text: # Lint as: python2, python3
  # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Functions used by multiple converter files."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import copy
  import datetime
  import sys
  
  from absl import logging
  import six
  from six.moves import range
  
  import flatbuffers
  from tensorflow.core.protobuf import config_pb2 as _config_pb2
  from tensorflow.core.protobuf import graph_debug_info_pb2
  from tensorflow.core.protobuf import meta_graph_pb2 as _meta_graph_pb2
  from tensorflow.lite.python import schema_py_generated as schema_fb
  from tensorflow.lite.python import schema_util
  from tensorflow.lite.python import tflite_keras_util as _tflite_keras_util
  from tensorflow.lite.python.op_hint import convert_op_hints_to_stubs
  from tensorflow.lite.python.op_hint import find_all_hinted_output_nodes
  from tensorflow.lite.toco import types_pb2 as _types_pb2
  from tensorflow.python.eager import function
  from tensorflow.python.framework import convert_to_constants as _convert_to_constants
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import error_interpolation as _error_interpolation
  from tensorflow.python.framework import graph_util as tf_graph_util
  from tensorflow.python.grappler import tf_optimizer
  from tensorflow.python.training.saver import export_meta_graph as _export_meta_graph
  
  # Keras functions used by TFLite
  model_input_signature = _tflite_keras_util.model_input_signature
  trace_model_call = _tflite_keras_util.trace_model_call
  
  # Map of tf.dtypes to TFLite types_flag_pb2.
  _MAP_TF_TO_TFLITE_TYPES = {
      dtypes.float32: _types_pb2.FLOAT,
      dtypes.float16: _types_pb2.FLOAT16,
      dtypes.int32: _types_pb2.INT32,
      dtypes.uint8: _types_pb2.QUANTIZED_UINT8,
      dtypes.int64: _types_pb2.INT64,
      dtypes.uint64: _types_pb2.UINT64,
      dtypes.string: _types_pb2.STRING,
      dtypes.bool: _types_pb2.BOOL,
      dtypes.int16: _types_pb2.QUANTIZED_INT16,
      dtypes.complex64: _types_pb2.COMPLEX64,
      dtypes.int8: _types_pb2.INT8,
      dtypes.float64: _types_pb2.FLOAT64,
      dtypes.complex128: _types_pb2.COMPLEX128,
  }
  
  _MAP_TFLITE_ENUM_TO_TF_TYPES = {
      0: dtypes.float32,
      1: dtypes.float16,
      2: dtypes.int32,
      3: dtypes.uint8,
      4: dtypes.int64,
      5: dtypes.string,
      6: dtypes.bool,
      7: dtypes.int16,
      8: dtypes.complex64,
      9: dtypes.int8,
      10: dtypes.float64,
      11: dtypes.complex128,
  }
  
  _TFLITE_FILE_IDENTIFIER = b"TFL3"
  
  _MAP_QUANT_TO_IO_TYPES = {
      dtypes.int8: {dtypes.int8, dtypes.uint8},
      dtypes.int16: {dtypes.int16},
  }
  
  
  def convert_dtype_to_tflite_type(tf_dtype):
    """Converts tf.dtype to TFLite proto type.
  
    Args:
      tf_dtype: tf.dtype
  
    Raises:
      ValueError: Unsupported tf.dtype.
  
    Returns:
      types_flag_pb2.
    """
    result = _MAP_TF_TO_TFLITE_TYPES.get(tf_dtype)
    if result is None:
      raise ValueError("Unsupported tf.dtype {0}".format(tf_dtype))
    return result
  
  
  def _convert_tflite_enum_type_to_tf_type(tflite_enum_type):
    """Converts tflite enum type (eg: 0) to tf type (eg: tf.float32).
  
    Args:
      tflite_enum_type: tflite enum type (eg: 0, that corresponds to float32)
  
    Raises:
      ValueError: If an invalid tflite enum type is provided.
  
