch(recognizer) # we know current token is correct + return matchedSymbol + else: + return None + + # Conjure up a missing token during error recovery. + # + # The recognizer attempts to recover from single missing + # symbols. But, actions might refer to that missing symbol. + # For example, x=ID {f($x);}. The action clearly assumes + # that there has been an identifier matched previously and that + # $x points at that token. If that token is missing, but + # the next token in the stream is what we want we assume that + # this token is missing and we keep going. Because we + # have to return some token to replace the missing token, + # we have to conjure one up. This method gives the user control + # over the tokens returned for missing tokens. Mostly, + # you will want to create something special for identifier + # tokens. For literals such as '{' and ',', the default + # action in the parser or tree parser works. It simply creates + # a CommonToken of the appropriate type. The text will be the token. + # If you change what tokens must be created by the lexer, + # override this method to create the appropriate tokens. + # + def getMissingSymbol(self, recognizer:Parser): + currentSymbol = recognizer.getCurrentToken() + expecting = self.getExpectedTokens(recognizer) + expectedTokenType = expecting[0] # get any element + if expectedTokenType==Token.EOF: + tokenText = "" + else: + name = None + if expectedTokenType < len(recognizer.literalNames): + name = recognizer.literalNames[expectedTokenType] + if name is None and expectedTokenType < len(recognizer.symbolicNames): + name = recognizer.symbolicNames[expectedTokenType] + tokenText = "" + current = currentSymbol + lookback = recognizer.getTokenStream().LT(-1) + if current.type==Token.EOF and lookback is not None: + current = lookback + return recognizer.getTokenFactory().create(current.source, + expectedTokenType, tokenText, Token.DEFAULT_CHANNEL, + -1, -1, current.line, current.column) + + def getExpectedTokens(self, recognizer:Parser): + return recognizer.getExpectedTokens() + + # How should a token be displayed in an error message? The default + # is to display just the text, but during development you might + # want to have a lot of information spit out. Override in that case + # to use t.toString() (which, for CommonToken, dumps everything about + # the token). This is better than forcing you to override a method in + # your token objects because you don't have to go modify your lexer + # so that it creates a new Java type. + # + def getTokenErrorDisplay(self, t:Token): + if t is None: + return "" + s = t.text + if s is None: + if t.type==Token.EOF: + s = "" + else: + s = "<" + str(t.type) + ">" + return self.escapeWSAndQuote(s) + + def escapeWSAndQuote(self, s:str): + s = s.replace("\n","\\n") + s = s.replace("\r","\\r") + s = s.replace("\t","\\t") + return "'" + s + "'" + + # Compute the error recovery set for the current rule. During + # rule invocation, the parser pushes the set of tokens that can + # follow that rule reference on the stack; this amounts to + # computing FIRST of what follows the rule reference in the + # enclosing rule. See LinearApproximator.FIRST(). + # This local follow set only includes tokens + # from within the rule; i.e., the FIRST computation done by + # ANTLR stops at the end of a rule. + # + # EXAMPLE + # + # When you find a "no viable alt exception", the input is not + # consistent with any of the alternatives for rule r. The best + # thing to do is to consume tokens until you see something that + # can legally follow a call to r#or* any rule that called r. + # You don't want the exact set of viable next tokens because the + # input might just be missing a token--you might consume the + # rest