diff --git a/.gitattributes b/.gitattributes
index df75ca26a0b787df37b6c87251adef008a2f29e1..f0c613eb78839418ba0bdc9ba5eaaaeb4d9abeda 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1140,3 +1140,6 @@ mgm/lib/python3.10/__pycache__/_pydecimal.cpython-310.pyc filter=lfs diff=lfs me
mgm/lib/python3.10/lib-dynload/readline.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
wemm/lib/python3.10/site-packages/torch/lib/libcudnn_adv_infer.so.8 filter=lfs diff=lfs merge=lfs -text
vlmpy310/lib/python3.10/site-packages/decord/libdecord.so filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/pillow.libs/libxcb-b8a56d01.so.1.1.0 filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/pillow.libs/libfreetype-be14bf51.so.6.20.1 filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/pillow.libs/libbrotlicommon-3ecfe81c.so.1 filter=lfs diff=lfs merge=lfs -text
diff --git a/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/INSTALLER b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/LICENSE b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/METADATA b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..98cb32fe27e6f88e0bbae29f75cae71c50b76439
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/accelerate-0.21.0.dist-info/METADATA
@@ -0,0 +1,359 @@
+Metadata-Version: 2.1
+Name: accelerate
+Version: 0.21.0
+Summary: Accelerate
+Home-page: https://github.com/huggingface/accelerate
+Author: The HuggingFace team
+Author-email: sylvain@huggingface.co
+License: Apache
+Keywords: deep learning
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.8.0
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy (>=1.17)
+Requires-Dist: packaging (>=20.0)
+Requires-Dist: psutil
+Requires-Dist: pyyaml
+Requires-Dist: torch (>=1.10.0)
+Provides-Extra: dev
+Requires-Dist: black (~=23.1) ; extra == 'dev'
+Requires-Dist: ruff (>=0.0.241) ; extra == 'dev'
+Requires-Dist: hf-doc-builder (>=0.3.0) ; extra == 'dev'
+Requires-Dist: urllib3 (<2.0.0) ; extra == 'dev'
+Requires-Dist: pytest ; extra == 'dev'
+Requires-Dist: pytest-xdist ; extra == 'dev'
+Requires-Dist: pytest-subtests ; extra == 'dev'
+Requires-Dist: parameterized ; extra == 'dev'
+Requires-Dist: datasets ; extra == 'dev'
+Requires-Dist: evaluate ; extra == 'dev'
+Requires-Dist: transformers ; extra == 'dev'
+Requires-Dist: scipy ; extra == 'dev'
+Requires-Dist: scikit-learn ; extra == 'dev'
+Requires-Dist: deepspeed ; extra == 'dev'
+Requires-Dist: tqdm ; extra == 'dev'
+Requires-Dist: rich ; extra == 'dev'
+Provides-Extra: docs
+Provides-Extra: quality
+Requires-Dist: black (~=23.1) ; extra == 'quality'
+Requires-Dist: ruff (>=0.0.241) ; extra == 'quality'
+Requires-Dist: hf-doc-builder (>=0.3.0) ; extra == 'quality'
+Requires-Dist: urllib3 (<2.0.0) ; extra == 'quality'
+Provides-Extra: rich
+Requires-Dist: rich ; extra == 'rich'
+Provides-Extra: sagemaker
+Requires-Dist: sagemaker ; extra == 'sagemaker'
+Provides-Extra: test_dev
+Requires-Dist: datasets ; extra == 'test_dev'
+Requires-Dist: evaluate ; extra == 'test_dev'
+Requires-Dist: transformers ; extra == 'test_dev'
+Requires-Dist: scipy ; extra == 'test_dev'
+Requires-Dist: scikit-learn ; extra == 'test_dev'
+Requires-Dist: deepspeed ; extra == 'test_dev'
+Requires-Dist: tqdm ; extra == 'test_dev'
+Provides-Extra: test_prod
+Requires-Dist: pytest ; extra == 'test_prod'
+Requires-Dist: pytest-xdist ; extra == 'test_prod'
+Requires-Dist: pytest-subtests ; extra == 'test_prod'
+Requires-Dist: parameterized ; extra == 'test_prod'
+Provides-Extra: test_trackers
+Requires-Dist: wandb ; extra == 'test_trackers'
+Requires-Dist: comet-ml ; extra == 'test_trackers'
+Requires-Dist: tensorboard ; extra == 'test_trackers'
+Provides-Extra: testing
+Requires-Dist: pytest ; extra == 'testing'
+Requires-Dist: pytest-xdist ; extra == 'testing'
+Requires-Dist: pytest-subtests ; extra == 'testing'
+Requires-Dist: parameterized ; extra == 'testing'
+Requires-Dist: datasets ; extra == 'testing'
+Requires-Dist: evaluate ; extra == 'testing'
+Requires-Dist: transformers ; extra == 'testing'
+Requires-Dist: scipy ; extra == 'testing'
+Requires-Dist: scikit-learn ; extra == 'testing'
+Requires-Dist: deepspeed ; extra == 'testing'
+Requires-Dist: tqdm ; extra == 'testing'
+
+
+
+
+
+ 
+
+
+
+
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+ 
+
+
+
+
+
Run your *raw* PyTorch training script on any kind of device
+
+
+
+ 
+
+
+## Easy to integrate
+
+🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16.
+
+🤗 Accelerate abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.
+
+Here is an example:
+
+```diff
+ import torch
+ import torch.nn.functional as F
+ from datasets import load_dataset
++ from accelerate import Accelerator
+
++ accelerator = Accelerator()
+- device = 'cpu'
++ device = accelerator.device
+
+ model = torch.nn.Transformer().to(device)
+ optimizer = torch.optim.Adam(model.parameters())
+
+ dataset = load_dataset('my_dataset')
+ data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+ model.train()
+ for epoch in range(10):
+ for source, targets in data:
+ source = source.to(device)
+ targets = targets.to(device)
+
+ optimizer.zero_grad()
+
+ output = model(source)
+ loss = F.cross_entropy(output, targets)
+
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+```
+
+As you can see in this example, by adding 5-lines to any standard PyTorch training script you can now run on any kind of single or distributed node setting (single CPU, single GPU, multi-GPUs and TPUs) as well as with or without mixed precision (fp8, fp16, bf16).
+
+In particular, the same code can then be run without modification on your local machine for debugging or your training environment.
+
+🤗 Accelerate even handles the device placement for you (which requires a few more changes to your code, but is safer in general), so you can even simplify your training loop further:
+
+```diff
+ import torch
+ import torch.nn.functional as F
+ from datasets import load_dataset
++ from accelerate import Accelerator
+
+- device = 'cpu'
++ accelerator = Accelerator()
+
+- model = torch.nn.Transformer().to(device)
++ model = torch.nn.Transformer()
+ optimizer = torch.optim.Adam(model.parameters())
+
+ dataset = load_dataset('my_dataset')
+ data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+ model.train()
+ for epoch in range(10):
+ for source, targets in data:
+- source = source.to(device)
+- targets = targets.to(device)
+
+ optimizer.zero_grad()
+
+ output = model(source)
+ loss = F.cross_entropy(output, targets)
+
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+```
+
+Want to learn more? Check out the [documentation](https://huggingface.co/docs/accelerate) or have a look at our [examples](https://github.com/huggingface/accelerate/tree/main/examples).
+
+## Launching script
+
+🤗 Accelerate also provides an optional CLI tool that allows you to quickly configure and test your training environment before launching the scripts. No need to remember how to use `torch.distributed.run` or to write a specific launcher for TPU training!
+On your machine(s) just run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. This will generate a config file that will be used automatically to properly set the default options when doing
+
+```bash
+accelerate launch my_script.py --args_to_my_script
+```
+
+For instance, here is how you would run the GLUE example on the MRPC task (from the root of the repo):
+
+```bash
+accelerate launch examples/nlp_example.py
+```
+
+This CLI tool is **optional**, and you can still use `python my_script.py` or `python -m torchrun my_script.py` at your convenience.
+
+You can also directly pass in the arguments you would to `torchrun` as arguments to `accelerate launch` if you wish to not run` accelerate config`.
+
+For example, here is how to launch on two GPUs:
+
+```bash
+accelerate launch --multi_gpu --num_processes 2 examples/nlp_example.py
+```
+
+To learn more, check the CLI documentation available [here](https://huggingface.co/docs/accelerate/package_reference/cli).
+
+## Launching multi-CPU run using MPI
+
+🤗 Here is another way to launch multi-CPU run using MPI. You can learn how to install Open MPI on [this page](https://www.open-mpi.org/faq/?category=building#easy-build). You can use Intel MPI or MVAPICH as well.
+Once you have MPI setup on your cluster, just run:
+
+```bash
+mpirun -np 2 python examples/nlp_example.py
+```
+
+## Launching training using DeepSpeed
+
+🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. To use it, you don't need to change anything in your training code; you can set everything using just `accelerate config`. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide you the `DeepSpeedPlugin`.
+
+```python
+from accelerate import Accelerator, DeepSpeedPlugin
+
+# deepspeed needs to know your gradient accumulation steps beforehand, so don't forget to pass it
+# Remember you still need to do gradient accumulation by yourself, just like you would have done without deepspeed
+deepspeed_plugin = DeepSpeedPlugin(zero_stage=2, gradient_accumulation_steps=2)
+accelerator = Accelerator(mixed_precision='fp16', deepspeed_plugin=deepspeed_plugin)
+
+# How to save your 🤗 Transformer?
+accelerator.wait_for_everyone()
+unwrapped_model = accelerator.unwrap_model(model)
+unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
+```
+
+Note: DeepSpeed support is experimental for now. In case you get into some problem, please open an issue.
+
+## Launching your training from a notebook
+
+🤗 Accelerate also provides a `notebook_launcher` function you can use in a notebook to launch a distributed training. This is especially useful for Colab or Kaggle notebooks with a TPU backend. Just define your training loop in a `training_function` then in your last cell, add:
+
+```python
+from accelerate import notebook_launcher
+
+notebook_launcher(training_function)
+```
+
+An example can be found in [this notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb). [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb)
+
+## Why should I use 🤗 Accelerate?
+
+You should use 🤗 Accelerate when you want to easily run your training scripts in a distributed environment without having to renounce full control over your training loop. This is not a high-level framework above PyTorch, just a thin wrapper so you don't have to learn a new library. In fact, the whole API of 🤗 Accelerate is in one class, the `Accelerator` object.
+
+## Why shouldn't I use 🤗 Accelerate?
+
+You shouldn't use 🤗 Accelerate if you don't want to write a training loop yourself. There are plenty of high-level libraries above PyTorch that will offer you that, 🤗 Accelerate is not one of them.
+
+## Frameworks using 🤗 Accelerate
+
+If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around its capabilities, some frameworks and libraries that are built on top of 🤗 Accelerate are listed below:
+
+* [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76).
+* [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model training, and inference logic.
+* [fastai](https://github.com/fastai/fastai#installing) is a PyTorch framework for Deep Learning that simplifies training fast and accurate neural nets using modern best practices. fastai provides a [Learner](https://docs.fast.ai/learner.html#Learner) to handle the training, fine-tuning, and inference of deep learning algorithms.
+* [Finetuner](https://github.com/jina-ai/finetuner) is a service that enables models to create higher-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses.
+* [InvokeAI](https://github.com/invoke-ai/InvokeAI) is a creative engine for Stable Diffusion models, offering industry-leading WebUI, terminal usage support, and serves as the foundation for many commercial products.
+* [Kornia](https://kornia.readthedocs.io/en/latest/get-started/introduction.html) is a differentiable library that allows classical computer vision to be integrated into deep learning models. Kornia provides a [Trainer](https://kornia.readthedocs.io/en/latest/x.html#kornia.x.Trainer) with the specific purpose to train and fine-tune the supported deep learning algorithms within the library.
+* [Open Assistant](https://projects.laion.ai/Open-Assistant/) is a chat-based assistant that understands tasks, can interact with their party systems, and retrieve information dynamically to do so.
+* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centered around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves!
+* [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is an open-source browser-based easy-to-use interface based on the Gradio library for Stable Diffusion.
+* [torchkeras](https://github.com/lyhue1991/torchkeras) is a simple tool for training pytorch model just in a keras style, a dynamic and beautiful plot is provided in notebook to monitor your loss or metric.
+* [transformers](https://github.com/huggingface/transformers) as a tool for helping train state-of-the-art machine learning models in PyTorch, Tensorflow, and JAX. (Accelerate is the backend for the PyTorch side).
+
+
+## Installation
+
+This repository is tested on Python 3.8+ and PyTorch 1.10.0+
+
+You should install 🤗 Accelerate in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+First, create a virtual environment with the version of Python you're going to use and activate it.
+
+Then, you will need to install PyTorch: refer to the [official installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Then 🤗 Accelerate can be installed using pip as follows:
+
+```bash
+pip install accelerate
+```
+
+## Supported integrations
+
+- CPU only
+- multi-CPU on one node (machine)
+- multi-CPU on several nodes (machines)
+- single GPU
+- multi-GPU on one node (machine)
+- multi-GPU on several nodes (machines)
+- TPU
+- FP16/BFloat16 mixed precision
+- FP8 mixed precision with [Transformer Engine](https://github.com/NVIDIA/TransformerEngine)
+- DeepSpeed support (Experimental)
+- PyTorch Fully Sharded Data Parallel (FSDP) support (Experimental)
+- Megatron-LM support (Experimental)
+
+## Citing 🤗 Accelerate
+
+If you use 🤗 Accelerate in your publication, please cite it by using the following BibTeX entry.
+
+```bibtex
+@Misc{accelerate,
+ title = {Accelerate: Training and inference at scale made simple, efficient and adaptable.},
+ author = {Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar},
+ howpublished = {\url{https://github.com/huggingface/accelerate}},
+ year = {2022}
+}
+```
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diff --git a/llava_next/lib/python3.10/site-packages/networkx/__init__.py b/llava_next/lib/python3.10/site-packages/networkx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5710827b61d5f5fa25dde778489c2d56677b9b37
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/__init__.py
@@ -0,0 +1,53 @@
+"""
+NetworkX
+========
+
+NetworkX is a Python package for the creation, manipulation, and study of the
+structure, dynamics, and functions of complex networks.
+
+See https://networkx.org for complete documentation.
+"""
+
+__version__ = "3.4.2"
+
+
+# These are imported in order as listed
+from networkx.lazy_imports import _lazy_import
+
+from networkx.exception import *
+
+from networkx import utils
+from networkx.utils import _clear_cache, _dispatchable
+
+# load_and_call entry_points, set configs
+config = utils.backends._set_configs_from_environment()
+utils.config = utils.configs.config = config # type: ignore[attr-defined]
+
+from networkx import classes
+from networkx.classes import filters
+from networkx.classes import *
+
+from networkx import convert
+from networkx.convert import *
+
+from networkx import convert_matrix
+from networkx.convert_matrix import *
+
+from networkx import relabel
+from networkx.relabel import *
+
+from networkx import generators
+from networkx.generators import *
+
+from networkx import readwrite
+from networkx.readwrite import *
+
+# Need to test with SciPy, when available
+from networkx import algorithms
+from networkx.algorithms import *
+
+from networkx import linalg
+from networkx.linalg import *
+
+from networkx import drawing
+from networkx.drawing import *
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/chains.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/chains.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae342d9c8669acd832a3bdb4fe8eecf3e300464f
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/chains.py
@@ -0,0 +1,172 @@
+"""Functions for finding chains in a graph."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["chain_decomposition"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def chain_decomposition(G, root=None):
+ """Returns the chain decomposition of a graph.
+
+ The *chain decomposition* of a graph with respect a depth-first
+ search tree is a set of cycles or paths derived from the set of
+ fundamental cycles of the tree in the following manner. Consider
+ each fundamental cycle with respect to the given tree, represented
+ as a list of edges beginning with the nontree edge oriented away
+ from the root of the tree. For each fundamental cycle, if it
+ overlaps with any previous fundamental cycle, just take the initial
+ non-overlapping segment, which is a path instead of a cycle. Each
+ cycle or path is called a *chain*. For more information, see [1]_.
+
+ Parameters
+ ----------
+ G : undirected graph
+
+ root : node (optional)
+ A node in the graph `G`. If specified, only the chain
+ decomposition for the connected component containing this node
+ will be returned. This node indicates the root of the depth-first
+ search tree.
+
+ Yields
+ ------
+ chain : list
+ A list of edges representing a chain. There is no guarantee on
+ the orientation of the edges in each chain (for example, if a
+ chain includes the edge joining nodes 1 and 2, the chain may
+ include either (1, 2) or (2, 1)).
+
+ Raises
+ ------
+ NodeNotFound
+ If `root` is not in the graph `G`.
+
+ Examples
+ --------
+ >>> G = nx.Graph([(0, 1), (1, 4), (3, 4), (3, 5), (4, 5)])
+ >>> list(nx.chain_decomposition(G))
+ [[(4, 5), (5, 3), (3, 4)]]
+
+ Notes
+ -----
+ The worst-case running time of this implementation is linear in the
+ number of nodes and number of edges [1]_.
+
+ References
+ ----------
+ .. [1] Jens M. Schmidt (2013). "A simple test on 2-vertex-
+ and 2-edge-connectivity." *Information Processing Letters*,
+ 113, 241–244. Elsevier.
+
+ """
+
+ def _dfs_cycle_forest(G, root=None):
+ """Builds a directed graph composed of cycles from the given graph.
+
+ `G` is an undirected simple graph. `root` is a node in the graph
+ from which the depth-first search is started.
+
+ This function returns both the depth-first search cycle graph
+ (as a :class:`~networkx.DiGraph`) and the list of nodes in
+ depth-first preorder. The depth-first search cycle graph is a
+ directed graph whose edges are the edges of `G` oriented toward
+ the root if the edge is a tree edge and away from the root if
+ the edge is a non-tree edge. If `root` is not specified, this
+ performs a depth-first search on each connected component of `G`
+ and returns a directed forest instead.
+
+ If `root` is not in the graph, this raises :exc:`KeyError`.
+
+ """
+ # Create a directed graph from the depth-first search tree with
+ # root node `root` in which tree edges are directed toward the
+ # root and nontree edges are directed away from the root. For
+ # each node with an incident nontree edge, this creates a
+ # directed cycle starting with the nontree edge and returning to
+ # that node.
+ #
+ # The `parent` node attribute stores the parent of each node in
+ # the DFS tree. The `nontree` edge attribute indicates whether
+ # the edge is a tree edge or a nontree edge.
+ #
+ # We also store the order of the nodes found in the depth-first
+ # search in the `nodes` list.
+ H = nx.DiGraph()
+ nodes = []
+ for u, v, d in nx.dfs_labeled_edges(G, source=root):
+ if d == "forward":
+ # `dfs_labeled_edges()` yields (root, root, 'forward')
+ # if it is beginning the search on a new connected
+ # component.
+ if u == v:
+ H.add_node(v, parent=None)
+ nodes.append(v)
+ else:
+ H.add_node(v, parent=u)
+ H.add_edge(v, u, nontree=False)
+ nodes.append(v)
+ # `dfs_labeled_edges` considers nontree edges in both
+ # orientations, so we need to not add the edge if it its
+ # other orientation has been added.
+ elif d == "nontree" and v not in H[u]:
+ H.add_edge(v, u, nontree=True)
+ else:
+ # Do nothing on 'reverse' edges; we only care about
+ # forward and nontree edges.
+ pass
+ return H, nodes
+
+ def _build_chain(G, u, v, visited):
+ """Generate the chain starting from the given nontree edge.
+
+ `G` is a DFS cycle graph as constructed by
+ :func:`_dfs_cycle_graph`. The edge (`u`, `v`) is a nontree edge
+ that begins a chain. `visited` is a set representing the nodes
+ in `G` that have already been visited.
+
+ This function yields the edges in an initial segment of the
+ fundamental cycle of `G` starting with the nontree edge (`u`,
+ `v`) that includes all the edges up until the first node that
+ appears in `visited`. The tree edges are given by the 'parent'
+ node attribute. The `visited` set is updated to add each node in
+ an edge yielded by this function.
+
+ """
+ while v not in visited:
+ yield u, v
+ visited.add(v)
+ u, v = v, G.nodes[v]["parent"]
+ yield u, v
+
+ # Check if the root is in the graph G. If not, raise NodeNotFound
+ if root is not None and root not in G:
+ raise nx.NodeNotFound(f"Root node {root} is not in graph")
+
+ # Create a directed version of H that has the DFS edges directed
+ # toward the root and the nontree edges directed away from the root
+ # (in each connected component).
+ H, nodes = _dfs_cycle_forest(G, root)
+
+ # Visit the nodes again in DFS order. For each node, and for each
+ # nontree edge leaving that node, compute the fundamental cycle for
+ # that nontree edge starting with that edge. If the fundamental
+ # cycle overlaps with any visited nodes, just take the prefix of the
+ # cycle up to the point of visited nodes.
+ #
+ # We repeat this process for each connected component (implicitly,
+ # since `nodes` already has a list of the nodes grouped by connected
+ # component).
+ visited = set()
+ for u in nodes:
+ visited.add(u)
+ # For each nontree edge going out of node u...
+ edges = ((u, v) for u, v, d in H.out_edges(u, data="nontree") if d)
+ for u, v in edges:
+ # Create the cycle or cycle prefix starting with the
+ # nontree edge.
+ chain = list(_build_chain(H, u, v, visited))
+ yield chain
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/chordal.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/chordal.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab71c243f314d02b74eac9a7b0b4e601ed7e484d
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/chordal.py
@@ -0,0 +1,443 @@
+"""
+Algorithms for chordal graphs.
+
+A graph is chordal if every cycle of length at least 4 has a chord
+(an edge joining two nodes not adjacent in the cycle).
+https://en.wikipedia.org/wiki/Chordal_graph
+"""
+
+import sys
+
+import networkx as nx
+from networkx.algorithms.components import connected_components
+from networkx.utils import arbitrary_element, not_implemented_for
+
+__all__ = [
+ "is_chordal",
+ "find_induced_nodes",
+ "chordal_graph_cliques",
+ "chordal_graph_treewidth",
+ "NetworkXTreewidthBoundExceeded",
+ "complete_to_chordal_graph",
+]
+
+
+class NetworkXTreewidthBoundExceeded(nx.NetworkXException):
+ """Exception raised when a treewidth bound has been provided and it has
+ been exceeded"""
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_chordal(G):
+ """Checks whether G is a chordal graph.
+
+ A graph is chordal if every cycle of length at least 4 has a chord
+ (an edge joining two nodes not adjacent in the cycle).
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph.
+
+ Returns
+ -------
+ chordal : bool
+ True if G is a chordal graph and False otherwise.
+
+ Raises
+ ------
+ NetworkXNotImplemented
+ The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+
+ Examples
+ --------
+ >>> e = [
+ ... (1, 2),
+ ... (1, 3),
+ ... (2, 3),
+ ... (2, 4),
+ ... (3, 4),
+ ... (3, 5),
+ ... (3, 6),
+ ... (4, 5),
+ ... (4, 6),
+ ... (5, 6),
+ ... ]
+ >>> G = nx.Graph(e)
+ >>> nx.is_chordal(G)
+ True
+
+ Notes
+ -----
+ The routine tries to go through every node following maximum cardinality
+ search. It returns False when it finds that the separator for any node
+ is not a clique. Based on the algorithms in [1]_.
+
+ Self loops are ignored.
+
+ References
+ ----------
+ .. [1] R. E. Tarjan and M. Yannakakis, Simple linear-time algorithms
+ to test chordality of graphs, test acyclicity of hypergraphs, and
+ selectively reduce acyclic hypergraphs, SIAM J. Comput., 13 (1984),
+ pp. 566–579.
+ """
+ if len(G.nodes) <= 3:
+ return True
+ return len(_find_chordality_breaker(G)) == 0
+
+
+@nx._dispatchable
+def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
+ """Returns the set of induced nodes in the path from s to t.
+
+ Parameters
+ ----------
+ G : graph
+ A chordal NetworkX graph
+ s : node
+ Source node to look for induced nodes
+ t : node
+ Destination node to look for induced nodes
+ treewidth_bound: float
+ Maximum treewidth acceptable for the graph H. The search
+ for induced nodes will end as soon as the treewidth_bound is exceeded.
+
+ Returns
+ -------
+ induced_nodes : Set of nodes
+ The set of induced nodes in the path from s to t in G
+
+ Raises
+ ------
+ NetworkXError
+ The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+ If the input graph is an instance of one of these classes, a
+ :exc:`NetworkXError` is raised.
+ The algorithm can only be applied to chordal graphs. If the input
+ graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+ Examples
+ --------
+ >>> G = nx.Graph()
+ >>> G = nx.generators.classic.path_graph(10)
+ >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
+ >>> sorted(induced_nodes)
+ [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+ Notes
+ -----
+ G must be a chordal graph and (s,t) an edge that is not in G.
+
+ If a treewidth_bound is provided, the search for induced nodes will end
+ as soon as the treewidth_bound is exceeded.
+
+ The algorithm is inspired by Algorithm 4 in [1]_.
+ A formal definition of induced node can also be found on that reference.
+
+ Self Loops are ignored
+
+ References
+ ----------
+ .. [1] Learning Bounded Treewidth Bayesian Networks.
+ Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
+ http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
+ """
+ if not is_chordal(G):
+ raise nx.NetworkXError("Input graph is not chordal.")
+
+ H = nx.Graph(G)
+ H.add_edge(s, t)
+ induced_nodes = set()
+ triplet = _find_chordality_breaker(H, s, treewidth_bound)
+ while triplet:
+ (u, v, w) = triplet
+ induced_nodes.update(triplet)
+ for n in triplet:
+ if n != s:
+ H.add_edge(s, n)
+ triplet = _find_chordality_breaker(H, s, treewidth_bound)
+ if induced_nodes:
+ # Add t and the second node in the induced path from s to t.
+ induced_nodes.add(t)
+ for u in G[s]:
+ if len(induced_nodes & set(G[u])) == 2:
+ induced_nodes.add(u)
+ break
+ return induced_nodes
+
+
+@nx._dispatchable
+def chordal_graph_cliques(G):
+ """Returns all maximal cliques of a chordal graph.
+
+ The algorithm breaks the graph in connected components and performs a
+ maximum cardinality search in each component to get the cliques.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ Yields
+ ------
+ frozenset of nodes
+ Maximal cliques, each of which is a frozenset of
+ nodes in `G`. The order of cliques is arbitrary.
+
+ Raises
+ ------
+ NetworkXError
+ The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+ The algorithm can only be applied to chordal graphs. If the input
+ graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+ Examples
+ --------
+ >>> e = [
+ ... (1, 2),
+ ... (1, 3),
+ ... (2, 3),
+ ... (2, 4),
+ ... (3, 4),
+ ... (3, 5),
+ ... (3, 6),
+ ... (4, 5),
+ ... (4, 6),
+ ... (5, 6),
+ ... (7, 8),
+ ... ]
+ >>> G = nx.Graph(e)
+ >>> G.add_node(9)
+ >>> cliques = [c for c in chordal_graph_cliques(G)]
+ >>> cliques[0]
+ frozenset({1, 2, 3})
+ """
+ for C in (G.subgraph(c).copy() for c in connected_components(G)):
+ if C.number_of_nodes() == 1:
+ if nx.number_of_selfloops(C) > 0:
+ raise nx.NetworkXError("Input graph is not chordal.")
+ yield frozenset(C.nodes())
+ else:
+ unnumbered = set(C.nodes())
+ v = arbitrary_element(C)
+ unnumbered.remove(v)
+ numbered = {v}
+ clique_wanna_be = {v}
+ while unnumbered:
+ v = _max_cardinality_node(C, unnumbered, numbered)
+ unnumbered.remove(v)
+ numbered.add(v)
+ new_clique_wanna_be = set(C.neighbors(v)) & numbered
+ sg = C.subgraph(clique_wanna_be)
+ if _is_complete_graph(sg):
+ new_clique_wanna_be.add(v)
+ if not new_clique_wanna_be >= clique_wanna_be:
+ yield frozenset(clique_wanna_be)
+ clique_wanna_be = new_clique_wanna_be
+ else:
+ raise nx.NetworkXError("Input graph is not chordal.")
+ yield frozenset(clique_wanna_be)
+
+
+@nx._dispatchable
+def chordal_graph_treewidth(G):
+ """Returns the treewidth of the chordal graph G.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ Returns
+ -------
+ treewidth : int
+ The size of the largest clique in the graph minus one.
+
+ Raises
+ ------
+ NetworkXError
+ The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+ The algorithm can only be applied to chordal graphs. If the input
+ graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+ Examples
+ --------
+ >>> e = [
+ ... (1, 2),
+ ... (1, 3),
+ ... (2, 3),
+ ... (2, 4),
+ ... (3, 4),
+ ... (3, 5),
+ ... (3, 6),
+ ... (4, 5),
+ ... (4, 6),
+ ... (5, 6),
+ ... (7, 8),
+ ... ]
+ >>> G = nx.Graph(e)
+ >>> G.add_node(9)
+ >>> nx.chordal_graph_treewidth(G)
+ 3
+
+ References
+ ----------
+ .. [1] https://en.wikipedia.org/wiki/Tree_decomposition#Treewidth
+ """
+ if not is_chordal(G):
+ raise nx.NetworkXError("Input graph is not chordal.")
+
+ max_clique = -1
+ for clique in nx.chordal_graph_cliques(G):
+ max_clique = max(max_clique, len(clique))
+ return max_clique - 1
+
+
+def _is_complete_graph(G):
+ """Returns True if G is a complete graph."""
+ if nx.number_of_selfloops(G) > 0:
+ raise nx.NetworkXError("Self loop found in _is_complete_graph()")
+ n = G.number_of_nodes()
+ if n < 2:
+ return True
+ e = G.number_of_edges()
+ max_edges = (n * (n - 1)) / 2
+ return e == max_edges
+
+
+def _find_missing_edge(G):
+ """Given a non-complete graph G, returns a missing edge."""
+ nodes = set(G)
+ for u in G:
+ missing = nodes - set(list(G[u].keys()) + [u])
+ if missing:
+ return (u, missing.pop())
+
+
+def _max_cardinality_node(G, choices, wanna_connect):
+ """Returns a the node in choices that has more connections in G
+ to nodes in wanna_connect.
+ """
+ max_number = -1
+ for x in choices:
+ number = len([y for y in G[x] if y in wanna_connect])
+ if number > max_number:
+ max_number = number
+ max_cardinality_node = x
+ return max_cardinality_node
+
+
+def _find_chordality_breaker(G, s=None, treewidth_bound=sys.maxsize):
+ """Given a graph G, starts a max cardinality search
+ (starting from s if s is given and from an arbitrary node otherwise)
+ trying to find a non-chordal cycle.
+
+ If it does find one, it returns (u,v,w) where u,v,w are the three
+ nodes that together with s are involved in the cycle.
+
+ It ignores any self loops.
+ """
+ if len(G) == 0:
+ raise nx.NetworkXPointlessConcept("Graph has no nodes.")
+ unnumbered = set(G)
+ if s is None:
+ s = arbitrary_element(G)
+ unnumbered.remove(s)
+ numbered = {s}
+ current_treewidth = -1
+ while unnumbered: # and current_treewidth <= treewidth_bound:
+ v = _max_cardinality_node(G, unnumbered, numbered)
+ unnumbered.remove(v)
+ numbered.add(v)
+ clique_wanna_be = set(G[v]) & numbered
+ sg = G.subgraph(clique_wanna_be)
+ if _is_complete_graph(sg):
+ # The graph seems to be chordal by now. We update the treewidth
+ current_treewidth = max(current_treewidth, len(clique_wanna_be))
+ if current_treewidth > treewidth_bound:
+ raise nx.NetworkXTreewidthBoundExceeded(
+ f"treewidth_bound exceeded: {current_treewidth}"
+ )
+ else:
+ # sg is not a clique,
+ # look for an edge that is not included in sg
+ (u, w) = _find_missing_edge(sg)
+ return (u, v, w)
+ return ()
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(returns_graph=True)
+def complete_to_chordal_graph(G):
+ """Return a copy of G completed to a chordal graph
+
+ Adds edges to a copy of G to create a chordal graph. A graph G=(V,E) is
+ called chordal if for each cycle with length bigger than 3, there exist
+ two non-adjacent nodes connected by an edge (called a chord).
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ Undirected graph
+
+ Returns
+ -------
+ H : NetworkX graph
+ The chordal enhancement of G
+ alpha : Dictionary
+ The elimination ordering of nodes of G
+
+ Notes
+ -----
+ There are different approaches to calculate the chordal
+ enhancement of a graph. The algorithm used here is called
+ MCS-M and gives at least minimal (local) triangulation of graph. Note
+ that this triangulation is not necessarily a global minimum.
+
+ https://en.wikipedia.org/wiki/Chordal_graph
+
+ References
+ ----------
+ .. [1] Berry, Anne & Blair, Jean & Heggernes, Pinar & Peyton, Barry. (2004)
+ Maximum Cardinality Search for Computing Minimal Triangulations of
+ Graphs. Algorithmica. 39. 287-298. 10.1007/s00453-004-1084-3.
+
+ Examples
+ --------
+ >>> from networkx.algorithms.chordal import complete_to_chordal_graph
+ >>> G = nx.wheel_graph(10)
+ >>> H, alpha = complete_to_chordal_graph(G)
+ """
+ H = G.copy()
+ alpha = {node: 0 for node in H}
+ if nx.is_chordal(H):
+ return H, alpha
+ chords = set()
+ weight = {node: 0 for node in H.nodes()}
+ unnumbered_nodes = list(H.nodes())
+ for i in range(len(H.nodes()), 0, -1):
+ # get the node in unnumbered_nodes with the maximum weight
+ z = max(unnumbered_nodes, key=lambda node: weight[node])
+ unnumbered_nodes.remove(z)
+ alpha[z] = i
+ update_nodes = []
+ for y in unnumbered_nodes:
+ if G.has_edge(y, z):
+ update_nodes.append(y)
+ else:
+ # y_weight will be bigger than node weights between y and z
+ y_weight = weight[y]
+ lower_nodes = [
+ node for node in unnumbered_nodes if weight[node] < y_weight
+ ]
+ if nx.has_path(H.subgraph(lower_nodes + [z, y]), y, z):
+ update_nodes.append(y)
+ chords.add((z, y))
+ # during calculation of paths the weights should not be updated
+ for node in update_nodes:
+ weight[node] += 1
+ H.add_edges_from(chords)
+ return H, alpha
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/graphical.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/graphical.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5d82dedda6f9810e3f51bc4c82a9a2b252fa998
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/graphical.py
@@ -0,0 +1,483 @@
+"""Test sequences for graphiness."""
+
+import heapq
+
+import networkx as nx
+
+__all__ = [
+ "is_graphical",
+ "is_multigraphical",
+ "is_pseudographical",
+ "is_digraphical",
+ "is_valid_degree_sequence_erdos_gallai",
+ "is_valid_degree_sequence_havel_hakimi",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_graphical(sequence, method="eg"):
+ """Returns True if sequence is a valid degree sequence.
+
+ A degree sequence is valid if some graph can realize it.
+
+ Parameters
+ ----------
+ sequence : list or iterable container
+ A sequence of integer node degrees
+
+ method : "eg" | "hh" (default: 'eg')
+ The method used to validate the degree sequence.
+ "eg" corresponds to the Erdős-Gallai algorithm
+ [EG1960]_, [choudum1986]_, and
+ "hh" to the Havel-Hakimi algorithm
+ [havel1955]_, [hakimi1962]_, [CL1996]_.
+
+ Returns
+ -------
+ valid : bool
+ True if the sequence is a valid degree sequence and False if not.
+
+ Examples
+ --------
+ >>> G = nx.path_graph(4)
+ >>> sequence = (d for n, d in G.degree())
+ >>> nx.is_graphical(sequence)
+ True
+
+ To test a non-graphical sequence:
+ >>> sequence_list = [d for n, d in G.degree()]
+ >>> sequence_list[-1] += 1
+ >>> nx.is_graphical(sequence_list)
+ False
+
+ References
+ ----------
+ .. [EG1960] Erdős and Gallai, Mat. Lapok 11 264, 1960.
+ .. [choudum1986] S.A. Choudum. "A simple proof of the Erdős-Gallai theorem on
+ graph sequences." Bulletin of the Australian Mathematical Society, 33,
+ pp 67-70, 1986. https://doi.org/10.1017/S0004972700002872
+ .. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs"
+ Casopis Pest. Mat. 80, 477-480, 1955.
+ .. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as
+ Degrees of the Vertices of a Graph." SIAM J. Appl. Math. 10, 496-506, 1962.
+ .. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
+ Chapman and Hall/CRC, 1996.
+ """
+ if method == "eg":
+ valid = is_valid_degree_sequence_erdos_gallai(list(sequence))
+ elif method == "hh":
+ valid = is_valid_degree_sequence_havel_hakimi(list(sequence))
+ else:
+ msg = "`method` must be 'eg' or 'hh'"
+ raise nx.NetworkXException(msg)
+ return valid
+
+
+def _basic_graphical_tests(deg_sequence):
+ # Sort and perform some simple tests on the sequence
+ deg_sequence = nx.utils.make_list_of_ints(deg_sequence)
+ p = len(deg_sequence)
+ num_degs = [0] * p
+ dmax, dmin, dsum, n = 0, p, 0, 0
+ for d in deg_sequence:
+ # Reject if degree is negative or larger than the sequence length
+ if d < 0 or d >= p:
+ raise nx.NetworkXUnfeasible
+ # Process only the non-zero integers
+ elif d > 0:
+ dmax, dmin, dsum, n = max(dmax, d), min(dmin, d), dsum + d, n + 1
+ num_degs[d] += 1
+ # Reject sequence if it has odd sum or is oversaturated
+ if dsum % 2 or dsum > n * (n - 1):
+ raise nx.NetworkXUnfeasible
+ return dmax, dmin, dsum, n, num_degs
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_degree_sequence_havel_hakimi(deg_sequence):
+ r"""Returns True if deg_sequence can be realized by a simple graph.
+
+ The validation proceeds using the Havel-Hakimi theorem
+ [havel1955]_, [hakimi1962]_, [CL1996]_.
+ Worst-case run time is $O(s)$ where $s$ is the sum of the sequence.
+
+ Parameters
+ ----------
+ deg_sequence : list
+ A list of integers where each element specifies the degree of a node
+ in a graph.
+
+ Returns
+ -------
+ valid : bool
+ True if deg_sequence is graphical and False if not.
+
+ Examples
+ --------
+ >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+ >>> sequence = (d for _, d in G.degree())
+ >>> nx.is_valid_degree_sequence_havel_hakimi(sequence)
+ True
+
+ To test a non-valid sequence:
+ >>> sequence_list = [d for _, d in G.degree()]
+ >>> sequence_list[-1] += 1
+ >>> nx.is_valid_degree_sequence_havel_hakimi(sequence_list)
+ False
+
+ Notes
+ -----
+ The ZZ condition says that for the sequence d if
+
+ .. math::
+ |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
+
+ then d is graphical. This was shown in Theorem 6 in [1]_.
+
+ References
+ ----------
+ .. [1] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
+ of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
+ .. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs"
+ Casopis Pest. Mat. 80, 477-480, 1955.
+ .. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as
+ Degrees of the Vertices of a Graph." SIAM J. Appl. Math. 10, 496-506, 1962.
+ .. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
+ Chapman and Hall/CRC, 1996.
+ """
+ try:
+ dmax, dmin, dsum, n, num_degs = _basic_graphical_tests(deg_sequence)
+ except nx.NetworkXUnfeasible:
+ return False
+ # Accept if sequence has no non-zero degrees or passes the ZZ condition
+ if n == 0 or 4 * dmin * n >= (dmax + dmin + 1) * (dmax + dmin + 1):
+ return True
+
+ modstubs = [0] * (dmax + 1)
+ # Successively reduce degree sequence by removing the maximum degree
+ while n > 0:
+ # Retrieve the maximum degree in the sequence
+ while num_degs[dmax] == 0:
+ dmax -= 1
+ # If there are not enough stubs to connect to, then the sequence is
+ # not graphical
+ if dmax > n - 1:
+ return False
+
+ # Remove largest stub in list
+ num_degs[dmax], n = num_degs[dmax] - 1, n - 1
+ # Reduce the next dmax largest stubs
+ mslen = 0
+ k = dmax
+ for i in range(dmax):
+ while num_degs[k] == 0:
+ k -= 1
+ num_degs[k], n = num_degs[k] - 1, n - 1
+ if k > 1:
+ modstubs[mslen] = k - 1
+ mslen += 1
+ # Add back to the list any non-zero stubs that were removed
+ for i in range(mslen):
+ stub = modstubs[i]
+ num_degs[stub], n = num_degs[stub] + 1, n + 1
+ return True
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_degree_sequence_erdos_gallai(deg_sequence):
+ r"""Returns True if deg_sequence can be realized by a simple graph.
+
+ The validation is done using the Erdős-Gallai theorem [EG1960]_.
+
+ Parameters
+ ----------
+ deg_sequence : list
+ A list of integers
+
+ Returns
+ -------
+ valid : bool
+ True if deg_sequence is graphical and False if not.
+
+ Examples
+ --------
+ >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+ >>> sequence = (d for _, d in G.degree())
+ >>> nx.is_valid_degree_sequence_erdos_gallai(sequence)
+ True
+
+ To test a non-valid sequence:
+ >>> sequence_list = [d for _, d in G.degree()]
+ >>> sequence_list[-1] += 1
+ >>> nx.is_valid_degree_sequence_erdos_gallai(sequence_list)
+ False
+
+ Notes
+ -----
+
+ This implementation uses an equivalent form of the Erdős-Gallai criterion.
+ Worst-case run time is $O(n)$ where $n$ is the length of the sequence.
+
+ Specifically, a sequence d is graphical if and only if the
+ sum of the sequence is even and for all strong indices k in the sequence,
+
+ .. math::
+
+ \sum_{i=1}^{k} d_i \leq k(k-1) + \sum_{j=k+1}^{n} \min(d_i,k)
+ = k(n-1) - ( k \sum_{j=0}^{k-1} n_j - \sum_{j=0}^{k-1} j n_j )
+
+ A strong index k is any index where d_k >= k and the value n_j is the
+ number of occurrences of j in d. The maximal strong index is called the
+ Durfee index.
+
+ This particular rearrangement comes from the proof of Theorem 3 in [2]_.
+
+ The ZZ condition says that for the sequence d if
+
+ .. math::
+ |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
+
+ then d is graphical. This was shown in Theorem 6 in [2]_.
+
+ References
+ ----------
+ .. [1] A. Tripathi and S. Vijay. "A note on a theorem of Erdős & Gallai",
+ Discrete Mathematics, 265, pp. 417-420 (2003).
+ .. [2] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
+ of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
+ .. [EG1960] Erdős and Gallai, Mat. Lapok 11 264, 1960.
+ """
+ try:
+ dmax, dmin, dsum, n, num_degs = _basic_graphical_tests(deg_sequence)
+ except nx.NetworkXUnfeasible:
+ return False
+ # Accept if sequence has no non-zero degrees or passes the ZZ condition
+ if n == 0 or 4 * dmin * n >= (dmax + dmin + 1) * (dmax + dmin + 1):
+ return True
+
+ # Perform the EG checks using the reformulation of Zverovich and Zverovich
+ k, sum_deg, sum_nj, sum_jnj = 0, 0, 0, 0
+ for dk in range(dmax, dmin - 1, -1):
+ if dk < k + 1: # Check if already past Durfee index
+ return True
+ if num_degs[dk] > 0:
+ run_size = num_degs[dk] # Process a run of identical-valued degrees
+ if dk < k + run_size: # Check if end of run is past Durfee index
+ run_size = dk - k # Adjust back to Durfee index
+ sum_deg += run_size * dk
+ for v in range(run_size):
+ sum_nj += num_degs[k + v]
+ sum_jnj += (k + v) * num_degs[k + v]
+ k += run_size
+ if sum_deg > k * (n - 1) - k * sum_nj + sum_jnj:
+ return False
+ return True
+
+
+@nx._dispatchable(graphs=None)
+def is_multigraphical(sequence):
+ """Returns True if some multigraph can realize the sequence.
+
+ Parameters
+ ----------
+ sequence : list
+ A list of integers
+
+ Returns
+ -------
+ valid : bool
+ True if deg_sequence is a multigraphic degree sequence and False if not.
+
+ Examples
+ --------
+ >>> G = nx.MultiGraph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+ >>> sequence = (d for _, d in G.degree())
+ >>> nx.is_multigraphical(sequence)
+ True
+
+ To test a non-multigraphical sequence:
+ >>> sequence_list = [d for _, d in G.degree()]
+ >>> sequence_list[-1] += 1
+ >>> nx.is_multigraphical(sequence_list)
+ False
+
+ Notes
+ -----
+ The worst-case run time is $O(n)$ where $n$ is the length of the sequence.
+
+ References
+ ----------
+ .. [1] S. L. Hakimi. "On the realizability of a set of integers as
+ degrees of the vertices of a linear graph", J. SIAM, 10, pp. 496-506
+ (1962).
+ """
+ try:
+ deg_sequence = nx.utils.make_list_of_ints(sequence)
+ except nx.NetworkXError:
+ return False
+ dsum, dmax = 0, 0
+ for d in deg_sequence:
+ if d < 0:
+ return False
+ dsum, dmax = dsum + d, max(dmax, d)
+ if dsum % 2 or dsum < 2 * dmax:
+ return False
+ return True
+
+
+@nx._dispatchable(graphs=None)
+def is_pseudographical(sequence):
+ """Returns True if some pseudograph can realize the sequence.
+
+ Every nonnegative integer sequence with an even sum is pseudographical
+ (see [1]_).
+
+ Parameters
+ ----------
+ sequence : list or iterable container
+ A sequence of integer node degrees
+
+ Returns
+ -------
+ valid : bool
+ True if the sequence is a pseudographic degree sequence and False if not.
+
+ Examples
+ --------
+ >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+ >>> sequence = (d for _, d in G.degree())
+ >>> nx.is_pseudographical(sequence)
+ True
+
+ To test a non-pseudographical sequence:
+ >>> sequence_list = [d for _, d in G.degree()]
+ >>> sequence_list[-1] += 1
+ >>> nx.is_pseudographical(sequence_list)
+ False
+
+ Notes
+ -----
+ The worst-case run time is $O(n)$ where n is the length of the sequence.
+
+ References
+ ----------
+ .. [1] F. Boesch and F. Harary. "Line removal algorithms for graphs
+ and their degree lists", IEEE Trans. Circuits and Systems, CAS-23(12),
+ pp. 778-782 (1976).
+ """
+ try:
+ deg_sequence = nx.utils.make_list_of_ints(sequence)
+ except nx.NetworkXError:
+ return False
+ return sum(deg_sequence) % 2 == 0 and min(deg_sequence) >= 0
+
+
+@nx._dispatchable(graphs=None)
+def is_digraphical(in_sequence, out_sequence):
+ r"""Returns True if some directed graph can realize the in- and out-degree
+ sequences.
+
+ Parameters
+ ----------
+ in_sequence : list or iterable container
+ A sequence of integer node in-degrees
+
+ out_sequence : list or iterable container
+ A sequence of integer node out-degrees
+
+ Returns
+ -------
+ valid : bool
+ True if in and out-sequences are digraphic False if not.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+ >>> in_seq = (d for n, d in G.in_degree())
+ >>> out_seq = (d for n, d in G.out_degree())
+ >>> nx.is_digraphical(in_seq, out_seq)
+ True
+
+ To test a non-digraphical scenario:
+ >>> in_seq_list = [d for n, d in G.in_degree()]
+ >>> in_seq_list[-1] += 1
+ >>> nx.is_digraphical(in_seq_list, out_seq)
+ False
+
+ Notes
+ -----
+ This algorithm is from Kleitman and Wang [1]_.
+ The worst case runtime is $O(s \times \log n)$ where $s$ and $n$ are the
+ sum and length of the sequences respectively.
+
+ References
+ ----------
+ .. [1] D.J. Kleitman and D.L. Wang
+ Algorithms for Constructing Graphs and Digraphs with Given Valences
+ and Factors, Discrete Mathematics, 6(1), pp. 79-88 (1973)
+ """
+ try:
+ in_deg_sequence = nx.utils.make_list_of_ints(in_sequence)
+ out_deg_sequence = nx.utils.make_list_of_ints(out_sequence)
+ except nx.NetworkXError:
+ return False
+ # Process the sequences and form two heaps to store degree pairs with
+ # either zero or non-zero out degrees
+ sumin, sumout, nin, nout = 0, 0, len(in_deg_sequence), len(out_deg_sequence)
+ maxn = max(nin, nout)
+ maxin = 0
+ if maxn == 0:
+ return True
+ stubheap, zeroheap = [], []
+ for n in range(maxn):
+ in_deg, out_deg = 0, 0
+ if n < nout:
+ out_deg = out_deg_sequence[n]
+ if n < nin:
+ in_deg = in_deg_sequence[n]
+ if in_deg < 0 or out_deg < 0:
+ return False
+ sumin, sumout, maxin = sumin + in_deg, sumout + out_deg, max(maxin, in_deg)
+ if in_deg > 0:
+ stubheap.append((-1 * out_deg, -1 * in_deg))
+ elif out_deg > 0:
+ zeroheap.append(-1 * out_deg)
+ if sumin != sumout:
+ return False
+ heapq.heapify(stubheap)
+ heapq.heapify(zeroheap)
+
+ modstubs = [(0, 0)] * (maxin + 1)
+ # Successively reduce degree sequence by removing the maximum out degree
+ while stubheap:
+ # Take the first value in the sequence with non-zero in degree
+ (freeout, freein) = heapq.heappop(stubheap)
+ freein *= -1
+ if freein > len(stubheap) + len(zeroheap):
+ return False
+
+ # Attach out stubs to the nodes with the most in stubs
+ mslen = 0
+ for i in range(freein):
+ if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0]):
+ stubout = heapq.heappop(zeroheap)
+ stubin = 0
+ else:
+ (stubout, stubin) = heapq.heappop(stubheap)
+ if stubout == 0:
+ return False
+ # Check if target is now totally connected
+ if stubout + 1 < 0 or stubin < 0:
+ modstubs[mslen] = (stubout + 1, stubin)
+ mslen += 1
+
+ # Add back the nodes to the heap that still have available stubs
+ for i in range(mslen):
+ stub = modstubs[i]
+ if stub[1] < 0:
+ heapq.heappush(stubheap, stub)
+ else:
+ heapq.heappush(zeroheap, stub[0])
+ if freeout < 0:
+ heapq.heappush(zeroheap, freeout)
+ return True
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/non_randomness.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/non_randomness.py
new file mode 100644
index 0000000000000000000000000000000000000000..137991157747e3720f026481ff1b0b3b5e8af1cf
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/non_randomness.py
@@ -0,0 +1,98 @@
+r"""Computation of graph non-randomness"""
+
+import math
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["non_randomness"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def non_randomness(G, k=None, weight="weight"):
+ """Compute the non-randomness of graph G.
