# Copyright 2017 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.
# ==============================================================================
"""Test utilities for tf.signal."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.core.protobuf import config_pb2
from tensorflow.lite.python import interpreter
from tensorflow.lite.python import lite
from tensorflow.python.eager import def_function
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.training import saver


def grappler_optimize(graph, fetches=None, config_proto=None):
  """Tries to optimize the provided graph using grappler.

  Args:
    graph: A `tf.Graph` instance containing the graph to optimize.
    fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away).
      Grappler uses the 'train_op' collection to look for fetches, so if not
      provided this collection should be non-empty.
    config_proto: An optional `tf.compat.v1.ConfigProto` to use when rewriting
      the graph.

  Returns:
    A `tf.compat.v1.GraphDef` containing the rewritten graph.
  """
  if config_proto is None:
    config_proto = config_pb2.ConfigProto()
    config_proto.graph_options.rewrite_options.min_graph_nodes = -1
  if fetches is not None:
    for fetch in fetches:
      graph.add_to_collection('train_op', fetch)
  metagraph = saver.export_meta_graph(graph_def=graph.as_graph_def())
  return tf_optimizer.OptimizeGraph(config_proto, metagraph)


def tflite_convert(fn, input_templates, use_mlir=False):
  """Converts the provided fn to tf.lite model.

  Args:
    fn: A callable that expects a list of inputs like input_templates that
      returns a tensor or structure of tensors.
    input_templates: A list of Tensors, ndarrays or TensorSpecs describing the
      inputs that fn expects. The actual values of the Tensors or ndarrays are
      unused.
    use_mlir: Experimental. Whether to use the tf.lite MLIR converter.

  Returns:
    The serialized tf.lite model.
  """
  fn = def_function.function(fn)
  concrete_func = fn.get_concrete_function(*input_templates)
  converter = lite.TFLiteConverterV2([concrete_func])
  converter.experimental_new_converter = use_mlir
  return converter.convert()


def evaluate_tflite_model(tflite_model, input_ndarrays):
  """Evaluates the provided tf.lite model with the given input ndarrays.

  Args:
    tflite_model: bytes. The serialized tf.lite model.
    input_ndarrays: A list of NumPy arrays to feed as input to the model.

  Returns:
    A list of ndarrays produced by the model.

  Raises:
    ValueError: If the number of input arrays does not match the number of
      inputs the model expects.
  """
  the_interpreter = interpreter.Interpreter(model_content=tflite_model)
  the_interpreter.allocate_tensors()

  input_details = the_interpreter.get_input_details()
  output_details = the_interpreter.get_output_details()

  if len(input_details) != len(input_ndarrays):
    raise ValueError('Wrong number of inputs: provided=%s, '
                     'input_details=%s output_details=%s' % (
                         input_ndarrays, input_details, output_details))
  for input_tensor, data in zip(input_details, input_ndarrays):
    the_interpreter.set_tensor(input_tensor['index'], data)
  the_interpreter.invoke()
  return [the_interpreter.get_tensor(details['index'])
          for details in output_details]