of the input looking for one of the missing tokens. + # + # Consider grammar: + # + # a : '[' b ']' + # | '(' b ')' + # ; + # b : c '^' INT ; + # c : ID + # | INT + # ; + # + # At each rule invocation, the set of tokens that could follow + # that rule is pushed on a stack. Here are the various + # context-sensitive follow sets: + # + # FOLLOW(b1_in_a) = FIRST(']') = ']' + # FOLLOW(b2_in_a) = FIRST(')') = ')' + # FOLLOW(c_in_b) = FIRST('^') = '^' + # + # Upon erroneous input "[]", the call chain is + # + # a -> b -> c + # + # and, hence, the follow context stack is: + # + # depth follow set start of rule execution + # 0 a (from main()) + # 1 ']' b + # 2 '^' c + # + # Notice that ')' is not included, because b would have to have + # been called from a different context in rule a for ')' to be + # included. + # + # For error recovery, we cannot consider FOLLOW(c) + # (context-sensitive or otherwise). We need the combined set of + # all context-sensitive FOLLOW sets--the set of all tokens that + # could follow any reference in the call chain. We need to + # resync to one of those tokens. Note that FOLLOW(c)='^' and if + # we resync'd to that token, we'd consume until EOF. We need to + # sync to context-sensitive FOLLOWs for a, b, and c: {']','^'}. + # In this case, for input "[]", LA(1) is ']' and in the set, so we would + # not consume anything. After printing an error, rule c would + # return normally. Rule b would not find the required '^' though. + # At this point, it gets a mismatched token error and throws an + # exception (since LA(1) is not in the viable following token + # set). The rule exception handler tries to recover, but finds + # the same recovery set and doesn't consume anything. Rule b + # exits normally returning to rule a. Now it finds the ']' (and + # with the successful match exits errorRecovery mode). + # + # So, you can see that the parser walks up the call chain looking + # for the token that was a member of the recovery set. + # + # Errors are not generated in errorRecovery mode. + # + # ANTLR's error recovery mechanism is based upon original ideas: + # + # "Algorithms + Data Structures = Programs" by Niklaus Wirth + # + # and + # + # "A note on error recovery in recursive descent parsers": + # http:#portal.acm.org/citation.cfm?id=947902.947905 + # + # Later, Josef Grosch had some good ideas: + # + # "Efficient and Comfortable Error Recovery in Recursive Descent + # Parsers": + # ftp:#www.cocolab.com/products/cocktail/doca4.ps/ell.ps.zip + # + # Like Grosch I implement context-sensitive FOLLOW sets that are combined + # at run-time upon error to avoid overhead during parsing. + # + def getErrorRecoverySet(self, recognizer:Parser): + atn = recognizer._interp.atn + ctx = recognizer._ctx + recoverSet = IntervalSet() + while ctx is not None and ctx.invokingState>=0: + # compute what follows who invoked us + invokingState = atn.states[ctx.invokingState] + rt = invokingState.transitions[0] + follow = atn.nextTokens(rt.followState) + recoverSet.addSet(follow) + ctx = ctx.parentCtx + recoverSet.removeOne(Token.EPSILON) + return recoverSet + + # Consume tokens until one matches the given token set.# + def consumeUntil(self, recognizer:Parser, set_:set): + ttype = recognizer.getTokenStream().LA(1) + while ttype != Token.EOF and not ttype in set_: + recognizer.consume() + ttype = recognizer.getTokenStream().LA(1) + + +# +# This implementation of {@link ANTLRErrorStrategy} responds to syntax errors +# by immediately canceling the parse operation with a +# {@link ParseCancellationException}. The implementation ensures that the +# {@link ParserRuleContext#exception} field is set for all parse tree nodes +# that were not completed prior to encountering the error. +# +#

