// Copyright (c) ONNX Project Contributors
//
// SPDX-License-Identifier: Apache-2.0

#include <algorithm>
#include <cmath>

#include "onnx/defs/doc_strings.h"
#include "onnx/defs/function.h"
#include "onnx/defs/generator/utils.h"
#include "onnx/defs/schema.h"

namespace ONNX_NAMESPACE {
static const char* const Constant_ver25_doc = kDoc_Constant_ver24;

ONNX_OPERATOR_SET_SCHEMA(
    Constant,
    25,
    OpSchema()
        .SetDoc(Constant_ver25_doc)
        .Attr("value", "The value for the elements of the output tensor.", AttributeProto::TENSOR, false)
        .Attr(
            "sparse_value",
            "The value for the elements of the output tensor in sparse format.",
            AttributeProto::SPARSE_TENSOR,
            false)
        .Attr(
            "value_int",
            "The value for the sole element for the scalar, int64, output tensor.",
            AttributeProto::INT,
            false)
        .Attr(
            "value_ints",
            "The values for the elements for the 1D, int64, output tensor.",
            AttributeProto::INTS,
            false)
        .Attr(
            "value_float",
            "The value for the sole element for the scalar, float32, output tensor.",
            AttributeProto::FLOAT,
            false)
        .Attr(
            "value_floats",
            "The values for the elements for the 1D, float32, output tensor.",
            AttributeProto::FLOATS,
            false)
        .Attr(
            "value_string",
            "The value for the sole element for the scalar, UTF-8 string, output tensor.",
            AttributeProto::STRING,
            false)
        .Attr(
            "value_strings",
            "The values for the elements for the 1D, UTF-8 string, output tensor.",
            AttributeProto::STRINGS,
            false)
        .Output(0, "output", "Output tensor containing the same value of the provided tensor.", "T")
        .TypeConstraint("T", OpSchema::all_tensor_types_ir13(), "Constrain input and output types to all tensor types.")
        .TypeAndShapeInferenceFunction(ConstantOpInference));

static const char* const ConstantOfShape_ver25_doc = kDoc_ConstantOfShape_ver24;

ONNX_OPERATOR_SET_SCHEMA(
    ConstantOfShape,
    25,
    OpSchema()
        .SetDoc(ConstantOfShape_ver25_doc)
        .Attr(
            "value",
            "(Optional) The value of the output elements."
            "Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32",
            AttributeProto::TENSOR,
            OPTIONAL_VALUE)
        .Input(
            0,
            "input",
            "1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar."
            " All values must be >= 0.",
            "T1")
        .Output(
            0,
            "output",
            "Output tensor of shape specified by 'input'."
            "If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'."
            "If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype "
            "defaults to float32.",
            "T2")
        .TypeConstraint("T1", {"tensor(int64)"}, "Constrain input types.")
        .TypeConstraint(
            "T2",
            {"tensor(float16)",
             "tensor(float)",
             "tensor(double)",
             "tensor(int8)",
             "tensor(int16)",
             "tensor(int32)",
             "tensor(int64)",
             "tensor(uint8)",
             "tensor(uint16)",
             "tensor(uint32)",
             "tensor(uint64)",
             "tensor(uint4)",
             "tensor(int4)",
             "tensor(bool)",
             "tensor(bfloat16)",
             "tensor(float8e4m3fn)",
             "tensor(float8e4m3fnuz)",
             "tensor(float8e5m2)",
             "tensor(float8e5m2fnuz)",
             "tensor(float4e2m1)",
             "tensor(float8e8m0)",
             "tensor(uint2)",
             "tensor(int2)"},
            "Constrain output types to be numerics or boolean.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          if (ctx.getAttribute("value") != nullptr) {
            propagateElemTypeFromDtypeToOutput(ctx, ctx.getAttribute("value"), 0);
          } else {
            propagateElemTypeFromDtypeToOutput(ctx, TensorProto::FLOAT, 0);
          }

          bool found = false;
          TensorShapeProto output_shape = getShapeInput(ctx, 0, true, found);
          if (found) {
            *ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape() = output_shape;
          }
        }));

static const char* const EyeLike_ver22_doc = kDoc_EyeLike_ver9;

