facebookexperimental / facebookexperimental/Robyn

Dependency Issue

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Description

**Message from pip install:**

Building wheels for collected packages: directsearch
Building wheel for directsearch (setup.py) ... done
Created wheel for directsearch: filename=directsearch-1.0-py3-none-any.whl size=16297 sha256=0d6296dce867099c16d8c105e6ccbc202b381672db79cbb08aa5e11a6ae5ea8a
Stored in directory: /root/.cache/pip/wheels/02/a0/13/8e9d14b8e38244bf712bc5608c641b47c50d96f6e0f11a3280
Successfully built directsearch
Installing collected packages: PyQt6-Qt6, uncertainties, PyQt6-sip, numpy, jedi, dill, colorama, asteval, PyQt6, nlopt, cma, lmfit, directsearch, bayesian-optimization, nevergrad, robynpy
Attempting uninstall: numpy
Found existing installation: numpy 1.26.4
Uninstalling numpy-1.26.4:
Successfully uninstalled numpy-1.26.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
numba 0.61.0 requires numpy<2.2,>=1.24, but you have numpy 2.2.3 which is incompatible.
gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.2.3 which is incompatible.
tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.3 which is incompatible.
thinc 8.2.5 requires numpy<2.0.0,>=1.19.0; python_version >= "3.9", but you have numpy 2.2.3 which is incompatible.
langchain 0.3.18 requires numpy<2,>=1.26.4; python_version < "3.12", but you have numpy 2.2.3 which is incompatible.
pytensor 2.27.1 requires numpy<2,>=1.17.0, but you have numpy 2.2.3 which is incompatible.

**This is model import message:**

![Image](https://github.com/user-attachments/assets/aa86c0b7-d718-4e62-a78e-000109e7aed1)

AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

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