code: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors.
  #
  # 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 unittest
  
  from transformers import GPT2Config, is_tf_available
  
  from .test_configuration_common import ConfigTester
  from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from .utils import CACHE_DIR, require_tf, slow
  
  
  if is_tf_available():
      import tensorflow as tf
      from transformers.modeling_tf_gpt2 import (
          TFGPT2Model,
          TFGPT2LMHeadModel,
          TFGPT2DoubleHeadsModel,
          TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
          shape_list,
      )
  
  
  @require_tf
  class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2DoubleHeadsModel) if is_tf_available() else ()
      all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else ()
  
      class TFGPT2ModelTester(object):
          def __init__(
              self,
              parent,
              batch_size=13,
              seq_length=7,
              is_training=True,
              use_token_type_ids=True,
              use_input_mask=True,
              use_labels=True,
              use_mc_token_ids=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 = batch_size
              self.seq_length = seq_length
              self.is_training = is_training
              self.use_token_type_ids = use_token_type_ids
              self.use_input_mask = use_input_mask
              self.use_labels = use_labels
              self.use_mc_token_ids = use_mc_token_ids
              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_act = hidden_act
              self.hidden_dropout_prob = hidden_dropout_prob
              self.attention_probs_dropout_prob = attention_probs_dropout_prob
              self.max_position_embeddings = max_position_embeddings
              self.type_vocab_size = type_vocab_size
              self.type_sequence_label_size = type_sequence_label_size
              self.initializer_range = initializer_range
              self.num_labels = num_labels
              self.num_choices = num_choices
              self.scope = scope
              self.bos_token_id = vocab_size - 1
              self.eos_token_id = vocab_size - 1
  
          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)
  
              mc_token_ids = None
              if self.use_mc_token_ids:
                  mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
  
              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 = GPT2Config(
                  vocab_size=self.vocab_size,
                  n_embd=self.hidden_size,
                  n_layer=self.num_hidden_layers,
                  n_head=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,
                  n_positions=self.max_position_embeddings,
                  n_ctx=self.max_position_embeddings,
                  # type_vocab_size=self.type_vocab_size,
                  # initializer_range=self.initializer_range
                  bos_token_id=self.bos_token_id,
                  eos_token_id=self.eos_token_id,
              )
  
              head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
  
              return (
                  config,
                  input_ids,
                  input_mask,
                  head_mask,
                  token_type_ids,
                  mc_token_ids,
                  sequence_labels,
                  token_labels,
                  choice_labels,
              )
  
          def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
              model = TFGPT2Model(config=config)
              inputs = {
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              sequence_output = model(inputs)[0]
  
              inputs = [input_ids, None, input_mask]  # None is the input for 'past'
              sequence_output = model(inputs)[0]
  
              sequence_output = model(input_ids)[0]
  
              result = {
                  "sequence_output": sequence_output.numpy(),
              }
              self.parent.assertListEqual(
                  list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size],
              )
  
          def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
              model = TFGPT2Model(config=config)
  
              # first forward pass
              output, past = model(input_ids, token_type_ids=token_type_ids)
  
              # create hypothetical next token and extent to next_input_ids
              next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
              next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
  
              # append to next input_ids and token_type_ids
              next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
              next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1)
  
              output_from_no_past, _ = model(next_input_ids, token_type_ids=next_token_type_ids)
              output_from_past, _ = model(next_tokens, token_type_ids=next_token_types, past=past)
  
              # select random slice
              random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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-12)
  
          def create_and_check_gpt2_model_attention_mask_past(
              self, config, input_ids, input_mask, head_mask, token_type_ids, *args
          ):
              model = TFGPT2Model(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
              output, past = model(input_ids, attention_mask=attn_mask)
  
              # create hypothetical next token and extent to next_input_ids
              next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
              # 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 attn_mask
              next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
              attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1)
  
              # get two different outputs
              output_from_no_past, _ = model(next_input_ids, attention_mask=attn_mask)
              output_from_past, _ = model(next_tokens, past=past, attention_mask=attn_mask)
  
              # select random slice
              random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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-12)
  
          def create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
              model = TFGPT2LMHeadModel(config=config)
              inputs = {
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              prediction_scores = model(inputs)[0]
              result = {
                  "prediction_scores": prediction_scores.numpy(),
              }
              self.parent.assertListEqual(
                  list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size],
              )
  
          def create_and_check_gpt2_double_head(
              self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
          ):
              model = TFGPT2DoubleHeadsModel(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,
                  "mc_token_ids": mc_token_ids,
                  "attention_mask": multiple_choice_input_mask,
                  "token_type_ids": multiple_choice_token_type_ids,
              }
              lm_logits, mc_logits = model(inputs)[:2]
              result = {"lm_logits": lm_logits.numpy(), "mc_logits": mc_logits.numpy()}
              self.parent.assertListEqual(
                  list(result["lm_logits"].shape), [self.batch_size, self.num_choices, self.seq_length, self.vocab_size],
              )
              self.parent.assertListEqual(list(result["mc_logits"].shape), [self.batch_size, self.num_choices])
  
          def prepare_config_and_inputs_for_common(self):
              config_and_inputs = self.prepare_config_and_inputs()
  
              (
                  config,
                  input_ids,
                  input_mask,
                  head_mask,
                  token_type_ids,
                  mc_token_ids,
                  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
  
      def setUp(self):
          self.model_tester = TFGPT2ModelTest.TFGPT2ModelTester(self)
          self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_gpt2_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gpt2_model(*config_and_inputs)
  
      def test_gpt2_model_past(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)
  
      def test_gpt2_model_att_mask_past(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)
  
      def test_gpt2_lm_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs)
  
      def test_gpt2_double_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
              model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
              self.assertIsNotNone(model)
  
  
  class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
      @slow
      def test_lm_generate_gpt2(self):
          model = TFGPT2LMHeadModel.from_pretrained("gpt2")
          input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32)  # The dog
          expected_output_ids = [
              464,
              3290,
              373,
              1043,
              287,
              257,
              2214,
              1474,
              262,
              16246,
              286,
              2688,
              290,
              2688,
              27262,
              13,
              198,
              198,
              464,
              3290,
          ]  # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
  
      @slow
      def test_lm_generate_distilgpt2(self):
          model = TFGPT2LMHeadModel.from_pretrained("distilgpt2")
          input_ids = tf.convert_to_tensor([[464, 1893]], dtype=tf.int32)  # The president
          expected_output_ids = [
              464,
              1893,
              286,
              262,
              1578,
              1829,
              11,
              290,
              262,
              1893,
              286,
              262,
              1578,
              7526,
              11,
              423,
              587,
              287,
              262,
              2635,
          ]  # The president of the United States, and the president of the United Kingdom, have been in the White
  
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
  