+
+ The first returned value nr is the sum of non-randomness values of all
+ edges within the graph (where the non-randomness of an edge tends to be
+ small when the two nodes linked by that edge are from two different
+ communities).
+
+ The second computed value nr_rd is a relative measure that indicates
+ to what extent graph G is different from random graphs in terms
+ of probability. When it is close to 0, the graph tends to be more
+ likely generated by an Erdos Renyi model.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ Graph must be symmetric, connected, and without self-loops.
+
+ k : int
+ The number of communities in G.
+ If k is not set, the function will use a default community
+ detection algorithm to set it.
+
+ weight : string or None, optional (default=None)
+ The name of an edge attribute that holds the numerical value used
+ as a weight. If None, then each edge has weight 1, i.e., the graph is
+ binary.
+
+ Returns
+ -------
+ non-randomness : (float, float) tuple
+ Non-randomness, Relative non-randomness w.r.t.
+ Erdos Renyi random graphs.
+
+ Raises
+ ------
+ NetworkXException
+ if the input graph is not connected.
+ NetworkXError
+ if the input graph contains self-loops or if graph has no edges.
+
+ Examples
+ --------
+ >>> G = nx.karate_club_graph()
+ >>> nr, nr_rd = nx.non_randomness(G, 2)
+ >>> nr, nr_rd = nx.non_randomness(G, 2, "weight")
+
+ Notes
+ -----
+ This computes Eq. (4.4) and (4.5) in Ref. [1]_.
+
+ If a weight field is passed, this algorithm will use the eigenvalues
+ of the weighted adjacency matrix to compute Eq. (4.4) and (4.5).
+
+ References
+ ----------
+ .. [1] Xiaowei Ying and Xintao Wu,
+ On Randomness Measures for Social Networks,
+ SIAM International Conference on Data Mining. 2009
+ """
+ import numpy as np
+
+ # corner case: graph has no edges
+ if nx.is_empty(G):
+ raise nx.NetworkXError("non_randomness not applicable to empty graphs")
+ if not nx.is_connected(G):
+ raise nx.NetworkXException("Non connected graph.")
+ if len(list(nx.selfloop_edges(G))) > 0:
+ raise nx.NetworkXError("Graph must not contain self-loops")
+
+ if k is None:
+ k = len(tuple(nx.community.label_propagation_communities(G)))
+
+ # eq. 4.4
+ eigenvalues = np.linalg.eigvals(nx.to_numpy_array(G, weight=weight))
+ nr = float(np.real(np.sum(eigenvalues[:k])))
+
+ n = G.number_of_nodes()
+ m = G.number_of_edges()
+ p = (2 * k * m) / (n * (n - k))
+
+ # eq. 4.5
+ nr_rd = (nr - ((n - 2 * k) * p + k)) / math.sqrt(2 * k * p * (1 - p))
+
+ return nr, nr_rd
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/reciprocity.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/reciprocity.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea7ed2ce26ab973e07bcc6ec0d92aa4799d9a6a
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/reciprocity.py
@@ -0,0 +1,98 @@
+"""Algorithms to calculate reciprocity in a directed graph."""
+
+import networkx as nx
+from networkx import NetworkXError
+
+from ..utils import not_implemented_for
+
+__all__ = ["reciprocity", "overall_reciprocity"]
+
+
+@not_implemented_for("undirected", "multigraph")
+@nx._dispatchable
+def reciprocity(G, nodes=None):
+ r"""Compute the reciprocity in a directed graph.
+
+ The reciprocity of a directed graph is defined as the ratio
+ of the number of edges pointing in both directions to the total
+ number of edges in the graph.
+ Formally, $r = |{(u,v) \in G|(v,u) \in G}| / |{(u,v) \in G}|$.
+
+ The reciprocity of a single node u is defined similarly,
+ it is the ratio of the number of edges in both directions to
+ the total number of edges attached to node u.
+
+ Parameters
+ ----------
+ G : graph
+ A networkx directed graph
+ nodes : container of nodes, optional (default=whole graph)
+ Compute reciprocity for nodes in this container.
+
+ Returns
+ -------
+ out : dictionary
+ Reciprocity keyed by node label.
+
+ Notes
+ -----
+ The reciprocity is not defined for isolated nodes.
+ In such cases this function will return None.
+
+ """
+ # If `nodes` is not specified, calculate the reciprocity of the graph.
+ if nodes is None:
+ return overall_reciprocity(G)
+
+ # If `nodes` represents a single node in the graph, return only its
+ # reciprocity.
+ if nodes in G:
+ reciprocity = next(_reciprocity_iter(G, nodes))[1]
+ if reciprocity is None:
+ raise NetworkXError("Not defined for isolated nodes.")
+ else:
+ return reciprocity
+
+ # Otherwise, `nodes` represents an iterable of nodes, so return a
+ # dictionary mapping node to its reciprocity.
+ return dict(_reciprocity_iter(G, nodes))
+
+
+def _reciprocity_iter(G, nodes):
+ """Return an iterator of (node, reciprocity)."""
+ n = G.nbunch_iter(nodes)
+ for node in n:
+ pred = set(G.predecessors(node))
+ succ = set(G.successors(node))
+ overlap = pred & succ
+ n_total = len(pred) + len(succ)
+
+ # Reciprocity is not defined for isolated nodes.
+ # Return None.
+ if n_total == 0:
+ yield (node, None)
+ else:
+ reciprocity = 2 * len(overlap) / n_total
+ yield (node, reciprocity)
+
+
+@not_implemented_for("undirected", "multigraph")
+@nx._dispatchable
+def overall_reciprocity(G):
+ """Compute the reciprocity for the whole graph.
+
+ See the doc of reciprocity for the definition.
+
+ Parameters
+ ----------
+ G : graph
+ A networkx graph
+
+ """
+ n_all_edge = G.number_of_edges()
+ n_overlap_edge = (n_all_edge - G.to_undirected().number_of_edges()) * 2
+
+ if n_all_edge == 0:
+ raise NetworkXError("Not defined for empty graphs")
+
+ return n_overlap_edge / n_all_edge
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/smetric.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/smetric.py
new file mode 100644
index 0000000000000000000000000000000000000000..d985aa805b4fb21300680afe389aae4732793a73
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/smetric.py
@@ -0,0 +1,30 @@
+import networkx as nx
+
+__all__ = ["s_metric"]
+
+
+@nx._dispatchable
+def s_metric(G):
+ """Returns the s-metric [1]_ of graph.
+
+ The s-metric is defined as the sum of the products ``deg(u) * deg(v)``
+ for every edge ``(u, v)`` in `G`.
+
+ Parameters
+ ----------
+ G : graph
+ The graph used to compute the s-metric.
+
+ Returns
+ -------
+ s : float
+ The s-metric of the graph.
+
+ References
+ ----------
+ .. [1] Lun Li, David Alderson, John C. Doyle, and Walter Willinger,
+ Towards a Theory of Scale-Free Graphs:
+ Definition, Properties, and Implications (Extended Version), 2005.
+ https://arxiv.org/abs/cond-mat/0501169
+ """
+ return float(sum(G.degree(u) * G.degree(v) for (u, v) in G.edges()))
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/structuralholes.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/structuralholes.py
new file mode 100644
index 0000000000000000000000000000000000000000..bae42d060af9e2c8bc5d9732b5bc7905d3d895b9
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/structuralholes.py
@@ -0,0 +1,283 @@
+"""Functions for computing measures of structural holes."""
+
+import networkx as nx
+
+__all__ = ["constraint", "local_constraint", "effective_size"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def mutual_weight(G, u, v, weight=None):
+ """Returns the sum of the weights of the edge from `u` to `v` and
+ the edge from `v` to `u` in `G`.
+
+ `weight` is the edge data key that represents the edge weight. If
+ the specified key is `None` or is not in the edge data for an edge,
+ that edge is assumed to have weight 1.
+
+ Pre-conditions: `u` and `v` must both be in `G`.
+
+ """
+ try:
+ a_uv = G[u][v].get(weight, 1)
+ except KeyError:
+ a_uv = 0
+ try:
+ a_vu = G[v][u].get(weight, 1)
+ except KeyError:
+ a_vu = 0
+ return a_uv + a_vu
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_mutual_weight(G, u, v, norm=sum, weight=None):
+ """Returns normalized mutual weight of the edges from `u` to `v`
+ with respect to the mutual weights of the neighbors of `u` in `G`.
+
+ `norm` specifies how the normalization factor is computed. It must
+ be a function that takes a single argument and returns a number.
+ The argument will be an iterable of mutual weights
+ of pairs ``(u, w)``, where ``w`` ranges over each (in- and
+ out-)neighbor of ``u``. Commons values for `normalization` are
+ ``sum`` and ``max``.
+
+ `weight` can be ``None`` or a string, if None, all edge weights
+ are considered equal. Otherwise holds the name of the edge
+ attribute used as weight.
+
+ """
+ scale = norm(mutual_weight(G, u, w, weight) for w in set(nx.all_neighbors(G, u)))
+ return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale
+
+
+@nx._dispatchable(edge_attrs="weight")
+def effective_size(G, nodes=None, weight=None):
+ r"""Returns the effective size of all nodes in the graph ``G``.
+
+ The *effective size* of a node's ego network is based on the concept
+ of redundancy. A person's ego network has redundancy to the extent
+ that her contacts are connected to each other as well. The
+ nonredundant part of a person's relationships is the effective
+ size of her ego network [1]_. Formally, the effective size of a
+ node $u$, denoted $e(u)$, is defined by
+
+ .. math::
+
+ e(u) = \sum_{v \in N(u) \setminus \{u\}}
+ \left(1 - \sum_{w \in N(v)} p_{uw} m_{vw}\right)
+
+ where $N(u)$ is the set of neighbors of $u$ and $p_{uw}$ is the
+ normalized mutual weight of the (directed or undirected) edges
+ joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. And $m_{vw}$
+ is the mutual weight of $v$ and $w$ divided by $v$ highest mutual
+ weight with any of its neighbors. The *mutual weight* of $u$ and $v$
+ is the sum of the weights of edges joining them (edge weights are
+ assumed to be one if the graph is unweighted).
+
+ For the case of unweighted and undirected graphs, Borgatti proposed
+ a simplified formula to compute effective size [2]_
+
+ .. math::
+
+ e(u) = n - \frac{2t}{n}
+
+ where `t` is the number of ties in the ego network (not including
+ ties to ego) and `n` is the number of nodes (excluding ego).
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ The graph containing ``v``. Directed graphs are treated like
+ undirected graphs when computing neighbors of ``v``.
+
+ nodes : container, optional
+ Container of nodes in the graph ``G`` to compute the effective size.
+ If None, the effective size of every node is computed.
+
+ weight : None or string, optional
+ If None, all edge weights are considered equal.
+ Otherwise holds the name of the edge attribute used as weight.
+
+ Returns
+ -------
+ dict
+ Dictionary with nodes as keys and the effective size of the node as values.
+
+ Notes
+ -----
+ Burt also defined the related concept of *efficiency* of a node's ego
+ network, which is its effective size divided by the degree of that
+ node [1]_. So you can easily compute efficiency:
+
+ >>> G = nx.DiGraph()
+ >>> G.add_edges_from([(0, 1), (0, 2), (1, 0), (2, 1)])
+ >>> esize = nx.effective_size(G)
+ >>> efficiency = {n: v / G.degree(n) for n, v in esize.items()}
+
+ See also
+ --------
+ constraint
+
+ References
+ ----------
+ .. [1] Burt, Ronald S.
+ *Structural Holes: The Social Structure of Competition.*
+ Cambridge: Harvard University Press, 1995.
+
+ .. [2] Borgatti, S.
+ "Structural Holes: Unpacking Burt's Redundancy Measures"
+ CONNECTIONS 20(1):35-38.
+ http://www.analytictech.com/connections/v20(1)/holes.htm
+
+ """
+
+ def redundancy(G, u, v, weight=None):
+ nmw = normalized_mutual_weight
+ r = sum(
+ nmw(G, u, w, weight=weight) * nmw(G, v, w, norm=max, weight=weight)
+ for w in set(nx.all_neighbors(G, u))
+ )
+ return 1 - r
+
+ effective_size = {}
+ if nodes is None:
+ nodes = G
+ # Use Borgatti's simplified formula for unweighted and undirected graphs
+ if not G.is_directed() and weight is None:
+ for v in nodes:
+ # Effective size is not defined for isolated nodes
+ if len(G[v]) == 0:
+ effective_size[v] = float("nan")
+ continue
+ E = nx.ego_graph(G, v, center=False, undirected=True)
+ effective_size[v] = len(E) - (2 * E.size()) / len(E)
+ else:
+ for v in nodes:
+ # Effective size is not defined for isolated nodes
+ if len(G[v]) == 0:
+ effective_size[v] = float("nan")
+ continue
+ effective_size[v] = sum(
+ redundancy(G, v, u, weight) for u in set(nx.all_neighbors(G, v))
+ )
+ return effective_size
+
+
+@nx._dispatchable(edge_attrs="weight")
+def constraint(G, nodes=None, weight=None):
+ r"""Returns the constraint on all nodes in the graph ``G``.
+
+ The *constraint* is a measure of the extent to which a node *v* is
+ invested in those nodes that are themselves invested in the
+ neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,
+ is defined by
+
+ .. math::
+
+ c(v) = \sum_{w \in N(v) \setminus \{v\}} \ell(v, w)
+
+ where $N(v)$ is the subset of the neighbors of `v` that are either
+ predecessors or successors of `v` and $\ell(v, w)$ is the local
+ constraint on `v` with respect to `w` [1]_. For the definition of local
+ constraint, see :func:`local_constraint`.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ The graph containing ``v``. This can be either directed or undirected.
+
+ nodes : container, optional
+ Container of nodes in the graph ``G`` to compute the constraint. If
+ None, the constraint of every node is computed.
+
+ weight : None or string, optional
+ If None, all edge weights are considered equal.
+ Otherwise holds the name of the edge attribute used as weight.
+
+ Returns
+ -------
+ dict
+ Dictionary with nodes as keys and the constraint on the node as values.
+
+ See also
+ --------
+ local_constraint
+
+ References
+ ----------
+ .. [1] Burt, Ronald S.
+ "Structural holes and good ideas".
+ American Journal of Sociology (110): 349–399.
+
+ """
+ if nodes is None:
+ nodes = G
+ constraint = {}
+ for v in nodes:
+ # Constraint is not defined for isolated nodes
+ if len(G[v]) == 0:
+ constraint[v] = float("nan")
+ continue
+ constraint[v] = sum(
+ local_constraint(G, v, n, weight) for n in set(nx.all_neighbors(G, v))
+ )
+ return constraint
+
+
+@nx._dispatchable(edge_attrs="weight")
+def local_constraint(G, u, v, weight=None):
+ r"""Returns the local constraint on the node ``u`` with respect to
+ the node ``v`` in the graph ``G``.
+
+ Formally, the *local constraint on u with respect to v*, denoted
+ $\ell(u, v)$, is defined by
+
+ .. math::
+
+ \ell(u, v) = \left(p_{uv} + \sum_{w \in N(v)} p_{uw} p_{wv}\right)^2,
+
+ where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the
+ normalized mutual weight of the (directed or undirected) edges
+ joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual
+ weight* of $u$ and $v$ is the sum of the weights of edges joining
+ them (edge weights are assumed to be one if the graph is
+ unweighted).
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ The graph containing ``u`` and ``v``. This can be either
+ directed or undirected.
+
+ u : node
+ A node in the graph ``G``.
+
+ v : node
+ A node in the graph ``G``.
+
+ weight : None or string, optional
+ If None, all edge weights are considered equal.
+ Otherwise holds the name of the edge attribute used as weight.
+
+ Returns
+ -------
+ float
+ The constraint of the node ``v`` in the graph ``G``.
+
+ See also
+ --------
+ constraint
+
+ References
+ ----------
+ .. [1] Burt, Ronald S.
+ "Structural holes and good ideas".
+ American Journal of Sociology (110): 349–399.
+
+ """
+ nmw = normalized_mutual_weight
+ direct = nmw(G, u, v, weight=weight)
+ indirect = sum(
+ nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)
+ for w in set(nx.all_neighbors(G, u))
+ )
+ return (direct + indirect) ** 2
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/tournament.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/tournament.py
new file mode 100644
index 0000000000000000000000000000000000000000..25c1983e6a6d0a5dbba05d28f127d3ef62140117
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/tournament.py
@@ -0,0 +1,403 @@
+"""Functions concerning tournament graphs.
+
+A `tournament graph`_ is a complete oriented graph. In other words, it
+is a directed graph in which there is exactly one directed edge joining
+each pair of distinct nodes. For each function in this module that
+accepts a graph as input, you must provide a tournament graph. The
+responsibility is on the caller to ensure that the graph is a tournament
+graph:
+
+ >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+ >>> nx.is_tournament(G)
+ True
+
+To access the functions in this module, you must access them through the
+:mod:`networkx.tournament` module::
+
+ >>> nx.tournament.is_reachable(G, 0, 1)
+ True
+
+.. _tournament graph: https://en.wikipedia.org/wiki/Tournament_%28graph_theory%29
+
+"""
+
+from itertools import combinations
+
+import networkx as nx
+from networkx.algorithms.simple_paths import is_simple_path as is_path
+from networkx.utils import arbitrary_element, not_implemented_for, py_random_state
+
+__all__ = [
+ "hamiltonian_path",
+ "is_reachable",
+ "is_strongly_connected",
+ "is_tournament",
+ "random_tournament",
+ "score_sequence",
+]
+
+
+def index_satisfying(iterable, condition):
+ """Returns the index of the first element in `iterable` that
+ satisfies the given condition.
+
+ If no such element is found (that is, when the iterable is
+ exhausted), this returns the length of the iterable (that is, one
+ greater than the last index of the iterable).
+
+ `iterable` must not be empty. If `iterable` is empty, this
+ function raises :exc:`ValueError`.
+
+ """
+ # Pre-condition: iterable must not be empty.
+ for i, x in enumerate(iterable):
+ if condition(x):
+ return i
+ # If we reach the end of the iterable without finding an element
+ # that satisfies the condition, return the length of the iterable,
+ # which is one greater than the index of its last element. If the
+ # iterable was empty, `i` will not be defined, so we raise an
+ # exception.
+ try:
+ return i + 1
+ except NameError as err:
+ raise ValueError("iterable must be non-empty") from err
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_tournament(G):
+ """Returns True if and only if `G` is a tournament.
+
+ A tournament is a directed graph, with neither self-loops nor
+ multi-edges, in which there is exactly one directed edge joining
+ each pair of distinct nodes.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ Returns
+ -------
+ bool
+ Whether the given graph is a tournament graph.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+ >>> nx.is_tournament(G)
+ True
+
+ Notes
+ -----
+ Some definitions require a self-loop on each node, but that is not
+ the convention used here.
+
+ """
+ # In a tournament, there is exactly one directed edge joining each pair.
+ return (
+ all((v in G[u]) ^ (u in G[v]) for u, v in combinations(G, 2))
+ and nx.number_of_selfloops(G) == 0
+ )
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def hamiltonian_path(G):
+ """Returns a Hamiltonian path in the given tournament graph.
+
+ Each tournament has a Hamiltonian path. If furthermore, the
+ tournament is strongly connected, then the returned Hamiltonian path
+ is a Hamiltonian cycle (by joining the endpoints of the path).
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ Returns
+ -------
+ path : list
+ A list of nodes which form a Hamiltonian path in `G`.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)])
+ >>> nx.is_tournament(G)
+ True
+ >>> nx.tournament.hamiltonian_path(G)
+ [0, 1, 2, 3]
+
+ Notes
+ -----
+ This is a recursive implementation with an asymptotic running time
+ of $O(n^2)$, ignoring multiplicative polylogarithmic factors, where
+ $n$ is the number of nodes in the graph.
+
+ """
+ if len(G) == 0:
+ return []
+ if len(G) == 1:
+ return [arbitrary_element(G)]
+ v = arbitrary_element(G)
+ hampath = hamiltonian_path(G.subgraph(set(G) - {v}))
+ # Get the index of the first node in the path that does *not* have
+ # an edge to `v`, then insert `v` before that node.
+ index = index_satisfying(hampath, lambda u: v not in G[u])
+ hampath.insert(index, v)
+ return hampath
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_tournament(n, seed=None):
+ r"""Returns a random tournament graph on `n` nodes.
+
+ Parameters
+ ----------
+ n : int
+ The number of nodes in the returned graph.
+ seed : integer, random_state, or None (default)
+ Indicator of random number generation state.
+ See :ref:`Randomness`.
+
+ Returns
+ -------
+ G : DiGraph
+ A tournament on `n` nodes, with exactly one directed edge joining
+ each pair of distinct nodes.
+
+ Notes
+ -----
+ This algorithm adds, for each pair of distinct nodes, an edge with
+ uniformly random orientation. In other words, `\binom{n}{2}` flips
+ of an unbiased coin decide the orientations of the edges in the
+ graph.
+
+ """
+ # Flip an unbiased coin for each pair of distinct nodes.
+ coins = (seed.random() for i in range((n * (n - 1)) // 2))
+ pairs = combinations(range(n), 2)
+ edges = ((u, v) if r < 0.5 else (v, u) for (u, v), r in zip(pairs, coins))
+ return nx.DiGraph(edges)
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def score_sequence(G):
+ """Returns the score sequence for the given tournament graph.
+
+ The score sequence is the sorted list of the out-degrees of the
+ nodes of the graph.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ Returns
+ -------
+ list
+ A sorted list of the out-degrees of the nodes of `G`.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 0), (1, 3), (0, 2), (0, 3), (2, 1), (3, 2)])
+ >>> nx.is_tournament(G)
+ True
+ >>> nx.tournament.score_sequence(G)
+ [1, 1, 2, 2]
+
+ """
+ return sorted(d for v, d in G.out_degree())
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def tournament_matrix(G):
+ r"""Returns the tournament matrix for the given tournament graph.
+
+ This function requires SciPy.
+
+ The *tournament matrix* of a tournament graph with edge set *E* is
+ the matrix *T* defined by
+
+ .. math::
+
+ T_{i j} =
+ \begin{cases}
+ +1 & \text{if } (i, j) \in E \\
+ -1 & \text{if } (j, i) \in E \\
+ 0 & \text{if } i == j.
+ \end{cases}
+
+ An equivalent definition is `T = A - A^T`, where *A* is the
+ adjacency matrix of the graph `G`.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ Returns
+ -------
+ SciPy sparse array
+ The tournament matrix of the tournament graph `G`.
+
+ Raises
+ ------
+ ImportError
+ If SciPy is not available.
+
+ """
+ A = nx.adjacency_matrix(G)
+ return A - A.T
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_reachable(G, s, t):
+ """Decides whether there is a path from `s` to `t` in the
+ tournament.
+
+ This function is more theoretically efficient than the reachability
+ checks than the shortest path algorithms in
+ :mod:`networkx.algorithms.shortest_paths`.
+
+ The given graph **must** be a tournament, otherwise this function's
+ behavior is undefined.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ s : node
+ A node in the graph.
+
+ t : node
+ A node in the graph.
+
+ Returns
+ -------
+ bool
+ Whether there is a path from `s` to `t` in `G`.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 0), (1, 3), (1, 2), (2, 3), (2, 0), (3, 0)])
+ >>> nx.is_tournament(G)
+ True
+ >>> nx.tournament.is_reachable(G, 1, 3)
+ True
+ >>> nx.tournament.is_reachable(G, 3, 2)
+ False
+
+ Notes
+ -----
+ Although this function is more theoretically efficient than the
+ generic shortest path functions, a speedup requires the use of
+ parallelism. Though it may in the future, the current implementation
+ does not use parallelism, thus you may not see much of a speedup.
+
+ This algorithm comes from [1].
+
+ References
+ ----------
+ .. [1] Tantau, Till.
+ "A note on the complexity of the reachability problem for
+ tournaments."
+ *Electronic Colloquium on Computational Complexity*. 2001.
+
+ """
+
+ def two_neighborhood(G, v):
+ """Returns the set of nodes at distance at most two from `v`.
+
+ `G` must be a graph and `v` a node in that graph.
+
+ The returned set includes the nodes at distance zero (that is,
+ the node `v` itself), the nodes at distance one (that is, the
+ out-neighbors of `v`), and the nodes at distance two.
+
+ """
+ return {
+ x for x in G if x == v or x in G[v] or any(is_path(G, [v, z, x]) for z in G)
+ }
+
+ def is_closed(G, nodes):
+ """Decides whether the given set of nodes is closed.
+
+ A set *S* of nodes is *closed* if for each node *u* in the graph
+ not in *S* and for each node *v* in *S*, there is an edge from
+ *u* to *v*.
+
+ """
+ return all(v in G[u] for u in set(G) - nodes for v in nodes)
+
+ neighborhoods = [two_neighborhood(G, v) for v in G]
+ return all(not (is_closed(G, S) and s in S and t not in S) for S in neighborhoods)
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(name="tournament_is_strongly_connected")
+def is_strongly_connected(G):
+ """Decides whether the given tournament is strongly connected.
+
+ This function is more theoretically efficient than the
+ :func:`~networkx.algorithms.components.is_strongly_connected`
+ function.
+
+ The given graph **must** be a tournament, otherwise this function's
+ behavior is undefined.
+
+ Parameters
+ ----------
+ G : NetworkX graph
+ A directed graph representing a tournament.
+
+ Returns
+ -------
+ bool
+ Whether the tournament is strongly connected.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 0)])
+ >>> nx.is_tournament(G)
+ True
+ >>> nx.tournament.is_strongly_connected(G)
+ True
+ >>> G.remove_edge(3, 0)
+ >>> G.add_edge(0, 3)
+ >>> nx.is_tournament(G)
+ True
+ >>> nx.tournament.is_strongly_connected(G)
+ False
+
+ Notes
+ -----
+ Although this function is more theoretically efficient than the
+ generic strong connectivity function, a speedup requires the use of
+ parallelism. Though it may in the future, the current implementation
+ does not use parallelism, thus you may not see much of a speedup.
+
+ This algorithm comes from [1].
+
+ References
+ ----------
+ .. [1] Tantau, Till.
+ "A note on the complexity of the reachability problem for
+ tournaments."
+ *Electronic Colloquium on Computational Complexity*. 2001.
+
+
+ """
+ return all(is_reachable(G, u, v) for u in G for v in G)
diff --git a/llava_next/lib/python3.10/site-packages/networkx/algorithms/triads.py b/llava_next/lib/python3.10/site-packages/networkx/algorithms/triads.py
new file mode 100644
index 0000000000000000000000000000000000000000..640fc304e2393bb0ee2a004f6bf9dc8d197c01b2
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/algorithms/triads.py
@@ -0,0 +1,604 @@
+# See https://github.com/networkx/networkx/pull/1474
+# Copyright 2011 Reya Group
+# Copyright 2011 Alex Levenson
+# Copyright 2011 Diederik van Liere
+"""Functions for analyzing triads of a graph."""
+
+from collections import defaultdict
+from itertools import combinations, permutations
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = [
+ "triadic_census",
+ "is_triad",
+ "all_triplets",
+ "all_triads",
+ "triads_by_type",
+ "triad_type",
+ "random_triad",
+]
+
+#: The integer codes representing each type of triad.
+#:
+#: Triads that are the same up to symmetry have the same code.
+TRICODES = (
+ 1,
+ 2,
+ 2,
+ 3,
+ 2,
+ 4,
+ 6,
+ 8,
+ 2,
+ 6,
+ 5,
+ 7,
+ 3,
+ 8,
+ 7,
+ 11,
+ 2,
+ 6,
+ 4,
+ 8,
+ 5,
+ 9,
+ 9,
+ 13,
+ 6,
+ 10,
+ 9,
+ 14,
+ 7,
+ 14,
+ 12,
+ 15,
+ 2,
+ 5,
+ 6,
+ 7,
+ 6,
+ 9,
+ 10,
+ 14,
+ 4,
+ 9,
+ 9,
+ 12,
+ 8,
+ 13,
+ 14,
+ 15,
+ 3,
+ 7,
+ 8,
+ 11,
+ 7,
+ 12,
+ 14,
+ 15,
+ 8,
+ 14,
+ 13,
+ 15,
+ 11,
+ 15,
+ 15,
+ 16,
+)
+
+#: The names of each type of triad. The order of the elements is
+#: important: it corresponds to the tricodes given in :data:`TRICODES`.
+TRIAD_NAMES = (
+ "003",
+ "012",
+ "102",
+ "021D",
+ "021U",
+ "021C",
+ "111D",
+ "111U",
+ "030T",
+ "030C",
+ "201",
+ "120D",
+ "120U",
+ "120C",
+ "210",
+ "300",
+)
+
+
+#: A dictionary mapping triad code to triad name.
+TRICODE_TO_NAME = {i: TRIAD_NAMES[code - 1] for i, code in enumerate(TRICODES)}
+
+
+def _tricode(G, v, u, w):
+ """Returns the integer code of the given triad.
+
+ This is some fancy magic that comes from Batagelj and Mrvar's paper. It
+ treats each edge joining a pair of `v`, `u`, and `w` as a bit in
+ the binary representation of an integer.
+
+ """
+ combos = ((v, u, 1), (u, v, 2), (v, w, 4), (w, v, 8), (u, w, 16), (w, u, 32))
+ return sum(x for u, v, x in combos if v in G[u])
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triadic_census(G, nodelist=None):
+ """Determines the triadic census of a directed graph.
+
+ The triadic census is a count of how many of the 16 possible types of
+ triads are present in a directed graph. If a list of nodes is passed, then
+ only those triads are taken into account which have elements of nodelist in them.
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph
+ nodelist : list
+ List of nodes for which you want to calculate triadic census
+
+ Returns
+ -------
+ census : dict
+ Dictionary with triad type as keys and number of occurrences as values.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1), (3, 4), (4, 1), (4, 2)])
+ >>> triadic_census = nx.triadic_census(G)
+ >>> for key, value in triadic_census.items():
+ ... print(f"{key}: {value}")
+ 003: 0
+ 012: 0
+ 102: 0
+ 021D: 0
+ 021U: 0
+ 021C: 0
+ 111D: 0
+ 111U: 0
+ 030T: 2
+ 030C: 2
+ 201: 0
+ 120D: 0
+ 120U: 0
+ 120C: 0
+ 210: 0
+ 300: 0
+
+ Notes
+ -----
+ This algorithm has complexity $O(m)$ where $m$ is the number of edges in
+ the graph.
+
+ For undirected graphs, the triadic census can be computed by first converting
+ the graph into a directed graph using the ``G.to_directed()`` method.
+ After this conversion, only the triad types 003, 102, 201 and 300 will be
+ present in the undirected scenario.
+
+ Raises
+ ------
+ ValueError
+ If `nodelist` contains duplicate nodes or nodes not in `G`.
+ If you want to ignore this you can preprocess with `set(nodelist) & G.nodes`
+
+ See also
+ --------
+ triad_graph
+
+ References
+ ----------
+ .. [1] Vladimir Batagelj and Andrej Mrvar, A subquadratic triad census
+ algorithm for large sparse networks with small maximum degree,
+ University of Ljubljana,
+ http://vlado.fmf.uni-lj.si/pub/networks/doc/triads/triads.pdf
+
+ """
+ nodeset = set(G.nbunch_iter(nodelist))
+ if nodelist is not None and len(nodelist) != len(nodeset):
+ raise ValueError("nodelist includes duplicate nodes or nodes not in G")
+
+ N = len(G)
+ Nnot = N - len(nodeset) # can signal special counting for subset of nodes
+
+ # create an ordering of nodes with nodeset nodes first
+ m = {n: i for i, n in enumerate(nodeset)}
+ if Nnot:
+ # add non-nodeset nodes later in the ordering
+ not_nodeset = G.nodes - nodeset
+ m.update((n, i + N) for i, n in enumerate(not_nodeset))
+
+ # build all_neighbor dicts for easy counting
+ # After Python 3.8 can leave off these keys(). Speedup also using G._pred
+ # nbrs = {n: G._pred[n].keys() | G._succ[n].keys() for n in G}
+ nbrs = {n: G.pred[n].keys() | G.succ[n].keys() for n in G}
+ dbl_nbrs = {n: G.pred[n].keys() & G.succ[n].keys() for n in G}
+
+ if Nnot:
+ sgl_nbrs = {n: G.pred[n].keys() ^ G.succ[n].keys() for n in not_nodeset}
+ # find number of edges not incident to nodes in nodeset
+ sgl = sum(1 for n in not_nodeset for nbr in sgl_nbrs[n] if nbr not in nodeset)
+ sgl_edges_outside = sgl // 2
+ dbl = sum(1 for n in not_nodeset for nbr in dbl_nbrs[n] if nbr not in nodeset)
+ dbl_edges_outside = dbl // 2
+
+ # Initialize the count for each triad to be zero.
+ census = {name: 0 for name in TRIAD_NAMES}
+ # Main loop over nodes
+ for v in nodeset:
+ vnbrs = nbrs[v]
+ dbl_vnbrs = dbl_nbrs[v]
+ if Nnot:
+ # set up counts of edges attached to v.
+ sgl_unbrs_bdy = sgl_unbrs_out = dbl_unbrs_bdy = dbl_unbrs_out = 0
+ for u in vnbrs:
+ if m[u] <= m[v]:
+ continue
+ unbrs = nbrs[u]
+ neighbors = (vnbrs | unbrs) - {u, v}
+ # Count connected triads.
+ for w in neighbors:
+ if m[u] < m[w] or (m[v] < m[w] < m[u] and v not in nbrs[w]):
+ code = _tricode(G, v, u, w)
+ census[TRICODE_TO_NAME[code]] += 1
+
+ # Use a formula for dyadic triads with edge incident to v
+ if u in dbl_vnbrs:
+ census["102"] += N - len(neighbors) - 2
+ else:
+ census["012"] += N - len(neighbors) - 2
+
+ # Count edges attached to v. Subtract later to get triads with v isolated
+ # _out are (u,unbr) for unbrs outside boundary of nodeset
+ # _bdy are (u,unbr) for unbrs on boundary of nodeset (get double counted)
+ if Nnot and u not in nodeset:
+ sgl_unbrs = sgl_nbrs[u]
+ sgl_unbrs_bdy += len(sgl_unbrs & vnbrs - nodeset)
+ sgl_unbrs_out += len(sgl_unbrs - vnbrs - nodeset)
+ dbl_unbrs = dbl_nbrs[u]
+ dbl_unbrs_bdy += len(dbl_unbrs & vnbrs - nodeset)
+ dbl_unbrs_out += len(dbl_unbrs - vnbrs - nodeset)
+ # if nodeset == G.nodes, skip this b/c we will find the edge later.
+ if Nnot:
+ # Count edges outside nodeset not connected with v (v isolated triads)
+ census["012"] += sgl_edges_outside - (sgl_unbrs_out + sgl_unbrs_bdy // 2)
+ census["102"] += dbl_edges_outside - (dbl_unbrs_out + dbl_unbrs_bdy // 2)
+
+ # calculate null triads: "003"
+ # null triads = total number of possible triads - all found triads
+ total_triangles = (N * (N - 1) * (N - 2)) // 6
+ triangles_without_nodeset = (Nnot * (Nnot - 1) * (Nnot - 2)) // 6
+ total_census = total_triangles - triangles_without_nodeset
+ census["003"] = total_census - sum(census.values())
+
+ return census
+
+
+@nx._dispatchable
+def is_triad(G):
+ """Returns True if the graph G is a triad, else False.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX Graph
+
+ Returns
+ -------
+ istriad : boolean
+ Whether G is a valid triad
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+ >>> nx.is_triad(G)
+ True
+ >>> G.add_edge(0, 1)
+ >>> nx.is_triad(G)
+ False
+ """
+ if isinstance(G, nx.Graph):
+ if G.order() == 3 and nx.is_directed(G):
+ if not any((n, n) in G.edges() for n in G.nodes()):
+ return True
+ return False
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def all_triplets(G):
+ """Returns a generator of all possible sets of 3 nodes in a DiGraph.
+
+ .. deprecated:: 3.3
+
+ all_triplets is deprecated and will be removed in NetworkX version 3.5.
+ Use `itertools.combinations` instead::
+
+ all_triplets = itertools.combinations(G, 3)
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph
+
+ Returns
+ -------
+ triplets : generator of 3-tuples
+ Generator of tuples of 3 nodes
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+ >>> list(nx.all_triplets(G))
+ [(1, 2, 3), (1, 2, 4), (1, 3, 4), (2, 3, 4)]
+
+ """
+ import warnings
+
+ warnings.warn(
+ (
+ "\n\nall_triplets is deprecated and will be removed in v3.5.\n"
+ "Use `itertools.combinations(G, 3)` instead."
+ ),
+ category=DeprecationWarning,
+ stacklevel=4,
+ )
+ triplets = combinations(G.nodes(), 3)
+ return triplets
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+def all_triads(G):
+ """A generator of all possible triads in G.
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph
+
+ Returns
+ -------
+ all_triads : generator of DiGraphs
+ Generator of triads (order-3 DiGraphs)
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1), (3, 4), (4, 1), (4, 2)])
+ >>> for triad in nx.all_triads(G):
+ ... print(triad.edges)
+ [(1, 2), (2, 3), (3, 1)]
+ [(1, 2), (4, 1), (4, 2)]
+ [(3, 1), (3, 4), (4, 1)]
+ [(2, 3), (3, 4), (4, 2)]
+
+ """
+ triplets = combinations(G.nodes(), 3)
+ for triplet in triplets:
+ yield G.subgraph(triplet).copy()
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triads_by_type(G):
+ """Returns a list of all triads for each triad type in a directed graph.
+ There are exactly 16 different types of triads possible. Suppose 1, 2, 3 are three
+ nodes, they will be classified as a particular triad type if their connections
+ are as follows:
+
+ - 003: 1, 2, 3
+ - 012: 1 -> 2, 3
+ - 102: 1 <-> 2, 3
+ - 021D: 1 <- 2 -> 3
+ - 021U: 1 -> 2 <- 3
+ - 021C: 1 -> 2 -> 3
+ - 111D: 1 <-> 2 <- 3
+ - 111U: 1 <-> 2 -> 3
+ - 030T: 1 -> 2 -> 3, 1 -> 3
+ - 030C: 1 <- 2 <- 3, 1 -> 3
+ - 201: 1 <-> 2 <-> 3
+ - 120D: 1 <- 2 -> 3, 1 <-> 3
+ - 120U: 1 -> 2 <- 3, 1 <-> 3
+ - 120C: 1 -> 2 -> 3, 1 <-> 3
+ - 210: 1 -> 2 <-> 3, 1 <-> 3
+ - 300: 1 <-> 2 <-> 3, 1 <-> 3
+
+ Refer to the :doc:`example gallery `
+ for visual examples of the triad types.
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph
+
+ Returns
+ -------
+ tri_by_type : dict
+ Dictionary with triad types as keys and lists of triads as values.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 3), (3, 1), (5, 6), (5, 4), (6, 7)])
+ >>> dict = nx.triads_by_type(G)
+ >>> dict["120C"][0].edges()
+ OutEdgeView([(1, 2), (1, 3), (2, 3), (3, 1)])
+ >>> dict["012"][0].edges()
+ OutEdgeView([(1, 2)])
+
+ References
+ ----------
+ .. [1] Snijders, T. (2012). "Transitivity and triads." University of
+ Oxford.
+ https://web.archive.org/web/20170830032057/http://www.stats.ox.ac.uk/~snijders/Trans_Triads_ha.pdf
+ """
+ # num_triads = o * (o - 1) * (o - 2) // 6
+ # if num_triads > TRIAD_LIMIT: print(WARNING)
+ all_tri = all_triads(G)
+ tri_by_type = defaultdict(list)
+ for triad in all_tri:
+ name = triad_type(triad)
+ tri_by_type[name].append(triad)
+ return tri_by_type
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triad_type(G):
+ """Returns the sociological triad type for a triad.
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph with 3 nodes
+
+ Returns
+ -------
+ triad_type : str
+ A string identifying the triad type
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+ >>> nx.triad_type(G)
+ '030C'
+ >>> G.add_edge(1, 3)
+ >>> nx.triad_type(G)
+ '120C'
+
+ Notes
+ -----
+ There can be 6 unique edges in a triad (order-3 DiGraph) (so 2^^6=64 unique
+ triads given 3 nodes). These 64 triads each display exactly 1 of 16
+ topologies of triads (topologies can be permuted). These topologies are
+ identified by the following notation:
+
+ {m}{a}{n}{type} (for example: 111D, 210, 102)
+
+ Here:
+
+ {m} = number of mutual ties (takes 0, 1, 2, 3); a mutual tie is (0,1)
+ AND (1,0)
+ {a} = number of asymmetric ties (takes 0, 1, 2, 3); an asymmetric tie
+ is (0,1) BUT NOT (1,0) or vice versa
+ {n} = number of null ties (takes 0, 1, 2, 3); a null tie is NEITHER
+ (0,1) NOR (1,0)
+ {type} = a letter (takes U, D, C, T) corresponding to up, down, cyclical
+ and transitive. This is only used for topologies that can have
+ more than one form (eg: 021D and 021U).
+
+ References
+ ----------
+ .. [1] Snijders, T. (2012). "Transitivity and triads." University of
+ Oxford.
+ https://web.archive.org/web/20170830032057/http://www.stats.ox.ac.uk/~snijders/Trans_Triads_ha.pdf
+ """
+ if not is_triad(G):
+ raise nx.NetworkXAlgorithmError("G is not a triad (order-3 DiGraph)")
+ num_edges = len(G.edges())
+ if num_edges == 0:
+ return "003"
+ elif num_edges == 1:
+ return "012"
+ elif num_edges == 2:
+ e1, e2 = G.edges()
+ if set(e1) == set(e2):
+ return "102"
+ elif e1[0] == e2[0]:
+ return "021D"
+ elif e1[1] == e2[1]:
+ return "021U"
+ elif e1[1] == e2[0] or e2[1] == e1[0]:
+ return "021C"
+ elif num_edges == 3:
+ for e1, e2, e3 in permutations(G.edges(), 3):
+ if set(e1) == set(e2):
+ if e3[0] in e1:
+ return "111U"
+ # e3[1] in e1:
+ return "111D"
+ elif set(e1).symmetric_difference(set(e2)) == set(e3):
+ if {e1[0], e2[0], e3[0]} == {e1[0], e2[0], e3[0]} == set(G.nodes()):
+ return "030C"
+ # e3 == (e1[0], e2[1]) and e2 == (e1[1], e3[1]):
+ return "030T"
+ elif num_edges == 4:
+ for e1, e2, e3, e4 in permutations(G.edges(), 4):
+ if set(e1) == set(e2):
+ # identify pair of symmetric edges (which necessarily exists)
+ if set(e3) == set(e4):
+ return "201"
+ if {e3[0]} == {e4[0]} == set(e3).intersection(set(e4)):
+ return "120D"
+ if {e3[1]} == {e4[1]} == set(e3).intersection(set(e4)):
+ return "120U"
+ if e3[1] == e4[0]:
+ return "120C"
+ elif num_edges == 5:
+ return "210"
+ elif num_edges == 6:
+ return "300"
+
+
+@not_implemented_for("undirected")
+@py_random_state(1)
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def random_triad(G, seed=None):
+ """Returns a random triad from a directed graph.
+
+ .. deprecated:: 3.3
+
+ random_triad is deprecated and will be removed in version 3.5.
+ Use random sampling directly instead::
+
+ G.subgraph(random.sample(list(G), 3))
+
+ Parameters
+ ----------
+ G : digraph
+ A NetworkX DiGraph
+ seed : integer, random_state, or None (default)
+ Indicator of random number generation state.
+ See :ref:`Randomness`.
+
+ Returns
+ -------
+ G2 : subgraph
+ A randomly selected triad (order-3 NetworkX DiGraph)
+
+ Raises
+ ------
+ NetworkXError
+ If the input Graph has less than 3 nodes.
+
+ Examples
+ --------
+ >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 3), (3, 1), (5, 6), (5, 4), (6, 7)])
+ >>> triad = nx.random_triad(G, seed=1)
+ >>> triad.edges
+ OutEdgeView([(1, 2)])
+
+ """
+ import warnings
+
+ warnings.warn(
+ (
+ "\n\nrandom_triad is deprecated and will be removed in NetworkX v3.5.\n"
+ "Use random.sample instead, e.g.::\n\n"
+ "\tG.subgraph(random.sample(list(G), 3))\n"
+ ),
+ category=DeprecationWarning,
+ stacklevel=5,
+ )
+ if len(G) < 3:
+ raise nx.NetworkXError(
+ f"G needs at least 3 nodes to form a triad; (it has {len(G)} nodes)"
+ )
+ nodes = seed.sample(list(G.nodes()), 3)
+ G2 = G.subgraph(nodes)
+ return G2
diff --git a/llava_next/lib/python3.10/site-packages/networkx/conftest.py b/llava_next/lib/python3.10/site-packages/networkx/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a261c6d0d6f1a31c55349c2cee6776f9b2ba6c4
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/conftest.py
@@ -0,0 +1,284 @@
+"""
+Testing
+=======
+
+General guidelines for writing good tests:
+
+- doctests always assume ``import networkx as nx`` so don't add that
+- prefer pytest fixtures over classes with setup methods.
+- use the ``@pytest.mark.parametrize`` decorator
+- use ``pytest.importorskip`` for numpy, scipy, pandas, and matplotlib b/c of PyPy.
+ and add the module to the relevant entries below.
+
+"""
+
+import os
+import sys
+import warnings
+from importlib.metadata import entry_points
+
+import pytest
+
+import networkx
+
+
+def pytest_addoption(parser):
+ parser.addoption(
+ "--runslow", action="store_true", default=False, help="run slow tests"
+ )
+ parser.addoption(
+ "--backend",
+ action="store",
+ default=None,
+ help="Run tests with a backend by auto-converting nx graphs to backend graphs",
+ )
+ parser.addoption(
+ "--fallback-to-nx",
+ action="store_true",
+ default=False,
+ help="Run nx function if a backend doesn't implement a dispatchable function"
+ " (use with --backend)",
+ )
+
+
+def pytest_configure(config):
+ config.addinivalue_line("markers", "slow: mark test as slow to run")
+ backend = config.getoption("--backend")
+ if backend is None:
+ backend = os.environ.get("NETWORKX_TEST_BACKEND")
+ # nx_loopback backend is only available when testing with a backend
+ loopback_ep = entry_points(name="nx_loopback", group="networkx.backends")
+ if not loopback_ep:
+ warnings.warn(
+ "\n\n WARNING: Mixed NetworkX configuration! \n\n"
+ " This environment has mixed configuration for networkx.\n"
+ " The test object nx_loopback is not configured correctly.\n"
+ " You should not be seeing this message.\n"
+ " Try `pip install -e .`, or change your PYTHONPATH\n"
+ " Make sure python finds the networkx repo you are testing\n\n"
+ )
+ config.backend = backend
+ if backend:
+ # We will update `networkx.config.backend_priority` below in `*_modify_items`
+ # to allow tests to get set up with normal networkx graphs.
+ networkx.utils.backends.backends["nx_loopback"] = loopback_ep["nx_loopback"]
+ networkx.utils.backends.backend_info["nx_loopback"] = {}
+ networkx.config.backends = networkx.utils.Config(
+ nx_loopback=networkx.utils.Config(),
+ **networkx.config.backends,
+ )
+ fallback_to_nx = config.getoption("--fallback-to-nx")
+ if not fallback_to_nx:
+ fallback_to_nx = os.environ.get("NETWORKX_FALLBACK_TO_NX")
+ networkx.config.fallback_to_nx = bool(fallback_to_nx)
+
+
+def pytest_collection_modifyitems(config, items):
+ # Setting this to True here allows tests to be set up before dispatching
+ # any function call to a backend.
+ if config.backend:
+ # Allow pluggable backends to add markers to tests (such as skip or xfail)
+ # when running in auto-conversion test mode
+ backend_name = config.backend
+ if backend_name != "networkx":
+ networkx.utils.backends._dispatchable._is_testing = True
+ networkx.config.backend_priority.algos = [backend_name]
+ networkx.config.backend_priority.generators = [backend_name]
+ backend = networkx.utils.backends.backends[backend_name].load()
+ if hasattr(backend, "on_start_tests"):
+ getattr(backend, "on_start_tests")(items)
+
+ if config.getoption("--runslow"):
+ # --runslow given in cli: do not skip slow tests
+ return
+ skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+ for item in items:
+ if "slow" in item.keywords:
+ item.add_marker(skip_slow)
+
+
+# TODO: The warnings below need to be dealt with, but for now we silence them.
