diff --git a/.gitattributes b/.gitattributes
index e74f24d7e3a47762ee2512ab854b73a029d0aede..16ba971f4457a872834c51a7f27e1f0ae3055562 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1565,3 +1565,12 @@ vllm/lib/python3.10/site-packages/torio/lib/libtorio_ffmpeg4.so filter=lfs diff=
 vllm/lib/python3.10/site-packages/torio/lib/libtorio_ffmpeg6.so filter=lfs diff=lfs merge=lfs -text
 vllm/lib/python3.10/site-packages/torio/lib/_torio_ffmpeg5.so filter=lfs diff=lfs merge=lfs -text
 vllm/lib/python3.10/site-packages/torio/lib/_torio_ffmpeg4.so filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/signal/tests/__pycache__/test_signaltools.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_filter_design.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_signaltools.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/interpolate/_rgi_cython.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_stats_py.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/cluster/__pycache__/hierarchy.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_distn_infrastructure.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
diff --git a/parrot/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so b/parrot/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..5f27c39598d6f57f5ee68f6d70d6ca7c94dcdeb7
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7c4ebb97b90fed30f3c3a62d1b4157dd99a13aad68363e8f4362f30baa1ba3c
+size 988040
diff --git a/parrot/lib/python3.10/site-packages/scipy/cluster/__pycache__/hierarchy.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/cluster/__pycache__/hierarchy.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fd448a392b7fa2fff1764b95cce7e384535bdee4
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/cluster/__pycache__/hierarchy.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f83546aeaf4bb5dc2874f93e104650528f90c4f4bde27ee2be1bed2e4ae85a64
+size 130975
diff --git a/parrot/lib/python3.10/site-packages/scipy/fftpack/tests/fftw_single_ref.npz b/parrot/lib/python3.10/site-packages/scipy/fftpack/tests/fftw_single_ref.npz
new file mode 100644
index 0000000000000000000000000000000000000000..a42748dba14b7ff0d2f53ce4cd5a86a4f08e5d93
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/fftpack/tests/fftw_single_ref.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:276a9141318e6fc36e4ab6ff54a61b64054ef8849b660f17359e5f541b43c526
+size 95144
diff --git a/parrot/lib/python3.10/site-packages/scipy/interpolate/_rgi_cython.cpython-310-x86_64-linux-gnu.so b/parrot/lib/python3.10/site-packages/scipy/interpolate/_rgi_cython.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..c1aa4d8f736e71129137e5294b5f8115de382001
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/interpolate/_rgi_cython.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fcadead067c87106c923a55256aa79741cf876f62a8ceb441526e7556813071
+size 295704
diff --git a/parrot/lib/python3.10/site-packages/scipy/interpolate/tests/data/bug-1310.npz b/parrot/lib/python3.10/site-packages/scipy/interpolate/tests/data/bug-1310.npz
new file mode 100644
index 0000000000000000000000000000000000000000..8bddf805c36b29dc449556c27a2b489691f841af
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/interpolate/tests/data/bug-1310.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d6803c0b398f2704c236f1d1b9e8e5ede06bd165a0abb0f228281abbd455ae9
+size 2648
diff --git a/parrot/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so b/parrot/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..646a71195cecbfcd728f17acf6bffd793ae21767
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/io/matlab/_streams.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a46d261a95b0a6e49625a78ec2063eaa154fe030ab29636492125a85b663ef36
+size 147488
diff --git a/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_filter_design.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_filter_design.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a7e64ca48207abe5a9eae41f7255af02de5042d
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_filter_design.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd6efa9a572b995bae93e5a6ce23030d5ed8c517d8b702ad8c97d19f1fe174b5
+size 168151
diff --git a/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_signaltools.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_signaltools.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4d05bff437f8af4e06cff31a34424fbe591f9d39
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/signal/__pycache__/_signaltools.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44113792e65f53fe8ead22380c953f676868f2ed811b917d96799d21b28403b8
+size 135055
diff --git a/parrot/lib/python3.10/site-packages/scipy/signal/tests/__pycache__/test_signaltools.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/signal/tests/__pycache__/test_signaltools.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b8c2c1ae072a9c7b82d6570627e8a64f6d3a3f62
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/signal/tests/__pycache__/test_signaltools.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cbe0dd7464159b234dd57ecf31a35d6759a38b1aa0fac882ab66c1378841661a
+size 115571
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__init__.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_bdtr.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_bdtr.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..af3da7e79b2980430be5867a164d867137dbc184
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_bdtr.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_boxcox.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_boxcox.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0114eee138b6fc8adb2ef0c04fc13e208c2b845a
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_boxcox.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdflib.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdflib.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d55aa89650eaad4ed820f5b28e48a086b9d23bf8
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdflib.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdft_asymptotic.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdft_asymptotic.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7e8730a5eeec4a5d31320149d425a46bdd0ca29f
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cdft_asymptotic.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cephes_intp_cast.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cephes_intp_cast.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..befc67a422030db11129485abab8bd2be5b4e011
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cephes_intp_cast.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cosine_distr.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cosine_distr.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9a21679eadecb7bd20cbc84671d3923c98958c86
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cosine_distr.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cython_special.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cython_special.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2c5f504bbd42ecc8a61aaf7b8ad867e3ea201da6
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_cython_special.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_data.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_data.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c73c54cadde384ccfc9a8689a0905e2b85ed93c9
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_data.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_dd.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_dd.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5536e4426c0f8f9d91bea8d13dfde5b8a9bcbb8e
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_dd.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_digamma.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_digamma.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a6ebda157bda44b420f4c0d86ac2589073546129
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_digamma.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ellip_harm.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ellip_harm.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..445187533de4861362d8eda48e5f96802d44c41c
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ellip_harm.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_erfinv.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_erfinv.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3c93ede22a61837caaa145242fd6fecd48be33a9
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_erfinv.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_exponential_integrals.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_exponential_integrals.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ded83b9beb885fcc5d1a8a0d89fb65820224d0eb
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_exponential_integrals.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_extending.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_extending.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d420d84aea5156f81934ae4a16c33ed606c8a884
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_extending.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_faddeeva.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_faddeeva.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..889d0f8df6bf55aa016fe900559d2175db237937
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_faddeeva.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gamma.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gamma.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c3ebed5cbdff796c84e3a6328047595f5e95f840
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gamma.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gammainc.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gammainc.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..717705ebe9439462e912e1c717628b3125084ee4
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_gammainc.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_hyp2f1.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_hyp2f1.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0ead35aa3e79bfe287abfee9463c6e2f3a11626b
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_hyp2f1.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_iv_ratio.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_iv_ratio.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7088c8d97eb04a5bc3a9c8469e9a8a81f1b15f07
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_iv_ratio.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_kolmogorov.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_kolmogorov.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..04ef80f45aef1c6942e8b72ba19cd35eda2d9adb
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_kolmogorov.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_log_softmax.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_log_softmax.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..38e01e476d5e16a0e493b24b64a8cbfbff46120b
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_log_softmax.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_loggamma.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_loggamma.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..db515c280a577962bccca4aad8336d3c37d632c3
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_loggamma.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logit.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logit.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7c918d15b30e51ccba2fd53b9a5915d70b89cee6
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logit.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logsumexp.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logsumexp.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..57915e7ba93baad7d8e38345d9fd1d5af9c949d3
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_logsumexp.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtr.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtr.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..35d89391458f56967691f59b084d1b75c8604380
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtr.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtri_exp.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtri_exp.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..97f8156883a277e733332894ac8826e3b3c1aa8c
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_ndtri_exp.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..92bb1e74409fe3dd3e340a575fbb238f6de63570
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal_eval.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal_eval.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..52e01ae01585f89f09d19386aa979f1045591df4
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_orthogonal_eval.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_owens_t.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_owens_t.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..06d501716a35f5c319d9fdff65e6b1099af38be9
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_owens_t.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_pdtr.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_pdtr.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3da16eef3a6f94ab3b59ea2a0cf243a9614602b4
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_pdtr.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_powm1.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_powm1.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bec06d97f5382a9f216c0ea881751621a3a2982c
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_powm1.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_expn_asy.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_expn_asy.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8159786480e89d7fe38bde2ac720b59f66ce5cb5
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_expn_asy.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_gammainc.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_gammainc.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cde0d127630c9af9099dd53f5277461e77ad5c12
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_gammainc.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_utils.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fb8e14881bb9f7bc7bec97e3041c63b6ac0e3267
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_precompute_utils.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sf_error.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sf_error.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5c914df4141b9af131fa66fc760971d008b6e040
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sf_error.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sici.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sici.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e1efe76d9e461ac12421123bff21c0baa20ba321
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sici.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_specfun.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_specfun.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dd6c227fc8d0ec7d7ffc702232572494bc9717d9
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_specfun.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spfun_stats.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spfun_stats.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6243e75a7cdf31d13860383ea55fa6afcfcb849c
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spfun_stats.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sph_harm.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sph_harm.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..da0c9bb87a186717009f47428e0e8e72c1849424
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_sph_harm.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spherical_bessel.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spherical_bessel.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..12a3d78cade3a15f3e0a32e9a4896cb1fe52ac60
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_spherical_bessel.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_support_alternative_backends.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_support_alternative_backends.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..98c277917cd3093bf7bd3ef1b5190750aa5868b1
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_support_alternative_backends.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_trig.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_trig.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f0389727c8aeeb6be2a27c8a7e8f65890f10eab2
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_trig.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wright_bessel.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wright_bessel.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6b7f2c1982256748a4d2551ae2be29e8285aee87
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wright_bessel.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wrightomega.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wrightomega.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..fc0842b3873edd8e6f9d21920f20c7be727b972c
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_wrightomega.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_zeta.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_zeta.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a80d61dac28933ac4c3080e8293af4065487199e
Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/tests/__pycache__/test_zeta.cpython-310.pyc differ
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/extending.pyx b/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/extending.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..ca3bf2167f0f7726f8b0acb60ed8b8798a518d79
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/extending.pyx
@@ -0,0 +1,12 @@
+#!/usr/bin/env python3
+#cython: language_level=3
+#cython: boundscheck=False
+#cython: wraparound=False
+
+from scipy.special.cython_special cimport beta, gamma
+
+cpdef double cy_beta(double a, double b):
+    return beta(a, b)
+
+cpdef double complex cy_gamma(double complex z):
+    return gamma(z)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/meson.build b/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..2a5e1535a16f840f31ca0207513e7c060767ea12
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/_cython_examples/meson.build
@@ -0,0 +1,25 @@
+project('random-build-examples', 'c', 'cpp', 'cython')
+
+fs = import('fs')
+
+py3 = import('python').find_installation(pure: false)
+
+cy = meson.get_compiler('cython')
+
+if not cy.version().version_compare('>=3.0.8')
+  error('tests requires Cython >= 3.0.8')
+endif
+
+py3.extension_module(
+  'extending',
+  'extending.pyx',
+  install: false,
+)
+
+extending_cpp = fs.copyfile('extending.pyx', 'extending_cpp.pyx')
+py3.extension_module(
+  'extending_cpp',
+  extending_cpp,
+  install: false,
+  override_options : ['cython_language=cpp']
+)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_bdtr.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_bdtr.py
new file mode 100644
index 0000000000000000000000000000000000000000..57694becc49b2028f17eac819b80a225ac010795
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_bdtr.py
@@ -0,0 +1,112 @@
+import numpy as np
+import scipy.special as sc
+import pytest
+from numpy.testing import assert_allclose, assert_array_equal, suppress_warnings
+
+
+class TestBdtr:
+    def test(self):
+        val = sc.bdtr(0, 1, 0.5)
+        assert_allclose(val, 0.5)
+
+    def test_sum_is_one(self):
+        val = sc.bdtr([0, 1, 2], 2, 0.5)
+        assert_array_equal(val, [0.25, 0.75, 1.0])
+
+    def test_rounding(self):
+        double_val = sc.bdtr([0.1, 1.1, 2.1], 2, 0.5)
+        int_val = sc.bdtr([0, 1, 2], 2, 0.5)
+        assert_array_equal(double_val, int_val)
+
+    @pytest.mark.parametrize('k, n, p', [
+        (np.inf, 2, 0.5),
+        (1.0, np.inf, 0.5),
+        (1.0, 2, np.inf)
+    ])
+    def test_inf(self, k, n, p):
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning)
+            val = sc.bdtr(k, n, p)
+        assert np.isnan(val)
+
+    def test_domain(self):
+        val = sc.bdtr(-1.1, 1, 0.5)
+        assert np.isnan(val)
+
+
+class TestBdtrc:
+    def test_value(self):
+        val = sc.bdtrc(0, 1, 0.5)
+        assert_allclose(val, 0.5)
+
+    def test_sum_is_one(self):
+        val = sc.bdtrc([0, 1, 2], 2, 0.5)
+        assert_array_equal(val, [0.75, 0.25, 0.0])
+
+    def test_rounding(self):
+        double_val = sc.bdtrc([0.1, 1.1, 2.1], 2, 0.5)
+        int_val = sc.bdtrc([0, 1, 2], 2, 0.5)
+        assert_array_equal(double_val, int_val)
+
+    @pytest.mark.parametrize('k, n, p', [
+        (np.inf, 2, 0.5),
+        (1.0, np.inf, 0.5),
+        (1.0, 2, np.inf)
+    ])
+    def test_inf(self, k, n, p):
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning)
+            val = sc.bdtrc(k, n, p)
+        assert np.isnan(val)
+
+    def test_domain(self):
+        val = sc.bdtrc(-1.1, 1, 0.5)
+        val2 = sc.bdtrc(2.1, 1, 0.5)
+        assert np.isnan(val2)
+        assert_allclose(val, 1.0)
+
+    def test_bdtr_bdtrc_sum_to_one(self):
+        bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
+        bdtrc_vals = sc.bdtrc([0, 1, 2], 2, 0.5)
+        vals = bdtr_vals + bdtrc_vals
+        assert_allclose(vals, [1.0, 1.0, 1.0])
+
+
+class TestBdtri:
+    def test_value(self):
+        val = sc.bdtri(0, 1, 0.5)
+        assert_allclose(val, 0.5)
+
+    def test_sum_is_one(self):
+        val = sc.bdtri([0, 1], 2, 0.5)
+        actual = np.asarray([1 - 1/np.sqrt(2), 1/np.sqrt(2)])
+        assert_allclose(val, actual)
+
+    def test_rounding(self):
+        double_val = sc.bdtri([0.1, 1.1], 2, 0.5)
+        int_val = sc.bdtri([0, 1], 2, 0.5)
+        assert_allclose(double_val, int_val)
+
+    @pytest.mark.parametrize('k, n, p', [
+        (np.inf, 2, 0.5),
+        (1.0, np.inf, 0.5),
+        (1.0, 2, np.inf)
+    ])
+    def test_inf(self, k, n, p):
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning)
+            val = sc.bdtri(k, n, p)
+        assert np.isnan(val)
+
+    @pytest.mark.parametrize('k, n, p', [
+        (-1.1, 1, 0.5),
+        (2.1, 1, 0.5)
+    ])
+    def test_domain(self, k, n, p):
+        val = sc.bdtri(k, n, p)
+        assert np.isnan(val)
+
+    def test_bdtr_bdtri_roundtrip(self):
+        bdtr_vals = sc.bdtr([0, 1, 2], 2, 0.5)
+        roundtrip_vals = sc.bdtri([0, 1, 2], 2, bdtr_vals)
+        assert_allclose(roundtrip_vals, [0.5, 0.5, np.nan])
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_cephes_intp_cast.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_cephes_intp_cast.py
new file mode 100644
index 0000000000000000000000000000000000000000..05f3d1ae5c101ff50c75d1065e5e234063d192e4
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_cephes_intp_cast.py
@@ -0,0 +1,29 @@
+import pytest
+import numpy as np
+from scipy.special._ufuncs import (
+    _smirnovc, _smirnovci, _smirnovp,
+    _struve_asymp_large_z, _struve_bessel_series, _struve_power_series,
+    bdtr, bdtrc, bdtri, expn, kn, nbdtr, nbdtrc, nbdtri, pdtri,
+    smirnov, smirnovi, yn
+)
+
+
+#
+# For each ufunc here, verify that the default integer type, np.intp,
+# can be safely cast to the integer type found in the input type signatures.
