def test_push_to_hub_in_organization(self): config = BertConfig(vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37) config.push_to_hub("valid_org/test-config-org", use_auth_token=self._token) new_config = BertConfig.from_pretrained("valid_org/test-config-org") for k, v in config.to_dict().items(): if k != "transformers_version": self.assertEqual(v, getattr(new_config, k)) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-config-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: config.save_pretrained(tmp_dir, repo_id="valid_org/test-config-org", push_to_hub=True, use_auth_token=self._token) new_config = BertConfig.from_pretrained("valid_org/test-config-org") for k, v in config.to_dict().items(): if k != "transformers_version": self.assertEqual(v, getattr(new_config, k))
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) # follow test_modeling_tf_ctrl.py 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 = BertConfig( 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, is_decoder=False, initializer_range=self.initializer_range, # MODIFY return_dict=False, ) config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels