text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. team. 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 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 require_sentencepiece, require_tf, require_tokenizers, slow
  
  from .test_configuration_common import ConfigTester
  from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers import TFAutoModelForSeq2SeqLM, TFMarianModel, TFMarianMTModel
  
  
  @require_tf
  class TFMarianModelTester:
      config_cls = MarianConfig
      config_updates = {}
      hidden_act = "gelu"
  
      def __init__(
          self,
          parent,
          batch_size=13,
          seq_length=7,
          is_training=True,
          use_labels=False,
          vocab_size=99,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=20,
          eos_token_id=2,
          pad_token_id=1,
          bos_token_id=0,
      ):
          self.parent = parent
          self.batch_size = batch_size
          self.seq_length = seq_length
          self.is_training = is_training
          self.use_labels = use_labels
          self.vocab_size = vocab_size
          self.hidden_size = hidden_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.intermediate_size = intermediate_size
  
          self.hidden_dropout_prob = hidden_dropout_prob
          self.attention_probs_dropout_prob = attention_probs_dropout_prob
          self.max_position_embeddings = max_position_embeddings
          self.eos_token_id = eos_token_id
          self.pad_token_id = pad_token_id
          self.bos_token_id = bos_token_id
  
      def prepare_config_and_inputs_for_common(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
          eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
          input_ids = tf.concat([input_ids, eos_tensor], axis=1)
  
          decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          config = self.config_cls(
              vocab_size=self.vocab_size,
              d_model=self.hidden_size,
              encoder_layers=self.num_hidden_layers,
              decoder_layers=self.num_hidden_layers,
              encoder_attention_heads=self.num_attention_heads,
              decoder_attention_heads=self.num_attention_heads,
              encoder_ffn_dim=self.intermediate_size,
              decoder_ffn_dim=self.intermediate_size,
              dropout=self.hidden_dropout_prob,
              attention_dropout=self.attention_probs_dropout_prob,
              max_position_embeddings=self.max_position_embeddings,
              eos_token_ids=[2],
              bos_token_id=self.bos_token_id,
              pad_token_id=self.pad_token_id,
              decoder_start_token_id=self.pad_token_id,
              **self.config_updates,
          )
          inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
          return config, inputs_dict
  
      def check_decoder_model_past_large_inputs(self, config, inputs_dict):
          model = TFMarianModel(config=config).get_decoder()
          input_ids = inputs_dict["input_ids"]
  
          input_ids = input_ids[:1, :]
          attention_mask = inputs_dict["attention_mask"][:1, :]
          self.batch_size = 1
  
          # first forward pass
          outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
  
          output, past_key_values = outputs.to_tuple()
          past_key_values = past_key_values[1]
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
  
          output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
          output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
  
          self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
          output_from_past_slice = output_from_past[:, :, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
  
  
  def prepare_marian_inputs_dict(
      config,
      input_ids,
      decoder_input_ids,
      attention_mask=None,
      decoder_attention_mask=None,
  ):
      if attention_mask is None:
          attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
      if decoder_attention_mask is None:
          decoder_attention_mask = tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
              ],
              axis=-1,
          )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }
  
  
  @require_tf
  class TFMarianModelTest(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      all_generative_model_classes = (TFMarianMTModel,) if is_tf_available() else ()
      is_encoder_decoder = True
      test_pruning = False
  
      def setUp(self):
          self.model_tester = TFMarianModelTester(self)
          self.config_tester = ConfigTester(self, config_class=MarianConfig)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_decoder_model_past_large_inputs(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
  
      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)
  
              if model_class in self.all_generative_model_classes:
                  x = model.get_output_embeddings()
                  assert isinstance(x, tf.keras.layers.Layer)
                  name = model.get_bias()
                  assert isinstance(name, dict)
                  for k, v in name.items():
                      assert isinstance(v, tf.Variable)
              else:
                  x = model.get_output_embeddings()
                  assert x is None
                  name = model.get_bias()
                  assert name is None
  
      def test_saved_model_creation(self):
          # This test is too long (>30sec) and makes fail the CI
          pass
  
      def test_resize_token_embeddings(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          def _get_word_embedding_weight(model, embedding_layer):
              if hasattr(embedding_layer, "weight"):
                  return embedding_layer.weight
              else:
                  # Here we build the word embeddings weights if not exists.
                  # And then we retry to get the attribute once built.
                  model(model.dummy_inputs)
                  if hasattr(embedding_layer, "weight"):
                      return embedding_layer.weight
                  else:
                      return None
  
          for model_class in self.all_model_classes:
              for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
                  # build the embeddings
                  model = model_class(config=config)
                  old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                  old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                  old_final_logits_bias = model.get_bias()
  
                  # reshape the embeddings
                  model.resize_token_embeddings(size)
                  new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                  new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                  new_final_logits_bias = model.get_bias()
  
                  # check that the resized embeddings size matches the desired size.
                  assert_size = size if size is not None else config.vocab_size
  
                  self.assertEqual(new_input_embeddings.shape[0], assert_size)
  
                  # check that weights remain the same after resizing
                  models_equal = True
                  for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
                      if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                          models_equal = False
                  self.assertTrue(models_equal)
  
                  if old_output_embeddings is not None and new_output_embeddings is not None:
                      self.assertEqual(new_output_embeddings.shape[0], assert_size)
  
                      models_equal = True
                      for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
                          if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                              models_equal = False
                      self.assertTrue(models_equal)
  
                  if old_final_logits_bias is not None and new_final_logits_bias is not None:
                      old_final_logits_bias = old_final_logits_bias["final_logits_bias"]
                      new_final_logits_bias = new_final_logits_bias["final_logits_bias"]
                      self.assertEqual(new_final_logits_bias.shape[0], 1)
                      self.assertEqual(new_final_logits_bias.shape[1], assert_size)
  
                      models_equal = True
                      for old, new in zip(old_final_logits_bias.value(), new_final_logits_bias.value()):
                          for p1, p2 in zip(old, new):
                              if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                                  models_equal = False
                      self.assertTrue(models_equal)
  
  
  def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
      """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
      if a is None and b is None:
          return True
      try:
          if tf.debugging.assert_near(a, b, atol=atol):
              return True
          raise
      except Exception:
          msg = "{} != {}".format(a, b)
          if prefix:
              msg = prefix + ": " + msg
          raise AssertionError(msg)
  
  
  def _long_tensor(tok_lst):
      return tf.constant(tok_lst, dtype=tf.int32)
  
  
  @require_tf
  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
  @require_tf
  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()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  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()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  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])
  