# 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/adapters/detect.py
# class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
#   def width(self):
#   def height(self):
#   def area(self):
#   def valid(self):
#   def scale(self, sx, sy):
#   def translate(self, dx, dy):
#   def map(self, f):
#   def intersect(a, b):
#   def union(a, b):
#   def iou(a, b):
# def get_objects(interpreter,
#                 score_threshold=-float('inf'),
#                 image_scale=(1.0, 1.0)):
#   def make(i):
# 
# 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: pycoral/utils/edgetpu.py
# def make_interpreter(model_path_or_content, device=None, delegate=None):
#   """Creates a new ``tf.lite.Interpreter`` instance using the given model.
# 
#   **Note:** If you have multiple Edge TPUs, you should always specify the
#   ``device`` argument.
# 
#   Args:
#      model_path_or_content (str or bytes): `str` object is interpreted as
#        model path, `bytes` object is interpreted as model content.
#      device (str): The Edge TPU device you want:
# 
#        + None      -- use any Edge TPU (this is the default)
#        + ":<N>"    -- use N-th Edge TPU (this corresponds to the enumerated
#          index position from :func:`list_edge_tpus`)
#        + "usb"     -- use any USB Edge TPU
#        + "usb:<N>" -- use N-th USB Edge TPU
#        + "pci"     -- use any PCIe Edge TPU
#        + "pci:<N>" -- use N-th PCIe Edge TPU
# 
#        If left as None, you cannot reliably predict which device you'll get.
#        So if you have multiple Edge TPUs and want to run a specific model on
#        each one, then you must specify the device.
#      delegate: A pre-loaded Edge TPU delegate object, as provided by
#        :func:`load_edgetpu_delegate`. If provided, the `device` argument
#        is ignored.
# 
#   Returns:
#      New ``tf.lite.Interpreter`` instance.
#   """
#   if delegate:
#     delegates = [delegate]
#   else:
#     delegates = [load_edgetpu_delegate({'device': device} if device else {})]
#   if isinstance(model_path_or_content, bytes):
#     return tflite.Interpreter(
#         model_content=model_path_or_content, experimental_delegates=delegates)
#   else:
#     return tflite.Interpreter(
#         model_path=model_path_or_content, experimental_delegates=delegates)


import argparse
import contextlib
import threading
import time
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import make_interpreter

  classification_thread.start()
  detection_thread.start()
  classification_thread.join()
  detection_thread.join()
  return time.perf_counter() - start_time


def main():
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--classification_model',
      help='Path of classification model.',
      required=True)
  parser.add_argument(
      '--detection_model', help='Path of detection model.', required=True)
  parser.add_argument('--image', help='Path of the image.', required=True)
  parser.add_argument(
      '--num_inferences',
      help='Number of inferences to run.',
      type=int,
      default=2000)
  parser.add_argument(
      '--batch_size',
      help='Runs one model batch_size times before switching to the other.',
      type=int,
      default=10)

  args = parser.parse_args()


# 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/adapters/detect.py
# class BBox(collections.namedtuple('BBox', ['xmin', 'ymin', 'xmax', 'ymax'])):
#   def width(self):
#   def height(self):
#   def area(self):
#   def valid(self):
#   def scale(self, sx, sy):
#   def translate(self, dx, dy):
#   def map(self, f):
#   def intersect(a, b):
#   def union(a, b):
#   def iou(a, b):
# def get_objects(interpreter,
#                 score_threshold=-float('inf'),
#                 image_scale=(1.0, 1.0)):
#   def make(i):
# 
# 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: pycoral/utils/edgetpu.py
# def make_interpreter(model_path_or_content, device=None, delegate=None):
#   """Creates a new ``tf.lite.Interpreter`` instance using the given model.
# 
#   **Note:** If you have multiple Edge TPUs, you should always specify the
#   ``device`` argument.
# 
#   Args:
#      model_path_or_content (str or bytes): `str` object is interpreted as
#        model path, `bytes` object is interpreted as model content.
#      device (str): The Edge TPU device you want:
# 
#        + None      -- use any Edge TPU (this is the default)
#        + ":<N>"    -- use N-th Edge TPU (this corresponds to the enumerated
#          index position from :func:`list_edge_tpus`)
#        + "usb"     -- use any USB Edge TPU
#        + "usb:<N>" -- use N-th USB Edge TPU
#        + "pci"     -- use any PCIe Edge TPU
#        + "pci:<N>" -- use N-th PCIe Edge TPU
# 
#        If left as None, you cannot reliably predict which device you'll get.
#        So if you have multiple Edge TPUs and want to run a specific model on
#        each one, then you must specify the device.
#      delegate: A pre-loaded Edge TPU delegate object, as provided by
#        :func:`load_edgetpu_delegate`. If provided, the `device` argument
#        is ignored.
# 
#   Returns:
#      New ``tf.lite.Interpreter`` instance.
#   """
#   if delegate:
#     delegates = [delegate]
#   else:
#     delegates = [load_edgetpu_delegate({'device': device} if device else {})]
#   if isinstance(model_path_or_content, bytes):
#     return tflite.Interpreter(
#         model_content=model_path_or_content, experimental_delegates=delegates)
#   else:
#     return tflite.Interpreter(
#         model_path=model_path_or_content, experimental_delegates=delegates)


# Path: examples/two_models_inference.py
import argparse
import contextlib
import threading
import time
from PIL import Image
from pycoral.adapters import classify
from pycoral.adapters import common
from pycoral.adapters import detect
from pycoral.utils.edgetpu import list_edge_tpus
from pycoral.utils.edgetpu import make_interpreter


  classification_thread.start()
  detection_thread.start()
  classification_thread.join()
  detection_thread.join()
  return time.perf_counter() - start_time


def main():
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--classification_model',
      help='Path of classification model.',
      required=True)
  parser.add_argument(
      '--detection_model', help='Path of detection model.', required=True)
  parser.add_argument('--image', help='Path of the image.', required=True)
  parser.add_argument(
      '--num_inferences',
      help='Number of inferences to run.',
      type=int,
      default=2000)
  parser.add_argument(
      '--batch_size',
      help='Runs one model batch_size times before switching to the other.',
      type=int,
      default=10)

  args = parser.parse_args()


  if len(list_edge_tpus()) <= 1: