xhs-note-crawling/python/py/Lib/site-packages/torch/include/ATen/Utils.h

#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once

#include <ATen/EmptyTensor.h>
#include <ATen/Formatting.h>
#include <ATen/core/ATenGeneral.h>
#include <ATen/core/Generator.h>
#include <c10/core/ScalarType.h>
#include <c10/core/StorageImpl.h>
#include <c10/core/UndefinedTensorImpl.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Exception.h>
#include <c10/util/accumulate.h>
#include <c10/util/irange.h>

#include <algorithm>

#define AT_DISALLOW_COPY_AND_ASSIGN(TypeName) \
  TypeName(const TypeName&) = delete;         \
  void operator=(const TypeName&) = delete

namespace at {

TORCH_API int _crash_if_asan(int /*arg*/);

// Converts a TensorList (i.e. ArrayRef<Tensor> to vector of TensorImpl*)
// NB: This is ONLY used by legacy TH bindings, and ONLY used by cat.
// Once cat is ported entirely to ATen this can be deleted!
inline std::vector<TensorImpl*> checked_dense_tensor_list_unwrap(
    ArrayRef<Tensor> tensors,
    const char* name,
    int pos,
    c10::DeviceType device_type,
    ScalarType scalar_type) {
  std::vector<TensorImpl*> unwrapped;
  unwrapped.reserve(tensors.size());
  for (const auto i : c10::irange(tensors.size())) {
    const auto& expr = tensors[i];
    if (expr.layout() != Layout::Strided) {
      TORCH_CHECK(
          false,
          "Expected dense tensor but got ",
          expr.layout(),
          " for sequence element ",
          i,
          " in sequence argument at position #",
          pos,
          " '",
          name,
          "'");
    }
    if (expr.device().type() != device_type) {
      TORCH_CHECK(
          false,
          "Expected object of device type ",
          device_type,
          " but got device type ",
          expr.device().type(),
          " for sequence element ",
          i,
          " in sequence argument at position #",
          pos,
          " '",
          name,
          "'");
    }
    if (expr.scalar_type() != scalar_type) {
      TORCH_CHECK(
          false,
          "Expected object of scalar type ",
          scalar_type,
          " but got scalar type ",
          expr.scalar_type(),
          " for sequence element ",
          i,
          " in sequence argument at position #",
          pos,
          " '",
          name,
          "'");
    }
    unwrapped.emplace_back(expr.unsafeGetTensorImpl());
  }
  return unwrapped;
}

template <size_t N>
std::array<int64_t, N> check_intlist(
    ArrayRef<int64_t> list,
    const char* name,
    int pos) {
  if (list.empty()) {
    // TODO: is this necessary?  We used to treat nullptr-vs-not in IntList
    // differently with strides as a way of faking optional.
    list = {};
  }
  auto res = std::array<int64_t, N>();
  if (list.size() == 1 && N > 1) {
    res.fill(list[0]);
    return res;
  }
  if (list.size() != N) {
    TORCH_CHECK(
        false,
        "Expected a list of ",
        N,
        " ints but got ",
        list.size(),
        " for argument #",
        pos,
        " '",
        name,
        "'");
  }
  std::copy_n(list.begin(), N, res.begin());
  return res;
}

using at::detail::check_size_nonnegative;

namespace detail {

template <typename T>
TORCH_API Tensor tensor_cpu(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API Tensor
tensor_backend(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API Tensor
tensor_complex_cpu(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API Tensor
tensor_complex_backend(ArrayRef<T> values, const TensorOptions& options);
} // namespace detail

} // namespace at

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)