# Path: pycoral/adapters/classify.py
# def num_classes(interpreter):
# def get_scores(interpreter):
# def get_classes_from_scores(scores,
#                             top_k=float('inf'),
#                             score_threshold=-float('inf')):
# def get_classes(interpreter, top_k=float('inf'), score_threshold=-float('inf')):
# 
# Path: pycoral/adapters/common.py
# def output_tensor(interpreter, i):
# def input_details(interpreter, key):
# def input_size(interpreter):
# def input_tensor(interpreter):
# def set_input(interpreter, data):
# def set_resized_input(interpreter, size, resize):
# 
# Path: pycoral/utils/edgetpu.py
# _EDGETPU_SHARED_LIB = {
#   'Linux': 'libedgetpu.so.1',
#   'Darwin': 'libedgetpu.1.dylib',
#   'Windows': 'edgetpu.dll'
# }[platform.system()]
# def load_edgetpu_delegate(options=None):
# def make_interpreter(model_path_or_content, device=None, delegate=None):
# def _is_valid_ctypes_input(input_data):
# def _gst_buffer_map(buffer):
# def _check_input_size(input_size, expected_input_size):
# def run_inference(interpreter, input_data):
# class _GstMapInfo(ctypes.Structure):
# 
# Path: tests/test_utils.py
# _TEST_DATA_DIR = ''
#   _TEST_DATA_DIR = args.test_data_dir
# def get_test_tmpdir():
# def test_data_path(path, *paths):
# def test_image(path, *paths):
# def temporary_file(suffix=None):
# def generate_random_input(seed, n):
# def prepare_images(image_list, directory, shape):
# def area(box):
# def iou(box_a, box_b):
# def prepare_data_set_from_directory(path, test_ratio, fixed_order):
# def coral_test_main():


import numpy as np
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.utils import edgetpu
from tests import test_utils

  if np.issubdtype(input_type, np.integer) and image_quantization:
    image = rescale_image(image, image_quantization,
                          common.input_details(interpreter, 'quantization'),
                          input_type)

  common.set_input(interpreter, image)
  interpreter.invoke()
  return classify.get_classes(interpreter)[0]


def mobilenet_v1(depth_multiplier, input_size):
  return 'mobilenet_v1_%s_%d_quant_edgetpu.tflite' % (depth_multiplier,
                                                      input_size)


def mobilenet_v1_float_io(depth_multiplier, input_size):
  return 'mobilenet_v1_%s_%d_ptq_float_io_legacy_edgetpu.tflite' % (
      depth_multiplier, input_size)


def efficientnet(input_type):
  return 'efficientnet-edgetpu-%s_quant_edgetpu.tflite' % input_type


class TestClassify(unittest.TestCase):

  @classmethod
  def setUpClass(cls):
    super(TestClassify, cls).setUpClass()

# Path: pycoral/adapters/classify.py
# def num_classes(interpreter):
# def get_scores(interpreter):
# def get_classes_from_scores(scores,
#                             top_k=float('inf'),
#                             score_threshold=-float('inf')):
# def get_classes(interpreter, top_k=float('inf'), score_threshold=-float('inf')):
# 
# Path: pycoral/adapters/common.py
# def output_tensor(interpreter, i):
# def input_details(interpreter, key):
# def input_size(interpreter):
# def input_tensor(interpreter):
# def set_input(interpreter, data):
# def set_resized_input(interpreter, size, resize):
# 
# Path: pycoral/utils/edgetpu.py
# _EDGETPU_SHARED_LIB = {
#   'Linux': 'libedgetpu.so.1',
#   'Darwin': 'libedgetpu.1.dylib',
#   'Windows': 'edgetpu.dll'
# }[platform.system()]
# def load_edgetpu_delegate(options=None):
# def make_interpreter(model_path_or_content, device=None, delegate=None):
# def _is_valid_ctypes_input(input_data):
# def _gst_buffer_map(buffer):
# def _check_input_size(input_size, expected_input_size):
# def run_inference(interpreter, input_data):
# class _GstMapInfo(ctypes.Structure):
# 
# Path: tests/test_utils.py
# _TEST_DATA_DIR = ''
#   _TEST_DATA_DIR = args.test_data_dir
# def get_test_tmpdir():
# def test_data_path(path, *paths):
# def test_image(path, *paths):
# def temporary_file(suffix=None):
# def generate_random_input(seed, n):
# def prepare_images(image_list, directory, shape):
# def area(box):
# def iou(box_a, box_b):
# def prepare_data_set_from_directory(path, test_ratio, fixed_order):
# def coral_test_main():


# Path: tests/classify_test.py
import numpy as np
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.utils import edgetpu
from tests import test_utils


  if np.issubdtype(input_type, np.integer) and image_quantization:
    image = rescale_image(image, image_quantization,
                          common.input_details(interpreter, 'quantization'),
                          input_type)

  common.set_input(interpreter, image)
  interpreter.invoke()
  return classify.get_classes(interpreter)[0]


def mobilenet_v1(depth_multiplier, input_size):
  return 'mobilenet_v1_%s_%d_quant_edgetpu.tflite' % (depth_multiplier,
                                                      input_size)


def mobilenet_v1_float_io(depth_multiplier, input_size):
  return 'mobilenet_v1_%s_%d_ptq_float_io_legacy_edgetpu.tflite' % (
      depth_multiplier, input_size)


def efficientnet(input_type):
  return 'efficientnet-edgetpu-%s_quant_edgetpu.tflite' % input_type


class TestClassify(unittest.TestCase):

  @classmethod
  def setUpClass(cls):
    super(TestClassify, cls).setUpClass()

    cls.delegate = edgetpu.load_edgetpu_delegate()