+# This error strategy is useful in the following scenarios.

+# +#
    +#
  • Two-stage parsing: This error strategy allows the first +# stage of two-stage parsing to immediately terminate if an error is +# encountered, and immediately fall back to the second stage. In addition to +# avoiding wasted work by attempting to recover from errors here, the empty +# implementation of {@link BailErrorStrategy#sync} improves the performance of +# the first stage.
    • +#
  • Silent validation: When syntax errors are not being +# reported or logged, and the parse result is simply ignored if errors occur, +# the {@link BailErrorStrategy} avoids wasting work on recovering from errors +# when the result will be ignored either way.
    • +#
+# +#

+# {@code myparser.setErrorHandler(new BailErrorStrategy());}

+# +# @see Parser#setErrorHandler(ANTLRErrorStrategy) +# +class BailErrorStrategy(DefaultErrorStrategy): + # Instead of recovering from exception {@code e}, re-throw it wrapped + # in a {@link ParseCancellationException} so it is not caught by the + # rule function catches. Use {@link Exception#getCause()} to get the + # original {@link RecognitionException}. + # + def recover(self, recognizer:Parser, e:RecognitionException): + context = recognizer._ctx + while context is not None: + context.exception = e + context = context.parentCtx + raise ParseCancellationException(e) + + # Make sure we don't attempt to recover inline; if the parser + # successfully recovers, it won't throw an exception. + # + def recoverInline(self, recognizer:Parser): + self.recover(recognizer, InputMismatchException(recognizer)) + + # Make sure we don't attempt to recover from problems in subrules.# + def sync(self, recognizer:Parser): + pass + +del Parser \ No newline at end of file diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/Errors.py b/vlmpy310/lib/python3.10/site-packages/antlr4/error/Errors.py new file mode 100644 index 0000000000000000000000000000000000000000..e78ac05911d3c9569441fe376ff7d6c686c05c95 --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/error/Errors.py @@ -0,0 +1,172 @@ +# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. +# Use of this file is governed by the BSD 3-clause license that +# can be found in the LICENSE.txt file in the project root. +# + +# need forward declaration +Token = None +Lexer = None +Parser = None +TokenStream = None +ATNConfigSet = None +ParserRulecontext = None +PredicateTransition = None +BufferedTokenStream = None + +class UnsupportedOperationException(Exception): + + def __init__(self, msg:str): + super().__init__(msg) + +class IllegalStateException(Exception): + + def __init__(self, msg:str): + super().__init__(msg) + +class CancellationException(IllegalStateException): + + def __init__(self, msg:str): + super().__init__(msg) + +# The root of the ANTLR exception hierarchy. In general, ANTLR tracks just +# 3 kinds of errors: prediction errors, failed predicate errors, and +# mismatched input errors. In each case, the parser knows where it is +# in the input, where it is in the ATN, the rule invocation stack, +# and what kind of problem occurred. + +from antlr4.InputStream import InputStream +from antlr4.ParserRuleContext import ParserRuleContext +from antlr4.Recognizer import Recognizer + +class RecognitionException(Exception): + + + def __init__(self, message:str=None, recognizer:Recognizer=None, input:InputStream=None, ctx:ParserRulecontext=None): + super().__init__(message) + self.message = message + self.recognizer = recognizer + self.input = input + self.ctx = ctx + # The current {@link Token} when an error occurred. Since not all streams + # support accessing symbols by index, we have to track the {@link Token} + # instance itself. + self.offendingToken = None + # Get the ATN state number the parser was in at the time the error + # occurred. For {@link NoViableAltException} and + # {@link LexerNoViableAltException} exceptions, this is the + # {@link DecisionState} number. For others, it is the state whose outgoing + # edge we couldn't match. + self.offendingState = -1 + if recognizer is not None: + self.offendingState = recognizer.state + + #

If the state number is not known, this method returns -1.

+ + # + # Gets the set of input symbols which could potentially follow the + # previously matched symbol at the time this exception was thrown. + # + #

If the set of expected tokens is not known and could not be computed, + # this method returns {@code null}.

