#pragma once

// Parse arguments to Python functions implemented in C++
// This is similar to PyArg_ParseTupleAndKeywords(), but specifically handles
// the types relevant to PyTorch and distinguishes between overloaded function
// signatures.
//
// Example:
//
//   static PythonArgParser parser({
//     "norm(Scalar p, int64_t dim, bool keepdim=False)",
//     "norm(Scalar p=2)",
//   });
//   ParsedArgs<3> parsed_args;
//   auto r = parser.parse(args, kwargs, parsed_args);
//   if (r.idx == 0) {
//     norm(r.scalar(0), r.int64(1), r.bool(0));
//   } else {
//     norm(r.scalar(0));
//   }
//
// We auto-generate most uses of PythonArgParser; the generated files
// are torch/csrc/autograd/generated/python_*.cpp
//
// Some gotchas that you should watch out for:
//
//    - Note [Order of overloads matters]
//      Order of overloads matters.  A set of input arguments may
//      bind to multiple argument specs; we will always pick the
//      first one in PythonArgParser.  However, when you are writing
//      overloads in, e.g., native_functions.yaml, you don't have to
//      worry about what order you write them, because the code
//      generation logic always gives the overloads a canonical
//      order, where Tensor overloads come first, before Scalar overloads.
//      This logic is in sort_declarations in
//      tools/autograd/gen_python_functions.py
//
//    - Zero-dim tensors (e.g., torch.tensor(2)) bind to both
//      Scalar and Tensor, UNLESS they require grad (in which case
//      they only bind to Tensor).


#include "torch/csrc/python_headers.h"

#include "torch/csrc/Device.h"
#include "torch/csrc/Dtype.h"
#include "torch/csrc/DynamicTypes.h"
#include "torch/csrc/Exceptions.h"
#include "torch/csrc/Generator.h"
#include "torch/csrc/autograd/generated/VariableType.h"
#include "torch/csrc/autograd/python_variable.h"
#include "torch/csrc/jit/tracer.h"
#include "torch/csrc/tensor/python_tensor.h"
#include "torch/csrc/utils/numpy_stub.h"
#include "torch/csrc/utils/object_ptr.h"
#include "torch/csrc/utils/python_numbers.h"
#include "torch/csrc/utils/python_strings.h"
#include "torch/csrc/autograd/variable.h"

#include <ATen/ATen.h>

#include <array>
#include <cstddef>
#include <memory>
#include <sstream>
#include <string>
#include <vector>

namespace torch {

enum class ParameterType {
  TENSOR, SCALAR, INT64, DOUBLE, TENSOR_LIST, INT_LIST, GENERATOR,
  BOOL, STORAGE, PYOBJECT, SCALARTYPE, LAYOUT, DEVICE, STRING
};

struct FunctionParameter;
struct FunctionSignature;
struct PythonArgs;

// Contains bound Python arguments in declaration order
template<int N>
struct ParsedArgs {
  PyObject* args[N];
};

struct PythonArgParser {
  explicit PythonArgParser(std::vector<std::string> fmts, bool traceable=false);

  template<int N>
  inline PythonArgs parse(PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst);

private:
  [[noreturn]]
  void print_error(PyObject* args, PyObject* kwargs, PyObject* parsed_args[]);
  PythonArgs raw_parse(PyObject* args, PyObject* kwargs, PyObject* parsed_args[]);

  std::vector<FunctionSignature> signatures_;
  std::string function_name;
  ssize_t max_args;
  bool traceable;
};

struct PythonArgs {
  PythonArgs(int idx, bool traceable, const FunctionSignature& signature, PyObject** args)
    : idx(idx)
    , traceable(traceable)
    , signature(signature)
    , args(args) {}

  int idx;
  bool traceable;
  const FunctionSignature& signature;
  PyObject** args;