+@pytest.fixture(autouse=True)
+def set_warnings():
+ warnings.filterwarnings(
+ "ignore",
+ category=FutureWarning,
+ message="\n\nsingle_target_shortest_path_length",
+ )
+ warnings.filterwarnings(
+ "ignore",
+ category=FutureWarning,
+ message="\n\nshortest_path",
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nThe `normalized`"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nall_triplets"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nrandom_triad"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="minimal_d_separator"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="d_separated"
+ )
+ warnings.filterwarnings("ignore", category=DeprecationWarning, message="\n\nk_core")
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nk_shell"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nk_crust"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\nk_corona"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\ntotal_spanning_tree_weight"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message=r"\n\nThe 'create=matrix'"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="\n\n`compute_v_structures"
+ )
+ warnings.filterwarnings(
+ "ignore", category=DeprecationWarning, message="Keyword argument 'link'"
+ )
+
+
+@pytest.fixture(autouse=True)
+def add_nx(doctest_namespace):
+ doctest_namespace["nx"] = networkx
+
+
+# What dependencies are installed?
+
+try:
+ import numpy
+
+ has_numpy = True
+except ImportError:
+ has_numpy = False
+
+try:
+ import scipy
+
+ has_scipy = True
+except ImportError:
+ has_scipy = False
+
+try:
+ import matplotlib
+
+ has_matplotlib = True
+except ImportError:
+ has_matplotlib = False
+
+try:
+ import pandas
+
+ has_pandas = True
+except ImportError:
+ has_pandas = False
+
+try:
+ import pygraphviz
+
+ has_pygraphviz = True
+except ImportError:
+ has_pygraphviz = False
+
+try:
+ import pydot
+
+ has_pydot = True
+except ImportError:
+ has_pydot = False
+
+try:
+ import sympy
+
+ has_sympy = True
+except ImportError:
+ has_sympy = False
+
+
+# List of files that pytest should ignore
+
+collect_ignore = []
+
+needs_numpy = [
+ "algorithms/approximation/traveling_salesman.py",
+ "algorithms/centrality/current_flow_closeness.py",
+ "algorithms/centrality/laplacian.py",
+ "algorithms/node_classification.py",
+ "algorithms/non_randomness.py",
+ "algorithms/polynomials.py",
+ "algorithms/shortest_paths/dense.py",
+ "algorithms/tree/mst.py",
+ "drawing/nx_latex.py",
+ "generators/expanders.py",
+ "linalg/bethehessianmatrix.py",
+ "linalg/laplacianmatrix.py",
+ "utils/misc.py",
+]
+needs_scipy = [
+ "algorithms/approximation/traveling_salesman.py",
+ "algorithms/assortativity/correlation.py",
+ "algorithms/assortativity/mixing.py",
+ "algorithms/assortativity/pairs.py",
+ "algorithms/bipartite/matrix.py",
+ "algorithms/bipartite/spectral.py",
+ "algorithms/centrality/current_flow_betweenness.py",
+ "algorithms/centrality/current_flow_betweenness_subset.py",
+ "algorithms/centrality/eigenvector.py",
+ "algorithms/centrality/katz.py",
+ "algorithms/centrality/laplacian.py",
+ "algorithms/centrality/second_order.py",
+ "algorithms/centrality/subgraph_alg.py",
+ "algorithms/communicability_alg.py",
+ "algorithms/community/divisive.py",
+ "algorithms/distance_measures.py",
+ "algorithms/link_analysis/hits_alg.py",
+ "algorithms/link_analysis/pagerank_alg.py",
+ "algorithms/node_classification.py",
+ "algorithms/similarity.py",
+ "algorithms/tree/mst.py",
+ "algorithms/walks.py",
+ "convert_matrix.py",
+ "drawing/layout.py",
+ "drawing/nx_pylab.py",
+ "generators/spectral_graph_forge.py",
+ "generators/expanders.py",
+ "linalg/algebraicconnectivity.py",
+ "linalg/attrmatrix.py",
+ "linalg/bethehessianmatrix.py",
+ "linalg/graphmatrix.py",
+ "linalg/laplacianmatrix.py",
+ "linalg/modularitymatrix.py",
+ "linalg/spectrum.py",
+ "utils/rcm.py",
+]
+needs_matplotlib = ["drawing/nx_pylab.py", "generators/classic.py"]
+needs_pandas = ["convert_matrix.py"]
+needs_pygraphviz = ["drawing/nx_agraph.py"]
+needs_pydot = ["drawing/nx_pydot.py"]
+needs_sympy = ["algorithms/polynomials.py"]
+
+if not has_numpy:
+ collect_ignore += needs_numpy
+if not has_scipy:
+ collect_ignore += needs_scipy
+if not has_matplotlib:
+ collect_ignore += needs_matplotlib
+if not has_pandas:
+ collect_ignore += needs_pandas
+if not has_pygraphviz:
+ collect_ignore += needs_pygraphviz
+if not has_pydot:
+ collect_ignore += needs_pydot
+if not has_sympy:
+ collect_ignore += needs_sympy
diff --git a/llava_next/lib/python3.10/site-packages/networkx/convert.py b/llava_next/lib/python3.10/site-packages/networkx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f89c35d8067bf06768109f73caa3436d3719662
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/convert.py
@@ -0,0 +1,502 @@
+"""Functions to convert NetworkX graphs to and from other formats.
+
+The preferred way of converting data to a NetworkX graph is through the
+graph constructor. The constructor calls the to_networkx_graph() function
+which attempts to guess the input type and convert it automatically.
+
+Examples
+--------
+Create a graph with a single edge from a dictionary of dictionaries
+
+>>> d = {0: {1: 1}} # dict-of-dicts single edge (0,1)
+>>> G = nx.Graph(d)
+
+See Also
+--------
+nx_agraph, nx_pydot
+"""
+
+import warnings
+from collections.abc import Collection, Generator, Iterator
+
+import networkx as nx
+
+__all__ = [
+ "to_networkx_graph",
+ "from_dict_of_dicts",
+ "to_dict_of_dicts",
+ "from_dict_of_lists",
+ "to_dict_of_lists",
+ "from_edgelist",
+ "to_edgelist",
+]
+
+
+def to_networkx_graph(data, create_using=None, multigraph_input=False):
+ """Make a NetworkX graph from a known data structure.
+
+ The preferred way to call this is automatically
+ from the class constructor
+
+ >>> d = {0: {1: {"weight": 1}}} # dict-of-dicts single edge (0,1)
+ >>> G = nx.Graph(d)
+
+ instead of the equivalent
+
+ >>> G = nx.from_dict_of_dicts(d)
+
+ Parameters
+ ----------
+ data : object to be converted
+
+ Current known types are:
+ any NetworkX graph
+ dict-of-dicts
+ dict-of-lists
+ container (e.g. set, list, tuple) of edges
+ iterator (e.g. itertools.chain) that produces edges
+ generator of edges
+ Pandas DataFrame (row per edge)
+ 2D numpy array
+ scipy sparse array
+ pygraphviz agraph
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ multigraph_input : bool (default False)
+ If True and data is a dict_of_dicts,
+ try to create a multigraph assuming dict_of_dict_of_lists.
+ If data and create_using are both multigraphs then create
+ a multigraph from a multigraph.
+
+ """
+ # NX graph
+ if hasattr(data, "adj"):
+ try:
+ result = from_dict_of_dicts(
+ data.adj,
+ create_using=create_using,
+ multigraph_input=data.is_multigraph(),
+ )
+ # data.graph should be dict-like
+ result.graph.update(data.graph)
+ # data.nodes should be dict-like
+ # result.add_node_from(data.nodes.items()) possible but
+ # for custom node_attr_dict_factory which may be hashable
+ # will be unexpected behavior
+ for n, dd in data.nodes.items():
+ result._node[n].update(dd)
+ return result
+ except Exception as err:
+ raise nx.NetworkXError("Input is not a correct NetworkX graph.") from err
+
+ # dict of dicts/lists
+ if isinstance(data, dict):
+ try:
+ return from_dict_of_dicts(
+ data, create_using=create_using, multigraph_input=multigraph_input
+ )
+ except Exception as err1:
+ if multigraph_input is True:
+ raise nx.NetworkXError(
+ f"converting multigraph_input raised:\n{type(err1)}: {err1}"
+ )
+ try:
+ return from_dict_of_lists(data, create_using=create_using)
+ except Exception as err2:
+ raise TypeError("Input is not known type.") from err2
+
+ # edgelists
+ if isinstance(data, list | tuple | nx.reportviews.EdgeViewABC | Iterator):
+ try:
+ return from_edgelist(data, create_using=create_using)
+ except:
+ pass
+
+ # pygraphviz agraph
+ if hasattr(data, "is_strict"):
+ try:
+ return nx.nx_agraph.from_agraph(data, create_using=create_using)
+ except Exception as err:
+ raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from err
+
+ # Pandas DataFrame
+ try:
+ import pandas as pd
+
+ if isinstance(data, pd.DataFrame):
+ if data.shape[0] == data.shape[1]:
+ try:
+ return nx.from_pandas_adjacency(data, create_using=create_using)
+ except Exception as err:
+ msg = "Input is not a correct Pandas DataFrame adjacency matrix."
+ raise nx.NetworkXError(msg) from err
+ else:
+ try:
+ return nx.from_pandas_edgelist(
+ data, edge_attr=True, create_using=create_using
+ )
+ except Exception as err:
+ msg = "Input is not a correct Pandas DataFrame edge-list."
+ raise nx.NetworkXError(msg) from err
+ except ImportError:
+ pass
+
+ # numpy array
+ try:
+ import numpy as np
+
+ if isinstance(data, np.ndarray):
+ try:
+ return nx.from_numpy_array(data, create_using=create_using)
+ except Exception as err:
+ raise nx.NetworkXError(
+ f"Failed to interpret array as an adjacency matrix."
+ ) from err
+ except ImportError:
+ pass
+
+ # scipy sparse array - any format
+ try:
+ import scipy
+
+ if hasattr(data, "format"):
+ try:
+ return nx.from_scipy_sparse_array(data, create_using=create_using)
+ except Exception as err:
+ raise nx.NetworkXError(
+ "Input is not a correct scipy sparse array type."
+ ) from err
+ except ImportError:
+ pass
+
+ # Note: most general check - should remain last in order of execution
+ # Includes containers (e.g. list, set, dict, etc.), generators, and
+ # iterators (e.g. itertools.chain) of edges
+
+ if isinstance(data, Collection | Generator | Iterator):
+ try:
+ return from_edgelist(data, create_using=create_using)
+ except Exception as err:
+ raise nx.NetworkXError("Input is not a valid edge list") from err
+
+ raise nx.NetworkXError("Input is not a known data type for conversion.")
+
+
+@nx._dispatchable
+def to_dict_of_lists(G, nodelist=None):
+ """Returns adjacency representation of graph as a dictionary of lists.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ nodelist : list
+ Use only nodes specified in nodelist
+
+ Notes
+ -----
+ Completely ignores edge data for MultiGraph and MultiDiGraph.
+
+ """
+ if nodelist is None:
+ nodelist = G
+
+ d = {}
+ for n in nodelist:
+ d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
+ return d
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_dict_of_lists(d, create_using=None):
+ """Returns a graph from a dictionary of lists.
+
+ Parameters
+ ----------
+ d : dictionary of lists
+ A dictionary of lists adjacency representation.
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ Examples
+ --------
+ >>> dol = {0: [1]} # single edge (0,1)
+ >>> G = nx.from_dict_of_lists(dol)
+
+ or
+
+ >>> G = nx.Graph(dol) # use Graph constructor
+
+ """
+ G = nx.empty_graph(0, create_using)
+ G.add_nodes_from(d)
+ if G.is_multigraph() and not G.is_directed():
+ # a dict_of_lists can't show multiedges. BUT for undirected graphs,
+ # each edge shows up twice in the dict_of_lists.
+ # So we need to treat this case separately.
+ seen = {}
+ for node, nbrlist in d.items():
+ for nbr in nbrlist:
+ if nbr not in seen:
+ G.add_edge(node, nbr)
+ seen[node] = 1 # don't allow reverse edge to show up
+ else:
+ G.add_edges_from(
+ ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
+ )
+ return G
+
+
+def to_dict_of_dicts(G, nodelist=None, edge_data=None):
+ """Returns adjacency representation of graph as a dictionary of dictionaries.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ nodelist : list
+ Use only nodes specified in nodelist
+
+ edge_data : scalar, optional
+ If provided, the value of the dictionary will be set to `edge_data` for
+ all edges. Usual values could be `1` or `True`. If `edge_data` is
+ `None` (the default), the edgedata in `G` is used, resulting in a
+ dict-of-dict-of-dicts. If `G` is a MultiGraph, the result will be a
+ dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize
+ handling edge data. `edge_data` should *not* be a container.
+
+ Returns
+ -------
+ dod : dict
+ A nested dictionary representation of `G`. Note that the level of
+ nesting depends on the type of `G` and the value of `edge_data`
+ (see Examples).
+
+ See Also
+ --------
+ from_dict_of_dicts, to_dict_of_lists
+
+ Notes
+ -----
+ For a more custom approach to handling edge data, try::
+
+ dod = {
+ n: {nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()}
+ for n, nbrdict in G.adj.items()
+ }
+
+ where `custom` returns the desired edge data for each edge between `n` and
+ `nbr`, given existing edge data `dd`.
+
+ Examples
+ --------
+ >>> G = nx.path_graph(3)
+ >>> nx.to_dict_of_dicts(G)
+ {0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}}
+
+ Edge data is preserved by default (``edge_data=None``), resulting
+ in dict-of-dict-of-dicts where the innermost dictionary contains the
+ edge data:
+
+ >>> G = nx.Graph()
+ >>> G.add_edges_from(
+ ... [
+ ... (0, 1, {"weight": 1.0}),
+ ... (1, 2, {"weight": 2.0}),
+ ... (2, 0, {"weight": 1.0}),
+ ... ]
+ ... )
+ >>> d = nx.to_dict_of_dicts(G)
+ >>> d # doctest: +SKIP
+ {0: {1: {'weight': 1.0}, 2: {'weight': 1.0}},
+ 1: {0: {'weight': 1.0}, 2: {'weight': 2.0}},
+ 2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}}
+ >>> d[1][2]["weight"]
+ 2.0
+
+ If `edge_data` is not `None`, edge data in the original graph (if any) is
+ replaced:
+
+ >>> d = nx.to_dict_of_dicts(G, edge_data=1)
+ >>> d
+ {0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}}
+ >>> d[1][2]
+ 1
+
+ This also applies to MultiGraphs: edge data is preserved by default:
+
+ >>> G = nx.MultiGraph()
+ >>> G.add_edge(0, 1, key="a", weight=1.0)
+ 'a'
+ >>> G.add_edge(0, 1, key="b", weight=5.0)
+ 'b'
+ >>> d = nx.to_dict_of_dicts(G)
+ >>> d # doctest: +SKIP
+ {0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}},
+ 1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}}
+ >>> d[0][1]["b"]["weight"]
+ 5.0
+
+ But multi edge data is lost if `edge_data` is not `None`:
+
+ >>> d = nx.to_dict_of_dicts(G, edge_data=10)
+ >>> d
+ {0: {1: 10}, 1: {0: 10}}
+ """
+ dod = {}
+ if nodelist is None:
+ if edge_data is None:
+ for u, nbrdict in G.adjacency():
+ dod[u] = nbrdict.copy()
+ else: # edge_data is not None
+ for u, nbrdict in G.adjacency():
+ dod[u] = dod.fromkeys(nbrdict, edge_data)
+ else: # nodelist is not None
+ if edge_data is None:
+ for u in nodelist:
+ dod[u] = {}
+ for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
+ dod[u][v] = data
+ else: # nodelist and edge_data are not None
+ for u in nodelist:
+ dod[u] = {}
+ for v in (v for v in G[u] if v in nodelist):
+ dod[u][v] = edge_data
+ return dod
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
+ """Returns a graph from a dictionary of dictionaries.
+
+ Parameters
+ ----------
+ d : dictionary of dictionaries
+ A dictionary of dictionaries adjacency representation.
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ multigraph_input : bool (default False)
+ When True, the dict `d` is assumed
+ to be a dict-of-dict-of-dict-of-dict structure keyed by
+ node to neighbor to edge keys to edge data for multi-edges.
+ Otherwise this routine assumes dict-of-dict-of-dict keyed by
+ node to neighbor to edge data.
+
+ Examples
+ --------
+ >>> dod = {0: {1: {"weight": 1}}} # single edge (0,1)
+ >>> G = nx.from_dict_of_dicts(dod)
+
+ or
+
+ >>> G = nx.Graph(dod) # use Graph constructor
+
+ """
+ G = nx.empty_graph(0, create_using)
+ G.add_nodes_from(d)
+ # does dict d represent a MultiGraph or MultiDiGraph?
+ if multigraph_input:
+ if G.is_directed():
+ if G.is_multigraph():
+ G.add_edges_from(
+ (u, v, key, data)
+ for u, nbrs in d.items()
+ for v, datadict in nbrs.items()
+ for key, data in datadict.items()
+ )
+ else:
+ G.add_edges_from(
+ (u, v, data)
+ for u, nbrs in d.items()
+ for v, datadict in nbrs.items()
+ for key, data in datadict.items()
+ )
+ else: # Undirected
+ if G.is_multigraph():
+ seen = set() # don't add both directions of undirected graph
+ for u, nbrs in d.items():
+ for v, datadict in nbrs.items():
+ if (u, v) not in seen:
+ G.add_edges_from(
+ (u, v, key, data) for key, data in datadict.items()
+ )
+ seen.add((v, u))
+ else:
+ seen = set() # don't add both directions of undirected graph
+ for u, nbrs in d.items():
+ for v, datadict in nbrs.items():
+ if (u, v) not in seen:
+ G.add_edges_from(
+ (u, v, data) for key, data in datadict.items()
+ )
+ seen.add((v, u))
+
+ else: # not a multigraph to multigraph transfer
+ if G.is_multigraph() and not G.is_directed():
+ # d can have both representations u-v, v-u in dict. Only add one.
+ # We don't need this check for digraphs since we add both directions,
+ # or for Graph() since it is done implicitly (parallel edges not allowed)
+ seen = set()
+ for u, nbrs in d.items():
+ for v, data in nbrs.items():
+ if (u, v) not in seen:
+ G.add_edge(u, v, key=0)
+ G[u][v][0].update(data)
+ seen.add((v, u))
+ else:
+ G.add_edges_from(
+ ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
+ )
+ return G
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def to_edgelist(G, nodelist=None):
+ """Returns a list of edges in the graph.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ nodelist : list
+ Use only nodes specified in nodelist
+
+ """
+ if nodelist is None:
+ return G.edges(data=True)
+ return G.edges(nodelist, data=True)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_edgelist(edgelist, create_using=None):
+ """Returns a graph from a list of edges.
+
+ Parameters
+ ----------
+ edgelist : list or iterator
+ Edge tuples
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ Examples
+ --------
+ >>> edgelist = [(0, 1)] # single edge (0,1)
+ >>> G = nx.from_edgelist(edgelist)
+
+ or
+
+ >>> G = nx.Graph(edgelist) # use Graph constructor
+
+ """
+ G = nx.empty_graph(0, create_using)
+ G.add_edges_from(edgelist)
+ return G
diff --git a/llava_next/lib/python3.10/site-packages/networkx/convert_matrix.py b/llava_next/lib/python3.10/site-packages/networkx/convert_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..8992627cbac970e46ca7dce0557611a51cea2c26
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/convert_matrix.py
@@ -0,0 +1,1317 @@
+"""Functions to convert NetworkX graphs to and from common data containers
+like numpy arrays, scipy sparse arrays, and pandas DataFrames.
+
+The preferred way of converting data to a NetworkX graph is through the
+graph constructor. The constructor calls the `~networkx.convert.to_networkx_graph`
+function which attempts to guess the input type and convert it automatically.
+
+Examples
+--------
+Create a 10 node random graph from a numpy array
+
+>>> import numpy as np
+>>> rng = np.random.default_rng()
+>>> a = rng.integers(low=0, high=2, size=(10, 10))
+>>> DG = nx.from_numpy_array(a, create_using=nx.DiGraph)
+
+or equivalently:
+
+>>> DG = nx.DiGraph(a)
+
+which calls `from_numpy_array` internally based on the type of ``a``.
+
+See Also
+--------
+nx_agraph, nx_pydot
+"""
+
+import itertools
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+ "from_pandas_adjacency",
+ "to_pandas_adjacency",
+ "from_pandas_edgelist",
+ "to_pandas_edgelist",
+ "from_scipy_sparse_array",
+ "to_scipy_sparse_array",
+ "from_numpy_array",
+ "to_numpy_array",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def to_pandas_adjacency(
+ G,
+ nodelist=None,
+ dtype=None,
+ order=None,
+ multigraph_weight=sum,
+ weight="weight",
+ nonedge=0.0,
+):
+ """Returns the graph adjacency matrix as a Pandas DataFrame.
+
+ Parameters
+ ----------
+ G : graph
+ The NetworkX graph used to construct the Pandas DataFrame.
+
+ nodelist : list, optional
+ The rows and columns are ordered according to the nodes in `nodelist`.
+ If `nodelist` is None, then the ordering is produced by G.nodes().
+
+ multigraph_weight : {sum, min, max}, optional
+ An operator that determines how weights in multigraphs are handled.
+ The default is to sum the weights of the multiple edges.
+
+ weight : string or None, optional
+ The edge attribute that holds the numerical value used for
+ the edge weight. If an edge does not have that attribute, then the
+ value 1 is used instead.
+
+ nonedge : float, optional
+ The matrix values corresponding to nonedges are typically set to zero.
+ However, this could be undesirable if there are matrix values
+ corresponding to actual edges that also have the value zero. If so,
+ one might prefer nonedges to have some other value, such as nan.
+
+ Returns
+ -------
+ df : Pandas DataFrame
+ Graph adjacency matrix
+
+ Notes
+ -----
+ For directed graphs, entry i,j corresponds to an edge from i to j.
+
+ The DataFrame entries are assigned to the weight edge attribute. When
+ an edge does not have a weight attribute, the value of the entry is set to
+ the number 1. For multiple (parallel) edges, the values of the entries
+ are determined by the 'multigraph_weight' parameter. The default is to
+ sum the weight attributes for each of the parallel edges.
+
+ When `nodelist` does not contain every node in `G`, the matrix is built
+ from the subgraph of `G` that is induced by the nodes in `nodelist`.
+
+ The convention used for self-loop edges in graphs is to assign the
+ diagonal matrix entry value to the weight attribute of the edge
+ (or the number 1 if the edge has no weight attribute). If the
+ alternate convention of doubling the edge weight is desired the
+ resulting Pandas DataFrame can be modified as follows::
+
+ >>> import pandas as pd
+ >>> G = nx.Graph([(1, 1), (2, 2)])
+ >>> df = nx.to_pandas_adjacency(G)
+ >>> df
+ 1 2
+ 1 1.0 0.0
+ 2 0.0 1.0
+ >>> diag_idx = list(range(len(df)))
+ >>> df.iloc[diag_idx, diag_idx] *= 2
+ >>> df
+ 1 2
+ 1 2.0 0.0
+ 2 0.0 2.0
+
+ Examples
+ --------
+ >>> G = nx.MultiDiGraph()
+ >>> G.add_edge(0, 1, weight=2)
+ 0
+ >>> G.add_edge(1, 0)
+ 0
+ >>> G.add_edge(2, 2, weight=3)
+ 0
+ >>> G.add_edge(2, 2)
+ 1
+ >>> nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int)
+ 0 1 2
+ 0 0 2 0
+ 1 1 0 0
+ 2 0 0 4
+
+ """
+ import pandas as pd
+
+ M = to_numpy_array(
+ G,
+ nodelist=nodelist,
+ dtype=dtype,
+ order=order,
+ multigraph_weight=multigraph_weight,
+ weight=weight,
+ nonedge=nonedge,
+ )
+ if nodelist is None:
+ nodelist = list(G)
+ return pd.DataFrame(data=M, index=nodelist, columns=nodelist)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pandas_adjacency(df, create_using=None):
+ r"""Returns a graph from Pandas DataFrame.
+
+ The Pandas DataFrame is interpreted as an adjacency matrix for the graph.
+
+ Parameters
+ ----------
+ df : Pandas DataFrame
+ An adjacency matrix representation of a graph
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ Notes
+ -----
+ For directed graphs, explicitly mention create_using=nx.DiGraph,
+ and entry i,j of df corresponds to an edge from i to j.
+
+ If `df` has a single data type for each entry it will be converted to an
+ appropriate Python data type.
+
+ If you have node attributes stored in a separate dataframe `df_nodes`,
+ you can load those attributes to the graph `G` using the following code:
+
+ ```
+ df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]})
+ G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows())
+ ```
+
+ If `df` has a user-specified compound data type the names
+ of the data fields will be used as attribute keys in the resulting
+ NetworkX graph.
+
+ See Also
+ --------
+ to_pandas_adjacency
+
+ Examples
+ --------
+ Simple integer weights on edges:
+
+ >>> import pandas as pd
+ >>> pd.options.display.max_columns = 20
+ >>> df = pd.DataFrame([[1, 1], [2, 1]])
+ >>> df
+ 0 1
+ 0 1 1
+ 1 2 1
+ >>> G = nx.from_pandas_adjacency(df)
+ >>> G.name = "Graph from pandas adjacency matrix"
+ >>> print(G)
+ Graph named 'Graph from pandas adjacency matrix' with 2 nodes and 3 edges
+ """
+
+ try:
+ df = df[df.index]
+ except Exception as err:
+ missing = list(set(df.index).difference(set(df.columns)))
+ msg = f"{missing} not in columns"
+ raise nx.NetworkXError("Columns must match Indices.", msg) from err
+
+ A = df.values
+ G = from_numpy_array(A, create_using=create_using, nodelist=df.columns)
+
+ return G
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def to_pandas_edgelist(
+ G,
+ source="source",
+ target="target",
+ nodelist=None,
+ dtype=None,
+ edge_key=None,
+):
+ """Returns the graph edge list as a Pandas DataFrame.
+
+ Parameters
+ ----------
+ G : graph
+ The NetworkX graph used to construct the Pandas DataFrame.
+
+ source : str or int, optional
+ A valid column name (string or integer) for the source nodes (for the
+ directed case).
+
+ target : str or int, optional
+ A valid column name (string or integer) for the target nodes (for the
+ directed case).
+
+ nodelist : list, optional
+ Use only nodes specified in nodelist
+
+ dtype : dtype, default None
+ Use to create the DataFrame. Data type to force.
+ Only a single dtype is allowed. If None, infer.
+
+ edge_key : str or int or None, optional (default=None)
+ A valid column name (string or integer) for the edge keys (for the
+ multigraph case). If None, edge keys are not stored in the DataFrame.
+
+ Returns
+ -------
+ df : Pandas DataFrame
+ Graph edge list
+
+ Examples
+ --------
+ >>> G = nx.Graph(
+ ... [
+ ... ("A", "B", {"cost": 1, "weight": 7}),
+ ... ("C", "E", {"cost": 9, "weight": 10}),
+ ... ]
+ ... )
+ >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"])
+ >>> df[["source", "target", "cost", "weight"]]
+ source target cost weight
+ 0 A B 1 7
+ 1 C E 9 10
+
+ >>> G = nx.MultiGraph([("A", "B", {"cost": 1}), ("A", "B", {"cost": 9})])
+ >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"], edge_key="ekey")
+ >>> df[["source", "target", "cost", "ekey"]]
+ source target cost ekey
+ 0 A B 1 0
+ 1 A B 9 1
+
+ """
+ import pandas as pd
+
+ if nodelist is None:
+ edgelist = G.edges(data=True)
+ else:
+ edgelist = G.edges(nodelist, data=True)
+ source_nodes = [s for s, _, _ in edgelist]
+ target_nodes = [t for _, t, _ in edgelist]
+
+ all_attrs = set().union(*(d.keys() for _, _, d in edgelist))
+ if source in all_attrs:
+ raise nx.NetworkXError(f"Source name {source!r} is an edge attr name")
+ if target in all_attrs:
+ raise nx.NetworkXError(f"Target name {target!r} is an edge attr name")
+
+ nan = float("nan")
+ edge_attr = {k: [d.get(k, nan) for _, _, d in edgelist] for k in all_attrs}
+
+ if G.is_multigraph() and edge_key is not None:
+ if edge_key in all_attrs:
+ raise nx.NetworkXError(f"Edge key name {edge_key!r} is an edge attr name")
+ edge_keys = [k for _, _, k in G.edges(keys=True)]
+ edgelistdict = {source: source_nodes, target: target_nodes, edge_key: edge_keys}
+ else:
+ edgelistdict = {source: source_nodes, target: target_nodes}
+
+ edgelistdict.update(edge_attr)
+ return pd.DataFrame(edgelistdict, dtype=dtype)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pandas_edgelist(
+ df,
+ source="source",
+ target="target",
+ edge_attr=None,
+ create_using=None,
+ edge_key=None,
+):
+ """Returns a graph from Pandas DataFrame containing an edge list.
+
+ The Pandas DataFrame should contain at least two columns of node names and
+ zero or more columns of edge attributes. Each row will be processed as one
+ edge instance.
+
+ Note: This function iterates over DataFrame.values, which is not
+ guaranteed to retain the data type across columns in the row. This is only
+ a problem if your row is entirely numeric and a mix of ints and floats. In
+ that case, all values will be returned as floats. See the
+ DataFrame.iterrows documentation for an example.
+
+ Parameters
+ ----------
+ df : Pandas DataFrame
+ An edge list representation of a graph
+
+ source : str or int
+ A valid column name (string or integer) for the source nodes (for the
+ directed case).
+
+ target : str or int
+ A valid column name (string or integer) for the target nodes (for the
+ directed case).
+
+ edge_attr : str or int, iterable, True, or None
+ A valid column name (str or int) or iterable of column names that are
+ used to retrieve items and add them to the graph as edge attributes.
+ If `True`, all columns will be added except `source`, `target` and `edge_key`.
+ If `None`, no edge attributes are added to the graph.
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ edge_key : str or None, optional (default=None)
+ A valid column name for the edge keys (for a MultiGraph). The values in
+ this column are used for the edge keys when adding edges if create_using
+ is a multigraph.
+
+ If you have node attributes stored in a separate dataframe `df_nodes`,
+ you can load those attributes to the graph `G` using the following code:
+
+ ```
+ df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]})
+ G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows())
+ ```
+
+ See Also
+ --------
+ to_pandas_edgelist
+
+ Examples
+ --------
+ Simple integer weights on edges:
+
+ >>> import pandas as pd
+ >>> pd.options.display.max_columns = 20
+ >>> import numpy as np
+ >>> rng = np.random.RandomState(seed=5)
+ >>> ints = rng.randint(1, 11, size=(3, 2))
+ >>> a = ["A", "B", "C"]
+ >>> b = ["D", "A", "E"]
+ >>> df = pd.DataFrame(ints, columns=["weight", "cost"])
+ >>> df[0] = a
+ >>> df["b"] = b
+ >>> df[["weight", "cost", 0, "b"]]
+ weight cost 0 b
+ 0 4 7 A D
+ 1 7 1 B A
+ 2 10 9 C E
+ >>> G = nx.from_pandas_edgelist(df, 0, "b", ["weight", "cost"])
+ >>> G["E"]["C"]["weight"]
+ 10
+ >>> G["E"]["C"]["cost"]
+ 9
+ >>> edges = pd.DataFrame(
+ ... {
+ ... "source": [0, 1, 2],
+ ... "target": [2, 2, 3],
+ ... "weight": [3, 4, 5],
+ ... "color": ["red", "blue", "blue"],
+ ... }
+ ... )
+ >>> G = nx.from_pandas_edgelist(edges, edge_attr=True)
+ >>> G[0][2]["color"]
+ 'red'
+
+ Build multigraph with custom keys:
+
+ >>> edges = pd.DataFrame(
+ ... {
+ ... "source": [0, 1, 2, 0],
+ ... "target": [2, 2, 3, 2],
+ ... "my_edge_key": ["A", "B", "C", "D"],
+ ... "weight": [3, 4, 5, 6],
+ ... "color": ["red", "blue", "blue", "blue"],
+ ... }
+ ... )
+ >>> G = nx.from_pandas_edgelist(
+ ... edges,
+ ... edge_key="my_edge_key",
+ ... edge_attr=["weight", "color"],
+ ... create_using=nx.MultiGraph(),
+ ... )
+ >>> G[0][2]
+ AtlasView({'A': {'weight': 3, 'color': 'red'}, 'D': {'weight': 6, 'color': 'blue'}})
+
+
+ """
+ g = nx.empty_graph(0, create_using)
+
+ if edge_attr is None:
+ if g.is_multigraph() and edge_key is not None:
+ for u, v, k in zip(df[source], df[target], df[edge_key]):
+ g.add_edge(u, v, k)
+ else:
+ g.add_edges_from(zip(df[source], df[target]))
+ return g
+
+ reserved_columns = [source, target]
+ if g.is_multigraph() and edge_key is not None:
+ reserved_columns.append(edge_key)
+
+ # Additional columns requested
+ attr_col_headings = []
+ attribute_data = []
+ if edge_attr is True:
+ attr_col_headings = [c for c in df.columns if c not in reserved_columns]
+ elif isinstance(edge_attr, list | tuple):
+ attr_col_headings = edge_attr
+ else:
+ attr_col_headings = [edge_attr]
+ if len(attr_col_headings) == 0:
+ raise nx.NetworkXError(
+ f"Invalid edge_attr argument: No columns found with name: {attr_col_headings}"
+ )
+
+ try:
+ attribute_data = zip(*[df[col] for col in attr_col_headings])
+ except (KeyError, TypeError) as err:
+ msg = f"Invalid edge_attr argument: {edge_attr}"
+ raise nx.NetworkXError(msg) from err
+
+ if g.is_multigraph():
+ # => append the edge keys from the df to the bundled data
+ if edge_key is not None:
+ try:
+ multigraph_edge_keys = df[edge_key]
+ attribute_data = zip(attribute_data, multigraph_edge_keys)
+ except (KeyError, TypeError) as err:
+ msg = f"Invalid edge_key argument: {edge_key}"
+ raise nx.NetworkXError(msg) from err
+
+ for s, t, attrs in zip(df[source], df[target], attribute_data):
+ if edge_key is not None:
+ attrs, multigraph_edge_key = attrs
+ key = g.add_edge(s, t, key=multigraph_edge_key)
+ else:
+ key = g.add_edge(s, t)
+
+ g[s][t][key].update(zip(attr_col_headings, attrs))
+ else:
+ for s, t, attrs in zip(df[source], df[target], attribute_data):
+ g.add_edge(s, t)
+ g[s][t].update(zip(attr_col_headings, attrs))
+
+ return g
+
+
+@nx._dispatchable(edge_attrs="weight")
+def to_scipy_sparse_array(G, nodelist=None, dtype=None, weight="weight", format="csr"):
+ """Returns the graph adjacency matrix as a SciPy sparse array.
+
+ Parameters
+ ----------
+ G : graph
+ The NetworkX graph used to construct the sparse array.
+
+ nodelist : list, optional
+ The rows and columns are ordered according to the nodes in `nodelist`.
+ If `nodelist` is None, then the ordering is produced by ``G.nodes()``.
+
+ dtype : NumPy data-type, optional
+ A valid NumPy dtype used to initialize the array. If None, then the
+ NumPy default is used.
+
+ weight : string or None, optional (default='weight')
+ The edge attribute that holds the numerical value used for
+ the edge weight. If None then all edge weights are 1.
+
+ format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
+ The format of the sparse array to be returned (default 'csr'). For
+ some algorithms different implementations of sparse arrays
+ can perform better. See [1]_ for details.
+
+ Returns
+ -------
+ A : SciPy sparse array
+ Graph adjacency matrix.
+
+ Notes
+ -----
+ For directed graphs, matrix entry ``i, j`` corresponds to an edge from
+ ``i`` to ``j``.
+
+ The values of the adjacency matrix are populated using the edge attribute held in
+ parameter `weight`. When an edge does not have that attribute, the
+ value of the entry is 1.
+
+ For multiple edges the matrix values are the sums of the edge weights.
+
+ When `nodelist` does not contain every node in `G`, the adjacency matrix
+ is built from the subgraph of `G` that is induced by the nodes in
+ `nodelist`.
+
+ The convention used for self-loop edges in graphs is to assign the
+ diagonal matrix entry value to the weight attribute of the edge
+ (or the number 1 if the edge has no weight attribute). If the
+ alternate convention of doubling the edge weight is desired the
+ resulting array can be modified as follows::
+
+ >>> G = nx.Graph([(1, 1)])
+ >>> A = nx.to_scipy_sparse_array(G)
+ >>> A.toarray()
+ array([[1]])
+ >>> A.setdiag(A.diagonal() * 2)
+ >>> A.toarray()
+ array([[2]])
+
+ Examples
+ --------
+
+ Basic usage:
+
+ >>> G = nx.path_graph(4)
+ >>> A = nx.to_scipy_sparse_array(G)
+ >>> A # doctest: +SKIP
+
+
+ >>> A.toarray()
+ array([[0, 1, 0, 0],
+ [1, 0, 1, 0],
+ [0, 1, 0, 1],
+ [0, 0, 1, 0]])
+
+ .. note:: The `toarray` method is used in these examples to better visualize
+ the adjacancy matrix. For a dense representation of the adjaceny matrix,
+ use `to_numpy_array` instead.
+
+ Directed graphs:
+
+ >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+ >>> nx.to_scipy_sparse_array(G).toarray()
+ array([[0, 1, 0, 0],
+ [0, 0, 1, 0],
+ [0, 0, 0, 1],
+ [0, 0, 0, 0]])
+
+ >>> H = G.reverse()
+ >>> H.edges
+ OutEdgeView([(1, 0), (2, 1), (3, 2)])
+ >>> nx.to_scipy_sparse_array(H).toarray()
+ array([[0, 0, 0, 0],
+ [1, 0, 0, 0],
+ [0, 1, 0, 0],
+ [0, 0, 1, 0]])
+
+ By default, the order of the rows/columns of the adjacency matrix is determined
+ by the ordering of the nodes in `G`:
+
+ >>> G = nx.Graph()
+ >>> G.add_nodes_from([3, 5, 0, 1])
+ >>> G.add_edges_from([(1, 3), (1, 5)])
+ >>> nx.to_scipy_sparse_array(G).toarray()
+ array([[0, 0, 0, 1],
+ [0, 0, 0, 1],
+ [0, 0, 0, 0],
+ [1, 1, 0, 0]])
+
+ The ordering of the rows can be changed with `nodelist`:
+
+ >>> ordered = [0, 1, 3, 5]
+ >>> nx.to_scipy_sparse_array(G, nodelist=ordered).toarray()
+ array([[0, 0, 0, 0],
+ [0, 0, 1, 1],
+ [0, 1, 0, 0],
+ [0, 1, 0, 0]])
+
+ If `nodelist` contains a subset of the nodes in `G`, the adjacency matrix
+ for the node-induced subgraph is produced:
+
+ >>> nx.to_scipy_sparse_array(G, nodelist=[1, 3, 5]).toarray()
+ array([[0, 1, 1],
+ [1, 0, 0],
+ [1, 0, 0]])
+
+ The values of the adjacency matrix are drawn from the edge attribute
+ specified by the `weight` parameter:
+
+ >>> G = nx.path_graph(4)
+ >>> nx.set_edge_attributes(
+ ... G, values={(0, 1): 1, (1, 2): 10, (2, 3): 2}, name="weight"
+ ... )
+ >>> nx.set_edge_attributes(
+ ... G, values={(0, 1): 50, (1, 2): 35, (2, 3): 10}, name="capacity"
+ ... )
+ >>> nx.to_scipy_sparse_array(G).toarray() # Default weight="weight"
+ array([[ 0, 1, 0, 0],
+ [ 1, 0, 10, 0],
+ [ 0, 10, 0, 2],
+ [ 0, 0, 2, 0]])
+ >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray()
+ array([[ 0, 50, 0, 0],
+ [50, 0, 35, 0],
+ [ 0, 35, 0, 10],
+ [ 0, 0, 10, 0]])
+
+ Any edges that don't have a `weight` attribute default to 1:
+
+ >>> G[1][2].pop("capacity")
+ 35
+ >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray()
+ array([[ 0, 50, 0, 0],
+ [50, 0, 1, 0],
+ [ 0, 1, 0, 10],
+ [ 0, 0, 10, 0]])
+
+ When `G` is a multigraph, the values in the adjacency matrix are given by
+ the sum of the `weight` edge attribute over each edge key:
+
+ >>> G = nx.MultiDiGraph([(0, 1), (0, 1), (0, 1), (2, 0)])
+ >>> nx.to_scipy_sparse_array(G).toarray()
+ array([[0, 3, 0],
+ [0, 0, 0],
+ [1, 0, 0]])
+
+ References
+ ----------
+ .. [1] Scipy Dev. References, "Sparse Arrays",
+ https://docs.scipy.org/doc/scipy/reference/sparse.html
+ """
+ import scipy as sp
+
+ if len(G) == 0:
+ raise nx.NetworkXError("Graph has no nodes or edges")
+
+ if nodelist is None:
+ nodelist = list(G)
+ nlen = len(G)
+ else:
+ nlen = len(nodelist)
+ if nlen == 0:
+ raise nx.NetworkXError("nodelist has no nodes")
+ nodeset = set(G.nbunch_iter(nodelist))
+ if nlen != len(nodeset):
+ for n in nodelist:
+ if n not in G:
+ raise nx.NetworkXError(f"Node {n} in nodelist is not in G")
+ raise nx.NetworkXError("nodelist contains duplicates.")
+ if nlen < len(G):
+ G = G.subgraph(nodelist)
+
+ index = dict(zip(nodelist, range(nlen)))
+ coefficients = zip(
+ *((index[u], index[v], wt) for u, v, wt in G.edges(data=weight, default=1))
+ )
+ try:
+ row, col, data = coefficients
+ except ValueError:
+ # there is no edge in the subgraph
+ row, col, data = [], [], []
+
+ if G.is_directed():
+ A = sp.sparse.coo_array((data, (row, col)), shape=(nlen, nlen), dtype=dtype)
+ else:
+ # symmetrize matrix
+ d = data + data
+ r = row + col
+ c = col + row
+ # selfloop entries get double counted when symmetrizing
+ # so we subtract the data on the diagonal
+ selfloops = list(nx.selfloop_edges(G, data=weight, default=1))
+ if selfloops:
+ diag_index, diag_data = zip(*((index[u], -wt) for u, v, wt in selfloops))
+ d += diag_data
+ r += diag_index
+ c += diag_index
+ A = sp.sparse.coo_array((d, (r, c)), shape=(nlen, nlen), dtype=dtype)
+ try:
+ return A.asformat(format)
+ except ValueError as err:
+ raise nx.NetworkXError(f"Unknown sparse matrix format: {format}") from err
+
+
+def _csr_gen_triples(A):
+ """Converts a SciPy sparse array in **Compressed Sparse Row** format to
+ an iterable of weighted edge triples.
+
+ """
+ nrows = A.shape[0]
+ indptr, dst_indices, data = A.indptr, A.indices, A.data
+ import numpy as np
+
+ src_indices = np.repeat(np.arange(nrows), np.diff(indptr))
+ return zip(src_indices.tolist(), dst_indices.tolist(), A.data.tolist())
+
+
+def _csc_gen_triples(A):
+ """Converts a SciPy sparse array in **Compressed Sparse Column** format to
+ an iterable of weighted edge triples.
+
+ """
+ ncols = A.shape[1]
+ indptr, src_indices, data = A.indptr, A.indices, A.data
+ import numpy as np
+
+ dst_indices = np.repeat(np.arange(ncols), np.diff(indptr))
+ return zip(src_indices.tolist(), dst_indices.tolist(), A.data.tolist())
+
+
+def _coo_gen_triples(A):
+ """Converts a SciPy sparse array in **Coordinate** format to an iterable
+ of weighted edge triples.
+
+ """
+ return zip(A.row.tolist(), A.col.tolist(), A.data.tolist())
+
+
+def _dok_gen_triples(A):
+ """Converts a SciPy sparse array in **Dictionary of Keys** format to an
+ iterable of weighted edge triples.
+
+ """
+ for (r, c), v in A.items():
+ # Use `v.item()` to convert a NumPy scalar to the appropriate Python scalar
+ yield int(r), int(c), v.item()
+
+
+def _generate_weighted_edges(A):
+ """Returns an iterable over (u, v, w) triples, where u and v are adjacent
+ vertices and w is the weight of the edge joining u and v.
+
+ `A` is a SciPy sparse array (in any format).
+
+ """
+ if A.format == "csr":
+ return _csr_gen_triples(A)
+ if A.format == "csc":
+ return _csc_gen_triples(A)
+ if A.format == "dok":
+ return _dok_gen_triples(A)
+ # If A is in any other format (including COO), convert it to COO format.
+ return _coo_gen_triples(A.tocoo())
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_scipy_sparse_array(
+ A, parallel_edges=False, create_using=None, edge_attribute="weight"
+):
+ """Creates a new graph from an adjacency matrix given as a SciPy sparse
+ array.
+
+ Parameters
+ ----------
+ A: scipy.sparse array
+ An adjacency matrix representation of a graph
+
+ parallel_edges : Boolean
+ If this is True, `create_using` is a multigraph, and `A` is an
+ integer matrix, then entry *(i, j)* in the matrix is interpreted as the
+ number of parallel edges joining vertices *i* and *j* in the graph.
+ If it is False, then the entries in the matrix are interpreted as
+ the weight of a single edge joining the vertices.
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ edge_attribute: string
+ Name of edge attribute to store matrix numeric value. The data will
+ have the same type as the matrix entry (int, float, (real,imag)).
+
+ Notes
+ -----
+ For directed graphs, explicitly mention create_using=nx.DiGraph,
+ and entry i,j of A corresponds to an edge from i to j.
+
+ If `create_using` is :class:`networkx.MultiGraph` or
+ :class:`networkx.MultiDiGraph`, `parallel_edges` is True, and the
+ entries of `A` are of type :class:`int`, then this function returns a
+ multigraph (constructed from `create_using`) with parallel edges.
+ In this case, `edge_attribute` will be ignored.
+
+ If `create_using` indicates an undirected multigraph, then only the edges
+ indicated by the upper triangle of the matrix `A` will be added to the
+ graph.
+
+ Examples
+ --------
+ >>> import scipy as sp
+ >>> A = sp.sparse.eye(2, 2, 1)
+ >>> G = nx.from_scipy_sparse_array(A)
+
+ If `create_using` indicates a multigraph and the matrix has only integer
+ entries and `parallel_edges` is False, then the entries will be treated
+ as weights for edges joining the nodes (without creating parallel edges):
+
+ >>> A = sp.sparse.csr_array([[1, 1], [1, 2]])
+ >>> G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
+ >>> G[1][1]
+ AtlasView({0: {'weight': 2}})
+
+ If `create_using` indicates a multigraph and the matrix has only integer
+ entries and `parallel_edges` is True, then the entries will be treated
+ as the number of parallel edges joining those two vertices:
+
+ >>> A = sp.sparse.csr_array([[1, 1], [1, 2]])
+ >>> G = nx.from_scipy_sparse_array(
+ ... A, parallel_edges=True, create_using=nx.MultiGraph
+ ... )
+ >>> G[1][1]
+ AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
+
+ """
+ G = nx.empty_graph(0, create_using)
+ n, m = A.shape
+ if n != m:
+ raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}")
+ # Make sure we get even the isolated nodes of the graph.
+ G.add_nodes_from(range(n))
+ # Create an iterable over (u, v, w) triples and for each triple, add an
+ # edge from u to v with weight w.
+ triples = _generate_weighted_edges(A)
+ # If the entries in the adjacency matrix are integers, the graph is a
+ # multigraph, and parallel_edges is True, then create parallel edges, each
+ # with weight 1, for each entry in the adjacency matrix. Otherwise, create
+ # one edge for each positive entry in the adjacency matrix and set the
+ # weight of that edge to be the entry in the matrix.
+ if A.dtype.kind in ("i", "u") and G.is_multigraph() and parallel_edges:
+ chain = itertools.chain.from_iterable
+ # The following line is equivalent to:
+ #
+ # for (u, v) in edges:
+ # for d in range(A[u, v]):
+ # G.add_edge(u, v, weight=1)
+ #
+ triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
+ # If we are creating an undirected multigraph, only add the edges from the
+ # upper triangle of the matrix. Otherwise, add all the edges. This relies
+ # on the fact that the vertices created in the
+ # `_generated_weighted_edges()` function are actually the row/column
+ # indices for the matrix `A`.
+ #
+ # Without this check, we run into a problem where each edge is added twice
+ # when `G.add_weighted_edges_from()` is invoked below.
+ if G.is_multigraph() and not G.is_directed():
+ triples = ((u, v, d) for u, v, d in triples if u <= v)
+ G.add_weighted_edges_from(triples, weight=edge_attribute)
+ return G
+
+
+@nx._dispatchable(edge_attrs="weight") # edge attrs may also be obtained from `dtype`
+def to_numpy_array(
+ G,
+ nodelist=None,
+ dtype=None,
+ order=None,
+ multigraph_weight=sum,
+ weight="weight",
+ nonedge=0.0,
+):
+ """Returns the graph adjacency matrix as a NumPy array.
+
+ Parameters
+ ----------
+ G : graph
+ The NetworkX graph used to construct the NumPy array.
+
+ nodelist : list, optional
+ The rows and columns are ordered according to the nodes in `nodelist`.
