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# from transformers import AutoTokenizer, RobertaModel
# model = RobertaModel.from_pretrained('sinhala-roberta-mc4', from_flax=True)
# tokenizer = AutoTokenizer.from_pretrained('sinhala-roberta-mc4')
# tokenizer.save_pretrained('sinhala-roberta-mc4')
# model.save_pretrained('sinhala-roberta-mc4')
from transformers import RobertaForMaskedLM, FlaxRobertaForMaskedLM, AutoTokenizer
import torch
import numpy as np
import jax
import jax.numpy as jnp
jax.config.update('jax_platform_name', 'cpu')
MODEL_PATH = "sinhala-roberta-oscar"
model = FlaxRobertaForMaskedLM.from_pretrained(MODEL_PATH)
def to_f32(t):
return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
model.params = to_f32(model.params)
model.save_pretrained(MODEL_PATH)
pt_model = RobertaForMaskedLM.from_pretrained(MODEL_PATH, from_flax=True).to('cpu')
input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
input_ids_pt = torch.tensor(input_ids)
logits_pt = pt_model(input_ids_pt).logits
print(logits_pt)
logits_fx = model(input_ids).logits
print(logits_fx)
pt_model.save_pretrained(MODEL_PATH)
# also save tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
tokenizer.save_pretrained(MODEL_PATH)
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