    Returns:
      tf type (eg: tf.float32)
    """
    tf_type = _MAP_TFLITE_ENUM_TO_TF_TYPES.get(tflite_enum_type)
    if tf_type is None:
      raise ValueError(
          "Unsupported enum {}. The valid map of enum to tf types is : {}"
          .format(tflite_enum_type, _MAP_TFLITE_ENUM_TO_TF_TYPES))
    return tf_type
  
  
  def _get_tf_type_name(tf_type):
    """Converts tf.dtype (eg: tf.float32) to str (eg: "tf.float32")."""
    return "tf." + tf_type.name
  
  
  def get_tensor_name(tensor):
    """Returns name of the input tensor.
  
    Args:
      tensor: tf.Tensor
  
    Returns:
      str
    """
    parts = six.ensure_str(tensor.name).split(":")
    if len(parts) > 2:
      raise ValueError("Tensor name invalid. Expect 0 or 1 colon, got {0}".format(
          len(parts) - 1))
  
    # To be consistent with the tensor naming scheme in tensorflow, we need
    # drop the ':0' suffix for the first tensor.
    if len(parts) > 1 and parts[1] != "0":
      return tensor.name
    return parts[0]
  
  
  def get_tensors_from_tensor_names(graph, tensor_names):
    """Gets the Tensors associated with the `tensor_names` in the provided graph.
  
    Args:
      graph: TensorFlow Graph.
      tensor_names: List of strings that represent names of tensors in the graph.
  
    Returns:
      A list of Tensor objects in the same order the names are provided.
  
    Raises:
      ValueError:
        tensor_names contains an invalid tensor name.
    """
    # Get the list of all of the tensors.
    tensor_name_to_tensor = {}
    for op in graph.get_operations():
      for tensor in op.values():
        tensor_name_to_tensor[get_tensor_name(tensor)] = tensor
  
    # Get the tensors associated with tensor_names.
    tensors = []
    invalid_tensors = []
    for name in tensor_names:
      if not isinstance(name, six.string_types):
        raise ValueError("Invalid type for a tensor name in the provided graph. "
                         "Expected type for a tensor name is 'str', instead got "
                         "type '{}' for tensor name '{}'".format(
                             type(name), name))
  
      tensor = tensor_name_to_tensor.get(name)
      if tensor is None:
        invalid_tensors.append(name)
      else:
        tensors.append(tensor)
  
    # Throw ValueError if any user input names are not valid tensors.
    if invalid_tensors:
      raise ValueError("Invalid tensors '{}' were found.".format(
          ",".join(invalid_tensors)))
    return tensors
  
  
  def set_tensor_shapes(tensors, shapes):
    """Sets Tensor shape for each tensor if the shape is defined.
  
    Args:
      tensors: TensorFlow ops.Tensor.
      shapes: Dict of strings representing input tensor names to list of
        integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
  
    Raises:
      ValueError:
        `shapes` contains an invalid tensor.
        `shapes` contains an invalid shape for a valid tensor.
    """
    if shapes:
      tensor_names_to_tensor = {
          get_tensor_name(tensor): tensor for tensor in tensors
      }
      for name, shape in shapes.items():
        if name not in tensor_names_to_tensor:
          raise ValueError("Invalid tensor \'{}\' found in tensor shapes "
                           "map.".format(name))
        if shape is not None:
          tensor = tensor_names_to_tensor[name]
          try:
            tensor.set_shape(shape)
          except ValueError as error:
            message = ("The shape of tensor '{0}' cannot be changed from {1} to "
                       "{2}. {3}".format(name, tensor.shape, shape, str(error)))
            raise ValueError(message)
  
  
  def get_grappler_config(optimizers_list):
    """Creates a tf.compat.v1.ConfigProto for configuring Grappler.
  
    Args:
      optimizers_list: List of strings that represents the list of optimizers.
  
    Returns:
      tf.ConfigProto.
    """
    config = _config_pb2.ConfigProto()
    rewrite_options = config.graph_options.rewrite_options
    for optimizer in optimizers_list:
      rewrite_options.optimizers.append(optimizer)
    return config
  
  
  def run_graph_optimizations(graph_def,
                              input_arrays,
                              output_arrays,
                              config,
                              graph=None):
    """Apply standard TensorFlow optimizations to the graph_def.
  
