# 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/learn/imprinting/engine.py
# class ImprintingEngine:
#   def __init__(self, model_path, keep_classes=False):
#   def embedding_dim(self):
#   def num_classes(self):
#   def serialize_extractor_model(self):
#   def serialize_model(self):
#   def train(self, embedding, class_id):
# 
# 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 collections
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.learn.imprinting import engine
from pycoral.utils import edgetpu
from tests import test_utils

    if not keep_classes:
      self.assertEqual(len(train_points), classify.num_classes(classifier))

    for point in test_points:
      with test_utils.test_image('imprinting', point.image) as img:
        set_input(classifier, img)
        classifier.invoke()
        top = classify.get_classes(classifier, top_k=1)[0]
        self.assertEqual(top.id, point.class_id)
        self.assertGreater(top.score, point.score)

    return trained_model

  # Test full model, not keeping base model classes.
  def test_training_l2_norm_model_not_keep_classes(self):
    train_points = [
        TrainPoint(images=['cat_train_0.bmp'], class_id=0),
        TrainPoint(images=['dog_train_0.bmp'], class_id=1),
        TrainPoint(
            images=['hotdog_train_0.bmp', 'hotdog_train_1.bmp'], class_id=2),
    ]
    test_points = [
        TestPoint(image='cat_test_0.bmp', class_id=0, score=0.99),
        TestPoint(image='dog_test_0.bmp', class_id=1, score=0.99),
        TestPoint(image='hotdog_test_0.bmp', class_id=2, score=0.99)
    ]
    for model_path in _MODEL_LIST:
      with self.subTest(model_path=model_path):
        self._train_and_test(

# 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/learn/imprinting/engine.py
# class ImprintingEngine:
#   def __init__(self, model_path, keep_classes=False):
#   def embedding_dim(self):
#   def num_classes(self):
#   def serialize_extractor_model(self):
#   def serialize_model(self):
#   def train(self, embedding, class_id):
# 
# 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/imprinting_engine_test.py
import collections
import unittest
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.learn.imprinting import engine
from pycoral.utils import edgetpu
from tests import test_utils


    if not keep_classes:
      self.assertEqual(len(train_points), classify.num_classes(classifier))

    for point in test_points:
      with test_utils.test_image('imprinting', point.image) as img:
        set_input(classifier, img)
        classifier.invoke()
        top = classify.get_classes(classifier, top_k=1)[0]
        self.assertEqual(top.id, point.class_id)
        self.assertGreater(top.score, point.score)

    return trained_model

  # Test full model, not keeping base model classes.
  def test_training_l2_norm_model_not_keep_classes(self):
    train_points = [
        TrainPoint(images=['cat_train_0.bmp'], class_id=0),
        TrainPoint(images=['dog_train_0.bmp'], class_id=1),
        TrainPoint(
            images=['hotdog_train_0.bmp', 'hotdog_train_1.bmp'], class_id=2),
    ]
    test_points = [
        TestPoint(image='cat_test_0.bmp', class_id=0, score=0.99),
        TestPoint(image='dog_test_0.bmp', class_id=1, score=0.99),
        TestPoint(image='hotdog_test_0.bmp', class_id=2, score=0.99)
    ]
    for model_path in _MODEL_LIST:
      with self.subTest(model_path=model_path):
        self._train_and_test(

            test_utils.test_data_path(model_path),