text: # 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
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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_tf_bart import TFBartModelTester
  from .test_modeling_tf_common import TFModelTesterMixin
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers import TFAutoModelForSeq2SeqLM, TFMarianMTModel
  
  
  class ModelTester(TFBartModelTester):
      config_updates = dict(static_position_embeddings=True, add_bias_logits=True)
      config_cls = MarianConfig
  
  
  @require_tf
  class TestTFMarianCommon(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (TFMarianMTModel,) if is_tf_available() else ()
      all_generative_model_classes = (TFMarianMTModel,) 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=MarianConfig)
  
      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):
          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 pre-trained 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])
  
      def test_model_common_attributes(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
              x = model.get_output_layer_with_bias()
              assert x is None
              name = model.get_prefix_bias_name()
              assert name is None
  
      def test_saved_model_creation(self):
          # This test is too long (>30sec) and makes fail the CI
          pass
  
  
  class AbstractMarianIntegrationTest(unittest.TestCase):
      maxDiff = 1000  # show more chars for failing integration tests
  
      @classmethod
      def setUpClass(cls) -> None:
          cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls
  
      @cached_property
      def tokenizer(self) -> MarianTokenizer:
          return AutoTokenizer.from_pretrained(self.model_name)
  
      @property
      def eos_token_id(self) -> int:
          return self.tokenizer.eos_token_id
  
      @cached_property
      def model(self):
          warnings.simplefilter("error")
          model: TFMarianMTModel = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
          assert isinstance(model, TFMarianMTModel)
          c = model.config
          self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
          self.assertEqual(c.max_length, 512)
          self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
          return model
  
      def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
          generated_words = self.translate_src_text(**tokenizer_kwargs)
          self.assertListEqual(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, max_length=128
          )
          generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
          return generated_words
  
  
  @require_sentencepiece
  @require_tokenizers
  @is_pt_tf_cross_test
  class TestMarian_MT_EN(AbstractMarianIntegrationTest):
      """Cover low resource/high perplexity setting. This breaks if pad_token_id logits not set to LARGE_NEGATIVE."""
  
      src = "mt"
      tgt = "en"
      src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
      expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
  
      @slow
      def test_batch_generation_mt_en(self):
          self._assert_generated_batch_equal_expected()
  
  
  @is_pt_tf_cross_test
  @require_sentencepiece
  @require_tokenizers
  class TestMarian_en_zh(AbstractMarianIntegrationTest):
      src = "en"
      tgt = "zh"
      src_text = ["My name is Wolfgang and I live in Berlin"]
      expected_text = ["我叫沃尔夫冈 我住在柏林"]
  
      @slow
      def test_batch_generation_en_zh(self):
          self._assert_generated_batch_equal_expected()
  
  
  @is_pt_tf_cross_test
  @require_sentencepiece
  @require_tokenizers
  class TestMarian_en_ROMANCE(AbstractMarianIntegrationTest):
      """Multilingual on target side."""
  
      src = "en"
      tgt = "ROMANCE"
      src_text = [
          ">>fr<< Don't spend so much time watching TV.",
          ">>pt<< Your message has been sent.",
          ">>es<< He's two years older than me.",
      ]
      expected_text = [
          "Ne passez pas autant de temps à regarder la télé.",
          "A sua mensagem foi enviada.",
          "Es dos años más viejo que yo.",
      ]
  
      @slow
      def test_batch_generation_en_ROMANCE_multi(self):
          self._assert_generated_batch_equal_expected()
  
      @slow
      def test_pipeline(self):
          pipeline = TranslationPipeline(self.model, self.tokenizer, framework="tf")
          output = pipeline(self.src_text)
          self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
  