    Args:
      graph_def: Frozen GraphDef to be optimized.
      input_arrays: List of arrays that are considered inputs of the graph.
      output_arrays: List of arrays that are considered outputs of the graph.
      config: tf.ConfigProto.
      graph: TensorFlow Graph. Required when Eager mode is enabled. (default None)
  
    Returns:
      A new, optimized GraphDef.
    """
    meta_graph = _export_meta_graph(graph_def=graph_def, graph=graph)
  
    signature = _meta_graph_pb2.SignatureDef()
    for array in input_arrays:
      signature.inputs[array.name].name = array.name
      signature.inputs[array.name].dtype = array.dtype.as_datatype_enum
      signature.inputs[array.name].tensor_shape.CopyFrom(array.shape.as_proto())
  
    for array in output_arrays:
      signature.outputs[array.name].name = array.name
      signature.outputs[array.name].dtype = array.dtype.as_datatype_enum
      signature.outputs[array.name].tensor_shape.CopyFrom(array.shape.as_proto())
  
    meta_graph.signature_def["not_used_key"].CopyFrom(signature)
  
    # We need to add a collection called 'train_op' so that grappler
    # knows what the outputs are.
    fetch_collection = _meta_graph_pb2.CollectionDef()
    for array in input_arrays + output_arrays:
      fetch_collection.node_list.value.append(array.name)
    meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
  
    return tf_optimizer.OptimizeGraph(config, meta_graph)
  
  
  def _convert_op_hints_if_present(sess, graph_def, output_tensors,
                                   hinted_outputs_nodes):
    if is_frozen_graph(sess):
      raise ValueError("Try to convert op hints, needs unfrozen graph.")
    output_arrays = [get_tensor_name(tensor) for tensor in output_tensors]
    graph_def = tf_graph_util.convert_variables_to_constants(
        sess, graph_def, output_arrays + hinted_outputs_nodes)
    graph_def = convert_op_hints_to_stubs(graph_def=graph_def)
    return graph_def
  
  
  def freeze_graph(sess, input_tensors, output_tensors):
    """Returns a frozen GraphDef.
  
    Runs a Grappler pass and freezes a graph with Variables in it. Otherwise the
    existing GraphDef is returned. The Grappler pass is only run on models that
    are frozen in order to inline the functions in the graph.
    If OpHints is present, it will try to convert the OpHint graph.
  
    Args:
      sess: TensorFlow Session.
      input_tensors: List of input tensors.
      output_tensors: List of output tensors (only .name is used from this).
  
    Returns:
      Frozen GraphDef.
    """
    # Runs a Grappler pass in order to inline any functions in the graph.
    # Asides from inlining any simple function, Grappler will also try to lower
    # while loop into switch merge representation which is undesired for Ophints,
    # so we simply remove those attributes to prevent Grappler from doing so.
    graph_def = _convert_to_constants.disable_lower_using_switch_merge(
        sess.graph_def)
    config = get_grappler_config(["function"])
    graph_def = run_graph_optimizations(
        graph_def, input_tensors, output_tensors, config, graph=sess.graph)
  
    # If ophints are present, just convert them.
    hinted_outputs_nodes = find_all_hinted_output_nodes(sess)
    if hinted_outputs_nodes:
      return _convert_op_hints_if_present(sess, graph_def, output_tensors,
                                          hinted_outputs_nodes)
  
    if not is_frozen_graph(sess):
      output_node_names = [tensor.name.split(":")[0] for tensor in output_tensors]
      return tf_graph_util.convert_variables_to_constants(sess, graph_def,
                                                          output_node_names)
    else:
      return sess.graph_def
  
  
  def is_frozen_graph(sess):
    """Determines if the graph is frozen.
  
    Determines if a graph has previously been frozen by checking for any
    operations of type Variable*. If variables are found, the graph is not frozen.
  
    Args:
      sess: TensorFlow Session.
  
    Returns:
      Bool.
    """
    for op in sess.graph.get_operations():
      if six.ensure_str(op.type).startswith("Variable") or six.ensure_str(
          op.type).endswith("VariableOp"):
        return False
    return True
  
  
  def build_debug_info_func(original_graph):
    """Returns a method to retrieve the `GraphDebugInfo` from the original graph.
  
