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# coding=utf-8
# Copyright 2024 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# 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 Kosmos2_5.
"""
from typing import List, Optional, Union
from transformers.image_processing_utils import BatchFeature
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, TextInput, TruncationStrategy
from transformers.utils import TensorType, is_torch_available
if is_torch_available():
import torch
class Kosmos2_5Processor(ProcessorMixin):
r"""
Constructs a Kosmos2_5 processor which wraps a BERT tokenizer and Kosmos2_5 image processor into a single
processor.
[`Kosmos2_5Processor`] offers all the functionalities of [`Kosmos2_5ImageProcessor`] and [`T5TokenizerFast`]. See
the docstring of [`~Kosmos2_5Processor.__call__`] and [`~Kosmos2_5Processor.decode`] for more information.
Args:
image_processor (`Kosmos2_5ImageProcessor`):
An instance of [`Kosmos2_5ImageProcessor`]. The image processor is a required input.
tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]):
An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "Kosmos2_5ImageProcessor"
tokenizer_class = "PreTrainedTokenizerFast"
def __init__(self, image_processor, tokenizer):
tokenizer.return_token_type_ids = False
super().__init__(image_processor, tokenizer)
def __call__(
self,
images=None,
text: Union[TextInput, List[TextInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = True,
truncation: Union[bool, str, TruncationStrategy] = True,
max_length: Optional[int] = None,
max_patches: Optional[int] = 4096,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = "pt",
**kwargs,
) -> BatchFeature:
"""
This method uses [`Kosmos2_5ImageProcessor.preprocess`] method to prepare image(s) for the model, and
[`PreTrainedTokenizerFast.__call__`] to prepare text for the model.
Please refer to the docstring of the above two methods for more information.
The rest of this documentation shows the arguments specific to `Kosmos2_5Processor`.
"""
if images is None and text is None:
raise ValueError("You have to specify either images or text.")
encoding = BatchFeature()
if images is not None:
image_encoding = self.image_processor(
images, return_tensors=return_tensors, max_patches=max_patches, **kwargs
)
image_encoding.pop("rows")
image_encoding.pop("cols")
encoding.update(image_encoding)
if text is not None:
# use updates or pop
input = self.tokenizer(
text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
return_tensors="pt",
)
batch_size, seq_len = input.input_ids.shape
additional_tokens = [0, 100283] + [0] * 2048 + [100284]
additional_tokens_tensor = torch.tensor(additional_tokens).unsqueeze(0).repeat(batch_size, 1)
input_ids = torch.cat([additional_tokens_tensor, input.input_ids], dim=1)
image_embeds_position_mask = [0, -1] + [1] * 2048 + [-1] + [0] * seq_len
image_embeds_position_mask = (
torch.LongTensor(image_embeds_position_mask).unsqueeze(0).repeat(batch_size, 1)
)
added_attention_mask = [1, 1] + [1] * 2048 + [1]
added_attention_mask_tensor = torch.tensor(added_attention_mask).unsqueeze(0).repeat(batch_size, 1)
attention_mask = torch.cat([added_attention_mask_tensor, input.attention_mask], dim=1)
encoding.update(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"image_embeds_position_mask": image_embeds_position_mask,
}
)
return encoding
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Kosmos2_5TokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Kosmos2_5TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
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)) |