# coding=utf-8
# Copyright 2023 The Tensor2Tensor Authors.
#
# 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.

"""Basic models for testing simple tasks."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensor2tensor.layers import common_hparams
from tensor2tensor.layers import common_layers
from tensor2tensor.utils import registry
from tensor2tensor.utils import t2t_model

import tensorflow.compat.v1 as tf


@registry.register_model
class BasicFcRelu(t2t_model.T2TModel):
  """Basic fully-connected + ReLU model."""

  def body(self, features):
    hparams = self.hparams
    x = features["inputs"]
    shape = common_layers.shape_list(x)
    x = tf.reshape(x, [-1, shape[1] * shape[2] * shape[3]])
    for i in range(hparams.num_hidden_layers):
      x = tf.layers.dense(x, hparams.hidden_size, name="layer_%d" % i)
      x = tf.nn.dropout(x, keep_prob=1.0 - hparams.dropout)
      x = tf.nn.relu(x)
    return tf.expand_dims(tf.expand_dims(x, axis=1), axis=1)  # 4D For T2T.


@registry.register_hparams
def basic_fc_small():
  """Small fully connected model."""
  hparams = common_hparams.basic_params1()
  hparams.learning_rate = 0.1
  hparams.batch_size = 128
  hparams.hidden_size = 256
  hparams.num_hidden_layers = 2
  hparams.initializer = "uniform_unit_scaling"
  hparams.initializer_gain = 1.0
  hparams.weight_decay = 0.0
  hparams.dropout = 0.0
  return hparams
