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@@ -76,3 +76,48 @@ graphrag-ollama/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1800 fil
 graphrag-ollama/lib/python3.12/site-packages/pyarrow/libarrow_python.so filter=lfs diff=lfs merge=lfs -text
 graphrag-ollama/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1800 filter=lfs diff=lfs merge=lfs -text
 graphrag-ollama/lib/python3.12/site-packages/pyarrow/libparquet.so.1800 filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/_cffi_backend.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/cryptography/hazmat/bindings/_rust.abi3.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/cu2qu/cu2qu.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/feaLib/lexer.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/misc/bezierTools.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/pens/momentsPen.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/qu2cu/qu2cu.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/fontTools/varLib/iup.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/_matutils.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/models/nmf_pgd.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/models/word2vec_corpusfile.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/doc2vec_old filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/doc2vec_old_sep filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/ft_model_2.3.0 filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/large_tag_doc_10_iter50 filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/lee_fasttext filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/gensim/test/test_data/small_tag_doc_5_iter50 filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/graspologic_native/graspologic_native.abi3.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/kiwisolver/_cext.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/lance/lance.abi3.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/lancedb/_lancedb.abi3.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/llvmlite/binding/libllvmlite.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/numba/np/ufunc/tbbpool.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/ot/lp/emd_wrap.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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+graphrag-ollama/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1500 filter=lfs diff=lfs merge=lfs -text
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+graphrag-ollama/lib/python3.12/site-packages/scipy/io/_fast_matrix_market/_fmm_core.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/scipy/linalg/_flapack.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+graphrag-ollama/lib/python3.12/site-packages/scipy/misc/face.dat filter=lfs diff=lfs merge=lfs -text
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+graphrag-ollama/lib/python3.12/site-packages/sklearn/_loss/_loss.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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graphrag-ollama/bin/fonttools

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.__main__ import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/futurize

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from libfuturize.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/jsonschema

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from jsonschema.cli import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/nltk

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from nltk.cli import cli
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(cli())

graphrag-ollama/bin/numba

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: UTF-8 -*-
+from __future__ import print_function, division, absolute_import
+
+from numba.misc.numba_entry import main
+
+if __name__ == "__main__":
+    main()

graphrag-ollama/bin/pasteurize

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from libpasteurize.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/pyftmerge

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.merge import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/pyftsubset

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.subset import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/bin/ttx

@@ -0,0 +1,8 @@
+#!/home/ryo_goto/GraphRAG-Ollama-UI/graphrag-ollama/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from fontTools.ttx import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

graphrag-ollama/lib/python3.12/site-packages/CHANGELOG.md

@@ -0,0 +1,55 @@
+# Changelog
+
+<!--
+    You should *NOT* be adding new change log entries to this file, this
+    file is managed by towncrier. You *may* edit previous change logs to
+    fix problems like typo corrections or such.
+    To add a new change log entry, please see
+    https://pip.pypa.io/en/latest/development/contributing/#news-entries
+    we named the news folder "changelog.d", and use Markdown to format
+    entries.
+
+    WARNING: Don't drop the next directive!
+-->
+
+<!-- Towncrier release notes start -->
+
+## Aiolimiter 1.1.0 (2023-05-08)
+
+
+
+### Features
+
+- Add ``__slots__`` to the ``AsyncLimiter`` class, reducing memory requirements. ([#85](https://github.com/mjpieters/aiolimiter/issues/85))
+
+
+
+### Deprecations and Removals
+
+- Dropped support for Python 3.6 ([#62](https://github.com/mjpieters/aiolimiter/issues/62))
+
+
+
+### Misc
+
+- [#95](https://github.com/mjpieters/aiolimiter/issues/95)
+
+
+## Aiolimiter 1.0.0 (2021-10-15)
+
+### Bugfixes
+
+- Avoid warnings on Python 3.8 and up by not passing in the loop to
+  ``asyncio.wait_for()``. ([#46](https://github.com/mjpieters/aiolimiter/issues/46))
+
+
+## Aiolimiter 1.0.0b1 (2019-12-01)
+
+### Improved Documentation
+
+- Corrected build process to ensure CHANGELOG.md is updated on release. ([#4](https://github.com/mjpieters/aiolimiter/issues/4))
+
+
+## Aiolimiter 1.0.0b0 (2019-11-30)
+
+_No significant changes_.

graphrag-ollama/lib/python3.12/site-packages/POT-0.9.4.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

graphrag-ollama/lib/python3.12/site-packages/POT-0.9.4.dist-info/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2016-2023 POT contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

