# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import io import json import os from collections import OrderedDict from ...utils.downloader import COMMUNITY_MODEL_PREFIX, get_path_from_url from ...utils.import_utils import import_module from ...utils.log import logger from ..utils import resolve_cache_dir __all__ = [ "AutoProcessor", ] PROCESSOR_MAPPING_NAMES = OrderedDict( [ ("ChineseCLIPProcessor", "chineseclip"), ("CLIPProcessor", "clip"), ("ErnieViLProcessor", "ernie_vil"), ] ) def get_configurations(): MAPPING_NAMES = OrderedDict() for key, class_name in PROCESSOR_MAPPING_NAMES.items(): import_class = importlib.import_module(f"paddlenlp.transformers.{class_name}.procesing") processor_name = getattr(import_class, key) name = tuple(processor_name.pretrained_init_configuration.keys()) if MAPPING_NAMES.get(name, None) is None: MAPPING_NAMES[name] = [] MAPPING_NAMES[name].append(processor_name) return MAPPING_NAMES class AutoProcessor: """ AutoClass can help you automatically retrieve the relevant model given the provided pretrained weights/vocabulary. Autoprocessor is a generic processor class that will be instantiated as one of the base processor classes when created with the Autoprocessor.from_pretrained() classmethod. """ MAPPING_NAMES = get_configurations() _processor_mapping = MAPPING_NAMES _name_mapping = PROCESSOR_MAPPING_NAMES processor_config_file = "preprocessor_config.json" def __init__(self, *args, **kwargs): raise EnvironmentError( f"{self.__class__.__name__} is designed to be instantiated " f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path).`" ) @classmethod def _get_processor_class_from_config(cls, pretrained_model_name_or_path, config_file_path): with io.open(config_file_path, encoding="utf-8") as f: init_kwargs = json.load(f) # class name corresponds to this configuration init_class = init_kwargs.pop("init_class", None) if init_class is None: init_class = init_kwargs.pop("processor_class", None) if init_class: class_name = cls._name_mapping[init_class] import_class = import_module(f"paddlenlp.transformers.{class_name}.procesing") processor_class = getattr(import_class, init_class) return processor_class # If no `init_class`, we use pattern recognition to recognize the processor class. else: logger.info("We use pattern recognition to recognize the processor class.") for key, pattern in cls._name_mapping.items(): if pattern in pretrained_model_name_or_path.lower(): init_class = key class_name = cls._name_mapping[init_class] import_class = import_module(f"paddlenlp.transformers.{class_name}.processor") processor_class = getattr(import_class, init_class) break return processor_class @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `Autoprocessor`. Related resources are loaded by specifying name of a built-in pretrained model, or a community-contributed pretrained model, or a local file directory path. Args: pretrained_model_name_or_path (str): Name of pretrained model or dir path to load from. The string can be: - Name of built-in pretrained model - Name of a community-contributed pretrained model. - Local directory path which contains processor related resources and processor config file ("processor_config.json"). *args (tuple): position arguments for model `__init__`. If provided, use these as position argument values for processor initialization. **kwargs (dict): keyword arguments for model `__init__`. If provided, use these to update pre-defined keyword argument values for processor initialization. Returns: Pretrainedprocessor: An instance of `Pretrainedprocessor`. Example: .. code-block:: from paddlenlp.transformers import AutoProcessor processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") processor.save_pretrained('clip_processor') """ cache_dir = resolve_cache_dir( pretrained_model_name_or_path=pretrained_model_name_or_path, from_hf_hub=False, # TODO: from_hf_hub not supported yet cache_dir=kwargs.pop("cache_dir", None), ) all_processor_names = [] for names, processor_class in cls._processor_mapping.items(): for name in names: all_processor_names.append(name) # From built-in pretrained models if pretrained_model_name_or_path in all_processor_names: for names, processor_classes in cls._processor_mapping.items(): for pattern in names: if pattern == pretrained_model_name_or_path: actual_processor_class = processor_classes[0] logger.info( "We are using %s to load '%s'." % (actual_processor_class, pretrained_model_name_or_path) ) return actual_processor_class.from_pretrained( pretrained_model_name_or_path, *model_args, **kwargs ) # From local dir path elif os.path.isdir(pretrained_model_name_or_path): config_file = os.path.join(pretrained_model_name_or_path, cls.processor_config_file) if os.path.exists(config_file): processor_class = cls._get_processor_class_from_config(pretrained_model_name_or_path, config_file) logger.info("We are using %s to load '%s'." % (processor_class, pretrained_model_name_or_path)) return processor_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) # Assuming from community-contributed pretrained models else: community_config_path = "/".join( [COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.processor_config_file] ) try: resolved_vocab_file = get_path_from_url(community_config_path, cache_dir) except RuntimeError as err: logger.error(err) raise RuntimeError( f"Can't load processor for '{pretrained_model_name_or_path}'.\n" f"Please make sure that '{pretrained_model_name_or_path}' is:\n" "- a correct model-identifier of built-in pretrained models,\n" "- or a correct model-identifier of community-contributed pretrained models,\n" "- or the correct path to a directory containing relevant processor files.\n" ) if os.path.exists(resolved_vocab_file): processor_class = cls._get_processor_class_from_config( pretrained_model_name_or_path, resolved_vocab_file ) logger.info("We are using %s to load '%s'." % (processor_class, pretrained_model_name_or_path)) return processor_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)