Delete files *cpmv.py with huggingface_hub

image_processing_minicpmv.py

@@ -1,418 +0,0 @@
-from typing import Optional, Union, Dict, Any, List
-
-import torch
-import math
-import PIL.Image
-import PIL.ImageSequence
-import numpy as np
-import PIL
-from PIL import Image
-
-from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
-from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
-from transformers import AutoImageProcessor
-from transformers.image_transforms import to_channel_dimension_format
-from transformers.image_utils import (
-    ImageInput, 
-    make_list_of_images, 
-    valid_images, 
-    is_torch_tensor, 
-    is_batched,
-    to_numpy_array, 
-    infer_channel_dimension_format,
-    ChannelDimension
-)
-
-
-def recursive_converter(converter, value):
-    if isinstance(value, list):
-        new_value = []
-        for v in value:
-            new_value += [recursive_converter(converter, v)]
-        return new_value
-    else:
-        return converter(value)
-
-
-class MiniCPMVBatchFeature(BatchFeature):
-    r"""
-    Extend from BatchFeature for supporting various image size
-    """
-    def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
-        super().__init__(data)
-        self.convert_to_tensors(tensor_type=tensor_type)
-
-    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
-        if tensor_type is None:
-            return self
-        
-        is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type)
-
-        def converter(value):
-            try:
-                if not is_tensor(value):
-                    tensor = as_tensor(value)
-                    return tensor
-            except:  # noqa E722
-                if key == "overflowing_values":
-                    raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
-                raise ValueError(
-                    "Unable to create tensor, you should probably activate padding "
-                    "with 'padding=True' to have batched tensors with the same length."
-                )
-
-
-        for key, value in self.items():
-            self[key] = recursive_converter(converter, value)
-        return self
-            
-    def to(self, *args, **kwargs) -> "MiniCPMVBatchFeature":
-        requires_backends(self, ["torch"])
-        import torch
-
-        def cast_tensor(v):
-            # check if v is a floating point
-            if torch.is_floating_point(v):
-                # cast and send to device
-                return v.to(*args, **kwargs)
-            elif device is not None:
-                return v.to(device=device)
-            else:
-                return v
-
-        new_data = {}
-        device = kwargs.get("device")
-        # Check if the args are a device or a dtype
-        if device is None and len(args) > 0:
-            # device should be always the first argument
-            arg = args[0]
-            if is_torch_dtype(arg):
-                # The first argument is a dtype
-                pass
-            elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
-                device = arg
-            else:
-                # it's something else
-                raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
-        # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
-        for k, v in self.items():
-            new_data[k] = recursive_converter(cast_tensor, v)
-        self.data = new_data
-        return self
-
-
-class MiniCPMVImageProcessor(BaseImageProcessor):
-    model_input_names = ["pixel_values"]
-
-    def __init__(
-            self, 
-            max_slice_nums=9,
-            scale_resolution=448,
-            patch_size=14,
-            **kwargs):
-        super().__init__(**kwargs)
-        self.max_slice_nums = max_slice_nums
-        self.scale_resolution = scale_resolution
-        self.patch_size = patch_size
-        self.use_image_id = kwargs.pop("use_image_id", False)
-        self.image_feature_size = kwargs.pop("image_feature_size", 64)
-        self.im_start_token = kwargs.pop("im_start", "<image>")
-        self.im_end_token = kwargs.pop("im_end", "</image>")
-        self.slice_start_token = kwargs.pop("slice_start", "<slice>")
-        self.slice_end_token = kwargs.pop("slice_end", "</slice>")
-        self.unk_token = kwargs.pop("unk", "<unk>")
-        self.im_id_start = kwargs.pop("im_id_start", "<image_id>")
-        self.im_id_end = kwargs.pop("im_id_end", "</image_id>")
-        self.slice_mode = kwargs.pop("slice_mode", True)
-        self.mean = np.array(kwargs.pop("norm_mean", [0.5, 0.5, 0.5]))
-        self.std = np.array(kwargs.pop("norm_std", [0.5, 0.5, 0.5]))
-        self.version = kwargs.pop("version", 2.0)
-
-    def ensure_divide(self, length, patch_size):
-        return max(round(length / patch_size) * patch_size, patch_size)
-
-    def find_best_resize(self,
-                         original_size,
-                         scale_resolution,
-                         patch_size,
-                         allow_upscale=False):
-        width, height = original_size
-        if (width * height >
-                scale_resolution * scale_resolution) or allow_upscale:
-            r = width / height
-            height = int(scale_resolution / math.sqrt(r))
-            width = int(height * r)
-        best_width = self.ensure_divide(width, patch_size)
-        best_height = self.ensure_divide(height, patch_size)
-        return (best_width, best_height)
-
-    def get_refine_size(self,
-                        original_size,
-                        grid,
-                        scale_resolution,
-                        patch_size,
-                        allow_upscale=False):
-        width, height = original_size
-        grid_x, grid_y = grid
-
-        refine_width = self.ensure_divide(width, grid_x)
-        refine_height = self.ensure_divide(height, grid_y)
-
-        grid_width = refine_width / grid_x
-        grid_height = refine_height / grid_y
