text: # coding=utf-8
  # Copyright 2022 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 is_tf_available, {{cookiecutter.camelcase_modelname}}Config
  from transformers.testing_utils import require_tf, slow
  
  from ..test_configuration_common import ConfigTester
  from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers import (
          TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
          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}}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 = random_attention_mask([self.batch_size, self.seq_length])
  
          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 prepare_config_and_inputs_for_decoder(self):
          (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = self.prepare_config_and_inputs()
  
          config.is_decoder = True
          encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
  
          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              encoder_hidden_states,
              encoder_attention_mask,
          )
  
      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_causal_lm_base_model(
          self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      ):
          config.is_decoder = True
  
          model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          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_model_as_decoder(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      ):
          config.add_cross_attention = True
  
          model = TF{{cookiecutter.camelcase_modelname}}Model(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
              "encoder_hidden_states": encoder_hidden_states,
              "encoder_attention_mask": encoder_attention_mask,
          }
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
  
          # Also check the case where encoder outputs are not passed
          result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_causal_lm_model(
          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_causal_lm_model_as_decoder(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      ):
          config.add_cross_attention = True
  
          model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
              "encoder_hidden_states": encoder_hidden_states,
              "encoder_attention_mask": encoder_attention_mask,
          }
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
  
          prediction_scores = result["logits"]
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
          )
  
      def create_and_check_causal_lm_model_past(
          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)
  
          # first forward pass
          outputs = model(input_ids, use_cache=True)
          outputs_use_cache_conf = model(input_ids)
          outputs_no_past = model(input_ids, use_cache=False)
  
          self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
          self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
  
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          # append to next input_ids and attn_mask
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
  
          output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
          output_from_past = model(
              next_tokens, past_key_values=past_key_values, output_hidden_states=True
          ).hidden_states[0]
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
          output_from_past_slice = output_from_past[:, 0, 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-6)
  
      def create_and_check_causal_lm_model_past_with_attn_mask(
          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)
  
          # create attention mask
          half_seq_length = self.seq_length // 2
          attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
          attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
          attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
  
          # first forward pass
          outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
          past_key_values = outputs.past_key_values
  
          # change a random masked slice from input_ids
          random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
          random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
          vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
          condition = tf.transpose(
              tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
          )
          input_ids = tf.where(condition, random_other_next_tokens, input_ids)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          attn_mask = tf.concat(
              [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
              axis=1,
          )
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=attn_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
          ).hidden_states[0]
  
          # select random slice
          random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
          output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
          output_from_past_slice = output_from_past[:, 0, 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-6)
  
      def create_and_check_causal_lm_model_past_large_inputs(
          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)
  
          input_ids = input_ids[:1, :]
          input_mask = input_mask[:1, :]
          self.batch_size = 1
  
          # first forward pass
          outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=next_attention_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens,
              attention_mask=next_attention_mask,
              past_key_values=past_key_values,
              output_hidden_states=True,
          ).hidden_states[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 create_and_check_decoder_model_past_large_inputs(
          self,
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      ):
          config.add_cross_attention = True
  
          model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
  
          input_ids = input_ids[:1, :]
          input_mask = input_mask[:1, :]
          encoder_hidden_states = encoder_hidden_states[:1, :, :]
          encoder_attention_mask = encoder_attention_mask[:1, :]
          self.batch_size = 1
  
          # first forward pass
          outputs = model(
              input_ids,
              attention_mask=input_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              use_cache=True,
          )
          past_key_values = outputs.past_key_values
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
          # append to next input_ids and
          next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
          next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
  
          output_from_no_past = model(
              next_input_ids,
              attention_mask=next_attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              output_hidden_states=True,
          ).hidden_states[0]
          output_from_past = model(
              next_tokens,
              attention_mask=next_attention_mask,
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=encoder_attention_mask,
              past_key_values=past_key_values,
              output_hidden_states=True,
          ).hidden_states[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 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 ()
      )
  
      test_head_masking = False
      test_onnx = False
  
      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):
          """Test the base model"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*config_and_inputs)
  
      def test_causal_lm_base_model(self):
          """Test the base model of the causal LM model
  
          is_deocder=True, no cross_attention, no encoder outputs
          """
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
  
      def test_model_as_decoder(self):
          """Test the base model as a decoder (of an encoder-decoder architecture)
  
          is_deocder=True + cross_attention + pass encoder outputs
          """
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*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):
          """Test the causal LM model"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
  
      def test_causal_lm_model_as_decoder(self):
          """Test the causal LM model as a decoder"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
  
      def test_causal_lm_model_past(self):
          """Test causal LM model with `past_key_values`"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
  
      def test_causal_lm_model_past_with_attn_mask(self):
          """Test the causal LM model with `past_key_values` and `attention_mask`"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
  
      def test_causal_lm_model_past_with_large_inputs(self):
          """Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
  
      def test_decoder_model_past_with_large_inputs(self):
          """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
          config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*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 (
      is_tf_available,
      {{cookiecutter.camelcase_modelname}}Config,
      {{cookiecutter.camelcase_modelname}}Tokenizer,
  )
  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 (
          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()
  
          # create hypothetical next token and extent to next_input_ids
          next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
          next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
          # 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.int32)
      if decoder_attention_mask is None:
          decoder_attention_mask = tf.concat([tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int32), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int32)], 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 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
      test_head_masking = False
      test_onnx = 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)
  
              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_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:
          if len(prefix) > 0:
              prefix = f"{prefix}: "
          raise AssertionError(f"{prefix}{a} != {b}")
  
  
  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]],
          )
          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]],
          )
          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 %}
  