+# For this particular set of functions, the code expects to find just one
+# integer type among the input signatures.
+#
+@pytest.mark.parametrize(
+    'ufunc',
+    [_smirnovc, _smirnovci, _smirnovp,
+     _struve_asymp_large_z, _struve_bessel_series, _struve_power_series,
+     bdtr, bdtrc, bdtri, expn, kn, nbdtr, nbdtrc, nbdtri, pdtri,
+     smirnov, smirnovi, yn],
+)
+def test_intp_safe_cast(ufunc):
+    int_chars = {'i', 'l', 'q'}
+    int_input = [set(sig.split('->')[0]) & int_chars for sig in ufunc.types]
+    int_char = ''.join(s.pop() if s else '' for s in int_input)
+    assert len(int_char) == 1, "More integer types in the signatures than expected"
+    assert np.can_cast(np.intp, np.dtype(int_char))
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_exponential_integrals.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_exponential_integrals.py
new file mode 100644
index 0000000000000000000000000000000000000000..8332a83267e2f75dded04e80443c150c832676c8
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_exponential_integrals.py
@@ -0,0 +1,118 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose
+import scipy.special as sc
+
+
+class TestExp1:
+
+    def test_branch_cut(self):
+        assert np.isnan(sc.exp1(-1))
+        assert sc.exp1(complex(-1, 0)).imag == (
+            -sc.exp1(complex(-1, -0.0)).imag
+        )
+
+        assert_allclose(
+            sc.exp1(complex(-1, 0)),
+            sc.exp1(-1 + 1e-20j),
+            atol=0,
+            rtol=1e-15
+        )
+        assert_allclose(
+            sc.exp1(complex(-1, -0.0)),
+            sc.exp1(-1 - 1e-20j),
+            atol=0,
+            rtol=1e-15
+        )
+
+    def test_834(self):
+        # Regression test for #834
+        a = sc.exp1(-complex(19.9999990))
+        b = sc.exp1(-complex(19.9999991))
+        assert_allclose(a.imag, b.imag, atol=0, rtol=1e-15)
+
+
+class TestScaledExp1:
+
+    @pytest.mark.parametrize('x, expected', [(0, 0), (np.inf, 1)])
+    def test_limits(self, x, expected):
+        y = sc._ufuncs._scaled_exp1(x)
+        assert y == expected
+
+    # The expected values were computed with mpmath, e.g.:
+    #
+    #   from mpmath import mp
+    #   mp.dps = 80
+    #   x = 1e-25
+    #   print(float(x*mp.exp(x)*np.expint(1, x)))
+    #
+    # prints 5.698741165994961e-24
+    #
+    # The method used to compute _scaled_exp1 changes at x=1
+    # and x=1250, so values at those inputs, and values just
+    # above and below them, are included in the test data.
+    @pytest.mark.parametrize('x, expected',
+                             [(1e-25, 5.698741165994961e-24),
+                              (0.1, 0.20146425447084518),
+                              (0.9995, 0.5962509885831002),
+                              (1.0, 0.5963473623231941),
+                              (1.0005, 0.5964436833238044),
+                              (2.5, 0.7588145912149602),
+                              (10.0, 0.9156333393978808),
+                              (100.0, 0.9901942286733019),
+                              (500.0, 0.9980079523802055),
+                              (1000.0, 0.9990019940238807),
+                              (1249.5, 0.9992009578306811),
+                              (1250.0, 0.9992012769377913),
+                              (1250.25, 0.9992014363957858),
+                              (2000.0, 0.9995004992514963),
+                              (1e4, 0.9999000199940024),
+                              (1e10, 0.9999999999),
+                              (1e15, 0.999999999999999),
+                              ])
+    def test_scaled_exp1(self, x, expected):
+        y = sc._ufuncs._scaled_exp1(x)
+        assert_allclose(y, expected, rtol=2e-15)
+
+
+class TestExpi:
+
+    @pytest.mark.parametrize('result', [
+        sc.expi(complex(-1, 0)),
+        sc.expi(complex(-1, -0.0)),
+        sc.expi(-1)
+    ])
+    def test_branch_cut(self, result):
+        desired = -0.21938393439552027368  # Computed using Mpmath
+        assert_allclose(result, desired, atol=0, rtol=1e-14)
+
+    def test_near_branch_cut(self):
+        lim_from_above = sc.expi(-1 + 1e-20j)
+        lim_from_below = sc.expi(-1 - 1e-20j)
+        assert_allclose(
+            lim_from_above.real,
+            lim_from_below.real,
+            atol=0,
+            rtol=1e-15
+        )
+        assert_allclose(
+            lim_from_above.imag,
+            -lim_from_below.imag,
+            atol=0,
+            rtol=1e-15
+        )
+
+    def test_continuity_on_positive_real_axis(self):
+        assert_allclose(
+            sc.expi(complex(1, 0)),
+            sc.expi(complex(1, -0.0)),
+            atol=0,
+            rtol=1e-15
+        )
+
+
+class TestExpn:
+
+    def test_out_of_domain(self):
+        assert all(np.isnan([sc.expn(-1, 1.0), sc.expn(1, -1.0)]))
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_extending.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..57ab39a9d489c2e5a624106a708b5b7f882a7d46
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_extending.py
@@ -0,0 +1,24 @@
+import os
+import platform
+
+import pytest
+
+from scipy._lib._testutils import IS_EDITABLE,_test_cython_extension, cython
+from scipy.special import beta, gamma
+
+
+@pytest.mark.fail_slow(20)
+# essential per https://github.com/scipy/scipy/pull/20487#discussion_r1567057247
+@pytest.mark.skipif(IS_EDITABLE,
+                    reason='Editable install cannot find .pxd headers.')
+@pytest.mark.skipif(platform.machine() in ["wasm32", "wasm64"],
+                    reason="Can't start subprocess")
+@pytest.mark.skipif(cython is None, reason="requires cython")
+def test_cython(tmp_path):
+    srcdir = os.path.dirname(os.path.dirname(__file__))
+    extensions, extensions_cpp = _test_cython_extension(tmp_path, srcdir)
+    # actually test the cython c-extensions
+    assert extensions.cy_beta(0.5, 0.1) == beta(0.5, 0.1)
+    assert extensions.cy_gamma(0.5 + 1.0j) == gamma(0.5 + 1.0j)
+    assert extensions_cpp.cy_beta(0.5, 0.1) == beta(0.5, 0.1)
+    assert extensions_cpp.cy_gamma(0.5 + 1.0j) == gamma(0.5 + 1.0j)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_iv_ratio.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_iv_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..d814d8d0669559f140b200361da74f38ec65d8db
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_iv_ratio.py
@@ -0,0 +1,136 @@
+# This file contains unit tests for the iv_ratio() function.
+
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy.special._ufuncs import _iv_ratio as iv_ratio  # type: ignore[attr-defined]
+
+
+class TestIvRatio:
+
+    @pytest.mark.parametrize('v,x,r', [
+        (1, 0.3380952380952381, 0.1666773049170313),
+        (1, 0.7083333333333333, 0.33366443586989925),
+        (1, 1.1666666666666667, 0.5023355231537423),
+        (1, 1.8666666666666665, 0.674616572252164),
+        (1, 3.560606060606061, 0.844207659503163),
+        (2.34, 0.7975238095238094, 0.16704903081553285),
+        (2.34, 1.7133333333333334, 0.3360215931268845),
+        (2.34, 2.953333333333333, 0.50681909317803),
+        (2.34, 5.0826666666666656, 0.6755252698800679),
+        (2.34, 10.869696969696973, 0.8379351104498762),
+        (56.789, 19.46575238095238, 0.1667020505391409),
+        (56.789, 42.55008333333333, 0.33353809996933026),
+        (56.789, 75.552, 0.5003932381177826),
+        (56.789, 135.76026666666667, 0.6670528221946127),
+        (56.789, 307.8642424242425, 0.8334999441460798),
+    ])
+    def test_against_reference_values(self, v, x, r):
+        """The reference values are computed using mpmath as follows.
+
+        from mpmath import mp
+        mp.dps = 100
+
+        def iv_ratio_mp(v, x):
+            return mp.besseli(v, x) / mp.besseli(v - 1, x)
+
+        def _sample(n, *, v):
+            '''Return n positive real numbers x such that iv_ratio(v, x) are
+            roughly evenly spaced over (0, 1).  The formula is taken from [1].
+
+            [1] Banerjee A., Dhillon, I. S., Ghosh, J., Sra, S. (2005).
+                "Clustering on the Unit Hypersphere using von Mises-Fisher
+                Distributions."  Journal of Machine Learning Research,
+                6(46):1345-1382.
+            '''
+            r = np.arange(1, n+1) / (n+1)
+            return r * (2*v-r*r) / (1-r*r)
+
+        for v in (1, 2.34, 56.789):
+            xs = _sample(5, v=v)
+            for x in xs:
+                print(f"({v}, {x}, {float(iv_ratio_mp_float(v,x))}),")
+        """
+        assert_allclose(iv_ratio(v, x), r, rtol=4e-16, atol=0)
+
+    @pytest.mark.parametrize('v,x,r', [
+        (1, np.inf, 1),
+        (np.inf, 1, 0),
+    ])
+    def test_inf(self, v, x, r):
+        """If exactly one of v or x is inf and the other is within domain,
+        should return 0 or 1 accordingly.
+
+        Also check that the function
+        never returns -0.0."""
+        assert_equal(iv_ratio(v, x), r)
+
+    @pytest.mark.parametrize('v', [np.nextafter(1, 0), -np.inf, np.nan, np.inf])
+    @pytest.mark.parametrize('x', [-np.finfo(float).smallest_normal,
+                                   -np.finfo(float).smallest_subnormal,
+                                   -np.inf, np.nan, np.inf])
+    def test_nan(self, v, x):
+        """If at least one argument is out of domain, or if v = x = inf,
+        the function should return nan."""
+        assert_equal(iv_ratio(v, x), np.nan)
+
+    @pytest.mark.parametrize('v', [1, np.finfo(float).max, np.inf])
+    def test_zero_x(self, v):
+        """If x is +/-0.0, return x to agree with the limiting behavior."""
+        assert_equal(iv_ratio(v, 0.0), 0.0)
+        assert_equal(iv_ratio(v, -0.0), -0.0)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, np.finfo(float).smallest_normal),
+        (1, np.finfo(float).smallest_subnormal),
+        (1, np.finfo(float).smallest_subnormal*2),
+        (1e20, 123),
+        (np.finfo(float).max, 1),
+        (np.finfo(float).max, np.sqrt(np.finfo(float).max)),
+    ])
+    def test_tiny_x(self, v, x):
+        """If x is much less than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to R ~= x/2v.  Test against this asymptotic expression.
+        """
+        assert_equal(iv_ratio(v, x), (0.5*x)/v)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, 1e16),
+        (1e20, 1e40),
+        (np.sqrt(np.finfo(float).max), np.finfo(float).max),
+    ])
+    def test_huge_x(self, v, x):
+        """If x is much greater than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to R ~= 1.  Test against this asymptotic expression.
+        """
+        assert_equal(iv_ratio(v, x), 1.0)
+
+    @pytest.mark.parametrize('v,x', [
+        (np.finfo(float).max, np.finfo(float).max),
+        (np.finfo(float).max / 3, np.finfo(float).max),
+        (np.finfo(float).max, np.finfo(float).max / 3),
+    ])
+    def test_huge_v_x(self, v, x):
+        """If both x and v are very large, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to R ~= x/(v+sqrt(x**2+v**2).  Test against this asymptotic
+        expression, and in particular that no numerical overflow occurs during
+        intermediate calculations.