-
-        best_grid_size = self.find_best_resize((grid_width, grid_height),
-                                               scale_resolution,
-                                               patch_size,
-                                               allow_upscale=allow_upscale)
-        refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
-        return refine_size
-
-    def split_to_patches(self, image, grid):
-        patches = []
-        width, height = image.size
-        grid_x = int(width / grid[0])
-        grid_y = int(height / grid[1])
-        for i in range(0, height, grid_y):
-            images = []
-            for j in range(0, width, grid_x):
-                box = (j, i, j + grid_x, i + grid_y)
-                patch = image.crop(box)
-                images.append(patch)
-            patches.append(images)
-        return patches
-
-    def slice_image(
-        self, image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
-    ):
-        original_size = image.size
-        source_image = None
-        best_grid = self.get_sliced_grid(original_size, max_slice_nums, never_split)
-        patches = []
-
-        if best_grid is None:
-            # dont need to slice, upsample
-            best_size = self.find_best_resize(
-                original_size, scale_resolution, patch_size, allow_upscale=True
-            )
-            source_image = image.resize(best_size, resample=Image.Resampling.BICUBIC)
-        else:
-            # source image, down-sampling and ensure divided by patch_size
-            best_resize = self.find_best_resize(original_size, scale_resolution, patch_size)
-            source_image = image.copy().resize(best_resize, resample=Image.Resampling.BICUBIC)
-            refine_size = self.get_refine_size(
-                original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
-            )
-            refine_image = image.resize(refine_size, resample=Image.Resampling.BICUBIC)
-            patches = self.split_to_patches(refine_image, best_grid)
-
-        return source_image, patches, best_grid
-
-    def get_grid_placeholder(self, grid):
-        if grid is None:
-            return ""
-        slice_image_placeholder = (
-            self.slice_start_token 
-            + self.unk_token * self.image_feature_size
-            + self.slice_end_token
-        )
-
-        cols = grid[0]
-        rows = grid[1]
-        slices = []
-        for i in range(rows):
-            lines = []
-            for j in range(cols):
-                lines.append(slice_image_placeholder)
-            slices.append("".join(lines))
-            
-        slice_placeholder = "\n".join(slices)
-        return slice_placeholder
-
-    def get_image_id_placeholder(self, idx=0):
-        return f"{self.im_id_start}{idx}{self.im_id_end}"
-    
-    def get_sliced_images(self, image, max_slice_nums=None):
-        slice_images = []
-
-        if not self.slice_mode:
-            return [image]
-
-        max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
-        assert max_slice_nums > 0 
-        source_image, patches, sliced_grid = self.slice_image(
-            image,
-            max_slice_nums,  # default: 9
-            self.scale_resolution,  # default: 448
-            self.patch_size  # default: 14
-        )
-
-        slice_images.append(source_image)
-        if len(patches) > 0:
-            for i in range(len(patches)):
-                for j in range(len(patches[0])):
-                    slice_images.append(patches[i][j])
-        return slice_images
-
-    def get_sliced_grid(self, image_size, max_slice_nums, nerver_split=False):
-        original_width, original_height = image_size
-        log_ratio = math.log(original_width / original_height)
-        ratio = original_width * original_height / (self.scale_resolution * self.scale_resolution)
-        multiple = min(math.ceil(ratio), max_slice_nums)
-        if multiple <= 1 or nerver_split:
-            return None
-        candidate_split_grids_nums = []
-        for i in [multiple - 1, multiple, multiple + 1]:
-            if i == 1 or i > max_slice_nums:
-                continue
-            candidate_split_grids_nums.append(i)
-        
-        candidate_grids = []
-        for split_grids_nums in candidate_split_grids_nums:
-            m = 1
-            while m <= split_grids_nums:
-                if split_grids_nums % m == 0:
-                    candidate_grids.append([m, split_grids_nums // m])
-                m += 1
-
-        best_grid = [1, 1]
-        min_error = float("inf")
-        for grid in candidate_grids:
-            error = abs(log_ratio - math.log(grid[0] / grid[1]))
-            if error < min_error:
-                best_grid = grid
-                min_error = error
-        
-        return best_grid
-    
-    def get_slice_image_placeholder(self, image_size, image_idx=0, max_slice_nums=None, use_image_id=None):
-        max_slice_nums = self.max_slice_nums if max_slice_nums is None else int(max_slice_nums)
-        assert max_slice_nums > 0        
-        grid = self.get_sliced_grid(image_size=image_size, max_slice_nums=max_slice_nums)
-
-        image_placeholder = (
-            self.im_start_token 
-            + self.unk_token * self.image_feature_size 
-            + self.im_end_token
-        )
-        use_image_id = self.use_image_id if use_image_id is None else bool(use_image_id)
-        if use_image_id:
-            final_placeholder = self.get_image_id_placeholder(image_idx) + image_placeholder
-        else:
-            final_placeholder = image_placeholder
-
-        if self.slice_mode:
-            final_placeholder = final_placeholder + self.get_grid_placeholder(grid=grid)
-        return final_placeholder
-        
-    def to_pil_image(self, image, rescale=None) -> PIL.Image.Image:
-        """
-        Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
-        needed.