+ If `nodelist` is ``None``, then the ordering is produced by ``G.nodes()``.
+
+ dtype : NumPy data type, optional
+ A NumPy data type used to initialize the array. If None, then the NumPy
+ default is used. The dtype can be structured if `weight=None`, in which
+ case the dtype field names are used to look up edge attributes. The
+ result is a structured array where each named field in the dtype
+ corresponds to the adjacency for that edge attribute. See examples for
+ details.
+
+ order : {'C', 'F'}, optional
+ Whether to store multidimensional data in C- or Fortran-contiguous
+ (row- or column-wise) order in memory. If None, then the NumPy default
+ is used.
+
+ multigraph_weight : callable, optional
+ An function that determines how weights in multigraphs are handled.
+ The function should accept a sequence of weights and return a single
+ value. The default is to sum the weights of the multiple edges.
+
+ weight : string or None optional (default = 'weight')
+ The edge attribute that holds the numerical value used for
+ the edge weight. If an edge does not have that attribute, then the
+ value 1 is used instead. `weight` must be ``None`` if a structured
+ dtype is used.
+
+ nonedge : array_like (default = 0.0)
+ The value used to represent non-edges in the adjacency matrix.
+ The array values corresponding to nonedges are typically set to zero.
+ However, this could be undesirable if there are array values
+ corresponding to actual edges that also have the value zero. If so,
+ one might prefer nonedges to have some other value, such as ``nan``.
+
+ Returns
+ -------
+ A : NumPy ndarray
+ Graph adjacency matrix
+
+ Raises
+ ------
+ NetworkXError
+ If `dtype` is a structured dtype and `G` is a multigraph
+ ValueError
+ If `dtype` is a structured dtype and `weight` is not `None`
+
+ See Also
+ --------
+ from_numpy_array
+
+ Notes
+ -----
+ For directed graphs, entry ``i, j`` corresponds to an edge from ``i`` to ``j``.
+
+ Entries in the adjacency matrix are given by the `weight` edge attribute.
+ When an edge does not have a weight attribute, the value of the entry is
+ set to the number 1. For multiple (parallel) edges, the values of the
+ entries are determined by the `multigraph_weight` parameter. The default is
+ to sum the weight attributes for each of the parallel edges.
+
+ When `nodelist` does not contain every node in `G`, the adjacency matrix is
+ built from the subgraph of `G` that is induced by the nodes in `nodelist`.
+
+ The convention used for self-loop edges in graphs is to assign the
+ diagonal array entry value to the weight attribute of the edge
+ (or the number 1 if the edge has no weight attribute). If the
+ alternate convention of doubling the edge weight is desired the
+ resulting NumPy array can be modified as follows:
+
+ >>> import numpy as np
+ >>> G = nx.Graph([(1, 1)])
+ >>> A = nx.to_numpy_array(G)
+ >>> A
+ array([[1.]])
+ >>> A[np.diag_indices_from(A)] *= 2
+ >>> A
+ array([[2.]])
+
+ Examples
+ --------
+ >>> G = nx.MultiDiGraph()
+ >>> G.add_edge(0, 1, weight=2)
+ 0
+ >>> G.add_edge(1, 0)
+ 0
+ >>> G.add_edge(2, 2, weight=3)
+ 0
+ >>> G.add_edge(2, 2)
+ 1
+ >>> nx.to_numpy_array(G, nodelist=[0, 1, 2])
+ array([[0., 2., 0.],
+ [1., 0., 0.],
+ [0., 0., 4.]])
+
+ When `nodelist` argument is used, nodes of `G` which do not appear in the `nodelist`
+ and their edges are not included in the adjacency matrix. Here is an example:
+
+ >>> G = nx.Graph()
+ >>> G.add_edge(3, 1)
+ >>> G.add_edge(2, 0)
+ >>> G.add_edge(2, 1)
+ >>> G.add_edge(3, 0)
+ >>> nx.to_numpy_array(G, nodelist=[1, 2, 3])
+ array([[0., 1., 1.],
+ [1., 0., 0.],
+ [1., 0., 0.]])
+
+ This function can also be used to create adjacency matrices for multiple
+ edge attributes with structured dtypes:
+
+ >>> G = nx.Graph()
+ >>> G.add_edge(0, 1, weight=10)
+ >>> G.add_edge(1, 2, cost=5)
+ >>> G.add_edge(2, 3, weight=3, cost=-4.0)
+ >>> dtype = np.dtype([("weight", int), ("cost", float)])
+ >>> A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+ >>> A["weight"]
+ array([[ 0, 10, 0, 0],
+ [10, 0, 1, 0],
+ [ 0, 1, 0, 3],
+ [ 0, 0, 3, 0]])
+ >>> A["cost"]
+ array([[ 0., 1., 0., 0.],
+ [ 1., 0., 5., 0.],
+ [ 0., 5., 0., -4.],
+ [ 0., 0., -4., 0.]])
+
+ As stated above, the argument "nonedge" is useful especially when there are
+ actually edges with weight 0 in the graph. Setting a nonedge value different than 0,
+ makes it much clearer to differentiate such 0-weighted edges and actual nonedge values.
+
+ >>> G = nx.Graph()
+ >>> G.add_edge(3, 1, weight=2)
+ >>> G.add_edge(2, 0, weight=0)
+ >>> G.add_edge(2, 1, weight=0)
+ >>> G.add_edge(3, 0, weight=1)
+ >>> nx.to_numpy_array(G, nonedge=-1.0)
+ array([[-1., 2., -1., 1.],
+ [ 2., -1., 0., -1.],
+ [-1., 0., -1., 0.],
+ [ 1., -1., 0., -1.]])
+ """
+ import numpy as np
+
+ if nodelist is None:
+ nodelist = list(G)
+ nlen = len(nodelist)
+
+ # Input validation
+ nodeset = set(nodelist)
+ if nodeset - set(G):
+ raise nx.NetworkXError(f"Nodes {nodeset - set(G)} in nodelist is not in G")
+ if len(nodeset) < nlen:
+ raise nx.NetworkXError("nodelist contains duplicates.")
+
+ A = np.full((nlen, nlen), fill_value=nonedge, dtype=dtype, order=order)
+
+ # Corner cases: empty nodelist or graph without any edges
+ if nlen == 0 or G.number_of_edges() == 0:
+ return A
+
+ # If dtype is structured and weight is None, use dtype field names as
+ # edge attributes
+ edge_attrs = None # Only single edge attribute by default
+ if A.dtype.names:
+ if weight is None:
+ edge_attrs = dtype.names
+ else:
+ raise ValueError(
+ "Specifying `weight` not supported for structured dtypes\n."
+ "To create adjacency matrices from structured dtypes, use `weight=None`."
+ )
+
+ # Map nodes to row/col in matrix
+ idx = dict(zip(nodelist, range(nlen)))
+ if len(nodelist) < len(G):
+ G = G.subgraph(nodelist).copy()
+
+ # Collect all edge weights and reduce with `multigraph_weights`
+ if G.is_multigraph():
+ if edge_attrs:
+ raise nx.NetworkXError(
+ "Structured arrays are not supported for MultiGraphs"
+ )
+ d = defaultdict(list)
+ for u, v, wt in G.edges(data=weight, default=1.0):
+ d[(idx[u], idx[v])].append(wt)
+ i, j = np.array(list(d.keys())).T # indices
+ wts = [multigraph_weight(ws) for ws in d.values()] # reduced weights
+ else:
+ i, j, wts = [], [], []
+
+ # Special branch: multi-attr adjacency from structured dtypes
+ if edge_attrs:
+ # Extract edges with all data
+ for u, v, data in G.edges(data=True):
+ i.append(idx[u])
+ j.append(idx[v])
+ wts.append(data)
+ # Map each attribute to the appropriate named field in the
+ # structured dtype
+ for attr in edge_attrs:
+ attr_data = [wt.get(attr, 1.0) for wt in wts]
+ A[attr][i, j] = attr_data
+ if not G.is_directed():
+ A[attr][j, i] = attr_data
+ return A
+
+ for u, v, wt in G.edges(data=weight, default=1.0):
+ i.append(idx[u])
+ j.append(idx[v])
+ wts.append(wt)
+
+ # Set array values with advanced indexing
+ A[i, j] = wts
+ if not G.is_directed():
+ A[j, i] = wts
+
+ return A
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_numpy_array(
+ A, parallel_edges=False, create_using=None, edge_attr="weight", *, nodelist=None
+):
+ """Returns a graph from a 2D NumPy array.
+
+ The 2D NumPy array is interpreted as an adjacency matrix for the graph.
+
+ Parameters
+ ----------
+ A : a 2D numpy.ndarray
+ An adjacency matrix representation of a graph
+
+ parallel_edges : Boolean
+ If this is True, `create_using` is a multigraph, and `A` is an
+ integer array, then entry *(i, j)* in the array is interpreted as the
+ number of parallel edges joining vertices *i* and *j* in the graph.
+ If it is False, then the entries in the array are interpreted as
+ the weight of a single edge joining the vertices.
+
+ create_using : NetworkX graph constructor, optional (default=nx.Graph)
+ Graph type to create. If graph instance, then cleared before populated.
+
+ edge_attr : String, optional (default="weight")
+ The attribute to which the array values are assigned on each edge. If
+ it is None, edge attributes will not be assigned.
+
+ nodelist : sequence of nodes, optional
+ A sequence of objects to use as the nodes in the graph. If provided, the
+ list of nodes must be the same length as the dimensions of `A`. The
+ default is `None`, in which case the nodes are drawn from ``range(n)``.
+
+ Notes
+ -----
+ For directed graphs, explicitly mention create_using=nx.DiGraph,
+ and entry i,j of A corresponds to an edge from i to j.
+
+ If `create_using` is :class:`networkx.MultiGraph` or
+ :class:`networkx.MultiDiGraph`, `parallel_edges` is True, and the
+ entries of `A` are of type :class:`int`, then this function returns a
+ multigraph (of the same type as `create_using`) with parallel edges.
+
+ If `create_using` indicates an undirected multigraph, then only the edges
+ indicated by the upper triangle of the array `A` will be added to the
+ graph.
+
+ If `edge_attr` is Falsy (False or None), edge attributes will not be
+ assigned, and the array data will be treated like a binary mask of
+ edge presence or absence. Otherwise, the attributes will be assigned
+ as follows:
+
+ If the NumPy array has a single data type for each array entry it
+ will be converted to an appropriate Python data type.
+
+ If the NumPy array has a user-specified compound data type the names
+ of the data fields will be used as attribute keys in the resulting
+ NetworkX graph.
+
+ See Also
+ --------
+ to_numpy_array
+
+ Examples
+ --------
+ Simple integer weights on edges:
+
+ >>> import numpy as np
+ >>> A = np.array([[1, 1], [2, 1]])
+ >>> G = nx.from_numpy_array(A)
+ >>> G.edges(data=True)
+ EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})])
+
+ If `create_using` indicates a multigraph and the array has only integer
+ entries and `parallel_edges` is False, then the entries will be treated
+ as weights for edges joining the nodes (without creating parallel edges):
+
+ >>> A = np.array([[1, 1], [1, 2]])
+ >>> G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
+ >>> G[1][1]
+ AtlasView({0: {'weight': 2}})
+
+ If `create_using` indicates a multigraph and the array has only integer
+ entries and `parallel_edges` is True, then the entries will be treated
+ as the number of parallel edges joining those two vertices:
+
+ >>> A = np.array([[1, 1], [1, 2]])
+ >>> temp = nx.MultiGraph()
+ >>> G = nx.from_numpy_array(A, parallel_edges=True, create_using=temp)
+ >>> G[1][1]
+ AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
+
+ User defined compound data type on edges:
+
+ >>> dt = [("weight", float), ("cost", int)]
+ >>> A = np.array([[(1.0, 2)]], dtype=dt)
+ >>> G = nx.from_numpy_array(A)
+ >>> G.edges()
+ EdgeView([(0, 0)])
+ >>> G[0][0]["cost"]
+ 2
+ >>> G[0][0]["weight"]
+ 1.0
+
+ """
+ kind_to_python_type = {
+ "f": float,
+ "i": int,
+ "u": int,
+ "b": bool,
+ "c": complex,
+ "S": str,
+ "U": str,
+ "V": "void",
+ }
+ G = nx.empty_graph(0, create_using)
+ if A.ndim != 2:
+ raise nx.NetworkXError(f"Input array must be 2D, not {A.ndim}")
+ n, m = A.shape
+ if n != m:
+ raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}")
+ dt = A.dtype
+ try:
+ python_type = kind_to_python_type[dt.kind]
+ except Exception as err:
+ raise TypeError(f"Unknown numpy data type: {dt}") from err
+ if _default_nodes := (nodelist is None):
+ nodelist = range(n)
+ else:
+ if len(nodelist) != n:
+ raise ValueError("nodelist must have the same length as A.shape[0]")
+
+ # Make sure we get even the isolated nodes of the graph.
+ G.add_nodes_from(nodelist)
+ # Get a list of all the entries in the array with nonzero entries. These
+ # coordinates become edges in the graph. (convert to int from np.int64)
+ edges = ((int(e[0]), int(e[1])) for e in zip(*A.nonzero()))
+ # handle numpy constructed data type
+ if python_type == "void":
+ # Sort the fields by their offset, then by dtype, then by name.
+ fields = sorted(
+ (offset, dtype, name) for name, (dtype, offset) in A.dtype.fields.items()
+ )
+ triples = (
+ (
+ u,
+ v,
+ {}
+ if edge_attr in [False, None]
+ else {
+ name: kind_to_python_type[dtype.kind](val)
+ for (_, dtype, name), val in zip(fields, A[u, v])
+ },
+ )
+ for u, v in edges
+ )
+ # If the entries in the adjacency matrix are integers, the graph is a
+ # multigraph, and parallel_edges is True, then create parallel edges, each
+ # with weight 1, for each entry in the adjacency matrix. Otherwise, create
+ # one edge for each positive entry in the adjacency matrix and set the
+ # weight of that edge to be the entry in the matrix.
+ elif python_type is int and G.is_multigraph() and parallel_edges:
+ chain = itertools.chain.from_iterable
+ # The following line is equivalent to:
+ #
+ # for (u, v) in edges:
+ # for d in range(A[u, v]):
+ # G.add_edge(u, v, weight=1)
+ #
+ if edge_attr in [False, None]:
+ triples = chain(((u, v, {}) for d in range(A[u, v])) for (u, v) in edges)
+ else:
+ triples = chain(
+ ((u, v, {edge_attr: 1}) for d in range(A[u, v])) for (u, v) in edges
+ )
+ else: # basic data type
+ if edge_attr in [False, None]:
+ triples = ((u, v, {}) for u, v in edges)
+ else:
+ triples = ((u, v, {edge_attr: python_type(A[u, v])}) for u, v in edges)
+ # If we are creating an undirected multigraph, only add the edges from the
+ # upper triangle of the matrix. Otherwise, add all the edges. This relies
+ # on the fact that the vertices created in the
+ # `_generated_weighted_edges()` function are actually the row/column
+ # indices for the matrix `A`.
+ #
+ # Without this check, we run into a problem where each edge is added twice
+ # when `G.add_edges_from()` is invoked below.
+ if G.is_multigraph() and not G.is_directed():
+ triples = ((u, v, d) for u, v, d in triples if u <= v)
+ # Remap nodes if user provided custom `nodelist`
+ if not _default_nodes:
+ idx_to_node = dict(enumerate(nodelist))
+ triples = ((idx_to_node[u], idx_to_node[v], d) for u, v, d in triples)
+ G.add_edges_from(triples)
+ return G
diff --git a/llava_next/lib/python3.10/site-packages/networkx/exception.py b/llava_next/lib/python3.10/site-packages/networkx/exception.py
new file mode 100644
index 0000000000000000000000000000000000000000..c960cf13fd5a8e4da0ca68c66350b8baa1728c34
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/exception.py
@@ -0,0 +1,131 @@
+"""
+**********
+Exceptions
+**********
+
+Base exceptions and errors for NetworkX.
+"""
+
+__all__ = [
+ "HasACycle",
+ "NodeNotFound",
+ "PowerIterationFailedConvergence",
+ "ExceededMaxIterations",
+ "AmbiguousSolution",
+ "NetworkXAlgorithmError",
+ "NetworkXException",
+ "NetworkXError",
+ "NetworkXNoCycle",
+ "NetworkXNoPath",
+ "NetworkXNotImplemented",
+ "NetworkXPointlessConcept",
+ "NetworkXUnbounded",
+ "NetworkXUnfeasible",
+]
+
+
+class NetworkXException(Exception):
+ """Base class for exceptions in NetworkX."""
+
+
+class NetworkXError(NetworkXException):
+ """Exception for a serious error in NetworkX"""
+
+
+class NetworkXPointlessConcept(NetworkXException):
+ """Raised when a null graph is provided as input to an algorithm
+ that cannot use it.
+
+ The null graph is sometimes considered a pointless concept [1]_,
+ thus the name of the exception.
+
+ Notes
+ -----
+ Null graphs and empty graphs are often used interchangeably but they
+ are well defined in NetworkX. An ``empty_graph`` is a graph with ``n`` nodes
+ and 0 edges, and a ``null_graph`` is a graph with 0 nodes and 0 edges.
+
+ References
+ ----------
+ .. [1] Harary, F. and Read, R. "Is the Null Graph a Pointless
+ Concept?" In Graphs and Combinatorics Conference, George
+ Washington University. New York: Springer-Verlag, 1973.
+
+ """
+
+
+class NetworkXAlgorithmError(NetworkXException):
+ """Exception for unexpected termination of algorithms."""
+
+
+class NetworkXUnfeasible(NetworkXAlgorithmError):
+ """Exception raised by algorithms trying to solve a problem
+ instance that has no feasible solution."""
+
+
+class NetworkXNoPath(NetworkXUnfeasible):
+ """Exception for algorithms that should return a path when running
+ on graphs where such a path does not exist."""
+
+
+class NetworkXNoCycle(NetworkXUnfeasible):
+ """Exception for algorithms that should return a cycle when running
+ on graphs where such a cycle does not exist."""
+
+
+class HasACycle(NetworkXException):
+ """Raised if a graph has a cycle when an algorithm expects that it
+ will have no cycles.
+
+ """
+
+
+class NetworkXUnbounded(NetworkXAlgorithmError):
+ """Exception raised by algorithms trying to solve a maximization
+ or a minimization problem instance that is unbounded."""
+
+
+class NetworkXNotImplemented(NetworkXException):
+ """Exception raised by algorithms not implemented for a type of graph."""
+
+
+class NodeNotFound(NetworkXException):
+ """Exception raised if requested node is not present in the graph"""
+
+
+class AmbiguousSolution(NetworkXException):
+ """Raised if more than one valid solution exists for an intermediary step
+ of an algorithm.
+
+ In the face of ambiguity, refuse the temptation to guess.
+ This may occur, for example, when trying to determine the
+ bipartite node sets in a disconnected bipartite graph when
+ computing bipartite matchings.
+
+ """
+
+
+class ExceededMaxIterations(NetworkXException):
+ """Raised if a loop iterates too many times without breaking.
+
+ This may occur, for example, in an algorithm that computes
+ progressively better approximations to a value but exceeds an
+ iteration bound specified by the user.
+
+ """
+
+
+class PowerIterationFailedConvergence(ExceededMaxIterations):
+ """Raised when the power iteration method fails to converge within a
+ specified iteration limit.
+
+ `num_iterations` is the number of iterations that have been
+ completed when this exception was raised.
+
+ """
+
+ def __init__(self, num_iterations, *args, **kw):
+ msg = f"power iteration failed to converge within {num_iterations} iterations"
+ exception_message = msg
+ superinit = super().__init__
+ superinit(self, exception_message, *args, **kw)
diff --git a/llava_next/lib/python3.10/site-packages/networkx/lazy_imports.py b/llava_next/lib/python3.10/site-packages/networkx/lazy_imports.py
new file mode 100644
index 0000000000000000000000000000000000000000..396404ba38f5885bfcc65af36d7b4655e94ccc27
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/lazy_imports.py
@@ -0,0 +1,188 @@
+import importlib
+import importlib.util
+import inspect
+import os
+import sys
+import types
+
+__all__ = ["attach", "_lazy_import"]
+
+
+def attach(module_name, submodules=None, submod_attrs=None):
+ """Attach lazily loaded submodules, and functions or other attributes.
+
+ Typically, modules import submodules and attributes as follows::
+
+ import mysubmodule
+ import anothersubmodule
+
+ from .foo import someattr
+
+ The idea of this function is to replace the `__init__.py`
+ module's `__getattr__`, `__dir__`, and `__all__` attributes such that
+ all imports work exactly the way they normally would, except that the
+ actual import is delayed until the resulting module object is first used.
+
+ The typical way to call this function, replacing the above imports, is::
+
+ __getattr__, __lazy_dir__, __all__ = lazy.attach(
+ __name__, ["mysubmodule", "anothersubmodule"], {"foo": "someattr"}
+ )
+
+ This functionality requires Python 3.7 or higher.
+
+ Parameters
+ ----------
+ module_name : str
+ Typically use __name__.
+ submodules : set
+ List of submodules to lazily import.
+ submod_attrs : dict
+ Dictionary of submodule -> list of attributes / functions.
+ These attributes are imported as they are used.
+
+ Returns
+ -------
+ __getattr__, __dir__, __all__
+
+ """
+ if submod_attrs is None:
+ submod_attrs = {}
+
+ if submodules is None:
+ submodules = set()
+ else:
+ submodules = set(submodules)
+
+ attr_to_modules = {
+ attr: mod for mod, attrs in submod_attrs.items() for attr in attrs
+ }
+
+ __all__ = list(submodules | attr_to_modules.keys())
+
+ def __getattr__(name):
+ if name in submodules:
+ return importlib.import_module(f"{module_name}.{name}")
+ elif name in attr_to_modules:
+ submod = importlib.import_module(f"{module_name}.{attr_to_modules[name]}")
+ return getattr(submod, name)
+ else:
+ raise AttributeError(f"No {module_name} attribute {name}")
+
+ def __dir__():
+ return __all__
+
+ if os.environ.get("EAGER_IMPORT", ""):
+ for attr in set(attr_to_modules.keys()) | submodules:
+ __getattr__(attr)
+
+ return __getattr__, __dir__, list(__all__)
+
+
+class DelayedImportErrorModule(types.ModuleType):
+ def __init__(self, frame_data, *args, **kwargs):
+ self.__frame_data = frame_data
+ super().__init__(*args, **kwargs)
+
+ def __getattr__(self, x):
+ if x in ("__class__", "__file__", "__frame_data"):
+ super().__getattr__(x)
+ else:
+ fd = self.__frame_data
+ raise ModuleNotFoundError(
+ f"No module named '{fd['spec']}'\n\n"
+ "This error is lazily reported, having originally occurred in\n"
+ f' File {fd["filename"]}, line {fd["lineno"]}, in {fd["function"]}\n\n'
+ f'----> {"".join(fd["code_context"] or "").strip()}'
+ )
+
+
+def _lazy_import(fullname):
+ """Return a lazily imported proxy for a module or library.
+
+ Warning
+ -------
+ Importing using this function can currently cause trouble
+ when the user tries to import from a subpackage of a module before
+ the package is fully imported. In particular, this idiom may not work:
+
+ np = lazy_import("numpy")
+ from numpy.lib import recfunctions
+
+ This is due to a difference in the way Python's LazyLoader handles
+ subpackage imports compared to the normal import process. Hopefully
+ we will get Python's LazyLoader to fix this, or find a workaround.
+ In the meantime, this is a potential problem.
+
+ The workaround is to import numpy before importing from the subpackage.
+
+ Notes
+ -----
+ We often see the following pattern::
+
+ def myfunc():
+ import scipy as sp
+ sp.argmin(...)
+ ....
+
+ This is to prevent a library, in this case `scipy`, from being
+ imported at function definition time, since that can be slow.
+
+ This function provides a proxy module that, upon access, imports
+ the actual module. So the idiom equivalent to the above example is::
+
+ sp = lazy.load("scipy")
+
+ def myfunc():
+ sp.argmin(...)
+ ....
+
+ The initial import time is fast because the actual import is delayed
+ until the first attribute is requested. The overall import time may
+ decrease as well for users that don't make use of large portions
+ of the library.
+
+ Parameters
+ ----------
+ fullname : str
+ The full name of the package or subpackage to import. For example::
+
+ sp = lazy.load("scipy") # import scipy as sp
+ spla = lazy.load("scipy.linalg") # import scipy.linalg as spla
+
+ Returns
+ -------
+ pm : importlib.util._LazyModule
+ Proxy module. Can be used like any regularly imported module.
+ Actual loading of the module occurs upon first attribute request.
+
+ """
+ try:
+ return sys.modules[fullname]
+ except:
+ pass
+
+ # Not previously loaded -- look it up
+ spec = importlib.util.find_spec(fullname)
+
+ if spec is None:
+ try:
+ parent = inspect.stack()[1]
+ frame_data = {
+ "spec": fullname,
+ "filename": parent.filename,
+ "lineno": parent.lineno,
+ "function": parent.function,
+ "code_context": parent.code_context,
+ }
+ return DelayedImportErrorModule(frame_data, "DelayedImportErrorModule")
+ finally:
+ del parent
+
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[fullname] = module
+
+ loader = importlib.util.LazyLoader(spec.loader)
+ loader.exec_module(module)
+
+ return module
diff --git a/llava_next/lib/python3.10/site-packages/networkx/relabel.py b/llava_next/lib/python3.10/site-packages/networkx/relabel.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b870f726ef42e0bcaa7bf724e2ae6ab4145f288
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/networkx/relabel.py
@@ -0,0 +1,285 @@
+import networkx as nx
+
+__all__ = ["convert_node_labels_to_integers", "relabel_nodes"]
+
+
+@nx._dispatchable(
+ preserve_all_attrs=True, mutates_input={"not copy": 2}, returns_graph=True
+)
+def relabel_nodes(G, mapping, copy=True):
+ """Relabel the nodes of the graph G according to a given mapping.
+
+ The original node ordering may not be preserved if `copy` is `False` and the
+ mapping includes overlap between old and new labels.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ mapping : dictionary
+ A dictionary with the old labels as keys and new labels as values.
+ A partial mapping is allowed. Mapping 2 nodes to a single node is allowed.
+ Any non-node keys in the mapping are ignored.
+
+ copy : bool (optional, default=True)
+ If True return a copy, or if False relabel the nodes in place.
+
+ Examples
+ --------
+ To create a new graph with nodes relabeled according to a given
+ dictionary:
+
+ >>> G = nx.path_graph(3)
+ >>> sorted(G)
+ [0, 1, 2]
+ >>> mapping = {0: "a", 1: "b", 2: "c"}
+ >>> H = nx.relabel_nodes(G, mapping)
+ >>> sorted(H)
+ ['a', 'b', 'c']
+
+ Nodes can be relabeled with any hashable object, including numbers
+ and strings:
+
+ >>> import string
+ >>> G = nx.path_graph(26) # nodes are integers 0 through 25
+ >>> sorted(G)[:3]
+ [0, 1, 2]
+ >>> mapping = dict(zip(G, string.ascii_lowercase))
+ >>> G = nx.relabel_nodes(G, mapping) # nodes are characters a through z
+ >>> sorted(G)[:3]
+ ['a', 'b', 'c']
+ >>> mapping = dict(zip(G, range(1, 27)))
+ >>> G = nx.relabel_nodes(G, mapping) # nodes are integers 1 through 26
+ >>> sorted(G)[:3]
+ [1, 2, 3]
+
+ To perform a partial in-place relabeling, provide a dictionary
+ mapping only a subset of the nodes, and set the `copy` keyword
+ argument to False:
+
+ >>> G = nx.path_graph(3) # nodes 0-1-2
+ >>> mapping = {0: "a", 1: "b"} # 0->'a' and 1->'b'
+ >>> G = nx.relabel_nodes(G, mapping, copy=False)
+ >>> sorted(G, key=str)
+ [2, 'a', 'b']
+
+ A mapping can also be given as a function:
+
+ >>> G = nx.path_graph(3)
+ >>> H = nx.relabel_nodes(G, lambda x: x**2)
+ >>> list(H)
+ [0, 1, 4]
+
+ In a multigraph, relabeling two or more nodes to the same new node
+ will retain all edges, but may change the edge keys in the process:
+
+ >>> G = nx.MultiGraph()
+ >>> G.add_edge(0, 1, value="a") # returns the key for this edge
+ 0
+ >>> G.add_edge(0, 2, value="b")
+ 0
+ >>> G.add_edge(0, 3, value="c")
+ 0
+ >>> mapping = {1: 4, 2: 4, 3: 4}
+ >>> H = nx.relabel_nodes(G, mapping, copy=True)
+ >>> print(H[0])
+ {4: {0: {'value': 'a'}, 1: {'value': 'b'}, 2: {'value': 'c'}}}
+
+ This works for in-place relabeling too:
+
+ >>> G = nx.relabel_nodes(G, mapping, copy=False)
+ >>> print(G[0])
+ {4: {0: {'value': 'a'}, 1: {'value': 'b'}, 2: {'value': 'c'}}}
+
+ Notes
+ -----
+ Only the nodes specified in the mapping will be relabeled.
+ Any non-node keys in the mapping are ignored.
+
+ The keyword setting copy=False modifies the graph in place.
+ Relabel_nodes avoids naming collisions by building a
+ directed graph from ``mapping`` which specifies the order of
+ relabelings. Naming collisions, such as a->b, b->c, are ordered
+ such that "b" gets renamed to "c" before "a" gets renamed "b".
+ In cases of circular mappings (e.g. a->b, b->a), modifying the
+ graph is not possible in-place and an exception is raised.
+ In that case, use copy=True.
+
+ If a relabel operation on a multigraph would cause two or more
+ edges to have the same source, target and key, the second edge must
+ be assigned a new key to retain all edges. The new key is set
+ to the lowest non-negative integer not already used as a key
+ for edges between these two nodes. Note that this means non-numeric
+ keys may be replaced by numeric keys.
+
+ See Also
+ --------
+ convert_node_labels_to_integers
+ """
+ # you can pass any callable e.g. f(old_label) -> new_label or
+ # e.g. str(old_label) -> new_label, but we'll just make a dictionary here regardless
+ m = {n: mapping(n) for n in G} if callable(mapping) else mapping
+
+ if copy:
+ return _relabel_copy(G, m)
+ else:
+ return _relabel_inplace(G, m)
+
+
+def _relabel_inplace(G, mapping):
+ if len(mapping.keys() & mapping.values()) > 0:
+ # labels sets overlap
+ # can we topological sort and still do the relabeling?
+ D = nx.DiGraph(list(mapping.items()))
+ D.remove_edges_from(nx.selfloop_edges(D))
+ try:
+ nodes = reversed(list(nx.topological_sort(D)))
+ except nx.NetworkXUnfeasible as err:
+ raise nx.NetworkXUnfeasible(
+ "The node label sets are overlapping and no ordering can "
+ "resolve the mapping. Use copy=True."
+ ) from err
+ else:
+ # non-overlapping label sets, sort them in the order of G nodes
+ nodes = [n for n in G if n in mapping]
+
+ multigraph = G.is_multigraph()
+ directed = G.is_directed()
+
+ for old in nodes:
+ # Test that old is in both mapping and G, otherwise ignore.
+ try:
+ new = mapping[old]
+ G.add_node(new, **G.nodes[old])
+ except KeyError:
+ continue
+ if new == old:
+ continue
+ if multigraph:
+ new_edges = [
+ (new, new if old == target else target, key, data)
+ for (_, target, key, data) in G.edges(old, data=True, keys=True)
+ ]
+ if directed:
+ new_edges += [
+ (new if old == source else source, new, key, data)
+ for (source, _, key, data) in G.in_edges(old, data=True, keys=True)
+ ]
+ # Ensure new edges won't overwrite existing ones
+ seen = set()
+ for i, (source, target, key, data) in enumerate(new_edges):
+ if target in G[source] and key in G[source][target]:
+ new_key = 0 if not isinstance(key, int | float) else key
+ while new_key in G[source][target] or (target, new_key) in seen:
+ new_key += 1
+ new_edges[i] = (source, target, new_key, data)
+ seen.add((target, new_key))
+ else:
+ new_edges = [
+ (new, new if old == target else target, data)
+ for (_, target, data) in G.edges(old, data=True)
+ ]
+ if directed:
+ new_edges += [
+ (new if old == source else source, new, data)
+ for (source, _, data) in G.in_edges(old, data=True)
+ ]
+ G.remove_node(old)
+ G.add_edges_from(new_edges)
+ return G
+
+
+def _relabel_copy(G, mapping):
+ H = G.__class__()
+ H.add_nodes_from(mapping.get(n, n) for n in G)
+ H._node.update((mapping.get(n, n), d.copy()) for n, d in G.nodes.items())
+ if G.is_multigraph():
+ new_edges = [
+ (mapping.get(n1, n1), mapping.get(n2, n2), k, d.copy())
+ for (n1, n2, k, d) in G.edges(keys=True, data=True)
+ ]
+
+ # check for conflicting edge-keys
+ undirected = not G.is_directed()
+ seen_edges = set()
+ for i, (source, target, key, data) in enumerate(new_edges):
+ while (source, target, key) in seen_edges:
+ if not isinstance(key, int | float):
+ key = 0
+ key += 1
+ seen_edges.add((source, target, key))
+ if undirected:
+ seen_edges.add((target, source, key))
+ new_edges[i] = (source, target, key, data)
+
+ H.add_edges_from(new_edges)
+ else:
+ H.add_edges_from(
+ (mapping.get(n1, n1), mapping.get(n2, n2), d.copy())
+ for (n1, n2, d) in G.edges(data=True)
+ )
+ H.graph.update(G.graph)
+ return H
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def convert_node_labels_to_integers(
+ G, first_label=0, ordering="default", label_attribute=None
+):
+ """Returns a copy of the graph G with the nodes relabeled using
+ consecutive integers.
+
+ Parameters
+ ----------
+ G : graph
+ A NetworkX graph
+
+ first_label : int, optional (default=0)
+ An integer specifying the starting offset in numbering nodes.
+ The new integer labels are numbered first_label, ..., n-1+first_label.
+
+ ordering : string
+ "default" : inherit node ordering from G.nodes()
+ "sorted" : inherit node ordering from sorted(G.nodes())
+ "increasing degree" : nodes are sorted by increasing degree
+ "decreasing degree" : nodes are sorted by decreasing degree
+
+ label_attribute : string, optional (default=None)
+ Name of node attribute to store old label. If None no attribute
+ is created.
+
+ Notes
+ -----
+ Node and edge attribute data are copied to the new (relabeled) graph.
+
+ There is no guarantee that the relabeling of nodes to integers will
+ give the same two integers for two (even identical graphs).
+ Use the `ordering` argument to try to preserve the order.
+
+ See Also
+ --------
+ relabel_nodes
+ """
+ N = G.number_of_nodes() + first_label
+ if ordering == "default":
+ mapping = dict(zip(G.nodes(), range(first_label, N)))
+ elif ordering == "sorted":
+ nlist = sorted(G.nodes())
+ mapping = dict(zip(nlist, range(first_label, N)))
+ elif ordering == "increasing degree":
+ dv_pairs = [(d, n) for (n, d) in G.degree()]
+ dv_pairs.sort() # in-place sort from lowest to highest degree
+ mapping = dict(zip([n for d, n in dv_pairs], range(first_label, N)))
+ elif ordering == "decreasing degree":
+ dv_pairs = [(d, n) for (n, d) in G.degree()]
+ dv_pairs.sort() # in-place sort from lowest to highest degree
+ dv_pairs.reverse()
+ mapping = dict(zip([n for d, n in dv_pairs], range(first_label, N)))
+ else:
+ raise nx.NetworkXError(f"Unknown node ordering: {ordering}")
+ H = relabel_nodes(G, mapping)
+ # create node attribute with the old label
+ if label_attribute is not None:
+ nx.set_node_attributes(H, {v: k for k, v in mapping.items()}, label_attribute)
+ return H
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diff --git a/llava_next/lib/python3.10/site-packages/rpds/__init__.py b/llava_next/lib/python3.10/site-packages/rpds/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..257da6a7bd439c46cb2409e77531dc4a4dc6295c
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/rpds/__init__.py
@@ -0,0 +1,5 @@
+from .rpds import *
+
+__doc__ = rpds.__doc__
+if hasattr(rpds, "__all__"):
+ __all__ = rpds.__all__
\ No newline at end of file
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+Metadata-Version: 2.2
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+Summary: Easily download, build, install, upgrade, and uninstall Python packages
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+Project-URL: Documentation, https://setuptools.pypa.io/
+Project-URL: Changelog, https://setuptools.pypa.io/en/stable/history.html
+Keywords: CPAN PyPI distutils eggs package management
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: System :: Archiving :: Packaging
+Classifier: Topic :: System :: Systems Administration
+Classifier: Topic :: Utilities
+Requires-Python: >=3.9
+Description-Content-Type: text/x-rst
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+Requires-Dist: jaraco.develop>=7.21; sys_platform != "cygwin" and extra == "type"
+
+.. |pypi-version| image:: https://img.shields.io/pypi/v/setuptools.svg
+ :target: https://pypi.org/project/setuptools
+
+.. |py-version| image:: https://img.shields.io/pypi/pyversions/setuptools.svg
+
+.. |test-badge| image:: https://github.com/pypa/setuptools/actions/workflows/main.yml/badge.svg
+ :target: https://github.com/pypa/setuptools/actions?query=workflow%3A%22tests%22
+ :alt: tests
+
+.. |ruff-badge| image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+ :target: https://github.com/astral-sh/ruff
+ :alt: Ruff
+
+.. |docs-badge| image:: https://img.shields.io/readthedocs/setuptools/latest.svg
+ :target: https://setuptools.pypa.io
+
+.. |skeleton-badge| image:: https://img.shields.io/badge/skeleton-2024-informational
+ :target: https://blog.jaraco.com/skeleton
+
+.. |codecov-badge| image:: https://img.shields.io/codecov/c/github/pypa/setuptools/master.svg?logo=codecov&logoColor=white
+ :target: https://codecov.io/gh/pypa/setuptools
+
+.. |tidelift-badge| image:: https://tidelift.com/badges/github/pypa/setuptools?style=flat
+ :target: https://tidelift.com/subscription/pkg/pypi-setuptools?utm_source=pypi-setuptools&utm_medium=readme
+
+.. |discord-badge| image:: https://img.shields.io/discord/803025117553754132
+ :target: https://discord.com/channels/803025117553754132/815945031150993468
+ :alt: Discord
+
+|pypi-version| |py-version| |test-badge| |ruff-badge| |docs-badge| |skeleton-badge| |codecov-badge| |discord-badge|
+
+See the `Quickstart `_
+and the `User's Guide `_ for
+instructions on how to use Setuptools.
+
+Questions and comments should be directed to `GitHub Discussions
+`_.
+Bug reports and especially tested patches may be
+submitted directly to the `bug tracker
+`_.
+
+
+Code of Conduct
+===============
+
+Everyone interacting in the setuptools project's codebases, issue trackers,
+chat rooms, and fora is expected to follow the
+`PSF Code of Conduct `_.
+
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+Setuptools and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more `_.
diff --git a/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/SOURCES.txt b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..29082151c0f8f791aadab0533d15c3e345d23835
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/SOURCES.txt
@@ -0,0 +1,571 @@
+LICENSE
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+setuptools/_vendor/jaraco/collections/py.typed
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+setuptools/_vendor/jaraco/functools/__init__.pyi
+setuptools/_vendor/jaraco/functools/py.typed
+setuptools/_vendor/jaraco/text/Lorem ipsum.txt
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+setuptools/_vendor/jaraco/text/layouts.py
+setuptools/_vendor/jaraco/text/show-newlines.py
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+setuptools/_vendor/jaraco/text/to-dvorak.py
+setuptools/_vendor/jaraco/text/to-qwerty.py
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+setuptools/_vendor/more_itertools/__init__.pyi
+setuptools/_vendor/more_itertools/more.py
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+setuptools/_vendor/more_itertools/py.typed
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+setuptools/_vendor/packaging/_musllinux.py
+setuptools/_vendor/packaging/_parser.py
+setuptools/_vendor/packaging/_structures.py
+setuptools/_vendor/packaging/_tokenizer.py
+setuptools/_vendor/packaging/markers.py
+setuptools/_vendor/packaging/metadata.py
+setuptools/_vendor/packaging/py.typed
+setuptools/_vendor/packaging/requirements.py
+setuptools/_vendor/packaging/specifiers.py
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+setuptools/_vendor/packaging/utils.py
+setuptools/_vendor/packaging/version.py
+setuptools/_vendor/packaging-24.2.dist-info/INSTALLER
+setuptools/_vendor/packaging-24.2.dist-info/LICENSE
+setuptools/_vendor/packaging-24.2.dist-info/LICENSE.APACHE
+setuptools/_vendor/packaging-24.2.dist-info/LICENSE.BSD
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+setuptools/_vendor/packaging/licenses/__init__.py
+setuptools/_vendor/packaging/licenses/_spdx.py
+setuptools/_vendor/platformdirs/__init__.py
+setuptools/_vendor/platformdirs/__main__.py
+setuptools/_vendor/platformdirs/android.py
+setuptools/_vendor/platformdirs/api.py
+setuptools/_vendor/platformdirs/macos.py
+setuptools/_vendor/platformdirs/py.typed
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+setuptools/_vendor/tomli/_parser.py
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+setuptools/_vendor/tomli/py.typed
+setuptools/_vendor/tomli-2.0.1.dist-info/INSTALLER
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+setuptools/_vendor/typeguard/_config.py
+setuptools/_vendor/typeguard/_decorators.py
+setuptools/_vendor/typeguard/_exceptions.py
+setuptools/_vendor/typeguard/_functions.py
+setuptools/_vendor/typeguard/_importhook.py
+setuptools/_vendor/typeguard/_memo.py
+setuptools/_vendor/typeguard/_pytest_plugin.py
+setuptools/_vendor/typeguard/_suppression.py
+setuptools/_vendor/typeguard/_transformer.py
+setuptools/_vendor/typeguard/_union_transformer.py
+setuptools/_vendor/typeguard/_utils.py
+setuptools/_vendor/typeguard/py.typed
+setuptools/_vendor/typeguard-4.3.0.dist-info/INSTALLER
+setuptools/_vendor/typeguard-4.3.0.dist-info/LICENSE
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+setuptools/_vendor/wheel/__main__.py
+setuptools/_vendor/wheel/_setuptools_logging.py
+setuptools/_vendor/wheel/bdist_wheel.py
+setuptools/_vendor/wheel/macosx_libfile.py
+setuptools/_vendor/wheel/metadata.py
+setuptools/_vendor/wheel/util.py
+setuptools/_vendor/wheel/wheelfile.py
+setuptools/_vendor/wheel-0.43.0.dist-info/INSTALLER
+setuptools/_vendor/wheel-0.43.0.dist-info/LICENSE.txt
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+setuptools/_vendor/wheel/cli/convert.py
+setuptools/_vendor/wheel/cli/pack.py
+setuptools/_vendor/wheel/cli/tags.py
+setuptools/_vendor/wheel/cli/unpack.py
+setuptools/_vendor/wheel/vendored/__init__.py
+setuptools/_vendor/wheel/vendored/vendor.txt
+setuptools/_vendor/wheel/vendored/packaging/__init__.py
+setuptools/_vendor/wheel/vendored/packaging/_elffile.py
+setuptools/_vendor/wheel/vendored/packaging/_manylinux.py
+setuptools/_vendor/wheel/vendored/packaging/_musllinux.py
+setuptools/_vendor/wheel/vendored/packaging/_parser.py
+setuptools/_vendor/wheel/vendored/packaging/_structures.py
+setuptools/_vendor/wheel/vendored/packaging/_tokenizer.py
+setuptools/_vendor/wheel/vendored/packaging/markers.py
+setuptools/_vendor/wheel/vendored/packaging/requirements.py
+setuptools/_vendor/wheel/vendored/packaging/specifiers.py
+setuptools/_vendor/wheel/vendored/packaging/tags.py
+setuptools/_vendor/wheel/vendored/packaging/utils.py
+setuptools/_vendor/wheel/vendored/packaging/version.py
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+setuptools/_vendor/zipp/compat/__init__.py
+setuptools/_vendor/zipp/compat/py310.py
+setuptools/command/__init__.py
+setuptools/command/_requirestxt.py
+setuptools/command/alias.py
+setuptools/command/bdist_egg.py
+setuptools/command/bdist_rpm.py
+setuptools/command/bdist_wheel.py
+setuptools/command/build.py
+setuptools/command/build_clib.py
+setuptools/command/build_ext.py
+setuptools/command/build_py.py
+setuptools/command/develop.py
+setuptools/command/dist_info.py
+setuptools/command/easy_install.py
+setuptools/command/editable_wheel.py
+setuptools/command/egg_info.py
+setuptools/command/install.py
+setuptools/command/install_egg_info.py
+setuptools/command/install_lib.py
+setuptools/command/install_scripts.py
+setuptools/command/launcher manifest.xml
+setuptools/command/rotate.py
+setuptools/command/saveopts.py
+setuptools/command/sdist.py
+setuptools/command/setopt.py
+setuptools/command/test.py
+setuptools/compat/__init__.py
+setuptools/compat/py310.py
+setuptools/compat/py311.py
+setuptools/compat/py312.py
+setuptools/compat/py39.py
+setuptools/config/NOTICE
+setuptools/config/__init__.py
+setuptools/config/_apply_pyprojecttoml.py
+setuptools/config/distutils.schema.json
+setuptools/config/expand.py
+setuptools/config/pyprojecttoml.py
+setuptools/config/setupcfg.py
+setuptools/config/setuptools.schema.json
+setuptools/config/_validate_pyproject/NOTICE
+setuptools/config/_validate_pyproject/__init__.py
+setuptools/config/_validate_pyproject/error_reporting.py
+setuptools/config/_validate_pyproject/extra_validations.py
+setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py
+setuptools/config/_validate_pyproject/fastjsonschema_validations.py
+setuptools/config/_validate_pyproject/formats.py
+setuptools/tests/__init__.py
+setuptools/tests/contexts.py
+setuptools/tests/environment.py
+setuptools/tests/fixtures.py
+setuptools/tests/mod_with_constant.py
+setuptools/tests/namespaces.py
+setuptools/tests/script-with-bom.py
+setuptools/tests/server.py
+setuptools/tests/test_archive_util.py
+setuptools/tests/test_bdist_deprecations.py
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+setuptools/tests/test_config_discovery.py
+setuptools/tests/test_core_metadata.py
+setuptools/tests/test_depends.py
+setuptools/tests/test_develop.py
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+setuptools/tests/test_dist_info.py
+setuptools/tests/test_distutils_adoption.py
+setuptools/tests/test_easy_install.py
+setuptools/tests/test_editable_install.py
+setuptools/tests/test_egg_info.py
+setuptools/tests/test_extern.py
+setuptools/tests/test_find_packages.py
+setuptools/tests/test_find_py_modules.py
+setuptools/tests/test_glob.py
+setuptools/tests/test_install_scripts.py
+setuptools/tests/test_logging.py
+setuptools/tests/test_manifest.py
+setuptools/tests/test_namespaces.py
+setuptools/tests/test_packageindex.py
+setuptools/tests/test_sandbox.py
+setuptools/tests/test_sdist.py
+setuptools/tests/test_setopt.py
+setuptools/tests/test_setuptools.py
+setuptools/tests/test_shutil_wrapper.py
+setuptools/tests/test_unicode_utils.py
+setuptools/tests/test_virtualenv.py
+setuptools/tests/test_warnings.py
+setuptools/tests/test_wheel.py
+setuptools/tests/test_windows_wrappers.py
+setuptools/tests/text.py
+setuptools/tests/textwrap.py
+setuptools/tests/compat/__init__.py
+setuptools/tests/compat/py39.py
+setuptools/tests/config/__init__.py
+setuptools/tests/config/setupcfg_examples.txt
+setuptools/tests/config/test_apply_pyprojecttoml.py
+setuptools/tests/config/test_expand.py
+setuptools/tests/config/test_pyprojecttoml.py
+setuptools/tests/config/test_pyprojecttoml_dynamic_deps.py
+setuptools/tests/config/test_setupcfg.py
+setuptools/tests/config/downloads/__init__.py
+setuptools/tests/config/downloads/preload.py
+setuptools/tests/indexes/test_links_priority/external.html
+setuptools/tests/indexes/test_links_priority/simple/foobar/index.html
+setuptools/tests/integration/__init__.py
+setuptools/tests/integration/helpers.py
+setuptools/tests/integration/test_pip_install_sdist.py
+tools/build_launchers.py
+tools/finalize.py
+tools/generate_validation_code.py
+tools/vendored.py
\ No newline at end of file
diff --git a/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/dependency_links.txt b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/entry_points.txt b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0db0a6c8f1b8d9c0ad4a25db6892e29f8988fcf2
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/entry_points.txt
@@ -0,0 +1,51 @@
+[distutils.commands]
+alias = setuptools.command.alias:alias
+bdist_egg = setuptools.command.bdist_egg:bdist_egg
+bdist_rpm = setuptools.command.bdist_rpm:bdist_rpm
+bdist_wheel = setuptools.command.bdist_wheel:bdist_wheel
+build = setuptools.command.build:build
+build_clib = setuptools.command.build_clib:build_clib
+build_ext = setuptools.command.build_ext:build_ext
+build_py = setuptools.command.build_py:build_py
+develop = setuptools.command.develop:develop
+dist_info = setuptools.command.dist_info:dist_info
+easy_install = setuptools.command.easy_install:easy_install
+editable_wheel = setuptools.command.editable_wheel:editable_wheel
+egg_info = setuptools.command.egg_info:egg_info
+install = setuptools.command.install:install
+install_egg_info = setuptools.command.install_egg_info:install_egg_info
+install_lib = setuptools.command.install_lib:install_lib
+install_scripts = setuptools.command.install_scripts:install_scripts
+rotate = setuptools.command.rotate:rotate
+saveopts = setuptools.command.saveopts:saveopts
+sdist = setuptools.command.sdist:sdist
+setopt = setuptools.command.setopt:setopt
+
+[distutils.setup_keywords]
+dependency_links = setuptools.dist:assert_string_list
+eager_resources = setuptools.dist:assert_string_list
+entry_points = setuptools.dist:check_entry_points
+exclude_package_data = setuptools.dist:check_package_data
+extras_require = setuptools.dist:check_extras
+include_package_data = setuptools.dist:assert_bool
+install_requires = setuptools.dist:check_requirements
+namespace_packages = setuptools.dist:check_nsp
+package_data = setuptools.dist:check_package_data
+packages = setuptools.dist:check_packages
+python_requires = setuptools.dist:check_specifier
+setup_requires = setuptools.dist:check_requirements
+use_2to3 = setuptools.dist:invalid_unless_false
+zip_safe = setuptools.dist:assert_bool
+
+[egg_info.writers]
+PKG-INFO = setuptools.command.egg_info:write_pkg_info
+dependency_links.txt = setuptools.command.egg_info:overwrite_arg
+eager_resources.txt = setuptools.command.egg_info:overwrite_arg
+entry_points.txt = setuptools.command.egg_info:write_entries
+namespace_packages.txt = setuptools.command.egg_info:overwrite_arg
+requires.txt = setuptools.command.egg_info:write_requirements
+top_level.txt = setuptools.command.egg_info:write_toplevel_names
+
+[setuptools.finalize_distribution_options]
+keywords = setuptools.dist:Distribution._finalize_setup_keywords
+parent_finalize = setuptools.dist:_Distribution.finalize_options
diff --git a/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/requires.txt b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4d40327a3cb30ab8ec29d99bb3f8a785174ae689
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/requires.txt
@@ -0,0 +1,85 @@
+
+[certs]
+
+[check]
+pytest-checkdocs>=2.4
+
+[check:sys_platform != "cygwin"]
+pytest-ruff>=0.2.1
+ruff>=0.8.0
+
+[core]
+packaging>=24.2
+more_itertools>=8.8
+jaraco.text>=3.7
+wheel>=0.43.0
+platformdirs>=4.2.2
+jaraco.collections
+jaraco.functools>=4
+packaging
+more_itertools
+
+[core:python_version < "3.10"]
+importlib_metadata>=6
+
+[core:python_version < "3.11"]
+tomli>=2.0.1
+
+[cover]
+pytest-cov
+
+[doc]
+sphinx>=3.5
+jaraco.packaging>=9.3
+rst.linker>=1.9
+furo
+sphinx-lint
+jaraco.tidelift>=1.4
+pygments-github-lexers==0.0.5
+sphinx-favicon
+sphinx-inline-tabs
+sphinx-reredirects
+sphinxcontrib-towncrier
+sphinx-notfound-page<2,>=1
+pyproject-hooks!=1.1
+towncrier<24.7
+
+[enabler]
+pytest-enabler>=2.2
+
+[ssl]
+
+[test]
+pytest!=8.1.*,>=6
+virtualenv>=13.0.0
+wheel>=0.44.0
+pip>=19.1
+packaging>=24.2
+jaraco.envs>=2.2
+pytest-xdist>=3
+jaraco.path>=3.7.2
+build[virtualenv]>=1.0.3
+filelock>=3.4.0
+ini2toml[lite]>=0.14
+tomli-w>=1.0.0
+pytest-timeout
+pytest-home>=0.5
+pytest-subprocess
+pyproject-hooks!=1.1
+jaraco.test>=5.5
+
+[test:python_version >= "3.9" and sys_platform != "cygwin"]
+jaraco.develop>=7.21
+
+[test:sys_platform != "cygwin"]
+pytest-perf
+
+[type]
+pytest-mypy
+mypy==1.14.*
+
+[type:python_version < "3.10"]
+importlib_metadata>=7.0.2
+
+[type:sys_platform != "cygwin"]
+jaraco.develop>=7.21
diff --git a/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/top_level.txt b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b5ac1070294b478b7cc2ce677207ee08813bfa37
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/setuptools-75.8.0-py3.10.egg-info/top_level.txt
@@ -0,0 +1,3 @@
+_distutils_hack
+pkg_resources
+setuptools
diff --git a/llava_next/lib/python3.10/site-packages/shellingham/__init__.py b/llava_next/lib/python3.10/site-packages/shellingham/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f7a90cbd02e5c2cc933cf6aa0374cca68035f1
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/shellingham/__init__.py
@@ -0,0 +1,23 @@
+import importlib
+import os
+
+from ._core import ShellDetectionFailure
+
+__version__ = "1.5.4"
+
+
+def detect_shell(pid=None, max_depth=10):
+ name = os.name
+ try:
+ impl = importlib.import_module(".{}".format(name), __name__)
+ except ImportError:
+ message = "Shell detection not implemented for {0!r}".format(name)
+ raise RuntimeError(message)
+ try:
+ get_shell = impl.get_shell
+ except AttributeError:
+ raise RuntimeError("get_shell not implemented for {0!r}".format(name))
+ shell = get_shell(pid, max_depth=max_depth)
+ if shell:
+ return shell
+ raise ShellDetectionFailure()
diff --git a/llava_next/lib/python3.10/site-packages/shellingham/__pycache__/__init__.cpython-310.pyc b/llava_next/lib/python3.10/site-packages/shellingham/__pycache__/__init__.cpython-310.pyc
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diff --git a/llava_next/lib/python3.10/site-packages/shellingham/posix/__init__.py b/llava_next/lib/python3.10/site-packages/shellingham/posix/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd2070db27189e62a1867e4de49f16f8c8841ff
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/shellingham/posix/__init__.py
@@ -0,0 +1,112 @@
+import os
+import re
+
+from .._core import SHELL_NAMES, ShellDetectionFailure
+from . import proc, ps
+
+# Based on QEMU docs: https://www.qemu.org/docs/master/user/main.html
+QEMU_BIN_REGEX = re.compile(
+ r"""qemu-
+ (alpha
+ |armeb
+ |arm
+ |m68k
+ |cris
+ |i386
+ |x86_64
+ |microblaze
+ |mips
+ |mipsel
+ |mips64
+ |mips64el
+ |mipsn32
+ |mipsn32el
+ |nios2
+ |ppc64
+ |ppc
+ |sh4eb
+ |sh4
+ |sparc
+ |sparc32plus
+ |sparc64
+ )""",
+ re.VERBOSE,
+)
+
+
+def _iter_process_parents(pid, max_depth=10):
+ """Select a way to obtain process information from the system.