graphrag-ollama/lib/python3.12/site-packages/POT-0.9.4.dist-info/METADATA

@@ -0,0 +1,445 @@
+Metadata-Version: 2.1
+Name: POT
+Version: 0.9.4
+Summary: Python Optimal Transport Library
+Home-page: https://github.com/PythonOT/POT
+Download-URL: https://github.com/PythonOT/POT/archive/0.9.4.tar.gz
+Author: Remi Flamary, Nicolas Courty, POT Contributors
+Author-email: [email protected], [email protected]
+License: MIT
+Platform: linux
+Platform: macosx
+Platform: windows
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Environment :: Console
+Classifier: Operating System :: OS Independent
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: C++
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Cython
+Classifier: Topic :: Utilities
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy >=1.16
+Requires-Dist: scipy >=1.6
+Provides-Extra: all
+Requires-Dist: jax ; extra == 'all'
+Requires-Dist: jaxlib ; extra == 'all'
+Requires-Dist: tensorflow ; extra == 'all'
+Requires-Dist: torch ; extra == 'all'
+Requires-Dist: cvxopt ; extra == 'all'
+Requires-Dist: scikit-learn ; extra == 'all'
+Requires-Dist: pymanopt ; extra == 'all'
+Requires-Dist: autograd ; extra == 'all'
+Requires-Dist: torch-geometric ; extra == 'all'
+Requires-Dist: matplotlib ; extra == 'all'
+Provides-Extra: backend-cupy
+Provides-Extra: backend-jax
+Requires-Dist: jax ; extra == 'backend-jax'
+Requires-Dist: jaxlib ; extra == 'backend-jax'
+Provides-Extra: backend-numpy
+Provides-Extra: backend-tf
+Requires-Dist: tensorflow ; extra == 'backend-tf'
+Provides-Extra: backend-torch
+Requires-Dist: torch ; extra == 'backend-torch'
+Provides-Extra: cvxopt
+Requires-Dist: cvxopt ; extra == 'cvxopt'
+Provides-Extra: dr
+Requires-Dist: scikit-learn ; extra == 'dr'
+Requires-Dist: pymanopt ; extra == 'dr'
+Requires-Dist: autograd ; extra == 'dr'
+Provides-Extra: gnn
+Requires-Dist: torch ; extra == 'gnn'
+Requires-Dist: torch-geometric ; extra == 'gnn'
+Provides-Extra: plot
+Requires-Dist: matplotlib ; extra == 'plot'
+
+# POT: Python Optimal Transport
+
+[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT)
+[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot)
+[![Build Status](https://github.com/PythonOT/POT/actions/workflows/build_tests.yml/badge.svg)](https://github.com/PythonOT/POT/actions)
+[![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT)
+[![Downloads](https://static.pepy.tech/badge/pot)](https://pepy.tech/project/pot)
+[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot)
+[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/PythonOT/POT/blob/master/LICENSE)
+
+This open source Python library provides several solvers for optimization
+problems related to Optimal Transport for signal, image processing and machine
+learning.
+
+Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
+
+Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
+
+POT provides the following generic OT solvers (links to examples):
+
+* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
+* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
+* Entropic regularization OT solver with [Sinkhorn Knopp
+  Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] ,
+  stabilized version [9] [10] [34], lazy CPU/GPU solver from geomloss [60] [61], greedy Sinkhorn [22] and [Screening
+  Sinkhorn [26]
+  ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html).
+* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21]  and unmixing [4].
+* Sinkhorn divergence [23] and entropic regularization OT from empirical data.
+* Debiased Sinkhorn barycenters [Sinkhorn divergence barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_debiased_barycenter.html) [37]
+* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
+* Weak OT solver between empirical distributions [39]
+* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) with LP solver (only small scale).
+* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html)  (exact [13] and regularized [12,51]), differentiable using gradients from Graph Dictionary Learning [38]
+ * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) (exact [24] and regularized [12,51]).
+* [Stochastic
+  solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and
+  [differentiable losses](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html) for
+  Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
+* [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
+* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
+* [One dimensional Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. Also [exact unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html) with KL and quadratic regularization and the [regularization path of UOT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html) [41]
+* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
+  formulations).
+* [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
+* [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45]
+* [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]
+* [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38].
+* [Semi-relaxed (Fused) Gromov-Wasserstein divergences](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html) (exact and regularized [48]).
+* [Quantized (Fused) Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_quantized_gromov_wasserstein.html) [68].
+* [Efficient Discrete Multi Marginal Optimal Transport Regularization](https://pythonot.github.io/auto_examples/others/plot_demd_gradient_minimize.html) [50].
+* [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with  [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/)/[Cupy](https://cupy.dev/)/[Tensorflow](https://www.tensorflow.org/) arrays.
+* Smooth Strongly Convex Nearest Brenier Potentials [58], with an extension to bounding potentials using [59].
+
+POT provides the following Machine Learning related solvers:
+
+* [Optimal transport for domain
+  adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html)
+  with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html),   [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi
+  supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html).
+* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8].
+* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt).
+* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27].
+* [Graph Neural Network OT layers TFGW](https://pythonot.github.io/auto_examples/gromov/plot_gnn_TFGW.html) [52] and TW (OT-GNN) [53] 
+
+Some other examples are available in the  [documentation](https://pythonot.github.io/auto_examples/index.html).
+
+#### Using and citing the toolbox
+
+If you use this toolbox in your research and find it useful, please cite POT
+using the following reference from our [JMLR paper](https://jmlr.org/papers/v22/20-451.html):
+
+    Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer,
+    POT Python Optimal Transport library,
+    Journal of Machine Learning Research, 22(78):1−8, 2021.
+    Website: https://pythonot.github.io/
+
+In Bibtex format:
+
+```bibtex
+@article{flamary2021pot,
+  author  = {R{\'e}mi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur{\'e}lie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L{\'e}o Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
+  title   = {POT: Python Optimal Transport},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {78},
+  pages   = {1-8},
+  url     = {http://jmlr.org/papers/v22/20-451.html}
+}
+```
+
+## Installation
+
+The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:
+
+- Numpy (>=1.16)
