jDbddlmZddlmZmZddgZGddeZGddeZdS) )default_collate)_CharacterGenerator_WordGeneratorCharacterGenerator WordGeneratorc$eZdZdZdfd ZxZS)raImplements a character image generation dataset >>> from doctr.datasets import CharacterGenerator >>> ds = CharacterGenerator(vocab='abdef', num_samples=100) >>> img, target = ds[0] Args: vocab: vocabulary to take the character from num_samples: number of samples that will be generated iterating over the dataset cache_samples: whether generated images should be cached firsthand font_family: font to use to generate the text images img_transforms: composable transformations that will be applied to each image sample_transforms: composable transformations that will be applied to both the image and the target returnNcftj|i|t|dtdS)N collate_fn)super__init__setattrr)selfargskwargs __class__s c/var/www/html/Carbon-Document/venv/lib/python3.11/site-packages/doctr/datasets/generator/pytorch.pyrzCharacterGenerator.__init__s7$)&)))lO44444)r N)__name__ __module__ __qualname____doc__r __classcell__)rs@rrr sG  5555555555rceZdZdZdS)ra/Implements a character image generation dataset >>> from doctr.datasets import WordGenerator >>> ds = WordGenerator(vocab='abdef', min_chars=1, max_chars=32, num_samples=100) >>> img, target = ds[0] Args: vocab: vocabulary to take the character from min_chars: minimum number of characters in a word max_chars: maximum number of characters in a word num_samples: number of samples that will be generated iterating over the dataset cache_samples: whether generated images should be cached firsthand font_family: font to use to generate the text images img_transforms: composable transformations that will be applied to each image sample_transforms: composable transformations that will be applied to both the image and the target N)rrrrrrrr"s" DrN)torch.utils.data._utils.collaterbaserr__all__rrrrrr s <;;;;;55555555  155555,555*     N     r