    Args:
      original_graph: The original `Graph` containing all the op stack traces.
  
    Returns:
      A function which retrieves the stack traces from the original graph and
      converts them to a `GraphDebugInfo` for a given set of nodes.
    """
  
    def f(original_nodes):
      """Function to create `GraphDebugInfo` for the given `original_nodes`."""
      if not original_graph:
        return None
      # For the given nodes, gets all the op definitions in the original graph.
      useful_ops = []
      for func, name in original_nodes:
        try:
          if not func:
            useful_ops.append((func, original_graph.get_operation_by_name(name)))
          else:
            sub_func = original_graph._get_function(func)  # pylint: disable=protected-access
            if isinstance(sub_func, function._EagerDefinedFunction):  # pylint: disable=protected-access
              useful_ops.append(
                  (func, sub_func.graph.get_operation_by_name(name)))
            else:
              sys.stderr.write(
                  "Use '@tf.function' or '@defun' to decorate the function.\n")
              continue
        except KeyError:
          # New node created by graph optimizer. No stack trace from source code.
          continue
      # Convert all the op definitions to stack traces in terms of GraphDebugInfo.
      return _error_interpolation.create_graph_debug_info_def(useful_ops)
  
    return f
  
  
  def convert_debug_info_func(saved_debug_info):
    """Returns a method to retrieve the `GraphDebugInfo` from the original graph.
  
    Args:
      saved_debug_info: The `GraphDebugInfo` containing all the debug info.
  
    Returns:
      A function which retrieves the stack traces from the original graph and
      converts them to a `GraphDebugInfo` for a given set of nodes.
    """
  
    def f(original_nodes):
      """Function to create `GraphDebugInfo` for the given `original_nodes`."""
      if not saved_debug_info:
        return None
  
      output_debug_info = graph_debug_info_pb2.GraphDebugInfo()
      # All the files are copied over, so the index wouldn't be changed.
      output_debug_info.files[:] = saved_debug_info.files
      # We only copy over the debug info for the input nodes
      for func, node in original_nodes:
        debug_key = node + "@" + func
        output_debug_info.traces[debug_key].CopyFrom(
            saved_debug_info.traces[debug_key])
      return output_debug_info
  
    return f
  
  
  def get_debug_info(nodes_to_debug_info_func, converted_graph):
    """Returns the debug info for the original nodes in the `converted_graph`.
  
    Args:
      nodes_to_debug_info_func: The method to collect the op debug info for the
        nodes.
      converted_graph: A `GraphDef` after optimization and transformation.
  
    Returns:
      `GraphDebugInfo` for all the original nodes in `converted_graph`.
    """
    if not nodes_to_debug_info_func:
      return None
  
    # Collect all the debug info nodes from the converted_graph
    original_nodes = set()
    for node in converted_graph.node:
      debug_nodes = node.experimental_debug_info.original_node_names
      debug_funcs = node.experimental_debug_info.original_func_names
      # If the `original_node_names` are empty, uses the node name directly.
      if not debug_nodes:
        original_nodes.add(("", node.name))
      else:
        for i in range(len(debug_nodes)):
          debug_func = "" if i >= len(debug_funcs) else debug_funcs[i]
          original_nodes.add((debug_func, debug_nodes[i]))
  
    # Convert the nodes to the debug info proto object.
    return nodes_to_debug_info_func(original_nodes)
  
  
  def convert_bytes_to_c_source(data,
                                array_name,
                                max_line_width=80,
                                include_guard=None,
                                include_path=None,
                                use_tensorflow_license=False):
    """Returns strings representing a C constant array containing `data`.
  
    Args:
      data: Byte array that will be converted into a C constant.
      array_name: String to use as the variable name for the constant array.
      max_line_width: The longest line length, for formatting purposes.
      include_guard: Name to use for the include guard macro definition.
      include_path: Optional path to include in the source file.
      use_tensorflow_license: Whether to include the standard TensorFlow Apache2
        license in the generated files.
  
