codingutf8 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 orglicenseslicense2 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 the greedy text should be printed to stdout except for the final n in the streamer the greedy text should be printed to stdout except for the final n in the streamer tests that we can pass decodekwargs to the streamer to control how the tokens are decoded must be tested with actual models the dummy models tokenizers are not aligned with their models and skipspecialtokenstrue has no effect on them the prompt contains a special token so the streamer should not print it as such the output text when retokenized must only contain one token the streamer will timeout after 0 001 seconds so an exception will be raised coding utf 8 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 the greedy text should be printed to stdout except for the final n in the streamer the greedy text should be printed to stdout except for the final n in the streamer tests that we can pass decode_kwargs to the streamer to control how the tokens are decoded must be tested with actual models the dummy models tokenizers are not aligned with their models and skip_special_tokens true has no effect on them the prompt contains a special token so the streamer should not print it as such the output text when re tokenized must only contain one token remove the final n the streamer will timeout after 0 001 seconds so an exception will be raised
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, 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():
    import torch

    from transformers import AutoModelForCausalLM


@require_torch
class StreamerTester(unittest.TestCase):
    def test_text_streamer_matches_non_streaming(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)
        
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, greedy_text)

    def test_iterator_streamer_matches_non_streaming(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])

        streamer = TextIteratorStreamer(tokenizer)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
        streamer_text = ""
        for new_text in streamer:
            streamer_text += new_text

        self.assertEqual(streamer_text, greedy_text)

    def test_text_streamer_skip_prompt(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)
        new_greedy_ids = greedy_ids[:, input_ids.shape[1] :]
        new_greedy_text = tokenizer.decode(new_greedy_ids[0])

        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_prompt=True)
            model.generate(input_ids, max_new_tokens=10, do_sample=False, streamer=streamer)
        
        streamer_text = cs.out[:-1]

        self.assertEqual(streamer_text, new_greedy_text)

    def test_text_streamer_decode_kwargs(self):
        
        
        
        tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
        model = AutoModelForCausalLM.from_pretrained("distilgpt2").to(torch_device)
        model.config.eos_token_id = -1

        input_ids = torch.ones((1, 5), device=torch_device).long() * model.config.bos_token_id
        with CaptureStdout() as cs:
            streamer = TextStreamer(tokenizer, skip_special_tokens=True)
            model.generate(input_ids, max_new_tokens=1, do_sample=False, streamer=streamer)

        
        
        streamer_text = cs.out[:-1]  
        streamer_text_tokenized = tokenizer(streamer_text, return_tensors="pt")
        self.assertEqual(streamer_text_tokenized.input_ids.shape, (1, 1))

    def test_iterator_streamer_timeout(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)
        streamer = TextIteratorStreamer(tokenizer, timeout=0.001)
        generation_kwargs = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        
        with self.assertRaises(Empty):
            streamer_text = ""
            for new_text in streamer:
                streamer_text += new_text