// Package math provides basic constants and mathematical functions.
//
// This package does not guarantee bit-identical results across architectures.
package math

// Mathematical constants.
const (
	E   = 2.71828182845904523536028747135266249775724709369995957496696763 // https://oeis.org/A001113
	Pi  = 3.14159265358979323846264338327950288419716939937510582097494459 // https://oeis.org/A000796

Brad Fitzpatrick <******@****.***> 1493866163 +0000

  // CHECK: %[[CONST1:.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
  %const1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  // CHECK: %[[SUB:.*]] = "tf.Sub"(%[[BARG1]], %[[CONST1]])
  %sub = "tf.Sub"(%arg1, %const1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %elem = "tf._SomeOp"() : () -> tensor<f32>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "bf16_scalar"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_BFLOAT16
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_BFLOAT16
        tensor_shape {
        }
        half_val: 0
        # CHECK: value = dense<0.000000e+00> : tensor<bf16>

    NEWARRAY(T_BYTE)
    for ((i, byte) in byteArray.withIndex()) {
        DUP()
        LDC(i)
        LDC(byte)
        BASTORE()
    }
}


internal
fun MethodVisitor.ICONST_0() {
    visitInsn(Opcodes.ICONST_0)
}


internal
fun MethodVisitor.NEW(type: InternalName) {
    visitTypeInsn(Opcodes.NEW, type)
}


internal

(Lsh32x64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Rsh32x64 x (Int64Make (Const32 [c]) _)) && c != 0 => (Signmask x)
(Rsh32Ux64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Lsh16x64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])
(Rsh16x64 x (Int64Make (Const32 [c]) _)) && c != 0 => (Signmask (SignExt16to32 x))
(Rsh16Ux64 _ (Int64Make (Const32 [c]) _)) && c != 0 => (Const32 [0])

node {
  name: "Quantized_Constant"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_QUINT8
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_QUINT8
        tensor_shape {
        }
        int_val: 7
      }
    }
  }
}
library {
}
versions {
  producer: 70
}

# CHECK: tf.Const

  Scope root = Scope::NewRootScope();
  ops::Const(root, {{2.0}, {"df"}});
  EXPECT_FALSE(root.status().ok());
}

TEST(ConstOpTest, Names) {
  Scope root = Scope::NewRootScope();
  auto c = ops::Const(root, {{2.0}, {3.0}});
  EXPECT_EQ(c.node()->name(), "Const");
  auto c_1 = ops::Const(root, {{2.0}, {3.0}});
  EXPECT_EQ(c_1.node()->name(), "Const_1");

  auto x = ops::Const(root.WithOpName("x"), 1);

  name: "bn_conv1_1/FusedBatchNorm"
  op: "FusedBatchNorm"
  input: "conv1_1/Conv2D"
  input: "bn_conv1_1/ReadVariableOp"
  input: "bn_conv1_1/ReadVariableOp_1"
  input: "bn5a_branch1_1/Const_1"
  input: "bn5a_branch1_1/Const_1"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"

}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test hoisting const op.

// CHECK-LABEL: func @_tfrt_resource_init
// CHECK: [[const:%.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> {device = "/CPU:0"} : () -> tensor<i32>
// CHECK: [[x:%.*]] = "tf.AddV2"([[const]], [[const]]) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>