    Returns:
      Text that can be compiled as a C source file to link in the data as a
      literal array of values.
      Text that can be used as a C header file to reference the literal array.
    """
  
    starting_pad = "   "
    array_lines = []
    array_line = starting_pad
    for value in bytearray(data):
      if (len(array_line) + 4) > max_line_width:
        array_lines.append(array_line + "\n")
        array_line = starting_pad
      array_line += " 0x%02x," % (value)
    if len(array_line) > len(starting_pad):
      array_lines.append(array_line + "\n")
    array_values = "".join(array_lines)
  
    if include_guard is None:
      include_guard = "TENSORFLOW_LITE_UTIL_" + array_name.upper() + "_DATA_H_"
  
    if include_path is not None:
      include_line = "#include \"{include_path}\"\n".format(
          include_path=include_path)
    else:
      include_line = ""
  
    if use_tensorflow_license:
      license_text = """
  /* Copyright {year} The TensorFlow Authors. All Rights Reserved.
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
      http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  ==============================================================================*/
  """.format(year=datetime.date.today().year)
    else:
      license_text = ""
  
    source_template = """{license_text}
  // This is a TensorFlow Lite model file that has been converted into a C data
  // array using the tensorflow.lite.util.convert_bytes_to_c_source() function.
  // This form is useful for compiling into a binary for devices that don't have a
  // file system.
  
  {include_line}
  // We need to keep the data array aligned on some architectures.
  #ifdef __has_attribute
  #define HAVE_ATTRIBUTE(x) __has_attribute(x)
  #else
  #define HAVE_ATTRIBUTE(x) 0
  #endif
  #if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
  #define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
  #else
  #define DATA_ALIGN_ATTRIBUTE
  #endif
  
  const unsigned char {array_name}[] DATA_ALIGN_ATTRIBUTE = {{
  {array_values}}};
  const int {array_name}_len = {array_length};
  """
  
    source_text = source_template.format(
        array_name=array_name,
        array_length=len(data),
        array_values=array_values,
        license_text=license_text,
        include_line=include_line)
  
    header_template = """
  {license_text}
  
  // This is a TensorFlow Lite model file that has been converted into a C data
  // array using the tensorflow.lite.util.convert_bytes_to_c_source() function.
  // This form is useful for compiling into a binary for devices that don't have a
  // file system.
  
  #ifndef {include_guard}
  #define {include_guard}
  
  extern const unsigned char {array_name}[];
  extern const int {array_name}_len;
  
  #endif  // {include_guard}
  """
  
    header_text = header_template.format(
        array_name=array_name,
        include_guard=include_guard,
        license_text=license_text)
  
    return source_text, header_text
  
  
  def _convert_model_from_bytearray_to_object(model_bytearray):
    """Converts a tflite model from a bytearray into a parsable object."""
    model_object = schema_fb.Model.GetRootAsModel(model_bytearray, 0)
    model_object = schema_fb.ModelT.InitFromObj(model_object)
    model_object = copy.deepcopy(model_object)
    model_object.subgraphs[0].inputs[0] = model_object.subgraphs[0].inputs[0]
    return model_object
  
  
  def _convert_model_from_object_to_bytearray(model_object):
    """Converts a tflite model from a parsable object into a bytearray."""
    # Initial size of the buffer, which will grow automatically if needed
    builder = flatbuffers.Builder(1024)
    model_offset = model_object.Pack(builder)
    builder.Finish(model_offset, file_identifier=_TFLITE_FILE_IDENTIFIER)
    return bytes(builder.Output())
  
  
  def _remove_tensors_from_model(model, remove_tensors_idxs):
    """Remove tensors from model."""
    if not remove_tensors_idxs:
      return
    if len(model.subgraphs) > 1:
      raise ValueError("Model must only have one subgraph. Instead, it has "
                       "{} subgraphs.".format(len(model.subgraphs)))
    subgraph = model.subgraphs[0]
    tensors = subgraph.tensors
    operators = subgraph.operators
  