+        """
+        t = x / v
+        expected = t / (1 + np.hypot(1, t))
+        assert_allclose(iv_ratio(v, x), expected, rtol=4e-16, atol=0)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_kolmogorov.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_kolmogorov.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc427b0584ab87307c50ffb120fb8bc66a26df5a
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_kolmogorov.py
@@ -0,0 +1,495 @@
+import itertools
+import sys
+import pytest
+
+import numpy as np
+from numpy.testing import assert_
+from scipy.special._testutils import FuncData
+
+from scipy.special import kolmogorov, kolmogi, smirnov, smirnovi
+from scipy.special._ufuncs import (_kolmogc, _kolmogci, _kolmogp,
+                                   _smirnovc, _smirnovci, _smirnovp)
+
+_rtol = 1e-10
+
+class TestSmirnov:
+    def test_nan(self):
+        assert_(np.isnan(smirnov(1, np.nan)))
+
+    def test_basic(self):
+        dataset = [(1, 0.1, 0.9),
+                   (1, 0.875, 0.125),
+                   (2, 0.875, 0.125 * 0.125),
+                   (3, 0.875, 0.125 * 0.125 * 0.125)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0(self):
+        dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_1(self):
+        dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0point5(self):
+        dataset = [(1, 0.5, 0.5),
+                   (2, 0.5, 0.25),
+                   (3, 0.5, 0.166666666667),
+                   (4, 0.5, 0.09375),
+                   (5, 0.5, 0.056),
+                   (6, 0.5, 0.0327932098765),
+                   (7, 0.5, 0.0191958707681),
+                   (8, 0.5, 0.0112953186035),
+                   (9, 0.5, 0.00661933257355),
+                   (10, 0.5, 0.003888705)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_1(self):
+        x = np.linspace(0, 1, 101, endpoint=True)
+        dataset = np.column_stack([[1]*len(x), x, 1-x])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_2(self):
+        x = np.linspace(0.5, 1, 101, endpoint=True)
+        p = np.power(1-x, 2)
+        n = np.array([2] * len(x))
+        dataset = np.column_stack([n, x, p])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_3(self):
+        x = np.linspace(0.7, 1, 31, endpoint=True)
+        p = np.power(1-x, 3)
+        n = np.array([3] * len(x))
+        dataset = np.column_stack([n, x, p])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_large(self):
+        # test for large values of n
+        # Probabilities should go down as n goes up
+        x = 0.4
+        pvals = np.array([smirnov(n, x) for n in range(400, 1100, 20)])
+        dfs = np.diff(pvals)
+        assert_(np.all(dfs <= 0), msg='Not all diffs negative %s' % dfs)
+
+
+class TestSmirnovi:
+    def test_nan(self):
+        assert_(np.isnan(smirnovi(1, np.nan)))
+
+    def test_basic(self):
+        dataset = [(1, 0.4, 0.6),
+                   (1, 0.6, 0.4),
+                   (1, 0.99, 0.01),
+                   (1, 0.01, 0.99),
+                   (2, 0.125 * 0.125, 0.875),
+                   (3, 0.125 * 0.125 * 0.125, 0.875),
+                   (10, 1.0 / 16 ** 10, 1 - 1.0 / 16)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0(self):
+        dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_1(self):
+        dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_1(self):
+        pp = np.linspace(0, 1, 101, endpoint=True)
+        # dataset = np.array([(1, p, 1-p) for p in pp])
+        dataset = np.column_stack([[1]*len(pp), pp, 1-pp])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_2(self):
+        x = np.linspace(0.5, 1, 101, endpoint=True)
+        p = np.power(1-x, 2)
+        n = np.array([2] * len(x))
+        dataset = np.column_stack([n, p, x])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_3(self):
+        x = np.linspace(0.7, 1, 31, endpoint=True)
+        p = np.power(1-x, 3)
+        n = np.array([3] * len(x))
+        dataset = np.column_stack([n, p, x])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_round_trip(self):
+        def _sm_smi(n, p):
+            return smirnov(n, smirnovi(n, p))
+
+        def _smc_smci(n, p):
+            return _smirnovc(n, _smirnovci(n, p))
+
+        dataset = [(1, 0.4, 0.4),
+                   (1, 0.6, 0.6),
+                   (2, 0.875, 0.875),
+                   (3, 0.875, 0.875),
+                   (3, 0.125, 0.125),
+                   (10, 0.999, 0.999),
+                   (10, 0.0001, 0.0001)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            _sm_smi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        FuncData(
+            _smc_smci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0point5(self):
+        dataset = [(1, 0.5, 0.5),
+                   (2, 0.5, 0.366025403784),
+                   (2, 0.25, 0.5),
+                   (3, 0.5, 0.297156508177),
+                   (4, 0.5, 0.255520481121),
+                   (5, 0.5, 0.234559536069),
+                   (6, 0.5, 0.21715965898),
+                   (7, 0.5, 0.202722580034),
+                   (8, 0.5, 0.190621765256),
+                   (9, 0.5, 0.180363501362),
+                   (10, 0.5, 0.17157867006)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+
+class TestSmirnovp:
+    def test_nan(self):
+        assert_(np.isnan(_smirnovp(1, np.nan)))
+
+    def test_basic(self):
+        # Check derivative at endpoints
+        n1_10 = np.arange(1, 10)
+        dataset0 = np.column_stack([n1_10,
+                                    np.full_like(n1_10, 0),
+                                    np.full_like(n1_10, -1)])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+        n2_10 = np.arange(2, 10)
+        dataset1 = np.column_stack([n2_10,
+                                    np.full_like(n2_10, 1.0),
+                                    np.full_like(n2_10, 0)])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneminusoneovern(self):
+        # Check derivative at x=1-1/n
+        n = np.arange(1, 20)
+        x = 1.0/n
+        xm1 = 1-1.0/n
+        pp1 = -n * x**(n-1)
+        pp1 -= (1-np.sign(n-2)**2) * 0.5  # n=2, x=0.5, 1-1/n = 0.5, need to adjust
+        dataset1 = np.column_stack([n, xm1, pp1])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneovertwon(self):
+        # Check derivative at x=1/2n  (Discontinuous at x=1/n, so check at x=1/2n)
+        n = np.arange(1, 20)
+        x = 1.0/2/n
+        pp = -(n*x+1) * (1+x)**(n-2)
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneovern(self):
+        # Check derivative at x=1/n
+        # (Discontinuous at x=1/n, hard to tell if x==1/n, only use n=power of 2)
+        n = 2**np.arange(1, 10)
+        x = 1.0/n
+        pp = -(n*x+1) * (1+x)**(n-2) + 0.5
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    @pytest.mark.xfail(sys.maxsize <= 2**32,
+                       reason="requires 64-bit platform")
+    def test_oneovernclose(self):
+        # Check derivative at x=1/n
+        # (Discontinuous at x=1/n, test on either side: x=1/n +/- 2epsilon)
+        n = np.arange(3, 20)
+
+        x = 1.0/n - 2*np.finfo(float).eps
+        pp = -(n*x+1) * (1+x)**(n-2)
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+        x = 1.0/n + 2*np.finfo(float).eps
+        pp = -(n*x+1) * (1+x)**(n-2) + 1
+        dataset1 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+
+class TestKolmogorov:
+    def test_nan(self):
+        assert_(np.isnan(kolmogorov(np.nan)))
+
+    def test_basic(self):
+        dataset = [(0, 1.0),
+                   (0.5, 0.96394524366487511),
+                   (0.8275735551899077, 0.5000000000000000),
+                   (1, 0.26999967167735456),
+                   (2, 0.00067092525577969533)]
+
+        dataset = np.asarray(dataset)
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_linspace(self):
+        x = np.linspace(0, 2.0, 21)
+        dataset = [1.0000000000000000, 1.0000000000000000, 0.9999999999994950,
+                   0.9999906941986655, 0.9971923267772983, 0.9639452436648751,
+                   0.8642827790506042, 0.7112351950296890, 0.5441424115741981,
+                   0.3927307079406543, 0.2699996716773546, 0.1777181926064012,
+                   0.1122496666707249, 0.0680922218447664, 0.0396818795381144,
+                   0.0222179626165251, 0.0119520432391966, 0.0061774306344441,
+                   0.0030676213475797, 0.0014636048371873, 0.0006709252557797]
+
+        dataset_c = [0.0000000000000000, 6.609305242245699e-53, 5.050407338670114e-13,
+                     9.305801334566668e-06, 0.0028076732227017, 0.0360547563351249,
+                     0.1357172209493958, 0.2887648049703110, 0.4558575884258019,
+                     0.6072692920593457, 0.7300003283226455, 0.8222818073935988,
+                     0.8877503333292751, 0.9319077781552336, 0.9603181204618857,
+                     0.9777820373834749, 0.9880479567608034, 0.9938225693655559,
+                     0.9969323786524203, 0.9985363951628127, 0.9993290747442203]
+
+        dataset = np.column_stack([x, dataset])
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+        dataset_c = np.column_stack([x, dataset_c])
+        FuncData(_kolmogc, dataset_c, (0,), 1, rtol=_rtol).check()
+
+    def test_linspacei(self):
+        p = np.linspace(0, 1.0, 21, endpoint=True)
+        dataset = [np.inf, 1.3580986393225507, 1.2238478702170823,
+                   1.1379465424937751, 1.0727491749396481, 1.0191847202536859,
+                   0.9730633753323726, 0.9320695842357622, 0.8947644549851197,
+                   0.8601710725555463, 0.8275735551899077, 0.7964065373291559,
+                   0.7661855555617682, 0.7364542888171910, 0.7067326523068980,
+                   0.6764476915028201, 0.6448126061663567, 0.6105590999244391,
+                   0.5711732651063401, 0.5196103791686224, 0.0000000000000000]
+
+        dataset_c = [0.0000000000000000, 0.5196103791686225, 0.5711732651063401,
+                     0.6105590999244391, 0.6448126061663567, 0.6764476915028201,
+                     0.7067326523068980, 0.7364542888171910, 0.7661855555617682,
+                     0.7964065373291559, 0.8275735551899077, 0.8601710725555463,
+                     0.8947644549851196, 0.9320695842357622, 0.9730633753323727,
+                     1.0191847202536859, 1.0727491749396481, 1.1379465424937754,
+                     1.2238478702170825, 1.3580986393225509, np.inf]
+
+        dataset = np.column_stack([p[1:], dataset[1:]])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+        dataset_c = np.column_stack([p[:-1], dataset_c[:-1]])
+        FuncData(_kolmogci, dataset_c, (0,), 1, rtol=_rtol).check()
+
+    def test_smallx(self):
+        epsilon = 0.1 ** np.arange(1, 14)
+        x = np.array([0.571173265106, 0.441027698518, 0.374219690278, 0.331392659217,
+                      0.300820537459, 0.277539353999, 0.259023494805, 0.243829561254,
+                      0.231063086389, 0.220135543236, 0.210641372041, 0.202290283658,
+                      0.19487060742])
+
+        dataset = np.column_stack([x, 1-epsilon])
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_round_trip(self):
+        def _ki_k(_x):
+            return kolmogi(kolmogorov(_x))
+
+        def _kci_kc(_x):
+            return _kolmogci(_kolmogc(_x))
+
+        x = np.linspace(0.0, 2.0, 21, endpoint=True)
+        # Exclude 0.1, 0.2.  0.2 almost makes succeeds, but 0.1 has no chance.
+        x02 = x[(x == 0) | (x > 0.21)]
+        dataset02 = np.column_stack([x02, x02])
+        FuncData(_ki_k, dataset02, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([x, x])
+        FuncData(_kci_kc, dataset, (0,), 1, rtol=_rtol).check()
+
+
+class TestKolmogi:
+    def test_nan(self):
+        assert_(np.isnan(kolmogi(np.nan)))
+
+    def test_basic(self):
+        dataset = [(1.0, 0),
+                   (0.96394524366487511, 0.5),
+                   (0.9, 0.571173265106),
+                   (0.5000000000000000, 0.8275735551899077),
+                   (0.26999967167735456, 1),
+                   (0.00067092525577969533, 2)]
+
+        dataset = np.asarray(dataset)
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_smallpcdf(self):
+        epsilon = 0.5 ** np.arange(1, 55, 3)
+        # kolmogi(1-p) == _kolmogci(p) if  1-(1-p) == p, but not necessarily otherwise
+        # Use epsilon s.t. 1-(1-epsilon)) == epsilon,
+        # so can use same x-array for both results
+
+        x = np.array([0.8275735551899077, 0.5345255069097583, 0.4320114038786941,
+                      0.3736868442620478, 0.3345161714909591, 0.3057833329315859,
+                      0.2835052890528936, 0.2655578150208676, 0.2506869966107999,
+                      0.2380971058736669, 0.2272549289962079, 0.2177876361600040,
+                      0.2094254686862041, 0.2019676748836232, 0.1952612948137504,
+                      0.1891874239646641, 0.1836520225050326, 0.1785795904846466])
+
+        dataset = np.column_stack([1-epsilon, x])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([epsilon, x])
+        FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_smallpsf(self):
+        epsilon = 0.5 ** np.arange(1, 55, 3)
+        # kolmogi(p) == _kolmogci(1-p) if  1-(1-p) == p, but not necessarily otherwise
+        # Use epsilon s.t. 1-(1-epsilon)) == epsilon,
+        # so can use same x-array for both results
+
+        x = np.array([0.8275735551899077, 1.3163786275161036, 1.6651092133663343,
+                      1.9525136345289607, 2.2027324540033235, 2.4272929437460848,
+                      2.6327688477341593, 2.8233300509220260, 3.0018183401530627,
+                      3.1702735084088891, 3.3302184446307912, 3.4828258153113318,
+                      3.6290214150152051, 3.7695513262825959, 3.9050272690877326,
+                      4.0359582187082550, 4.1627730557884890, 4.2858371743264527])
+
+        dataset = np.column_stack([epsilon, x])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([1-epsilon, x])
+        FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_round_trip(self):
+        def _k_ki(_p):
+            return kolmogorov(kolmogi(_p))
+
+        p = np.linspace(0.1, 1.0, 10, endpoint=True)
+        dataset = np.column_stack([p, p])
+        FuncData(_k_ki, dataset, (0,), 1, rtol=_rtol).check()
+
+
+class TestKolmogp:
+    def test_nan(self):
+        assert_(np.isnan(_kolmogp(np.nan)))
+
+    def test_basic(self):
+        dataset = [(0.000000, -0.0),
+                   (0.200000, -1.532420541338916e-10),
+                   (0.400000, -0.1012254419260496),
+                   (0.600000, -1.324123244249925),
+                   (0.800000, -1.627024345636592),
+                   (1.000000, -1.071948558356941),
+                   (1.200000, -0.538512430720529),
+                   (1.400000, -0.2222133182429472),
+                   (1.600000, -0.07649302775520538),
+                   (1.800000, -0.02208687346347873),
+                   (2.000000, -0.005367402045629683)]
+
+        dataset = np.asarray(dataset)
+        FuncData(_kolmogp, dataset, (0,), 1, rtol=_rtol).check()
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_owens_t.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_owens_t.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d15aead25302023c5f07d8392c0931995764ced
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_owens_t.py
@@ -0,0 +1,53 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+
+import scipy.special as sc
+
+
+def test_symmetries():
+    np.random.seed(1234)
+    a, h = np.random.rand(100), np.random.rand(100)
+    assert_equal(sc.owens_t(h, a), sc.owens_t(-h, a))
+    assert_equal(sc.owens_t(h, a), -sc.owens_t(h, -a))
+
+
+def test_special_cases():
+    assert_equal(sc.owens_t(5, 0), 0)
+    assert_allclose(sc.owens_t(0, 5), 0.5*np.arctan(5)/np.pi,
+                    rtol=5e-14)
+    # Target value is 0.5*Phi(5)*(1 - Phi(5)) for Phi the CDF of the
+    # standard normal distribution
+    assert_allclose(sc.owens_t(5, 1), 1.4332574485503512543e-07,
+                    rtol=5e-14)
+
+
+def test_nans():
+    assert_equal(sc.owens_t(20, np.nan), np.nan)
+    assert_equal(sc.owens_t(np.nan, 20), np.nan)
+    assert_equal(sc.owens_t(np.nan, np.nan), np.nan)
+
+
+def test_infs():
+    h, a = 0, np.inf
+    # T(0, a) = 1/2π * arctan(a)
+    res = 1/(2*np.pi) * np.arctan(a)
+    assert_allclose(sc.owens_t(h, a), res, rtol=5e-14)
+    assert_allclose(sc.owens_t(h, -a), -res, rtol=5e-14)
+
+    h = 1
+    # Refer Owens T function definition in Wikipedia
+    # https://en.wikipedia.org/wiki/Owen%27s_T_function
+    # Value approximated through Numerical Integration
+    # using scipy.integrate.quad
+    # quad(lambda x: 1/(2*pi)*(exp(-0.5*(1*1)*(1+x*x))/(1+x*x)), 0, inf)
+    res = 0.07932762696572854
+    assert_allclose(sc.owens_t(h, np.inf), res, rtol=5e-14)
+    assert_allclose(sc.owens_t(h, -np.inf), -res, rtol=5e-14)
+
+    assert_equal(sc.owens_t(np.inf, 1), 0)
+    assert_equal(sc.owens_t(-np.inf, 1), 0)
+
+    assert_equal(sc.owens_t(np.inf, np.inf), 0)
+    assert_equal(sc.owens_t(-np.inf, np.inf), 0)
+    assert_equal(sc.owens_t(np.inf, -np.inf), -0.0)
+    assert_equal(sc.owens_t(-np.inf, -np.inf), -0.0)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_pcf.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_pcf.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8c42aa688081fb58f79ad2c8ea932d03b33523b
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_pcf.py
@@ -0,0 +1,24 @@
+"""Tests for parabolic cylinder functions.