  inline at::Tensor tensor(int i);
  inline at::Scalar scalar(int i);
  inline at::Scalar scalarWithDefault(int i, at::Scalar default_scalar);
  inline std::vector<at::Tensor> tensorlist(int i);
  template<int N>
  inline std::array<at::Tensor, N> tensorlist_n(int i);
  inline std::vector<int64_t> intlist(int i);
  inline std::vector<int64_t> intlistWithDefault(int i, std::vector<int64_t> default_intlist);
  inline at::Generator* generator(int i);
  inline at::Storage storage(int i);
  inline at::ScalarType scalartype(int i);
  inline at::ScalarType scalartypeWithDefault(int i, at::ScalarType default_scalartype);
  inline c10::optional<at::ScalarType> scalartypeOptional(int i);
  inline const THPLayout& layout(int i);
  inline const THPLayout& layoutWithDefault(int i, const THPLayout& default_layout);
  inline at::Device device(int i);
  inline at::Device deviceWithDefault(int i, const at::Device& default_device);
  inline c10::optional<at::Device> deviceOptional(int i);
  inline std::string string(int i);
  inline PyObject* pyobject(int i);
  inline int64_t toInt64(int i);
  inline int64_t toInt64WithDefault(int i, int64_t default_int);
  inline double toDouble(int i);
  inline double toDoubleWithDefault(int i, double default_double);
  inline bool toBool(int i);
  inline bool toBoolWithDefault(int i, bool default_bool);
  inline bool isNone(int i);
};

struct FunctionSignature {
  explicit FunctionSignature(const std::string& fmt);

  bool parse(PyObject* args, PyObject* kwargs, PyObject* dst[], bool raise_exception);
  std::string toString() const;

  std::string name;
  std::vector<FunctionParameter> params;
  ssize_t min_args;
  ssize_t max_args;
  ssize_t max_pos_args;
  bool hidden;
  bool deprecated;
};

struct FunctionParameter {
  FunctionParameter(const std::string& fmt, bool keyword_only);

  bool check(PyObject* obj);
  void set_default_str(const std::string& str);
  std::string type_name() const;

  ParameterType type_;
  bool optional;
  bool allow_none;
  bool keyword_only;
  bool allow_numbers_as_tensors = false;
  int size;
  std::string name;
  // having this as a raw PyObject * will presumably leak it, but these are only held by static objects
  // anyway, and Py_Finalize can already be called when this is destructed.
  PyObject *python_name;
  at::Scalar default_scalar;
  std::vector<int64_t> default_intlist;
  union {
    bool default_bool;
    int64_t default_int;
    double default_double;
    at::ScalarType default_scalartype;
    THPLayout* default_layout;
  };
};

template<int N>
inline PythonArgs PythonArgParser::parse(PyObject* args, PyObject* kwargs, ParsedArgs<N>& dst) {
  if (N < max_args) {
    throw ValueError("PythonArgParser: dst ParsedArgs buffer does not have enough capacity, expected %d (got %d)",
        (int)max_args, N);
  }
  return raw_parse(args, kwargs, dst.args);
}

inline at::Tensor PythonArgs::tensor(int i) {
  PyObject* obj = args[i];
  if (!obj) return at::Tensor();
  if (!THPVariable_Check(obj)) {
    at::Scalar scalar;
    if (THPUtils_checkLong(obj)) {
      scalar = at::Scalar(THPUtils_unpackLong(obj));
    } else if (THPUtils_checkDouble(obj)) {
      scalar = at::Scalar(THPUtils_unpackDouble(obj));
    } else {
      // NB: Are you here because you passed None to a Variable method,
      // and you expected an undefined tensor to be returned?   Don't add
      // a test for Py_None here; instead, you need to mark the argument
      // as *allowing none*; you can do this by writing 'Tensor?' instead
      // of 'Tensor' in the ATen metadata.
      throw TypeError("expected Tensor as argument %d, but got %s", i,
          Py_TYPE(obj)->tp_name);
    }
    auto tensor = scalar_to_tensor(scalar);
    tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
    return autograd::make_variable(tensor);
  }
  return reinterpret_cast<THPVariable*>(obj)->cdata;
}

inline at::Scalar PythonArgs::scalar(int i) {
  return scalarWithDefault(i, signature.params[i].default_scalar);
}

inline at::Scalar PythonArgs::scalarWithDefault(int i, at::Scalar default_scalar) {
  if (!args[i]) return default_scalar;
  // Zero-dim tensors are converted to Scalars as-is. Note this doesn't currently
  // handle most NumPy scalar types except np.float64.
  if (THPVariable_Check(args[i])) {
    return at::_local_scalar(((THPVariable*)args[i])->cdata);
  }
  if (THPUtils_checkLong(args[i])) {
    return at::Scalar(static_cast<int64_t>(THPUtils_unpackLong(args[i])));
  }

