#include "tensorflow/compiler/mlir/quantization/stablehlo/utils/fill_quantization_options.h"

// NOLINTNEXTLINE
//===----------------------------------------------------------------------===//
// The Quantization Pass for Weight.
//===----------------------------------------------------------------------===//

namespace mlir::quant::stablehlo {

// Put the definitions inside the ::mlir::quant::stablehlo namespace, to match

                values:
                - valuesValue
              matchFields:
              - key: keyValue
                operator: operatorValue
                values:
                - valuesValue
            weight: 1
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: keyValue
                operator: operatorValue
                values:

                values:
                - valuesValue
              matchFields:
              - key: keyValue
                operator: operatorValue
                values:
                - valuesValue
            weight: 1
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: keyValue
                operator: operatorValue
                values:

                values:
                - valuesValue
              matchFields:
              - key: keyValue
                operator: operatorValue
                values:
                - valuesValue
            weight: 1
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: keyValue
                operator: operatorValue
                values:

		destLocalities := make([]string, 0)
		for loc, weight := range locality.To {
			destLocalities = append(destLocalities, loc)
			if weight <= 0 || weight > 100 {
				errs = AppendValidation(errs, fmt.Errorf("locality weight must be in range [1, 100]"))
				return
			}
			totalWeight += weight
		}
		if totalWeight != 100 {
			errs = AppendValidation(errs, fmt.Errorf("total locality weight %v != 100", totalWeight))
			return
		}

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=false" | FileCheck --check-prefix=CustomOpNotWeightOnly %s

// CHECK-LABEL: QuantizeConv2D

  // function attribute prefixed with `"indy_lstm_"`.
  // IndyLSTMs have diagonal recurrent weight matrices and can benefit from
  // more efficent operations in TFLite with the correct conversion (i.e. when
  // the diagonal recurrent weight matrices are provided as vectors).
  if (attr.getValue().starts_with("indy_lstm_")) {

                  matchFields:
                  - key: keyValue
                    operator: operatorValue
                    values:
                    - valuesValue
                weight: 1
              requiredDuringSchedulingIgnoredDuringExecution:
                nodeSelectorTerms:
                - matchExpressions:
                  - key: keyValue
                    operator: operatorValue

                - valuesValue
              matchFields:
              - key: keyValue
                operator: operatorValue
                values:
                - valuesValue
            weight: 1
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
            - matchExpressions:
              - key: keyValue
                operator: operatorValue

        enable_per_channel_quantized_weight_ &&
            IsWeightPerChannelQuantized(quantization_method));
  }

  // Returns true if the quantization method indicates per-channel quantization
  // for convolution weights. This method specifically matches a quantization
  // dimension of 3 for the input index 1 or unspecified quantization dimension
  // for the input index 1.