+- Scipy (>=1.0)
+- Cython (>=0.23) (build only, not necessary when installing from pip or conda)
+
+#### Pip installation
+
+
+You can install the toolbox through PyPI with:
+
+```console
+pip install POT
+```
+
+or get the very latest version by running:
+
+```console
+pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root)
+```
+
+Optional dependencies may be installed with
+```console
+pip install POT[all]
+```
+Note that this installs `cvxopt`, which is licensed under GPL 3.0. Alternatively, if you cannot use GPL-licensed software, the specific optional dependencies may be installed individually, or per-submodule. The available optional installations are `backend-jax, backend-tf, backend-torch, cvxopt, dr, gnn, all`.
+
+#### Anaconda installation with conda-forge
+
+If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies:
+
+```console
+conda install -c conda-forge pot
+```
+
+#### Post installation check
+After a correct installation, you should be able to import the module without errors:
+
+```python
+import ot
+```
+
+Note that for easier access the module is named `ot` instead of `pot`.
+
+
+### Dependencies
+
+Some sub-modules require additional dependencies which are discussed below
+
+* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
+
+```shell
+pip install pymanopt autograd
+```
+
+
+## Examples
+
+### Short examples
+
+* Import the toolbox
+
+```python
+import ot
+```
+
+* Compute Wasserstein distances
+
+```python
+# a,b are 1D histograms (sum to 1 and positive)
+# M is the ground cost matrix
+Wd = ot.emd2(a, b, M) # exact linear program
+Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT
+# if b is a matrix compute all distances to a and return a vector
+```
+
+* Compute OT matrix
+
+```python
+# a,b are 1D histograms (sum to 1 and positive)
+# M is the ground cost matrix
+T = ot.emd(a, b, M) # exact linear program
+T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT
+```
+
+* Compute Wasserstein barycenter
+
+```python
+# A is a n*d matrix containing d  1D histograms
+# M is the ground cost matrix
+ba = ot.barycenter(A, M, reg) # reg is regularization parameter
+```
+
+### Examples and Notebooks
+
+The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/).
+
+
+## Acknowledgements
+
+This toolbox has been created by
+
+* [Rémi Flamary](https://remi.flamary.com/)
+* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
+
+It is currently maintained by 
+
+* [Rémi Flamary](https://remi.flamary.com/)
+* [Cédric Vincent-Cuaz](https://cedricvincentcuaz.github.io/)
+
+The numerous contributors to this library are listed [here](CONTRIBUTORS.md).
+
+POT has benefited from the financing or manpower from the following partners:
+
+<img src="https://pythonot.github.io/master/_static/images/logo_anr.jpg" alt="ANR" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_cnrs.jpg" alt="CNRS" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_3ia.jpg" alt="3IA" style="height:60px;"/><img src="https://pythonot.github.io/master/_static/images/logo_hiparis.png" alt="Hi!PARIS" style="height:60px;"/>
+
+
+
+## Contributions and code of conduct
+
+Every contribution is welcome and should respect the [contribution guidelines](https://pythonot.github.io/master/contributing.html). Each member of the project is expected to follow the [code of conduct](https://pythonot.github.io/master/code_of_conduct.html).
+
+## Support
+
+You can ask questions and join the development discussion:
+
+* On the POT [slack channel](https://pot-toolbox.slack.com)
+* On the POT [gitter channel](https://gitter.im/PythonOT/community)
+* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/)
+
+You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](.github/CONTRIBUTING.md) first.
+
+## References
+
+[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
+
+[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300).
+
+[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138.
+
+[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
+
+[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+
+[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+
+[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567.
+
+[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS).
+
+[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519.
+
+[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816.
+
+[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063.
+
+[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html)  International Conference on Machine Learning (ICML).
+
+[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487.
+
+[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43.
+
+[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) .
+
+[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924.
+
+[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).
+
+[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016).
+
+[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018)
+
+[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning
+
+[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66.
+
+[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31
+
+[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, (AISTATS) 21, 2018
+
+[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML).
+
+[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/)  Advances in Neural Information Processing Systems (NIPS).
+
+[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.
+
+[28] Caffarelli, L. A., McCann, R. J. (2010). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730.
+
+[29] Chapel, L., Alaya, M., Gasso, G. (2020). [Partial Optimal Transport with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), Advances in Neural Information Processing Systems (NeurIPS), 2020.
+
+[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014.
+
+[31] Bonneel, Nicolas, et al. [Sliced and radon wasserstein barycenters of measures](https://perso.liris.cnrs.fr/nicolas.bonneel/WassersteinSliced-JMIV.pdf), Journal of Mathematical Imaging and Vision 51.1 (2015): 22-45
+
+[32] Huang, M., Ma S., Lai, L. (2021). [A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance](http://proceedings.mlr.press/v139/huang21e.html), Proceedings of the 38th International Conference on Machine Learning (ICML).
+
+[33] Kerdoncuff T., Emonet R., Marc S. [Sampled Gromov Wasserstein](https://hal.archives-ouvertes.fr/hal-03232509/document), Machine Learning Journal (MJL), 2021
+
+[34] Feydy, J., Séjourné, T., Vialard, F. X., Amari, S. I., Trouvé, A., & Peyré, G. (2019, April). [Interpolating between optimal transport and MMD using Sinkhorn divergences](http://proceedings.mlr.press/v89/feydy19a/feydy19a.pdf). In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2681-2690). PMLR.
+
+[35] Deshpande, I., Hu, Y. T., Sun, R., Pyrros, A., Siddiqui, N., Koyejo, S., ... & Schwing, A. G. (2019). [Max-sliced wasserstein distance and its use for gans](https://openaccess.thecvf.com/content_CVPR_2019/papers/Deshpande_Max-Sliced_Wasserstein_Distance_and_Its_Use_for_GANs_CVPR_2019_paper.pdf). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10648-10656).
+
+[36] Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., & Stöter, F. R.
+(2019, May). [Sliced-Wasserstein flows: Nonparametric generative modeling
+via optimal transport and diffusions](http://proceedings.mlr.press/v97/liutkus19a/liutkus19a.pdf). In International Conference on
+Machine Learning (pp. 4104-4113). PMLR.
+
