Z j?4 S SK Jr S SKrS SKrS SKJr S SKJrJrJ r \( a SSK
J r " S S5 r " S S
\ 5 r
" S S \
5 r " S
S\
5 rg) ) annotationsN)Queue)
TYPE_CHECKINGAnycast )PreTrainedTokenizerBasec $ \ rS rSrSrS rS rSrg) BaseStreamer z?
Base class from which `.generate()` streamers should inherit.
c [ 5 e)z;Function that is called by `.generate()` to push new tokensNotImplementedErrorselfvalues r/root/GenerationalWealth/GenerationalWealth/venv/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__
__module__ __qualname____firstlineno____doc__r r __static_attributes__r r r r r s $$r r c F \ rS rSrSrS
S S jjrS rS rS
S S jjrS r Sr
g )
TextStreamer) a
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
The API for the streamer classes is still under development and may change in the future.
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,
```
c R Xl X l X0l / U l SU l SU l g )Nr T) tokenizer skip_prompt
decode_kwargs token_cache print_lennext_tokens_are_prompt)r r' r( r) s r __init__TextStreamer.__init__K s, "& * ')&*#r c [ UR 5 S: a UR S S: a [ S5 e[ UR 5 S: a US nU R ( a U R ( a SU l gU R
R
UR 5 5 [ [ U R R " U R
40 U R D65 nUR S5 ( a X R S n/ U l SU l O[ U5 S: aO U R [! US 5 5 ( a- X R S nU =R [ U5 -
sl O>X R UR# S5 S- nU =R [ U5 -
sl U R% U5 g) z]
Receives tokens, decodes them, and prints them to stdout as soon as they form entire words.
r z'TextStreamer only supports batch size 1FN
)lenshape
ValueErrorr( r, r* extendtolistr strr' decoder) endswithr+ _is_chinese_charordrfindon_finalized_text)r r textprintable_texts r r TextStreamer.putU s` u{{ a EKKNQ$6 FG G
!!HE ; ;*/D '
/C ..t/?/?V4CUCUVW == !.."2 3N !D DN
Y]t 4 4Sb] C C !.."2 3N NNc. 1 1N "..4::c?Q3F GN NNc. 1 1N ~.r c [ U R 5 S: a\ [ [ U R R
" U R 40 U R D65 nXR S n/ U l SU l OSnSU l U R USS9 g)z;Flushes any remaining cache and prints a newline to stdout.r N T)
stream_end)
r4 r* r r9 r' r: r) r+ r, r? )r r@ rA s r r TextStreamer.endw s t 1 $T^^ 2 243C3C ZtGYGY Z[D !.."2 3N !D DN N&*# ~$ ?r c , [ USU( d SOSS9 g)zNPrints the new text to stdout. If the stream is ending, also prints a newline.TrD N)flushr )printr r@ rE s r r? TextStreamer.on_finalized_text s
d$jBdCr c US: a US:: dT US: a US:: dH US: a US:: d< US: a US:: d0 US : a US
:: d$ US : a US :: d US
: a US:: d US: a US:: a gg)z6Checks whether CP is the codepoint of a CJK character.i N i i 4 iM i iߦ i i? i@ i i i i i i i TFr )r cps r r< TextStreamer._is_chinese_char sr 6\bFlf v g
"-g
"-g
"-g
"-f v g
"-r )r) r, r+ r( r* r' NF)r' r r( boolr) r r@ r9 rE rP ) r r r r r! r- r r r? r< r" r r r r$ r$ ) s$ B+ /D @ Dr r$ c ` ^ \ rS rSrSr S S U 4S jjjrS
S S jjrS rS rSr U =r
$ ) 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).
The API for the streamer classes is still under development and may change in the future.
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
text_queue stop_signaltimeout)r r' r( rZ r) __class__s r r- TextIteratorStreamer.__init__ s- A=A ' r c U R R XR S9 U( a/ U R R U R U R S9 gg)\Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.rZ N)rX r rZ rY rJ s r r? &TextIteratorStreamer.on_finalized_text sD D,,7 OO 0 0$,, G r c U $ rV r r s r __iter__ TextIteratorStreamer.__iter__ r c ~ U R R U R S9nXR :X a
[ 5 eU$ Nr_ )rX getrZ rY
StopIterationr s r __next__ TextIteratorStreamer.__next__ s6 ##DLL#9 $$ $ / !Lr )rY rX rZ FNr' r r( rP rZ z float | Noner) r rO rQ ) r r r r r! r- r? rb ri r"
__classcell__r[ s @r rS rS sV +` " $
*
H r rS c ` ^ \ rS rSrSr S S U 4S jjjrS
S S jjrS rS rSr U =r
$ ) 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).
The API for the streamer classes is still under development and may change in the future.
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
U R ( a XPl g S U l g )NrZ ) )rW r- asyncior rX rY rZ get_running_looploopgetattrsys version_infocallablehas_asyncio_timeoutasyncio_timeout)r r' r( rZ r) timeout_contextr[ s r r- "AsyncTextIteratorStreamer.__init__" s A=A!--/ ,,. !'9d;##3w#>#\8OC\ 262J2J PT r c U R R U R R U5 U( a; U R R U R R U R 5 gg)r^ N)rw call_soon_threadsaferX
put_nowaitrY rJ s r r? +AsyncTextIteratorStreamer.on_finalized_text2 sL &&t'A'A4H II * *4??+E+EtGWGW X r c U $ rV r r s r __aiter__#AsyncTextIteratorStreamer.__aiter__8 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?[
R " U R R 5 U R S9I S h vN nWU R :X a
[ 5 eU$ N Nr Nd! , IS h vN ( d f N9= f N@! [
R a [ 5 ef = f7frf )
r| r} rZ rX rg ru wait_forrY StopAsyncIteration TimeoutErrorr s r __anext__#AsyncTextIteratorStreamer.__anext__; s ''D,@,@,L // =="&//"5"5"7 7E >== & . .t/B/B/Ddll [[ ((((** > 7 >=== \## ! . !s D=C6 CC6 C#C$C( C6 3C4?C6 3C44C6 8 DC6 CC6 C1 C# ! C1-C6 6 DD)r} r| rw rY rX rZ rk rl rO rQ ) r r r r r! r- r? r r r" rm rn s @r rp rp s\ 2n " $ U*U U U
U U Y
r rp )
__future__r ru ry queuer typingr r r tokenization_utils_baser r r$ rS rp r r r r sW #
+ + A $ $ v< vrH< HV[ [ r