// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def quantfork_StorageCastOp : quantfork_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

        """Initializes a MatmulModel.

        Args:
          weight_shape: Shape of the weight tensor.
        """
        self.filters = np.random.uniform(low=-1.0, high=1.0, size=weight_shape)

        if bias_fn is not None:
          self.bias = np.random.uniform(
              low=-1.0, high=1.0, size=weight_shape[-1]
          )

      def has_reshape(self) -> bool:

^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)
  %2 = "tf.Transpose"(%1, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %2 : tensor<2x1xf32>

        self.filters_0 = np.random.uniform(
            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_0 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(
            'f4'
        )

        self.filters_1 = np.random.uniform(
            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_1 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(

    FileTypes(String fileExtension, String identifier) {
        this.fileExtension = fileExtension;
        this.identifier = identifier;
    }


    /**
     * Map of file extension to Apple UTI (Uniform Type Identifier).
     */
    public static final ImmutableMap<String, String> FILE_EXTENSION_TO_UTI;

    static {
        ImmutableMap.Builder<String, String> builder = ImmutableMap.builder();

namespace mlir::quant::stablehlo {

// Creates an instance of the ConvertTFQuantOpsToMHLOPass pass, which will
// convert TF uniform quantized ops to the corresponding quantized MHLO ops.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateConvertTFQuantOpsToMHLOPass();

// TODO(b/288094093): Migrate uniform quantization legalization in a separate
// pass.
void PopulateLegalizeTfQuantizationPatterns(MLIRContext *context,

std::unique_ptr<Pass> createOptimizePass();

// Creates a pass that finds quantization patterns and compose them to uniform
// quantized types.
std::unique_ptr<OperationPass<ModuleOp>>
CreateComposeUniformQuantizedTypePass();

// Creates a pass that finds stablehlo ops that accept or produce uniform
// quantized typed tensors and converts them to equivalent ops in the TFLite
// dialect.
std::unique_ptr<OperationPass<func::FuncOp>>

  }
  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"