+
+ * `/proc` is used if supported.
+ * The system `ps` utility is used as a fallback option.
+ """
+ for impl in (proc, ps):
+ try:
+ iterator = impl.iter_process_parents(pid, max_depth)
+ except EnvironmentError:
+ continue
+ return iterator
+ raise ShellDetectionFailure("compatible proc fs or ps utility is required")
+
+
+def _get_login_shell(proc_cmd):
+ """Form shell information from SHELL environ if possible."""
+ login_shell = os.environ.get("SHELL", "")
+ if login_shell:
+ proc_cmd = login_shell
+ else:
+ proc_cmd = proc_cmd[1:]
+ return (os.path.basename(proc_cmd).lower(), proc_cmd)
+
+
+_INTERPRETER_SHELL_NAMES = [
+ (re.compile(r"^python(\d+(\.\d+)?)?$"), {"xonsh"}),
+]
+
+
+def _get_interpreter_shell(proc_name, proc_args):
+ """Get shell invoked via an interpreter.
+
+ Some shells are implemented on, and invoked with an interpreter, e.g. xonsh
+ is commonly executed with an executable Python script. This detects what
+ script the interpreter is actually running, and check whether that looks
+ like a shell.
+
+ See sarugaku/shellingham#26 for rational.
+ """
+ for pattern, shell_names in _INTERPRETER_SHELL_NAMES:
+ if not pattern.match(proc_name):
+ continue
+ for arg in proc_args:
+ name = os.path.basename(arg).lower()
+ if os.path.isfile(arg) and name in shell_names:
+ return (name, arg)
+ return None
+
+
+def _get_shell(cmd, *args):
+ if cmd.startswith("-"): # Login shell! Let's use this.
+ return _get_login_shell(cmd)
+ name = os.path.basename(cmd).lower()
+ if name == "rosetta" or QEMU_BIN_REGEX.fullmatch(name):
+ # If the current process is Rosetta or QEMU, this likely is a
+ # containerized process. Parse out the actual command instead.
+ cmd = args[0]
+ args = args[1:]
+ name = os.path.basename(cmd).lower()
+ if name in SHELL_NAMES: # Command looks like a shell.
+ return (name, cmd)
+ shell = _get_interpreter_shell(name, args)
+ if shell:
+ return shell
+ return None
+
+
+def get_shell(pid=None, max_depth=10):
+ """Get the shell that the supplied pid or os.getpid() is running in."""
+ pid = str(pid or os.getpid())
+ for proc_args, _, _ in _iter_process_parents(pid, max_depth):
+ shell = _get_shell(*proc_args)
+ if shell:
+ return shell
+ return None
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diff --git a/llava_next/lib/python3.10/site-packages/shellingham/posix/__pycache__/ps.cpython-310.pyc b/llava_next/lib/python3.10/site-packages/shellingham/posix/__pycache__/ps.cpython-310.pyc
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diff --git a/llava_next/lib/python3.10/site-packages/shellingham/posix/_core.py b/llava_next/lib/python3.10/site-packages/shellingham/posix/_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc49e6e7a9d3edf062c55e0078136899f78d30d
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/shellingham/posix/_core.py
@@ -0,0 +1,3 @@
+import collections
+
+Process = collections.namedtuple("Process", "args pid ppid")
diff --git a/llava_next/lib/python3.10/site-packages/shellingham/posix/proc.py b/llava_next/lib/python3.10/site-packages/shellingham/posix/proc.py
new file mode 100644
index 0000000000000000000000000000000000000000..950f63228e5b328f82b70da8851ec60c6a2ff029
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/shellingham/posix/proc.py
@@ -0,0 +1,83 @@
+import io
+import os
+import re
+import sys
+
+from ._core import Process
+
+# FreeBSD: https://www.freebsd.org/cgi/man.cgi?query=procfs
+# NetBSD: https://man.netbsd.org/NetBSD-9.3-STABLE/mount_procfs.8
+# DragonFlyBSD: https://www.dragonflybsd.org/cgi/web-man?command=procfs
+BSD_STAT_PPID = 2
+
+# See https://docs.kernel.org/filesystems/proc.html
+LINUX_STAT_PPID = 3
+
+STAT_PATTERN = re.compile(r"\(.+\)|\S+")
+
+
+def detect_proc():
+ """Detect /proc filesystem style.
+
+ This checks the /proc/{pid} directory for possible formats. Returns one of
+ the following as str:
+
+ * `stat`: Linux-style, i.e. ``/proc/{pid}/stat``.
+ * `status`: BSD-style, i.e. ``/proc/{pid}/status``.
+ """
+ pid = os.getpid()
+ for name in ("stat", "status"):
+ if os.path.exists(os.path.join("/proc", str(pid), name)):
+ return name
+ raise ProcFormatError("unsupported proc format")
+
+
+def _use_bsd_stat_format():
+ try:
+ return os.uname().sysname.lower() in ("freebsd", "netbsd", "dragonfly")
+ except Exception:
+ return False
+
+
+def _get_ppid(pid, name):
+ path = os.path.join("/proc", str(pid), name)
+ with io.open(path, encoding="ascii", errors="replace") as f:
+ parts = STAT_PATTERN.findall(f.read())
+ # We only care about TTY and PPID -- both are numbers.
+ if _use_bsd_stat_format():
+ return parts[BSD_STAT_PPID]
+ return parts[LINUX_STAT_PPID]
+
+
+def _get_cmdline(pid):
+ path = os.path.join("/proc", str(pid), "cmdline")
+ encoding = sys.getfilesystemencoding() or "utf-8"
+ with io.open(path, encoding=encoding, errors="replace") as f:
+ # XXX: Command line arguments can be arbitrary byte sequences, not
+ # necessarily decodable. For Shellingham's purpose, however, we don't
+ # care. (pypa/pipenv#2820)
+ # cmdline appends an extra NULL at the end, hence the [:-1].
+ return tuple(f.read().split("\0")[:-1])
+
+
+class ProcFormatError(EnvironmentError):
+ pass
+
+
+def iter_process_parents(pid, max_depth=10):
+ """Try to look up the process tree via the /proc interface."""
+ stat_name = detect_proc()
+
+ # Inner generator function so we correctly throw an error eagerly if proc
+ # is not supported, rather than on the first call to the iterator. This
+ # allows the call site detects the correct implementation.
+ def _iter_process_parents(pid, max_depth):
+ for _ in range(max_depth):
+ ppid = _get_ppid(pid, stat_name)
+ args = _get_cmdline(pid)
+ yield Process(args=args, pid=pid, ppid=ppid)
+ if ppid == "0":
+ break
+ pid = ppid
+
+ return _iter_process_parents(pid, max_depth)
diff --git a/llava_next/lib/python3.10/site-packages/shellingham/posix/ps.py b/llava_next/lib/python3.10/site-packages/shellingham/posix/ps.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bc39a74a56390c263e63bfead028f6bce4df3cb
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/shellingham/posix/ps.py
@@ -0,0 +1,51 @@
+import errno
+import subprocess
+import sys
+
+from ._core import Process
+
+
+class PsNotAvailable(EnvironmentError):
+ pass
+
+
+def iter_process_parents(pid, max_depth=10):
+ """Try to look up the process tree via the output of `ps`."""
+ try:
+ cmd = ["ps", "-ww", "-o", "pid=", "-o", "ppid=", "-o", "args="]
+ output = subprocess.check_output(cmd)
+ except OSError as e: # Python 2-compatible FileNotFoundError.
+ if e.errno != errno.ENOENT:
+ raise
+ raise PsNotAvailable("ps not found")
+ except subprocess.CalledProcessError as e:
+ # `ps` can return 1 if the process list is completely empty.
+ # (sarugaku/shellingham#15)
+ if not e.output.strip():
+ return
+ raise
+ if not isinstance(output, str):
+ encoding = sys.getfilesystemencoding() or sys.getdefaultencoding()
+ output = output.decode(encoding)
+
+ processes_mapping = {}
+ for line in output.split("\n"):
+ try:
+ _pid, ppid, args = line.strip().split(None, 2)
+ # XXX: This is not right, but we are really out of options.
+ # ps does not offer a sane way to decode the argument display,
+ # and this is "Good Enough" for obtaining shell names. Hopefully
+ # people don't name their shell with a space, or have something
+ # like "/usr/bin/xonsh is uber". (sarugaku/shellingham#14)
+ args = tuple(a.strip() for a in args.split(" "))
+ except ValueError:
+ continue
+ processes_mapping[_pid] = Process(args=args, pid=_pid, ppid=ppid)
+
+ for _ in range(max_depth):
+ try:
+ process = processes_mapping[pid]
+ except KeyError:
+ return
+ yield process
+ pid = process.ppid
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbc0474d307a9fc87ab35067c08771adffe9d3b6
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/__init__.py
@@ -0,0 +1,57 @@
+from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
+
+from . import functional # usort: skip
+
+from ._transform import Transform # usort: skip
+
+from ._augment import CutMix, MixUp, RandomErasing
+from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
+from ._color import (
+ ColorJitter,
+ Grayscale,
+ RandomAdjustSharpness,
+ RandomAutocontrast,
+ RandomChannelPermutation,
+ RandomEqualize,
+ RandomGrayscale,
+ RandomInvert,
+ RandomPhotometricDistort,
+ RandomPosterize,
+ RandomSolarize,
+)
+from ._container import Compose, RandomApply, RandomChoice, RandomOrder
+from ._geometry import (
+ CenterCrop,
+ ElasticTransform,
+ FiveCrop,
+ Pad,
+ RandomAffine,
+ RandomCrop,
+ RandomHorizontalFlip,
+ RandomIoUCrop,
+ RandomPerspective,
+ RandomResize,
+ RandomResizedCrop,
+ RandomRotation,
+ RandomShortestSize,
+ RandomVerticalFlip,
+ RandomZoomOut,
+ Resize,
+ ScaleJitter,
+ TenCrop,
+)
+from ._meta import ClampBoundingBoxes, ConvertBoundingBoxFormat
+from ._misc import (
+ ConvertImageDtype,
+ GaussianBlur,
+ Identity,
+ Lambda,
+ LinearTransformation,
+ Normalize,
+ SanitizeBoundingBoxes,
+ ToDtype,
+)
+from ._temporal import UniformTemporalSubsample
+from ._type_conversion import PILToTensor, ToImage, ToPILImage, ToPureTensor
+
+from ._deprecated import ToTensor # usort: skip
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_augment.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad7fb861be268ee7c6f205a608be90ee21b91639
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_augment.py
@@ -0,0 +1,325 @@
+import math
+import numbers
+import warnings
+from typing import Any, Callable, Dict, List, Tuple
+
+import PIL.Image
+import torch
+from torch.nn.functional import one_hot
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torchvision import transforms as _transforms, tv_tensors
+from torchvision.transforms.v2 import functional as F
+
+from ._transform import _RandomApplyTransform, Transform
+from ._utils import _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size
+
+
+class RandomErasing(_RandomApplyTransform):
+ """[BETA] Randomly select a rectangle region in the input image or video and erase its pixels.
+
+ .. v2betastatus:: RandomErasing transform
+
+ This transform does not support PIL Image.
+ 'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
+
+ Args:
+ p (float, optional): probability that the random erasing operation will be performed.
+ scale (tuple of float, optional): range of proportion of erased area against input image.
+ ratio (tuple of float, optional): range of aspect ratio of erased area.
+ value (number or tuple of numbers): erasing value. Default is 0. If a single int, it is used to
+ erase all pixels. If a tuple of length 3, it is used to erase
+ R, G, B channels respectively.
+ If a str of 'random', erasing each pixel with random values.
+ inplace (bool, optional): boolean to make this transform inplace. Default set to False.
+
+ Returns:
+ Erased input.
+
+ Example:
+ >>> from torchvision.transforms import v2 as transforms
+ >>>
+ >>> transform = transforms.Compose([
+ >>> transforms.RandomHorizontalFlip(),
+ >>> transforms.PILToTensor(),
+ >>> transforms.ConvertImageDtype(torch.float),
+ >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+ >>> transforms.RandomErasing(),
+ >>> ])
+ """
+
+ _v1_transform_cls = _transforms.RandomErasing
+
+ def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+ return dict(
+ super()._extract_params_for_v1_transform(),
+ value="random" if self.value is None else self.value,
+ )
+
+ def __init__(
+ self,
+ p: float = 0.5,
+ scale: Tuple[float, float] = (0.02, 0.33),
+ ratio: Tuple[float, float] = (0.3, 3.3),
+ value: float = 0.0,
+ inplace: bool = False,
+ ):
+ super().__init__(p=p)
+ if not isinstance(value, (numbers.Number, str, tuple, list)):
+ raise TypeError("Argument value should be either a number or str or a sequence")
+ if isinstance(value, str) and value != "random":
+ raise ValueError("If value is str, it should be 'random'")
+ if not isinstance(scale, (tuple, list)):
+ raise TypeError("Scale should be a sequence")
+ if not isinstance(ratio, (tuple, list)):
+ raise TypeError("Ratio should be a sequence")
+ if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+ warnings.warn("Scale and ratio should be of kind (min, max)")
+ if scale[0] < 0 or scale[1] > 1:
+ raise ValueError("Scale should be between 0 and 1")
+ self.scale = scale
+ self.ratio = ratio
+ if isinstance(value, (int, float)):
+ self.value = [float(value)]
+ elif isinstance(value, str):
+ self.value = None
+ elif isinstance(value, (list, tuple)):
+ self.value = [float(v) for v in value]
+ else:
+ self.value = value
+ self.inplace = inplace
+
+ self._log_ratio = torch.log(torch.tensor(self.ratio))
+
+ def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any:
+ if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)):
+ warnings.warn(
+ f"{type(self).__name__}() is currently passing through inputs of type "
+ f"tv_tensors.{type(inpt).__name__}. This will likely change in the future."
+ )
+ return super()._call_kernel(functional, inpt, *args, **kwargs)
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ img_c, img_h, img_w = query_chw(flat_inputs)
+
+ if self.value is not None and not (len(self.value) in (1, img_c)):
+ raise ValueError(
+ f"If value is a sequence, it should have either a single value or {img_c} (number of inpt channels)"
+ )
+
+ area = img_h * img_w
+
+ log_ratio = self._log_ratio
+ for _ in range(10):
+ erase_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
+ aspect_ratio = torch.exp(
+ torch.empty(1).uniform_(
+ log_ratio[0], # type: ignore[arg-type]
+ log_ratio[1], # type: ignore[arg-type]
+ )
+ ).item()
+
+ h = int(round(math.sqrt(erase_area * aspect_ratio)))
+ w = int(round(math.sqrt(erase_area / aspect_ratio)))
+ if not (h < img_h and w < img_w):
+ continue
+
+ if self.value is None:
+ v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
+ else:
+ v = torch.tensor(self.value)[:, None, None]
+
+ i = torch.randint(0, img_h - h + 1, size=(1,)).item()
+ j = torch.randint(0, img_w - w + 1, size=(1,)).item()
+ break
+ else:
+ i, j, h, w, v = 0, 0, img_h, img_w, None
+
+ return dict(i=i, j=j, h=h, w=w, v=v)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ if params["v"] is not None:
+ inpt = self._call_kernel(F.erase, inpt, **params, inplace=self.inplace)
+
+ return inpt
+
+
+class _BaseMixUpCutMix(Transform):
+ def __init__(self, *, alpha: float = 1.0, num_classes: int, labels_getter="default") -> None:
+ super().__init__()
+ self.alpha = float(alpha)
+ self._dist = torch.distributions.Beta(torch.tensor([alpha]), torch.tensor([alpha]))
+
+ self.num_classes = num_classes
+
+ self._labels_getter = _parse_labels_getter(labels_getter)
+
+ def forward(self, *inputs):
+ inputs = inputs if len(inputs) > 1 else inputs[0]
+ flat_inputs, spec = tree_flatten(inputs)
+ needs_transform_list = self._needs_transform_list(flat_inputs)
+
+ if has_any(flat_inputs, PIL.Image.Image, tv_tensors.BoundingBoxes, tv_tensors.Mask):
+ raise ValueError(f"{type(self).__name__}() does not support PIL images, bounding boxes and masks.")
+
+ labels = self._labels_getter(inputs)
+ if not isinstance(labels, torch.Tensor):
+ raise ValueError(f"The labels must be a tensor, but got {type(labels)} instead.")
+ elif labels.ndim != 1:
+ raise ValueError(
+ f"labels tensor should be of shape (batch_size,) " f"but got shape {labels.shape} instead."
+ )
+
+ params = {
+ "labels": labels,
+ "batch_size": labels.shape[0],
+ **self._get_params(
+ [inpt for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list) if needs_transform]
+ ),
+ }
+
+ # By default, the labels will be False inside needs_transform_list, since they are a torch.Tensor coming
+ # after an image or video. However, we need to handle them in _transform, so we make sure to set them to True
+ needs_transform_list[next(idx for idx, inpt in enumerate(flat_inputs) if inpt is labels)] = True
+ flat_outputs = [
+ self._transform(inpt, params) if needs_transform else inpt
+ for (inpt, needs_transform) in zip(flat_inputs, needs_transform_list)
+ ]
+
+ return tree_unflatten(flat_outputs, spec)
+
+ def _check_image_or_video(self, inpt: torch.Tensor, *, batch_size: int):
+ expected_num_dims = 5 if isinstance(inpt, tv_tensors.Video) else 4
+ if inpt.ndim != expected_num_dims:
+ raise ValueError(
+ f"Expected a batched input with {expected_num_dims} dims, but got {inpt.ndim} dimensions instead."
+ )
+ if inpt.shape[0] != batch_size:
+ raise ValueError(
+ f"The batch size of the image or video does not match the batch size of the labels: "
+ f"{inpt.shape[0]} != {batch_size}."
+ )
+
+ def _mixup_label(self, label: torch.Tensor, *, lam: float) -> torch.Tensor:
+ label = one_hot(label, num_classes=self.num_classes)
+ if not label.dtype.is_floating_point:
+ label = label.float()
+ return label.roll(1, 0).mul_(1.0 - lam).add_(label.mul(lam))
+
+
+class MixUp(_BaseMixUpCutMix):
+ """[BETA] Apply MixUp to the provided batch of images and labels.
+
+ .. v2betastatus:: MixUp transform
+
+ Paper: `mixup: Beyond Empirical Risk Minimization `_.
+
+ .. note::
+ This transform is meant to be used on **batches** of samples, not
+ individual images. See
+ :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage
+ examples.
+ The sample pairing is deterministic and done by matching consecutive
+ samples in the batch, so the batch needs to be shuffled (this is an
+ implementation detail, not a guaranteed convention.)
+
+ In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed
+ into a tensor of shape ``(batch_size, num_classes)``.
+
+ Args:
+ alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
+ num_classes (int): number of classes in the batch. Used for one-hot-encoding.
+ labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
+ By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
+ common scenario where this transform is called as ``MixUp()(imgs_batch, labels_batch)``.
+ It can also be a callable that takes the same input as the transform, and returns the labels.
+ """
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ return dict(lam=float(self._dist.sample(()))) # type: ignore[arg-type]
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ lam = params["lam"]
+
+ if inpt is params["labels"]:
+ return self._mixup_label(inpt, lam=lam)
+ elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt):
+ self._check_image_or_video(inpt, batch_size=params["batch_size"])
+
+ output = inpt.roll(1, 0).mul_(1.0 - lam).add_(inpt.mul(lam))
+
+ if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)):
+ output = tv_tensors.wrap(output, like=inpt)
+
+ return output
+ else:
+ return inpt
+
+
+class CutMix(_BaseMixUpCutMix):
+ """[BETA] Apply CutMix to the provided batch of images and labels.
+
+ .. v2betastatus:: CutMix transform
+
+ Paper: `CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
+ `_.
+
+ .. note::
+ This transform is meant to be used on **batches** of samples, not
+ individual images. See
+ :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py` for detailed usage
+ examples.
+ The sample pairing is deterministic and done by matching consecutive
+ samples in the batch, so the batch needs to be shuffled (this is an
+ implementation detail, not a guaranteed convention.)
+
+ In the input, the labels are expected to be a tensor of shape ``(batch_size,)``. They will be transformed
+ into a tensor of shape ``(batch_size, num_classes)``.
+
+ Args:
+ alpha (float, optional): hyperparameter of the Beta distribution used for mixup. Default is 1.
+ num_classes (int): number of classes in the batch. Used for one-hot-encoding.
+ labels_getter (callable or "default", optional): indicates how to identify the labels in the input.
+ By default, this will pick the second parameter as the labels if it's a tensor. This covers the most
+ common scenario where this transform is called as ``CutMix()(imgs_batch, labels_batch)``.
+ It can also be a callable that takes the same input as the transform, and returns the labels.
+ """
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ lam = float(self._dist.sample(())) # type: ignore[arg-type]
+
+ H, W = query_size(flat_inputs)
+
+ r_x = torch.randint(W, size=(1,))
+ r_y = torch.randint(H, size=(1,))
+
+ r = 0.5 * math.sqrt(1.0 - lam)
+ r_w_half = int(r * W)
+ r_h_half = int(r * H)
+
+ x1 = int(torch.clamp(r_x - r_w_half, min=0))
+ y1 = int(torch.clamp(r_y - r_h_half, min=0))
+ x2 = int(torch.clamp(r_x + r_w_half, max=W))
+ y2 = int(torch.clamp(r_y + r_h_half, max=H))
+ box = (x1, y1, x2, y2)
+
+ lam_adjusted = float(1.0 - (x2 - x1) * (y2 - y1) / (W * H))
+
+ return dict(box=box, lam_adjusted=lam_adjusted)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ if inpt is params["labels"]:
+ return self._mixup_label(inpt, lam=params["lam_adjusted"])
+ elif isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)) or is_pure_tensor(inpt):
+ self._check_image_or_video(inpt, batch_size=params["batch_size"])
+
+ x1, y1, x2, y2 = params["box"]
+ rolled = inpt.roll(1, 0)
+ output = inpt.clone()
+ output[..., y1:y2, x1:x2] = rolled[..., y1:y2, x1:x2]
+
+ if isinstance(inpt, (tv_tensors.Image, tv_tensors.Video)):
+ output = tv_tensors.wrap(output, like=inpt)
+
+ return output
+ else:
+ return inpt
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_auto_augment.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_auto_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ddd5aacdc396a357723380e0fd2493cb9ce5cb7
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_auto_augment.py
@@ -0,0 +1,635 @@
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
+
+import PIL.Image
+import torch
+
+from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
+from torchvision import transforms as _transforms, tv_tensors
+from torchvision.transforms import _functional_tensor as _FT
+from torchvision.transforms.v2 import AutoAugmentPolicy, functional as F, InterpolationMode, Transform
+from torchvision.transforms.v2.functional._geometry import _check_interpolation
+from torchvision.transforms.v2.functional._meta import get_size
+from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
+
+from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
+
+
+ImageOrVideo = Union[torch.Tensor, PIL.Image.Image, tv_tensors.Image, tv_tensors.Video]
+
+
+class _AutoAugmentBase(Transform):
+ def __init__(
+ self,
+ *,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
+ ) -> None:
+ super().__init__()
+ self.interpolation = _check_interpolation(interpolation)
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+ params = super()._extract_params_for_v1_transform()
+
+ if isinstance(params["fill"], dict):
+ raise ValueError(f"{type(self).__name__}() can not be scripted for when `fill` is a dictionary.")
+
+ return params
+
+ def _get_random_item(self, dct: Dict[str, Tuple[Callable, bool]]) -> Tuple[str, Tuple[Callable, bool]]:
+ keys = tuple(dct.keys())
+ key = keys[int(torch.randint(len(keys), ()))]
+ return key, dct[key]
+
+ def _flatten_and_extract_image_or_video(
+ self,
+ inputs: Any,
+ unsupported_types: Tuple[Type, ...] = (tv_tensors.BoundingBoxes, tv_tensors.Mask),
+ ) -> Tuple[Tuple[List[Any], TreeSpec, int], ImageOrVideo]:
+ flat_inputs, spec = tree_flatten(inputs if len(inputs) > 1 else inputs[0])
+ needs_transform_list = self._needs_transform_list(flat_inputs)
+
+ image_or_videos = []
+ for idx, (inpt, needs_transform) in enumerate(zip(flat_inputs, needs_transform_list)):
+ if needs_transform and check_type(
+ inpt,
+ (
+ tv_tensors.Image,
+ PIL.Image.Image,
+ is_pure_tensor,
+ tv_tensors.Video,
+ ),
+ ):
+ image_or_videos.append((idx, inpt))
+ elif isinstance(inpt, unsupported_types):
+ raise TypeError(f"Inputs of type {type(inpt).__name__} are not supported by {type(self).__name__}()")
+
+ if not image_or_videos:
+ raise TypeError("Found no image in the sample.")
+ if len(image_or_videos) > 1:
+ raise TypeError(
+ f"Auto augment transformations are only properly defined for a single image or video, "
+ f"but found {len(image_or_videos)}."
+ )
+
+ idx, image_or_video = image_or_videos[0]
+ return (flat_inputs, spec, idx), image_or_video
+
+ def _unflatten_and_insert_image_or_video(
+ self,
+ flat_inputs_with_spec: Tuple[List[Any], TreeSpec, int],
+ image_or_video: ImageOrVideo,
+ ) -> Any:
+ flat_inputs, spec, idx = flat_inputs_with_spec
+ flat_inputs[idx] = image_or_video
+ return tree_unflatten(flat_inputs, spec)
+
+ def _apply_image_or_video_transform(
+ self,
+ image: ImageOrVideo,
+ transform_id: str,
+ magnitude: float,
+ interpolation: Union[InterpolationMode, int],
+ fill: Dict[Union[Type, str], _FillTypeJIT],
+ ) -> ImageOrVideo:
+ fill_ = _get_fill(fill, type(image))
+
+ if transform_id == "Identity":
+ return image
+ elif transform_id == "ShearX":
+ # magnitude should be arctan(magnitude)
+ # official autoaug: (1, level, 0, 0, 1, 0)
+ # https://github.com/tensorflow/models/blob/dd02069717128186b88afa8d857ce57d17957f03/research/autoaugment/augmentation_transforms.py#L290
+ # compared to
+ # torchvision: (1, tan(level), 0, 0, 1, 0)
+ # https://github.com/pytorch/vision/blob/0c2373d0bba3499e95776e7936e207d8a1676e65/torchvision/transforms/functional.py#L976
+ return F.affine(
+ image,
+ angle=0.0,
+ translate=[0, 0],
+ scale=1.0,
+ shear=[math.degrees(math.atan(magnitude)), 0.0],
+ interpolation=interpolation,
+ fill=fill_,
+ center=[0, 0],
+ )
+ elif transform_id == "ShearY":
+ # magnitude should be arctan(magnitude)
+ # See above
+ return F.affine(
+ image,
+ angle=0.0,
+ translate=[0, 0],
+ scale=1.0,
+ shear=[0.0, math.degrees(math.atan(magnitude))],
+ interpolation=interpolation,
+ fill=fill_,
+ center=[0, 0],
+ )
+ elif transform_id == "TranslateX":
+ return F.affine(
+ image,
+ angle=0.0,
+ translate=[int(magnitude), 0],
+ scale=1.0,
+ interpolation=interpolation,
+ shear=[0.0, 0.0],
+ fill=fill_,
+ )
+ elif transform_id == "TranslateY":
+ return F.affine(
+ image,
+ angle=0.0,
+ translate=[0, int(magnitude)],
+ scale=1.0,
+ interpolation=interpolation,
+ shear=[0.0, 0.0],
+ fill=fill_,
+ )
+ elif transform_id == "Rotate":
+ return F.rotate(image, angle=magnitude, interpolation=interpolation, fill=fill_)
+ elif transform_id == "Brightness":
+ return F.adjust_brightness(image, brightness_factor=1.0 + magnitude)
+ elif transform_id == "Color":
+ return F.adjust_saturation(image, saturation_factor=1.0 + magnitude)
+ elif transform_id == "Contrast":
+ return F.adjust_contrast(image, contrast_factor=1.0 + magnitude)
+ elif transform_id == "Sharpness":
+ return F.adjust_sharpness(image, sharpness_factor=1.0 + magnitude)
+ elif transform_id == "Posterize":
+ return F.posterize(image, bits=int(magnitude))
+ elif transform_id == "Solarize":
+ bound = _FT._max_value(image.dtype) if isinstance(image, torch.Tensor) else 255.0
+ return F.solarize(image, threshold=bound * magnitude)
+ elif transform_id == "AutoContrast":
+ return F.autocontrast(image)
+ elif transform_id == "Equalize":
+ return F.equalize(image)
+ elif transform_id == "Invert":
+ return F.invert(image)
+ else:
+ raise ValueError(f"No transform available for {transform_id}")
+
+
+class AutoAugment(_AutoAugmentBase):
+ r"""[BETA] AutoAugment data augmentation method based on
+ `"AutoAugment: Learning Augmentation Strategies from Data" `_.
+
+ .. v2betastatus:: AutoAugment transform
+
+ This transformation works on images and videos only.
+
+ If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
+ to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+ If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+ Args:
+ policy (AutoAugmentPolicy, optional): Desired policy enum defined by
+ :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+ image. If given a number, the value is used for all bands respectively.
+ """
+ _v1_transform_cls = _transforms.AutoAugment
+
+ _AUGMENTATION_SPACE = {
+ "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "TranslateX": (
+ lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins),
+ True,
+ ),
+ "TranslateY": (
+ lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins),
+ True,
+ ),
+ "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
+ "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Posterize": (
+ lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
+ False,
+ ),
+ "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
+ "AutoContrast": (lambda num_bins, height, width: None, False),
+ "Equalize": (lambda num_bins, height, width: None, False),
+ "Invert": (lambda num_bins, height, width: None, False),
+ }
+
+ def __init__(
+ self,
+ policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
+ ) -> None:
+ super().__init__(interpolation=interpolation, fill=fill)
+ self.policy = policy
+ self._policies = self._get_policies(policy)
+
+ def _get_policies(
+ self, policy: AutoAugmentPolicy
+ ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]:
+ if policy == AutoAugmentPolicy.IMAGENET:
+ return [
+ (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)),
+ (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+ (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+ (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)),
+ (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+ (("Equalize", 0.4, None), ("Rotate", 0.8, 8)),
+ (("Solarize", 0.6, 3), ("Equalize", 0.6, None)),
+ (("Posterize", 0.8, 5), ("Equalize", 1.0, None)),
+ (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)),
+ (("Equalize", 0.6, None), ("Posterize", 0.4, 6)),
+ (("Rotate", 0.8, 8), ("Color", 0.4, 0)),
+ (("Rotate", 0.4, 9), ("Equalize", 0.6, None)),
+ (("Equalize", 0.0, None), ("Equalize", 0.8, None)),
+ (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+ (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+ (("Rotate", 0.8, 8), ("Color", 1.0, 2)),
+ (("Color", 0.8, 8), ("Solarize", 0.8, 7)),
+ (("Sharpness", 0.4, 7), ("Invert", 0.6, None)),
+ (("ShearX", 0.6, 5), ("Equalize", 1.0, None)),
+ (("Color", 0.4, 0), ("Equalize", 0.6, None)),
+ (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+ (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+ (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+ (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+ (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+ ]
+ elif policy == AutoAugmentPolicy.CIFAR10:
+ return [
+ (("Invert", 0.1, None), ("Contrast", 0.2, 6)),
+ (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)),
+ (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
+ (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)),
+ (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)),
+ (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)),
+ (("Color", 0.4, 3), ("Brightness", 0.6, 7)),
+ (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)),
+ (("Equalize", 0.6, None), ("Equalize", 0.5, None)),
+ (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)),
+ (("Color", 0.7, 7), ("TranslateX", 0.5, 8)),
+ (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)),
+ (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)),
+ (("Brightness", 0.9, 6), ("Color", 0.2, 8)),
+ (("Solarize", 0.5, 2), ("Invert", 0.0, None)),
+ (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)),
+ (("Equalize", 0.2, None), ("Equalize", 0.6, None)),
+ (("Color", 0.9, 9), ("Equalize", 0.6, None)),
+ (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)),
+ (("Brightness", 0.1, 3), ("Color", 0.7, 0)),
+ (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)),
+ (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)),
+ (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)),
+ (("Equalize", 0.8, None), ("Invert", 0.1, None)),
+ (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)),
+ ]
+ elif policy == AutoAugmentPolicy.SVHN:
+ return [
+ (("ShearX", 0.9, 4), ("Invert", 0.2, None)),
+ (("ShearY", 0.9, 8), ("Invert", 0.7, None)),
+ (("Equalize", 0.6, None), ("Solarize", 0.6, 6)),
+ (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+ (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+ (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)),
+ (("ShearY", 0.9, 8), ("Invert", 0.4, None)),
+ (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)),
+ (("Invert", 0.9, None), ("AutoContrast", 0.8, None)),
+ (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+ (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)),
+ (("ShearY", 0.8, 8), ("Invert", 0.7, None)),
+ (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)),
+ (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+ (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)),
+ (("Invert", 0.8, None), ("TranslateY", 0.0, 2)),
+ (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)),
+ (("Invert", 0.6, None), ("Rotate", 0.8, 4)),
+ (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)),
+ (("ShearX", 0.1, 6), ("Invert", 0.6, None)),
+ (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)),
+ (("ShearY", 0.8, 4), ("Invert", 0.8, None)),
+ (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)),
+ (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)),
+ (("ShearX", 0.7, 2), ("Invert", 0.1, None)),
+ ]
+ else:
+ raise ValueError(f"The provided policy {policy} is not recognized.")
+
+ def forward(self, *inputs: Any) -> Any:
+ flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
+ height, width = get_size(image_or_video)
+
+ policy = self._policies[int(torch.randint(len(self._policies), ()))]
+
+ for transform_id, probability, magnitude_idx in policy:
+ if not torch.rand(()) <= probability:
+ continue
+
+ magnitudes_fn, signed = self._AUGMENTATION_SPACE[transform_id]
+
+ magnitudes = magnitudes_fn(10, height, width)
+ if magnitudes is not None:
+ magnitude = float(magnitudes[magnitude_idx])
+ if signed and torch.rand(()) <= 0.5:
+ magnitude *= -1
+ else:
+ magnitude = 0.0
+
+ image_or_video = self._apply_image_or_video_transform(
+ image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
+ )
+
+ return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
+
+
+class RandAugment(_AutoAugmentBase):
+ r"""[BETA] RandAugment data augmentation method based on
+ `"RandAugment: Practical automated data augmentation with a reduced search space"
+ `_.
+
+ .. v2betastatus:: RandAugment transform
+
+ This transformation works on images and videos only.
+
+ If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
+ to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+ If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+ Args:
+ num_ops (int, optional): Number of augmentation transformations to apply sequentially.
+ magnitude (int, optional): Magnitude for all the transformations.
+ num_magnitude_bins (int, optional): The number of different magnitude values.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+ image. If given a number, the value is used for all bands respectively.
+ """
+
+ _v1_transform_cls = _transforms.RandAugment
+ _AUGMENTATION_SPACE = {
+ "Identity": (lambda num_bins, height, width: None, False),
+ "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "TranslateX": (
+ lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * width, num_bins),
+ True,
+ ),
+ "TranslateY": (
+ lambda num_bins, height, width: torch.linspace(0.0, 150.0 / 331.0 * height, num_bins),
+ True,
+ ),
+ "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
+ "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Posterize": (
+ lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
+ False,
+ ),
+ "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
+ "AutoContrast": (lambda num_bins, height, width: None, False),
+ "Equalize": (lambda num_bins, height, width: None, False),
+ }
+
+ def __init__(
+ self,
+ num_ops: int = 2,
+ magnitude: int = 9,
+ num_magnitude_bins: int = 31,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
+ ) -> None:
+ super().__init__(interpolation=interpolation, fill=fill)
+ self.num_ops = num_ops
+ self.magnitude = magnitude
+ self.num_magnitude_bins = num_magnitude_bins
+
+ def forward(self, *inputs: Any) -> Any:
+ flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
+ height, width = get_size(image_or_video)
+
+ for _ in range(self.num_ops):
+ transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
+ magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width)
+ if magnitudes is not None:
+ magnitude = float(magnitudes[self.magnitude])
+ if signed and torch.rand(()) <= 0.5:
+ magnitude *= -1
+ else:
+ magnitude = 0.0
+ image_or_video = self._apply_image_or_video_transform(
+ image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
+ )
+
+ return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
+
+
+class TrivialAugmentWide(_AutoAugmentBase):
+ r"""[BETA] Dataset-independent data-augmentation with TrivialAugment Wide, as described in
+ `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" `_.
+
+ .. v2betastatus:: TrivialAugmentWide transform
+
+ This transformation works on images and videos only.
+
+ If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
+ to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+ If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+ Args:
+ num_magnitude_bins (int, optional): The number of different magnitude values.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+ image. If given a number, the value is used for all bands respectively.
+ """
+
+ _v1_transform_cls = _transforms.TrivialAugmentWide
+ _AUGMENTATION_SPACE = {
+ "Identity": (lambda num_bins, height, width: None, False),
+ "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True),
+ "TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, 32.0, num_bins), True),
+ "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 135.0, num_bins), True),
+ "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.99, num_bins), True),
+ "Posterize": (
+ lambda num_bins, height, width: (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6))).round().int(),
+ False,
+ ),
+ "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
+ "AutoContrast": (lambda num_bins, height, width: None, False),
+ "Equalize": (lambda num_bins, height, width: None, False),
+ }
+
+ def __init__(
+ self,
+ num_magnitude_bins: int = 31,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
+ ):
+ super().__init__(interpolation=interpolation, fill=fill)
+ self.num_magnitude_bins = num_magnitude_bins
+
+ def forward(self, *inputs: Any) -> Any:
+ flat_inputs_with_spec, image_or_video = self._flatten_and_extract_image_or_video(inputs)
+ height, width = get_size(image_or_video)
+
+ transform_id, (magnitudes_fn, signed) = self._get_random_item(self._AUGMENTATION_SPACE)
+
+ magnitudes = magnitudes_fn(self.num_magnitude_bins, height, width)
+ if magnitudes is not None:
+ magnitude = float(magnitudes[int(torch.randint(self.num_magnitude_bins, ()))])
+ if signed and torch.rand(()) <= 0.5:
+ magnitude *= -1
+ else:
+ magnitude = 0.0
+
+ image_or_video = self._apply_image_or_video_transform(
+ image_or_video, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
+ )
+ return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video)
+
+
+class AugMix(_AutoAugmentBase):
+ r"""[BETA] AugMix data augmentation method based on
+ `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" `_.
+
+ .. v2betastatus:: AugMix transform
+
+ This transformation works on images and videos only.
+
+ If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
+ to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+ If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+ Args:
+ severity (int, optional): The severity of base augmentation operators. Default is ``3``.
+ mixture_width (int, optional): The number of augmentation chains. Default is ``3``.
+ chain_depth (int, optional): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3].
+ Default is ``-1``.
+ alpha (float, optional): The hyperparameter for the probability distributions. Default is ``1.0``.
+ all_ops (bool, optional): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+ image. If given a number, the value is used for all bands respectively.
+ """
+
+ _v1_transform_cls = _transforms.AugMix
+
+ _PARTIAL_AUGMENTATION_SPACE = {
+ "ShearX": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "ShearY": (lambda num_bins, height, width: torch.linspace(0.0, 0.3, num_bins), True),
+ "TranslateX": (lambda num_bins, height, width: torch.linspace(0.0, width / 3.0, num_bins), True),
+ "TranslateY": (lambda num_bins, height, width: torch.linspace(0.0, height / 3.0, num_bins), True),
+ "Rotate": (lambda num_bins, height, width: torch.linspace(0.0, 30.0, num_bins), True),
+ "Posterize": (
+ lambda num_bins, height, width: (4 - (torch.arange(num_bins) / ((num_bins - 1) / 4))).round().int(),
+ False,
+ ),
+ "Solarize": (lambda num_bins, height, width: torch.linspace(1.0, 0.0, num_bins), False),
+ "AutoContrast": (lambda num_bins, height, width: None, False),
+ "Equalize": (lambda num_bins, height, width: None, False),
+ }
+ _AUGMENTATION_SPACE: Dict[str, Tuple[Callable[[int, int, int], Optional[torch.Tensor]], bool]] = {
+ **_PARTIAL_AUGMENTATION_SPACE,
+ "Brightness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Color": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Contrast": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ "Sharpness": (lambda num_bins, height, width: torch.linspace(0.0, 0.9, num_bins), True),
+ }
+
+ def __init__(
+ self,
+ severity: int = 3,
+ mixture_width: int = 3,
+ chain_depth: int = -1,
+ alpha: float = 1.0,
+ all_ops: bool = True,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = None,
+ ) -> None:
+ super().__init__(interpolation=interpolation, fill=fill)
+ self._PARAMETER_MAX = 10
+ if not (1 <= severity <= self._PARAMETER_MAX):
+ raise ValueError(f"The severity must be between [1, {self._PARAMETER_MAX}]. Got {severity} instead.")
+ self.severity = severity
+ self.mixture_width = mixture_width
+ self.chain_depth = chain_depth
+ self.alpha = alpha
+ self.all_ops = all_ops
+
+ def _sample_dirichlet(self, params: torch.Tensor) -> torch.Tensor:
+ # Must be on a separate method so that we can overwrite it in tests.
+ return torch._sample_dirichlet(params)
+
+ def forward(self, *inputs: Any) -> Any:
+ flat_inputs_with_spec, orig_image_or_video = self._flatten_and_extract_image_or_video(inputs)
+ height, width = get_size(orig_image_or_video)
+
+ if isinstance(orig_image_or_video, torch.Tensor):
+ image_or_video = orig_image_or_video
+ else: # isinstance(inpt, PIL.Image.Image):
+ image_or_video = F.pil_to_tensor(orig_image_or_video)
+
+ augmentation_space = self._AUGMENTATION_SPACE if self.all_ops else self._PARTIAL_AUGMENTATION_SPACE
+
+ orig_dims = list(image_or_video.shape)
+ expected_ndim = 5 if isinstance(orig_image_or_video, tv_tensors.Video) else 4
+ batch = image_or_video.reshape([1] * max(expected_ndim - image_or_video.ndim, 0) + orig_dims)
+ batch_dims = [batch.size(0)] + [1] * (batch.ndim - 1)
+
+ # Sample the beta weights for combining the original and augmented image or video. To get Beta, we use a
+ # Dirichlet with 2 parameters. The 1st column stores the weights of the original and the 2nd the ones of
+ # augmented image or video.
+ m = self._sample_dirichlet(
+ torch.tensor([self.alpha, self.alpha], device=batch.device).expand(batch_dims[0], -1)
+ )
+
+ # Sample the mixing weights and combine them with the ones sampled from Beta for the augmented images or videos.