+[37] Janati, H., Cuturi, M., Gramfort, A. [Debiased sinkhorn barycenters](http://proceedings.mlr.press/v119/janati20a/janati20a.pdf) Proceedings of the 37th International
+Conference on Machine Learning, PMLR 119:4692-4701, 2020
+
+[38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty, [Online Graph
+Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Conference on Machine Learning (ICML), 2021.
+
+[39] Gozlan, N., Roberto, C., Samson, P. M., & Tetali, P. (2017). [Kantorovich duality for general transport costs and applications](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.1825&rep=rep1&type=pdf). Journal of Functional Analysis, 273(11), 3327-3405.
+
+[40] Forrow, A., Hütter, J. C., Nitzan, M., Rigollet, P., Schiebinger, G., & Weed, J. (2019, April). [Statistical optimal transport via factored couplings](http://proceedings.mlr.press/v89/forrow19a/forrow19a.pdf). In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 2454-2465). PMLR.
+
+[41] Chapel*, L., Flamary*, R., Wu, H., Févotte, C., Gasso, G. (2021). [Unbalanced Optimal Transport through Non-negative Penalized Linear Regression](https://proceedings.neurips.cc/paper/2021/file/c3c617a9b80b3ae1ebd868b0017cc349-Paper.pdf) Advances in Neural Information Processing Systems (NeurIPS), 2020. (Two first co-authors)
+
+[42] Delon, J., Gozlan, N., and Saint-Dizier, A. [Generalized Wasserstein barycenters between probability measures living on different subspaces](https://arxiv.org/pdf/2105.09755). arXiv preprint arXiv:2105.09755, 2021.
+
+[43]  Álvarez-Esteban, Pedro C., et al. [A fixed-point approach to barycenters in Wasserstein space.](https://arxiv.org/pdf/1511.05355.pdf) Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
+
+[44] Delon, Julie, Julien Salomon, and Andrei Sobolevski. [Fast transport optimization for Monge costs on the circle.](https://arxiv.org/abs/0902.3527) SIAM Journal on Applied Mathematics 70.7 (2010): 2239-2258.
+
+[45] Hundrieser, Shayan, Marcel Klatt, and Axel Munk. [The statistics of circular optimal transport.](https://arxiv.org/abs/2103.15426) Directional Statistics for Innovative Applications: A Bicentennial Tribute to Florence Nightingale. Singapore: Springer Nature Singapore, 2022. 57-82.
+
+[46] Bonet, C., Berg, P., Courty, N., Septier, F., Drumetz, L., & Pham, M. T. (2023). [Spherical Sliced-Wasserstein](https://openreview.net/forum?id=jXQ0ipgMdU). International Conference on Learning Representations.
+
+[47] Chowdhury, S., & Mémoli, F. (2019). [The gromov–wasserstein distance between networks and stable network invariants](https://academic.oup.com/imaiai/article/8/4/757/5627736). Information and Inference: A Journal of the IMA, 8(4), 757-787.
+
+[48] Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty (2022). [Semi-relaxed Gromov-Wasserstein divergence and applications on graphs](https://openreview.net/pdf?id=RShaMexjc-x). International Conference on Learning Representations (ICLR), 2022.
+
+[49] Redko, I., Vayer, T., Flamary, R., and Courty, N. (2020). [CO-Optimal Transport](https://proceedings.neurips.cc/paper/2020/file/cc384c68ad503482fb24e6d1e3b512ae-Paper.pdf). Advances in Neural Information Processing Systems, 33.
+
+[50] Liu, T., Puigcerver, J., & Blondel, M. (2023). [Sparsity-constrained optimal transport](https://openreview.net/forum?id=yHY9NbQJ5BP). Proceedings of the Eleventh International Conference on Learning Representations (ICLR).
+
+[51] Xu, H., Luo, D., Zha, H., & Duke, L. C. (2019). [Gromov-wasserstein learning for graph matching and node embedding](http://proceedings.mlr.press/v97/xu19b.html). In International Conference on Machine Learning (ICML), 2019.
+
+[52] Collas, A., Vayer, T., Flamary, F., & Breloy, A. (2023). [Entropic Wasserstein Component Analysis](https://arxiv.org/abs/2303.05119). ArXiv.
+
+[53] C. Vincent-Cuaz, R. Flamary, M. Corneli, T. Vayer, N. Courty (2022). [Template based graph neural network with optimal transport distances](https://papers.nips.cc/paper_files/paper/2022/file/4d3525bc60ba1adc72336c0392d3d902-Paper-Conference.pdf). Advances in Neural Information Processing Systems, 35.
+
+[54] Bécigneul, G., Ganea, O. E., Chen, B., Barzilay, R., & Jaakkola, T. S. (2020). [Optimal transport graph neural networks](https://arxiv.org/pdf/2006.04804).
+
+[55] Ronak Mehta, Jeffery Kline, Vishnu Suresh Lokhande, Glenn Fung, & Vikas Singh (2023). [Efficient Discrete Multi Marginal Optimal Transport Regularization](https://openreview.net/forum?id=R98ZfMt-jE). In The Eleventh International Conference on Learning Representations (ICLR).
+
+[56] Jeffery Kline. [Properties of the d-dimensional earth mover’s problem](https://www.sciencedirect.com/science/article/pii/S0166218X19301441). Discrete Applied Mathematics, 265: 128–141, 2019.
+
+[57] Delon, J., Desolneux, A., & Salmona, A. (2022). [Gromov–Wasserstein
+distances between Gaussian distributions](https://hal.science/hal-03197398v2/file/main.pdf). Journal of Applied Probability, 59(4),
+1178-1198.
+
+[58] Paty F-P., d’Aspremont 1., & Cuturi M. (2020). [Regularity as regularization:Smooth and strongly convex brenier potentials in optimal transport.](http://proceedings.mlr.press/v108/paty20a/paty20a.pdf) In International Conference on Artificial Intelligence and Statistics, pages 1222–1232. PMLR, 2020.
+
+[59] Taylor A. B. (2017). [Convex interpolation and performance estimation of first-order methods for convex optimization.](https://dial.uclouvain.be/pr/boreal/object/boreal%3A182881/datastream/PDF_01/view) PhD thesis, Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2017.
+
+[60] Feydy, J., Roussillon, P., Trouvé, A., & Gori, P. (2019). [Fast and scalable optimal transport for brain tractograms](https://arxiv.org/pdf/2107.02010.pdf). In Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22 (pp. 636-644). Springer International Publishing.
+
+[61] Charlier, B., Feydy, J., Glaunes, J. A., Collin, F. D., & Durif, G. (2021). [Kernel operations on the gpu, with autodiff, without memory overflows](https://www.jmlr.org/papers/volume22/20-275/20-275.pdf). The Journal of Machine Learning Research, 22(1), 3457-3462.
+
+[62] H. Van Assel, C. Vincent-Cuaz, T. Vayer, R. Flamary, N. Courty (2023). [Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein](https://arxiv.org/pdf/2310.03398.pdf). NeurIPS 2023 Workshop Optimal Transport and Machine Learning.
+
+[63] Li, J., Tang, J., Kong, L., Liu, H., Li, J., So, A. M. C., & Blanchet, J. (2022). [A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data](https://openreview.net/pdf?id=0jxPyVWmiiF). In The Eleventh International Conference on Learning Representations.
+
+[64] Ma, X., Chu, X., Wang, Y., Lin, Y., Zhao, J., Ma, L., & Zhu, W. (2023). [Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications](https://openreview.net/pdf?id=uqkUguNu40). In Thirty-seventh Conference on Neural Information Processing Systems.
+
+[65] Scetbon, M., Cuturi, M., & Peyré, G. (2021). [Low-Rank Sinkhorn Factorization](https://arxiv.org/pdf/2103.04737.pdf).
+
+[66] Pooladian, Aram-Alexandre, and Jonathan Niles-Weed. [Entropic estimation of optimal transport maps](https://arxiv.org/pdf/2109.12004.pdf). arXiv preprint arXiv:2109.12004 (2021).
+
+[67] Scetbon, M., Peyré, G. & Cuturi, M. (2022). [Linear-Time Gromov-Wasserstein Distances using Low Rank Couplings and Costs](https://proceedings.mlr.press/v162/scetbon22b/scetbon22b.pdf). In International Conference on Machine Learning (ICML), 2022.
+
+[68] Chowdhury, S., Miller, D., & Needham, T. (2021). [Quantized gromov-wasserstein](https://link.springer.com/chapter/10.1007/978-3-030-86523-8_49). ECML PKDD 2021. Springer International Publishing.

