import os
import json
import warnings
from pathlib import Path
import torch
import torch.nn as nn
from transformers import (
PreTrainedTokenizer,
PreTrainedTokenizerBase,
ProcessorMixin,
BatchFeature,
)
from transformers.utils import (
logging,
direct_transformers_import,
PROCESSOR_NAME,
CHAT_TEMPLATE_NAME,
)
from transformers.image_utils import ImageInput
from transformers.dynamic_module_utils import custom_object_save
logger = logging.get_logger(__name__)
# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
transformers_module = direct_transformers_import(Path(__file__).parent)
class MultiProcessorKwargs:
_defaults = {
"tokenizer_1_kwargs": {
"padding": False,
},
"tokenizer_2_kwargs": {
"padding": False,
},
}
class MultiProcessor(ProcessorMixin):
attributes = ["tokenizer_1", "tokenizer_2"]
valid_kwargs = ["chat_template"]
tokenizer_1_class = "AutoTokenizer"
tokenizer_2_class = "AutoTokenizer"
tokenizer_1: PreTrainedTokenizer
tokenizer_2: PreTrainedTokenizer
def __init__(
self,
tokenizer_1=None,
tokenizer_2=None,
chat_template=None,
**kwargs,
):
super().__init__(
tokenizer_1,
tokenizer_2,
chat_template=chat_template,
**kwargs,
)
def __call__(
self,
text_1: str | list[str] | None = None,
text_2: str | list[str] | None = None,
**kwargs,
) -> BatchFeature:
def _validate_text_input(text) -> str | list[str]:
if isinstance(text, list):
assert all(
isinstance(t, str) for t in text
), f"Expected list of str but got {type(text)}"
assert all(len(t) > 0 for t in text), "Expected non-empty strings"
else:
assert isinstance(text, str), f"Expected str but got {type(text)}"
return text
def _normalize_text_input(text: str | list[str]) -> list[str]:
if isinstance(text, str):
return [text]
return text
_text_1: str | list[str] = _validate_text_input(text_1)
text_1_list: list[str] = _normalize_text_input(_text_1)
_text_2: str | list[str] = _validate_text_input(text_2)
text_2_list: list[str] = _normalize_text_input(_text_2)
tokenizer_1_output_kwargs = {
**MultiProcessorKwargs._defaults["tokenizer_1_kwargs"],
"return_tensors": "pt",
**kwargs,
}
tokenizer_2_output_kwargs = {
**MultiProcessorKwargs._defaults["tokenizer_2_kwargs"],
"return_tensors": "pt",
**kwargs,
}
# tokenize
text_1_inputs = self.tokenizer_1(
text_1_list,
**tokenizer_1_output_kwargs,
)
text_2_inputs = self.tokenizer_2(
text_2_list,
**tokenizer_2_output_kwargs,
)
return BatchFeature(
data={
"input_ids": text_1_inputs.get("input_ids"),
"attention_mask": text_1_inputs.get("attention_mask"),
"input_ids_2": text_2_inputs.get("input_ids"),
"attention_mask_2": text_2_inputs.get("attention_mask"),
}
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer_2_tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer_2_tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
return ["text_1", "text_2"]
# edit from: https://github.com/huggingface/transformers/blob/1d063793318b20654ebb850f48f43e0a247ab7bb/src/transformers/processing_utils.py#L980-L995
@classmethod
def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
args = []
for attribute_name in cls.attributes:
class_name = getattr(cls, f"{attribute_name}_class")
subfolder = attribute_name # subfolder is the same as attribute_name
if isinstance(class_name, tuple):
classes = tuple(
getattr(transformers_module, n) if n is not None else None
for n in class_name
)
use_fast = kwargs.get("use_fast", True)
if use_fast and classes[1] is not None:
attribute_class = classes[1]
else:
attribute_class = classes[0]
else:
attribute_class = getattr(transformers_module, class_name)
assert attribute_class is not None, f"Missing attribute class: {class_name}"
args.append(
attribute_class.from_pretrained(
pretrained_model_name_or_path,
subfolder=subfolder,
**kwargs,
)
)
return args
# edit from: https://github.com/huggingface/transformers/blob/1d063793318b20654ebb850f48f43e0a247ab7bb/src/transformers/processing_utils.py#L460-L560
def save_pretrained(self, save_directory, push_to_hub: bool = False, **kwargs):
"""
Saves the attributes of this processor (feature extractor, tokenizer...) in the specified directory so that it
can be reloaded using the [`~ProcessorMixin.from_pretrained`] method.
<Tip>
This class method is simply calling [`~feature_extraction_utils.FeatureExtractionMixin.save_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`]. Please refer to the docstrings of the
methods above for more information.
</Tip>
Args:
save_directory (`str` or `os.PathLike`):
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
namespace).
kwargs (`Dict[str, Any]`, *optional*):
Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
"""
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if kwargs.get("token", None) is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
kwargs["token"] = use_auth_token
os.makedirs(save_directory, exist_ok=True)
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory)
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
attrs = [
getattr(self, attribute_name) for attribute_name in self.attributes
]
configs = [
(a.init_kwargs if isinstance(a, PreTrainedTokenizerBase) else a)
for a in attrs
]
configs.append(self)
custom_object_save(self, save_directory, config=configs)
for attribute_name in self.attributes:
attribute = getattr(self, attribute_name)
# Include the processor class in the attribute config so this processor can then be reloaded with the
# `AutoProcessor` API.
if hasattr(attribute, "_set_processor_class"):
attribute._set_processor_class(self.__class__.__name__)
attribute.save_pretrained(
os.path.join(
save_directory,
attribute_name, # CHANGED: save to subfolder
),
)
if self._auto_class is not None:
# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
for attribute_name in self.attributes:
attribute = getattr(self, attribute_name)
if isinstance(attribute, PreTrainedTokenizerBase):
del attribute.init_kwargs["auto_map"]
# If we save using the predefined names, we can load using `from_pretrained`
# plus we save chat_template in its own file
output_processor_file = os.path.join(save_directory, PROCESSOR_NAME)
output_chat_template_file = os.path.join(save_directory, CHAT_TEMPLATE_NAME)
processor_dict = self.to_dict()
# Save `chat_template` in its own file. We can't get it from `processor_dict` as we popped it in `to_dict`
# to avoid serializing chat template in json config file. So let's get it from `self` directly
if self.chat_template is not None:
chat_template_json_string = (
json.dumps(
{"chat_template": self.chat_template}, indent=2, sort_keys=True
)
+ "\n"
)
with open(output_chat_template_file, "w", encoding="utf-8") as writer:
writer.write(chat_template_json_string)
logger.info(f"chat template saved in {output_chat_template_file}")
# For now, let's not save to `processor_config.json` if the processor doesn't have extra attributes and
# `auto_map` is not specified.
if set(processor_dict.keys()) != {"processor_class"}:
self.to_json_file(output_processor_file)
logger.info(f"processor saved in {output_processor_file}")
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=kwargs.get("token"),
)
if set(processor_dict.keys()) == {"processor_class"}:
return []
return [output_processor_file]