# Path: studio/model_util.py
# class BufferedPipe:
# class ModelPipe:
#     def __init__(self,
#                  func=lambda x: x,
#                  parent=None,
#                  q_in=None,
#                  q_out=None,
#                  num_workers=0,
#                  q_size=None,
#                  batch_size=1,
#                  filterf=lambda x: x is not None,
#                  batcher=lambda x: x,
#                  timeout=1):
#     def __call__(self, data_gen):
#     def add(self, func,
#             num_workers=None,
#             batch_size=None,
#             filterf=None,
#             batcher=None,
#             timeout=None):
#     def _wrapped_func(self, x):
#     def _wrapped_filter(self, x):
#     def __init__(self):
#     def add(self, func,
#             num_workers=None,
#             batch_size=None,
#             filterf=None,
#             batcher=None,
#             timeout=None):
#     def apply_unordered(self, data):
#     def apply_ordered(self, data):
#     def __call__(self, data):
# def resize_to_model_input(model, input_index=0):
#     def _run_resize(input_img):
# def _q2q_batch(
#         func,
#         queue_in,
#         queue_out,
#         filterf=lambda x: x is not None,
#         batch_size=32,
#         timeout=1):
# def _q2q_single(func, queue_in, queue_out, filterf=lambda x: True, timeout=1):
# def _gen2q(data, queue):
# def _q2gen(queue, timeout=1):
# def _prime_keras_model(model):


import unittest
import numpy as np
import six
    import keras
from PIL import Image
from io import BytesIO
    from keras.layers import Dense, Flatten
    from keras.models import Sequential
    from keras.datasets import mnist
    from keras.utils import to_categorical
from timeout_decorator import timeout
    from Queue import Queue, Empty
    from queue import Queue, Empty
from studio import model_util
        while True:
            try:
                actual_out.append(q.get(True, 1))
            except Empty:
                break

        self.assertEqual(expected_out, actual_out)

        model_util._gen2q((x for x in range(10)), q)
        gen = model_util._q2gen(q)

        self.assertEqual(expected_out, list(gen))


class BufferedPipeTest(unittest.TestCase):
    _multiprocess_shared_ = True

    def test_pipe_simple(self):
        p = model_util.BufferedPipe() \
            .add(lambda x: x + 1) \
            .add(lambda x: x * x)

        lst = list(p((x for x in range(1, 5))))

        expected_l = [4, 9, 16, 25]

        self.assertEqual(lst, expected_l)

    @unittest.skip('ordering problem - peterz to fix')
    def test_pipe_buffer(self):

# Path: studio/model_util.py
# class BufferedPipe:
# class ModelPipe:
#     def __init__(self,
#                  func=lambda x: x,
#                  parent=None,
#                  q_in=None,
#                  q_out=None,
#                  num_workers=0,
#                  q_size=None,
#                  batch_size=1,
#                  filterf=lambda x: x is not None,
#                  batcher=lambda x: x,
#                  timeout=1):
#     def __call__(self, data_gen):
#     def add(self, func,
#             num_workers=None,
#             batch_size=None,
#             filterf=None,
#             batcher=None,
#             timeout=None):
#     def _wrapped_func(self, x):
#     def _wrapped_filter(self, x):
#     def __init__(self):
#     def add(self, func,
#             num_workers=None,
#             batch_size=None,
#             filterf=None,
#             batcher=None,
#             timeout=None):
#     def apply_unordered(self, data):
#     def apply_ordered(self, data):
#     def __call__(self, data):
# def resize_to_model_input(model, input_index=0):
#     def _run_resize(input_img):
# def _q2q_batch(
#         func,
#         queue_in,
#         queue_out,
#         filterf=lambda x: x is not None,
#         batch_size=32,
#         timeout=1):
# def _q2q_single(func, queue_in, queue_out, filterf=lambda x: True, timeout=1):
# def _gen2q(data, queue):
# def _q2gen(queue, timeout=1):
# def _prime_keras_model(model):


# Path: tests/model_util_test.py
import unittest
import numpy as np
import six
    import keras
from PIL import Image
from io import BytesIO
    from keras.layers import Dense, Flatten
    from keras.models import Sequential
    from keras.datasets import mnist
    from keras.utils import to_categorical
from timeout_decorator import timeout
    from Queue import Queue, Empty
    from queue import Queue, Empty
from studio import model_util

        while True:
            try:
                actual_out.append(q.get(True, 1))
            except Empty:
                break

        self.assertEqual(expected_out, actual_out)

        model_util._gen2q((x for x in range(10)), q)
        gen = model_util._q2gen(q)

        self.assertEqual(expected_out, list(gen))


class BufferedPipeTest(unittest.TestCase):
    _multiprocess_shared_ = True

    def test_pipe_simple(self):
        p = model_util.BufferedPipe() \
            .add(lambda x: x + 1) \
            .add(lambda x: x * x)

        lst = list(p((x for x in range(1, 5))))

        expected_l = [4, 9, 16, 25]

        self.assertEqual(lst, expected_l)

    @unittest.skip('ordering problem - peterz to fix')
    def test_pipe_buffer(self):

        p = model_util.BufferedPipe() \