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Base class from which `.generate()` streamers should inherit.
c [ 5 e)z;Function that is called by `.generate()` to push new tokensNotImplementedErrorselfvalues [/home/wildlama/miniconda3/lib/python3.13/site-packages/transformers/generation/streamers.pyputBaseStreamer.put! !## c [ 5 e)zHFunction that is called by `.generate()` to signal the end of generationr r s r endBaseStreamer.end% r r N)__name__
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TextStreamer* aT
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
Parameters:
tokenizer (`AutoTokenizer`):
The tokenizer used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextStreamer(tok)
>>> # Despite returning the usual output, the streamer will also print the generated text to stdout.
>>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20)
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
```
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$ ) TextIteratorStreamer a
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from accessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
Parameters:
tokenizer (`AutoTokenizer`):
The tokenizer used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
>>> from threading import Thread
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextIteratorStreamer(tok)
>>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
>>> thread = Thread(target=model.generate, kwargs=generation_kwargs)
>>> thread.start()
>>> generated_text = ""
>>> for new_text in streamer:
... generated_text += new_text
>>> generated_text
'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,'
```
c ` > [ TU ] " X40 UD6 [ 5 U l S U l X0l g N)superr- r
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$ ) AsyncTextIteratorStreamer a
Streamer that stores print-ready text in a queue, to be used by a downstream application as an async iterator.
This is useful for applications that benefit from accessing the generated text asynchronously (e.g. in an
interactive Gradio demo).
Parameters:
tokenizer (`AutoTokenizer`):
The tokenizer used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
timeout (`float`, *optional*):
The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions
in `.generate()`, when it is called in a separate thread.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Raises:
TimeoutError: If token generation time exceeds timeout value.
Examples:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, AsyncTextIteratorStreamer
>>> from threading import Thread
>>> import asyncio
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
>>> async def main():
... # Important: AsyncTextIteratorStreamer must be initialized inside a coroutine!
... streamer = AsyncTextIteratorStreamer(tok)
... generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)
... thread = Thread(target=model.generate, kwargs=generation_kwargs)
... thread.start()
... generated_text = ""
... async for new_text in streamer:
... generated_text += new_text
>>> print(generated_text)
>>> asyncio.run(main())
An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,
```
c ^ > [ TU ] " X40 UD6 [ R " 5 U l S U l X0l [ R " 5 U l [ [ SS 5 n[ R S: =( a [ U5 U l
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put_nowaitrY rJ s r r? +AsyncTextIteratorStreamer.on_finalized_text! sL &&t'A'A4H II * *4??+E+EtGWGW X r c U $ rV r r s r __aiter__#AsyncTextIteratorStreamer.__aiter__' rd r c 4 # U R ( ad U R bW U R U R 5 IS h vN U R R 5 I S h vN nS S S 5 IS h vN O?[
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U =r $ ) TextDiffusionStreameri: a
Streamer that prints text diffusion outputs. Intermediate diffusion steps (drafts) are temporary
and overwritten by subsequent drafts, and removed when confirmed text is printed.
If you're running on an environment like tmux, the draft text may fail to overwrite itself.
Parameters:
tokenizer (`AutoTokenizer`):
The tokenized used to decode the tokens.
skip_prompt (`bool`, *optional*, defaults to `False`):
Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots.
sleep_time (`float`, *optional*):
Time to sleep between diffusion drafts, which may be helpful to visualize intermediate outputs.
decode_kwargs (`dict`, *optional*):
Additional keyword arguments to pass to the tokenizer's `decode` method.
Examples:
```python
>>> from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor, TextDiffusionStreamer
>>> model = DiffusionGemmaForBlockDiffusion.from_pretrained(
... "google/diffusiongemma-26B-A4B-it", device_map="auto",
... )
>>> processor = AutoProcessor.from_pretrained("google/diffusiongemma-26B-A4B-it")
>>> chat = [{"role": "user", "content": "Why is the sky blue?"},]
>>> input_ids = processor.apply_chat_template(
... chat, tokenize=True, return_tensors="pt", add_generation_prompt=True
... )
>>> streamer = TextDiffusionStreamer(tokenizer=processor.tokenizer)
>>> model.generate(input_ids.to(model.device), max_new_tokens=512, streamer=streamer)
```
c P > [ TU ] " X40 UD6 SU l SU l X0l g )NF)rW r-
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Overwrites previous draft.
r0 r z0TextDiffusionStreamer only supports batch size 1z7rD Tr z[33mz[0mN) r r4 r5 r6 r' r: r) rI r r timesleep)r r kwargsr@ s r put_draft TextDiffusionStreamer.put_draftx s
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