ONNX_OPERATOR_SET_SCHEMA(
    EyeLike,
    22,
    OpSchema()
        .SetDoc(EyeLike_ver22_doc)
        .Attr(
            "k",
            "(Optional) Index of the diagonal to be populated with ones. Default is 0."
            " If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, "
            "k > 0 populates an upper diagonal,  and k < 0 populates a lower diagonal.",
            AttributeProto::INT,
            static_cast<int64_t>(0))
        .Attr(
            "dtype",
            "(Optional) The data type for the elements of the output tensor. If not specified,"
            " the data type of the input tensor T1 is used.",
            AttributeProto::INT,
            OPTIONAL_VALUE)
        .Input(0, "input", "2D input tensor to copy shape, and optionally, type information from.", "T1")
        .Output(0, "output", "Output tensor, same shape as input tensor T1.", "T2")
        .TypeConstraint(
            "T1",
            OpSchema::all_non_complex_numeric_types_plus_bool_ir4(),
            "Constrain input types. Strings and complex are not supported.")
        .TypeConstraint(
            "T2",
            OpSchema::all_non_complex_numeric_types_plus_bool_ir4(),
            "Constrain output types. Strings and complex are not supported.")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          if (ctx.getAttribute("dtype") != nullptr) {
            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
          } else {
            propagateElemTypeFromInputToOutput(ctx, 0, 0);
          }
          if (hasInputShape(ctx, 0)) {
            auto& input_shape = getInputShape(ctx, 0);
            if (input_shape.dim_size() != 2) {
              fail_shape_inference("Input tensor must be 2-dimensional");
            }
          }
          propagateShapeFromInputToOutput(ctx, 0, 0);
        }));

static const char* const RandomUniform_ver22_doc = kDoc_RandomUniform_ver1;

ONNX_OPERATOR_SET_SCHEMA(
    RandomUniform,
    22,
    OpSchema()
        .SetDoc(RandomUniform_ver22_doc)
        .Attr("low", "Lower boundary of the output values.", AttributeProto::FLOAT, 0.0f)
        .Attr("high", "Upper boundary of the output values.", AttributeProto::FLOAT, 1.0f)
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.",
            AttributeProto::INT,
            static_cast<int64_t>(TensorProto::FLOAT))
        .Attr("shape", "The shape of the output tensor.", AttributeProto::INTS)
        .Output(0, "output", "Output tensor of random values drawn from uniform distribution", "T")
        .TypeConstraint("T", OpSchema::all_float_types_ir4(), "Constrain output types to float tensors.")
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0, TensorProto::FLOAT);
          propagateShapeFromAttributeToOutput(ctx, "shape", 0);
        }));

static const char* const RandomNormal_ver22_doc = kDoc_RandomNormal_ver1;

ONNX_OPERATOR_SET_SCHEMA(
    RandomNormal,
    22,
    OpSchema()
        .SetDoc(RandomNormal_ver22_doc)
        .Attr("mean", "The mean of the normal distribution.", AttributeProto::FLOAT, 0.0f)
        .Attr("scale", "The standard deviation of the normal distribution.", AttributeProto::FLOAT, 1.0f)
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "The data type for the elements of the output tensor. Default is TensorProto::FLOAT.",
            AttributeProto::INT,
            static_cast<int64_t>(TensorProto::FLOAT))
        .Attr("shape", "The shape of the output tensor.", AttributeProto::INTS)
        .Output(0, "output", "Output tensor of random values drawn from normal distribution", "T")
        .TypeConstraint("T", OpSchema::all_float_types_ir4(), "Constrain output types to float tensors.")
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0, TensorProto::FLOAT);
          propagateShapeFromAttributeToOutput(ctx, "shape", 0);
        }));

static const char* const RandomUniformLike_ver22_doc = kDoc_RandomUniformLike_ver1;