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+Authors
+=======
+
+``pyjwt`` is currently written and maintained by `Jose Padilla <https://github.com/jpadilla>`_.
+Originally written and maintained by `Jeff Lindsay <https://github.com/progrium>`_.
+
+A full list of contributors can be found on GitHub’s `overview <https://github.com/jpadilla/pyjwt/graphs/contributors>`_.

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graphrag-ollama/lib/python3.12/site-packages/PyJWT-2.9.0.dist-info/LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2015-2022 José Padilla
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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+Metadata-Version: 2.1
+Name: PyJWT
+Version: 2.9.0
+Summary: JSON Web Token implementation in Python
+Home-page: https://github.com/jpadilla/pyjwt
+Author: Jose Padilla
+Author-email: [email protected]
+License: MIT
+Keywords: json,jwt,security,signing,token,web
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Natural Language :: English
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Topic :: Utilities
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+License-File: AUTHORS.rst
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+Requires-Dist: coverage[toml] ==5.0.4 ; extra == 'tests'
+
+PyJWT
+=====
+
+.. image:: https://github.com/jpadilla/pyjwt/workflows/CI/badge.svg
+   :target: https://github.com/jpadilla/pyjwt/actions?query=workflow%3ACI
+
+.. image:: https://img.shields.io/pypi/v/pyjwt.svg
+   :target: https://pypi.python.org/pypi/pyjwt
+
+.. image:: https://codecov.io/gh/jpadilla/pyjwt/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/jpadilla/pyjwt
+
+.. image:: https://readthedocs.org/projects/pyjwt/badge/?version=stable
+   :target: https://pyjwt.readthedocs.io/en/stable/
+
+A Python implementation of `RFC 7519 <https://tools.ietf.org/html/rfc7519>`_. Original implementation was written by `@progrium <https://github.com/progrium>`_.
+
+Installing
+----------
+
+Install with **pip**:
+
+.. code-block:: console
+
+    $ pip install PyJWT
+
+
+Usage
+-----
+
+.. code-block:: pycon
+
+    >>> import jwt
+    >>> encoded = jwt.encode({"some": "payload"}, "secret", algorithm="HS256")
+    >>> print(encoded)
+    eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzb21lIjoicGF5bG9hZCJ9.4twFt5NiznN84AWoo1d7KO1T_yoc0Z6XOpOVswacPZg
+    >>> jwt.decode(encoded, "secret", algorithms=["HS256"])
+    {'some': 'payload'}
+
+Documentation
+-------------
+
+View the full docs online at https://pyjwt.readthedocs.io/en/stable/
+
+
+Tests
+-----
+
+You can run tests from the project root after cloning with:
+
+.. code-block:: console
+
+    $ tox

