// While the last several tensors could be optional tensors for an tfl op, the
// number of input operands could vary. This function gets the min/max number of
// operands from tflite op name.
llvm::MinMax OperandNumbersMinMax(llvm::StringRef op_name);

// Populates the `custom_code` and `custom_options` to attributes.
// `custom_code` is used to identify CustomOp.

// Return true if the op has non-empty "minmax" attribute.
static inline bool HasValidMinMaxAttribute(Operation* op) {
  auto minmax = op->getAttrOfType<ArrayAttr>("minmax");
  return minmax && minmax.getValue().size() == 2;
}

namespace {

/// This pattern matches and remove a tfl.fake_quant if all the users of this op
/// and itself have "minmax" attribute set.

:"12",13:"13"},gridTemplateColumns:{none:"none",subgrid:"subgrid",1:"repeat(1, minmax(0, 1fr))",2:"repeat(2, minmax(0, 1fr))",3:"repeat(3, minmax(0, 1fr))",4:"repeat(4, minmax(0, 1fr))",5:"repeat(5, minmax(0, 1fr))",6:"repeat(6, minmax(0, 1fr))",7:"repeat(7, minmax(0, 1fr))",8:"repeat(8, minmax(0, 1fr))",9:"repeat(9, minmax(0, 1fr))",10:"repeat(10, minmax(0, 1fr))",11:"repeat(11, minmax(0, 1fr))",12:"repeat(12, minmax(0, 1fr))"},gridTemplateRows:{none:"none",subgrid:"subgrid",1:"repeat(1, minmax(0,...

    (replaceWithValue $input),
    [(IsTailOfShape $input, $output),
     (IsTailOfShape $output, $input)]>;
}

// Remove (log-)softmax before arg-minmax as (log-)softmax is monotonic.
foreach ArgMinMaxOp = [TFL_ArgMinOp, TFL_ArgMaxOp] in {
  def RemoveSoftmaxOpBefore#ArgMinMaxOp : Pat<
    (ArgMinMaxOp (TFL_SoftmaxOp:$softmax $logits, TFL_FloatNonNegative:$beta),

  // operands from tflite op name.
  // Also, since the above code special-handles the `tfl.reshape` op and add an
  // additional input, we put these function block here.
  llvm::MinMax input_min_max = mlir::OperandNumbersMinMax(op_name);
  int input_max_num = input_min_max.Max;
  int op_input_num = op_state.operands.size();
  if (input_max_num != 0 && input_max_num > op_input_num) {

		delete(s.vars, capVar)
	}

	// make result
	if inplace {
		return nil
	}
	return s.newValue3(ssa.OpSliceMake, n.Type(), p, l, c)
}

// minMax converts an OMIN/OMAX builtin call into SSA.
func (s *state) minMax(n *ir.CallExpr) *ssa.Value {
	// The OMIN/OMAX builtin is variadic, but its semantics are
	// equivalent to left-folding a binary min/max operation across the
	// arguments list.

// RUN: tf-opt %s -quant-import-stats --quant-test-stats='entries { name: "op" params { min_max { min: -1 max: 1 } } } entries { name: "op_0:0" params { min_max { min: -2 max: 2 } } }  entries { name_regex: "op_*" params { min_max { min: -3 max: 3 } } }' | FileCheck %s


// CHECK-LABEL: import_stats_skip
func.func @import_stats_skip(%arg0: tensor<4xf32>, %cst: tensor<i32>) -> (tensor<2xf32>,tensor<2xf32>) {

}

func min3(b []*batchCursor, i0, i1, i2 int) int {
	minIdx := i0
	minT := maxTime
	if i0 < len(b) {
		minT = b[i0].ev.time
	}
	if i1 < len(b) {
		if t := b[i1].ev.time; t < minT {
			minT = t
			minIdx = i1
		}
	}
	if i2 < len(b) {
		if t := b[i2].ev.time; t < minT {
			minT = t
			minIdx = i2
		}
	}
	return minIdx

          return;
        }

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),

          return;
        }

        auto min_max = GetMinMaxValuesForArgument(func_name, i);
        // The input min/max or mean/std are not specified, then skip.
        if (!min_max.first.has_value() || !min_max.second.has_value()) return;

        TypeAttr params = quant::GetQuantizedTypeAttr(
            builder, input_type, builder.getF64FloatAttr(min_max.first.value()),