text: # 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.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import unittest
  import random
  import shutil
  
  from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
  from .configuration_common_test import ConfigTester
  from .utils import require_tf, slow
  
  from transformers import TransfoXLConfig, is_tf_available
  
  if is_tf_available():
      import tensorflow as tf
      from transformers.modeling_tf_transfo_xl import (TFTransfoXLModel,
                                                               TFTransfoXLLMHeadModel,
                                                               TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
  
  
  @require_tf
  class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
  
      all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
      test_pruning = False
      test_torchscript = False
      test_resize_embeddings = False
  
      class TFTransfoXLModelTester(object):
  
          def __init__(self,
                       parent,
                       batch_size=13,
                       seq_length=7,
                       mem_len=30,
                       clamp_len=15,
                       is_training=True,
                       use_labels=True,
                       vocab_size=99,
                       cutoffs=[10, 50, 80],
                       hidden_size=32,
                       d_embed=32,
                       num_attention_heads=4,
                       d_head=8,
                       d_inner=128,
                       div_val=2,
                       num_hidden_layers=5,
                       scope=None,
                       seed=1,
                       ):
              self.parent = parent
              self.batch_size = batch_size
              self.seq_length = seq_length
              self.mem_len = mem_len
              self.key_length = seq_length + mem_len
              self.clamp_len = clamp_len
              self.is_training = is_training
              self.use_labels = use_labels
              self.vocab_size = vocab_size
              self.cutoffs = cutoffs
              self.hidden_size = hidden_size
              self.d_embed = d_embed
              self.num_attention_heads = num_attention_heads
              self.d_head = d_head
              self.d_inner = d_inner
              self.div_val = div_val
              self.num_hidden_layers = num_hidden_layers
              self.scope = scope
              self.seed = seed
  
          def prepare_config_and_inputs(self):
              input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
              input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
              lm_labels = None
              if self.use_labels:
                  lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
              config = TransfoXLConfig(
                  vocab_size_or_config_json_file=self.vocab_size,
                  mem_len=self.mem_len,
                  clamp_len=self.clamp_len,
                  cutoffs=self.cutoffs,
                  d_model=self.hidden_size,
                  d_embed=self.d_embed,
                  n_head=self.num_attention_heads,
                  d_head=self.d_head,
                  d_inner=self.d_inner,
                  div_val=self.div_val,
                  n_layer=self.num_hidden_layers)
  
              return (config, input_ids_1, input_ids_2, lm_labels)
  
          def set_seed(self):
              random.seed(self.seed)
              tf.random.set_seed(self.seed)
  
          def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
              model = TFTransfoXLModel(config)
  
              hidden_states_1, mems_1 = model(input_ids_1)
  
              inputs = {'input_ids': input_ids_2,
                        'mems': mems_1}
  
              hidden_states_2, mems_2 = model(inputs)
  
              result = {
                  "hidden_states_1": hidden_states_1.numpy(),
                  "mems_1": [mem.numpy() for mem in mems_1],
                  "hidden_states_2": hidden_states_2.numpy(),
                  "mems_2": [mem.numpy() for mem in mems_2],
              }
  
              self.parent.assertListEqual(
                  list(result["hidden_states_1"].shape),
                  [self.batch_size, self.seq_length, self.hidden_size])
              self.parent.assertListEqual(
                  list(result["hidden_states_2"].shape),
                  [self.batch_size, self.seq_length, self.hidden_size])
              self.parent.assertListEqual(
                  list(list(mem.shape) for mem in result["mems_1"]),
                  [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
              self.parent.assertListEqual(
                  list(list(mem.shape) for mem in result["mems_2"]),
                  [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
  
  
          def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
              model = TFTransfoXLLMHeadModel(config)
  
              lm_logits_1, mems_1 = model(input_ids_1)
  
              inputs = {'input_ids': input_ids_1,
                        'labels': lm_labels}
              _, mems_1 = model(inputs)
  
              lm_logits_2, mems_2 = model([input_ids_2, mems_1])
  
              inputs = {'input_ids': input_ids_1,
                        'mems': mems_1,
                        'labels': lm_labels}
  
              _, mems_2 = model(inputs)
  
              result = {
                  "mems_1": [mem.numpy() for mem in mems_1],
                  "lm_logits_1": lm_logits_1.numpy(),
                  "mems_2": [mem.numpy() for mem in mems_2],
                  "lm_logits_2": lm_logits_2.numpy(),
              }
  
              self.parent.assertListEqual(
                  list(result["lm_logits_1"].shape),
                  [self.batch_size, self.seq_length, self.vocab_size])
              self.parent.assertListEqual(
                  list(list(mem.shape) for mem in result["mems_1"]),
                  [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
  
              self.parent.assertListEqual(
                  list(result["lm_logits_2"].shape),
                  [self.batch_size, self.seq_length, self.vocab_size])
              self.parent.assertListEqual(
                  list(list(mem.shape) for mem in result["mems_2"]),
                  [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
  
          def prepare_config_and_inputs_for_common(self):
              config_and_inputs = self.prepare_config_and_inputs()
              (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
              inputs_dict = {'input_ids': input_ids_1}
              return config, inputs_dict
  
  
      def setUp(self):
          self.model_tester = TFTransfoXLModelTest.TFTransfoXLModelTester(self)
          self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_transfo_xl_model(self):
          self.model_tester.set_seed()
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)
  
      def test_transfo_xl_lm_head(self):
          self.model_tester.set_seed()
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          cache_dir = "/tmp/transformers_test/"
          for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
              model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=cache_dir)
              shutil.rmtree(cache_dir)
              self.assertIsNotNone(model)
  
  
  if __name__ == "__main__":
      unittest.main()
  