+
+"""
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import scipy.special as sc
+
+
+def test_pbwa_segfault():
+    # Regression test for https://github.com/scipy/scipy/issues/6208.
+    #
+    # Data generated by mpmath.
+    #
+    w = 1.02276567211316867161
+    wp = -0.48887053372346189882
+    assert_allclose(sc.pbwa(0, 0), (w, wp), rtol=1e-13, atol=0)
+
+
+def test_pbwa_nan():
+    # Check that NaN's are returned outside of the range in which the
+    # implementation is accurate.
+    pts = [(-6, -6), (-6, 6), (6, -6), (6, 6)]
+    for p in pts:
+        assert_equal(sc.pbwa(*p), (np.nan, np.nan))
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_powm1.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_powm1.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d809963f64ddaedf6b59de80dcd5f7ca8fa18a9
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_powm1.py
@@ -0,0 +1,65 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose
+from scipy.special import powm1
+
+
+# Expected values were computed with mpmath, e.g.
+#
+#   >>> import mpmath
+#   >>> mpmath.np.dps = 200
+#   >>> print(float(mpmath.powm1(2.0, 1e-7))
+#   6.931472045825965e-08
+#
+powm1_test_cases = [
+    (1.25, 0.75, 0.18217701125396976, 1e-15),
+    (2.0, 1e-7, 6.931472045825965e-08, 1e-15),
+    (25.0, 5e-11, 1.6094379125636148e-10, 1e-15),
+    (0.99996, 0.75, -3.0000150002530058e-05, 1e-15),
+    (0.9999999999990905, 20, -1.81898940353014e-11, 1e-15),
+    (-1.25, 751.0, -6.017550852453444e+72, 2e-15)
+]
+
+
+@pytest.mark.parametrize('x, y, expected, rtol', powm1_test_cases)
+def test_powm1(x, y, expected, rtol):
+    p = powm1(x, y)
+    assert_allclose(p, expected, rtol=rtol)
+
+
+@pytest.mark.parametrize('x, y, expected',
+                         [(0.0, 0.0, 0.0),
+                          (0.0, -1.5, np.inf),
+                          (0.0, 1.75, -1.0),
+                          (-1.5, 2.0, 1.25),
+                          (-1.5, 3.0, -4.375),
+                          (np.nan, 0.0, 0.0),
+                          (1.0, np.nan, 0.0),
+                          (1.0, np.inf, 0.0),
+                          (1.0, -np.inf, 0.0),
+                          (np.inf, 7.5, np.inf),
+                          (np.inf, -7.5, -1.0),
+                          (3.25, np.inf, np.inf),
+                          (np.inf, np.inf, np.inf),
+                          (np.inf, -np.inf, -1.0),
+                          (np.inf, 0.0, 0.0),
+                          (-np.inf, 0.0, 0.0),
+                          (-np.inf, 2.0, np.inf),
+                          (-np.inf, 3.0, -np.inf),
+                          (-1.0, float(2**53 - 1), -2.0)])
+def test_powm1_exact_cases(x, y, expected):
+    # Test cases where we have an exact expected value.
+    p = powm1(x, y)
+    assert p == expected
+
+
+@pytest.mark.parametrize('x, y',
+                         [(-1.25, 751.03),
+                          (-1.25, np.inf),
+                          (np.nan, np.nan),
+                          (-np.inf, -np.inf),
+                          (-np.inf, 2.5)])
+def test_powm1_return_nan(x, y):
+    # Test cases where the expected return value is nan.
+    p = powm1(x, y)
+    assert np.isnan(p)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_precompute_utils.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_precompute_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..89616b92329691ca76039fe11a7e08f7f3db1150
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_precompute_utils.py
@@ -0,0 +1,36 @@
+import pytest
+
+from scipy.special._testutils import MissingModule, check_version
+from scipy.special._mptestutils import mp_assert_allclose
+from scipy.special._precompute.utils import lagrange_inversion
+
+try:
+    import sympy
+except ImportError:
+    sympy = MissingModule('sympy')
+
+try:
+    import mpmath as mp
+except ImportError:
+    mp = MissingModule('mpmath')
+
+
+@pytest.mark.slow
+@check_version(sympy, '0.7')
+@check_version(mp, '0.19')
+class TestInversion:
+    @pytest.mark.xfail_on_32bit("rtol only 2e-9, see gh-6938")
+    def test_log(self):
+        with mp.workdps(30):
+            logcoeffs = mp.taylor(lambda x: mp.log(1 + x), 0, 10)
+            expcoeffs = mp.taylor(lambda x: mp.exp(x) - 1, 0, 10)
+            invlogcoeffs = lagrange_inversion(logcoeffs)
+            mp_assert_allclose(invlogcoeffs, expcoeffs)
+
+    @pytest.mark.xfail_on_32bit("rtol only 1e-15, see gh-6938")
+    def test_sin(self):
+        with mp.workdps(30):
+            sincoeffs = mp.taylor(mp.sin, 0, 10)
+            asincoeffs = mp.taylor(mp.asin, 0, 10)
+            invsincoeffs = lagrange_inversion(sincoeffs)
+            mp_assert_allclose(invsincoeffs, asincoeffs, atol=1e-30)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_specfun.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_specfun.py
new file mode 100644
index 0000000000000000000000000000000000000000..d096f60dd65380a9847d5d5beb7380523445fc5c
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_specfun.py
@@ -0,0 +1,40 @@
+"""
+Various made-up tests to hit different branches of the code in specfun.c
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose
+from scipy import special
+
+
+def test_cva2_cv0_branches():
+    res, resp = special.mathieu_cem([40, 129], [13, 14], [30, 45])
+    assert_allclose(res, np.array([-0.3741211, 0.74441928]))
+    assert_allclose(resp, np.array([-37.02872758, -86.13549877]))
+
+    res, resp = special.mathieu_sem([40, 129], [13, 14], [30, 45])
+    assert_allclose(res, np.array([0.92955551, 0.66771207]))
+    assert_allclose(resp, np.array([-14.91073448, 96.02954185]))
+
+
+def test_chgm_branches():
+    res = special.eval_genlaguerre(-3.2, 3, 2.5)
+    assert_allclose(res, -0.7077721935779854)
+
+
+def test_hygfz_branches():
+    """(z == 1.0) && (c-a-b > 0.0)"""
+    res = special.hyp2f1(1.5, 2.5, 4.5, 1.+0.j)
+    assert_allclose(res, 10.30835089459151+0j)
+    """(cabs(z+1) < eps) && (fabs(c-a+b - 1.0) < eps)"""
+    res = special.hyp2f1(5+5e-16, 2, 2, -1.0 + 5e-16j)
+    assert_allclose(res, 0.031249999999999986+3.9062499999999994e-17j)
+
+
+def test_pro_rad1():
+    # https://github.com/scipy/scipy/issues/21058
+    # Reference values taken from WolframAlpha
+    # SpheroidalS1(1, 1, 30, 1.1)
+    # SpheroidalS1Prime(1, 1, 30, 1.1)
+    res = special.pro_rad1(1, 1, 30, 1.1)
+    assert_allclose(res, (0.009657872296166435, 3.253369651472877), rtol=2e-5)
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_spence.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_spence.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbb26ac281dff81ea71b30318731065fe5a78f94
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_spence.py
@@ -0,0 +1,32 @@
+import numpy as np
+from numpy import sqrt, log, pi
+from scipy.special._testutils import FuncData
+from scipy.special import spence
+
+
+def test_consistency():
+    # Make sure the implementation of spence for real arguments
+    # agrees with the implementation of spence for imaginary arguments.
+
+    x = np.logspace(-30, 300, 200)
+    dataset = np.vstack((x + 0j, spence(x))).T
+    FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
+
+
+def test_special_points():
+    # Check against known values of Spence's function.
+
+    phi = (1 + sqrt(5))/2
+    dataset = [(1, 0),
+               (2, -pi**2/12),
+               (0.5, pi**2/12 - log(2)**2/2),
+               (0, pi**2/6),
+               (-1, pi**2/4 - 1j*pi*log(2)),
+               ((-1 + sqrt(5))/2, pi**2/15 - log(phi)**2),
+               ((3 - sqrt(5))/2, pi**2/10 - log(phi)**2),
+               (phi, -pi**2/15 + log(phi)**2/2),
+               # Corrected from Zagier, "The Dilogarithm Function"
+               ((3 + sqrt(5))/2, -pi**2/10 - log(phi)**2)]
+
+    dataset = np.asarray(dataset)
+    FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
diff --git a/parrot/lib/python3.10/site-packages/scipy/special/tests/test_ufunc_signatures.py b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_ufunc_signatures.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bc3ffae15ab4620c4e752df166721825cb7449c
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/special/tests/test_ufunc_signatures.py
@@ -0,0 +1,46 @@
+"""Test that all ufuncs have float32-preserving signatures.
+
+This was once guaranteed through the code generation script for
+generating ufuncs, `scipy/special/_generate_pyx.py`. Starting with
+gh-20260, SciPy developers have begun moving to generate ufuncs
+through direct use of the NumPy C API (through C++). Existence of
+float32 preserving signatures must now be tested since it is no
+longer guaranteed.
+"""
+
+import numpy as np
+import pytest
+import scipy.special._ufuncs
+import scipy.special._gufuncs
+
+_ufuncs = []
+for funcname in dir(scipy.special._ufuncs):
+    _ufuncs.append(getattr(scipy.special._ufuncs, funcname))
+for funcname in dir(scipy.special._gufuncs):
+    _ufuncs.append(getattr(scipy.special._gufuncs, funcname))
+
+# Not all module members are actually ufuncs
+_ufuncs = [func for func in _ufuncs if isinstance(func, np.ufunc)]
+
+@pytest.mark.parametrize("ufunc", _ufuncs)
+def test_ufunc_signatures(ufunc):
+
+    # From _generate_pyx.py
+    # "Don't add float32 versions of ufuncs with integer arguments, as this
+    # can lead to incorrect dtype selection if the integer arguments are
+    # arrays, but float arguments are scalars.
+    # For instance sph_harm(0,[0],0,0).dtype == complex64
+    # This may be a NumPy bug, but we need to work around it.
+    # cf. gh-4895, https://github.com/numpy/numpy/issues/5895"
+    types = set(sig for sig in ufunc.types
+                if not ("l" in sig or "i" in sig or "q" in sig or "p" in sig))
+
+    # Generate the full expanded set of signatures which should exist. There
+    # should be matching float and double versions of any existing signature.
+    expanded_types = set()
+    for sig in types:
+        expanded_types.update(
+            [sig.replace("d", "f").replace("D", "F"),
+             sig.replace("f", "d").replace("F", "D")]
+        )
+    assert types == expanded_types
diff --git a/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_distn_infrastructure.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_distn_infrastructure.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d132871135e72f46172326d4d316c30acc89122e
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_distn_infrastructure.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43c4accafa80076ab0261a902011d20b792a10db7e2ea1faba4f83d66254dce9
+size 124305
diff --git a/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_stats_py.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_stats_py.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c3e6b95e9bf01f8224b5289cd7b530be5b15f607
--- /dev/null
+++ b/parrot/lib/python3.10/site-packages/scipy/stats/__pycache__/_stats_py.cpython-310.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:539512de2d73fa90cbfb11dd98f04856c6b14a3ff710712e8ec26f41224d97db
+size 368146
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_per_channel_affine_quantized_ops.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_per_channel_affine_quantized_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7930f0898c0521da266ebbe79d0ec5cd23fb5bbd
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_empty_per_channel_affine_quantized_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _empty_per_channel_affine_quantized {
+  using schema = at::Tensor (c10::SymIntArrayRef, const at::Tensor &, const at::Tensor &, int64_t, c10::optional<at::ScalarType>, c10::optional<at::Layout>, c10::optional<at::Device>, c10::optional<bool>, c10::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_empty_per_channel_affine_quantized")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor")
+  static at::Tensor call(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API _empty_per_channel_affine_quantized_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, const at::Tensor &, const at::Tensor &, int64_t, c10::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_empty_per_channel_affine_quantized")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_floor_compositeexplicitautograd_dispatch.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_floor_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b87f4ea671785326b61c63e983bf4357c7fc8e5b
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_floor_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void _foreach_floor_out(at::TensorList out, at::TensorList self);
+TORCH_API void _foreach_floor_outf(at::TensorList self, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_lstm_mps_ops.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_lstm_mps_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff2a91c12644e69fd44ab8f252c262f57610d561
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_lstm_mps_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _lstm_mps {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> (const at::Tensor &, at::TensorList, at::TensorList, bool, int64_t, double, bool, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_lstm_mps")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor)")
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> call(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first);
+};
+
+struct TORCH_API _lstm_mps_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> (const at::Tensor &, at::TensorList, at::TensorList, bool, int64_t, double, bool, bool, bool, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_lstm_mps")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!))")