    logging.debug("Removing tensors at indices : %s", remove_tensors_idxs)
    # An optimized check to validate if "remove_tensors_idxs" (eg: [4,5,6]) is an
    # exact subset, with ordering, of "tensors" indices (eg: [0,1,2,3,4,5,6]).
    if min(remove_tensors_idxs) == len(tensors) - len(remove_tensors_idxs):
      logging.debug("Removing tensors only at the end of the tensor list")
      del tensors[min(remove_tensors_idxs):]
    else:
      logging.debug("Removing tensors requires updating the model")
      # Map the old tensor indices to new tensor indices
      d_old_to_new_tensors = {}
      left_shift_by = 0
      for idx in range(len(tensors)):
        if idx in remove_tensors_idxs:
          left_shift_by += 1
        else:
          d_old_to_new_tensors[idx] = idx - left_shift_by
      logging.debug("Old to new tensors map: %s", d_old_to_new_tensors.__str__())
      # Update tensor indices referenced throughout the model
      def update_tensors(tensor_idxs):
        for i, ti in enumerate(tensor_idxs):
          tensor_idxs[i] = d_old_to_new_tensors.get(ti, -1)
      update_tensors(subgraph.inputs)
      update_tensors(subgraph.outputs)
      for op in operators:
        update_tensors(op.inputs)
        update_tensors(op.outputs)
      # Delete the tensors
      for idx in sorted(remove_tensors_idxs, reverse=True):
        tensors.pop(idx)
      logging.debug("Removed tensors marked for deletion")
  
  
  def _modify_model_input_type(model, inference_input_type=dtypes.float32):
    """Modify model input type."""
  
    if inference_input_type == dtypes.float32:
      return
  
    subgraph = model.subgraphs[0]
    tensors = subgraph.tensors
    operators = subgraph.operators
  
    # Find all quantize operators
    quant_opcode_idxs = []
    for idx, opcode in enumerate(model.operatorCodes):
      builtin_code = schema_util.get_builtin_code_from_operator_code(opcode)
      if builtin_code == schema_fb.BuiltinOperator.QUANTIZE:
        quant_opcode_idxs.append(idx)
    if operators and not quant_opcode_idxs:
      for input_idx in subgraph.inputs:
        input_type = _convert_tflite_enum_type_to_tf_type(tensors[input_idx].type)
        if input_type == dtypes.float32:
          raise ValueError("Model input is not dequantized.")
      # None of the inputs have float32, then they must be int16, int8, or bool
      return
  
    # Validate that the model input is quantized
    input_quant_ops = []
    for op in operators:
      # Find operators that quantize model input
      if op.opcodeIndex in quant_opcode_idxs and op.inputs[0] in subgraph.inputs:
        float_tensor, quant_tensor = tensors[op.inputs[0]], tensors[op.outputs[0]]
        # If found, validate that the operator's input type is float
        float_type = _convert_tflite_enum_type_to_tf_type(float_tensor.type)
        if float_type != dtypes.float32:
          if float_type == inference_input_type:
            continue
          else:
            raise ValueError(
                "Initial model input type must be tf.float32. Expected type for "
                "tensor with name '{}' is tf.float32, instead type is {}".format(
                    float_tensor.name, _get_tf_type_name(float_type)))
        # If found, validate that the operator output is quantized and compatible
        # with the final model input type
        quant_type = _convert_tflite_enum_type_to_tf_type(quant_tensor.type)
        if quant_type not in _MAP_QUANT_TO_IO_TYPES:
          raise ValueError(
              "Initial model input is not quantized. Expected type for "
              "tensor with name '{}' should be in {}, instead type is {}".format(
                  quant_tensor.name,
                  tuple(_get_tf_type_name(t) for t in
                        _MAP_QUANT_TO_IO_TYPES.keys()),
                  _get_tf_type_name(quant_type)))
        else:
          inference_io_types = _MAP_QUANT_TO_IO_TYPES[quant_type]
          if inference_input_type not in inference_io_types:
            raise ValueError(
                "Unsupported `inference_input_type` value. Expected to be in "
                "{}, instead got {}.".format(
                    tuple(_get_tf_type_name(t) for t in inference_io_types),
                    _get_tf_type_name(inference_input_type)))
        input_quant_ops.append(op)
  
    if len(subgraph.inputs) != len(input_quant_ops):
      logging.warning(
          "For model inputs containing unsupported operations which cannot be "
          "quantized, the `inference_input_type` attribute will default to the "
          "original type."
          )
  
