o 8wi(@sddlmZddlmZmZmZmZmZddlm Z ddl Z ddl m Z ddl m Z ee e fZ dded ed efd d Zd eeeeeeefffd efddZdeeefd efddZdS))Path)AnyDictTextOptionalUnion) import_moduleN)Compose)BaseWaveformTransformtorch_audiomentations class_namedefault_module_namereturncCs`|d}t|dkr|durd|d}t||}n d|dd}|d}tt||S)asLoad class by its name Parameters ---------- class_name : `str` default_module_name : `str`, optional When provided and `class_name` does not contain the absolute path. Defaults to "torch_audiomentations". Returns ------- Klass : `type` Class. Example ------- >>> YourAugmentation = get_class_by_name('your_package.your_module.YourAugmentation') >>> YourAugmentation = get_class_by_name('YourAugmentation', default_module_name='your_package.your_module') >>> from torch_audiomentations import Gain >>> assert Gain == get_class_by_name('Gain') .Nz-Could not infer module name from class name "z%".Please provide default module name.)splitlen ValueErrorjoingetattrr)r r tokensmsg module_namer_/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torch_audiomentations/utils/config.pyget_class_by_names   rconfigcCsz|d}Wn tytdwzt|}Wnty'td|dw|dt}t|ts8td|dvrGdd |d D|d <|d i|S) aInstantiate a transform from a configuration dictionary. `from_dict` can be used to instantiate a transform from its class name. For instance, these two pieces of code are equivalent: >>> from torch_audiomentations import Gain >>> transform = Gain(min_gain_in_db=-12.0) >>> transform = from_dict({'transform': 'Gain', ... 'params': {'min_gain_in_db': -12.0}}) Transforms composition is also supported: >>> compose = from_dict( ... {'transform': 'Compose', ... 'params': {'transforms': [{'transform': 'Gain', ... 'params': {'min_gain_in_db': -12.0, ... 'mode': 'per_channel'}}, ... {'transform': 'PolarityInversion'}], ... 'shuffle': True}}) :param config: configuration - a configuration dictionary :returns: A transform. :rtype Transform: transformzRA (currently missing) 'transform' key should be used to define the transform type.z)torch_audiomentations does not implement z transform.paramszPTransform parameters must be provided as {'param_name': param_value} dictionary.)r OneOfSomeOfcSsg|]}t|qSr) from_dict).0sub_transform_configrrr iszfrom_dict.. transformsNr)KeyErrorrrAttributeErrorgetdict isinstance)rTransformClassNameTransformClasstransform_paramsrrrr"9s0       r"file_ymlc Csvzddl}Wnty}ztdd}~wwt|d}|j||jd}Wdt|S1s2wYt|S)a`Instantiate a transform from a YAML configuration file. `from_yaml` can be used to instantiate a transform from a YAML file. For instance, these two pieces of code are equivalent: >>> from torch_audiomentations import Gain >>> transform = Gain(min_gain_in_db=-12.0, mode="per_channel") >>> transform = from_yaml("config.yml") where the content of `config.yml` is something like: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # config.yml transform: Gain params: min_gain_in_db: -12.0 mode: per_channel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Transforms composition is also supported: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # config.yml transform: Compose params: shuffle: True transforms: - transform: Gain params: min_gain_in_db: -12.0 mode: per_channel - transform: PolarityInversion ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :param file_yml: configuration file - a path to a YAML file with the following structure: :returns: A transform. :rtype Transform: rNzAPyYAML package is needed by `from_yaml`: please install it first.r)Loader)yaml ImportErroropenload SafeLoaderr")r/r2efrrrr from_yamlqs'   r9)r )pathlibrtypingrrrrr importlibrr r /torch_audiomentations.core.transforms_interfacer Transformstrtyperr"r9rrrrs"      **8