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# 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
#
# 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.
"""
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,
max_length: Optional[int] = None,
do_pad: Optional[bool] = True,
max_slice_nums: int = None,
use_image_id: bool = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> MiniCPMVBatchFeature:
if images is not None:
image_inputs = self.image_processor(images, do_pad=do_pad, max_slice_nums=max_slice_nums, return_tensors=return_tensors)
return self._convert_images_texts_to_inputs(image_inputs, text, max_slice_nums=max_slice_nums, use_image_id=use_image_id, 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 not getattr(self.tokenizer, "add_bos_token", False):
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)
start_cond = (input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id)
end_cond = (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
image_start_tokens = torch.where(start_cond)[0]
image_start_tokens += 1
image_end_tokens = torch.where(end_cond)[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, image_bounds
def _convert_images_texts_to_inputs(
self,
images,
texts: Union[str, List[str]],
truncation=None,
max_length=None,
max_slice_nums=None,
use_image_id=None,
return_tensors=None
):
if images is None or not len(images):
model_inputs = self.tokenizer(texts, return_tensors=return_tensors, 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"]
if isinstance(texts, str):
texts = [texts]
input_ids_list = []
image_bounds_list = []
for index, text in enumerate(texts):
image_tags = re.findall(pattern, text)
assert len(image_tags) == len(image_sizes[index])
text_chunks = text.split(pattern)
final_text = ""
for i in range(len(image_tags)):
final_text = final_text + text_chunks[i] + \
self.image_processor.get_slice_image_placeholder(
image_sizes[index][i],
i,
max_slice_nums,
use_image_id
)
final_text += text_chunks[-1]
input_ids, image_bounds = self._convert(final_text, max_length)
input_ids_list.append(input_ids)
image_bounds_list.append(image_bounds)
padded_input_ids, padding_lengths = self.pad(
input_ids_list,
padding_side="left"
)
for i, length in enumerate(padding_lengths):
image_bounds_list[i] = image_bounds_list[i] + length
attention_mask = padded_input_ids.ne(0)
return MiniCPMVBatchFeature(data={
"input_ids": padded_input_ids,
"attention_mask": attention_mask,
"pixel_values": images,
"image_sizes": image_sizes,
"image_bound": image_bounds_list,
"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, inputs, max_length=None, padding_value=0, padding_side="left"):
items = []
if isinstance(inputs[0], list):
assert isinstance(inputs[0][0], torch.Tensor)
for it in inputs:
for tr in it:
items.append(tr)
else:
assert isinstance(inputs[0], torch.Tensor)
items = inputs
batch_size = len(items)
shape = items[0].shape
dim = len(shape)
assert dim <= 2
if max_length is None:
max_length = 0
max_length = max(max_length, max(item.shape[-1] for item in items))
min_length = min(item.shape[-1] for item in items)
dtype = items[0].dtype
if dim == 0:
return torch.stack([item for item in items], dim=0), [0]
elif dim == 1:
if max_length == min_length:
return torch.stack([item for item in items], dim=0), [0] * batch_size
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
)
padding_length = []
for i, item in enumerate(items):
if dim == 1:
if padding_side == "left":
tensor[i, -len(item) :] = item.clone()
else:
tensor[i, : len(item)] = item.clone()
elif dim == 2:
if padding_side == "left":
tensor[i, -len(item) :, :] = item.clone()
else:
tensor[i, : len(item), :] = item.clone()
padding_length.append(tensor.shape[-1] - len(item))
return tensor, padding_length
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