# coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# 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 tempfile
import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.file_utils import cached_property
from transformers.testing_utils import is_pt_tf_cross_test, require_sentencepiece, require_tf, require_tokenizers, slow

from .test_configuration_common import ConfigTester
from .test_modeling_pegasus import PGE_ARTICLE, XSUM_ENTRY_LONGER
from .test_modeling_tf_bart import TFBartModelTester
from .test_modeling_tf_common import TFModelTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration


class ModelTester(TFBartModelTester):
    config_updates = dict(
        normalize_before=True,
        static_position_embeddings=True,
    )
    hidden_act = "relu"
    config_cls = PegasusConfig


@require_tf
class TestTFPegasusCommon(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    model_tester_cls = ModelTester
    is_encoder_decoder = True
    test_pruning = False

    def setUp(self):
        self.model_tester = self.model_tester_cls(self)
        self.config_tester = ConfigTester(self, config_class=PegasusConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_inputs_embeds(self):
        # inputs_embeds not supported
        pass

    def test_saved_model_with_hidden_states_output(self):
        # Should be uncommented during patrick TF refactor
        pass

    def test_saved_model_with_attentions_output(self):
        # Should be uncommented during patrick TF refactor
        pass

    def test_compile_tf_model(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
        metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")

        model_class = self.all_generative_model_classes[0]
        input_ids = {
            "decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
            "input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
        }

        # Prepare our model
        model = model_class(config)
        model(self._prepare_for_class(inputs_dict, model_class))  # Model must be called before saving.
        # Let's load it from the disk to be sure we can use pretrained weights
        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname)
            model = model_class.from_pretrained(tmpdirname)

        outputs_dict = model(input_ids)
        hidden_states = outputs_dict[0]

        # Add a dense layer on top to test integration with other keras modules
        outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)

        # Compile extended model
        extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
        extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])


@is_pt_tf_cross_test
@require_sentencepiece
@require_tokenizers
class TFPegasusIntegrationTests(unittest.TestCase):
    src_text = [PGE_ARTICLE, XSUM_ENTRY_LONGER]
    expected_text = [
        "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to reduce the risk of wildfires.",
        'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    model_name = "google/pegasus-xsum"

    @cached_property
    def tokenizer(self):
        return AutoTokenizer.from_pretrained(self.model_name)

    @cached_property
    def model(self):
        model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
        return model

    def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
        generated_words = self.translate_src_text(**tokenizer_kwargs)
        assert self.expected_text == generated_words

    def translate_src_text(self, **tokenizer_kwargs):
        model_inputs = self.tokenizer.prepare_seq2seq_batch(
            src_texts=self.src_text, **tokenizer_kwargs, return_tensors="tf"
        )
        generated_ids = self.model.generate(
            model_inputs.input_ids,
            attention_mask=model_inputs.attention_mask,
            num_beams=2,
            use_cache=True,
        )
        generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
        return generated_words

    @slow
    def test_batch_generation(self):
        self._assert_generated_batch_equal_expected()