+ combined_weights = self._sample_dirichlet(
+ torch.tensor([self.alpha] * self.mixture_width, device=batch.device).expand(batch_dims[0], -1)
+ ) * m[:, 1].reshape([batch_dims[0], -1])
+
+ mix = m[:, 0].reshape(batch_dims) * batch
+ for i in range(self.mixture_width):
+ aug = batch
+ depth = self.chain_depth if self.chain_depth > 0 else int(torch.randint(low=1, high=4, size=(1,)).item())
+ for _ in range(depth):
+ transform_id, (magnitudes_fn, signed) = self._get_random_item(augmentation_space)
+
+ magnitudes = magnitudes_fn(self._PARAMETER_MAX, height, width)
+ if magnitudes is not None:
+ magnitude = float(magnitudes[int(torch.randint(self.severity, ()))])
+ if signed and torch.rand(()) <= 0.5:
+ magnitude *= -1
+ else:
+ magnitude = 0.0
+
+ aug = self._apply_image_or_video_transform(
+ aug, transform_id, magnitude, interpolation=self.interpolation, fill=self._fill
+ )
+ mix.add_(combined_weights[:, i].reshape(batch_dims) * aug)
+ mix = mix.reshape(orig_dims).to(dtype=image_or_video.dtype)
+
+ if isinstance(orig_image_or_video, (tv_tensors.Image, tv_tensors.Video)):
+ mix = tv_tensors.wrap(mix, like=orig_image_or_video)
+ elif isinstance(orig_image_or_video, PIL.Image.Image):
+ mix = F.to_pil_image(mix)
+
+ return self._unflatten_and_insert_image_or_video(flat_inputs_with_spec, mix)
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_container.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_container.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f591c49707b00c0bb6256ae3d74ad1f662c0bbf
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_container.py
@@ -0,0 +1,180 @@
+from typing import Any, Callable, Dict, List, Optional, Sequence, Union
+
+import torch
+
+from torch import nn
+from torchvision import transforms as _transforms
+from torchvision.transforms.v2 import Transform
+
+
+class Compose(Transform):
+ """[BETA] Composes several transforms together.
+
+ .. v2betastatus:: Compose transform
+
+ This transform does not support torchscript.
+ Please, see the note below.
+
+ Args:
+ transforms (list of ``Transform`` objects): list of transforms to compose.
+
+ Example:
+ >>> transforms.Compose([
+ >>> transforms.CenterCrop(10),
+ >>> transforms.PILToTensor(),
+ >>> transforms.ConvertImageDtype(torch.float),
+ >>> ])
+
+ .. note::
+ In order to script the transformations, please use ``torch.nn.Sequential`` as below.
+
+ >>> transforms = torch.nn.Sequential(
+ >>> transforms.CenterCrop(10),
+ >>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+ >>> )
+ >>> scripted_transforms = torch.jit.script(transforms)
+
+ Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
+ `lambda` functions or ``PIL.Image``.
+
+ """
+
+ def __init__(self, transforms: Sequence[Callable]) -> None:
+ super().__init__()
+ if not isinstance(transforms, Sequence):
+ raise TypeError("Argument transforms should be a sequence of callables")
+ elif not transforms:
+ raise ValueError("Pass at least one transform")
+ self.transforms = transforms
+
+ def forward(self, *inputs: Any) -> Any:
+ needs_unpacking = len(inputs) > 1
+ for transform in self.transforms:
+ outputs = transform(*inputs)
+ inputs = outputs if needs_unpacking else (outputs,)
+ return outputs
+
+ def extra_repr(self) -> str:
+ format_string = []
+ for t in self.transforms:
+ format_string.append(f" {t}")
+ return "\n".join(format_string)
+
+
+class RandomApply(Transform):
+ """[BETA] Apply randomly a list of transformations with a given probability.
+
+ .. v2betastatus:: RandomApply transform
+
+ .. note::
+ In order to script the transformation, please use ``torch.nn.ModuleList`` as input instead of list/tuple of
+ transforms as shown below:
+
+ >>> transforms = transforms.RandomApply(torch.nn.ModuleList([
+ >>> transforms.ColorJitter(),
+ >>> ]), p=0.3)
+ >>> scripted_transforms = torch.jit.script(transforms)
+
+ Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
+ `lambda` functions or ``PIL.Image``.
+
+ Args:
+ transforms (sequence or torch.nn.Module): list of transformations
+ p (float): probability of applying the list of transforms
+ """
+
+ _v1_transform_cls = _transforms.RandomApply
+
+ def __init__(self, transforms: Union[Sequence[Callable], nn.ModuleList], p: float = 0.5) -> None:
+ super().__init__()
+
+ if not isinstance(transforms, (Sequence, nn.ModuleList)):
+ raise TypeError("Argument transforms should be a sequence of callables or a `nn.ModuleList`")
+ self.transforms = transforms
+
+ if not (0.0 <= p <= 1.0):
+ raise ValueError("`p` should be a floating point value in the interval [0.0, 1.0].")
+ self.p = p
+
+ def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+ return {"transforms": self.transforms, "p": self.p}
+
+ def forward(self, *inputs: Any) -> Any:
+ sample = inputs if len(inputs) > 1 else inputs[0]
+
+ if torch.rand(1) >= self.p:
+ return sample
+
+ for transform in self.transforms:
+ sample = transform(sample)
+ return sample
+
+ def extra_repr(self) -> str:
+ format_string = []
+ for t in self.transforms:
+ format_string.append(f" {t}")
+ return "\n".join(format_string)
+
+
+class RandomChoice(Transform):
+ """[BETA] Apply single transformation randomly picked from a list.
+
+ .. v2betastatus:: RandomChoice transform
+
+ This transform does not support torchscript.
+
+ Args:
+ transforms (sequence or torch.nn.Module): list of transformations
+ p (list of floats or None, optional): probability of each transform being picked.
+ If ``p`` doesn't sum to 1, it is automatically normalized. If ``None``
+ (default), all transforms have the same probability.
+ """
+
+ def __init__(
+ self,
+ transforms: Sequence[Callable],
+ p: Optional[List[float]] = None,
+ ) -> None:
+ if not isinstance(transforms, Sequence):
+ raise TypeError("Argument transforms should be a sequence of callables")
+
+ if p is None:
+ p = [1] * len(transforms)
+ elif len(p) != len(transforms):
+ raise ValueError(f"Length of p doesn't match the number of transforms: {len(p)} != {len(transforms)}")
+
+ super().__init__()
+
+ self.transforms = transforms
+ total = sum(p)
+ self.p = [prob / total for prob in p]
+
+ def forward(self, *inputs: Any) -> Any:
+ idx = int(torch.multinomial(torch.tensor(self.p), 1))
+ transform = self.transforms[idx]
+ return transform(*inputs)
+
+
+class RandomOrder(Transform):
+ """[BETA] Apply a list of transformations in a random order.
+
+ .. v2betastatus:: RandomOrder transform
+
+ This transform does not support torchscript.
+
+ Args:
+ transforms (sequence or torch.nn.Module): list of transformations
+ """
+
+ def __init__(self, transforms: Sequence[Callable]) -> None:
+ if not isinstance(transforms, Sequence):
+ raise TypeError("Argument transforms should be a sequence of callables")
+ super().__init__()
+ self.transforms = transforms
+
+ def forward(self, *inputs: Any) -> Any:
+ sample = inputs if len(inputs) > 1 else inputs[0]
+ for idx in torch.randperm(len(self.transforms)):
+ transform = self.transforms[idx]
+ sample = transform(sample)
+ return sample
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_geometry.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d79fb6fd24b2c0a3d96c2b877d0f0299466e28b
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_geometry.py
@@ -0,0 +1,1447 @@
+import math
+import numbers
+import warnings
+from typing import Any, Callable, cast, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+
+import PIL.Image
+import torch
+
+from torchvision import transforms as _transforms, tv_tensors
+from torchvision.ops.boxes import box_iou
+from torchvision.transforms.functional import _get_perspective_coeffs
+from torchvision.transforms.v2 import functional as F, InterpolationMode, Transform
+from torchvision.transforms.v2.functional._geometry import _check_interpolation
+from torchvision.transforms.v2.functional._utils import _FillType
+
+from ._transform import _RandomApplyTransform
+from ._utils import (
+ _check_padding_arg,
+ _check_padding_mode_arg,
+ _check_sequence_input,
+ _get_fill,
+ _setup_angle,
+ _setup_fill_arg,
+ _setup_number_or_seq,
+ _setup_size,
+ get_bounding_boxes,
+ has_all,
+ has_any,
+ is_pure_tensor,
+ query_size,
+)
+
+
+class RandomHorizontalFlip(_RandomApplyTransform):
+ """[BETA] Horizontally flip the input with a given probability.
+
+ .. v2betastatus:: RandomHorizontalFlip transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ p (float, optional): probability of the input being flipped. Default value is 0.5
+ """
+
+ _v1_transform_cls = _transforms.RandomHorizontalFlip
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(F.horizontal_flip, inpt)
+
+
+class RandomVerticalFlip(_RandomApplyTransform):
+ """[BETA] Vertically flip the input with a given probability.
+
+ .. v2betastatus:: RandomVerticalFlip transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ p (float, optional): probability of the input being flipped. Default value is 0.5
+ """
+
+ _v1_transform_cls = _transforms.RandomVerticalFlip
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(F.vertical_flip, inpt)
+
+
+class Resize(Transform):
+ """[BETA] Resize the input to the given size.
+
+ .. v2betastatus:: Resize transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ .. warning::
+ The output image might be different depending on its type: when downsampling, the interpolation of PIL images
+ and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
+ in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
+ types. See also below the ``antialias`` parameter, which can help making the output of PIL images and tensors
+ closer.
+
+ Args:
+ size (sequence or int): Desired output size. If size is a sequence like
+ (h, w), output size will be matched to this. If size is an int,
+ smaller edge of the image will be matched to this number.
+ i.e, if height > width, then image will be rescaled to
+ (size * height / width, size).
+
+ .. note::
+ In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+ ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ max_size (int, optional): The maximum allowed for the longer edge of
+ the resized image. If the longer edge of the image is greater
+ than ``max_size`` after being resized according to ``size``,
+ ``size`` will be overruled so that the longer edge is equal to
+ ``max_size``.
+ As a result, the smaller edge may be shorter than ``size``. This
+ is only supported if ``size`` is an int (or a sequence of length
+ 1 in torchscript mode).
+ antialias (bool, optional): Whether to apply antialiasing.
+ It only affects **tensors** with bilinear or bicubic modes and it is
+ ignored otherwise: on PIL images, antialiasing is always applied on
+ bilinear or bicubic modes; on other modes (for PIL images and
+ tensors), antialiasing makes no sense and this parameter is ignored.
+ Possible values are:
+
+ - ``True``: will apply antialiasing for bilinear or bicubic modes.
+ Other mode aren't affected. This is probably what you want to use.
+ - ``False``: will not apply antialiasing for tensors on any mode. PIL
+ images are still antialiased on bilinear or bicubic modes, because
+ PIL doesn't support no antialias.
+ - ``None``: equivalent to ``False`` for tensors and ``True`` for
+ PIL images. This value exists for legacy reasons and you probably
+ don't want to use it unless you really know what you are doing.
+
+ The current default is ``None`` **but will change to** ``True`` **in
+ v0.17** for the PIL and Tensor backends to be consistent.
+ """
+
+ _v1_transform_cls = _transforms.Resize
+
+ def __init__(
+ self,
+ size: Union[int, Sequence[int]],
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ max_size: Optional[int] = None,
+ antialias: Optional[Union[str, bool]] = "warn",
+ ) -> None:
+ super().__init__()
+
+ if isinstance(size, int):
+ size = [size]
+ elif isinstance(size, (list, tuple)) and len(size) in {1, 2}:
+ size = list(size)
+ else:
+ raise ValueError(
+ f"size can either be an integer or a list or tuple of one or two integers, " f"but got {size} instead."
+ )
+ self.size = size
+
+ self.interpolation = _check_interpolation(interpolation)
+ self.max_size = max_size
+ self.antialias = antialias
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(
+ F.resize,
+ inpt,
+ self.size,
+ interpolation=self.interpolation,
+ max_size=self.max_size,
+ antialias=self.antialias,
+ )
+
+
+class CenterCrop(Transform):
+ """[BETA] Crop the input at the center.
+
+ .. v2betastatus:: CenterCrop transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
+
+ Args:
+ size (sequence or int): Desired output size of the crop. If size is an
+ int instead of sequence like (h, w), a square crop (size, size) is
+ made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+ """
+
+ _v1_transform_cls = _transforms.CenterCrop
+
+ def __init__(self, size: Union[int, Sequence[int]]):
+ super().__init__()
+ self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(F.center_crop, inpt, output_size=self.size)
+
+
+class RandomResizedCrop(Transform):
+ """[BETA] Crop a random portion of the input and resize it to a given size.
+
+ .. v2betastatus:: RandomResizedCrop transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ A crop of the original input is made: the crop has a random area (H * W)
+ and a random aspect ratio. This crop is finally resized to the given
+ size. This is popularly used to train the Inception networks.
+
+ Args:
+ size (int or sequence): expected output size of the crop, for each edge. If size is an
+ int instead of sequence like (h, w), a square output size ``(size, size)`` is
+ made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+
+ .. note::
+ In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
+ scale (tuple of float, optional): Specifies the lower and upper bounds for the random area of the crop,
+ before resizing. The scale is defined with respect to the area of the original image.
+ ratio (tuple of float, optional): lower and upper bounds for the random aspect ratio of the crop, before
+ resizing.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+ ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ antialias (bool, optional): Whether to apply antialiasing.
+ It only affects **tensors** with bilinear or bicubic modes and it is
+ ignored otherwise: on PIL images, antialiasing is always applied on
+ bilinear or bicubic modes; on other modes (for PIL images and
+ tensors), antialiasing makes no sense and this parameter is ignored.
+ Possible values are:
+
+ - ``True``: will apply antialiasing for bilinear or bicubic modes.
+ Other mode aren't affected. This is probably what you want to use.
+ - ``False``: will not apply antialiasing for tensors on any mode. PIL
+ images are still antialiased on bilinear or bicubic modes, because
+ PIL doesn't support no antialias.
+ - ``None``: equivalent to ``False`` for tensors and ``True`` for
+ PIL images. This value exists for legacy reasons and you probably
+ don't want to use it unless you really know what you are doing.
+
+ The current default is ``None`` **but will change to** ``True`` **in
+ v0.17** for the PIL and Tensor backends to be consistent.
+ """
+
+ _v1_transform_cls = _transforms.RandomResizedCrop
+
+ def __init__(
+ self,
+ size: Union[int, Sequence[int]],
+ scale: Tuple[float, float] = (0.08, 1.0),
+ ratio: Tuple[float, float] = (3.0 / 4.0, 4.0 / 3.0),
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ antialias: Optional[Union[str, bool]] = "warn",
+ ) -> None:
+ super().__init__()
+ self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+
+ if not isinstance(scale, Sequence):
+ raise TypeError("Scale should be a sequence")
+ scale = cast(Tuple[float, float], scale)
+ if not isinstance(ratio, Sequence):
+ raise TypeError("Ratio should be a sequence")
+ ratio = cast(Tuple[float, float], ratio)
+ if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+ warnings.warn("Scale and ratio should be of kind (min, max)")
+
+ self.scale = scale
+ self.ratio = ratio
+ self.interpolation = _check_interpolation(interpolation)
+ self.antialias = antialias
+
+ self._log_ratio = torch.log(torch.tensor(self.ratio))
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ height, width = query_size(flat_inputs)
+ area = height * width
+
+ log_ratio = self._log_ratio
+ for _ in range(10):
+ target_area = area * torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
+ aspect_ratio = torch.exp(
+ torch.empty(1).uniform_(
+ log_ratio[0], # type: ignore[arg-type]
+ log_ratio[1], # type: ignore[arg-type]
+ )
+ ).item()
+
+ w = int(round(math.sqrt(target_area * aspect_ratio)))
+ h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+ if 0 < w <= width and 0 < h <= height:
+ i = torch.randint(0, height - h + 1, size=(1,)).item()
+ j = torch.randint(0, width - w + 1, size=(1,)).item()
+ break
+ else:
+ # Fallback to central crop
+ in_ratio = float(width) / float(height)
+ if in_ratio < min(self.ratio):
+ w = width
+ h = int(round(w / min(self.ratio)))
+ elif in_ratio > max(self.ratio):
+ h = height
+ w = int(round(h * max(self.ratio)))
+ else: # whole image
+ w = width
+ h = height
+ i = (height - h) // 2
+ j = (width - w) // 2
+
+ return dict(top=i, left=j, height=h, width=w)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(
+ F.resized_crop, inpt, **params, size=self.size, interpolation=self.interpolation, antialias=self.antialias
+ )
+
+
+class FiveCrop(Transform):
+ """[BETA] Crop the image or video into four corners and the central crop.
+
+ .. v2betastatus:: FiveCrop transform
+
+ If the input is a :class:`torch.Tensor` or a :class:`~torchvision.tv_tensors.Image` or a
+ :class:`~torchvision.tv_tensors.Video` it can have arbitrary number of leading batch dimensions.
+ For example, the image can have ``[..., C, H, W]`` shape.
+
+ .. Note::
+ This transform returns a tuple of images and there may be a mismatch in the number of
+ inputs and targets your Dataset returns. See below for an example of how to deal with
+ this.
+
+ Args:
+ size (sequence or int): Desired output size of the crop. If size is an ``int``
+ instead of sequence like (h, w), a square crop of size (size, size) is made.
+ If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+
+ Example:
+ >>> class BatchMultiCrop(transforms.Transform):
+ ... def forward(self, sample: Tuple[Tuple[Union[tv_tensors.Image, tv_tensors.Video], ...], int]):
+ ... images_or_videos, labels = sample
+ ... batch_size = len(images_or_videos)
+ ... image_or_video = images_or_videos[0]
+ ... images_or_videos = tv_tensors.wrap(torch.stack(images_or_videos), like=image_or_video)
+ ... labels = torch.full((batch_size,), label, device=images_or_videos.device)
+ ... return images_or_videos, labels
+ ...
+ >>> image = tv_tensors.Image(torch.rand(3, 256, 256))
+ >>> label = 3
+ >>> transform = transforms.Compose([transforms.FiveCrop(224), BatchMultiCrop()])
+ >>> images, labels = transform(image, label)
+ >>> images.shape
+ torch.Size([5, 3, 224, 224])
+ >>> labels
+ tensor([3, 3, 3, 3, 3])
+ """
+
+ _v1_transform_cls = _transforms.FiveCrop
+
+ def __init__(self, size: Union[int, Sequence[int]]) -> None:
+ super().__init__()
+ self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+
+ def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any:
+ if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)):
+ warnings.warn(
+ f"{type(self).__name__}() is currently passing through inputs of type "
+ f"tv_tensors.{type(inpt).__name__}. This will likely change in the future."
+ )
+ return super()._call_kernel(functional, inpt, *args, **kwargs)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(F.five_crop, inpt, self.size)
+
+ def _check_inputs(self, flat_inputs: List[Any]) -> None:
+ if has_any(flat_inputs, tv_tensors.BoundingBoxes, tv_tensors.Mask):
+ raise TypeError(f"BoundingBoxes'es and Mask's are not supported by {type(self).__name__}()")
+
+
+class TenCrop(Transform):
+ """[BETA] Crop the image or video into four corners and the central crop plus the flipped version of
+ these (horizontal flipping is used by default).
+
+ .. v2betastatus:: TenCrop transform
+
+ If the input is a :class:`torch.Tensor` or a :class:`~torchvision.tv_tensors.Image` or a
+ :class:`~torchvision.tv_tensors.Video` it can have arbitrary number of leading batch dimensions.
+ For example, the image can have ``[..., C, H, W]`` shape.
+
+ See :class:`~torchvision.transforms.v2.FiveCrop` for an example.
+
+ .. Note::
+ This transform returns a tuple of images and there may be a mismatch in the number of
+ inputs and targets your Dataset returns. See below for an example of how to deal with
+ this.
+
+ Args:
+ size (sequence or int): Desired output size of the crop. If size is an
+ int instead of sequence like (h, w), a square crop (size, size) is
+ made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+ vertical_flip (bool, optional): Use vertical flipping instead of horizontal
+ """
+
+ _v1_transform_cls = _transforms.TenCrop
+
+ def __init__(self, size: Union[int, Sequence[int]], vertical_flip: bool = False) -> None:
+ super().__init__()
+ self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+ self.vertical_flip = vertical_flip
+
+ def _call_kernel(self, functional: Callable, inpt: Any, *args: Any, **kwargs: Any) -> Any:
+ if isinstance(inpt, (tv_tensors.BoundingBoxes, tv_tensors.Mask)):
+ warnings.warn(
+ f"{type(self).__name__}() is currently passing through inputs of type "
+ f"tv_tensors.{type(inpt).__name__}. This will likely change in the future."
+ )
+ return super()._call_kernel(functional, inpt, *args, **kwargs)
+
+ def _check_inputs(self, flat_inputs: List[Any]) -> None:
+ if has_any(flat_inputs, tv_tensors.BoundingBoxes, tv_tensors.Mask):
+ raise TypeError(f"BoundingBoxes'es and Mask's are not supported by {type(self).__name__}()")
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(F.ten_crop, inpt, self.size, vertical_flip=self.vertical_flip)
+
+
+class Pad(Transform):
+ """[BETA] Pad the input on all sides with the given "pad" value.
+
+ .. v2betastatus:: Pad transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ padding (int or sequence): Padding on each border. If a single int is provided this
+ is used to pad all borders. If sequence of length 2 is provided this is the padding
+ on left/right and top/bottom respectively. If a sequence of length 4 is provided
+ this is the padding for the left, top, right and bottom borders respectively.
+
+ .. note::
+ In torchscript mode padding as single int is not supported, use a sequence of
+ length 1: ``[padding, ]``.
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric.
+ Default is "constant".
+
+ - constant: pads with a constant value, this value is specified with fill
+
+ - edge: pads with the last value at the edge of the image.
+
+ - reflect: pads with reflection of image without repeating the last value on the edge.
+ For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+ will result in [3, 2, 1, 2, 3, 4, 3, 2]
+
+ - symmetric: pads with reflection of image repeating the last value on the edge.
+ For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+ will result in [2, 1, 1, 2, 3, 4, 4, 3]
+ """
+
+ _v1_transform_cls = _transforms.Pad
+
+ def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+ params = super()._extract_params_for_v1_transform()
+
+ if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
+ raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
+
+ return params
+
+ def __init__(
+ self,
+ padding: Union[int, Sequence[int]],
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
+ ) -> None:
+ super().__init__()
+
+ _check_padding_arg(padding)
+ _check_padding_mode_arg(padding_mode)
+
+ # This cast does Sequence[int] -> List[int] and is required to make mypy happy
+ if not isinstance(padding, int):
+ padding = list(padding)
+ self.padding = padding
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+ self.padding_mode = padding_mode
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(F.pad, inpt, padding=self.padding, fill=fill, padding_mode=self.padding_mode) # type: ignore[arg-type]
+
+
+class RandomZoomOut(_RandomApplyTransform):
+ """[BETA] "Zoom out" transformation from
+ `"SSD: Single Shot MultiBox Detector" `_.
+
+ .. v2betastatus:: RandomZoomOut transform
+
+ This transformation randomly pads images, videos, bounding boxes and masks creating a zoom out effect.
+ Output spatial size is randomly sampled from original size up to a maximum size configured
+ with ``side_range`` parameter:
+
+ .. code-block:: python
+
+ r = uniform_sample(side_range[0], side_range[1])
+ output_width = input_width * r
+ output_height = input_height * r
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ side_range (sequence of floats, optional): tuple of two floats defines minimum and maximum factors to
+ scale the input size.
+ p (float, optional): probability that the zoom operation will be performed.
+ """
+
+ def __init__(
+ self,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ side_range: Sequence[float] = (1.0, 4.0),
+ p: float = 0.5,
+ ) -> None:
+ super().__init__(p=p)
+
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ _check_sequence_input(side_range, "side_range", req_sizes=(2,))
+
+ self.side_range = side_range
+ if side_range[0] < 1.0 or side_range[0] > side_range[1]:
+ raise ValueError(f"Invalid canvas side range provided {side_range}.")
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ orig_h, orig_w = query_size(flat_inputs)
+
+ r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
+ canvas_width = int(orig_w * r)
+ canvas_height = int(orig_h * r)
+
+ r = torch.rand(2)
+ left = int((canvas_width - orig_w) * r[0])
+ top = int((canvas_height - orig_h) * r[1])
+ right = canvas_width - (left + orig_w)
+ bottom = canvas_height - (top + orig_h)
+ padding = [left, top, right, bottom]
+
+ return dict(padding=padding)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(F.pad, inpt, **params, fill=fill)
+
+
+class RandomRotation(Transform):
+ """[BETA] Rotate the input by angle.
+
+ .. v2betastatus:: RandomRotation transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ degrees (sequence or number): Range of degrees to select from.
+ If degrees is a number instead of sequence like (min, max), the range of degrees
+ will be (-degrees, +degrees).
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ expand (bool, optional): Optional expansion flag.
+ If true, expands the output to make it large enough to hold the entire rotated image.
+ If false or omitted, make the output image the same size as the input image.
+ Note that the expand flag assumes rotation around the center (see note below) and no translation.
+ center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
+ Default is the center of the image.
+
+ .. note::
+
+ In theory, setting ``center`` has no effect if ``expand=True``, since the image center will become the
+ center of rotation. In practice however, due to numerical precision, this can lead to off-by-one
+ differences of the resulting image size compared to using the image center in the first place. Thus, when
+ setting ``expand=True``, it's best to leave ``center=None`` (default).
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+
+ .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
+
+ """
+
+ _v1_transform_cls = _transforms.RandomRotation
+
+ def __init__(
+ self,
+ degrees: Union[numbers.Number, Sequence],
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ expand: bool = False,
+ center: Optional[List[float]] = None,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ ) -> None:
+ super().__init__()
+ self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
+ self.interpolation = _check_interpolation(interpolation)
+ self.expand = expand
+
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ if center is not None:
+ _check_sequence_input(center, "center", req_sizes=(2,))
+
+ self.center = center
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item()
+ return dict(angle=angle)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(
+ F.rotate,
+ inpt,
+ **params,
+ interpolation=self.interpolation,
+ expand=self.expand,
+ center=self.center,
+ fill=fill,
+ )
+
+
+class RandomAffine(Transform):
+ """[BETA] Random affine transformation the input keeping center invariant.
+
+ .. v2betastatus:: RandomAffine transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ degrees (sequence or number): Range of degrees to select from.
+ If degrees is a number instead of sequence like (min, max), the range of degrees
+ will be (-degrees, +degrees). Set to 0 to deactivate rotations.
+ translate (tuple, optional): tuple of maximum absolute fraction for horizontal
+ and vertical translations. For example translate=(a, b), then horizontal shift
+ is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
+ randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.
+ scale (tuple, optional): scaling factor interval, e.g (a, b), then scale is
+ randomly sampled from the range a <= scale <= b. Will keep original scale by default.
+ shear (sequence or number, optional): Range of degrees to select from.
+ If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear)
+ will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the
+ range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values,
+ an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
+ Will not apply shear by default.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
+ Default is the center of the image.
+
+ .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
+
+ """
+
+ _v1_transform_cls = _transforms.RandomAffine
+
+ def __init__(
+ self,
+ degrees: Union[numbers.Number, Sequence],
+ translate: Optional[Sequence[float]] = None,
+ scale: Optional[Sequence[float]] = None,
+ shear: Optional[Union[int, float, Sequence[float]]] = None,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ center: Optional[List[float]] = None,
+ ) -> None:
+ super().__init__()
+ self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
+ if translate is not None:
+ _check_sequence_input(translate, "translate", req_sizes=(2,))
+ for t in translate:
+ if not (0.0 <= t <= 1.0):
+ raise ValueError("translation values should be between 0 and 1")
+ self.translate = translate
+ if scale is not None:
+ _check_sequence_input(scale, "scale", req_sizes=(2,))
+ for s in scale:
+ if s <= 0:
+ raise ValueError("scale values should be positive")
+ self.scale = scale
+
+ if shear is not None:
+ self.shear = _setup_angle(shear, name="shear", req_sizes=(2, 4))
+ else:
+ self.shear = shear
+
+ self.interpolation = _check_interpolation(interpolation)
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ if center is not None:
+ _check_sequence_input(center, "center", req_sizes=(2,))
+
+ self.center = center
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ height, width = query_size(flat_inputs)
+
+ angle = torch.empty(1).uniform_(self.degrees[0], self.degrees[1]).item()
+ if self.translate is not None:
+ max_dx = float(self.translate[0] * width)
+ max_dy = float(self.translate[1] * height)
+ tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))
+ ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))
+ translate = (tx, ty)
+ else:
+ translate = (0, 0)
+
+ if self.scale is not None:
+ scale = torch.empty(1).uniform_(self.scale[0], self.scale[1]).item()
+ else:
+ scale = 1.0
+
+ shear_x = shear_y = 0.0
+ if self.shear is not None:
+ shear_x = torch.empty(1).uniform_(self.shear[0], self.shear[1]).item()
+ if len(self.shear) == 4:
+ shear_y = torch.empty(1).uniform_(self.shear[2], self.shear[3]).item()
+
+ shear = (shear_x, shear_y)
+ return dict(angle=angle, translate=translate, scale=scale, shear=shear)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(
+ F.affine,
+ inpt,
+ **params,
+ interpolation=self.interpolation,
+ fill=fill,
+ center=self.center,
+ )
+
+
+class RandomCrop(Transform):
+ """[BETA] Crop the input at a random location.
+
+ .. v2betastatus:: RandomCrop transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ size (sequence or int): Desired output size of the crop. If size is an
+ int instead of sequence like (h, w), a square crop (size, size) is
+ made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+ padding (int or sequence, optional): Optional padding on each border
+ of the image. Default is None. If a single int is provided this
+ is used to pad all borders. If sequence of length 2 is provided this is the padding
+ on left/right and top/bottom respectively. If a sequence of length 4 is provided
+ this is the padding for the left, top, right and bottom borders respectively.
+
+ .. note::
+ In torchscript mode padding as single int is not supported, use a sequence of
+ length 1: ``[padding, ]``.
+ pad_if_needed (boolean, optional): It will pad the image if smaller than the
+ desired size to avoid raising an exception. Since cropping is done
+ after padding, the padding seems to be done at a random offset.
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ padding_mode (str, optional): Type of padding. Should be: constant, edge, reflect or symmetric.
+ Default is constant.
+
+ - constant: pads with a constant value, this value is specified with fill
+
+ - edge: pads with the last value at the edge of the image.
+
+ - reflect: pads with reflection of image without repeating the last value on the edge.
+ For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+ will result in [3, 2, 1, 2, 3, 4, 3, 2]
+
+ - symmetric: pads with reflection of image repeating the last value on the edge.
+ For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+ will result in [2, 1, 1, 2, 3, 4, 4, 3]
+ """
+
+ _v1_transform_cls = _transforms.RandomCrop
+
+ def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+ params = super()._extract_params_for_v1_transform()
+
+ if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
+ raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
+
+ padding = self.padding
+ if padding is not None:
+ pad_left, pad_right, pad_top, pad_bottom = padding
+ padding = [pad_left, pad_top, pad_right, pad_bottom]
+ params["padding"] = padding
+
+ return params
+
+ def __init__(
+ self,
+ size: Union[int, Sequence[int]],
+ padding: Optional[Union[int, Sequence[int]]] = None,
+ pad_if_needed: bool = False,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ padding_mode: Literal["constant", "edge", "reflect", "symmetric"] = "constant",
+ ) -> None:
+ super().__init__()
+
+ self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+
+ if pad_if_needed or padding is not None:
+ if padding is not None:
+ _check_padding_arg(padding)
+ _check_padding_mode_arg(padding_mode)
+
+ self.padding = F._geometry._parse_pad_padding(padding) if padding else None # type: ignore[arg-type]
+ self.pad_if_needed = pad_if_needed
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+ self.padding_mode = padding_mode
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ padded_height, padded_width = query_size(flat_inputs)
+
+ if self.padding is not None:
+ pad_left, pad_right, pad_top, pad_bottom = self.padding
+ padded_height += pad_top + pad_bottom
+ padded_width += pad_left + pad_right
+ else:
+ pad_left = pad_right = pad_top = pad_bottom = 0
+
+ cropped_height, cropped_width = self.size
+
+ if self.pad_if_needed:
+ if padded_height < cropped_height:
+ diff = cropped_height - padded_height
+
+ pad_top += diff
+ pad_bottom += diff
+ padded_height += 2 * diff
+
+ if padded_width < cropped_width:
+ diff = cropped_width - padded_width
+
+ pad_left += diff
+ pad_right += diff
+ padded_width += 2 * diff
+
+ if padded_height < cropped_height or padded_width < cropped_width:
+ raise ValueError(
+ f"Required crop size {(cropped_height, cropped_width)} is larger than "
+ f"{'padded ' if self.padding is not None else ''}input image size {(padded_height, padded_width)}."
+ )
+
+ # We need a different order here than we have in self.padding since this padding will be parsed again in `F.pad`
+ padding = [pad_left, pad_top, pad_right, pad_bottom]
+ needs_pad = any(padding)
+
+ needs_vert_crop, top = (
+ (True, int(torch.randint(0, padded_height - cropped_height + 1, size=())))
+ if padded_height > cropped_height
+ else (False, 0)
+ )
+ needs_horz_crop, left = (
+ (True, int(torch.randint(0, padded_width - cropped_width + 1, size=())))
+ if padded_width > cropped_width
+ else (False, 0)
+ )
+
+ return dict(
+ needs_crop=needs_vert_crop or needs_horz_crop,
+ top=top,
+ left=left,
+ height=cropped_height,
+ width=cropped_width,
+ needs_pad=needs_pad,
+ padding=padding,
+ )
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ if params["needs_pad"]:
+ fill = _get_fill(self._fill, type(inpt))
+ inpt = self._call_kernel(F.pad, inpt, padding=params["padding"], fill=fill, padding_mode=self.padding_mode)
+
+ if params["needs_crop"]:
+ inpt = self._call_kernel(
+ F.crop, inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"]
+ )
+
+ return inpt
+
+
+class RandomPerspective(_RandomApplyTransform):
+ """[BETA] Perform a random perspective transformation of the input with a given probability.
+
+ .. v2betastatus:: RandomPerspective transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ distortion_scale (float, optional): argument to control the degree of distortion and ranges from 0 to 1.
+ Default is 0.5.
+ p (float, optional): probability of the input being transformed. Default is 0.5.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ """
+
+ _v1_transform_cls = _transforms.RandomPerspective
+
+ def __init__(
+ self,
+ distortion_scale: float = 0.5,
+ p: float = 0.5,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ ) -> None:
+ super().__init__(p=p)
+
+ if not (0 <= distortion_scale <= 1):
+ raise ValueError("Argument distortion_scale value should be between 0 and 1")
+
+ self.distortion_scale = distortion_scale
+ self.interpolation = _check_interpolation(interpolation)
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ height, width = query_size(flat_inputs)
+
+ distortion_scale = self.distortion_scale
+
+ half_height = height // 2
+ half_width = width // 2
+ bound_height = int(distortion_scale * half_height) + 1
+ bound_width = int(distortion_scale * half_width) + 1
+ topleft = [
+ int(torch.randint(0, bound_width, size=(1,))),
+ int(torch.randint(0, bound_height, size=(1,))),
+ ]
+ topright = [
+ int(torch.randint(width - bound_width, width, size=(1,))),
+ int(torch.randint(0, bound_height, size=(1,))),
+ ]
+ botright = [
+ int(torch.randint(width - bound_width, width, size=(1,))),
+ int(torch.randint(height - bound_height, height, size=(1,))),
+ ]
+ botleft = [
+ int(torch.randint(0, bound_width, size=(1,))),
+ int(torch.randint(height - bound_height, height, size=(1,))),
+ ]
+ startpoints = [[0, 0], [width - 1, 0], [width - 1, height - 1], [0, height - 1]]
+ endpoints = [topleft, topright, botright, botleft]
+ perspective_coeffs = _get_perspective_coeffs(startpoints, endpoints)
+ return dict(coefficients=perspective_coeffs)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(
+ F.perspective,
+ inpt,
+ None,
+ None,
+ fill=fill,
+ interpolation=self.interpolation,
+ **params,
+ )
+
+
+class ElasticTransform(Transform):
+ """[BETA] Transform the input with elastic transformations.
+
+ .. v2betastatus:: RandomPerspective transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Given alpha and sigma, it will generate displacement
+ vectors for all pixels based on random offsets. Alpha controls the strength
+ and sigma controls the smoothness of the displacements.
+ The displacements are added to an identity grid and the resulting grid is
+ used to transform the input.
+
+ .. note::
+ Implementation to transform bounding boxes is approximative (not exact).
+ We construct an approximation of the inverse grid as ``inverse_grid = identity - displacement``.
+ This is not an exact inverse of the grid used to transform images, i.e. ``grid = identity + displacement``.
+ Our assumption is that ``displacement * displacement`` is small and can be ignored.
+ Large displacements would lead to large errors in the approximation.
+
+ Applications:
+ Randomly transforms the morphology of objects in images and produces a
+ see-through-water-like effect.
+
+ Args:
+ alpha (float or sequence of floats, optional): Magnitude of displacements. Default is 50.0.
+ sigma (float or sequence of floats, optional): Smoothness of displacements. Default is 5.0.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ fill (number or tuple or dict, optional): Pixel fill value used when the ``padding_mode`` is constant.
+ Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ Fill value can be also a dictionary mapping data type to the fill value, e.g.
+ ``fill={tv_tensors.Image: 127, tv_tensors.Mask: 0}`` where ``Image`` will be filled with 127 and
+ ``Mask`` will be filled with 0.
+ """
+
+ _v1_transform_cls = _transforms.ElasticTransform
+
+ def __init__(
+ self,
+ alpha: Union[float, Sequence[float]] = 50.0,
+ sigma: Union[float, Sequence[float]] = 5.0,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ fill: Union[_FillType, Dict[Union[Type, str], _FillType]] = 0,
+ ) -> None:
+ super().__init__()
+ self.alpha = _setup_number_or_seq(alpha, "alpha")
+ self.sigma = _setup_number_or_seq(sigma, "sigma")
+
+ self.interpolation = _check_interpolation(interpolation)
+ self.fill = fill
+ self._fill = _setup_fill_arg(fill)
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ size = list(query_size(flat_inputs))
+
+ dx = torch.rand([1, 1] + size) * 2 - 1
+ if self.sigma[0] > 0.0:
+ kx = int(8 * self.sigma[0] + 1)
+ # if kernel size is even we have to make it odd
+ if kx % 2 == 0:
+ kx += 1
+ dx = self._call_kernel(F.gaussian_blur, dx, [kx, kx], list(self.sigma))
+ dx = dx * self.alpha[0] / size[0]
+
+ dy = torch.rand([1, 1] + size) * 2 - 1
+ if self.sigma[1] > 0.0:
+ ky = int(8 * self.sigma[1] + 1)
+ # if kernel size is even we have to make it odd
+ if ky % 2 == 0:
+ ky += 1
+ dy = self._call_kernel(F.gaussian_blur, dy, [ky, ky], list(self.sigma))
+ dy = dy * self.alpha[1] / size[1]
+ displacement = torch.concat([dx, dy], 1).permute([0, 2, 3, 1]) # 1 x H x W x 2
+ return dict(displacement=displacement)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ fill = _get_fill(self._fill, type(inpt))
+ return self._call_kernel(
+ F.elastic,
+ inpt,
+ **params,
+ fill=fill,
+ interpolation=self.interpolation,
+ )
+
+
+class RandomIoUCrop(Transform):
+ """[BETA] Random IoU crop transformation from
+ `"SSD: Single Shot MultiBox Detector" `_.
+
+ .. v2betastatus:: RandomIoUCrop transform
+
+ This transformation requires an image or video data and ``tv_tensors.BoundingBoxes`` in the input.
+
+ .. warning::
+ In order to properly remove the bounding boxes below the IoU threshold, `RandomIoUCrop`
+ must be followed by :class:`~torchvision.transforms.v2.SanitizeBoundingBoxes`, either immediately
+ after or later in the transforms pipeline.
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ min_scale (float, optional): Minimum factors to scale the input size.
+ max_scale (float, optional): Maximum factors to scale the input size.
+ min_aspect_ratio (float, optional): Minimum aspect ratio for the cropped image or video.
+ max_aspect_ratio (float, optional): Maximum aspect ratio for the cropped image or video.
+ sampler_options (list of float, optional): List of minimal IoU (Jaccard) overlap between all the boxes and
+ a cropped image or video. Default, ``None`` which corresponds to ``[0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]``
+ trials (int, optional): Number of trials to find a crop for a given value of minimal IoU (Jaccard) overlap.
+ Default, 40.
+ """
+
+ def __init__(
+ self,
+ min_scale: float = 0.3,
+ max_scale: float = 1.0,
+ min_aspect_ratio: float = 0.5,
+ max_aspect_ratio: float = 2.0,
+ sampler_options: Optional[List[float]] = None,
+ trials: int = 40,
+ ):
+ super().__init__()
+ # Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174
+ self.min_scale = min_scale
+ self.max_scale = max_scale
+ self.min_aspect_ratio = min_aspect_ratio
+ self.max_aspect_ratio = max_aspect_ratio
+ if sampler_options is None:
+ sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
+ self.options = sampler_options
+ self.trials = trials
+
+ def _check_inputs(self, flat_inputs: List[Any]) -> None:
+ if not (
+ has_all(flat_inputs, tv_tensors.BoundingBoxes)
+ and has_any(flat_inputs, PIL.Image.Image, tv_tensors.Image, is_pure_tensor)
+ ):
+ raise TypeError(
+ f"{type(self).__name__}() requires input sample to contain tensor or PIL images "
+ "and bounding boxes. Sample can also contain masks."
+ )
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ orig_h, orig_w = query_size(flat_inputs)
+ bboxes = get_bounding_boxes(flat_inputs)
+
+ while True:
+ # sample an option
+ idx = int(torch.randint(low=0, high=len(self.options), size=(1,)))
+ min_jaccard_overlap = self.options[idx]
+ if min_jaccard_overlap >= 1.0: # a value larger than 1 encodes the leave as-is option
+ return dict()
+
+ for _ in range(self.trials):
+ # check the aspect ratio limitations
+ r = self.min_scale + (self.max_scale - self.min_scale) * torch.rand(2)
+ new_w = int(orig_w * r[0])
+ new_h = int(orig_h * r[1])
+ aspect_ratio = new_w / new_h
+ if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio):
+ continue
+
+ # check for 0 area crops
+ r = torch.rand(2)
+ left = int((orig_w - new_w) * r[0])
+ top = int((orig_h - new_h) * r[1])
+ right = left + new_w
+ bottom = top + new_h
+ if left == right or top == bottom:
+ continue
+
+ # check for any valid boxes with centers within the crop area
+ xyxy_bboxes = F.convert_bounding_box_format(
+ bboxes.as_subclass(torch.Tensor),
+ bboxes.format,
+ tv_tensors.BoundingBoxFormat.XYXY,
+ )
+ cx = 0.5 * (xyxy_bboxes[..., 0] + xyxy_bboxes[..., 2])
+ cy = 0.5 * (xyxy_bboxes[..., 1] + xyxy_bboxes[..., 3])
+ is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom)
+ if not is_within_crop_area.any():
+ continue
+
+ # check at least 1 box with jaccard limitations
+ xyxy_bboxes = xyxy_bboxes[is_within_crop_area]
+ ious = box_iou(
+ xyxy_bboxes,
+ torch.tensor([[left, top, right, bottom]], dtype=xyxy_bboxes.dtype, device=xyxy_bboxes.device),
+ )
+ if ious.max() < min_jaccard_overlap:
+ continue
+
+ return dict(top=top, left=left, height=new_h, width=new_w, is_within_crop_area=is_within_crop_area)
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+
+ if len(params) < 1:
+ return inpt
+
+ output = self._call_kernel(
+ F.crop, inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"]
+ )
+
+ if isinstance(output, tv_tensors.BoundingBoxes):
+ # We "mark" the invalid boxes as degenreate, and they can be
+ # removed by a later call to SanitizeBoundingBoxes()
+ output[~params["is_within_crop_area"]] = 0
+
+ return output
+
+
+class ScaleJitter(Transform):
+ """[BETA] Perform Large Scale Jitter on the input according to
+ `"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation" `_.
+
+ .. v2betastatus:: ScaleJitter transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ target_size (tuple of int): Target size. This parameter defines base scale for jittering,
+ e.g. ``min(target_size[0] / width, target_size[1] / height)``.
+ scale_range (tuple of float, optional): Minimum and maximum of the scale range. Default, ``(0.1, 2.0)``.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+ ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ antialias (bool, optional): Whether to apply antialiasing.
+ It only affects **tensors** with bilinear or bicubic modes and it is
+ ignored otherwise: on PIL images, antialiasing is always applied on
+ bilinear or bicubic modes; on other modes (for PIL images and
+ tensors), antialiasing makes no sense and this parameter is ignored.
+ Possible values are:
+
+ - ``True``: will apply antialiasing for bilinear or bicubic modes.
+ Other mode aren't affected. This is probably what you want to use.
+ - ``False``: will not apply antialiasing for tensors on any mode. PIL
+ images are still antialiased on bilinear or bicubic modes, because
+ PIL doesn't support no antialias.
+ - ``None``: equivalent to ``False`` for tensors and ``True`` for
+ PIL images. This value exists for legacy reasons and you probably
+ don't want to use it unless you really know what you are doing.
+
+ The current default is ``None`` **but will change to** ``True`` **in
+ v0.17** for the PIL and Tensor backends to be consistent.
+ """
+
+ def __init__(
+ self,
+ target_size: Tuple[int, int],
+ scale_range: Tuple[float, float] = (0.1, 2.0),
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ antialias: Optional[Union[str, bool]] = "warn",
+ ):
+ super().__init__()
+ self.target_size = target_size
+ self.scale_range = scale_range
+ self.interpolation = _check_interpolation(interpolation)
+ self.antialias = antialias
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ orig_height, orig_width = query_size(flat_inputs)
+
+ scale = self.scale_range[0] + torch.rand(1) * (self.scale_range[1] - self.scale_range[0])
+ r = min(self.target_size[1] / orig_height, self.target_size[0] / orig_width) * scale
+ new_width = int(orig_width * r)
+ new_height = int(orig_height * r)
+
+ return dict(size=(new_height, new_width))
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(
+ F.resize, inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias
+ )
+
+
+class RandomShortestSize(Transform):
+ """[BETA] Randomly resize the input.
+
+ .. v2betastatus:: RandomShortestSize transform
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ min_size (int or sequence of int): Minimum spatial size. Single integer value or a sequence of integer values.
+ max_size (int, optional): Maximum spatial size. Default, None.
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+ ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ antialias (bool, optional): Whether to apply antialiasing.
+ It only affects **tensors** with bilinear or bicubic modes and it is
+ ignored otherwise: on PIL images, antialiasing is always applied on
+ bilinear or bicubic modes; on other modes (for PIL images and
+ tensors), antialiasing makes no sense and this parameter is ignored.
+ Possible values are:
+
+ - ``True``: will apply antialiasing for bilinear or bicubic modes.
+ Other mode aren't affected. This is probably what you want to use.
+ - ``False``: will not apply antialiasing for tensors on any mode. PIL
+ images are still antialiased on bilinear or bicubic modes, because
+ PIL doesn't support no antialias.
+ - ``None``: equivalent to ``False`` for tensors and ``True`` for
+ PIL images. This value exists for legacy reasons and you probably
+ don't want to use it unless you really know what you are doing.
+
+ The current default is ``None`` **but will change to** ``True`` **in
+ v0.17** for the PIL and Tensor backends to be consistent.
+ """
+
+ def __init__(
+ self,
+ min_size: Union[List[int], Tuple[int], int],
+ max_size: Optional[int] = None,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ antialias: Optional[Union[str, bool]] = "warn",
+ ):
+ super().__init__()
+ self.min_size = [min_size] if isinstance(min_size, int) else list(min_size)
+ self.max_size = max_size
+ self.interpolation = _check_interpolation(interpolation)
+ self.antialias = antialias
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ orig_height, orig_width = query_size(flat_inputs)
+
+ min_size = self.min_size[int(torch.randint(len(self.min_size), ()))]
+ r = min_size / min(orig_height, orig_width)
+ if self.max_size is not None:
+ r = min(r, self.max_size / max(orig_height, orig_width))
+
+ new_width = int(orig_width * r)
+ new_height = int(orig_height * r)
+
+ return dict(size=(new_height, new_width))
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(
+ F.resize, inpt, size=params["size"], interpolation=self.interpolation, antialias=self.antialias
+ )
+
+
+class RandomResize(Transform):
+ """[BETA] Randomly resize the input.
+
+ .. v2betastatus:: RandomResize transform
+
+ This transformation can be used together with ``RandomCrop`` as data augmentations to train
+ models on image segmentation task.
+
+ Output spatial size is randomly sampled from the interval ``[min_size, max_size]``:
+
+ .. code-block:: python
+
+ size = uniform_sample(min_size, max_size)
+ output_width = size
+ output_height = size
+
+ If the input is a :class:`torch.Tensor` or a ``TVTensor`` (e.g. :class:`~torchvision.tv_tensors.Image`,
+ :class:`~torchvision.tv_tensors.Video`, :class:`~torchvision.tv_tensors.BoundingBoxes` etc.)
+ it can have arbitrary number of leading batch dimensions. For example,
+ the image can have ``[..., C, H, W]`` shape. A bounding box can have ``[..., 4]`` shape.
+
+ Args:
+ min_size (int): Minimum output size for random sampling
+ max_size (int): Maximum output size for random sampling
+ interpolation (InterpolationMode, optional): Desired interpolation enum defined by
+ :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
+ If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
+ ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
+ The corresponding Pillow integer constants, e.g. ``PIL.Image.BILINEAR`` are accepted as well.