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+Version: 24.1.0
+Summary: File support for asyncio.
+Project-URL: Changelog, https://github.com/Tinche/aiofiles#history
+Project-URL: Bug Tracker, https://github.com/Tinche/aiofiles/issues
+Project-URL: repository, https://github.com/Tinche/aiofiles
+Author-email: Tin Tvrtkovic <[email protected]>
+License: Apache-2.0
+License-File: LICENSE
+License-File: NOTICE
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Framework :: AsyncIO
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+
+# aiofiles: file support for asyncio
+
+[![PyPI](https://img.shields.io/pypi/v/aiofiles.svg)](https://pypi.python.org/pypi/aiofiles)
+[![Build](https://github.com/Tinche/aiofiles/workflows/CI/badge.svg)](https://github.com/Tinche/aiofiles/actions)
+[![Coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/Tinche/882f02e3df32136c847ba90d2688f06e/raw/covbadge.json)](https://github.com/Tinche/aiofiles/actions/workflows/main.yml)
+[![Supported Python versions](https://img.shields.io/pypi/pyversions/aiofiles.svg)](https://github.com/Tinche/aiofiles)
+[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+**aiofiles** is an Apache2 licensed library, written in Python, for handling local
+disk files in asyncio applications.
+
+Ordinary local file IO is blocking, and cannot easily and portably be made
+asynchronous. This means doing file IO may interfere with asyncio applications,
+which shouldn't block the executing thread. aiofiles helps with this by
+introducing asynchronous versions of files that support delegating operations to
+a separate thread pool.
+
+```python
+async with aiofiles.open('filename', mode='r') as f:
+    contents = await f.read()
+print(contents)
+'My file contents'
+```
+
+Asynchronous iteration is also supported.
+
+```python
+async with aiofiles.open('filename') as f:
+    async for line in f:
+        ...
+```
+
+Asynchronous interface to tempfile module.
+
+```python
+async with aiofiles.tempfile.TemporaryFile('wb') as f:
+    await f.write(b'Hello, World!')
+```
+
+## Features
+
+- a file API very similar to Python's standard, blocking API
+- support for buffered and unbuffered binary files, and buffered text files
+- support for `async`/`await` ([PEP 492](https://peps.python.org/pep-0492/)) constructs
+- async interface to tempfile module
+
+## Installation
+
+To install aiofiles, simply:
+
+```bash
+$ pip install aiofiles
+```
+
+## Usage
+
+Files are opened using the `aiofiles.open()` coroutine, which in addition to
+mirroring the builtin `open` accepts optional `loop` and `executor`
+arguments. If `loop` is absent, the default loop will be used, as per the
+set asyncio policy. If `executor` is not specified, the default event loop
+executor will be used.
+
+In case of success, an asynchronous file object is returned with an
+API identical to an ordinary file, except the following methods are coroutines
+and delegate to an executor:
+
+- `close`
+- `flush`
+- `isatty`
+- `read`
+- `readall`
+- `read1`
+- `readinto`
+- `readline`
+- `readlines`
+- `seek`
+- `seekable`
+- `tell`
+- `truncate`
+- `writable`
+- `write`
+- `writelines`
+
+In case of failure, one of the usual exceptions will be raised.
+
+`aiofiles.stdin`, `aiofiles.stdout`, `aiofiles.stderr`,
+`aiofiles.stdin_bytes`, `aiofiles.stdout_bytes`, and
+`aiofiles.stderr_bytes` provide async access to `sys.stdin`,
+`sys.stdout`, `sys.stderr`, and their corresponding `.buffer` properties.
+
+The `aiofiles.os` module contains executor-enabled coroutine versions of
+several useful `os` functions that deal with files:
+
+- `stat`
+- `statvfs`
+- `sendfile`
+- `rename`
+- `renames`
+- `replace`
+- `remove`
+- `unlink`
+- `mkdir`
+- `makedirs`
+- `rmdir`
+- `removedirs`
+- `link`
+- `symlink`
+- `readlink`
+- `listdir`
+- `scandir`
+- `access`
+- `getcwd`
+- `path.abspath`
+- `path.exists`
+- `path.isfile`
+- `path.isdir`
+- `path.islink`
+- `path.ismount`
+- `path.getsize`
+- `path.getatime`
+- `path.getctime`
+- `path.samefile`
+- `path.sameopenfile`
+
+### Tempfile
+
+**aiofiles.tempfile** implements the following interfaces:
+
+- TemporaryFile
+- NamedTemporaryFile
+- SpooledTemporaryFile
+- TemporaryDirectory
+
+Results return wrapped with a context manager allowing use with async with and async for.
+
+```python
+async with aiofiles.tempfile.NamedTemporaryFile('wb+') as f:
+    await f.write(b'Line1\n Line2')
+    await f.seek(0)
+    async for line in f:
+        print(line)
+
+async with aiofiles.tempfile.TemporaryDirectory() as d:
+    filename = os.path.join(d, "file.ext")
+```
+
+### Writing tests for aiofiles
+
+Real file IO can be mocked by patching `aiofiles.threadpool.sync_open`
+as desired. The return type also needs to be registered with the
+`aiofiles.threadpool.wrap` dispatcher:
+
+```python
+aiofiles.threadpool.wrap.register(mock.MagicMock)(
+    lambda *args, **kwargs: aiofiles.threadpool.AsyncBufferedIOBase(*args, **kwargs)
+)
+
+async def test_stuff():
+    write_data = 'data'
+    read_file_chunks = [
+        b'file chunks 1',
+        b'file chunks 2',
+        b'file chunks 3',
+        b'',
+    ]
+    file_chunks_iter = iter(read_file_chunks)
+