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> call(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5);
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5);
+};
+
+}} // namespace at::_ops
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_strides_compositeexplicitautograd_dispatch.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_strides_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..406098009966baf37efea23e78407119aacdd936
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_nested_tensor_strides_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _nested_tensor_strides_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & _nested_tensor_strides_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_prelu_kernel_ops.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_prelu_kernel_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5c48829c5b9e271af872659253938357dcbb2b9
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_prelu_kernel_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _prelu_kernel {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_prelu_kernel")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_prelu_kernel(Tensor self, Tensor weight) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & weight);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight);
+};
+
+}} // namespace at::_ops
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_flash_attention_backward.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_flash_attention_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7130019366087af497a9d1dde6b4b50552b48ee
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_flash_attention_backward.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+
+
+
+#include <ATen/ops/_scaled_dot_product_flash_attention_backward_ops.h>
+
+namespace at {
+
+
+// aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional<double> scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional<double> scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
+  }
+}
+
+// aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward_symint(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional<double> scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  ::std::tuple<at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention_backward(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional<double> scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale);
+  }
+}
+
+}
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_to_dense_native.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_to_dense_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..62f812925f15c4ff5556f1bb3f7eb648df15a61e
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_to_dense_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & _to_dense_out(const at::Tensor & self, c10::optional<at::ScalarType> dtype, c10::optional<bool> masked_grad, at::Tensor & out);
+TORCH_API at::Tensor sparse_to_dense(const at::Tensor & self, c10::optional<at::ScalarType> dtype=c10::nullopt, c10::optional<bool> masked_grad=c10::nullopt);
+TORCH_API at::Tensor sparse_compressed_to_dense(const at::Tensor & self, c10::optional<at::ScalarType> dtype=c10::nullopt, c10::optional<bool> masked_grad=c10::nullopt);
+TORCH_API at::Tensor mkldnn_to_dense(const at::Tensor & self, c10::optional<at::ScalarType> dtype=c10::nullopt, c10::optional<bool> masked_grad=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/as_strided_native.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/as_strided_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7503e6723adf9f6cd23f8dac5d9cdda29b2be6a1
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/as_strided_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor as_strided_tensorimpl(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<int64_t> storage_offset=c10::nullopt);
+TORCH_API at::Tensor as_strided_tensorimpl_meta_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset=c10::nullopt);
+TORCH_API at::Tensor as_strided_qtensorimpl(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional<int64_t> storage_offset=c10::nullopt);
+TORCH_API const at::Tensor & as_strided__symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional<c10::SymInt> storage_offset=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/fill_diagonal_ops.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/fill_diagonal_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f7930cf8f1730d785d335f5e4ca2bd9aec9c56dd
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/fill_diagonal_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API fill_diagonal_ {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fill_diagonal_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & fill_value, bool wrap);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & fill_value, bool wrap);
+};
+
+}} // namespace at::_ops
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/index_put_compositeexplicitautograd_dispatch.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/index_put_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6fcd9d57a143ee5564ce762bcdb7bfb3d24f8c52
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/index_put_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor index_put(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false);
+TORCH_API at::Tensor & index_put_out(at::Tensor & out, const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false);
+TORCH_API at::Tensor & index_put_outf(const at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate, at::Tensor & out);
+TORCH_API at::Tensor & index_put_(at::Tensor & self, const c10::List<c10::optional<at::Tensor>> & indices, const at::Tensor & values, bool accumulate=false);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_compositeexplicitautograd_dispatch.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe65643c17f8337f74995bc6491175664d66c6f2
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/mps_convolution_backward_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_symint_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> mps_convolution_backward_symint_outf(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array<bool,3> output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/result_type_native.h b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/result_type_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..65afcffbbbb12c753476be7708674d2aa133cb1f
--- /dev/null
+++ b/videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/result_type_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::ScalarType result_type(const at::Tensor & tensor, const at::Tensor & other);
+TORCH_API at::ScalarType result_type(const at::Tensor & tensor, const at::Scalar & other);
+TORCH_API at::ScalarType result_type(const at::Scalar & scalar, const at::Tensor & tensor);
+TORCH_API at::ScalarType result_type(const at::Scalar & scalar1, const at::Scalar & scalar2);
+} // namespace native
+} // namespace at
diff --git a/vllm/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc b/vllm/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..df90b5e7a13fbff858b95e5c97d0a29025233768
Binary files /dev/null and b/vllm/lib/python3.10/site-packages/triton/compiler/__pycache__/errors.cpython-310.pyc differ
diff --git a/vllm/lib/python3.10/site-packages/triton/compiler/code_generator.py b/vllm/lib/python3.10/site-packages/triton/compiler/code_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..6903052ca21966fcf4edb29d8429458457c51051
--- /dev/null
+++ b/vllm/lib/python3.10/site-packages/triton/compiler/code_generator.py
@@ -0,0 +1,1302 @@
+import ast
+import inspect
+import re
+import sys
+import warnings
+import os
+import textwrap
+from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
+from .. import language
+from .._C.libtriton import ir
+from ..language import constexpr, tensor, str_to_ty
+from ..runtime.jit import _normalize_ty
+# ideally we wouldn't need any runtime component
+from ..runtime import JITFunction
+from .errors import (CompilationError, CompileTimeAssertionFailure, UnsupportedLanguageConstruct)
+from types import ModuleType
+
+
+def mangle_ty(ty):
+    if ty.is_ptr():
+        return 'P' + mangle_ty(ty.element_ty)
+    if ty.is_int():
+        SIGNED = language.dtype.SIGNEDNESS.SIGNED
+        prefix = 'i' if ty.int_signedness == SIGNED else 'u'
+        return prefix + str(ty.int_bitwidth)
+    if ty.is_floating():
+        return str(ty)
+    if ty.is_block():
+        elt = mangle_ty(ty.scalar)
+        shape = '_'.join(map(str, ty.shape))
+        return f'{elt}S{shape}S'
+    if ty.is_void():
+        return 'V'
+    assert False, "Unsupported type"
+
+
+def mangle_fn(name, arg_tys, constants):
+    # doesn't mangle ret type, which must be a function of arg tys
+    mangled_arg_names = '_'.join([mangle_ty(ty) for ty in arg_tys])
+    mangled_constants = '_'.join([f'{i}c{repr(constants[i])}' for i in sorted(constants)])
+    mangled_constants = mangled_constants.replace('.', '_d_')
+    mangled_constants = mangled_constants.replace("'", '_sq_')
+    # [ and ] are not allowed in LLVM identifiers
+    mangled_constants = mangled_constants.replace('[', '_').replace(']', '_')
+    ret = f'{name}__{mangled_arg_names}__{mangled_constants}'
+    return ret
+
+
+def _is_triton_tensor(o: Any) -> bool:
+    return isinstance(o, tensor)
+
+
+def _is_constexpr(o: Any) -> bool:
+    return isinstance(o, constexpr)
+
+
+def _is_triton_scalar(o: Any) -> bool:
+    return _is_triton_tensor(o) and (not o.type.is_block() or o.type.numel == 1)
+
+
+def _is_list_like(o: Any) -> bool:
+    return isinstance(o, (list, tuple))
+
+
+def _unwrap_if_constexpr(o: Any):
+    return o.value if isinstance(o, constexpr) else o
+
+
+def _check_fn_args(node, fn, args):
+    if fn.noinline:
+        for idx, arg in enumerate(args):
+            if not _is_constexpr(arg) and not _is_triton_scalar(arg):
+                raise UnsupportedLanguageConstruct(
+                    fn.src, node,
+                    f'Function {fn.__name__} is marked noinline, but was called with non-scalar argument {fn.arg_names[idx]}:{arg}'
+                )
+
+
+def _get_fn_file_line(fn):
+    base_fn = fn
+    while not isinstance(base_fn, JITFunction):
+        base_fn = base_fn.fn
+    file_name = base_fn.fn.__code__.co_filename
+    lines, begin_line = inspect.getsourcelines(base_fn.fn)
+    # Match the following pattern:
+    # @triton.autotune(...) <- foo.__code__.co_firstlineno
+    # @triton.heuristics(...)
+    # @triton.jit
+    # def foo(...): <- this line is the first line
+    for idx, line in enumerate(lines):
+        if line.strip().startswith("def "):
+            begin_line += idx
+            break
+    return file_name, begin_line
+
+
+_condition_types = {bool, int, type(None)}  # Python types accepted for conditionals inside kernels
+
+
+class enter_sub_region:
+
+    def __init__(self, generator):
+        self.generator = generator
+
+    def __enter__(self):
+        # record lscope & local_defs in the parent scope
+        self.liveins = self.generator.lscope.copy()
+        self.prev_defs = self.generator.local_defs.copy()
+        self.generator.local_defs = {}
+        self.insert_block = self.generator.builder.get_insertion_block()
+        self.insert_point = self.generator.builder.get_insertion_point()
+        return self.liveins, self.insert_block
+
+    def __exit__(self, *args, **kwargs):
+        self.generator.builder.restore_insertion_point(self.insert_point)
+        self.generator.lscope = self.liveins
+        self.generator.local_defs = self.prev_defs
+
+
+# Check if the given syntax node has an "early" return
+class ContainsReturnChecker(ast.NodeVisitor):
+
+    def __init__(self, gscope):
+        self.gscope = gscope
+
+    def _visit_stmts(self, body) -> bool:
+        for s in body:
+            if self.visit(s):
+                return True
+        return False
+
+    def _visit_function(self, fn) -> bool:
+        # Currently we only support JITFunctions defined in the global scope
+        if isinstance(fn, JITFunction) and not fn.noinline:
+            fn_node = fn.parse()
+            return ContainsReturnChecker(self.gscope).visit(fn_node)
+        return False
+
+    def generic_visit(self, node) -> bool:
+        ret = False
+        for _, value in ast.iter_fields(node):
+            if isinstance(value, list):
+                for item in value:
+                    if isinstance(item, ast.AST):
+                        ret = ret or self.visit(item)
+            elif isinstance(value, ast.AST):
+                ret = ret or self.visit(value)
+        return ret
+
+    def visit_Attribute(self, node: ast.Attribute) -> bool:
+        # If the left part is a name, it's possible that
+        # we call triton native function or a jit function from another module.
+        # If the left part is not a name, it must return a tensor or a constexpr
+        # whose methods do not contain return statements
+        # e.g., (tl.load(x)).to(y)
+        # So we only check if the expressions within value have return or not
+        if isinstance(node.value, ast.Name):
+            if node.value.id in self.gscope:
+                value = self.gscope[node.value.id]
+                fn = getattr(value, node.attr)
+                return self._visit_function(fn)
+            return False
+        return self.visit(node.value)
+
+    def visit_Name(self, node: ast.Name) -> bool:
+        if type(node.ctx) == ast.Store:
+            return False
+        if node.id in self.gscope:
+            fn = self.gscope[node.id]
+            return self._visit_function(fn)
+        return False
+
+    def visit_Return(self, node: ast.Return) -> bool:
+        return True
+
+    def visit_Assign(self, node: ast.Assign) -> bool:
+        # There couldn't be an early return
+        # x = ...
+        return False
+
+    def visit_AugAssign(self, node: ast.AugAssign) -> bool:
+        # There couldn't be an early return
+        # x += ...
+        return False
+
+    def visit_Module(self, node: ast.Module) -> bool:
+        return self._visit_stmts(node.body)
+
+    def visit_FunctionDef(self, node: ast.FunctionDef) -> bool:
+        return self._visit_stmts(node.body)
+
+    def visit_If(self, node: ast.If) -> bool:
+        # TODO: optimize the following case in which we actually don't have
+        # a return when static_cond is false:
+        # if dynamic_cond
+        #   if static_cond
+        #     func_with_return
+        #   else
+        #     func_without_return
+        ret = self._visit_stmts(node.body)
+        if node.orelse:
+            ret = ret or self._visit_stmts(node.orelse)
+        return ret
+
+    def visit_IfExp(self, node: ast.IfExp) -> bool:
+        return self.visit(node.body) or self.visit(node.orelse)
+
+    def visit_Call(self, node: ast.Call) -> bool:
+        return self.visit(node.func)
+
+
+class CodeGenerator(ast.NodeVisitor):
+
+    def __init__(self, context, prototype, gscope, attributes, constants, function_name, jit_fn: JITFunction, options,
+                 codegen_fns, debug=None, module=None, is_kernel=False, function_types: Optional[Dict] = None,
+                 noinline=False, file_name: Optional[str] = None, begin_line=0):
+        self.context = context
+        self.builder = ir.builder(context)
+        self.file_name = file_name
+        # node.lineno starts from 1, so we need to subtract 1
+        self.begin_line = begin_line - 1
+        self.builder.set_loc(file_name, begin_line, 0)
+        self.builder.options = options
+        # dict of functions provided by the backend. Below are the list of possible functions:
+        # Convert custom types not natively supported on HW.
+        # convert_custom_types(intput_tensor, dtype, fp_downcast_rounding=None, _builder=None)
+        self.builder.codegen_fns = codegen_fns
+        self.module = self.builder.create_module() if module is None else module
+        self.function_ret_types = {} if function_types is None else function_types
+        self.prototype = prototype
+        self.gscope = gscope
+        self.lscope = dict()
+        self.attributes = attributes
+        self.constants = constants
+        self.jit_fn = jit_fn
+        self.function_name = function_name
+        self.is_kernel = is_kernel
+        self.cur_node = None
+        self.debug = options.debug if debug is None else debug
+        self.noinline = noinline
+        self.scf_stack = []
+        self.ret_type = None
+        # SSA-construction
+        # name => language.tensor
+        self.local_defs: Dict[str, tensor] = {}
+        self.dereference_name: Callable[[str], Any] = self._define_name_lookup()
+        self.fn = None
+        # Are we currently visiting an ast.arg's default value?  These have some
+        # special handling.
+        self.visiting_arg_default_value = False
+
+    builtin_namespace: Dict[str, Any] = {_.__name__: _ for _ in (len, list, range, float, int, isinstance, getattr)}
+    builtin_namespace.update((
+        ('print', language.core.device_print),
+        ('min', language.minimum),
+        ('max', language.maximum),
+    ))
+
+    def _unsupported(self, node, message):
+        return UnsupportedLanguageConstruct(self.jit_fn.src, node, message)
+
+    def _is_constexpr_global(self, name):
+        absent_marker = object()
+        val = self.gscope.get(name, absent_marker)
+        if val is absent_marker:
+            return False
+
+        if _is_constexpr(val):
+            return True
+
+        if a := self.gscope.get("__annotations__", {}).get(name):
+            return _normalize_ty(a) == "constexpr"
+
+        return False
+
+    def _define_name_lookup(self):
+
+        def local_lookup(name: str, absent):
+            # this needs to be re-fetched from `self` every time, because it gets switched occasionally
+            return self.lscope.get(name, absent)
+
+        def global_lookup(name: str, absent):
+            val = self.gscope.get(name, absent)
+            # The high-level rule is that only constexpr globals are allowed.
