U
9%e# @ s^ d dl mZ d dlmZmZ er,ddlmZ G dd dZG dd deZG d d
d
eZ d S ) )Queue)
TYPE_CHECKINGOptional )
AutoTokenizerc @ s e Zd ZdZdd Zdd ZdS ) BaseStreamerzG
Base class from which `.generate()` streamers should inherit.
c C s
t dS )z;Function that is called by `.generate()` to push new tokensNNotImplementedErrorselfvalue r
`/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/generation/streamers.pyput s zBaseStreamer.putc C s
t dS )zHFunction that is called by `.generate()` to signal the end of generationNr r r
r
r end! s zBaseStreamer.endN)__name__
__module__ __qualname____doc__r r r
r
r
r r s r c @ sL e Zd ZdZddedddZdd Zd d
Zdeed d d
Z dd Z
dS ) TextStreamera
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 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.
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("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("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,
```
Fr ) tokenizer skip_promptc K s( || _ || _|| _g | _d| _d| _d S )Nr T)r r
decode_kwargs token_cache print_lennext_tokens_are_prompt)r r r r r
r
r __init__H s zTextStreamer.__init__c C s t |jdkr&|jd dkr&tdnt |jdkr<|d }| jrR| jrRd| _dS | j| | jj | jf| j
}| dr|| j d }g | _d| _ njt |dkr|
t|d r|| j d }| j t |7 _ n*|| j |dd }| j t |7 _ | | dS ) zm
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 decoder endswithr _is_chinese_charordrfindon_finalized_text)r r textprintable_textr
r
r r R s&
zTextStreamer.putc C sZ t | jdkr>| jj| jf| j}|| jd }g | _d| _nd}d| _| j|dd dS )z;Flushes any remaining cache and prints a newline to stdout.r N T)
stream_end)r" r r r' r r r r, )r r- r. r
r
r r t s zTextStreamer.endr- r0 c C s t |d|sdndd dS )zNPrints the new text to stdout. If the stream is ending, also prints a newline.Tr/ N)flushr )printr r- r0 r
r
r r, s z TextStreamer.on_finalized_textc C s |dkr|dks|dkr |dks|dkr0|dks|dkr@|dks|d krP|d
ks|d kr`|d ks|d
krp|dks|dkr|dkrdS dS )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 cpr
r
r r) sD
z TextStreamer._is_chinese_charN)F)F) r r r r boolr r r strr, r) r
r
r
r r & s !
"r c sR e Zd ZdZddeee d fdd
Zdeedd d
Z d d Z
d
d Z Z S )TextIteratorStreamera
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 acessing 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 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.
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("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("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,'
```
FNr )r r timeoutc s* t j||f| t | _d | _|| _d S N)superr r
text_queue stop_signalr9 )r r r r9 r __class__r
r r s z TextIteratorStreamer.__init__r1 c C s. | j j|| jd |r*| j j| j| jd dS )z\Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.r9 N)r< r r9 r= r4 r
r
r r, s z&TextIteratorStreamer.on_finalized_textc C s | S r: r
r r
r
r __iter__ s z TextIteratorStreamer.__iter__c C s* | j j| jd}|| jkr"t n|S d S )Nr@ )r< getr9 r=
StopIterationr
r
r
r __next__ s
z TextIteratorStreamer.__next__)FN)F)
r r r r r6 r floatr r7 r, rA rD
__classcell__r
r
r> r r8 s . r8 N)
queuer typingr r Z models.autor r r r8 r
r
r
r s y