| import torch | |
| import numpy as np | |
| import jax.numpy as jnp | |
| from transformers import AutoTokenizer | |
| from transformers import FlaxT5ForConditionalGeneration | |
| from transformers import TFT5ForConditionalGeneration | |
| tokenizer = AutoTokenizer.from_pretrained("../") | |
| model_fx = FlaxT5ForConditionalGeneration.from_pretrained("../") | |
| model_tf = TFT5ForConditionalGeneration.from_pretrained("./", from_pt=True) | |
| model_tf.save_pretrained("./") | |
| text = "Hello To You" | |
| e_input_ids_fx = tokenizer(text, return_tensors="np", padding=True, max_length=128, truncation=True) | |
| d_input_ids_fx = jnp.ones((e_input_ids_fx.input_ids.shape[0], 1), dtype="i4") * model_fx.config.decoder_start_token_id | |
| e_input_ids_tf = tokenizer(text, return_tensors="tf", padding=True, max_length=128, truncation=True) | |
| d_input_ids_tf = np.ones((e_input_ids_tf.input_ids.shape[0], 1), dtype="i4") * model_tf.config.decoder_start_token_id | |
| print(e_input_ids_fx) | |
| print(d_input_ids_fx) | |
| print() | |
| encoder_tf = model_fx.encode(**e_input_ids_tf) | |
| decoder_tf = model_fx.decode(d_input_ids_tf, encoder_tf) | |
| logits_tf = decoder_tf.logits | |
| print(logits_tf) | |
| encoder_fx = model_fx.encode(**e_input_ids_fx) | |
| decoder_fx = model_fx.decode(d_input_ids_fx, encoder_fx) | |
| logits_fx = decoder_fx.logits | |
| print(logits_fx) |