+            # But actually a bunch of other things, such as module imports, are
+            # technically Python globals. We have to allow these too!
+            if (val is absent  #
+                    or name in self.builtin_namespace  #
+                    or type(val) == ModuleType  #
+                    or isinstance(val, JITFunction)  #
+                    or getattr(val, "__triton_builtin__", False)  #
+                    or getattr(val, "__module__", "").startswith("triton.language")  #
+                    or isinstance(val, language.dtype)  #
+                    or self._is_constexpr_global(name)  #
+                    # Allow accesses to globals while visiting an ast.arg
+                    # because you should be able to do
+                    #   @triton.jit def fn(x: tl.constexpr = GLOBAL): ...
+                    or self.visiting_arg_default_value  #
+                    or os.environ.get("TRITON_ALLOW_NON_CONSTEXPR_GLOBALS", "0") == "1"):
+                return val
+            raise NameError(
+                textwrap.dedent(f"""\
+                Cannot access global variable {name} from within @jit'ed
+                function. Triton kernels can only access global variables that
+                are annotated as constexpr (`x: triton.language.constexpr = 42`
+                or `x = triton.language.constexpr(42)`).  Alternatively, set the
+                envvar TRITON_ALLOW_NON_CONSTEXPR_GLOBALS=1, but we do not
+                promise to support this forever.""").replace("\n", " "))
+
+        absent_marker = object()
+
+        def name_lookup(name: str) -> Any:
+            absent = absent_marker
+            for lookup_function in local_lookup, global_lookup, self.builtin_namespace.get:
+                value = lookup_function(name, absent)
+                if value is not absent:
+                    return value
+            raise NameError(f'{name} is not defined')
+
+        return name_lookup
+
+    def set_value(self, name: str, value: Union[tensor, constexpr]) -> None:
+        ''' This function:
+            called by visit_Assign() & visit_FunctionDef() to store left value (lvalue)
+        1. record local defined name (FIXME: should consider control flow)
+        2. store tensor in self.lvalue
+        '''
+        self.lscope[name] = value
+        self.local_defs[name] = value
+
+    def _get_insertion_point_and_loc(self):
+        # XXX: this is a hack to get the location of the insertion point.
+        # The insertion point's location could be invalid sometimes,
+        # so we need to explicitly set the location
+        loc = self.builder.get_loc()
+        ip = self.builder.get_insertion_point()
+        return ip, loc
+
+    def _set_insertion_point_and_loc(self, ip, loc):
+        self.builder.restore_insertion_point(ip)
+        self.builder.set_loc(loc)
+
+    #
+    # AST visitor
+    #
+    def visit_compound_statement(self, stmts):
+        # Ensure that stmts is iterable
+        if not _is_list_like(stmts):
+            stmts = [stmts]
+        for stmt in stmts:
+            self.visit(stmt)
+
+            # Stop parsing as soon as we hit a `return` statement; everything
+            # after this is dead code.
+            if isinstance(stmt, ast.Return):
+                break
+
+    def visit_Module(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+
+    def visit_List(self, node):
+        ctx = self.visit(node.ctx)
+        assert ctx is None
+        elts = [self.visit(elt) for elt in node.elts]
+        return elts
+
+    # By design, only non-kernel functions can return
+    def visit_Return(self, node):
+        ret_value = self.visit(node.value)
+        # ret_block = self.builder.create_block()
+        # post_ret_block = self.builder.create_block()
+        # self.builder.create_branch(ret_block)
+        # self.builder.set_insertion_point_to_end(ret_block)
+        if ret_value is None:
+            self.builder.ret([])
+            ret_ty = language.void
+        elif isinstance(ret_value, tuple):
+            ret_values = [language.core._to_tensor(v, self.builder) for v in ret_value]
+            ret_types = [v.type for v in ret_values]
+            self.builder.ret([v.handle for v in ret_values])
+            ret_ty = tuple(ret_types)
+        else:
+            ret = language.core._to_tensor(ret_value, self.builder)
+            self.builder.ret([ret.handle])
+            ret_ty = ret.type
+        # self.builder.create_branch(post_ret_block)
+        # self.builder.set_insertion_point_to_end(post_ret_block)
+
+        if self.ret_type is None:
+            self.ret_type = ret_ty
+        elif self.ret_type != ret_ty:
+            raise TypeError(f'Inconsistent return types: {self.ret_type} and {ret_ty}')
+
+    def visit_FunctionDef(self, node):
+        arg_names, kwarg_names = self.visit(node.args)
+        if self.fn:
+            raise self._unsupported(node, "nested function definition is not supported.")
+        # initialize defaults
+        for i, default_value in enumerate(node.args.defaults):
+            arg_node = node.args.args[-i - 1]
+            annotation = arg_node.annotation
+            name = arg_node.arg
+            st_target = ast.Name(id=name, ctx=ast.Store())
+            if annotation is None:
+                init_node = ast.Assign(targets=[st_target], value=default_value)
+            else:
+                init_node = ast.AnnAssign(target=st_target, value=default_value, annotation=annotation)
+
+            try:
+                assert not self.visiting_arg_default_value
+                self.visiting_arg_default_value = True
+                self.visit(init_node)
+            finally:
+                self.visiting_arg_default_value = False
+
+        # initialize function
+        visibility = "public" if self.is_kernel else "private"
+        self.fn = self.builder.get_or_insert_function(self.module, self.function_name,
+                                                      self.prototype.to_ir(self.builder), visibility, self.noinline)
+        self.module.push_back(self.fn)
+        entry = self.fn.add_entry_block()
+        arg_values = []
+        idx = 0
+        for i, arg_name in enumerate(arg_names):
+            if i in self.constants:
+                cst = self.constants[i]
+                if not _is_constexpr(cst):
+                    cst = constexpr(self.constants[i])
+                arg_values.append(cst)
+                continue
+            else:
+                if i in self.attributes:
+                    for name, value in self.attributes[i]:
+                        self.fn.set_arg_attr(idx, name, value)
+                arg_values.append(tensor(self.fn.args(idx), self.prototype.param_types[idx]))
+                idx += 1
+
+        insert_pt = self.builder.get_insertion_block()
+        for arg_name, arg_value in zip(arg_names, arg_values):
+            self.set_value(arg_name, arg_value)
+        self.builder.set_insertion_point_to_start(entry)
+        # visit function body
+        self.visit_compound_statement(node.body)
+        # finalize function
+        if self.ret_type is None or self.ret_type == language.void:
+            self.ret_type = language.void
+            self.builder.ret([])
+        else:
+            # update return type
+            if isinstance(self.ret_type, tuple):
+                self.prototype.ret_types = list(self.ret_type)
+                self.fn.reset_type(self.prototype.to_ir(self.builder))
+            else:
+                self.prototype.ret_types = [self.ret_type]
+                self.fn.reset_type(self.prototype.to_ir(self.builder))
+        if insert_pt:
+            self.builder.set_insertion_point_to_end(insert_pt)
+        # Remove dead code
+        self.fn.finalize()
+
+    def visit_arguments(self, node):
+        arg_names = []
+        for arg in node.args:
+            arg_names += [self.visit(arg)]
+        kwarg_names = self.visit(node.kwarg)
+        return arg_names, kwarg_names
+
+    def visit_arg(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+        return node.arg
+
+    def visit_AnnAssign(self, node):
+        # extract attributes
+        annotation = self.visit(node.annotation)
+        target = self.visit(node.target)
+        value = self.visit(node.value)
+        # constexpr
+        if annotation == constexpr:
+            if target in self.lscope:
+                raise ValueError(f'{target} is already defined.'
+                                 f' constexpr cannot be reassigned.')
+            if not _is_constexpr(value):
+                value = constexpr(value)
+            self.lscope[target] = value
+            return self.lscope[target]
+        # default: call visit_Assign
+        return self.visit_Assign(node)
+
+    def visit_Assign(self, node):
+        _names = []
+        for target in node.targets:
+            _names += [self.visit(target)]
+        if len(_names) > 1:
+            raise self._unsupported(node, "simultaneous multiple assignment is not supported.")
+        names = _names[0]
+        values = self.visit(node.value)
+        if not _is_list_like(names):
+            names = [names]
+        if not _is_list_like(values):
+            values = [values]
+        native_nontensor_types = (language.dtype, )
+        for name, value in zip(names, values):
+            # by default, constexpr are assigned into python variable
+            value = _unwrap_if_constexpr(value)
+            if value is not None and \
+               not _is_triton_tensor(value) and \
+               not isinstance(value, native_nontensor_types):
+                value = language.core._to_tensor(value, self.builder)
+            self.set_value(name, value)
+
+    def visit_AugAssign(self, node):
+        name = node.target.id
+        lhs = ast.Name(id=name, ctx=ast.Load())
+        rhs = ast.BinOp(lhs, node.op, node.value)
+        assign = ast.Assign(targets=[node.target], value=rhs)
+        self.visit(assign)
+        return self.dereference_name(name)
+
+    def visit_Name(self, node):
+        if type(node.ctx) == ast.Store:
+            return node.id
+        return self.dereference_name(node.id)
+
+    def visit_Store(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+
+    def visit_Load(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+
+    def visit_Tuple(self, node):
+        args = [self.visit(x) for x in node.elts]
+        return tuple(args)
+
+    def _apply_binary_method(self, method_name, lhs, rhs):
+        # TODO: raise something meaningful if getattr fails below, esp for reverse method
+        if _is_triton_tensor(lhs):
+            return getattr(lhs, method_name)(rhs, _builder=self.builder)
+        if _is_triton_tensor(rhs):
+            reverse_method_name = re.sub(r"__(.*)__", r"__r\1__", method_name)
+            return getattr(rhs, reverse_method_name)(lhs, _builder=self.builder)
+        return getattr(lhs, method_name)(rhs)
+
+    def visit_BinOp(self, node):
+        lhs = self.visit(node.left)
+        rhs = self.visit(node.right)
+        method_name = self._method_name_for_bin_op.get(type(node.op))
+        if method_name is None:
+            raise self._unsupported(node,
+                                    "AST binary operator '{}' is not (currently) implemented.".format(node.op.__name__))
+        return self._apply_binary_method(method_name, lhs, rhs)
+
+    _method_name_for_bin_op: Dict[Type[ast.operator], str] = {
+        ast.Add: '__add__',
+        ast.Sub: '__sub__',
+        ast.Mult: '__mul__',
+        ast.Div: '__truediv__',
+        ast.FloorDiv: '__floordiv__',
+        ast.Mod: '__mod__',
+        ast.Pow: '__pow__',
+        ast.LShift: '__lshift__',
+        ast.RShift: '__rshift__',
+        ast.BitAnd: '__and__',
+        ast.BitOr: '__or__',
+        ast.BitXor: '__xor__',
+    }
+
+    def visit_then_else_blocks(self, node, liveins, then_block, else_block):
+        # then block
+        self.builder.set_insertion_point_to_start(then_block)
+        self.visit_compound_statement(node.body)
+        then_block = self.builder.get_insertion_block()
+        then_defs = self.local_defs.copy()
+        # else block
+        else_defs = {}
+        if node.orelse:
+            self.builder.set_insertion_point_to_start(else_block)
+            self.lscope = liveins.copy()
+            self.local_defs = {}
+            self.visit_compound_statement(node.orelse)
+            else_defs = self.local_defs.copy()
+            else_block = self.builder.get_insertion_block()
+
+        # update block arguments
+        names = []
+        ret_types = []
+        ir_ret_types = []
+        # variables in livein whose value is updated in `if`
+        for name in liveins:
+            # check type
+            for defs, block_name in [(then_defs, 'then'), (else_defs, 'else')]:
+                if name in defs:
+                    assert defs[name].type == liveins[name].type, \
+                        f'initial value for `{name}` is of type {liveins[name].type}, '\
+                        f'but the {block_name} block redefines it as {defs[name].type}'
+            if name in then_defs or name in else_defs:
+                names.append(name)
+                ret_types.append(then_defs[name].type if name in then_defs else else_defs[name].type)
+                ir_ret_types.append(then_defs[name].handle.get_type() if name in
+                                    then_defs else else_defs[name].handle.get_type())
+            # variable defined in then but not in else
+            if name in then_defs and name not in else_defs:
+                else_defs[name] = liveins[name]
+            # variable defined in else but not in then
+            if name in else_defs and name not in then_defs:
+                then_defs[name] = liveins[name]
+        # variables that are both in then and else but not in liveins
+        # TODO: could probably be cleaned up
+        for name in then_defs.keys() & else_defs.keys():
+            if name in names:
+                continue
+            then_ty = then_defs[name].type
+            else_ty = else_defs[name].type
+            assert then_ty == else_ty, \
+                f'mismatched type for {name} between then block ({then_ty}) '\
+                f'and else block ({else_ty})'
+            names.append(name)
+            ret_types.append(then_ty)
+            ir_ret_types.append(then_defs[name].handle.get_type())
+
+        return then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types
+
+    def visit_if_top_level(self, cond, node):
+        has_endif_block = True
+        with enter_sub_region(self) as sr:
+            liveins, ip_block = sr
+            then_block = self.builder.create_block()
+            else_block = self.builder.create_block()
+            # create basic-block after conditional
+            endif_block = self.builder.create_block()
+            # create branch
+            self.builder.set_insertion_point_to_end(ip_block)
+            self.builder.create_cond_branch(cond.handle, then_block, else_block)
+            # visit then and else blocks
+            then_defs, else_defs, then_block, else_block, names, ret_types, ir_ret_types = \
+                self.visit_then_else_blocks(node, liveins, then_block, else_block)
+            # then terminator
+            self.builder.set_insertion_point_to_end(then_block)
+            if then_block.has_return() and else_block.has_return():
+                has_endif_block = False
+                endif_block.erase()
+            if not then_block.has_terminator() and has_endif_block:
+                self.builder.create_branch(endif_block, [then_defs[n].handle for n in names])
+            # else terminator
+            self.builder.set_insertion_point_to_end(else_block)
+            if not else_block.has_terminator() and has_endif_block:
+                self.builder.create_branch(endif_block, [else_defs[n].handle for n in names])
+            if has_endif_block:
+                for ty in ir_ret_types:
+                    endif_block.add_argument(ty)
+        if has_endif_block:
+            # change block
+            self.builder.set_insertion_point_to_start(endif_block)
+            # update value
+            for i, name in enumerate(names):
+                new_tensor = language.core.tensor(endif_block.arg(i), ret_types[i])
+                self.set_value(name, new_tensor)
+
+    # TODO: refactor
+    def visit_if_scf(self, cond, node):
+        with enter_sub_region(self) as sr:
+            liveins, _ = sr
+            ip, last_loc = self._get_insertion_point_and_loc()