    # Modify model input type
    if inference_input_type == dtypes.uint8:
      # Change quant op (float to int8) to quant op (uint8 to int8)
      for op in input_quant_ops:
        int8_quantization = tensors[op.outputs[0]].quantization
        uint8_quantization = schema_fb.QuantizationParametersT()
        uint8_quantization.scale = [int8_quantization.scale[0]]
        uint8_quantization.zeroPoint = [int8_quantization.zeroPoint[0] + 128]
        tensors[op.inputs[0]].quantization = uint8_quantization
        tensors[op.inputs[0]].type = schema_fb.TensorType.UINT8
    elif inference_input_type in _MAP_QUANT_TO_IO_TYPES:
      # Remove the inputs and the quant operator
      remove_tensors_idxs = set()
      for op in input_quant_ops:
        subgraph.inputs[subgraph.inputs == op.inputs[0]] = op.outputs[0]
        remove_tensors_idxs.add(op.inputs[0])
        operators.remove(op)
      # Remove tensors marked for deletion.
      _remove_tensors_from_model(model, remove_tensors_idxs)
    else:
      raise ValueError(
          "Unsupported `inference_input_type` value {}.".format(
              _get_tf_type_name(inference_input_type)))
  
  
  def _modify_model_output_type(model, inference_output_type=dtypes.float32):
    """Modify model output type."""
  
    if inference_output_type == dtypes.float32:
      return
  
    subgraph = model.subgraphs[0]
    tensors = subgraph.tensors
    operators = subgraph.operators
  
    # Find all dequantize operators
    dequant_opcode_idxs = []
    for idx, opcode in enumerate(model.operatorCodes):
      builtin_code = schema_util.get_builtin_code_from_operator_code(opcode)
      if builtin_code == schema_fb.BuiltinOperator.DEQUANTIZE:
        dequant_opcode_idxs.append(idx)
    if operators and not dequant_opcode_idxs:
      for output in subgraph.outputs:
        output_type = _convert_tflite_enum_type_to_tf_type(tensors[output].type)
        if output_type == dtypes.float32:
          raise ValueError("Model output is not dequantized.")
      # None of the outputs have float32, then they must be int16, int8, or bool
      return
  
    # Validate that the model output is dequantized
    output_dequant_ops = []
    for op in operators:
      # Find operators that dequantize model output
      if op.opcodeIndex in dequant_opcode_idxs and \
          op.outputs[0] in subgraph.outputs:
        # If found, validate that the operator's output type is float
        quant_tensor, float_tensor = tensors[op.inputs[0]], tensors[op.outputs[0]]
        float_type = _convert_tflite_enum_type_to_tf_type(float_tensor.type)
        if float_type != dtypes.float32:
          if float_type == inference_output_type:
            continue
          else:
            raise ValueError(
                "Initial model output type must be tf.float32. Expected type for "
                "tensor with name '{}' is tf.float32, instead type is {}".format(
                    float_tensor.name, _get_tf_type_name(float_type)))
        # If found, validate that the operator input is quantized and compatible
        # with the final model output type
        quant_type = _convert_tflite_enum_type_to_tf_type(quant_tensor.type)
        if quant_type not in _MAP_QUANT_TO_IO_TYPES:
          raise ValueError(
              "Initial model output is not dequantized. Expected type for "
              "tensor with name '{}' should be in {}, instead type is {}".format(
                  quant_tensor.name,
                  tuple(_get_tf_type_name(t) for t in
                        _MAP_QUANT_TO_IO_TYPES.keys()),
                  _get_tf_type_name(quant_type)))
        else:
          inference_io_types = _MAP_QUANT_TO_IO_TYPES[quant_type]
          if inference_output_type not in inference_io_types:
            raise ValueError(
                "Unsupported `inference_output_type` value. Expected to be in "
                "{}, instead got {}.".format(
                    tuple(_get_tf_type_name(t) for t in inference_io_types),
                    _get_tf_type_name(inference_output_type)))
        output_dequant_ops.append(op)
  
    if len(subgraph.outputs) != len(output_dequant_ops):
      logging.warning(
          "For model outputs containing unsupported operations which cannot be "
          "quantized, the `inference_output_type` attribute will default to the "
          "original type."
          )
  
