| import torch |
| import numpy as np |
| import jax.numpy as jnp |
| from transformers import AutoTokenizer |
| from transformers import FlaxT5ForConditionalGeneration |
| from transformers import T5ForConditionalGeneration |
| tokenizer = AutoTokenizer.from_pretrained(".") |
| model_fx = FlaxT5ForConditionalGeneration.from_pretrained(".") |
| model_pt = T5ForConditionalGeneration.from_pretrained(".", from_flax=True) |
| model_pt.save_pretrained("./") |
| text = "Hoe gaat het?" |
| 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_pt = tokenizer(text, return_tensors="pt", padding=True, max_length=128, truncation=True) |
| d_input_ids_pt = np.ones((e_input_ids_pt.input_ids.shape[0], 1), dtype="i4") * model_pt.config.decoder_start_token_id |
| print(e_input_ids_fx) |
| print(d_input_ids_fx) |
| print() |
| encoder_pt = model_fx.encode(**e_input_ids_pt) |
| decoder_pt = model_fx.decode(d_input_ids_pt, encoder_pt) |
| logits_pt = decoder_pt.logits |
| print(logits_pt) |
| 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) |
|
|