+            then_block = self.builder.create_block()
+            else_block = self.builder.create_block() if node.orelse else None
+            then_defs, else_defs, then_block, else_block, names, ret_types, _ = \
+                self.visit_then_else_blocks(node, liveins, then_block, else_block)
+            # create if op
+            self._set_insertion_point_and_loc(ip, last_loc)
+            if_op = self.builder.create_if_op([ty.to_ir(self.builder) for ty in ret_types], cond.handle, True)
+            then_block.merge_block_before(if_op.get_then_block())
+            self.builder.set_insertion_point_to_end(if_op.get_then_block())
+            if len(names) > 0:
+                self.builder.create_yield_op([then_defs[n].handle for n in names])
+            if not node.orelse:
+                else_block = if_op.get_else_block()
+            else:
+                else_block.merge_block_before(if_op.get_else_block())
+            self.builder.set_insertion_point_to_end(if_op.get_else_block())
+            if len(names) > 0:
+                self.builder.create_yield_op([else_defs[n].handle for n in names])
+        # update values
+        for i, name in enumerate(names):
+            new_tensor = language.core.tensor(if_op.get_result(i), ret_types[i])
+            self.set_value(name, new_tensor)
+
+    def visit_If(self, node):
+        cond = self.visit(node.test)
+        if _is_triton_tensor(cond):
+            cond = cond.to(language.int1, _builder=self.builder)
+            contains_return = ContainsReturnChecker(self.gscope).visit(node)
+            if self.scf_stack and contains_return:
+                raise self._unsupported(
+                    node, "Cannot have `return` statements inside `while` or `for` statements in triton "
+                    "(note that this also applies to `return` statements that are inside functions "
+                    "transitively called from within `while`/`for` statements)")
+            elif self.scf_stack or not contains_return:
+                self.visit_if_scf(cond, node)
+            else:
+                self.visit_if_top_level(cond, node)
+        else:
+            cond = _unwrap_if_constexpr(cond)
+            # not isinstance - we insist the real thing, no subclasses and no ducks
+            if type(cond) not in _condition_types:
+                raise self._unsupported(
+                    node, "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format(
+                        ', '.join(_.__name__ for _ in _condition_types),
+                        type(cond).__name__))
+            if cond:
+                self.visit_compound_statement(node.body)
+            else:
+                self.visit_compound_statement(node.orelse)
+
+    def visit_IfExp(self, node):
+        cond = self.visit(node.test)
+        if _is_triton_tensor(cond):
+            cond = cond.to(language.int1, _builder=self.builder)
+            # TODO: Deal w/ more complicated return types (e.g tuple)
+            with enter_sub_region(self):
+                ip, last_loc = self._get_insertion_point_and_loc()
+
+                then_block = self.builder.create_block()
+                self.builder.set_insertion_point_to_start(then_block)
+                then_val = language.core._to_tensor(self.visit(node.body), self.builder)
+                then_block = self.builder.get_insertion_block()
+
+                else_block = self.builder.create_block()
+                self.builder.set_insertion_point_to_start(else_block)
+                # do not need to reset lscope since
+                # ternary expressions cannot define new variables
+                else_val = language.core._to_tensor(self.visit(node.orelse), self.builder)
+                else_block = self.builder.get_insertion_block()
+
+                self._set_insertion_point_and_loc(ip, last_loc)
+
+                assert then_val.type == else_val.type, \
+                    f'ternary expression with dynamic condition has inconsistent types {then_val.type} and {else_val.type}'
+                ret_type = then_val.type
+
+                ret_type_ir = [ret_type.to_ir(self.builder)] if ret_type != language.void else []
+                if_op = self.builder.create_if_op(ret_type_ir, cond.handle, True)
+                then_block.merge_block_before(if_op.get_then_block())
+                if ret_type_ir:
+                    self.builder.set_insertion_point_to_end(if_op.get_then_block())
+                    self.builder.create_yield_op([then_val.handle])
+
+                self.builder.set_insertion_point_to_end(if_op.get_then_block())
+                else_block.merge_block_before(if_op.get_else_block())
+                if ret_type_ir:
+                    self.builder.set_insertion_point_to_end(if_op.get_else_block())
+                    self.builder.create_yield_op([else_val.handle])
+                return language.core.tensor(if_op.get_result(0), ret_type) if ret_type_ir else None
+        else:
+            cond = _unwrap_if_constexpr(cond)
+
+            # not isinstance - we insist the real thing, no subclasses and no ducks
+            if type(cond) not in _condition_types:
+                raise self._unsupported(
+                    node, "`if` conditionals can only accept values of type {{{}}}, not objects of type {}".format(
+                        ', '.join(_.__name__ for _ in _condition_types),
+                        type(cond).__name__))
+            if cond:
+                return self.visit(node.body)
+            else:
+                return self.visit(node.orelse)
+
+    def visit_Pass(self, node):
+        pass
+
+    def visit_Compare(self, node):
+        if not (len(node.comparators) == 1 and len(node.ops) == 1):
+            raise self._unsupported(node, "simultaneous multiple comparison is not supported")
+        lhs = self.visit(node.left)
+        rhs = self.visit(node.comparators[0])
+        lhs_value = _unwrap_if_constexpr(lhs)
+        rhs_value = _unwrap_if_constexpr(rhs)
+        if type(node.ops[0]) == ast.Is:
+            return constexpr(lhs_value is rhs_value)
+        if type(node.ops[0]) == ast.IsNot:
+            return constexpr(lhs_value is not rhs_value)
+        method_name = self._method_name_for_comp_op.get(type(node.ops[0]))
+        if method_name is None:
+            raise self._unsupported(
+                node, "AST comparison operator '{}' is not (currently) implemented.".format(node.ops[0].__name__))
+        return self._apply_binary_method(method_name, lhs, rhs)
+
+    _method_name_for_comp_op: Dict[Type[ast.cmpop], str] = {
+        ast.Eq: '__eq__', ast.NotEq: '__ne__', ast.Lt: '__lt__', ast.LtE: '__le__', ast.Gt: '__gt__', ast.GtE: '__ge__'
+    }
+
+    def visit_UnaryOp(self, node):
+        operand = self.visit(node.operand)
+        fn = self._method_name_for_unary_op.get(type(node.op))
+        if fn is None:
+            raise self._unsupported(node, f"AST unary operator '{node.op.__name__}' is not (currently) implemented.")
+        if _is_triton_tensor(operand):
+            return getattr(operand, fn)(_builder=self.builder)
+        try:
+            return getattr(operand, fn)()
+        except AttributeError:
+            raise self._unsupported(
+                node, f"AST unary operator '{fn}' is not (currently) implemented on type {type(operand).__name__}")
+
+    _method_name_for_unary_op: Dict[Type[ast.unaryop], str] = {
+        ast.USub: '__neg__', ast.UAdd: '__pos__', ast.Not: '__not__', ast.Invert: '__invert__'
+    }
+
+    def visit_While(self, node):
+        with enter_sub_region(self) as sr:
+            liveins, insert_block = sr
+            ip, last_loc = self._get_insertion_point_and_loc()
+
+            # loop body (the after region)
+            # loop_block = self.builder.create_block()
+            dummy = self.builder.create_block()
+            self.builder.set_insertion_point_to_start(dummy)
+            self.scf_stack.append(node)
+            self.visit_compound_statement(node.body)
+            self.scf_stack.pop()
+            loop_defs = self.local_defs
+            dummy.erase()
+
+            # collect loop-carried values
+            names = []
+            ret_types = []
+            init_args = []
+            for name in loop_defs:
+                if name in liveins:
+                    # We should not def new constexpr
+                    assert _is_triton_tensor(loop_defs[name]), f'cannot reassign constxpr {name} in the loop'
+                    assert _is_triton_tensor(liveins[name]), f'cannot reasign constexpr {name} in the loop'
+                    assert loop_defs[name].type == liveins[name].type, \
+                        f'Loop-carried variable {name} has initial type {liveins[name].type} '\
+                        f'but is re-assigned to {loop_defs[name].type} in loop! '\
+                        f'Please make sure that the type stays consistent.'
+
+                    # these are loop-carried values
+                    names.append(name)
+                    ret_types.append(loop_defs[name].type)
+                    init_args.append(liveins[name])
+
+            self._set_insertion_point_and_loc(ip, last_loc)
+            while_op = self.builder.create_while_op([ty.to_ir(self.builder) for ty in ret_types],
+                                                    [arg.handle for arg in init_args])
+            # merge the condition region
+            before_block = self.builder.create_block_with_parent(while_op.get_before(),
+                                                                 [ty.to_ir(self.builder) for ty in ret_types])
+            self.builder.set_insertion_point_to_start(before_block)
+            for i, name in enumerate(names):
+                self.lscope[name] = language.core.tensor(before_block.arg(i), ret_types[i])
+                self.local_defs[name] = self.lscope[name]
+            cond = self.visit(node.test)
+            self.builder.set_insertion_point_to_end(before_block)
+            # create ConditionOp: e.g., scf.condition(%cond) %arg0, %arg1, ...
+            self.builder.create_condition_op(cond.handle, [before_block.arg(i) for i in range(len(init_args))])
+            # merge the loop body
+            after_block = self.builder.create_block_with_parent(while_op.get_after(),
+                                                                [ty.to_ir(self.builder) for ty in ret_types])
+
+            # generate loop body
+            self.builder.set_insertion_point_to_start(after_block)
+            for i, name in enumerate(names):
+                self.lscope[name] = language.core.tensor(after_block.arg(i), ret_types[i])
+                self.local_defs[name] = self.lscope[name]
+            self.scf_stack.append(node)
+            self.visit_compound_statement(node.body)
+            self.scf_stack.pop()
+            loop_defs = self.local_defs
+            yields = []
+            for name in loop_defs:
+                if name in liveins:
+                    yields.append(loop_defs[name])
+            self.builder.create_yield_op([y.handle for y in yields])
+
+        # WhileOp defines new values, update the symbol table (lscope, local_defs)
+        for i, name in enumerate(names):
+            new_def = language.core.tensor(while_op.get_result(i), ret_types[i])
+            self.lscope[name] = new_def
+            self.local_defs[name] = new_def
+
+        for stmt in node.orelse:
+            assert False, "Not implemented"
+            ast.NodeVisitor.generic_visit(self, stmt)
+
+    def visit_Subscript(self, node):
+        assert node.ctx.__class__.__name__ == "Load"
+        lhs = self.visit(node.value)
+        slices = self.visit(node.slice)
+        if _is_triton_tensor(lhs):
+            return lhs.__getitem__(slices, _builder=self.builder)
+        return lhs[slices]
+
+    def visit_ExtSlice(self, node):
+        return [self.visit(dim) for dim in node.dims]
+
+    def visit_For(self, node):
+        IteratorClass = self.visit(node.iter.func)
+        iter_args = [self.visit(arg) for arg in node.iter.args]
+        iter_kwargs = dict(self.visit(keyword) for keyword in node.iter.keywords)
+        if IteratorClass == language.static_range:
+            iterator = IteratorClass(*iter_args, **iter_kwargs)
+            static_range = range(iterator.start.value, iterator.end.value, iterator.step.value)
+            for i in static_range:
+                self.lscope[node.target.id] = constexpr(i)
+                self.visit_compound_statement(node.body)
+                for stmt in node.orelse:
+                    ast.NodeVisitor.generic_visit(self, stmt)
+            return
+        num_stages = None
+        if IteratorClass is language.range:
+            iterator = IteratorClass(*iter_args, **iter_kwargs)
+            # visit iterator arguments
+            # note: only `range` iterator is supported now
+            # collect lower bound (lb), upper bound (ub), and step
+            lb = iterator.start
+            ub = iterator.end
+            step = iterator.step
+            num_stages = iterator.num_stages
+        elif IteratorClass is range:
+            # visit iterator arguments
+            # note: only `range` iterator is supported now
+            # collect lower bound (lb), upper bound (ub), and step
+            lb = iter_args[0] if len(iter_args) > 1 else self.visit(ast.Num(0))
+            ub = iter_args[1] if len(iter_args) > 1 else self.visit(node.iter.args[0])
+            step = iter_args[2] if len(iter_args) > 2 else self.visit(ast.Num(1))
+        else:
+            raise RuntimeError('Only `range` and `static_range` iterators are currently supported')
+        # handle negative constant step (not supported by scf.for in MLIR)
+        negative_step = False
+        if _is_constexpr(step) and step.value < 0:
+            step = constexpr(-step.value)
+            negative_step = True
+            lb, ub = ub, lb
+        lb = language.core._to_tensor(lb, self.builder)
+        ub = language.core._to_tensor(ub, self.builder)
+        step = language.core._to_tensor(step, self.builder)
+        # induction variable type
+        if not lb.dtype.is_int() or not ub.dtype.is_int() or not step.dtype.is_int():
+            raise TypeError(f"For loop bounds and step must all be ints, are ({lb.dtype}, {ub.dtype}, {step.dtype})")
+        iv_type = language.semantic.integer_promote_impl(lb.dtype, ub.dtype)
+        iv_type = language.semantic.integer_promote_impl(iv_type, step.dtype)
+        iv_ir_type = iv_type.to_ir(self.builder)
+        iv_is_signed = iv_type.int_signedness == language.core.dtype.SIGNEDNESS.SIGNED
+        # lb/ub/step might be constexpr, we need to cast them to tensor
+        lb = lb.handle
+        ub = ub.handle
+        step = step.handle
+        # ForOp can only accept IndexType as lb/ub/step. Cast integer to Index
+        lb = self.builder.create_int_cast(lb, iv_ir_type, iv_is_signed)
+        ub = self.builder.create_int_cast(ub, iv_ir_type, iv_is_signed)
+        step = self.builder.create_int_cast(step, iv_ir_type, iv_is_signed)
+        # Create placeholder for the loop induction variable
+        iv = self.builder.create_undef(iv_ir_type)
+        self.set_value(node.target.id, language.core.tensor(iv, iv_type))
+
+        with enter_sub_region(self) as sr:
+            liveins, insert_block = sr
+            ip, last_loc = self._get_insertion_point_and_loc()
+
+            # create loop body block
+            block = self.builder.create_block()
+            self.builder.set_insertion_point_to_start(block)
+            # dry visit loop body
+            self.scf_stack.append(node)
+            self.visit_compound_statement(node.body)
+            self.scf_stack.pop()
+            block.erase()
+
+            # If a variable (name) is defined in both its parent & itself, then it's
+            # a loop-carried variable. (They must be of the same type)
+            init_args = []
+            yields = []
+            names = []
+            for name in self.local_defs:
+                if name in liveins:
+                    assert _is_triton_tensor(self.local_defs[name]), f'{name} is not tensor'
+                    assert _is_triton_tensor(liveins[name])
+                    assert self.local_defs[name].type == liveins[name].type, \
+                        f'Loop-carried variable {name} has initial type {liveins[name].type} '\
+                        f'but is re-assigned to {self.local_defs[name].type} in loop! '\
+                        f'Please make sure that the type stays consistent.'
