// 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"
#include "onnx/defs/type_builders.h"

namespace ONNX_NAMESPACE {
ONNX_OPERATOR_SET_SCHEMA(
    Constant,
    25,
    OpSchema()
        .SetDoc(kDoc_Constant_ver24)
        .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));

ONNX_OPERATOR_SET_SCHEMA(
    ConstantOfShape,
    25,
    OpSchema()
        .SetDoc(kDoc_ConstantOfShape_ver24)
        .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", {types::Int64}, "Constrain input types.")
        .TypeConstraint(
            "T2",
            {types::Float16,      types::Float,          types::Double,     types::Int8,           types::Int16,
             types::Int32,        types::Int64,          types::UInt8,      types::UInt16,         types::UInt32,
             types::UInt64,       types::UInt4,          types::Int4,       types::Bool,           types::BFloat16,
             types::Float8E4M3FN, types::Float8E4M3FNUZ, types::Float8E5M2, types::Float8E5M2FNUZ, types::Float4E2M1,
             types::Float8E8M0,   types::UInt2,          types::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;
          }
        }));

ONNX_OPERATOR_SET_SCHEMA(
    EyeLike,
    22,
    OpSchema()
        .SetDoc(kDoc_EyeLike_ver9)
        .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);
        }));

ONNX_OPERATOR_SET_SCHEMA(
    RandomUniform,
    22,
    OpSchema()
        .SetDoc(kDoc_RandomUniform_ver1)
        .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);
        }));

ONNX_OPERATOR_SET_SCHEMA(
    RandomNormal,
    22,
    OpSchema()
        .SetDoc(kDoc_RandomNormal_ver1)
        .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);
        }));

ONNX_OPERATOR_SET_SCHEMA(
    RandomUniformLike,
    22,
    OpSchema()
        .SetDoc(kDoc_RandomUniformLike_ver1)
        .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);
        }));

ONNX_OPERATOR_SET_SCHEMA(
    RandomNormalLike,
    22,
    OpSchema()
        .SetDoc(kDoc_RandomNormalLike_ver1)
        .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);
        }));

ONNX_OPERATOR_SET_SCHEMA(
    Multinomial,
    22,
    OpSchema()
        .SetDoc(kDoc_Multinomial_ver7)
        .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", {types::Int32, types::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 bool
BuildFunctionBodyRange27(const FunctionBodyBuildContext& ctx, const OpSchema& schema, FunctionProto& functionProto) {
  if (ctx.getInputType(0) == nullptr) {
    return false;
  }
  int64_t T = ctx.getInputType(0)->tensor_type().elem_type();
  bool needs_stash =
      (T == static_cast<int64_t>(TensorProto_DataType_FLOAT16) ||
       T == static_cast<int64_t>(TensorProto_DataType_BFLOAT16));

  int64_t stash_type = T;
  if (needs_stash) {
    const auto* stash_attr = ctx.getAttribute("stash_type");
    stash_type = (stash_attr != nullptr) ? stash_attr->i() : static_cast<int64_t>(TensorProto_DataType_FLOAT);
    if (stash_type != static_cast<int64_t>(TensorProto_DataType_FLOAT) &&
        stash_type != static_cast<int64_t>(TensorProto_DataType_DOUBLE))
      // return false so no inline function body is emitted; the model checker
      // will catch the invalid attribute value during validation.
      return false;
  }

  FunctionBuilder builder(functionProto);
  if (needs_stash && stash_type != T) {
    // Cast inputs to stash_type for higher-precision loop accumulation,
    // then cast the collected output back to T.
    builder.Add("start_s = Cast (start)", "to", stash_type)
        .Add("limit_s = Cast (limit)", "to", stash_type)
        .Add("delta_s = Cast (delta)", "to", stash_type)
        .Add("sub_result = Sub (limit_s, start_s)")
        .Add("div_result = Div (sub_result, delta_s)")
        .Add("ceil_result = Ceil (div_result)")
        .Add("ceil_relu = Relu (ceil_result)")
        .Add("n = Cast (ceil_relu)", "to", static_cast<int64_t>(TensorProto_DataType_INT64))
        .Add("loop_cond = Cast (ceil_relu)", "to", static_cast<int64_t>(TensorProto_DataType_BOOL))
        .Add(R"ONNX(variadic_output, output_s = Loop (n, loop_cond, start_s)
          <body = loop_body (int64 i, bool cond_in, prev) => (cond_out, current, range) {
            cond_out = Identity (cond_in)
            current = Add (prev, delta_s)
            range = Identity (prev)
          }>)ONNX")
        .Add("output = Cast (output_s)", "to", T);
  } else {
    builder.Add("sub_result = Sub (limit, start)")
        .Add("sub_result_casted = Cast (sub_result)", "to", static_cast<int64_t>(TensorProto_DataType_FLOAT))
        .Add("delta_casted = Cast (delta)", "to", static_cast<int64_t>(TensorProto_DataType_FLOAT))
        .Add("div_result = Div (sub_result_casted, delta_casted)")
        .Add("ceil_result = Ceil (div_result)")
        .Add("ceil_result_relu = Relu (ceil_result)")
        .Add("ceil_result_relu_int = Cast (ceil_result_relu)", "to", static_cast<int64_t>(TensorProto_DataType_INT64))
        .Add("ceil_result_relu_bool = Cast (ceil_result_relu)", "to", static_cast<int64_t>(TensorProto_DataType_BOOL))
        .Add(R"ONNX(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");
  }
  schema.BuildFunction(functionProto);
  return true;
}

ONNX_OPERATOR_SET_SCHEMA(
    Range,
    27,
    OpSchema()
        .SetDoc(kDoc_Range_ver27)
        .Attr(
            "stash_type",
            "The data type used for intermediate computation when T is float16 or bfloat16. "
            "Defaults to 1 (float). Has no effect for other types.",
            AttributeProto::INT,
            static_cast<int64_t>(TensorProto_DataType_FLOAT))
        .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",
            {types::Float, types::Double, types::Int16, types::Int32, types::Int64, types::Float16, types::BFloat16},
            "Constrain input types to common numeric type tensors.")
        .SetContextDependentFunctionBodyBuilder(BuildFunctionBodyRange27)
        .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, bfloat16, int16, and other types without a native C++
              // range-computation type — stop with rank inference, no action here
            }

            return;
          }
        }));

ONNX_OPERATOR_SET_SCHEMA(
    Bernoulli,
    22,
    OpSchema()
        .SetDoc(kDoc_Bernoulli_ver15)
        .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