  if (PyComplex_Check(args[i])) {
    return at::Scalar(THPUtils_unpackComplexDouble(args[i]));
  }
  return at::Scalar(THPUtils_unpackDouble(args[i]));
}

inline std::vector<at::Tensor> PythonArgs::tensorlist(int i) {
  if (!args[i]) return std::vector<at::Tensor>();
  PyObject* arg = args[i];
  auto tuple = PyTuple_Check(arg);
  auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
  std::vector<at::Tensor> res(size);
  for (int idx = 0; idx < size; idx++) {
    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
    if (!THPVariable_Check(obj)) {
      throw TypeError("expected Tensor as element %d in argument %d, but got %s",
                 idx, i, Py_TYPE(args[i])->tp_name);
    }
    res[idx] = reinterpret_cast<THPVariable*>(obj)->cdata;
  }
  return res;
}

template<int N>
inline std::array<at::Tensor, N> PythonArgs::tensorlist_n(int i) {
  auto res = std::array<at::Tensor, N>();
  PyObject* arg = args[i];
  if (!arg) return res;
  auto tuple = PyTuple_Check(arg);
  auto size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
  if (size != N) {
    throw TypeError("expected tuple of %d elements but got %d", N, (int)size);
  }
  for (int idx = 0; idx < size; idx++) {
    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
    if (!THPVariable_Check(obj)) {
      throw TypeError("expected Tensor as element %d in argument %d, but got %s",
                 idx, i, Py_TYPE(args[i])->tp_name);
    }
    res[idx] = reinterpret_cast<THPVariable*>(obj)->cdata;
  }
  return res;
}

inline std::vector<int64_t> PythonArgs::intlist(int i) {
  return intlistWithDefault(i, signature.params[i].default_intlist);
}

inline std::vector<int64_t> PythonArgs::intlistWithDefault(int i, std::vector<int64_t> default_intlist) {
  if (!args[i]) return default_intlist;
  PyObject* arg = args[i];
  auto size = signature.params[i].size;
  if (size > 0 && THPUtils_checkLong(arg)) {
    return std::vector<int64_t>(size, THPUtils_unpackIndex(arg));
  }
  auto tuple = PyTuple_Check(arg);
  size = tuple ? PyTuple_GET_SIZE(arg) : PyList_GET_SIZE(arg);
  std::vector<int64_t> res(size);
  for (int idx = 0; idx < size; idx++) {
    PyObject* obj = tuple ? PyTuple_GET_ITEM(arg, idx) : PyList_GET_ITEM(arg, idx);
    try {
      // Elements of torch.Size are tensors during tracing, and we need to record extra
      // information before they are turned into an IntList
      if (traceable && jit::tracer::isTracing() && THPVariable_Check(obj)) {
        auto & var = THPVariable_Unpack(obj);
        jit::tracer::ArgumentStash::stashIntListElem(
            signature.params[i].name, size, idx, var);
        res[idx] = var.item<int64_t>();
        continue;
      } else {
        res[idx] = THPUtils_unpackIndex(obj);
      }
    } catch (std::runtime_error &e) {
      throw TypeError("%s(): argument '%s' must be %s, but found element of type %s at pos %d",
          signature.name.c_str(), signature.params[i].name.c_str(),
          signature.params[i].type_name().c_str(), Py_TYPE(obj)->tp_name, idx + 1);
    }
  }
  return res;
}

inline at::ScalarType PythonArgs::scalartypeWithDefault(int i, at::ScalarType default_scalartype) {
  if (!args[i]) return default_scalartype;
  return scalartype(i);
}

inline at::ScalarType PythonArgs::scalartype(int i) {
  if (!args[i]) {
    auto scalartype = signature.params[i].default_scalartype;
    return (scalartype == at::ScalarType::Undefined) ?
            torch::tensors::get_default_tensor_type().scalarType() : scalartype;
  }
  return reinterpret_cast<THPDtype*>(args[i])->scalar_type;
}