+
+                    names.append(name)
+                    init_args.append(language.core._to_tensor(liveins[name], self.builder))
+                    yields.append(language.core._to_tensor(self.local_defs[name], self.builder))
+
+            # create ForOp
+            self._set_insertion_point_and_loc(ip, last_loc)
+            for_op = self.builder.create_for_op(lb, ub, step, [arg.handle for arg in init_args])
+            if num_stages is not None:
+                for_op.set_attr("tt.num_stages", self.builder.get_int32_attr(num_stages))
+
+            self.scf_stack.append(node)
+            self.builder.set_insertion_point_to_start(for_op.get_body(0))
+            # reset local scope to not pick up local defs from the previous dry run.
+            self.lscope = liveins.copy()
+            self.local_defs = {}
+            for i, name in enumerate(names):
+                self.set_value(name, language.core.tensor(for_op.get_body(0).arg(i + 1), yields[i].type))
+            self.visit_compound_statement(node.body)
+            self.scf_stack.pop()
+            yields = []
+            for name in self.local_defs:
+                if name in liveins:
+                    yields.append(language.core._to_tensor(self.local_defs[name], self.builder))
+
+            # create YieldOp
+            if len(yields) > 0:
+                self.builder.create_yield_op([y.handle for y in yields])
+            for_op_region = for_op.get_body(0).get_parent()
+            assert for_op_region.size() == 1, "We use SCF, so the loop body should only have one block"
+
+            # update induction variable with actual value, and replace all uses
+            self.builder.set_insertion_point_to_start(for_op.get_body(0))
+            iv = for_op.get_induction_var()
+            if negative_step:
+                iv = self.builder.create_sub(ub, iv)
+                iv = self.builder.create_add(iv, lb)
+            self.lscope[node.target.id].handle.replace_all_uses_with(iv)
+            self.set_value(node.target.id, language.core.tensor(iv, iv_type))
+
+        # update lscope & local_defs (ForOp defines new values)
+        for i, name in enumerate(names):
+            self.set_value(name, language.core.tensor(for_op.get_result(i), yields[i].type))
+
+        for stmt in node.orelse:
+            assert False, "Don't know what to do with else after for"
+            ast.NodeVisitor.generic_visit(self, stmt)
+
+    def visit_Slice(self, node):
+        lower = self.visit(node.lower)
+        upper = self.visit(node.upper)
+        step = self.visit(node.step)
+        return slice(lower, upper, step)
+
+    def visit_Index(self, node):
+        return self.visit(node.value)
+
+    def visit_keyword(self, node) -> Tuple[str, Any]:
+        return node.arg, self.visit(node.value)
+
+    def visit_Assert(self, node) -> Any:
+        if not self.debug:
+            return
+        test = self.visit(node.test)
+        msg = self.visit(node.msg) if node.msg is not None else ""
+        # Convert assert to triton's device_assert which happens on the device
+        return language.core.device_assert(test, msg, _builder=self.builder)
+
+    def call_JitFunction(self, fn: JITFunction, args, kwargs):
+        args = inspect.getcallargs(fn.fn, *args, **kwargs)
+        args = [args[name] for name in fn.arg_names]
+        args = [arg if _is_triton_tensor(arg) else constexpr(arg) for arg in args]
+        # generate function def
+        attributes = dict()
+        constexprs = [i for i, arg in enumerate(args) if _is_constexpr(arg)]
+        constants = {i: args[i] for i in constexprs}
+        # generate call
+        args = [None if i in constexprs else arg for i, arg in enumerate(args)]
+        arg_vals = [arg.handle for arg in args if arg is not None]
+        arg_types = [arg.type for arg in args if arg is not None]
+        fn_name = mangle_fn(fn.__name__, arg_types, constants)
+        # generate function def if necessary
+        if not self.module.has_function(fn_name):
+            prototype = language.function_type([], arg_types)
+            gscope = fn.__globals__
+            # If the callee is not set, we use the same debug setting as the caller
+            file_name, begin_line = _get_fn_file_line(fn)
+            debug = self.debug if fn.debug is None else fn.debug
+            generator = CodeGenerator(self.context, prototype, gscope, attributes, constants, module=self.module,
+                                      jit_fn=fn, function_name=fn_name, function_types=self.function_ret_types,
+                                      noinline=fn.noinline, file_name=file_name, begin_line=begin_line,
+                                      options=self.builder.options, codegen_fns=self.builder.codegen_fns, debug=debug)
+            try:
+                generator.visit(fn.parse())
+            except Exception as e:
+                # Wrap the error in the callee with the location of the call.
+                raise CompilationError(self.jit_fn.src, self.cur_node, None) from e
+
+            callee_ret_type = generator.ret_type
+            self.function_ret_types[fn_name] = callee_ret_type
+        else:
+            callee_ret_type = self.function_ret_types[fn_name]
+        symbol = self.module.get_function(fn_name)
+        call_op = self.builder.call(symbol, arg_vals)
+        if call_op.get_num_results() == 0 or callee_ret_type is None:
+            return None
+        elif call_op.get_num_results() == 1:
+            return tensor(call_op.get_result(0), callee_ret_type)
+        else:
+            # should return a tuple of tl.tensor
+            results = []
+            for i in range(call_op.get_num_results()):
+                results.append(tensor(call_op.get_result(i), callee_ret_type[i]))
+            return tuple(results)
+
+    def visit_Call(self, node):
+        fn = _unwrap_if_constexpr(self.visit(node.func))
+        static_implementation = self.statically_implemented_functions.get(fn)
+        if static_implementation is not None:
+            return static_implementation(self, node)
+
+        kws = dict(self.visit(keyword) for keyword in node.keywords)
+        args = [self.visit(arg) for arg in node.args]
+        if fn is language.core.device_assert:  # TODO: this should not be so hardcoded
+            if not self.debug:
+                return
+        if isinstance(fn, JITFunction):
+            _check_fn_args(node, fn, args)
+            return self.call_JitFunction(fn, args, kws)
+        if (hasattr(fn, '__self__') and _is_triton_tensor(fn.__self__)) or language.core.is_builtin(fn):
+            extra_kwargs = dict(_builder=self.builder)
+            sig = inspect.signature(fn)
+            if '_generator' in sig.parameters:
+                extra_kwargs['_generator'] = self
+            try:
+                return fn(*args, **extra_kwargs, **kws)
+            except Exception as e:
+                # Normally when we raise a CompilationError, we raise it as
+                # `from None`, because the original fileline from the exception
+                # is not relevant (and often points into code_generator.py
+                # itself).  But when calling a function, we raise as `from e` to
+                # preserve the traceback of the original error, which may e.g.
+                # be in core.py.
+                raise CompilationError(self.jit_fn.src, node, None) from e
+
+        if fn in self.builtin_namespace.values():
+            args = map(_unwrap_if_constexpr, args)
+        return fn(*args, **kws)
+
+    def visit_Constant(self, node):
+        return constexpr(node.value)
+
+    def visit_BoolOp(self, node: ast.BoolOp):
+        if len(node.values) != 2:
+            raise self._unsupported(
+                node, "chained boolean operators (A or B or C) are not supported; use parentheses to split the chain.")
+        lhs = self.visit(node.values[0])
+        rhs = self.visit(node.values[1])
+        method_name = self._method_name_for_bool_op.get(type(node.op))
+        if method_name is None:
+            raise self._unsupported(
+                node, "AST boolean operator '{}' is not (currently) implemented.".format(node.op.__name__))
+        return self._apply_binary_method(method_name, lhs, rhs)
+
+    _method_name_for_bool_op: Dict[Type[ast.boolop], str] = {ast.And: 'logical_and', ast.Or: 'logical_or'}
+
+    if sys.version_info < (3, 8):
+
+        def visit_NameConstant(self, node):
+            return constexpr(node.value)
+
+        def visit_Num(self, node):
+            return constexpr(node.n)
+
+        def visit_Str(self, node):
+            return constexpr(ast.literal_eval(node))
+
+    def visit_Attribute(self, node):
+        lhs = self.visit(node.value)
+        if _is_triton_tensor(lhs):
+            if node.attr == "T":
+                return language.semantic.permute(lhs, (1, 0), builder=self.builder)
+        return getattr(lhs, node.attr)
+
+    def visit_Expr(self, node):
+        ast.NodeVisitor.generic_visit(self, node)
+
+    def visit_NoneType(self, node):
+        return None
+
+    def visit_JoinedStr(self, node):
+        values = list(node.values)
+        for i, value in enumerate(values):
+            if isinstance(value, ast.Constant):
+                values[i] = str(value.value)
+            elif isinstance(value, ast.FormattedValue):
+                conversion_code = value.conversion
+                evaluated = self.visit(value.value)
+                if not _is_constexpr(evaluated):
+                    raise self._unsupported(
+                        node,
+                        "Cannot evaluate f-string containing non-constexpr conversion values, found conversion of type "
+                        + str(type(evaluated)))
+                values[i] = ("{}" if conversion_code < 0 else "{!" + chr(conversion_code) + "}").format(evaluated.value)
+            else:
+                raise AssertionError("encountered unexpected node of type {} in a JoinedStr node".format(type(value)))
+        return ''.join(values)
+
+    def visit(self, node):
+        if node is None:
+            return
+        with warnings.catch_warnings():
+            # The ast library added visit_Constant and deprecated some other
+            # methods but we can't move to that without breaking Python 3.6 and 3.7.
+            warnings.simplefilter("ignore", DeprecationWarning)  # python 3.9
+            warnings.simplefilter("ignore", PendingDeprecationWarning)  # python 3.8
+            last_node = self.cur_node
+            last_loc = self.builder.get_loc()
+            self.cur_node = node
+            if hasattr(node, 'lineno') and hasattr(node, 'col_offset'):
+                self.builder.set_loc(self.file_name, self.begin_line + node.lineno, node.col_offset)
+                last_loc = self.builder.get_loc()
+            try:
+                ret = super().visit(node)
+            except CompilationError:
+                raise
+            except Exception as e:
+                # Wrap the error in a CompilationError which contains the source
+                # of the @jit function.
+                raise CompilationError(self.jit_fn.src, self.cur_node, repr(e)) from None
+
+            # Reset the location to the last one before the visit
+            if last_loc:
+                self.cur_node = last_node
+                self.builder.set_loc(last_loc)
+            return ret
+
+    def generic_visit(self, node):
+        raise self._unsupported(node, "unsupported AST node type: {}".format(type(node).__name__))
+
+    def execute_static_assert(self, node: ast.Call) -> None:
+        arg_count = len(node.args)
+        if not (0 < arg_count <= 2) or len(node.keywords):
+            raise TypeError("`static_assert` requires one or two positional arguments only")
+
+        passed = _unwrap_if_constexpr(self.visit(node.args[0]))
+        if not isinstance(passed, bool):
+            raise NotImplementedError(
+                "Assertion condition could not be determined at compile-time. Make sure that it depends only on `constexpr` values"
+            )
+        if not passed:
+            if arg_count == 1:
+                message = ""
+            else:
+                try:
+                    message = self.visit(node.args[1])
+                except Exception as e:
+                    message = "<failed to evaluate assertion message: " + repr(e) + ">"
+
+            raise CompileTimeAssertionFailure(self.jit_fn.src, node, _unwrap_if_constexpr(message))
+        return None
+
+    def static_executor(python_fn):
+
+        def ret(self, node: ast.Call):
+            kws = {
+                name: _unwrap_if_constexpr(value)
+                for name, value in (self.visit(keyword) for keyword in node.keywords)
+            }
+            args = [_unwrap_if_constexpr(self.visit(arg)) for arg in node.args]
+            return constexpr(python_fn(*args, **kws))
+
+        return ret
+
+    statically_implemented_functions: Dict[object, Callable[[ast.Call], Any]] = {
+        language.core.static_assert: execute_static_assert,
+        language.core.static_print: static_executor(print),
+        int: static_executor(int),
+        len: static_executor(len),
+    }
+
+
+def kernel_suffix(signature, specialization):
+    # suffix format:
+    # <argid><'c' if equal to 1><'d' if divisible by 16><'e' if divisible by 8>
+    suffix = ''
+    for i, _ in enumerate(signature):
+        suffix += str(i)
+        if i in specialization.equal_to_1:
+            suffix += 'c'
+        if i in specialization.divisible_by_16:
+            suffix += 'd'
+    return suffix
+
+
+def ast_to_ttir(fn, specialization, context, options, codegen_fns):
+    attrs = specialization.attrs
+    # create kernel prototype
+    cst_key = lambda i: fn.arg_names.index(i) if isinstance(i, str) else i
+    constants = {cst_key(key): value for key, value in specialization.constants.items()}
+    # visit kernel AST
+    gscope = fn.__globals__.copy()
+    function_name = fn.repr(specialization)
+    tys = list(specialization.signature.values())
+    new_constants = {k: True if k in tys and tys[k] == "i1" else 1 for k in attrs.equal_to_1}
+    new_attrs = {k: [("tt.divisibility", 16)] for k in attrs.divisible_by_16}
+
+    all_constants = constants.copy()
+    all_constants.update(new_constants)
+    arg_types = [str_to_ty(v) for k, v in specialization.signature.items() if k not in specialization.constants]
+    file_name, begin_line = _get_fn_file_line(fn)
+
+    prototype = language.function_type([], arg_types)
+    generator = CodeGenerator(context, prototype, gscope=gscope, constants=all_constants, function_name=function_name,
+                              jit_fn=fn, attributes=new_attrs, is_kernel=True, file_name=file_name,
+                              begin_line=begin_line, options=options, codegen_fns=codegen_fns)
+    generator.visit(fn.parse())
+
+    ret = generator.module
+    # module takes ownership of the context
+    ret.context = context
+    return ret