    # Modify model output type
    if inference_output_type == dtypes.uint8:
      # Find a quantize operator
      quant_opcode_idx = -1
      for idx, opcode in enumerate(model.operatorCodes):
        builtin_code = schema_util.get_builtin_code_from_operator_code(opcode)
        if builtin_code == schema_fb.BuiltinOperator.QUANTIZE:
          quant_opcode_idx = idx
          break
      # Create a quantize operator, if none exist
      if quant_opcode_idx == -1:
        quant_op = schema_fb.OperatorCodeT()
        quant_op.builtinCode = schema_fb.BuiltinOperator.QUANTIZE
        quant_op.deprecatedBuiltinCode = schema_fb.BuiltinOperator.QUANTIZE
        model.operatorCodes.append(quant_op)
        quant_opcode_idx = len(model.operatorCodes) - 1
      # Change dequant op (int8 to float) to quant op (int8 to uint8)
      for op in output_dequant_ops:
        op.opcodeIndex = quant_opcode_idx
        int8_quantization = tensors[op.inputs[0]].quantization
        uint8_quantization = schema_fb.QuantizationParametersT()
        uint8_quantization.scale = [int8_quantization.scale[0]]
        uint8_quantization.zeroPoint = [int8_quantization.zeroPoint[0] + 128]
        tensors[op.outputs[0]].quantization = uint8_quantization
        tensors[op.outputs[0]].type = schema_fb.TensorType.UINT8
    elif inference_output_type in _MAP_QUANT_TO_IO_TYPES:
      # Remove the outputs and the dequant operator
      remove_tensors_idxs = set()
      for op in output_dequant_ops:
        subgraph.outputs[subgraph.outputs == op.outputs[0]] = op.inputs[0]
        remove_tensors_idxs.add(op.outputs[0])
        operators.remove(op)
      # Remove tensors marked for deletion.
      _remove_tensors_from_model(model, remove_tensors_idxs)
    else:
      raise ValueError(
          "Unsupported `inference_output_type` value {}.".format(
              _get_tf_type_name(inference_output_type)))
  
  
  def modify_model_io_type(
      model, inference_input_type=dtypes.float32,
      inference_output_type=dtypes.float32):
    """Modify the input/output type of a tflite model.
  
    Args:
      model: A tflite model.
      inference_input_type: tf.DType representing modified input type.
        (default tf.float32. If model input is int8 quantized, it must be in
        {tf.float32, tf.int8,tf.uint8}, else if model input is int16 quantized,
        it must be in {tf.float32, tf.int16}, else it must be tf.float32)
      inference_output_type: tf.DType representing modified output type.
        (default tf.float32. If model output is int8 dequantized, it must be in
        {tf.float32, tf.int8,tf.uint8}, else if model output is int16 dequantized,
        it must be in {tf.float32, tf.int16}, else it must be tf.float32)
    Returns:
      A tflite model with modified input/output type.
  
    Raises:
      ValueError: If `inference_input_type`/`inference_output_type` is unsupported
        or a supported integer type is specified for a model whose input/output is
        not quantized/dequantized.
      RuntimeError: If the modification was unsuccessful.
  
    """
    if inference_input_type == dtypes.float32 and \
        inference_output_type == dtypes.float32:
      return model
  
    model_object = _convert_model_from_bytearray_to_object(model)
  
    if len(model_object.subgraphs) > 1:
      raise ValueError("Model must only have one subgraph. Instead, it has "
                       "{} subgraphs.".format(len(model_object.subgraphs)))
  
    _modify_model_input_type(model_object, inference_input_type)
  
    _modify_model_output_type(model_object, inference_output_type)
  
    return _convert_model_from_object_to_bytearray(model_object)
  