# Path: reservoir_nn/models/convolutional.py
# _VERY_SMALL_FLOAT = 1e-12
# def inception_inspired_reservoir_model(
#     input_shape: Tuple[int, int, int],
#     reservoir_weight: np.ndarray,
#     num_output_channels: int,
#     seed: Optional[int] = None,
#     num_filters: int = 32,
#     reservoir_base: str = 'DenseReservoir',
#     reservoir_params: Optional[Dict[str, Any]] = None,
#     final_activation: Optional[str] = 'sigmoid',
#     task: str = 'segmentation',
# ) -> tf.keras.Model:
# def unet_reservoir_sandwich_model(
#     input_shape: Tuple[int, ...],
#     reservoir_weight: np.ndarray,
#     num_output_channels: int,
#     reservoir_base: str = 'DenseReservoir',
#     reservoir_params: Optional[Dict[str, Any]] = None,
#     final_activation: Optional[str] = 'sigmoid',
#     noise_layer_stddev: float = 0.0,
#     task: str = 'segmentation',
#     match_tutorial: bool = False,
# ) -> tf.keras.Model:


from absl.testing import absltest
from reservoir_nn.models import convolutional
import numpy as np
import tensorflow as tf
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for convolutional.py."""



class ReservoirInceptionTest(absltest.TestCase):

  def test_reservoir_inception_output_produce_correct_shape(self):
    reservoir = np.ones((42, 42))
    batch = 6
    image_size = (2, 3, 4)
    image_set = tf.range(batch * image_size[0] * image_size[1] * image_size[2])
    image_set = tf.reshape(image_set, (batch, *image_size))

# Path: reservoir_nn/models/convolutional.py
# _VERY_SMALL_FLOAT = 1e-12
# def inception_inspired_reservoir_model(
#     input_shape: Tuple[int, int, int],
#     reservoir_weight: np.ndarray,
#     num_output_channels: int,
#     seed: Optional[int] = None,
#     num_filters: int = 32,
#     reservoir_base: str = 'DenseReservoir',
#     reservoir_params: Optional[Dict[str, Any]] = None,
#     final_activation: Optional[str] = 'sigmoid',
#     task: str = 'segmentation',
# ) -> tf.keras.Model:
# def unet_reservoir_sandwich_model(
#     input_shape: Tuple[int, ...],
#     reservoir_weight: np.ndarray,
#     num_output_channels: int,
#     reservoir_base: str = 'DenseReservoir',
#     reservoir_params: Optional[Dict[str, Any]] = None,
#     final_activation: Optional[str] = 'sigmoid',
#     noise_layer_stddev: float = 0.0,
#     task: str = 'segmentation',
#     match_tutorial: bool = False,
# ) -> tf.keras.Model:


# Path: reservoir_nn/models/convolutional_test.py
from absl.testing import absltest
from reservoir_nn.models import convolutional
import numpy as np
import tensorflow as tf

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for convolutional.py."""



class ReservoirInceptionTest(absltest.TestCase):

  def test_reservoir_inception_output_produce_correct_shape(self):
    reservoir = np.ones((42, 42))
    batch = 6
    image_size = (2, 3, 4)
    image_set = tf.range(batch * image_size[0] * image_size[1] * image_size[2])
    image_set = tf.reshape(image_set, (batch, *image_size))

    model = convolutional.inception_inspired_reservoir_model(