+ antialias (bool, optional): Whether to apply antialiasing.
+ It only affects **tensors** with bilinear or bicubic modes and it is
+ ignored otherwise: on PIL images, antialiasing is always applied on
+ bilinear or bicubic modes; on other modes (for PIL images and
+ tensors), antialiasing makes no sense and this parameter is ignored.
+ Possible values are:
+
+ - ``True``: will apply antialiasing for bilinear or bicubic modes.
+ Other mode aren't affected. This is probably what you want to use.
+ - ``False``: will not apply antialiasing for tensors on any mode. PIL
+ images are still antialiased on bilinear or bicubic modes, because
+ PIL doesn't support no antialias.
+ - ``None``: equivalent to ``False`` for tensors and ``True`` for
+ PIL images. This value exists for legacy reasons and you probably
+ don't want to use it unless you really know what you are doing.
+
+ The current default is ``None`` **but will change to** ``True`` **in
+ v0.17** for the PIL and Tensor backends to be consistent.
+ """
+
+ def __init__(
+ self,
+ min_size: int,
+ max_size: int,
+ interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+ antialias: Optional[Union[str, bool]] = "warn",
+ ) -> None:
+ super().__init__()
+ self.min_size = min_size
+ self.max_size = max_size
+ self.interpolation = _check_interpolation(interpolation)
+ self.antialias = antialias
+
+ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+ size = int(torch.randint(self.min_size, self.max_size, ()))
+ return dict(size=[size])
+
+ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+ return self._call_kernel(
+ F.resize, inpt, params["size"], interpolation=self.interpolation, antialias=self.antialias
+ )
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_meta.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fa31ebef94f61eead6064ad065e141e3db23a6e
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_meta.py
@@ -0,0 +1,42 @@
+from typing import Any, Dict, Union
+
+from torchvision import tv_tensors
+from torchvision.transforms.v2 import functional as F, Transform
+
+
+class ConvertBoundingBoxFormat(Transform):
+ """[BETA] Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
+
+ .. v2betastatus:: ConvertBoundingBoxFormat transform
+
+ Args:
+ format (str or tv_tensors.BoundingBoxFormat): output bounding box format.
+ Possible values are defined by :class:`~torchvision.tv_tensors.BoundingBoxFormat` and
+ string values match the enums, e.g. "XYXY" or "XYWH" etc.
+ """
+
+ _transformed_types = (tv_tensors.BoundingBoxes,)
+
+ def __init__(self, format: Union[str, tv_tensors.BoundingBoxFormat]) -> None:
+ super().__init__()
+ if isinstance(format, str):
+ format = tv_tensors.BoundingBoxFormat[format]
+ self.format = format
+
+ def _transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes:
+ return F.convert_bounding_box_format(inpt, new_format=self.format) # type: ignore[return-value]
+
+
+class ClampBoundingBoxes(Transform):
+ """[BETA] Clamp bounding boxes to their corresponding image dimensions.
+
+ The clamping is done according to the bounding boxes' ``canvas_size`` meta-data.
+
+ .. v2betastatus:: ClampBoundingBoxes transform
+
+ """
+
+ _transformed_types = (tv_tensors.BoundingBoxes,)
+
+ def _transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes:
+ return F.clamp_bounding_boxes(inpt) # type: ignore[return-value]
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_utils.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6147180a986f0b2272b1f4f24654b6cbe46edff1
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/_utils.py
@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import collections.abc
+import numbers
+from contextlib import suppress
+
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+
+import PIL.Image
+import torch
+
+from torchvision import tv_tensors
+
+from torchvision._utils import sequence_to_str
+
+from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size # noqa: F401
+from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
+from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
+
+
+def _setup_number_or_seq(arg: Union[int, float, Sequence[Union[int, float]]], name: str) -> Sequence[float]:
+ if not isinstance(arg, (int, float, Sequence)):
+ raise TypeError(f"{name} should be a number or a sequence of numbers. Got {type(arg)}")
+ if isinstance(arg, Sequence) and len(arg) not in (1, 2):
+ raise ValueError(f"If {name} is a sequence its length should be 1 or 2. Got {len(arg)}")
+ if isinstance(arg, Sequence):
+ for element in arg:
+ if not isinstance(element, (int, float)):
+ raise ValueError(f"{name} should be a sequence of numbers. Got {type(element)}")
+
+ if isinstance(arg, (int, float)):
+ arg = [float(arg), float(arg)]
+ elif isinstance(arg, Sequence):
+ if len(arg) == 1:
+ arg = [float(arg[0]), float(arg[0])]
+ else:
+ arg = [float(arg[0]), float(arg[1])]
+ return arg
+
+
+def _check_fill_arg(fill: Union[_FillType, Dict[Union[Type, str], _FillType]]) -> None:
+ if isinstance(fill, dict):
+ for value in fill.values():
+ _check_fill_arg(value)
+ else:
+ if fill is not None and not isinstance(fill, (numbers.Number, tuple, list)):
+ raise TypeError("Got inappropriate fill arg, only Numbers, tuples, lists and dicts are allowed.")
+
+
+def _convert_fill_arg(fill: _FillType) -> _FillTypeJIT:
+ # Fill = 0 is not equivalent to None, https://github.com/pytorch/vision/issues/6517
+ # So, we can't reassign fill to 0
+ # if fill is None:
+ # fill = 0
+ if fill is None:
+ return fill
+
+ if not isinstance(fill, (int, float)):
+ fill = [float(v) for v in list(fill)]
+ return fill # type: ignore[return-value]
+
+
+def _setup_fill_arg(fill: Union[_FillType, Dict[Union[Type, str], _FillType]]) -> Dict[Union[Type, str], _FillTypeJIT]:
+ _check_fill_arg(fill)
+
+ if isinstance(fill, dict):
+ for k, v in fill.items():
+ fill[k] = _convert_fill_arg(v)
+ return fill # type: ignore[return-value]
+ else:
+ return {"others": _convert_fill_arg(fill)}
+
+
+def _get_fill(fill_dict, inpt_type):
+ if inpt_type in fill_dict:
+ return fill_dict[inpt_type]
+ elif "others" in fill_dict:
+ return fill_dict["others"]
+ else:
+ RuntimeError("This should never happen, please open an issue on the torchvision repo if you hit this.")
+
+
+def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
+ if not isinstance(padding, (numbers.Number, tuple, list)):
+ raise TypeError("Got inappropriate padding arg")
+
+ if isinstance(padding, (tuple, list)) and len(padding) not in [1, 2, 4]:
+ raise ValueError(f"Padding must be an int or a 1, 2, or 4 element tuple, not a {len(padding)} element tuple")
+
+
+# TODO: let's use torchvision._utils.StrEnum to have the best of both worlds (strings and enums)
+# https://github.com/pytorch/vision/issues/6250
+def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect", "symmetric"]) -> None:
+ if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+ raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
+
+
+def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
+ """
+ This heuristic covers three cases:
+
+ 1. The input is tuple or list whose second item is a labels tensor. This happens for already batched
+ classification inputs for MixUp and CutMix (typically after the Dataloder).
+ 2. The input is a tuple or list whose second item is a dictionary that contains the labels tensor
+ under a label-like (see below) key. This happens for the inputs of detection models.
+ 3. The input is a dictionary that is structured as the one from 2.
+
+ What is "label-like" key? We first search for an case-insensitive match of 'labels' inside the keys of the
+ dictionary. This is the name our detection models expect. If we can't find that, we look for a case-insensitive
+ match of the term 'label' anywhere inside the key, i.e. 'FooLaBeLBar'. If we can't find that either, the dictionary
+ contains no "label-like" key.
+ """
+
+ if isinstance(inputs, (tuple, list)):
+ inputs = inputs[1]
+
+ # MixUp, CutMix
+ if is_pure_tensor(inputs):
+ return inputs
+
+ if not isinstance(inputs, collections.abc.Mapping):
+ raise ValueError(
+ f"When using the default labels_getter, the input passed to forward must be a dictionary or a two-tuple "
+ f"whose second item is a dictionary or a tensor, but got {inputs} instead."
+ )
+
+ candidate_key = None
+ with suppress(StopIteration):
+ candidate_key = next(key for key in inputs.keys() if key.lower() == "labels")
+ if candidate_key is None:
+ with suppress(StopIteration):
+ candidate_key = next(key for key in inputs.keys() if "label" in key.lower())
+ if candidate_key is None:
+ raise ValueError(
+ "Could not infer where the labels are in the sample. Try passing a callable as the labels_getter parameter?"
+ "If there are no labels in the sample by design, pass labels_getter=None."
+ )
+
+ return inputs[candidate_key]
+
+
+def _parse_labels_getter(
+ labels_getter: Union[str, Callable[[Any], Optional[torch.Tensor]], None]
+) -> Callable[[Any], Optional[torch.Tensor]]:
+ if labels_getter == "default":
+ return _find_labels_default_heuristic
+ elif callable(labels_getter):
+ return labels_getter
+ elif labels_getter is None:
+ return lambda _: None
+ else:
+ raise ValueError(f"labels_getter should either be 'default', a callable, or None, but got {labels_getter}.")
+
+
+def get_bounding_boxes(flat_inputs: List[Any]) -> tv_tensors.BoundingBoxes:
+ # This assumes there is only one bbox per sample as per the general convention
+ try:
+ return next(inpt for inpt in flat_inputs if isinstance(inpt, tv_tensors.BoundingBoxes))
+ except StopIteration:
+ raise ValueError("No bounding boxes were found in the sample")
+
+
+def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]:
+ chws = {
+ tuple(get_dimensions(inpt))
+ for inpt in flat_inputs
+ if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video))
+ }
+ if not chws:
+ raise TypeError("No image or video was found in the sample")
+ elif len(chws) > 1:
+ raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}")
+ c, h, w = chws.pop()
+ return c, h, w
+
+
+def query_size(flat_inputs: List[Any]) -> Tuple[int, int]:
+ sizes = {
+ tuple(get_size(inpt))
+ for inpt in flat_inputs
+ if check_type(
+ inpt,
+ (
+ is_pure_tensor,
+ tv_tensors.Image,
+ PIL.Image.Image,
+ tv_tensors.Video,
+ tv_tensors.Mask,
+ tv_tensors.BoundingBoxes,
+ ),
+ )
+ }
+ if not sizes:
+ raise TypeError("No image, video, mask or bounding box was found in the sample")
+ elif len(sizes) > 1:
+ raise ValueError(f"Found multiple HxW dimensions in the sample: {sequence_to_str(sorted(sizes))}")
+ h, w = sizes.pop()
+ return h, w
+
+
+def check_type(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool:
+ for type_or_check in types_or_checks:
+ if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj):
+ return True
+ return False
+
+
+def has_any(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
+ for inpt in flat_inputs:
+ if check_type(inpt, types_or_checks):
+ return True
+ return False
+
+
+def has_all(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
+ for type_or_check in types_or_checks:
+ for inpt in flat_inputs:
+ if isinstance(inpt, type_or_check) if isinstance(type_or_check, type) else type_or_check(inpt):
+ break
+ else:
+ return False
+ return True
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_misc.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a6468bb46a7c3b75f9c0d301fc419f7a0c5a55a
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_misc.py
@@ -0,0 +1,277 @@
+import math
+from typing import List, Optional
+
+import PIL.Image
+import torch
+from torch.nn.functional import conv2d, pad as torch_pad
+
+from torchvision import tv_tensors
+from torchvision.transforms._functional_tensor import _max_value
+from torchvision.transforms.functional import pil_to_tensor, to_pil_image
+
+from torchvision.utils import _log_api_usage_once
+
+from ._utils import _get_kernel, _register_kernel_internal
+
+
+def normalize(
+ inpt: torch.Tensor,
+ mean: List[float],
+ std: List[float],
+ inplace: bool = False,
+) -> torch.Tensor:
+ """[BETA] See :class:`~torchvision.transforms.v2.Normalize` for details."""
+ if torch.jit.is_scripting():
+ return normalize_image(inpt, mean=mean, std=std, inplace=inplace)
+
+ _log_api_usage_once(normalize)
+
+ kernel = _get_kernel(normalize, type(inpt))
+ return kernel(inpt, mean=mean, std=std, inplace=inplace)
+
+
+@_register_kernel_internal(normalize, torch.Tensor)
+@_register_kernel_internal(normalize, tv_tensors.Image)
+def normalize_image(image: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor:
+ if not image.is_floating_point():
+ raise TypeError(f"Input tensor should be a float tensor. Got {image.dtype}.")
+
+ if image.ndim < 3:
+ raise ValueError(f"Expected tensor to be a tensor image of size (..., C, H, W). Got {image.shape}.")
+
+ if isinstance(std, (tuple, list)):
+ divzero = not all(std)
+ elif isinstance(std, (int, float)):
+ divzero = std == 0
+ else:
+ divzero = False
+ if divzero:
+ raise ValueError("std evaluated to zero, leading to division by zero.")
+
+ dtype = image.dtype
+ device = image.device
+ mean = torch.as_tensor(mean, dtype=dtype, device=device)
+ std = torch.as_tensor(std, dtype=dtype, device=device)
+ if mean.ndim == 1:
+ mean = mean.view(-1, 1, 1)
+ if std.ndim == 1:
+ std = std.view(-1, 1, 1)
+
+ if inplace:
+ image = image.sub_(mean)
+ else:
+ image = image.sub(mean)
+
+ return image.div_(std)
+
+
+@_register_kernel_internal(normalize, tv_tensors.Video)
+def normalize_video(video: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor:
+ return normalize_image(video, mean, std, inplace=inplace)
+
+
+def gaussian_blur(inpt: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> torch.Tensor:
+ """[BETA] See :class:`~torchvision.transforms.v2.GaussianBlur` for details."""
+ if torch.jit.is_scripting():
+ return gaussian_blur_image(inpt, kernel_size=kernel_size, sigma=sigma)
+
+ _log_api_usage_once(gaussian_blur)
+
+ kernel = _get_kernel(gaussian_blur, type(inpt))
+ return kernel(inpt, kernel_size=kernel_size, sigma=sigma)
+
+
+def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
+ lim = (kernel_size - 1) / (2.0 * math.sqrt(2.0) * sigma)
+ x = torch.linspace(-lim, lim, steps=kernel_size, dtype=dtype, device=device)
+ kernel1d = torch.softmax(x.pow_(2).neg_(), dim=0)
+ return kernel1d
+
+
+def _get_gaussian_kernel2d(
+ kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device
+) -> torch.Tensor:
+ kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device)
+ kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device)
+ kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x
+ return kernel2d
+
+
+@_register_kernel_internal(gaussian_blur, torch.Tensor)
+@_register_kernel_internal(gaussian_blur, tv_tensors.Image)
+def gaussian_blur_image(
+ image: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
+) -> torch.Tensor:
+ # TODO: consider deprecating integers from sigma on the future
+ if isinstance(kernel_size, int):
+ kernel_size = [kernel_size, kernel_size]
+ elif len(kernel_size) != 2:
+ raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}")
+ for ksize in kernel_size:
+ if ksize % 2 == 0 or ksize < 0:
+ raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}")
+
+ if sigma is None:
+ sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]
+ else:
+ if isinstance(sigma, (list, tuple)):
+ length = len(sigma)
+ if length == 1:
+ s = float(sigma[0])
+ sigma = [s, s]
+ elif length != 2:
+ raise ValueError(f"If sigma is a sequence, its length should be 2. Got {length}")
+ elif isinstance(sigma, (int, float)):
+ s = float(sigma)
+ sigma = [s, s]
+ else:
+ raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}")
+ for s in sigma:
+ if s <= 0.0:
+ raise ValueError(f"sigma should have positive values. Got {sigma}")
+
+ if image.numel() == 0:
+ return image
+
+ dtype = image.dtype
+ shape = image.shape
+ ndim = image.ndim
+ if ndim == 3:
+ image = image.unsqueeze(dim=0)
+ elif ndim > 4:
+ image = image.reshape((-1,) + shape[-3:])
+
+ fp = torch.is_floating_point(image)
+ kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype if fp else torch.float32, device=image.device)
+ kernel = kernel.expand(shape[-3], 1, kernel.shape[0], kernel.shape[1])
+
+ output = image if fp else image.to(dtype=torch.float32)
+
+ # padding = (left, right, top, bottom)
+ padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2]
+ output = torch_pad(output, padding, mode="reflect")
+ output = conv2d(output, kernel, groups=shape[-3])
+
+ if ndim == 3:
+ output = output.squeeze(dim=0)
+ elif ndim > 4:
+ output = output.reshape(shape)
+
+ if not fp:
+ output = output.round_().to(dtype=dtype)
+
+ return output
+
+
+@_register_kernel_internal(gaussian_blur, PIL.Image.Image)
+def _gaussian_blur_image_pil(
+ image: PIL.Image.Image, kernel_size: List[int], sigma: Optional[List[float]] = None
+) -> PIL.Image.Image:
+ t_img = pil_to_tensor(image)
+ output = gaussian_blur_image(t_img, kernel_size=kernel_size, sigma=sigma)
+ return to_pil_image(output, mode=image.mode)
+
+
+@_register_kernel_internal(gaussian_blur, tv_tensors.Video)
+def gaussian_blur_video(
+ video: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
+) -> torch.Tensor:
+ return gaussian_blur_image(video, kernel_size, sigma)
+
+
+def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
+ """[BETA] See :func:`~torchvision.transforms.v2.ToDtype` for details."""
+ if torch.jit.is_scripting():
+ return to_dtype_image(inpt, dtype=dtype, scale=scale)
+
+ _log_api_usage_once(to_dtype)
+
+ kernel = _get_kernel(to_dtype, type(inpt))
+ return kernel(inpt, dtype=dtype, scale=scale)
+
+
+def _num_value_bits(dtype: torch.dtype) -> int:
+ if dtype == torch.uint8:
+ return 8
+ elif dtype == torch.int8:
+ return 7
+ elif dtype == torch.int16:
+ return 15
+ elif dtype == torch.int32:
+ return 31
+ elif dtype == torch.int64:
+ return 63
+ else:
+ raise TypeError(f"Number of value bits is only defined for integer dtypes, but got {dtype}.")
+
+
+@_register_kernel_internal(to_dtype, torch.Tensor)
+@_register_kernel_internal(to_dtype, tv_tensors.Image)
+def to_dtype_image(image: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
+
+ if image.dtype == dtype:
+ return image
+ elif not scale:
+ return image.to(dtype)
+
+ float_input = image.is_floating_point()
+ if torch.jit.is_scripting():
+ # TODO: remove this branch as soon as `dtype.is_floating_point` is supported by JIT
+ float_output = torch.tensor(0, dtype=dtype).is_floating_point()
+ else:
+ float_output = dtype.is_floating_point
+
+ if float_input:
+ # float to float
+ if float_output:
+ return image.to(dtype)
+
+ # float to int
+ if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or (
+ image.dtype == torch.float64 and dtype == torch.int64
+ ):
+ raise RuntimeError(f"The conversion from {image.dtype} to {dtype} cannot be performed safely.")
+
+ # For data in the range `[0.0, 1.0]`, just multiplying by the maximum value of the integer range and converting
+ # to the integer dtype is not sufficient. For example, `torch.rand(...).mul(255).to(torch.uint8)` will only
+ # be `255` if the input is exactly `1.0`. See https://github.com/pytorch/vision/pull/2078#issuecomment-612045321
+ # for a detailed analysis.
+ # To mitigate this, we could round before we convert to the integer dtype, but this is an extra operation.
+ # Instead, we can also multiply by the maximum value plus something close to `1`. See
+ # https://github.com/pytorch/vision/pull/2078#issuecomment-613524965 for details.
+ eps = 1e-3
+ max_value = float(_max_value(dtype))
+ # We need to scale first since the conversion would otherwise turn the input range `[0.0, 1.0]` into the
+ # discrete set `{0, 1}`.
+ return image.mul(max_value + 1.0 - eps).to(dtype)
+ else:
+ # int to float
+ if float_output:
+ return image.to(dtype).mul_(1.0 / _max_value(image.dtype))
+
+ # int to int
+ num_value_bits_input = _num_value_bits(image.dtype)
+ num_value_bits_output = _num_value_bits(dtype)
+
+ if num_value_bits_input > num_value_bits_output:
+ return image.bitwise_right_shift(num_value_bits_input - num_value_bits_output).to(dtype)
+ else:
+ return image.to(dtype).bitwise_left_shift_(num_value_bits_output - num_value_bits_input)
+
+
+# We encourage users to use to_dtype() instead but we keep this for BC
+def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float32) -> torch.Tensor:
+ """[BETA] [DEPRECATED] Use to_dtype() instead."""
+ return to_dtype_image(image, dtype=dtype, scale=True)
+
+
+@_register_kernel_internal(to_dtype, tv_tensors.Video)
+def to_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
+ return to_dtype_image(video, dtype, scale=scale)
+
+
+@_register_kernel_internal(to_dtype, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False)
+@_register_kernel_internal(to_dtype, tv_tensors.Mask, tv_tensor_wrapper=False)
+def _to_dtype_tensor_dispatch(inpt: torch.Tensor, dtype: torch.dtype, scale: bool = False) -> torch.Tensor:
+ # We don't need to unwrap and rewrap here, since TVTensor.to() preserves the type
+ return inpt.to(dtype)
diff --git a/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_type_conversion.py b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_type_conversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..062f85198eeee6af73eebedc1ac34916c4c1623f
--- /dev/null
+++ b/llava_next/lib/python3.10/site-packages/torchvision/transforms/v2/functional/_type_conversion.py
@@ -0,0 +1,25 @@
+from typing import Union
+
+import numpy as np
+import PIL.Image
+import torch
+from torchvision import tv_tensors
+from torchvision.transforms import functional as _F
+
+
+@torch.jit.unused
+def to_image(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> tv_tensors.Image:
+ """[BETA] See :class:`~torchvision.transforms.v2.ToImage` for details."""
+ if isinstance(inpt, np.ndarray):
+ output = torch.from_numpy(inpt).permute((2, 0, 1)).contiguous()
+ elif isinstance(inpt, PIL.Image.Image):
+ output = pil_to_tensor(inpt)
+ elif isinstance(inpt, torch.Tensor):
+ output = inpt
+ else:
+ raise TypeError(f"Input can either be a numpy array or a PIL image, but got {type(inpt)} instead.")
+ return tv_tensors.Image(output)
+
+
+to_pil_image = _F.to_pil_image
+pil_to_tensor = _F.pil_to_tensor
diff --git a/vlmpy310/lib/python3.10/site-packages/notebook/static/1684.17fc47c7fb30c0a8e713.js b/vlmpy310/lib/python3.10/site-packages/notebook/static/1684.17fc47c7fb30c0a8e713.js
new file mode 100644
index 0000000000000000000000000000000000000000..0509b6f103776512be3d616743401387e404da81
--- /dev/null
+++ b/vlmpy310/lib/python3.10/site-packages/notebook/static/1684.17fc47c7fb30c0a8e713.js
@@ -0,0 +1,99 @@
+"use strict";
+(self["webpackChunk_JUPYTERLAB_CORE_OUTPUT"] = self["webpackChunk_JUPYTERLAB_CORE_OUTPUT"] || []).push([[1684,5601],{
+
+/***/ 95601:
+/***/ ((__unused_webpack_module, __webpack_exports__, __webpack_require__) => {
+
+__webpack_require__.r(__webpack_exports__);
+/* harmony export */ __webpack_require__.d(__webpack_exports__, {
+/* harmony export */ "default": () => (__WEBPACK_DEFAULT_EXPORT__)
+/* harmony export */ });
+/* harmony import */ var _jupyterlab_application__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(38075);
+/* harmony import */ var _jupyterlab_application__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(_jupyterlab_application__WEBPACK_IMPORTED_MODULE_0__);
+/* harmony import */ var _jupyterlab_coreutils__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(76107);
+/* harmony import */ var _jupyterlab_coreutils__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(_jupyterlab_coreutils__WEBPACK_IMPORTED_MODULE_1__);
+/* harmony import */ var _jupyterlab_terminal__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(27368);
+/* harmony import */ var _jupyterlab_terminal__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(_jupyterlab_terminal__WEBPACK_IMPORTED_MODULE_2__);
+/* harmony import */ var _jupyter_notebook_application__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(87362);
+/* harmony import */ var _jupyter_notebook_application__WEBPACK_IMPORTED_MODULE_3___default = /*#__PURE__*/__webpack_require__.n(_jupyter_notebook_application__WEBPACK_IMPORTED_MODULE_3__);
+/* harmony import */ var _lumino_algorithm__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(14931);
+/* harmony import */ var _lumino_algorithm__WEBPACK_IMPORTED_MODULE_4___default = /*#__PURE__*/__webpack_require__.n(_lumino_algorithm__WEBPACK_IMPORTED_MODULE_4__);
+// Copyright (c) Jupyter Development Team.
+// Distributed under the terms of the Modified BSD License.
+
+
+
+
+
+/**
+ * A plugin to open terminals in a new tab
+ */
+const opener = {
+ id: '@jupyter-notebook/terminal-extension:opener',
+ description: 'A plugin to open terminals in a new tab.',
+ requires: [_jupyterlab_application__WEBPACK_IMPORTED_MODULE_0__.IRouter, _jupyterlab_terminal__WEBPACK_IMPORTED_MODULE_2__.ITerminalTracker],
+ autoStart: true,
+ activate: (app, router, tracker) => {
+ const { commands } = app;
+ const terminalPattern = new RegExp('/terminals/(.*)');
+ const command = 'router:terminal';
+ commands.addCommand(command, {
+ execute: (args) => {
+ const parsed = args;
+ const matches = parsed.path.match(terminalPattern);
+ if (!matches) {
+ return;
+ }
+ const [, name] = matches;
+ if (!name) {
+ return;
+ }
+ tracker.widgetAdded.connect((send, terminal) => {
+ terminal.content.setOption('closeOnExit', false);
+ });
+ commands.execute('terminal:open', { name });
+ },
+ });
+ router.register({ command, pattern: terminalPattern });
+ },
+};
+/**
+ * Open terminals in a new tab.
+ */
+const redirect = {
+ id: '@jupyter-notebook/terminal-extension:redirect',
+ description: 'Open terminals in a new tab.',
+ requires: [_jupyterlab_terminal__WEBPACK_IMPORTED_MODULE_2__.ITerminalTracker],
+ optional: [_jupyter_notebook_application__WEBPACK_IMPORTED_MODULE_3__.INotebookPathOpener],
+ autoStart: true,
+ activate: (app, tracker, notebookPathOpener) => {
+ const baseUrl = _jupyterlab_coreutils__WEBPACK_IMPORTED_MODULE_1__.PageConfig.getBaseUrl();
+ const opener = notebookPathOpener !== null && notebookPathOpener !== void 0 ? notebookPathOpener : _jupyter_notebook_application__WEBPACK_IMPORTED_MODULE_3__.defaultNotebookPathOpener;
+ tracker.widgetAdded.connect((send, terminal) => {
+ const widget = (0,_lumino_algorithm__WEBPACK_IMPORTED_MODULE_4__.find)(app.shell.widgets('main'), (w) => w.id === terminal.id);
+ if (widget) {
+ // bail if the terminal is already added to the main area
+ return;
+ }
+ const name = terminal.content.session.name;
+ opener.open({
+ prefix: _jupyterlab_coreutils__WEBPACK_IMPORTED_MODULE_1__.URLExt.join(baseUrl, 'terminals'),
+ path: name,
+ target: '_blank',
+ });
+ // dispose the widget since it is not used on this page
+ terminal.dispose();
+ });
+ },
+};
+/**
+ * Export the plugins as default.
+ */
+const plugins = [opener, redirect];
+/* harmony default export */ const __WEBPACK_DEFAULT_EXPORT__ = (plugins);
+
+
+/***/ })
+
+}]);
+//# sourceMappingURL=1684.17fc47c7fb30c0a8e713.js.map?v=17fc47c7fb30c0a8e713
\ No newline at end of file
diff --git a/vlmpy310/lib/python3.10/site-packages/notebook/static/2324.4c423682e2c93316a122.js b/vlmpy310/lib/python3.10/site-packages/notebook/static/2324.4c423682e2c93316a122.js
new file mode 100644
index 0000000000000000000000000000000000000000..2030dc11712209269cef5dc57a1074e421c3be05
--- /dev/null
+++ b/vlmpy310/lib/python3.10/site-packages/notebook/static/2324.4c423682e2c93316a122.js
@@ -0,0 +1,7542 @@
+(self["webpackChunk_JUPYTERLAB_CORE_OUTPUT"] = self["webpackChunk_JUPYTERLAB_CORE_OUTPUT"] || []).push([[2324],{
+
+/***/ 26746:
+/***/ ((module, __unused_webpack_exports, __webpack_require__) => {
+
+(function(f){if(true){module.exports=f()}else { var g; }})(function(){var define,module,exports;return (function(){function r(e,n,t){function o(i,f){if(!n[i]){if(!e[i]){var c=undefined;if(!f&&c)return require(i,!0);if(u)return u(i,!0);var a=new Error("Cannot find module '"+i+"'");throw a.code="MODULE_NOT_FOUND",a}var p=n[i]={exports:{}};e[i][0].call(p.exports,function(r){var n=e[i][1][r];return o(n||r)},p,p.exports,r,e,n,t)}return n[i].exports}for(var u=undefined,i=0;i 0 && arguments[0] !== undefined ? arguments[0] : {},
+ _ref$defaultLayoutOpt = _ref.defaultLayoutOptions,
+ defaultLayoutOptions = _ref$defaultLayoutOpt === undefined ? {} : _ref$defaultLayoutOpt,
+ _ref$algorithms = _ref.algorithms,
+ algorithms = _ref$algorithms === undefined ? ['layered', 'stress', 'mrtree', 'radial', 'force', 'disco', 'sporeOverlap', 'sporeCompaction', 'rectpacking'] : _ref$algorithms,
+ workerFactory = _ref.workerFactory,
+ workerUrl = _ref.workerUrl;
+
+ _classCallCheck(this, ELK);
+
+ this.defaultLayoutOptions = defaultLayoutOptions;
+ this.initialized = false;
+
+ // check valid worker construction possible
+ if (typeof workerUrl === 'undefined' && typeof workerFactory === 'undefined') {
+ throw new Error("Cannot construct an ELK without both 'workerUrl' and 'workerFactory'.");
+ }
+ var factory = workerFactory;
+ if (typeof workerUrl !== 'undefined' && typeof workerFactory === 'undefined') {
+ // use default Web Worker
+ factory = function factory(url) {
+ return new Worker(url);
+ };
+ }
+
+ // create the worker
+ var worker = factory(workerUrl);
+ if (typeof worker.postMessage !== 'function') {
+ throw new TypeError("Created worker does not provide" + " the required 'postMessage' function.");
+ }
+
+ // wrap the worker to return promises
+ this.worker = new PromisedWorker(worker);
+
+ // initially register algorithms
+ this.worker.postMessage({
+ cmd: 'register',
+ algorithms: algorithms
+ }).then(function (r) {
+ return _this.initialized = true;
+ }).catch(console.err);
+ }
+
+ _createClass(ELK, [{
+ key: 'layout',
+ value: function layout(graph) {
+ var _ref2 = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {},
+ _ref2$layoutOptions = _ref2.layoutOptions,
+ layoutOptions = _ref2$layoutOptions === undefined ? this.defaultLayoutOptions : _ref2$layoutOptions,
+ _ref2$logging = _ref2.logging,
+ logging = _ref2$logging === undefined ? false : _ref2$logging,
+ _ref2$measureExecutio = _ref2.measureExecutionTime,
+ measureExecutionTime = _ref2$measureExecutio === undefined ? false : _ref2$measureExecutio;
+
+ if (!graph) {
+ return Promise.reject(new Error("Missing mandatory parameter 'graph'."));
+ }
+ return this.worker.postMessage({
+ cmd: 'layout',
+ graph: graph,
+ layoutOptions: layoutOptions,
+ options: {
+ logging: logging,
+ measureExecutionTime: measureExecutionTime
+ }
+ });
+ }
+ }, {
+ key: 'knownLayoutAlgorithms',
+ value: function knownLayoutAlgorithms() {
+ return this.worker.postMessage({ cmd: 'algorithms' });
+ }
+ }, {
+ key: 'knownLayoutOptions',
+ value: function knownLayoutOptions() {
+ return this.worker.postMessage({ cmd: 'options' });
+ }
+ }, {
+ key: 'knownLayoutCategories',
+ value: function knownLayoutCategories() {
+ return this.worker.postMessage({ cmd: 'categories' });
+ }
+ }, {
+ key: 'terminateWorker',
+ value: function terminateWorker() {
+ this.worker.terminate();
+ }
+ }]);
+
+ return ELK;
+}();
+
+exports.default = ELK;
+
+var PromisedWorker = function () {
+ function PromisedWorker(worker) {
+ var _this2 = this;
+
+ _classCallCheck(this, PromisedWorker);
+
+ if (worker === undefined) {
+ throw new Error("Missing mandatory parameter 'worker'.");
+ }
+ this.resolvers = {};
+ this.worker = worker;
+ this.worker.onmessage = function (answer) {
+ // why is this necessary?
+ setTimeout(function () {
+ _this2.receive(_this2, answer);
+ }, 0);
+ };
+ }
+
+ _createClass(PromisedWorker, [{
+ key: 'postMessage',
+ value: function postMessage(msg) {
+ var id = this.id || 0;
+ this.id = id + 1;
+ msg.id = id;
+ var self = this;
+ return new Promise(function (resolve, reject) {
+ // prepare the resolver
+ self.resolvers[id] = function (err, res) {
+ if (err) {
+ self.convertGwtStyleError(err);
+ reject(err);
+ } else {
+ resolve(res);
+ }
+ };
+ // post the message
+ self.worker.postMessage(msg);
+ });
+ }
+ }, {
+ key: 'receive',
+ value: function receive(self, answer) {
+ var json = answer.data;
+ var resolver = self.resolvers[json.id];
+ if (resolver) {
+ delete self.resolvers[json.id];
+ if (json.error) {
+ resolver(json.error);
+ } else {
+ resolver(null, json.data);
+ }
+ }
+ }
+ }, {
+ key: 'terminate',
+ value: function terminate() {
+ if (this.worker.terminate) {
+ this.worker.terminate();
+ }
+ }
+ }, {
+ key: 'convertGwtStyleError',
+ value: function convertGwtStyleError(err) {
+ if (!err) {
+ return;
+ }
+ // Somewhat flatten the way GWT stores nested exception(s)
+ var javaException = err['__java$exception'];
+ if (javaException) {
+ // Note that the property name of the nested exception is different
+ // in the non-minified ('cause') and the minified (not deterministic) version.
+ // Hence, the version below only works for the non-minified version.
+ // However, as the minified stack trace is not of much use anyway, one
+ // should switch the used version for debugging in such a case.
+ if (javaException.cause && javaException.cause.backingJsObject) {
+ err.cause = javaException.cause.backingJsObject;
+ this.convertGwtStyleError(err.cause);
+ }
+ delete err['__java$exception'];
+ }
+ }
+ }]);
+
+ return PromisedWorker;
+}();
+},{}],2:[function(require,module,exports){
+(function (global){(function (){
+'use strict';
+
+// -------------- FAKE ELEMENTS GWT ASSUMES EXIST --------------
+var $wnd;
+if (typeof window !== 'undefined')
+ $wnd = window
+else if (typeof global !== 'undefined')
+ $wnd = global // nodejs
+else if (typeof self !== 'undefined')
+ $wnd = self // web worker
+
+var $moduleName,
+ $moduleBase;
+
+// -------------- WORKAROUND STRICT MODE, SEE #127 --------------
+var g, i, o;
+
+// -------------- GENERATED CODE --------------
+function nb(){}
+function xb(){}
+function Fd(){}
+function hh(){}
+function lq(){}
+function Nq(){}
+function ir(){}
+function Ws(){}
+function Zw(){}
+function jx(){}
+function rx(){}
+function sx(){}
+function My(){}
+function bA(){}
+function mA(){}
+function tA(){}
+function aB(){}
+function dB(){}
+function jB(){}
+function dC(){}
+function keb(){}
+function geb(){}
+function oeb(){}
+function iob(){}
+function Job(){}
+function Rob(){}
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+function zob(a,b){yob();var c,d,e,f,g;g=false;for(d=b,e=0,f=d.length;e1||b>=0&&a.b<3}
+function nD(a){var b,c,d;b=~a.l+1&dxe;c=~a.m+(b==0?1:0)&dxe;d=~a.h+(b==0&&c==0?1:0)&exe;a.l=b;a.m=c;a.h=d}
+function Cob(a){yob();var b,c,d;d=1;for(c=a.Kc();c.Ob();){b=c.Pb();d=31*d+(b!=null?tb(b):0);d=d|0}return d}
+function kD(a,b,c,d,e){var f;f=BD(a,b);c&&nD(f);if(e){a=mD(a,b);d?(eD=xD(a)):(eD=hD(a.l,a.m,a.h))}return f}
+function Qlc(a,b,c){a.g=Wlc(a,b,(qpd(),Xod),a.b);a.d=Wlc(a,c,Xod,a.b);if(a.g.c==0||a.d.c==0){return}Tlc(a)}
+function Rlc(a,b,c){a.g=Wlc(a,b,(qpd(),ppd),a.j);a.d=Wlc(a,c,ppd,a.j);if(a.g.c==0||a.d.c==0){return}Tlc(a)}
+function Xyd(a,b){switch(b){case 7:return !!a.e&&a.e.i!=0;case 8:return !!a.d&&a.d.i!=0;}return wyd(a,b)}
+function STb(a,b){switch(b.g){case 0:ZD(a.b,641)||(a.b=new tUb);break;case 1:ZD(a.b,642)||(a.b=new zUb);}}
+function tbd(a){switch(a.g){case 0:return new _dd;default:throw Adb(new agb(eGe+(a.f!=null?a.f:''+a.g)));}}
+function bdd(a){switch(a.g){case 0:return new vdd;default:throw Adb(new agb(eGe+(a.f!=null?a.f:''+a.g)));}}
+function LCc(a,b,c){return !QDb(CDb(new SDb(null,new Swb(a.c,16)),new PAb(new gsd(b,c)))).Bd((xDb(),wDb))}
