# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Image/Text processor class for CLIP
"""
from transformers.tokenization_utils_base import BatchEncoding
from .feature_extraction_clip import CLIPFeatureExtractor
from .tokenization_clip import CLIPTokenizer
[docs]class CLIPProcessor:
r"""
Constructs a CLIP processor which wraps a CLIP feature extractor and a CLIP tokenizer into a single processor.
[`CLIPProcessor`] offers all the functionalities of [`CLIPFeatureExtractor`] and [`CLIPTokenizer`]. See the
[`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information.
Args:
feature_extractor ([`CLIPFeatureExtractor`]):
The feature extractor is a required input.
tokenizer ([`CLIPTokenizer`]):
The tokenizer is a required input.
"""
def __init__(self, feature_extractor, tokenizer):
if not isinstance(feature_extractor, CLIPFeatureExtractor):
raise ValueError(
f"`feature_extractor` has to be of type CLIPFeatureExtractor, but is {type(feature_extractor)}"
)
if not isinstance(tokenizer, CLIPTokenizer):
raise ValueError(f"`tokenizer` has to be of type CLIPTokenizer, but is {type(tokenizer)}")
self.feature_extractor = feature_extractor
self.tokenizer = tokenizer
self.current_processor = self.feature_extractor
[docs] def save_pretrained(self, save_directory):
"""
Save a CLIP feature extractor object and CLIP tokenizer object to the directory `save_directory`, so that it
can be re-loaded using the [`~CLIPProcessor.from_pretrained`] class method.
<Tip>
This class method is simply calling [`~PreTrainedFeatureExtractor.save_pretrained`] and
[`~tokenization_utils_base.PreTrainedTokenizer.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).
"""
self.feature_extractor._set_processor_class(self.__class__.__name__)
self.feature_extractor.save_pretrained(save_directory)
self.tokenizer._set_processor_class(self.__class__.__name__)
self.tokenizer.save_pretrained(save_directory)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a [`CLIPProcessor`] from a pretrained CLIP processor.
<Tip>
This class method is simply calling CLIPFeatureExtractor's [`~PreTrainedFeatureExtractor.from_pretrained`] and
CLIPTokenizer's [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`]. Please refer to the
docstrings of the methods above for more information.
</Tip>
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
This can be either:
- a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like `clip-vit-base-patch32`, or
namespaced under a user or organization name, like `openai/clip-vit-base-patch32`.
- a path to a *directory* containing a feature extractor file saved using the
[`~PreTrainedFeatureExtractor.save_pretrained`] method, e.g., `./my_model_directory/`.
- a path or url to a saved feature extractor JSON *file*, e.g.,
`./my_model_directory/preprocessor_config.json`.
**kwargs
Additional keyword arguments passed along to both [`PreTrainedFeatureExtractor`] and
[`PreTrainedTokenizer`]
"""
feature_extractor = CLIPFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizer's [`~CLIPTokenizer.__call__`] if `text` is not `None` to encode the
text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
doctsring of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
if images is not None:
image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
[docs] def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
[docs] def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)