inline c10::optional<at::ScalarType> PythonArgs::scalartypeOptional(int i) {
  if (!args[i])
    return c10::nullopt;
  return scalartype(i);
}

inline const THPLayout& PythonArgs::layout(int i) {
  if (!args[i]) return *signature.params[i].default_layout;
  return *reinterpret_cast<THPLayout*>(args[i]);
}

inline const THPLayout& PythonArgs::layoutWithDefault(int i, const THPLayout& default_layout) {
  if (!args[i]) return default_layout;
  return layout(i);
}

static std::string cuda_str = "cuda";
static std::string cpu_str = "cpu";
static std::string cuda_prefix = "cuda:";
static std::string cpu_prefix = "cpu:";

inline at::Device PythonArgs::device(int i) {
  if (!args[i]) {
    const auto& default_tensor_type = torch::tensors::get_default_tensor_type();
    return at::Device(default_tensor_type.device_type());
  }
  if (THPDevice_Check(args[i])) {
    const auto device = reinterpret_cast<THPDevice*>(args[i]);
    return device->device;
  }
  if (THPUtils_checkLong(args[i])) {
    const auto device_index = THPUtils_unpackLong(args[i]);
    AT_CHECK(device_index >= 0, "Device index must not be negative");
    return at::Device(at::DeviceType::CUDA, device_index);
  }
  const std::string device_str = THPUtils_unpackString(args[i]);
  if (device_str == cpu_str) {
    return at::Device(at::DeviceType::CPU);
  } else if (device_str == cuda_str) {
    return at::Device(at::DeviceType::CUDA);
  } else if (device_str.compare(0, cpu_prefix.length(), cpu_prefix) == 0) {
    const auto device_index = std::stoi(device_str.substr(cpu_prefix.length()));
    AT_CHECK(device_index >= 0, "Device index must not be negative");
    return at::Device(at::DeviceType::CPU, device_index);
  } else if (device_str.compare(0, cuda_prefix.length(), cuda_prefix) == 0) {
    const auto device_index = std::stoi(device_str.substr(cuda_prefix.length()));
    AT_CHECK(device_index >= 0, "Device index must not be negative");
    return at::Device(at::DeviceType::CUDA, device_index);
  }
  throw torch::TypeError("only \"cuda\" and \"cpu\" are valid device types, got %s", device_str.c_str());
}

inline at::Device PythonArgs::deviceWithDefault(int i, const at::Device& default_device) {
  if (!args[i]) return default_device;
  return device(i);
}

inline c10::optional<at::Device> PythonArgs::deviceOptional(int i) {
  if (!args[i])
    return c10::nullopt;
  return device(i);
}

inline std::string PythonArgs::string(int i) {
  if (!args[i]) return "";
  return THPUtils_unpackString(args[i]);
}

inline int64_t PythonArgs::toInt64(int i) {
  if (!args[i]) return signature.params[i].default_int;
  return THPUtils_unpackLong(args[i]);
}

inline int64_t PythonArgs::toInt64WithDefault(int i, int64_t default_int) {
  if (!args[i]) return default_int;
  return toInt64(i);
}

inline double PythonArgs::toDouble(int i) {
  if (!args[i]) return signature.params[i].default_double;
  return THPUtils_unpackDouble(args[i]);
}

inline double PythonArgs::toDoubleWithDefault(int i, double default_double) {
  if (!args[i]) return default_double;
  return toDouble(i);
}

inline bool PythonArgs::toBool(int i) {
  if (!args[i]) return signature.params[i].default_bool;
  return args[i] == Py_True;
}

inline bool PythonArgs::toBoolWithDefault(int i, bool default_bool) {
  if (!args[i]) return default_bool;
  return toBool(i);
}

inline bool PythonArgs::isNone(int i) {
  return args[i] == nullptr;
}

inline at::Generator* PythonArgs::generator(int i) {
  if (!args[i]) return nullptr;
  return reinterpret_cast<THPGenerator*>(args[i])->cdata;
}

inline at::Storage PythonArgs::storage(int i) {
  if (!args[i]) return at::Storage();
  return createStorage(args[i]);
}

inline PyObject* PythonArgs::pyobject(int i) {
  if (!args[i]) return Py_None;
  return args[i];
}

} // namespace torch