+function mWc(a,b){return cjd(jWc(RD(mQb(b,(h_c(),H$c)),88)),new rjd(a.c.e.a-a.b.e.a,a.c.e.b-a.b.e.b))<=0}
+function dve(a,b){while(a.g==null&&!a.c?sId(a):a.g==null||a.i!=0&&RD(a.g[a.i-1],51).Ob()){mFd(b,tId(a))}}
+function sYb(a){var b,c;for(c=new Anb(a.a.b);c.ad?1:0}
+function ICc(a){Rmb(a.c,(hed(),fed));if(_y(a.a,Kfb(UD(iGd((QCc(),OCc)))))){return new asd}return new csd(a)}
+function fs(a){while(!a.d||!a.d.Ob()){if(!!a.b&&!nmb(a.b)){a.d=RD(smb(a.b),51)}else{return null}}return a.d}
+function BVc(a){switch(a.g){case 1:return EEe;default:case 2:return 0;case 3:return Gze;case 4:return FEe;}}
+function fte(){Vse();var a;if(Cse)return Cse;a=Zse(hte('M',true));a=$se(hte('M',false),a);Cse=a;return Cse}
+function ttd(){ttd=geb;qtd=new utd('ELK',0);rtd=new utd('JSON',1);ptd=new utd('DOT',2);std=new utd('SVG',3)}
+function TEc(){TEc=geb;SEc=new UEc('STACKED',0);QEc=new UEc('REVERSE_STACKED',1);REc=new UEc('SEQUENCED',2)}
+function LZc(){LZc=geb;KZc=new MZc(LAe,0);JZc=new MZc('MIDDLE_TO_MIDDLE',1);IZc=new MZc('AVOID_OVERLAP',2)}
+function sgc(){sgc=geb;qgc=new Lgc;rgc=new Ngc;pgc=new Dgc;ogc=new Pgc;ngc=new Hgc;mgc=(uFb(ngc),new nrb)}
+function vnd(){vnd=geb;tnd=new A3b(15);snd=new mGd((umd(),tld),tnd);und=Qld;ond=Ekd;pnd=kld;rnd=nld;qnd=mld}
+function wgd(a,b){var c,d,e,f,g;for(d=b,e=0,f=d.length;e=a.b.c.length){return}jwb(a,2*b+1);c=2*b+2;c0){b.Cd(c);c.i&&zKc(c)}}}
+function Ejb(a,b,c){var d;for(d=c-1;d>=0&&a[d]===b[d];d--);return d<0?0:Ldb(Cdb(a[d],yxe),Cdb(b[d],yxe))?-1:1}
+function it(a,b,c){var d,e;this.g=a;this.c=b;this.a=this;this.d=this;e=Wp(c);d=$C(UG,ewe,227,e,0,1);this.b=d}
+function fQb(a,b,c,d,e){var f,g;for(g=c;g<=e;g++){for(f=b;f<=d;f++){if(PPb(a,f,g)){return true}}}return false}
+function Dc(a,b){var c,d;for(d=a.Zb().Cc().Kc();d.Ob();){c=RD(d.Pb(),16);if(c.Hc(b)){return true}}return false}
+function iu(a,b,c){var d,e,f,g;uFb(c);g=false;f=a.fd(b);for(e=c.Kc();e.Ob();){d=e.Pb();f.Rb(d);g=true}return g}
+function NMd(a,b){var c,d;d=RD(Ywd(a.a,4),129);c=$C(d6,IJe,424,b,0,1);d!=null&&hib(d,0,c,0,d.length);return c}
+function hSd(a,b){var c;c=new lSd((a.f&256)!=0,a.i,a.a,a.d,(a.f&16)!=0,a.j,a.g,b);a.e!=null||(c.c=a);return c}
+function Tv(a,b){var c;if(a===b){return true}else if(ZD(b,85)){c=RD(b,85);return Rx(gn(a),c.vc())}return false}
+function Vjb(a,b,c){var d,e;for(e=c.Kc();e.Ob();){d=RD(e.Pb(),44);if(a.Be(b,d.md())){return true}}return false}
+function lmc(a,b,c){if(!a.d[b.p][c.p]){kmc(a,b,c);a.d[b.p][c.p]=true;a.d[c.p][b.p]=true}return a.a[b.p][c.p]}
+function vMc(a,b){var c;if(!a||a==b||!nQb(b,(Ywc(),pwc))){return false}c=RD(mQb(b,(Ywc(),pwc)),10);return c!=a}
+function Bhe(a){switch(a.i){case 2:{return true}case 1:{return false}case -1:{++a.c}default:{return a.$l()}}}
+function Che(a){switch(a.i){case -2:{return true}case -1:{return false}case 1:{--a.c}default:{return a._l()}}}
+function bgb(a){oz.call(this,'The given string does not match the expected format for individual spacings.',a)}
+function J6c(a,b){var c;b.Ug('Min Size Preprocessing',1);c=vsd(a);Ixd(a,(X6c(),U6c),c.a);Ixd(a,R6c,c.b);b.Vg()}
+function Djd(a){var b,c,d;b=0;d=$C(l3,Nve,8,a.b,0,1);c=Sub(a,0);while(c.b!=c.d.c){d[b++]=RD(evb(c),8)}return d}
+function Ajd(a,b,c){var d,e,f;d=new Yub;for(f=Sub(c,0);f.b!=f.d.c;){e=RD(evb(f),8);Mub(d,new sjd(e))}iu(a,b,d)}
+function az(a,b){var c;c=Bdb(a,b);if(Ldb($db(a,b),0)|Jdb($db(a,c),0)){return c}return Bdb(Sve,$db(Udb(c,63),1))}
+function le(a,b){var c,d;c=RD(a.d.Bc(b),16);if(!c){return null}d=a.e.hc();d.Gc(c);a.e.d-=c.gc();c.$b();return d}
+function Dyb(a){var b;b=a.a.c.length;if(b>0){return lyb(b-1,a.a.c.length),Xmb(a.a,b-1)}else{throw Adb(new Srb)}}
+function nFb(a,b,c){if(a>b){throw Adb(new agb(_xe+a+aye+b))}if(a<0||b>c){throw Adb(new xeb(_xe+a+bye+b+Qxe+c))}}
+function yXd(a,b){if(a.D==null&&a.B!=null){a.D=a.B;a.B=null}JXd(a,b==null?null:(uFb(b),b));!!a.C&&a.hl(null)}
+function JCc(a,b){var c;c=iGd((QCc(),OCc))!=null&&b.Sg()!=null?Kfb(UD(b.Sg()))/Kfb(UD(iGd(OCc))):1;Zjb(a.b,b,c)}
+function $Lc(a,b){var c,d;d=a.c[b];if(d==0){return}a.c[b]=0;a.d-=d;c=b+1;while(cDEe?a-c>DEe:c-a>DEe}
+function vjd(a,b){var c;for(c=0;ce){ead(b.q,e);d=c!=b.q.d}}return d}
+function C3c(a,b){var c,d,e,f,g,h,i,j;i=b.i;j=b.j;d=a.f;e=d.i;f=d.j;g=i-e;h=j-f;c=$wnd.Math.sqrt(g*g+h*h);return c}
+function pBd(a,b){var c,d;d=Hvd(a);if(!d){!$Ad&&($Ad=new L5d);c=(gSd(),nSd(b));d=new Sde(c);WGd(d.El(),a)}return d}
+function Sc(a,b){var c,d;c=RD(a.c.Bc(b),16);if(!c){return a.jc()}d=a.hc();d.Gc(c);a.d-=c.gc();c.$b();return a.mc(d)}
+function tKc(a,b){var c,d;d=Kwb(a.d,1)!=0;c=true;while(c){c=false;c=b.c.mg(b.e,d);c=c|DKc(a,b,d,false);d=!d}yKc(a)}
+function omc(a,b,c,d){var e,f;a.a=b;f=d?0:1;a.f=(e=new mmc(a.c,a.a,c,f),new Pmc(c,a.a,e,a.e,a.b,a.c==(RKc(),PKc)))}
+function Imb(a){var b;sFb(a.a!=a.b);b=a.d.a[a.a];zmb(a.b==a.d.c&&b!=null);a.c=a.a;a.a=a.a+1&a.d.a.length-1;return b}
+function Vib(a){var b;if(a.c!=0){return a.c}for(b=0;b=a.c.b:a.a<=a.c.b)){throw Adb(new Dvb)}b=a.a;a.a+=a.c.c;++a.b;return sgb(b)}
+function h5b(a){var b;b=new y2b(a.a);kQb(b,a);pQb(b,(Ywc(),Awc),a);b.o.a=a.g;b.o.b=a.f;b.n.a=a.i;b.n.b=a.j;return b}
+function tVc(a){return (qpd(),hpd).Hc(a.j)?Kfb(UD(mQb(a,(Ywc(),Swc)))):xjd(cD(WC(l3,1),Nve,8,0,[a.i.n,a.n,a.a])).b}
+function ZJc(a){var b;b=vfd(XJc);RD(mQb(a,(Ywc(),kwc)),21).Hc((ovc(),kvc))&&pfd(b,(sXb(),pXb),(hcc(),Ybc));return b}
+function M2c(a){var b,c,d,e;e=new _sb;for(d=new Anb(a);d.a=0?b:-b;while(d>0){if(d%2==0){c*=c;d=d/2|0}else{e*=c;d-=1}}return b<0?1/e:e}
+function Jid(a,b){var c,d,e;e=1;c=a;d=b>=0?b:-b;while(d>0){if(d%2==0){c*=c;d=d/2|0}else{e*=c;d-=1}}return b<0?1/e:e}
+function Vvd(a,b){var c,d,e,f;f=(e=a?Hvd(a):null,Pje((d=b,e?e.Gl():null,d)));if(f==b){c=Hvd(a);!!c&&c.Gl()}return f}
+function g2d(a,b,c){var d,e;e=a.f;a.f=b;if((a.Db&4)!=0&&(a.Db&1)==0){d=new N3d(a,1,0,e,b);!c?(c=d):c.nj(d)}return c}
+function e2d(a,b,c){var d,e;e=a.b;a.b=b;if((a.Db&4)!=0&&(a.Db&1)==0){d=new N3d(a,1,3,e,b);!c?(c=d):c.nj(d)}return c}
+function rAd(a,b,c){var d,e;e=a.a;a.a=b;if((a.Db&4)!=0&&(a.Db&1)==0){d=new N3d(a,1,1,e,b);!c?(c=d):c.nj(d)}return c}
+function SNd(a){var b,c,d,e;if(a!=null){for(c=0;c=d||b-129&&a<128){return ugb(),b=a+128,c=tgb[b],!c&&(c=tgb[b]=new fgb(a)),c}return new fgb(a)}
+function bhb(a){var b,c;if(a>-129&&a<128){return dhb(),b=a+128,c=chb[b],!c&&(c=chb[b]=new Xgb(a)),c}return new Xgb(a)}
+function M$b(a,b){var c;if(a.a.c.length>0){c=RD(Vmb(a.a,a.a.c.length-1),579);if(Q_b(c,b)){return}}Rmb(a.a,new S_b(b))}
+function Ekc(a){lkc();var b,c;b=a.d.c-a.e.c;c=RD(a.g,154);Umb(c.b,new Ykc(b));Umb(c.c,new $kc(b));xgb(c.i,new alc(b))}
+function Mlc(a){var b;b=new bib;b.a+='VerticalSegment ';Yhb(b,a.e);b.a+=' ';Zhb(b,Eb(new Gb,new Anb(a.k)));return b.a}
+function Fmc(a,b){var c,d,e;c=0;for(e=b3b(a,b).Kc();e.Ob();){d=RD(e.Pb(),12);c+=mQb(d,(Ywc(),Iwc))!=null?1:0}return c}
+function VTc(a,b,c){var d,e,f;d=0;for(f=Sub(a,0);f.b!=f.d.c;){e=Kfb(UD(evb(f)));if(e>c){break}else e>=b&&++d}return d}
+function Wv(b,c){Qb(b);try{return b._b(c)}catch(a){a=zdb(a);if(ZD(a,212)||ZD(a,169)){return false}else throw Adb(a)}}
+function Nk(b,c){Qb(b);try{return b.Hc(c)}catch(a){a=zdb(a);if(ZD(a,212)||ZD(a,169)){return false}else throw Adb(a)}}
+function Ok(b,c){Qb(b);try{return b.Mc(c)}catch(a){a=zdb(a);if(ZD(a,212)||ZD(a,169)){return false}else throw Adb(a)}}
+function Xv(b,c){Qb(b);try{return b.xc(c)}catch(a){a=zdb(a);if(ZD(a,212)||ZD(a,169)){return null}else throw Adb(a)}}
+function Yv(b,c){Qb(b);try{return b.Bc(c)}catch(a){a=zdb(a);if(ZD(a,212)||ZD(a,169)){return null}else throw Adb(a)}}
+function aMc(a,b){switch(b.g){case 2:case 1:return b3b(a,b);case 3:case 4:return hv(b3b(a,b));}return yob(),yob(),vob}
+function QAd(a){var b;if((a.Db&64)!=0)return awd(a);b=new Shb(awd(a));b.a+=' (name: ';Nhb(b,a.zb);b.a+=')';return b.a}
+function Fgd(a){var b;b=RD(cub(a.c.c,''),233);if(!b){b=new fgd(ogd(ngd(new pgd,''),'Other'));dub(a.c.c,'',b)}return b}
+function hBd(a,b,c){var d,e;e=a.sb;a.sb=b;if((a.Db&4)!=0&&(a.Db&1)==0){d=new N3d(a,1,4,e,b);!c?(c=d):c.nj(d)}return c}
+function ZVd(a,b,c){var d,e;e=a.r;a.r=b;if((a.Db&4)!=0&&(a.Db&1)==0){d=new N3d(a,1,8,e,a.r);!c?(c=d):c.nj(d)}return c}
+function q5d(a,b,c){var d,e;d=new P3d(a.e,4,13,(e=b.c,e?e:(JTd(),wTd)),null,fZd(a,b),false);!c?(c=d):c.nj(d);return c}
+function p5d(a,b,c){var d,e;d=new P3d(a.e,3,13,null,(e=b.c,e?e:(JTd(),wTd)),fZd(a,b),false);!c?(c=d):c.nj(d);return c}
+function Oee(a,b){var c,d;c=RD(b,691);d=c.el();!d&&c.fl(d=ZD(b,90)?new afe(a,RD(b,29)):new mfe(a,RD(b,156)));return d}
+function KHd(a,b,c){var d;a._i(a.i+1);d=a.Zi(b,c);b!=a.i&&hib(a.g,b,a.g,b+1,a.i-b);bD(a.g,b,d);++a.i;a.Mi(b,c);a.Ni()}
+function Hyb(a,b){var c;if(b.a){c=b.a.a.length;!a.a?(a.a=new dib(a.d)):Zhb(a.a,a.b);Xhb(a.a,b.a,b.d.length,c)}return a}
+function wib(a,b){var c;a.c=b;a.a=pjb(b);a.a<54&&(a.f=(c=b.d>1?DFb(b.a[0],b.a[1]):DFb(b.a[0],0),Xdb(b.e>0?c:Odb(c))))}
+function MDb(a,b){var c;c=new IEb;if(!a.a.Bd(c)){LCb(a);return Kvb(),Kvb(),Jvb}return Kvb(),new Ovb(uFb(LDb(a,c.a,b)))}
+function t9b(a,b){var c;if(a.c.length==0){return}c=RD(anb(a,$C(jR,WAe,10,a.c.length,0,1)),199);Znb(c,new F9b);q9b(c,b)}
+function z9b(a,b){var c;if(a.c.length==0){return}c=RD(anb(a,$C(jR,WAe,10,a.c.length,0,1)),199);Znb(c,new K9b);q9b(c,b)}
+function pb(a,b){return bE(a)?lhb(a,b):_D(a)?Lfb(a,b):$D(a)?(uFb(a),dE(a)===dE(b)):YD(a)?a.Fb(b):aD(a)?mb(a,b):Hz(a,b)}
+function Cvd(a,b,c){if(b<0){Tvd(a,c)}else{if(!c.rk()){throw Adb(new agb(KHe+c.xe()+LHe))}RD(c,69).wk().Ek(a,a.hi(),b)}}
+function xFb(a,b,c){if(a<0||b>c){throw Adb(new veb(_xe+a+bye+b+', size: '+c))}if(a>b){throw Adb(new agb(_xe+a+aye+b))}}
+function oVd(a){var b;if((a.Db&64)!=0)return awd(a);b=new Shb(awd(a));b.a+=' (source: ';Nhb(b,a.d);b.a+=')';return b.a}
+function JSd(a){if(a>=65&&a<=70){return a-65+10}if(a>=97&&a<=102){return a-97+10}if(a>=48&&a<=57){return a-48}return 0}
+function lMb(a){hMb();var b,c,d,e;for(c=nMb(),d=0,e=c.length;d=0?jjb(a):Xib(jjb(Odb(a)))))}
+function G0b(a,b,c,d,e,f){this.e=new bnb;this.f=(BEc(),AEc);Rmb(this.e,a);this.d=b;this.a=c;this.b=d;this.f=e;this.c=f}
+function bQb(a,b,c){a.n=YC(lE,[Nve,rxe],[376,28],14,[c,eE($wnd.Math.ceil(b/32))],2);a.o=b;a.p=c;a.j=b-1>>1;a.k=c-1>>1}
+function ggb(a){a-=a>>1&1431655765;a=(a>>2&858993459)+(a&858993459);a=(a>>4)+a&252645135;a+=a>>8;a+=a>>16;return a&63}
+function C4d(a,b){var c,d;for(d=new dMd(a);d.e!=d.i.gc();){c=RD(bMd(d),142);if(dE(b)===dE(c)){return true}}return false}
+function Iee(a,b,c){var d,e,f;f=(e=N5d(a.b,b),e);if(f){d=RD(tfe(Pee(a,f),''),29);if(d){return Ree(a,d,b,c)}}return null}
+function Lee(a,b,c){var d,e,f;f=(e=N5d(a.b,b),e);if(f){d=RD(tfe(Pee(a,f),''),29);if(d){return See(a,d,b,c)}}return null}
+function IDd(a,b){var c;c=Ao(a.i,b);if(c==null){throw Adb(new CDd('Node did not exist in input.'))}wEd(b,c);return null}
+function wvd(a,b){var c;c=wYd(a,b);if(ZD(c,331)){return RD(c,35)}throw Adb(new agb(KHe+b+"' is not a valid attribute"))}
+function VGd(a,b,c){var d;d=a.gc();if(b>d)throw Adb(new aMd(b,d));if(a.Si()&&a.Hc(c)){throw Adb(new agb(LIe))}a.Gi(b,c)}
+function w7b(a,b){b.Ug('Sort end labels',1);FDb(CDb(EDb(new SDb(null,new Swb(a.b,16)),new H7b),new J7b),new L7b);b.Vg()}
+function Cmd(){Cmd=geb;Amd=new Gmd(Sye,0);zmd=new Gmd(Oye,1);ymd=new Gmd(Nye,2);xmd=new Gmd(Zye,3);Bmd=new Gmd('UP',4)}
+function gbd(){gbd=geb;dbd=new hbd('P1_STRUCTURE',0);ebd=new hbd('P2_PROCESSING_ORDER',1);fbd=new hbd('P3_EXECUTION',2)}
+function r0c(){r0c=geb;q0c=mfd(mfd(rfd(mfd(mfd(rfd(pfd(new ufd,(YVc(),VVc),(WYc(),VYc)),WVc),RYc),TYc),XVc),NYc),UYc)}
+function s8b(a){switch(RD(mQb(a,(Ywc(),owc)),311).g){case 1:pQb(a,owc,(Gvc(),Dvc));break;case 2:pQb(a,owc,(Gvc(),Fvc));}}
+function bUc(a){switch(a){case 0:return new mUc;case 1:return new cUc;case 2:return new hUc;default:throw Adb(new _fb);}}
+function Fmd(a){switch(a.g){case 2:return zmd;case 1:return ymd;case 4:return xmd;case 3:return Bmd;default:return Amd;}}
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+function udc(a){var b;b=a.a;do{b=RD(hs(new is(Mr(Z2b(b).a.Kc(),new ir))),18).c.i;b.k==(r3b(),o3b)&&a.b.Fc(b)}while(b.k==(r3b(),o3b));a.b=hv(a.b)}
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+function Y6b(a){switch(a.g){case 1:return mOb(),lOb;case 3:return mOb(),iOb;case 2:return mOb(),kOb;case 4:return mOb(),jOb;default:return null;}}
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+function p_b(a){var b,c;this.b=new bnb;this.c=a;this.a=false;for(c=new Anb(a.a);c.a0);if((b&-b)==b){return eE(b*Kwb(a,31)*4.6566128730773926E-10)}do{c=Kwb(a,31);d=c%b}while(c-d+(b-1)<0);return eE(d)}
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+function Dzd(a){var b;if(!!a.f&&a.f.Vh()){b=RD(a.f,54);a.f=RD(Vvd(a,b),84);a.f!=b&&(a.Db&4)!=0&&(a.Db&1)==0&&qvd(a,new N3d(a,9,8,b,a.f))}return a.f}
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+function j3b(a){v2b.call(this);this.k=(r3b(),p3b);this.j=(dk(6,iwe),new cnb(6));this.b=(dk(2,iwe),new cnb(2));this.d=new T2b;this.f=new C3b;this.a=a}
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+function ck(a,b){if(a==null){throw Adb(new Ogb('null key in entry: null='+b))}else if(b==null){throw Adb(new Ogb('null value in entry: '+a+'=null'))}}
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+function sUc(a){var b,c;a.c||vUc(a);c=new Ejd;b=new Anb(a.a);ynb(b);while(b.a0&&(BFb(0,b.length),b.charCodeAt(0)==43)?(BFb(1,b.length+1),b.substr(1)):b))}
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+function Syb(a,b,c,d,e,f,g,h){var i,j;if(!d){return}i=d.a[0];!!i&&Syb(a,b,c,i,e,f,g,h);Tyb(a,c,d.d,e,f,g,h)&&b.Fc(d);j=d.a[1];!!j&&Syb(a,b,c,j,e,f,g,h)}
+function PPb(b,c,d){try{return Gdb(SPb(b,c,d),1)}catch(a){a=zdb(a);if(ZD(a,333)){throw Adb(new veb(fze+b.o+'*'+b.p+gze+c+pve+d+hze))}else throw Adb(a)}}
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+function RPb(b,c,d){try{return Gdb(SPb(b,c,d),2)}catch(a){a=zdb(a);if(ZD(a,333)){throw Adb(new veb(fze+b.o+'*'+b.p+gze+c+pve+d+hze))}else throw Adb(a)}}
+function lMd(b,c){if(b.g==-1){throw Adb(new cgb)}b.Xj();try{b.d.hd(b.g,c);b.f=b.d.j}catch(a){a=zdb(a);if(ZD(a,77)){throw Adb(new Jrb)}else throw Adb(a)}}
+function Y7b(a){var b,c,d,e,f;for(d=new Anb(a.b);d.af&&bD(b,f,null);return b}
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+function pDd(a,b){var c,d;d=false;if(bE(b)){d=true;oDd(a,new OC(WD(b)))}if(!d){if(ZD(b,242)){d=true;oDd(a,(c=Qeb(RD(b,242)),new hC(c)))}}if(!d){throw Adb(new Aeb(tIe))}}
+function g$d(a,b,c,d){var e,f,g;e=new P3d(a.e,1,10,(g=b.c,ZD(g,90)?RD(g,29):(JTd(),zTd)),(f=c.c,ZD(f,90)?RD(f,29):(JTd(),zTd)),fZd(a,b),false);!d?(d=e):d.nj(e);return d}
+function _2b(a){var b,c;switch(RD(mQb(Y2b(a),(yCc(),QAc)),429).g){case 0:b=a.n;c=a.o;return new rjd(b.a+c.a/2,b.b+c.b/2);case 1:return new sjd(a.n);default:return null;}}
+function Ouc(){Ouc=geb;Luc=new Puc(LAe,0);Kuc=new Puc('LEFTUP',1);Nuc=new Puc('RIGHTUP',2);Juc=new Puc('LEFTDOWN',3);Muc=new Puc('RIGHTDOWN',4);Iuc=new Puc('BALANCED',5)}
+function dKc(a,b,c){var d,e,f;d=Qfb(a.a[b.p],a.a[c.p]);if(d==0){e=RD(mQb(b,(Ywc(),qwc)),15);f=RD(mQb(c,qwc),15);if(e.Hc(c)){return -1}else if(f.Hc(b)){return 1}}return d}
+function k5c(a){switch(a.g){case 1:return new K3c;case 2:return new M3c;case 3:return new I3c;case 0:return null;default:throw Adb(new agb(mFe+(a.f!=null?a.f:''+a.g)));}}
+function gyd(a,b,c){switch(b){case 1:!a.n&&(a.n=new C5d(I4,a,1,7));sLd(a.n);!a.n&&(a.n=new C5d(I4,a,1,7));YGd(a.n,RD(c,16));return;case 2:jyd(a,WD(c));return;}Dxd(a,b,c)}
+function xyd(a,b,c){switch(b){case 3:Ayd(a,Kfb(UD(c)));return;case 4:Cyd(a,Kfb(UD(c)));return;case 5:Dyd(a,Kfb(UD(c)));return;case 6:Eyd(a,Kfb(UD(c)));return;}gyd(a,b,c)}
+function dBd(a,b,c){var d,e,f;f=(d=new R5d,d);e=XVd(f,b,null);!!e&&e.oj();PAd(f,c);WGd((!a.c&&(a.c=new C5d(u7,a,12,10)),a.c),f);$Vd(f,0);bWd(f,1);aWd(f,true);_Vd(f,true)}
+function M5d(a,b){var c,d,e;c=Ktb(a.i,b);if(ZD(c,241)){e=RD(c,241);e.zi()==null&&undefined;return e.wi()}else if(ZD(c,507)){d=RD(c,2037);e=d.b;return e}else{return null}}
+function aj(a,b,c,d){var e,f;Qb(b);Qb(c);f=RD(Fn(a.d,b),17);Ob(!!f,'Row %s not in %s',b,a.e);e=RD(Fn(a.b,c),17);Ob(!!e,'Column %s not in %s',c,a.c);return cj(a,f.a,e.a,d)}
+function ZC(a,b,c,d,e,f,g){var h,i,j,k,l;k=e[f];j=f==g-1;h=j?d:0;l=_C(h,k);d!=10&&cD(WC(a,g-f),b[f],c[f],h,l);if(!j){++f;for(i=0;i1||h==-1){f=RD(i,15);e.Wb(Sje(a,f))}else{e.Wb(Rje(a,RD(i,58)))}}}}
+function ceb(b,c,d,e){beb();var f=_db;$moduleName=c;$moduleBase=d;ydb=e;function g(){for(var a=0;a0){return false}}return true}
+function okc(a){var b,c,d,e,f;for(d=new vkb((new mkb(a.b)).a);d.b;){c=tkb(d);b=RD(c.ld(),10);f=RD(RD(c.md(),42).a,10);e=RD(RD(c.md(),42).b,8);$id(hjd(b.n),$id(ajd(f.n),e))}}
+function Roc(a){switch(RD(mQb(a.b,(yCc(),BAc)),387).g){case 1:FDb(GDb(EDb(new SDb(null,new Swb(a.d,16)),new kpc),new mpc),new opc);break;case 2:Toc(a);break;case 0:Soc(a);}}
+function SVc(a,b,c){var d,e,f;d=c;!d&&(d=new Oqd);d.Ug('Layout',a.a.c.length);for(f=new Anb(a.a);f.aAEe){return c}else e>-1.0E-6&&++c}return c}
+function n2d(a,b){var c;if(b!=a.b){c=null;!!a.b&&(c=Jvd(a.b,a,-4,c));!!b&&(c=Ivd(b,a,-4,c));c=e2d(a,b,c);!!c&&c.oj()}else (a.Db&4)!=0&&(a.Db&1)==0&&qvd(a,new N3d(a,1,3,b,b))}
+function q2d(a,b){var c;if(b!=a.f){c=null;!!a.f&&(c=Jvd(a.f,a,-1,c));!!b&&(c=Ivd(b,a,-1,c));c=g2d(a,b,c);!!c&&c.oj()}else (a.Db&4)!=0&&(a.Db&1)==0&&qvd(a,new N3d(a,1,0,b,b))}
+function Lge(a,b,c,d){var e,f,g,h;if(Mvd(a.e)){e=b.Lk();h=b.md();f=c.md();g=fge(a,1,e,h,f,e.Jk()?kge(a,e,f,ZD(e,102)&&(RD(e,19).Bb&txe)!=0):-1,true);d?d.nj(g):(d=g)}return d}
+function bne(a){var b,c,d;if(a==null)return null;c=RD(a,15);if(c.dc())return '';d=new Qhb;for(b=c.Kc();b.Ob();){Nhb(d,(nme(),WD(b.Pb())));d.a+=' '}return qeb(d,d.a.length-1)}
+function fne(a){var b,c,d;if(a==null)return null;c=RD(a,15);if(c.dc())return '';d=new Qhb;for(b=c.Kc();b.Ob();){Nhb(d,(nme(),WD(b.Pb())));d.a+=' '}return qeb(d,d.a.length-1)}
+function QIc(a,b,c){var d,e;d=a.c[b.c.p][b.p];e=a.c[c.c.p][c.p];if(d.a!=null&&e.a!=null){return Jfb(d.a,e.a)}else if(d.a!=null){return -1}else if(e.a!=null){return 1}return 0}
+function RVc(a,b,c){c.Ug('Tree layout',1);Sed(a.b);Ved(a.b,(YVc(),UVc),UVc);Ved(a.b,VVc,VVc);Ved(a.b,WVc,WVc);Ved(a.b,XVc,XVc);a.a=Qed(a.b,b);SVc(a,b,c.eh(1));c.Vg();return b}
+function ZDd(a,b){var c,d,e,f,g,h;if(b){f=b.a.length;c=new vue(f);for(h=(c.b-c.a)*c.c<0?(uue(),tue):new Rue(c);h.Ob();){g=RD(h.Pb(),17);e=xDd(b,g.a);d=new aFd(a);$Dd(d.a,e)}}}
+function oEd(a,b){var c,d,e,f,g,h;if(b){f=b.a.length;c=new vue(f);for(h=(c.b-c.a)*c.c<0?(uue(),tue):new Rue(c);h.Ob();){g=RD(h.Pb(),17);e=xDd(b,g.a);d=new LEd(a);NDd(d.a,e)}}}
+function ESd(b){var c;if(b!=null&&b.length>0&&ihb(b,b.length-1)==33){try{c=nSd(zhb(b,0,b.length-1));return c.e==null}catch(a){a=zdb(a);if(!ZD(a,33))throw Adb(a)}}return false}
+function u0b(a,b,c){var d,e,f;d=Y2b(b);e=i2b(d);f=new R3b;P3b(f,b);switch(c.g){case 1:Q3b(f,spd(vpd(e)));break;case 2:Q3b(f,vpd(e));}pQb(f,(yCc(),ABc),UD(mQb(a,ABc)));return f}
+function jdc(a){var b,c;b=RD(hs(new is(Mr(Z2b(a.a).a.Kc(),new ir))),18);c=RD(hs(new is(Mr(a3b(a.a).a.Kc(),new ir))),18);return Heb(TD(mQb(b,(Ywc(),Nwc))))||Heb(TD(mQb(c,Nwc)))}
+function Bnc(){Bnc=geb;xnc=new Cnc('ONE_SIDE',0);znc=new Cnc('TWO_SIDES_CORNER',1);Anc=new Cnc('TWO_SIDES_OPPOSING',2);ync=new Cnc('THREE_SIDES',3);wnc=new Cnc('FOUR_SIDES',4)}
+function Usc(a,b){var c,d,e,f;f=new bnb;e=0;d=b.Kc();while(d.Ob()){c=sgb(RD(d.Pb(),17).a+e);while(c.a=a.f){break}ZEb(f.c,c)}return f}
+function iIc(a,b){var c,d,e,f,g;for(f=new Anb(b.a);f.a0&&Xlc(this,this.c-1,(qpd(),Xod));this.c0&&a[0].length>0&&(this.c=Heb(TD(mQb(Y2b(a[0][0]),(Ywc(),rwc)))));this.a=$C(aY,Nve,2117,a.length,0,2);this.b=$C(dY,Nve,2118,a.length,0,2);this.d=new Ks}
+function TOc(a){if(a.c.length==0){return false}if((tFb(0,a.c.length),RD(a.c[0],18)).c.i.k==(r3b(),o3b)){return true}return yDb(GDb(new SDb(null,new Swb(a,16)),new WOc),new YOc)}
+function I5c(a,b){var c,d,e,f,g,h,i;h=Q2c(b);f=b.f;i=b.g;g=$wnd.Math.sqrt(f*f+i*i);e=0;for(d=new Anb(h);d.a=0){c=Fdb(a,ixe);d=Mdb(a,ixe)}else{b=Udb(a,1);c=Fdb(b,500000000);d=Mdb(b,500000000);d=Bdb(Sdb(d,1),Cdb(a,1))}return Rdb(Sdb(d,32),Cdb(c,yxe))}
+function fTb(a,b,c){var d,e;d=(sFb(b.b!=0),RD(Wub(b,b.a.a),8));switch(c.g){case 0:d.b=0;break;case 2:d.b=a.f;break;case 3:d.a=0;break;default:d.a=a.g;}e=Sub(b,0);cvb(e,d);return b}
+function Vpc(a,b,c,d){var e,f,g,h,i;i=a.b;f=b.d;g=f.j;h=$pc(g,i.d[g.g],c);e=$id(ajd(f.n),f.a);switch(f.j.g){case 1:case 3:h.a+=e.a;break;case 2:case 4:h.b+=e.b;}Pub(d,h,d.c.b,d.c)}
+function YNc(a,b,c){var d,e,f,g;g=Wmb(a.e,b,0);f=new ZNc;f.b=c;d=new Jkb(a.e,g);while(d.b1;b>>=1){(b&1)!=0&&(d=Wib(d,c));c.d==1?(c=Wib(c,c)):(c=new djb(Tjb(c.a,c.d,$C(kE,Pwe,28,c.d<<1,15,1))))}d=Wib(d,c);return d}
+function Hwb(){Hwb=geb;var a,b,c,d;Ewb=$C(iE,vxe,28,25,15,1);Fwb=$C(iE,vxe,28,33,15,1);d=1.52587890625E-5;for(b=32;b>=0;b--){Fwb[b]=d;d*=0.5}c=1;for(a=24;a>=0;a--){Ewb[a]=c;c*=0.5}}
+function a5b(a){var b,c;if(Heb(TD(Gxd(a,(yCc(),NAc))))){for(c=new is(Mr(zGd(a).a.Kc(),new ir));gs(c);){b=RD(hs(c),74);if(ozd(b)){if(Heb(TD(Gxd(b,OAc)))){return true}}}}return false}
+function Qmc(a,b){var c,d,e;if(Ysb(a.f,b)){b.b=a;d=b.c;Wmb(a.j,d,0)!=-1||Rmb(a.j,d);e=b.d;Wmb(a.j,e,0)!=-1||Rmb(a.j,e);c=b.a.b;if(c.c.length!=0){!a.i&&(a.i=new _mc(a));Wmc(a.i,c)}}}
+function Xpc(a){var b,c,d,e,f;c=a.c.d;d=c.j;e=a.d.d;f=e.j;if(d==f){return c.p=0&&lhb(a.substr(b,'GMT'.length),'GMT')){c[0]=b+3;return JA(a,c,d)}if(b>=0&&lhb(a.substr(b,'UTC'.length),'UTC')){c[0]=b+3;return JA(a,c,d)}return JA(a,c,d)}
+function Zmc(a,b){var c,d,e,f,g;f=a.g.a;g=a.g.b;for(d=new Anb(a.d);d.ac;f--){a[f]|=b[f-c-1]>>>g;a[f-1]=b[f-c-1]<0&&hib(a.g,b,a.g,b+d,h);g=c.Kc();a.i+=d;for(e=0;e>4&15;f=a[d]&15;g[e++]=oAd[c];g[e++]=oAd[f]}return Ihb(g,0,g.length)}}
+function Fhb(a){var b,c;if(a>=txe){b=uxe+(a-txe>>10&1023)&Bwe;c=56320+(a-txe&1023)&Bwe;return String.fromCharCode(b)+(''+String.fromCharCode(c))}else{return String.fromCharCode(a&Bwe)}}
+function UMb(a,b){RMb();var c,d,e,f;e=RD(RD(Qc(a.r,b),21),87);if(e.gc()>=2){d=RD(e.Kc().Pb(),117);c=a.u.Hc((Pod(),Kod));f=a.u.Hc(Ood);return !d.a&&!c&&(e.gc()==2||f)}else{return false}}
+function v3c(a,b,c,d,e){var f,g,h;f=w3c(a,b,c,d,e);h=false;while(!f){n3c(a,e,true);h=true;f=w3c(a,b,c,d,e)}h&&n3c(a,e,false);g=N2c(e);if(g.c.length!=0){!!a.d&&a.d.Gg(g);v3c(a,e,c,d,g)}}
+function ind(){ind=geb;gnd=new jnd(LAe,0);end=new jnd('DIRECTED',1);hnd=new jnd('UNDIRECTED',2);cnd=new jnd('ASSOCIATION',3);fnd=new jnd('GENERALIZATION',4);dnd=new jnd('DEPENDENCY',5)}
+function nsd(a,b){var c;if(!MCd(a)){throw Adb(new dgb(sHe))}c=MCd(a);switch(b.g){case 1:return -(a.j+a.f);case 2:return a.i-c.g;case 3:return a.j-c.f;case 4:return -(a.i+a.g);}return 0}
+function Jge(a,b,c){var d,e,f;d=b.Lk();f=b.md();e=d.Jk()?fge(a,4,d,f,null,kge(a,d,f,ZD(d,102)&&(RD(d,19).Bb&txe)!=0),true):fge(a,d.tk()?2:1,d,f,d.ik(),-1,true);c?c.nj(e):(c=e);return c}
+function lwb(a,b){var c,d;uFb(b);d=a.b.c.length;Rmb(a.b,b);while(d>0){c=d;d=(d-1)/2|0;if(a.a.Ne(Vmb(a.b,d),b)<=0){$mb(a.b,c,b);return true}$mb(a.b,c,Vmb(a.b,d))}$mb(a.b,d,b);return true}
+function sKb(a,b,c,d){var e,f;e=0;if(!c){for(f=0;f=h}
+function A8c(a){switch(a.g){case 0:return new o8c;case 1:return new u8c;default:throw Adb(new agb('No implementation is available for the width approximator '+(a.f!=null?a.f:''+a.g)));}}
+function rDd(a,b,c,d){var e;e=false;if(bE(d)){e=true;sDd(b,c,WD(d))}if(!e){if($D(d)){e=true;rDd(a,b,c,d)}}if(!e){if(ZD(d,242)){e=true;qDd(b,c,RD(d,242))}}if(!e){throw Adb(new Aeb(tIe))}}
+function uee(a,b){var c,d,e;c=b.qi(a.a);if(c){e=$Nd((!c.b&&(c.b=new SVd((JTd(),FTd),C8,c)),c.b),rKe);if(e!=null){for(d=1;d<(lke(),hke).length;++d){if(lhb(hke[d],e)){return d}}}}return 0}
+function vee(a,b){var c,d,e;c=b.qi(a.a);if(c){e=$Nd((!c.b&&(c.b=new SVd((JTd(),FTd),C8,c)),c.b),rKe);if(e!=null){for(d=1;d<(lke(),ike).length;++d){if(lhb(ike[d],e)){return d}}}}return 0}
+function Ve(a,b){var c,d,e,f;uFb(b);f=a.a.gc();if(f0?1:0;while(f.a[e]!=c){f=f.a[e];e=a.a.Ne(c.d,f.d)>0?1:0}f.a[e]=d;d.b=c.b;d.a[0]=c.a[0];d.a[1]=c.a[1];c.a[0]=null;c.a[1]=null}
+function zIb(a){var b,c,d,e;b=new bnb;c=$C(xdb,Hye,28,a.a.c.length,16,1);Snb(c,c.length);for(e=new Anb(a.a);e.a0&&O9b((tFb(0,c.c.length),RD(c.c[0],30)),a);c.c.length>1&&O9b(RD(Vmb(c,c.c.length-1),30),a);b.Vg()}
+function Sod(a){Pod();var b,c;b=ysb(Lod,cD(WC(D3,1),jwe,279,0,[Nod]));if(dy(Tx(b,a))>1){return false}c=ysb(Kod,cD(WC(D3,1),jwe,279,0,[Jod,Ood]));if(dy(Tx(c,a))>1){return false}return true}
+function FBd(a,b){var c;c=Xjb((YSd(),XSd),a);ZD(c,507)?$jb(XSd,a,new B5d(this,b)):$jb(XSd,a,this);BBd(this,b);if(b==(jTd(),iTd)){this.wb=RD(this,2038);RD(b,2040)}else{this.wb=(lTd(),kTd)}}
+function Lae(b){var c,d,e;if(b==null){return null}c=null;for(d=0;d=Awe?'error':d>=900?'warn':d>=800?'info':'log');eFb(c,a.a);!!a.b&&fFb(b,c,a.b,'Exception: ',true)}
+function mQb(a,b){var c,d;d=(!a.q&&(a.q=new Tsb),Wjb(a.q,b));if(d!=null){return d}c=b.Sg();ZD(c,4)&&(c==null?(!a.q&&(a.q=new Tsb),_jb(a.q,b)):(!a.q&&(a.q=new Tsb),Zjb(a.q,b,c)),a);return c}
+function sXb(){sXb=geb;nXb=new tXb('P1_CYCLE_BREAKING',0);oXb=new tXb('P2_LAYERING',1);pXb=new tXb('P3_NODE_ORDERING',2);qXb=new tXb('P4_NODE_PLACEMENT',3);rXb=new tXb('P5_EDGE_ROUTING',4)}
+function KZb(a,b){CZb();var c;if(a.c==b.c){if(a.b==b.b||rZb(a.b,b.b)){c=oZb(a.b)?1:-1;if(a.a&&!b.a){return c}else if(!a.a&&b.a){return -c}}return hgb(a.b.g,b.b.g)}else{return Qfb(a.c,b.c)}}
+function E3c(a,b){var c,d,e;if(p3c(a,b)){return true}for(d=new Anb(b);d.a=e||b<0)throw Adb(new veb(MIe+b+NIe+e));if(c>=e||c<0)throw Adb(new veb(OIe+c+NIe+e));b!=c?(d=(f=a.Cj(c),a.qj(b,f),f)):(d=a.xj(c));return d}
+function Lje(a){var b,c,d;d=a;if(a){b=0;for(c=a.Eh();c;c=c.Eh()){if(++b>wxe){return Lje(c)}d=c;if(c==a){throw Adb(new dgb('There is a cycle in the containment hierarchy of '+a))}}}return d}
+function Fe(a){var b,c,d;d=new Jyb(pve,'[',']');for(c=a.Kc();c.Ob();){b=c.Pb();Gyb(d,dE(b)===dE(a)?'(this Collection)':b==null?vve:jeb(b))}return !d.a?d.c:d.e.length==0?d.a.a:d.a.a+(''+d.e)}
+function p3c(a,b){var c,d;d=false;if(b.gc()<2){return false}for(c=0;c1&&(a.j.b+=a.e)}else{a.j.a+=c.a;a.j.b=$wnd.Math.max(a.j.b,c.b);a.d.c.length>1&&(a.j.a+=a.e)}}
+function Mnc(){Mnc=geb;Jnc=cD(WC(E3,1),NAe,64,0,[(qpd(),Yod),Xod,npd]);Inc=cD(WC(E3,1),NAe,64,0,[Xod,npd,ppd]);Knc=cD(WC(E3,1),NAe,64,0,[npd,ppd,Yod]);Lnc=cD(WC(E3,1),NAe,64,0,[ppd,Yod,Xod])}
+function Upc(a,b,c,d){var e,f,g,h,i,j,k;g=a.c.d;h=a.d.d;if(g.j==h.j){return}k=a.b;e=g.j;i=null;while(e!=h.j){i=b==0?tpd(e):rpd(e);f=$pc(e,k.d[e.g],c);j=$pc(i,k.d[i.g],c);Mub(d,$id(f,j));e=i}}
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+function JVc(a,b){var c,d,e;c=RD(mQb(b,(h_c(),X$c)),17).a-RD(mQb(a,X$c),17).a;if(c==0){d=ojd(ajd(RD(mQb(a,(q$c(),RZc)),8)),RD(mQb(a,SZc),8));e=ojd(ajd(RD(mQb(b,RZc),8)),RD(mQb(b,SZc),8));return Qfb(d.a*d.b,e.a*e.b)}return c}
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+function Sqe(a,b){var c,d,e,f;Mqe(a);if(a.c!=0||a.a!=123)throw Adb(new Lqe(TId((Hde(),eJe))));f=b==112;d=a.d;c=phb(a.i,125,d);if(c<0)throw Adb(new Lqe(TId((Hde(),fJe))));e=zhb(a.i,d,c);a.d=c+1;return ite(e,f,(a.e&512)==512)}
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+function Vfe(a,b){var c,d,e,f;a.j=-1;if(Mvd(a.e)){c=a.i;f=a.i!=0;LHd(a,b);d=new P3d(a.e,3,a.c,null,b,c,f);e=b.zl(a.e,a.c,null);e=Hge(a,b,e);if(!e){qvd(a.e,d)}else{e.nj(d);e.oj()}}else{LHd(a,b);e=b.zl(a.e,a.c,null);!!e&&e.oj()}}
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+function KCb(a){var b,c,d,e,f;f=new bnb;Umb(a.b,new SEb(f));a.b.c.length=0;if(f.c.length!=0){b=(tFb(0,f.c.length),RD(f.c[0],82));for(c=1,d=f.c.length;c=-b&&d==b){return new Ptd(sgb(c-1),sgb(d))}return new Ptd(sgb(c),sgb(d-1))}
+function lcc(){hcc();return cD(WC(YS,1),jwe,81,0,[nbc,kbc,obc,Ebc,Xbc,Ibc,bcc,Nbc,Vbc,zbc,Rbc,Mbc,Wbc,vbc,dcc,ebc,Qbc,Zbc,Fbc,Ybc,fcc,Tbc,fbc,Ubc,gcc,_bc,ecc,Gbc,sbc,Hbc,Dbc,ccc,ibc,qbc,Kbc,hbc,Lbc,Bbc,wbc,Obc,ybc,lbc,jbc,Cbc,xbc,Pbc,acc,gbc,Sbc,Abc,Jbc,tbc,rbc,$bc,pbc,ubc,mbc])}
+function Cmc(a,b,c){a.d=0;a.b=0;b.k==(r3b(),q3b)&&c.k==q3b&&RD(mQb(b,(Ywc(),Awc)),10)==RD(mQb(c,Awc),10)&&(Gmc(b).j==(qpd(),Yod)?Dmc(a,b,c):Dmc(a,c,b));b.k==q3b&&c.k==o3b?Gmc(b).j==(qpd(),Yod)?(a.d=1):(a.b=1):c.k==q3b&&b.k==o3b&&(Gmc(c).j==(qpd(),Yod)?(a.b=1):(a.d=1));Imc(a,b,c)}
+function EFd(a){var b,c,d,e,f,g,h,i,j,k,l;l=HFd(a);b=a.a;i=b!=null;i&&sDd(l,'category',a.a);e=cve(new Xkb(a.d));g=!e;if(g){j=new MB;sC(l,'knownOptions',j);c=new MFd(j);xgb(new Xkb(a.d),c)}f=cve(a.g);h=!f;if(h){k=new MB;sC(l,'supportedFeatures',k);d=new OFd(k);xgb(a.g,d)}return l}
+function Ly(a){var b,c,d,e,f,g,h,i,j;d=false;b=336;c=0;f=new hq(a.length);for(h=a,i=0,j=h.length;i>16!=7&&!!b){if(Oje(a,b))throw Adb(new agb(UHe+gCd(a)));d=null;!!a.Cb&&(d=(c=a.Db>>16,c>=0?cCd(a,d):a.Cb.Th(a,-1-c,null,d)));!!b&&(d=RD(b,54).Rh(a,1,H4,d));d=bCd(a,b,d);!!d&&d.oj()}else (a.Db&4)!=0&&(a.Db&1)==0&&qvd(a,new N3d(a,1,7,b,b))}
+function lVd(a,b){var c,d;if(b!=a.Cb||a.Db>>16!=3&&!!b){if(Oje(a,b))throw Adb(new agb(UHe+oVd(a)));d=null;!!a.Cb&&(d=(c=a.Db>>16,c>=0?iVd(a,d):a.Cb.Th(a,-1-c,null,d)));!!b&&(d=RD(b,54).Rh(a,0,p7,d));d=hVd(a,b,d);!!d&&d.oj()}else (a.Db&4)!=0&&(a.Db&1)==0&&qvd(a,new N3d(a,1,3,b,b))}
+function Mjb(a,b){Ljb();var c,d,e,f,g,h,i,j,k;if(b.d>a.d){h=a;a=b;b=h}if(b.d<63){return Qjb(a,b)}g=(a.d&-2)<<4;j=$ib(a,g);k=$ib(b,g);d=Gjb(a,Zib(j,g));e=Gjb(b,Zib(k,g));i=Mjb(j,k);c=Mjb(d,e);f=Mjb(Gjb(j,d),Gjb(e,k));f=Bjb(Bjb(f,i),c);f=Zib(f,g);i=Zib(i,g<<1);return Bjb(Bjb(i,f),c)}
+function _Cc(){_Cc=geb;ZCc=new bDc(lEe,0);WCc=new bDc('LONGEST_PATH',1);XCc=new bDc('LONGEST_PATH_SOURCE',2);TCc=new bDc('COFFMAN_GRAHAM',3);VCc=new bDc(BBe,4);$Cc=new bDc('STRETCH_WIDTH',5);YCc=new bDc('MIN_WIDTH',6);SCc=new bDc('BF_MODEL_ORDER',7);UCc=new bDc('DF_MODEL_ORDER',8)}
+function AKc(a,b,c){var d,e,f,g,h;g=aMc(a,c);h=$C(jR,WAe,10,b.length,0,1);d=0;for(f=g.Kc();f.Ob();){e=RD(f.Pb(),12);Heb(TD(mQb(e,(Ywc(),nwc))))&&(h[d++]=RD(mQb(e,Iwc),10))}if(d=0;f+=c?1:-1){g=g|b.c.lg(i,f,c,d&&!Heb(TD(mQb(b.j,(Ywc(),jwc))))&&!Heb(TD(mQb(b.j,(Ywc(),Owc)))));g=g|b.q.ug(i,f,c);g=g|CKc(a,i[f],c,d)}Ysb(a.c,b);return g}
+function F6b(a,b,c){var d,e,f,g,h,i,j,k,l,m;for(k=u2b(a.j),l=0,m=k.length;l1&&(a.a=true);QQb(RD(c.b,68),$id(ajd(RD(b.b,68).c),ijd(ojd(ajd(RD(c.b,68).a),RD(b.b,68).a),e)));Odd(a,b);Qdd(a,c)}}
+function tYb(a){var b,c,d,e,f,g,h;for(f=new Anb(a.a.a);f.a0&&f>0?(g.p=b++):d>0?(g.p=c++):f>0?(g.p=e++):(g.p=c++);}}yob();_mb(a.j,new Lfc)}
+function zic(a){var b,c;c=null;b=RD(Vmb(a.g,0),18);do{c=b.d.i;if(nQb(c,(Ywc(),wwc))){return RD(mQb(c,wwc),12).i}if(c.k!=(r3b(),p3b)&&gs(new is(Mr(a3b(c).a.Kc(),new ir)))){b=RD(hs(new is(Mr(a3b(c).a.Kc(),new ir))),18)}else if(c.k!=p3b){return null}}while(!!c&&c.k!=(r3b(),p3b));return c}
+function sqc(a,b){var c,d,e,f,g,h,i,j,k;h=b.j;g=b.g;i=RD(Vmb(h,h.c.length-1),113);k=(tFb(0,h.c.length),RD(h.c[0],113));j=oqc(a,g,i,k);for(f=1;fj){i=c;k=e;j=d}}b.a=k;b.c=i}
+function fMc(a,b,c){var d,e,f,g,h,i,j;j=new yAb(new TMc(a));for(g=cD(WC(xR,1),XAe,12,0,[b,c]),h=0,i=g.length;hi-a.b&&hi-a.a&&h0){if(f.a){h=f.b.Mf().a;if(c>h){e=(c-h)/2;f.d.b=e;f.d.c=e}}else{f.d.c=a.s+c}}else if(Rod(a.u)){d=wsd(f.b);d.c<0&&(f.d.b=-d.c);d.c+d.b>f.b.Mf().a&&(f.d.c=d.c+d.b-f.b.Mf().a)}}}
+function RUc(a,b){var c,d,e,f,g;g=new bnb;c=b;do{f=RD(Wjb(a.b,c),131);f.B=c.c;f.D=c.d;ZEb(g.c,f);c=RD(Wjb(a.k,c),18)}while(c);d=(tFb(0,g.c.length),RD(g.c[0],131));d.j=true;d.A=RD(d.d.a.ec().Kc().Pb(),18).c.i;e=RD(Vmb(g,g.c.length-1),131);e.q=true;e.C=RD(e.d.a.ec().Kc().Pb(),18).d.i;return g}
+function pPb(a){var b,c;b=RD(a.a,17).a;c=RD(a.b,17).a;if(b>=0){if(b==c){return new Ptd(sgb(-b-1),sgb(-b-1))}if(b==-c){return new Ptd(sgb(-b),sgb(c+1))}}if($wnd.Math.abs(b)>$wnd.Math.abs(c)){if(b<0){return new Ptd(sgb(-b),sgb(c))}return new Ptd(sgb(-b),sgb(c+1))}return new Ptd(sgb(b+1),sgb(c))}
+function H8b(a){var b,c;c=RD(mQb(a,(yCc(),UAc)),171);b=RD(mQb(a,(Ywc(),owc)),311);if(c==(cxc(),$wc)){pQb(a,UAc,bxc);pQb(a,owc,(Gvc(),Fvc))}else if(c==axc){pQb(a,UAc,bxc);pQb(a,owc,(Gvc(),Dvc))}else if(b==(Gvc(),Fvc)){pQb(a,UAc,$wc);pQb(a,owc,Evc)}else if(b==Dvc){pQb(a,UAc,axc);pQb(a,owc,Evc)}}
+function dSc(){dSc=geb;bSc=new pSc;ZRc=pfd(new ufd,(sXb(),pXb),(hcc(),Fbc));aSc=nfd(pfd(new ufd,pXb,Tbc),rXb,Sbc);cSc=mfd(mfd(rfd(nfd(pfd(new ufd,nXb,bcc),rXb,acc),qXb),_bc),ccc);$Rc=nfd(pfd(pfd(pfd(new ufd,oXb,Ibc),qXb,Kbc),qXb,Lbc),rXb,Jbc);_Rc=nfd(pfd(pfd(new ufd,qXb,Lbc),qXb,qbc),rXb,pbc)}
+function HUc(){HUc=geb;CUc=pfd(nfd(new ufd,(sXb(),rXb),(hcc(),tbc)),pXb,Fbc);GUc=mfd(mfd(rfd(nfd(pfd(new ufd,nXb,bcc),rXb,acc),qXb),_bc),ccc);DUc=nfd(pfd(pfd(pfd(new ufd,oXb,Ibc),qXb,Kbc),qXb,Lbc),rXb,Jbc);FUc=pfd(pfd(new ufd,pXb,Tbc),rXb,Sbc);EUc=nfd(pfd(pfd(new ufd,qXb,Lbc),qXb,qbc),rXb,pbc)}
+function eSc(a,b,c,d,e){var f,g;if((!W0b(b)&&b.c.i.c==b.d.i.c||!djd(xjd(cD(WC(l3,1),Nve,8,0,[e.i.n,e.n,e.a])),c))&&!W0b(b)){b.c==e?hu(b.a,0,new sjd(c)):Mub(b.a,new sjd(c));if(d&&!Zsb(a.a,c)){g=RD(mQb(b,(yCc(),RAc)),75);if(!g){g=new Ejd;pQb(b,RAc,g)}f=new sjd(c);Pub(g,f,g.c.b,g.c);Ysb(a.a,f)}}}
+function ht(a,b){var c,d,e,f;f=Ydb(Ndb(cwe,qgb(Ydb(Ndb(b==null?0:tb(b),dwe)),15)));c=f&a.b.length-1;e=null;for(d=a.b[c];d;e=d,d=d.a){if(d.d==f&&Hb(d.i,b)){!e?(a.b[c]=d.a):(e.a=d.a);Ts(RD(Hvb(d.c),604),RD(Hvb(d.f),604));Ss(RD(Hvb(d.b),227),RD(Hvb(d.e),227));--a.f;++a.e;return true}}return false}
+function dec(a){var b,c;for(c=new is(Mr(Z2b(a).a.Kc(),new ir));gs(c);){b=RD(hs(c),18);if(b.c.i.k!=(r3b(),n3b)){throw Adb(new Jed(nBe+X2b(a)+"' has its layer constraint set to FIRST, but has at least one incoming edge that "+' does not come from a FIRST_SEPARATE node. That must not happen.'))}}}
+function Twd(a,b,c){var d,e,f,g,h,i,j;e=ggb(a.Db&254);if(e==0){a.Eb=c}else{if(e==1){h=$C(jJ,rve,1,2,5,1);f=Xwd(a,b);if(f==0){h[0]=c;h[1]=a.Eb}else{h[0]=a.Eb;h[1]=c}}else{h=$C(jJ,rve,1,e+1,5,1);g=SD(a.Eb);for(d=2,i=0,j=0;d<=128;d<<=1){d==b?(h[j++]=c):(a.Db&d)!=0&&(h[j++]=g[i++])}}a.Eb=h}a.Db|=b}
+function vQb(a,b,c){var d,e,f,g;this.b=new bnb;e=0;d=0;for(g=new Anb(a);g.a0){f=RD(Vmb(this.b,0),176);e+=f.o;d+=f.p}e*=2;d*=2;b>1?(e=eE($wnd.Math.ceil(e*b))):(d=eE($wnd.Math.ceil(d/b)));this.a=new gQb(e,d)}
+function mkc(a,b,c,d,e,f){var g,h,i,j,k,l,m,n,o,p,q,r;k=d;if(b.j&&b.o){n=RD(Wjb(a.f,b.A),60);p=n.d.c+n.d.b;--k}else{p=b.a.c+b.a.b}l=e;if(c.q&&c.o){n=RD(Wjb(a.f,c.C),60);j=n.d.c;++l}else{j=c.a.c}q=j-p;i=$wnd.Math.max(2,l-k);h=q/i;o=p+h;for(m=k;m=0;g+=e?1:-1){h=b[g];i=d==(qpd(),Xod)?e?b3b(h,d):hv(b3b(h,d)):e?hv(b3b(h,d)):b3b(h,d);f&&(a.c[h.p]=i.gc());for(l=i.Kc();l.Ob();){k=RD(l.Pb(),12);a.d[k.p]=j++}Tmb(c,i)}}
+function AUc(a,b,c){var d,e,f,g,h,i,j,k;f=Kfb(UD(a.b.Kc().Pb()));j=Kfb(UD(fr(b.b)));d=ijd(ajd(a.a),j-c);e=ijd(ajd(b.a),c-f);k=$id(d,e);ijd(k,1/(j-f));this.a=k;this.b=new bnb;h=true;g=a.b.Kc();g.Pb();while(g.Ob()){i=Kfb(UD(g.Pb()));if(h&&i-c>AEe){this.b.Fc(c);h=false}this.b.Fc(i)}h&&this.b.Fc(c)}
+function mJb(a){var b,c,d,e;pJb(a,a.n);if(a.d.c.length>0){Nnb(a.c);while(xJb(a,RD(ynb(new Anb(a.e.a)),125))>5;b&=31;if(d>=a.d){return a.e<0?(Pib(),Jib):(Pib(),Oib)}f=a.d-d;e=$C(kE,Pwe,28,f+1,15,1);ujb(e,f,a.a,d,b);if(a.e<0){for(c=0;c0&&a.a[c]<<32-b!=0){for(c=0;c=0){return false}else{c=Eee((lke(),jke),e,b);if(!c){return true}else{d=c.Ik();return (d>1||d==-1)&&yfe(Qee(jke,c))!=3}}}}else{return false}}
+function _4b(a,b,c,d){var e,f,g,h,i;h=AGd(RD(QHd((!b.b&&(b.b=new Yie(E4,b,4,7)),b.b),0),84));i=AGd(RD(QHd((!b.c&&(b.c=new Yie(E4,b,5,8)),b.c),0),84));if(vCd(h)==vCd(i)){return null}if(NGd(i,h)){return null}g=kzd(b);if(g==c){return d}else{f=RD(Wjb(a.a,g),10);if(f){e=f.e;if(e){return e}}}return null}
+function uHc(a,b,c){var d,e,f,g,h;c.Ug('Longest path to source layering',1);a.a=b;h=a.a.a;a.b=$C(kE,Pwe,28,h.c.length,15,1);d=0;for(g=new Anb(h);g.a