# coding=utf-8
# Copyright 2024 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Processor class for MiniCPMV.
"""
from typing import List, Optional, Union, Dict, Any
import torch
import re
from transformers.image_processing_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
from .image_processing_minicpmv import MiniCPMVBatchFeature
class MiniCPMVProcessor(ProcessorMixin):
r"""
Constructs a MiniCPMV processor which wraps a MiniCPMV image processor and a MiniCPMV tokenizer into a single processor.
[`MiniCPMVProcessor`] offers all the functionalities of [`MiniCPMVImageProcessor`] and [`LlamaTokenizerWrapper`]. See the
[`~MiniCPMVProcessor.__call__`] and [`~MiniCPMVProcessor.decode`] for more information.
Args:
image_processor ([`MiniCPMVImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerWrapper`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self, image_processor=None, tokenizer=None):
super().__init__(image_processor, tokenizer)
self.version = image_processor.version
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
images: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
do_pad: Optional[bool] = True,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> MiniCPMVBatchFeature:
"""
Only support for single input for now. Batched input is coming soon.
Args:
text (`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. Both channels-first and channels-last formats are supported.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
do_pad (`bool`, *optional*, defaults to self.do_pad):
Whether to pad the image. If `True` will pad the images in the batch to the largest image in the batch
and create a pixel mask. Padding will be applied to the bottom and right of the image with zeros.
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
return_tensors (`str` or [`~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:
[`BatchFeature`]: A [`BatchFeature`] 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 images is not None:
image_inputs = self.image_processor(images, do_pad=do_pad, return_tensors=return_tensors)
return self._convert_images_texts_to_inputs(image_inputs, text, max_length=max_length)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
output_ids = args[0]
result_text = []
for result in output_ids:
result = result[result != 0]
if result[0] == self.tokenizer.bos_id:
result = result[1:]
if result[-1] == self.tokenizer.eos_id:
result = result[:-1]
result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
return result_text
# return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
result = args[0]
result = result[result != 0]
if result[0] == self.tokenizer.bos_id:
result = result[1:]
if result[-1] == self.tokenizer.eos_id or (hasattr(self.tokenizer, "eot_id") and result[-1] == self.tokenizer.eot_id):
result = result[:-1]
return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
def _convert(
self, input_str, max_inp_length: Optional[int] = None
):
if self.version == 2.5 or self.tokenizer.add_bos_token:
input_ids = self.tokenizer.encode(input_str)
else:
input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
if max_inp_length is not None:
input_ids = input_ids[:max_inp_length]
input_ids = torch.tensor(input_ids, dtype=torch.int32)
image_start_tokens = torch.where(input_ids == self.tokenizer.im_start_id)[0]
image_start_tokens += 1
image_end_tokens = torch.where(input_ids == self.tokenizer.im_end_id)[0]
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
image_bounds = torch.hstack(
[
image_start_tokens[:valid_image_nums].unsqueeze(-1),
image_end_tokens[:valid_image_nums].unsqueeze(-1),
]
)
return input_ids.unsqueeze(0), image_bounds
def _convert_images_texts_to_inputs(self, images, texts, do_pad=False, truncation=None, max_length=None, return_tensors=None):
if not len(images):
model_inputs = self.tokenizer(texts, return_tensors=return_tensors, padding=do_pad, truncation=truncation, max_length=max_length)
return MiniCPMVBatchFeature(data={**model_inputs})
pattern = "(<image>./</image>)"
images, image_sizes, tgt_sizes = images["pixel_values"], images["image_sizes"], images["tgt_sizes"]
image_tags = re.findall(pattern, texts)
assert len(image_tags) == len(image_sizes[0])
text_chunks = texts.split(pattern)
final_texts = ""
for i in range(len(image_tags)):
final_texts = final_texts + text_chunks[i] + self.image_processor.get_slice_image_placeholder(image_sizes[0][i])
final_texts += text_chunks[-1]
input_ids, image_bounds = self._convert(final_texts, max_length)
return MiniCPMVBatchFeature(data={
"input_ids": input_ids,
"pixel_values": images,
"image_sizes": image_sizes,
"image_bound": [image_bounds],
"tgt_sizes": tgt_sizes
})
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def pad(self, orig_items, key, max_length=None, padding_value=0, padding_side="left"):
items = []
if isinstance(orig_items[0][key], list):
assert isinstance(orig_items[0][key][0], torch.Tensor)
for it in orig_items:
for tr in it[key]:
items.append({key: tr})
else:
assert isinstance(orig_items[0][key], torch.Tensor)
items = orig_items
batch_size = len(items)
shape = items[0][key].shape
dim = len(shape)
assert dim <= 3
if max_length is None:
max_length = 0
max_length = max(max_length, max(item[key].shape[-1] for item in items))
min_length = min(item[key].shape[-1] for item in items)
dtype = items[0][key].dtype
if dim == 1:
return torch.cat([item[key] for item in items], dim=0)
elif dim == 2:
if max_length == min_length:
return torch.cat([item[key] for item in items], dim=0)
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
else:
tensor = (
torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
+ padding_value
)
for i, item in enumerate(items):
if dim == 2:
if padding_side == "left":
tensor[i, -len(item[key][0]) :] = item[key][0].clone()
else:
tensor[i, : len(item[key][0])] = item[key][0].clone()
elif dim == 3:
if padding_side == "left":
tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
else:
tensor[i, : len(item[key][0]), :] = item[key][0].clone()
return tensor