+    mock_file_stream = mock.MagicMock(
+        read=lambda *args, **kwargs: next(file_chunks_iter)
+    )
+
+    with mock.patch('aiofiles.threadpool.sync_open', return_value=mock_file_stream) as mock_open:
+        async with aiofiles.open('filename', 'w') as f:
+            await f.write(write_data)
+            assert f.read() == b'file chunks 1'
+
+        mock_file_stream.write.assert_called_once_with(write_data)
+```
+
+### History
+
+#### 24.1.0 (2024-06-24)
+
+- Import `os.link` conditionally to fix importing on android.
+  [#175](https://github.com/Tinche/aiofiles/issues/175)
+- Remove spurious items from `aiofiles.os.__all__` when running on Windows.
+- Switch to more modern async idioms: Remove types.coroutine and make AiofilesContextManager an awaitable instead a coroutine.
+- Add `aiofiles.os.path.abspath` and `aiofiles.os.getcwd`.
+  [#174](https://github.com/Tinche/aiofiles/issues/181)
+- _aiofiles_ is now tested on Python 3.13 too.
+  [#184](https://github.com/Tinche/aiofiles/pull/184)
+- Dropped Python 3.7 support. If you require it, use version 23.2.1.
+
+#### 23.2.1 (2023-08-09)
+
+- Import `os.statvfs` conditionally to fix importing on non-UNIX systems.
+  [#171](https://github.com/Tinche/aiofiles/issues/171) [#172](https://github.com/Tinche/aiofiles/pull/172)
+- aiofiles is now also tested on Windows.
+
+#### 23.2.0 (2023-08-09)
+
+- aiofiles is now tested on Python 3.12 too.
+  [#166](https://github.com/Tinche/aiofiles/issues/166) [#168](https://github.com/Tinche/aiofiles/pull/168)
+- On Python 3.12, `aiofiles.tempfile.NamedTemporaryFile` now accepts a `delete_on_close` argument, just like the stdlib version.
+- On Python 3.12, `aiofiles.tempfile.NamedTemporaryFile` no longer exposes a `delete` attribute, just like the stdlib version.
+- Added `aiofiles.os.statvfs` and `aiofiles.os.path.ismount`.
+  [#162](https://github.com/Tinche/aiofiles/pull/162)
+- Use [PDM](https://pdm.fming.dev/latest/) instead of Poetry.
+  [#169](https://github.com/Tinche/aiofiles/pull/169)
+
+#### 23.1.0 (2023-02-09)
+
+- Added `aiofiles.os.access`.
+  [#146](https://github.com/Tinche/aiofiles/pull/146)
+- Removed `aiofiles.tempfile.temptypes.AsyncSpooledTemporaryFile.softspace`.
+  [#151](https://github.com/Tinche/aiofiles/pull/151)
+- Added `aiofiles.stdin`, `aiofiles.stdin_bytes`, and other stdio streams.
+  [#154](https://github.com/Tinche/aiofiles/pull/154)
+- Transition to `asyncio.get_running_loop` (vs `asyncio.get_event_loop`) internally.
+
+#### 22.1.0 (2022-09-04)
+
+- Added `aiofiles.os.path.islink`.
+  [#126](https://github.com/Tinche/aiofiles/pull/126)
+- Added `aiofiles.os.readlink`.
+  [#125](https://github.com/Tinche/aiofiles/pull/125)
+- Added `aiofiles.os.symlink`.
+  [#124](https://github.com/Tinche/aiofiles/pull/124)
+- Added `aiofiles.os.unlink`.
+  [#123](https://github.com/Tinche/aiofiles/pull/123)
+- Added `aiofiles.os.link`.
+  [#121](https://github.com/Tinche/aiofiles/pull/121)
+- Added `aiofiles.os.renames`.
+  [#120](https://github.com/Tinche/aiofiles/pull/120)
+- Added `aiofiles.os.{listdir, scandir}`.
+  [#143](https://github.com/Tinche/aiofiles/pull/143)
+- Switched to CalVer.
+- Dropped Python 3.6 support. If you require it, use version 0.8.0.
+- aiofiles is now tested on Python 3.11.
+
+#### 0.8.0 (2021-11-27)
+
+- aiofiles is now tested on Python 3.10.
+- Added `aiofiles.os.replace`.
+  [#107](https://github.com/Tinche/aiofiles/pull/107)
+- Added `aiofiles.os.{makedirs, removedirs}`.
+- Added `aiofiles.os.path.{exists, isfile, isdir, getsize, getatime, getctime, samefile, sameopenfile}`.
+  [#63](https://github.com/Tinche/aiofiles/pull/63)
+- Added `suffix`, `prefix`, `dir` args to `aiofiles.tempfile.TemporaryDirectory`.
+  [#116](https://github.com/Tinche/aiofiles/pull/116)
+
+#### 0.7.0 (2021-05-17)
+
+- Added the `aiofiles.tempfile` module for async temporary files.
+  [#56](https://github.com/Tinche/aiofiles/pull/56)
+- Switched to Poetry and GitHub actions.
+- Dropped 3.5 support.
+
+#### 0.6.0 (2020-10-27)
+
+- `aiofiles` is now tested on ppc64le.
+- Added `name` and `mode` properties to async file objects.
+  [#82](https://github.com/Tinche/aiofiles/pull/82)
+- Fixed a DeprecationWarning internally.
+  [#75](https://github.com/Tinche/aiofiles/pull/75)
+- Python 3.9 support and tests.
+
+#### 0.5.0 (2020-04-12)
+
+- Python 3.8 support. Code base modernization (using `async/await` instead of `asyncio.coroutine`/`yield from`).
+- Added `aiofiles.os.remove`, `aiofiles.os.rename`, `aiofiles.os.mkdir`, `aiofiles.os.rmdir`.
+  [#62](https://github.com/Tinche/aiofiles/pull/62)
+
+#### 0.4.0 (2018-08-11)
+
+- Python 3.7 support.
+- Removed Python 3.3/3.4 support. If you use these versions, stick to aiofiles 0.3.x.
+
+#### 0.3.2 (2017-09-23)
+
+- The LICENSE is now included in the sdist.
+  [#31](https://github.com/Tinche/aiofiles/pull/31)
+
+#### 0.3.1 (2017-03-10)
+
+- Introduced a changelog.
+- `aiofiles.os.sendfile` will now work if the standard `os` module contains a `sendfile` function.
+
+### Contributing
+
+Contributions are very welcome. Tests can be run with `tox`, please ensure
+the coverage at least stays the same before you submit a pull request.