-
-        Args:
-            image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`):
-                The image to convert to the PIL Image format.
-            rescale (`bool`, *optional*):
-                Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
-                default to `True` if the image type is a floating type, `False` otherwise.
-        """
-        if isinstance(image, PIL.Image.Image):
-            return image
-        if is_torch_tensor(image):
-            image = image.numpy()
-
-        if isinstance(image, np.ndarray):
-            if rescale is None:
-                # rescale default to the array being of floating type.
-                rescale = isinstance(image.flat[0], np.floating)
-            # If the channel as been moved to first dim, we put it back at the end.
-            if image.ndim == 3 and image.shape[0] in [1, 3]:
-                image = image.transpose(1, 2, 0)
-            if rescale:
-                image = image * 255
-            image = image.astype(np.uint8)
-            return PIL.Image.fromarray(image)
-        return image
-
-    def reshape_by_patch(self, image):
-        """
-        :param image: shape [3, H, W]
-        :param patch_size:
-        :return: [3, patch_size, HW/patch_size]
-        """
-        image = torch.from_numpy(image)
-        patch_size = self.patch_size
-        patches = torch.nn.functional.unfold(
-            image,
-            (patch_size, patch_size),
-            stride=(patch_size, patch_size)
-        )
-
-        patches = patches.reshape(image.size(0), patch_size, patch_size, -1)
-        patches = patches.permute(0, 1, 3, 2).reshape(image.size(0), patch_size, -1)
-        return patches.numpy()
-
-    def preprocess(
-            self, 
-            images: Union[Image.Image, List[Image.Image], List[List[Image.Image]]],
-            do_pad: Optional[bool] = True, # TODO: add pad for MiniCPM-Llama3-V-2_5
-            max_slice_nums: int = None,
-            return_tensors: Optional[Union[str, TensorType]] = None,
-            **kwargs
-        ) -> MiniCPMVBatchFeature:
-        if isinstance(images, Image.Image):
-            images_list = [[images]]
-        elif isinstance(images[0], Image.Image):
-            images_list = [images]
-        else:
-            images_list = images
-
-        new_images_list = []
-        image_sizes_list = []
-        tgt_sizes_list = []
-        
-        for _images in images_list:
-            if _images is None or len(_images) == 0:
-                new_images_list.append([])
-                image_sizes_list.append([])
-                tgt_sizes_list.append([])
-                continue             
-            if not valid_images(_images):
-                raise ValueError(
-                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                    "torch.Tensor, tf.Tensor or jax.ndarray."
-                )
-            
-            _images = [self.to_pil_image(image).convert("RGB") for image in _images]
-            input_data_format = infer_channel_dimension_format(np.array(_images[0]))
-
-            new_images = []
-            image_sizes = [image.size for image in _images]
-            tgt_sizes = []
-            for image in _images:
-                image_patches = self.get_sliced_images(image, max_slice_nums)
-                image_patches = [to_numpy_array(image).astype(np.float32) / 255 for image in image_patches]
-                image_patches = [
-                    self.normalize(image=image, mean=self.mean, std=self.std, input_data_format=input_data_format)
-                        for image in image_patches
-                ]
-                image_patches = [
-                    to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=input_data_format) 
-                        for image in image_patches
-                ]
-                for slice_image in image_patches:
-                    new_images.append(self.reshape_by_patch(slice_image))
-                    tgt_sizes.append(np.array((slice_image.shape[1] // self.patch_size, slice_image.shape[2] // self.patch_size)))
-
-            if tgt_sizes:
-                tgt_sizes = np.vstack(tgt_sizes)
-            
-            new_images_list.append(new_images)
-            image_sizes_list.append(image_sizes)
-            tgt_sizes_list.append(tgt_sizes)
-        return MiniCPMVBatchFeature(
-            data={"pixel_values": new_images_list, "image_sizes": image_sizes_list, "tgt_sizes": tgt_sizes_list}, tensor_type=return_tensors
-        )
-
-AutoImageProcessor.register("MiniCPMVImageProcessor", MiniCPMVImageProcessor)

processing_minicpmv.py

@@ -1,238 +0,0 @@
-# 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, **kwargs):
-        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,
-        **kwargs
-    ) -> 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, **kwargs)
-    
-    # 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
-    ):
-        input_ids = 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,
-            **kwargs
-        ):
-        if images is None or not len(images):
-            model_inputs = self.tokenizer(texts, return_tensors=return_tensors, truncation=truncation, max_length=max_length, **kwargs)
-            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"
-        )
-        attention_mask = torch.ones_like(padded_input_ids, dtype=torch.bool)
-        for i, length in enumerate(padding_lengths):
-            image_bounds_list[i] = image_bounds_list[i] + length
-            attention_mask[i, :length] = False
-
-        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