U -e;@sUddlZddlZddlZddlmZmZddlmZddlm Z m Z m Z m Z m Z mZmZddlZddlZddlmZddlmZddlmZdd lmZdd lmZdd lmZm Z m!Z!e rddl"Z#d d l$m%Z%da&ee e'e(d<ej)dkrdndZ*e+de+de+de+de+de+de+de+de+de+de+de+de+de+dgZ,eGd d!d!Z-e e'd"d#d$Z.d%e/d&d'd(Z0d%e1d&d)d*Z2ej3e1d+d,d-Z4ee e'e e1e ej3e d%fe e1d.d/d0Z5dS)1N) dataclassfield)BytesIO) TYPE_CHECKINGAnyClassVarDictListOptionalUnion)config)DownloadConfig)xopen) array_cast) is_local_path)first_non_null_valueno_op_if_value_is_nullstring_to_dict) FeatureType_IMAGE_COMPRESSION_FORMATSlittle<>z|b1|u1zu2zi2zu4zi4zf4zf8c@seZdZUdZdZeed<dZee ed<dZ e e ed<e e e dZe eed <edd d d Ze ed <d dZee eeejdfedddZdeddddZedee dffdddZee je je j fe jdddZ!e je jdddZ"dS)ImageaImage [`Feature`] to read image data from an image file. Input: The Image feature accepts as input: - A `str`: Absolute path to the image file (i.e. random access is allowed). - A `dict` with the keys: - `path`: String with relative path of the image file to the archive file. - `bytes`: Bytes of the image file. This is useful for archived files with sequential access. - An `np.ndarray`: NumPy array representing an image. - A `PIL.Image.Image`: PIL image object. Args: decode (`bool`, defaults to `True`): Whether to decode the image data. If `False`, returns the underlying dictionary in the format `{"path": image_path, "bytes": image_bytes}`. Examples: ```py >>> from datasets import load_dataset, Image >>> ds = load_dataset("beans", split="train") >>> ds.features["image"] Image(decode=True, id=None) >>> ds[0]["image"] >>> ds = ds.cast_column('image', Image(decode=False)) {'bytes': None, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/b0a21163f78769a2cf11f58dfc767fb458fc7cea5c05dccc0144a2c0f0bc1292/train/healthy/healthy_train.85.jpg'} ``` TdecodeNidPIL.Image.Imagedtypebytespathpa_typeF)defaultinitrepr_typecCs|jSN)r$)selfr+X/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/datasets/features/image.py__call__YszImage.__call__)valuereturncCstjrddl}ntdt|tr,t|}t|tr@|ddSt|t rTd|dSt|tj rht |St||j j r~t |S|ddk rtj|drd|ddS|ddk s|ddk r|d|ddStd|d dS) aEncode example into a format for Arrow. Args: value (`str`, `np.ndarray`, `PIL.Image.Image` or `dict`): Data passed as input to Image feature. 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The Arrow types that can be converted to the Image pyarrow storage type are: - `pa.string()` - it must contain the "path" data - `pa.binary()` - it must contain the image bytes - `pa.struct({"bytes": pa.binary()})` - `pa.struct({"path": pa.string()})` - `pa.struct({"bytes": pa.binary(), "path": pa.string()})` - order doesn't matter - `pa.list(*)` - it must contain the image array data Args: storage (`Union[pa.StringArray, pa.StructArray, pa.ListArray]`): PyArrow array to cast. Returns: `pa.StructArray`: Array in the Image arrow storage type, that is `pa.struct({"bytes": pa.binary(), "path": pa.string()})`. Ntyper"r#maskrcSs*g|]"}|dk r"tt|dndqS)Nr")r<r8r9).0Zarrr+r+r, sz&Image.cast_storage..)patypes is_stringr]r9lenrW StructArray from_arraysis_nullZ is_binaryrXZ is_structZget_field_indexrZis_list to_pylistrr$r*r[Z bytes_arrayZ path_arrayr+r+r, cast_storages4""   zImage.cast_storagecstddtjfdd|Dtd}tjdd|dDtd}tjj||gddg| d }t ||j S) a8Embed image files into the Arrow array. 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