| """ DalleBart processor """ |
|
|
| from typing import List |
|
|
| import jax.numpy as jnp |
|
|
| from .configuration import DalleBartConfig |
| from .text import TextNormalizer |
| from .tokenizer import DalleBartTokenizer |
| from .utils import PretrainedFromWandbMixin |
|
|
|
|
| class DalleBartProcessorBase: |
| def __init__( |
| self, tokenizer: DalleBartTokenizer, normalize_text: bool, max_text_length: int |
| ): |
| self.tokenizer = tokenizer |
| self.normalize_text = normalize_text |
| self.max_text_length = max_text_length |
| if normalize_text: |
| self.text_processor = TextNormalizer() |
| |
| uncond = self.tokenizer( |
| "", |
| return_tensors="jax", |
| padding="max_length", |
| truncation=True, |
| max_length=self.max_text_length, |
| ).data |
| self.input_ids_uncond = uncond["input_ids"] |
| self.attention_mask_uncond = uncond["attention_mask"] |
|
|
| def __call__(self, text: List[str] = None): |
| |
| assert not isinstance(text, str), "text must be a list of strings" |
|
|
| if self.normalize_text: |
| text = [self.text_processor(t) for t in text] |
| res = self.tokenizer( |
| text, |
| return_tensors="jax", |
| padding="max_length", |
| truncation=True, |
| max_length=self.max_text_length, |
| ).data |
| |
| n = len(text) |
| res["input_ids_uncond"] = jnp.repeat(self.input_ids_uncond, n, axis=0) |
| res["attention_mask_uncond"] = jnp.repeat(self.attention_mask_uncond, n, axis=0) |
| return res |
|
|
| @classmethod |
| def from_pretrained(cls, *args, **kwargs): |
| tokenizer = DalleBartTokenizer.from_pretrained(*args, **kwargs) |
| config = DalleBartConfig.from_pretrained(*args, **kwargs) |
| return cls(tokenizer, config.normalize_text, config.max_text_length) |
|
|
|
|
| class DalleBartProcessor(PretrainedFromWandbMixin, DalleBartProcessorBase): |
| pass |
|
|