Delete processing_kimi_vl.py

processing_kimi_vl.py

@@ -1,170 +0,0 @@
-# coding=utf-8
-# Copyright 2025 The Moonshot Team and HuggingFace Inc. team. All rights reserved.
-#
-# The code is based on the Qwen2VL processor (qwen2_vl/processing_qwen2_vl.py), but modified for KimiVL.
-#
-# 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 KimiVL.
-"""
-
-from typing import List, Union
-
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_utils import ImageInput
-from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class KimiVLProcessorKwargs(ProcessingKwargs, total=False):
-    _defaults = {
-        "text_kwargs": {
-            "padding": False,
-        },
-        "images_kwargs": {},
-    }
-
-
-class KimiVLProcessor(ProcessorMixin):
-    r"""
-    Constructs a KimiVL processor which wraps a KimiVL image processor and a tokenizer into a single processor.
-
-    [`KimiVLProcessor`] offers all the functionalities of [`KimiVLImageProcessor`] and [`TikTokenTokenizer`]. See the
-    [`~KimiVLProcessor.__call__`] and [`~KimiVLProcessor.decode`] for more information.
-
-    Args:
-        image_processor ([`KimiVLImageProcessor`], *optional*):
-            The image processor is a required input.
-        tokenizer ([`TikTokenTokenizer`], *optional*):
-            The tokenizer is a required input.
-        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
-            in a chat into a tokenizable string.
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    valid_kwargs = [ "chat_template"]
-    image_processor_class = "AutoImageProcessor"
-    tokenizer_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        image_processor=None,
-        tokenizer=None,
-        chat_template=None,
-        **kwargs,
-    ):
-        self.image_token = "<|media_pad|>"
-        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
-        **kwargs: Unpack[KimiVLProcessorKwargs],
-    ) -> BatchFeature:
-        """
-        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
-        and `kwargs` arguments to TikTokenTokenizer's [`~TikTokenTokenizer.__call__`] if `text` is not `None` to encode
-        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
-        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
-        of the above two methods for more information.
-
-        Args:
-            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.
-            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).
-            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 None and text is None:
-            raise ValueError("You have to specify at least one of `images` or `text`.")
-
-        # check if images and text inputs are reversed for BC
-        images, text = _validate_images_text_input_order(images, text)
-
-        output_kwargs = self._merge_kwargs(
-            KimiVLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-            image_grid_hws = image_inputs["image_grid_hws"]
-        else:
-            image_inputs = {}
-            image_grid_hws = None
-
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-
-        if image_grid_hws is not None:
-            merge_length = self.image_processor.merge_kernel_size[0] * self.image_processor.merge_kernel_size[1]
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    text[i] = text[i].replace(
-                        self.image_token,
-                        "<|placeholder|>" * (image_grid_hws[index].prod() // merge_length),
-                        1,
-                    )
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        return BatchFeature(data={**text_inputs, **image_inputs})
-
-    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.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    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.
-        """
-        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))
-
-
-__all__ = ["KimiVLProcessorKwargs"]