# 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


def run_two_models_one_tpu(classification_model, detection_model, image_name,
                           num_inferences, batch_size):
  """Runs two models ALTERNATIVELY using one Edge TPU.

  It runs classification model `batch_size` times and then switch to run
  detection model `batch_size` time until each model is run `num_inferences`
  times.

  Args:
    classification_model: string, path to classification model
    detection_model: string, path to detection model.
    image_name: string, path to input image.
    num_inferences: int, number of inferences to run for each model.
    batch_size: int, indicates how many inferences to run one model before
      switching to the other one.

  Returns:
    double, wall time it takes to finish the job.
  """
  start_time = time.perf_counter()
  interpreter_a = make_interpreter(classification_model, device=':0')
  interpreter_a.allocate_tensors()
  interpreter_b = make_interpreter(detection_model, device=':0')
  interpreter_b.allocate_tensors()

  with open_image(image_name) as image:
    size_a = common.input_size(interpreter_a)
    common.set_input(interpreter_a, image.resize(size_a, Image.NEAREST))

# 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



def run_two_models_one_tpu(classification_model, detection_model, image_name,
                           num_inferences, batch_size):
  """Runs two models ALTERNATIVELY using one Edge TPU.

  It runs classification model `batch_size` times and then switch to run
  detection model `batch_size` time until each model is run `num_inferences`
  times.

  Args:
    classification_model: string, path to classification model
    detection_model: string, path to detection model.
    image_name: string, path to input image.
    num_inferences: int, number of inferences to run for each model.
    batch_size: int, indicates how many inferences to run one model before
      switching to the other one.

  Returns:
    double, wall time it takes to finish the job.
  """
  start_time = time.perf_counter()
  interpreter_a = make_interpreter(classification_model, device=':0')
  interpreter_a.allocate_tensors()
  interpreter_b = make_interpreter(detection_model, device=':0')
  interpreter_b.allocate_tensors()

  with open_image(image_name) as image:
    size_a = common.input_size(interpreter_a)
    common.set_input(interpreter_a, image.resize(size_a, Image.NEAREST))

    _, scale_b = common.set_resized_input(