#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import unittest

import numpy as np

import paddle
import paddle.static
from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest


class TestBase(IPUOpTest):
    def setUp(self):
        self.set_atol()
        self.set_training()
        self.set_data_feed()
        self.set_feed_attr()
        self.set_op_attrs()

    def set_training(self):
        # ctcloss only support training currently.
        self.is_training = True
        self.epoch = 1

    def set_data_feed(self):
        self.batch_size = 16
        self.max_seq_length = 5
        self.max_label_length = 3
        self.num_classes = 5
        self.logits_length = np.array(
            [self.max_seq_length] * self.batch_size, dtype=np.int64
        )
        self.labels_length = np.array(
            [self.max_label_length] * self.batch_size, dtype=np.int64
        )
        self.blank = self.num_classes - 1
        self.norm_by_times = False

        logits = np.random.uniform(
            0.1, 1.0, [self.max_seq_length, self.batch_size, self.num_classes]
        ).astype("float32")
        labels = np.random.randint(
            0,
            self.num_classes - 1,
            [self.batch_size, self.max_label_length],
            dtype="int32",
        )

        self.feed_fp32 = {
            "Logits": logits,
            "Label": labels,
            "input_length": self.logits_length.astype("int64"),
            "label_length": self.labels_length.astype("int64"),
        }
        self.feed_fp16 = {
            "Logits": logits.astype(np.float16),
            "Label": labels,
            "input_length": self.logits_length.astype("int64"),
            "label_length": self.labels_length.astype("int64"),
        }

    def set_feed_attr(self):
        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
        self.feed_list = list(self.feed_fp32.keys())

    def set_op_attrs(self):
        self.attrs = {
            "blank": self.blank,
            "norm_by_times": self.norm_by_times,
        }

    @IPUOpTest.static_graph
    def build_model(self):
        data = paddle.static.data(
            name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
        )
        logits = paddle.nn.Linear(
            self.num_classes, self.num_classes, bias_attr=False
        )(data)
        labels = paddle.static.data(
            name=self.feed_list[1], shape=self.feed_shape[1], dtype='int32'
        )
        input_length = paddle.static.data(
            name=self.feed_list[2], shape=self.feed_shape[2], dtype='int64'
        )
        label_length = paddle.static.data(
            name=self.feed_list[3], shape=self.feed_shape[3], dtype='int64'
        )
        out = paddle.nn.functional.ctc_loss(
            logits,
            labels,
            input_length=input_length,
            label_length=label_length,
            reduction='mean',
            **self.attrs
        )
        loss = paddle.mean(out)
        adam = paddle.optimizer.Adam(learning_rate=1e-2)
        adam.minimize(loss)
        self.fetch_list = [loss.name, out.name]

    def run_model(self, exec_mode):
        self.run_op_test(exec_mode)

    def test(self):
        for m in IPUOpTest.ExecutionMode:
            if not self.skip_mode(m):
                self.build_model()
                self.run_model(m)
        self.check()


if __name__ == "__main__":
    unittest.main()
