def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CanineModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path):
# Initialize PyTorch model
config = CanineConfig()
model = CanineModel(config)
model.eval()
print(f"Building PyTorch model from configuration: {config}")
# Load weights from tf checkpoint
load_tf_weights_in_canine(model, config, tf_checkpoint_path)
# Save pytorch-model (weights and configuration)
print(f"Save PyTorch model to {pytorch_dump_path}")
model.save_pretrained(pytorch_dump_path)
# Save tokenizer files
tokenizer = CanineTokenizer()
print(f"Save tokenizer files to {pytorch_dump_path}")
tokenizer.save_pretrained(pytorch_dump_path)