ONNX_OPERATOR_SET_SCHEMA(
    RandomUniformLike,
    22,
    OpSchema()
        .SetDoc(RandomUniformLike_ver22_doc)
        .Attr("low", "Lower boundary of the output values.", AttributeProto::FLOAT, 0.0f)
        .Attr("high", "Upper boundary of the output values.", AttributeProto::FLOAT, 1.0f)
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "(Optional) The data type for the elements of the output tensor, if not specified, we will use "
            "the data type of the input tensor.",
            AttributeProto::INT,
            OPTIONAL_VALUE)
        .Input(0, "input", "Input tensor to copy shape and optionally type information from.", "T1")
        .Output(0, "output", "Output tensor of random values drawn from uniform distribution", "T2")
        .TypeConstraint(
            "T1",
            OpSchema::all_tensor_types_ir4(),
            "Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.")
        .TypeConstraint("T2", OpSchema::all_float_types_ir4(), "Constrain output types to float tensors.")
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          if (ctx.getAttribute("dtype") != nullptr)
            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
          else
            propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasNInputShapes(ctx, 1)) {
            return;
          }
          propagateShapeFromInputToOutput(ctx, 0, 0);
        }));

static const char* const RandomNormalLike_ver22_doc = kDoc_RandomNormalLike_ver1;

ONNX_OPERATOR_SET_SCHEMA(
    RandomNormalLike,
    22,
    OpSchema()
        .SetDoc(RandomNormalLike_ver22_doc)
        .Attr("mean", "The mean of the normal distribution.", AttributeProto::FLOAT, 0.0f)
        .Attr("scale", "The standard deviation of the normal distribution.", AttributeProto::FLOAT, 1.0f)
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "(Optional) The data type for the elements of the output tensor, if not specified, we will use "
            "the data type of the input tensor.",
            AttributeProto::INT,
            OPTIONAL_VALUE)
        .Input(0, "input", "Input tensor to copy shape and optionally type information from.", "T1")
        .Output(0, "output", "Output tensor of random values drawn from normal distribution", "T2")
        .TypeConstraint(
            "T1",
            OpSchema::all_tensor_types_ir4(),
            "Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.")
        .TypeConstraint("T2", OpSchema::all_float_types_ir4(), "Constrain output types to float tensors.")
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          if (ctx.getAttribute("dtype") != nullptr)
            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
          else
            propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasNInputShapes(ctx, 1)) {
            return;
          }
          propagateShapeFromInputToOutput(ctx, 0, 0);
        }));

static const char* const Multinomial_ver22_doc = kDoc_Multinomial_ver7;

ONNX_OPERATOR_SET_SCHEMA(
    Multinomial,
    22,
    OpSchema()
        .SetDoc(Multinomial_ver22_doc)
        .Attr("sample_size", "Number of times to sample.", AttributeProto::INT, static_cast<int64_t>(1))
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.",
            AttributeProto::INT,
            static_cast<int64_t>(TensorProto::INT32))
        .Input(
            0,
            "input",
            "Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.",
            "T1")
        .Output(
            0,
            "output",
            "Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.",
            "T2")
        .TypeConstraint("T1", OpSchema::all_float_types_ir4(), "Constrain input types to float tensors.")
        .TypeConstraint("T2", {"tensor(int32)", "tensor(int64)"}, "Constrain output types to integral tensors.")
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          auto dtype = ctx.getAttribute("dtype");
          auto dataType = TensorProto_DataType::TensorProto_DataType_INT32;
          if (dtype != nullptr) {
            dataType = static_cast<TensorProto_DataType>(dtype->i());
            if (dataType != TensorProto_DataType::TensorProto_DataType_INT32 &&
                dataType != TensorProto_DataType::TensorProto_DataType_INT64) {
              fail_type_inference("Output type must be int32 or int64");
            }
          }
          updateOutputElemType(ctx, 0, dataType);

          TensorShapeProto::Dimension batch_size, sample_size;
          if (hasInputShape(ctx, 0)) {
            auto& input_shape = getInputShape(ctx, 0);
            if (input_shape.dim_size() != 2) {
              fail_shape_inference("Input tensor must have rank 2");
            }
            batch_size = input_shape.dim(0);
          } // else statically-unknown batch-size
          sample_size.set_dim_value(getAttribute(ctx, "sample_size", 1));
          updateOutputShape(ctx, 0, {batch_size, sample_size});
        }));

static constexpr const char* Range_ver11_doc = R"DOC(
Generate a tensor containing a sequence of numbers that begin at `start` and extends by increments of `delta`
up to `limit` (exclusive).

