code: # coding=utf-8
  # Copyright {{cookiecutter.authors}} and 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.
  
  {% if cookiecutter.is_encoder_decoder_model == "False" %}
  
  import unittest
  
  from transformers import {{cookiecutter.camelcase_modelname}}Config, is_tf_available
  from transformers.testing_utils import require_tf, 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 (
          TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
          TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
          TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
          TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
          TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
          TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
          TF{{cookiecutter.camelcase_modelname}}Model,
      )
  
  
  class TF{{cookiecutter.camelcase_modelname}}ModelTester:
      def __init__(
          self,
          parent,
          batch_size=13,
          seq_length=7,
          is_training=True,
          use_input_mask=True,
          use_token_type_ids=True,
          use_labels=True,
          vocab_size=99,
          hidden_size=32,
          num_hidden_layers=5,
          num_attention_heads=4,
          intermediate_size=37,
          hidden_act="gelu",
          hidden_dropout_prob=0.1,
          attention_probs_dropout_prob=0.1,
          max_position_embeddings=512,
          type_vocab_size=16,
          type_sequence_label_size=2,
          initializer_range=0.02,
          num_labels=3,
          num_choices=4,
          scope=None,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_input_mask = True
          self.use_token_type_ids = True
          self.use_labels = True
          self.vocab_size = 99
          self.hidden_size = 32
          self.num_hidden_layers = 5
          self.num_attention_heads = 4
          self.intermediate_size = 37
          self.hidden_act = "gelu"
          self.hidden_dropout_prob = 0.1
          self.attention_probs_dropout_prob = 0.1
          self.max_position_embeddings = 512
          self.type_vocab_size = 16
          self.type_sequence_label_size = 2
          self.initializer_range = 0.02
          self.num_labels = 3
          self.num_choices = 4
          self.scope = None
  
      def prepare_config_and_inputs(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
  
          sequence_labels = None
          token_labels = None
          choice_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = {{cookiecutter.camelcase_modelname}}Config(
              vocab_size=self.vocab_size,
              hidden_size=self.hidden_size,
              num_hidden_layers=self.num_hidden_layers,
              num_attention_heads=self.num_attention_heads,
              intermediate_size=self.intermediate_size,
              hidden_act=self.hidden_act,
              hidden_dropout_prob=self.hidden_dropout_prob,
              attention_probs_dropout_prob=self.attention_probs_dropout_prob,
              max_position_embeddings=self.max_position_embeddings,
              type_vocab_size=self.type_vocab_size,
              initializer_range=self.initializer_range,
              return_dict=True,
          )
  
          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
      def create_and_check_model(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  
          inputs = [input_ids, input_mask]
          result = model(inputs)
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_lm_head(
              self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.is_decoder = True
          model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          prediction_scores = model(inputs)["logits"]
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
          )
  
      def create_and_check_for_masked_lm(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_for_sequence_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
  
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def create_and_check_for_multiple_choice(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_choices = self.num_choices
          model = TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(config=config)
          multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
          multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
          multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
          inputs = {
              "input_ids": multiple_choice_inputs_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
  
      def create_and_check_for_token_classification(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.num_labels = self.num_labels
          model = TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
  
      def create_and_check_for_question_answering(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          model = TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
  
          result = model(inputs)
          self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
          (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = config_and_inputs
          inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_tf
  class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (
              TF{{cookiecutter.camelcase_modelname}}Model,
              TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
              TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
              TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
              TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
              TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
              TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
  
      def setUp(self):
          self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
          self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config, hidden_size=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_for_masked_lm(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
  
      def test_for_causal_lm(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*config_and_inputs)
  
      def test_for_multiple_choice(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
  
      def test_for_question_answering(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
  
      def test_for_sequence_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
  
      def test_for_token_classification(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
          self.assertIsNotNone(model)
  
  @require_tf
  class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
      @slow
      def test_inference_masked_lm(self):
          model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
          input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
          output = model(input_ids)[0]
  
          # TODO Replace vocab size
          vocab_size = 32000
  
          expected_shape = [1, 6, vocab_size]
          self.assertEqual(output.shape, expected_shape)
  
          print(output[:, :3, :3])
  
          # TODO Replace values below with what was printed above.
          expected_slice = tf.constant(
              [
                  [
                      [-0.05243197, -0.04498899, 0.05512108],
                      [-0.07444685, -0.01064632, 0.04352357],
                      [-0.05020351, 0.05530146, 0.00700043],
                  ]
              ]
          )
          tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
  
  {% else %}
  import unittest
  
  from transformers import {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_tf, 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 TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model
  
  
  @require_tf
  class TF{{cookiecutter.camelcase_modelname}}ModelTester:
      config_cls = {{cookiecutter.camelcase_modelname}}Config
      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_{{cookiecutter.lowercase_modelname}}_inputs_dict(config, input_ids, decoder_input_ids)
          return config, inputs_dict
  
      def check_decoder_model_past_large_inputs(self, config, inputs_dict):
          model = TF{{cookiecutter.camelcase_modelname}}Model(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_{{cookiecutter.lowercase_modelname}}_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.cast(tf.math.not_equal(decoder_input_ids, config.pad_token_id), tf.int8)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
      }
  
  
  @require_tf
  class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unittest.TestCase):
      all_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration, TF{{cookiecutter.camelcase_modelname}}Model) if is_tf_available() else ()
      all_generative_model_classes = (TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration,) if is_tf_available() else ()
      is_encoder_decoder = True
      test_pruning = False
  
      def setUp(self):
          self.model_tester = TF{{cookiecutter.camelcase_modelname}}ModelTester(self)
          self.config_tester = ConfigTester(self, config_class={{cookiecutter.camelcase_modelname}}Config)
  
      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_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 _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)
  
  
  TOLERANCE = 1e-4
  
  
  @slow
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
      def test_inference_no_head(self):
          model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
          # change to intended input here
          input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
          decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
          inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
          output = model(**inputs_dict)[0]
          expected_shape = (1, 11, 1024)
          self.assertEqual(output.shape, expected_shape)
          # change to expected output here
          expected_slice = tf.Tensor(
              [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
          )
          self.assertTrue(tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE))
  
      def test_inference_with_head(self):
          model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
          # change to intended input here
          input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
          decoder_input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
          inputs_dict = prepare_{{cookiecutter.lowercase_modelname}}_inputs_dict(model.config, input_ids, decoder_input_ids)
          output = model(**inputs_dict)[0]
          expected_shape = (1, 11, 1024)
          self.assertEqual(output.shape, expected_shape)
          # change to expected output here
          expected_slice = tf.Tensor(
              [[0.7144, 0.8143, -1.2813], [0.7144, 0.8143, -1.2813], [-0.0467, 2.5911, -2.1845]],
          )
          self.assertTrue(tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=TOLERANCE))
  
      def test_seq_to_seq_generation(self):
          hf = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
          tok = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
  
          batch_input = [
              # string 1,
              # string 2,
              # string 3,
              # string 4,
          ]
  
          # The below article tests that we don't add any hypotheses outside of the top n_beams
          dct = tok.batch_encode_plus(
              batch_input,
              max_length=512,
              padding="max_length",
              truncation_strategy="only_first",
              truncation=True,
              return_tensors="tf",
          )
  
          hypotheses_batch = hf.generate(
              input_ids=dct["input_ids"],
              attention_mask=dct["attention_mask"],
              num_beams=2,
          )
  
          EXPECTED = [
              # here expected 1,
              # here expected 2,
              # here expected 3,
              # here expected 4,
          ]
  
          generated = tok.batch_decode(
              hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
          )
          assert generated == EXPECTED
  {%- endif %}
  