+ # + # @return The set of token types that could potentially follow the current + # state in the ATN, or {@code null} if the information is not available. + #/ + def getExpectedTokens(self): + if self.recognizer is not None: + return self.recognizer.atn.getExpectedTokens(self.offendingState, self.ctx) + else: + return None + + +class LexerNoViableAltException(RecognitionException): + + def __init__(self, lexer:Lexer, input:InputStream, startIndex:int, deadEndConfigs:ATNConfigSet): + super().__init__(message=None, recognizer=lexer, input=input, ctx=None) + self.startIndex = startIndex + self.deadEndConfigs = deadEndConfigs + + def __str__(self): + symbol = "" + if self.startIndex >= 0 and self.startIndex < self.input.size: + symbol = self.input.getText(self.startIndex, self.startIndex) + # TODO symbol = Utils.escapeWhitespace(symbol, false); + return "LexerNoViableAltException('" + symbol + "')" + +# Indicates that the parser could not decide which of two or more paths +# to take based upon the remaining input. It tracks the starting token +# of the offending input and also knows where the parser was +# in the various paths when the error. Reported by reportNoViableAlternative() +# +class NoViableAltException(RecognitionException): + + def __init__(self, recognizer:Parser, input:TokenStream=None, startToken:Token=None, + offendingToken:Token=None, deadEndConfigs:ATNConfigSet=None, ctx:ParserRuleContext=None): + if ctx is None: + ctx = recognizer._ctx + if offendingToken is None: + offendingToken = recognizer.getCurrentToken() + if startToken is None: + startToken = recognizer.getCurrentToken() + if input is None: + input = recognizer.getInputStream() + super().__init__(recognizer=recognizer, input=input, ctx=ctx) + # Which configurations did we try at input.index() that couldn't match input.LT(1)?# + self.deadEndConfigs = deadEndConfigs + # The token object at the start index; the input stream might + # not be buffering tokens so get a reference to it. (At the + # time the error occurred, of course the stream needs to keep a + # buffer all of the tokens but later we might not have access to those.) + self.startToken = startToken + self.offendingToken = offendingToken + +# This signifies any kind of mismatched input exceptions such as +# when the current input does not match the expected token. +# +class InputMismatchException(RecognitionException): + + def __init__(self, recognizer:Parser): + super().__init__(recognizer=recognizer, input=recognizer.getInputStream(), ctx=recognizer._ctx) + self.offendingToken = recognizer.getCurrentToken() + + +# A semantic predicate failed during validation. Validation of predicates +# occurs when normally parsing the alternative just like matching a token. +# Disambiguating predicate evaluation occurs when we test a predicate during +# prediction. + +class FailedPredicateException(RecognitionException): + + def __init__(self, recognizer:Parser, predicate:str=None, message:str=None): + super().__init__(message=self.formatMessage(predicate,message), recognizer=recognizer, + input=recognizer.getInputStream(), ctx=recognizer._ctx) + s = recognizer._interp.atn.states[recognizer.state] + trans = s.transitions[0] + from antlr4.atn.Transition import PredicateTransition + if isinstance(trans, PredicateTransition): + self.ruleIndex = trans.ruleIndex + self.predicateIndex = trans.predIndex + else: + self.ruleIndex = 0 + self.predicateIndex = 0 + self.predicate = predicate + self.offendingToken = recognizer.getCurrentToken() + + def formatMessage(self, predicate:str, message:str): + if message is not None: + return message + else: + return "failed predicate: {" + predicate + "}?" + +class ParseCancellationException(CancellationException): + + pass + +del Token +del Lexer +del Parser +del TokenStream +del ATNConfigSet +del ParserRulecontext +del PredicateTransition +del BufferedTokenStream diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__init__.py b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..216c000dc5ffc8e53cc9c596e420c1e67604d1aa --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__init__.py @@ -0,0 +1 @@ +__author__ = 'ericvergnaud' diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/DiagnosticErrorListener.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/DiagnosticErrorListener.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..041f0263c7c74bf5685820261a7bccd59ac7a6c4 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/DiagnosticErrorListener.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorListener.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorListener.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1734b6707f3701532a429f501f198688548bf2c7 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorListener.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorStrategy.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorStrategy.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5f05b559da32cf3b46c1bd0e78a4342880e46dbd Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/ErrorStrategy.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/Errors.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/Errors.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..54132ed277e0f6a9b85ba3cabf1037f81359953f Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/Errors.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/__init__.cpython-310.pyc b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4409a988d5b0c47b370285885221f700aad00ee5 Binary files /dev/null and b/vlmpy310/lib/python3.10/site-packages/antlr4/error/__pycache__/__init__.cpython-310.pyc differ diff --git a/vlmpy310/lib/python3.10/site-packages/docker_pycreds-0.4.0.dist-info/METADATA b/vlmpy310/lib/python3.10/site-packages/docker_pycreds-0.4.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..f242f009c225b7773415bd4c4fd07d6ab8c4588d --- /dev/null +++ b/vlmpy310/lib/python3.10/site-packages/docker_pycreds-0.4.0.dist-info/METADATA @@ -0,0 +1,62 @@ +Metadata-Version: 2.1 +Name: docker-pycreds +Version: 0.4.0 +Summary: Python bindings for the docker credentials store API +Home-page: https://github.com/shin-/dockerpy-creds +Author: UNKNOWN +Author-email: UNKNOWN +License: Apache License 2.0 +Platform: UNKNOWN +Classifier: Development Status :: 4 - Beta +Classifier: Environment :: Other Environment +Classifier: Intended Audience :: Developers +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 2.6 +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.3 +Classifier: Programming Language :: Python :: 3.4 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Topic :: Utilities +Classifier: License :: OSI Approved :: Apache Software License +Requires-Dist: six (>=1.4.0) + +# docker-pycreds + +[![CircleCI](https://circleci.com/gh/shin-/dockerpy-creds/tree/master.svg?style=svg)](https://circleci.com/gh/shin-/dockerpy-creds/tree/master) + +Python bindings for the docker credentials store API + +## Credentials store info + +[Docker documentation page](https://docs.docker.com/engine/reference/commandline/login/#/credentials-store) + +## Requirements + +On top of the dependencies in `requirements.txt`, the `docker-credential` +executable for the platform must be installed on the user's system. + +## API usage + +```python + +import dockerpycreds + +store = dockerpycreds.Store('secretservice') +store.store( + server='https://index.docker.io/v1/', username='johndoe', + secret='hunter2' +) + +print(store.list()) + +print(store.get('https://index.docker.io/v1/')) + + +store.erase('https://index.docker.io/v1/') +``` + +