The number of elements in the output of range is computed as below:

```
number_of_elements = max( ceil( (limit - start) / delta ) , 0 )
```

The pseudocode determining the contents of the output is shown below:

```
for(int i=0; i<number_of_elements; ++i) {
  output[i] =  start + (i * delta);
}
```

Example 1

```
Inputs: start = 3, limit = 9, delta = 3
Output: [3, 6]
```

Example 2

```
Inputs: start = 10, limit = 4, delta = -2
Output: [10, 8, 6]
```
)DOC";

template <typename T>
static int64_t
compute_output_dim_for_range(const TensorProto* start, const TensorProto* limit, const TensorProto* delta) {
  if (!start->dims().empty() || !limit->dims().empty() || !delta->dims().empty()) {
    fail_shape_inference("Input to 'Range' op should be scalars (Tensor with only one element and shape empty)");
  }

  const auto start_data = ParseData<T>(start);
  const auto limit_data = ParseData<T>(limit);
  const auto delta_data = ParseData<T>(delta);

  int64_t n = static_cast<int64_t>(ceil((1.0 * (limit_data[0] - start_data[0])) / delta_data[0]));

  n = std::max<int64_t>(n, 0);

  return n;
}

ONNX_OPERATOR_SET_SCHEMA(
    Range,
    11,
    OpSchema()
        .SetDoc(Range_ver11_doc)
        .Input(0, "start", "Scalar. First entry for the range of output values.", "T")
        .Input(1, "limit", "Scalar. Exclusive upper limit for the range of output values.", "T")
        .Input(2, "delta", "Scalar. Value to step by.", "T")
        .Output(0, "output", "A 1-D tensor with same type as the inputs containing generated range of values.", "T")
        .TypeConstraint(
            "T",
            {"tensor(float)", "tensor(double)", "tensor(int16)", "tensor(int32)", "tensor(int64)"},
            "Constrain input types to common numeric type tensors.")
        .FunctionBody(R"ONNX(
          {
            sub_result = Sub (limit, start)
            sub_result_casted = Cast <to = 1> (sub_result)
            delta_casted = Cast <to = 1> (delta)
            div_result = Div (sub_result_casted, delta_casted)
            ceil_result = Ceil (div_result)
            ceil_result_relu = Relu (ceil_result)
            ceil_result_relu_int = Cast <to = 7> (ceil_result_relu)
            ceil_result_relu_bool = Cast <to = 9> (ceil_result_relu)
            variadic_output, output = Loop (ceil_result_relu_int, ceil_result_relu_bool, start)
              <body = loop_body_attribute (int64 i, bool cond, prev) => (cond_out, current, range) {
                cond_out = Identity (cond)
                current = Add (prev, delta)
                range = Identity (prev)
              }>
          }
        )ONNX")
        .TypeAndShapeInferenceFunction([](InferenceContext& ctx) {
          // Type inference
          propagateElemTypeFromInputToOutput(ctx, 0, 0);

          // Shape inference
          const auto start_initializer = ctx.getInputData(0);
          const auto limit_initializer = ctx.getInputData(1);
          const auto delta_initializer = ctx.getInputData(2);

          // Output is always 1-D
          auto output_dim = ctx.getOutputType(0)->mutable_tensor_type()->mutable_shape()->add_dim();