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+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright {yyyy} {name of copyright owner}
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+

graphrag-ollama/lib/python3.12/site-packages/aiofiles-24.1.0.dist-info/licenses/NOTICE

@@ -0,0 +1,2 @@
+Asyncio support for files
+Copyright 2016 Tin Tvrtkovic

graphrag-ollama/lib/python3.12/site-packages/aiofiles/base.py

@@ -1,6 +1,6 @@
 """Various base classes."""
-from types import coroutine
-from collections.abc import Coroutine
+from collections.abc import Awaitable
+from contextlib import AbstractAsyncContextManager
 from asyncio import get_running_loop
 
 
@@ -45,66 +45,22 @@ class AsyncIndirectBase(AsyncBase):
         pass  # discard writes
 
 
-class _ContextManager(Coroutine):
+class AiofilesContextManager(Awaitable, AbstractAsyncContextManager):
+    """An adjusted async context manager for aiofiles."""
+
     __slots__ = ("_coro", "_obj")
 
     def __init__(self, coro):
         self._coro = coro
         self._obj = None
 
-    def send(self, value):
-        return self._coro.send(value)
-
-    def throw(self, typ, val=None, tb=None):
-        if val is None:
-            return self._coro.throw(typ)
-        elif tb is None:
-            return self._coro.throw(typ, val)
-        else:
-            return self._coro.throw(typ, val, tb)
-
-    def close(self):
-        return self._coro.close()
-
-    @property
-    def gi_frame(self):
-        return self._coro.gi_frame
-
-    @property
-    def gi_running(self):
-        return self._coro.gi_running
-
-    @property
-    def gi_code(self):
-        return self._coro.gi_code
-
-    def __next__(self):
-        return self.send(None)
-
-    @coroutine
-    def __iter__(self):
-        resp = yield from self._coro
-        return resp
-
     def __await__(self):
-        resp = yield from self._coro
-        return resp
-
-    async def __anext__(self):
-        resp = await self._coro
-        return resp
-
-    async def __aenter__(self):
-        self._obj = await self._coro
+        if self._obj is None:
+            self._obj = yield from self._coro.__await__()
         return self._obj
 
-    async def __aexit__(self, exc_type, exc, tb):
-        self._obj.close()
-        self._obj = None
-
-
-class AiofilesContextManager(_ContextManager):
-    """An adjusted async context manager for aiofiles."""
+    async def __aenter__(self):
+        return await self
 
     async def __aexit__(self, exc_type, exc_val, exc_tb):
         await get_running_loop().run_in_executor(

graphrag-ollama/lib/python3.12/site-packages/aiofiles/os.py

@@ -1,4 +1,5 @@
 """Async executor versions of file functions from the os module."""
+
 import os
 
 from . import ospath as path
@@ -7,7 +8,6 @@ from .ospath import wrap
 __all__ = [
     "path",
     "stat",
-    "statvfs",
     "rename",
     "renames",
     "replace",
@@ -17,15 +17,20 @@ __all__ = [
     "makedirs",
     "rmdir",
     "removedirs",
-    "link",
     "symlink",
     "readlink",
     "listdir",
     "scandir",
     "access",
-    "sendfile",
     "wrap",
+    "getcwd",
 ]
+if hasattr(os, "link"):
+    __all__ += ["link"]
+if hasattr(os, "sendfile"):
+    __all__ += ["sendfile"]
+if hasattr(os, "statvfs"):
+    __all__ += ["statvfs"]
 
 
 stat = wrap(os.stat)
@@ -38,13 +43,15 @@ mkdir = wrap(os.mkdir)
 makedirs = wrap(os.makedirs)
 rmdir = wrap(os.rmdir)
 removedirs = wrap(os.removedirs)
-link = wrap(os.link)
 symlink = wrap(os.symlink)
 readlink = wrap(os.readlink)
 listdir = wrap(os.listdir)
 scandir = wrap(os.scandir)
 access = wrap(os.access)
+getcwd = wrap(os.getcwd)
 
+if hasattr(os, "link"):
+    link = wrap(os.link)
 if hasattr(os, "sendfile"):
     sendfile = wrap(os.sendfile)
 if hasattr(os, "statvfs"):

graphrag-ollama/lib/python3.12/site-packages/aiofiles/ospath.py

@@ -1,4 +1,5 @@
 """Async executor versions of file functions from the os.path module."""
+
 import asyncio
 from functools import partial, wraps
 from os import path
@@ -26,3 +27,4 @@ getatime = wrap(path.getatime)
 getctime = wrap(path.getctime)
 samefile = wrap(path.samefile)
 sameopenfile = wrap(path.sameopenfile)
+abspath = wrap(path.abspath)

graphrag-ollama/lib/python3.12/site-packages/aiofiles/threadpool/__init__.py

@@ -10,7 +10,6 @@ from io import (
     FileIO,
     TextIOBase,
 )
-from types import coroutine
 
 from ..base import AiofilesContextManager
 from .binary import (
@@ -63,8 +62,7 @@ def open(
     )
 
 
-@coroutine
-def _open(
+async def _open(
     file,
     mode="r",
     buffering=-1,
@@ -91,7 +89,7 @@ def _open(
         closefd=closefd,
         opener=opener,
     )
-    f = yield from loop.run_in_executor(executor, cb)
+    f = await loop.run_in_executor(executor, cb)
 
     return wrap(f, loop=loop, executor=executor)