mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-23 22:38:58 +06:00
45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 The HuggingFace Team Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a clone of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
from transformers import AutoTokenizer, TextStreamer, is_torch_available
|
|
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
|
|
|
|
from ..test_modeling_common import ids_tensor
|
|
|
|
|
|
if is_torch_available():
|
|
from transformers import AutoModelForCausalLM
|
|
|
|
|
|
@require_torch
|
|
class StreamerTester(unittest.TestCase):
|
|
def test_text_streamer_stdout(self):
|
|
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
|
|
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
|
|
model.config.eos_token_id = -1
|
|
|
|
input_ids = ids_tensor((1, 5), vocab_size=model.config.vocab_size).to(torch_device)
|
|
greedy_ids = model.generate(input_ids, max_new_tokens=10, do_sample=False)
|
|
greedy_text = tokenizer.decode(greedy_ids[0])
|
|
|
|
with CaptureStdout() as cs:
|
|
streamer = TextStreamer(tokenizer)
|
|
model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
|
|
|
|
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
|
|
self.assertEqual(cs.out[:-1], greedy_text)
|