          // If any of Range's inputs are not initializers, the output dimension
          // value would remain unknown.
          if (start_initializer != nullptr && limit_initializer != nullptr && delta_initializer != nullptr) {
            // Make sure the input types are homogeneous
            if ((start_initializer->data_type() != limit_initializer->data_type()) ||
                (start_initializer->data_type() != delta_initializer->data_type())) {
              fail_shape_inference("All inputs to 'Range' op must be of the same type");
            }

            // Explicitly compute the output dimension if Range's inputs are
            // stored in initializer list.
            if (start_initializer->data_type() == TensorProto::FLOAT) {
              output_dim->set_dim_value(
                  compute_output_dim_for_range<float>(start_initializer, limit_initializer, delta_initializer));
            } else if (start_initializer->data_type() == TensorProto::INT32) {
              output_dim->set_dim_value(
                  compute_output_dim_for_range<int32_t>(start_initializer, limit_initializer, delta_initializer));
            } else if (start_initializer->data_type() == TensorProto::INT64) {
              output_dim->set_dim_value(
                  compute_output_dim_for_range<int64_t>(start_initializer, limit_initializer, delta_initializer));
            } else if (start_initializer->data_type() == TensorProto::DOUBLE) {
              output_dim->set_dim_value(
                  compute_output_dim_for_range<double>(start_initializer, limit_initializer, delta_initializer));
            } else {
              // 'float16' has no native CPU type -
              // stop with rank inference, no action here
            }

            return;
          }
        }));

static const char* const Bernoulli_ver22_doc = kDoc_Bernoulli_ver15;

ONNX_OPERATOR_SET_SCHEMA(
    Bernoulli,
    22,
    OpSchema()
        .SetDoc(Bernoulli_ver22_doc)
        .Attr(
            "seed",
            "(Optional) Seed to the random generator, if not specified we will auto generate one.",
            AttributeProto::FLOAT,
            OPTIONAL_VALUE)
        .Attr(
            "dtype",
            "The data type for the elements of the output tensor. if not specified, we will use "
            "the data type of the input tensor.",
            AttributeProto::INT,
            OPTIONAL_VALUE)
        .Input(0, "input", "All values in input have to be in the range:[0, 1].", "T1")
        .Output(0, "output", "The returned output tensor only has values 0 or 1, same shape as input tensor.", "T2")
        .TypeConstraint("T1", OpSchema::all_float_types_ir4(), "Constrain input types to float tensors.")
        .TypeConstraint(
            "T2",
            OpSchema::all_non_complex_numeric_types_plus_bool_ir4(),
            "Constrain output types to all numeric tensors and bool tensors.")
        .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) {
          if (ctx.getAttribute("dtype") != nullptr)
            propagateElemTypeFromAttributeToOutput(ctx, "dtype", 0);
          else
            propagateElemTypeFromInputToOutput(ctx, 0, 0);
          if (!hasNInputShapes(ctx, 1)) {
            return;
          }
          propagateShapeFromInputToOutput(ctx, 0, 0);
        })
        .SetNodeDeterminism(OpSchema::NodeDeterminism::NonDeterministic)
        .SetContextDependentFunctionBodyBuilder(
            [](const FunctionBodyBuildContext& ctx, const OpSchema& schema, FunctionProto& functionProto) -> bool {
              if (ctx.getInputType(0) == nullptr) {
                // we cannot create a correct function body without knowing the input type
                return false;
              }
              auto input_type = ctx.getInputType(0)->tensor_type().elem_type();
              auto dtype = ctx.getAttribute("dtype") != nullptr
                  ? static_cast<TensorProto_DataType>(ctx.getAttribute("dtype")->i())
                  : input_type;
              FunctionBuilder builder(functionProto);
              builder
                  .Add(
                      "X_random = RandomUniformLike <low = 0.0, high = 1.0, seed = @seed> (input)",
                      "dtype",
                      static_cast<int64_t>(input_type))
                  .Add("X_greater = Greater (X_random, input)")
                  .Add("output = Cast (X_greater)", "to", static_cast<int64_t>(dtype));
              schema.BuildFunction(functionProto);
              return true;
            }));
} // namespace ONNX_NAMESPACE
