# Copyright 2023 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.

# A simple test to check whether the tflite_micro package works after it is
# installed.

# To test from the perspective of a package user, use import paths to locations
# in the Python installation environment rather than to locations in the tflm
# source tree.
from tflite_micro import runtime
from tflite_micro import compression

import numpy as np
try:
  from importlib import resources
except ImportError:
  import importlib_resources as resources
import sys
import tempfile
import os


def runtime_test():
  """Test the runtime interpreter functionality."""
  # Create an interpreter with a sine model
  with resources.as_file(
      resources.files(__name__).joinpath("sine_float.tflite")) as model_path:
    interpreter = runtime.Interpreter.from_file(str(model_path))
    OUTPUT_INDEX = 0
    INPUT_INDEX = 0
    input_shape = interpreter.get_input_details(INPUT_INDEX).get("shape")

    # The interpreter infers sin(x)
    def infer(x):
      tensor = np.array(x, np.float32).reshape(input_shape)
      interpreter.set_input(tensor, INPUT_INDEX)
      interpreter.invoke()
      return interpreter.get_output(OUTPUT_INDEX).squeeze()

    # Check a few inferred values against a numerical computation
    PI = 3.14
    inputs = (0.0, PI / 2, PI, 3 * PI / 2, 2 * PI)
    outputs = [infer(x) for x in inputs]
    goldens = np.sin(inputs)

    return np.allclose(outputs, goldens, atol=0.05)


def compression_test():
  """Test that the compression module is available and functional."""

  # Test that compress function is available
  # We don't actually compress here as it requires a properly structured model
  # with compressible tensors, but we verify the function is importable
  assert callable(compression.compress)

  # Test availability of the SpecBuilder
  _ = (compression.SpecBuilder().add_tensor(
      subgraph=0, tensor=0).with_lut(index_bitwidth=4).build())

  return True


def passed():
  """Run all postinstall checks."""
  runtime_passed = runtime_test()
  compression_passed = compression_test()
  return runtime_passed and compression_passed


if __name__ == "__main__":
  sys.exit(0 if passed() else 1)