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@@ -1504,3 +1504,32 @@ vllm/lib/python3.10/site-packages/av.libs/libopus-21bd4123.so.0.10.1 filter=lfs
 parrot/lib/python3.10/site-packages/scipy/fftpack/convolve.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/scipy/stats/_stats_pythran.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/scipy/stats/tests/__pycache__/test_stats.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+vllm/lib/python3.10/site-packages/av.libs/libtwolame-72d74ef7.so.0.0.0 filter=lfs diff=lfs merge=lfs -text
+vllm/lib/python3.10/site-packages/av.libs/libvorbis-f4a9a6fd.so.0.4.9 filter=lfs diff=lfs merge=lfs -text
+vllm/lib/python3.10/site-packages/av.libs/libxcb-65da195c.so.1.1.0 filter=lfs diff=lfs merge=lfs -text
+vllm/lib/python3.10/site-packages/av.libs/libpcre-9513aab5.so.1.2.0 filter=lfs diff=lfs merge=lfs -text
+vllm/lib/python3.10/site-packages/av.libs/libcrypto-d3570994.so.1.0.2k filter=lfs diff=lfs merge=lfs -text
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parrot/lib/python3.10/site-packages/scipy/fft/tests/test_helper.py

@@ -0,0 +1,570 @@
+"""Includes test functions for fftpack.helper module
+
+Copied from fftpack.helper by Pearu Peterson, October 2005
+Modified for Array API, 2023
+
+"""
+from scipy.fft._helper import next_fast_len, prev_fast_len, _init_nd_shape_and_axes
+from numpy.testing import assert_equal
+from pytest import raises as assert_raises
+import pytest
+import numpy as np
+import sys
+from scipy.conftest import array_api_compatible
+from scipy._lib._array_api import (
+    xp_assert_close, get_xp_devices, device, array_namespace
+)
+from scipy import fft
+
+pytestmark = [array_api_compatible, pytest.mark.usefixtures("skip_xp_backends")]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+_5_smooth_numbers = [
+    2, 3, 4, 5, 6, 8, 9, 10,
+    2 * 3 * 5,
+    2**3 * 3**5,
+    2**3 * 3**3 * 5**2,
+]
+
+def test_next_fast_len():
+    for n in _5_smooth_numbers:
+        assert_equal(next_fast_len(n), n)
+
+
+def _assert_n_smooth(x, n):
+    x_orig = x
+    if n < 2:
+        assert False
+
+    while True:
+        q, r = divmod(x, 2)
+        if r != 0:
+            break
+        x = q
+
+    for d in range(3, n+1, 2):
+        while True:
+            q, r = divmod(x, d)
+            if r != 0:
+                break
+            x = q
+
+    assert x == 1, \
+           f'x={x_orig} is not {n}-smooth, remainder={x}'
+
+
+@skip_xp_backends(np_only=True)
+class TestNextFastLen:
+
+    def test_next_fast_len(self):
+        np.random.seed(1234)
+
+        def nums():
+            yield from range(1, 1000)
+            yield 2**5 * 3**5 * 4**5 + 1
+
+        for n in nums():
+            m = next_fast_len(n)
+            _assert_n_smooth(m, 11)
+            assert m == next_fast_len(n, False)
+
+            m = next_fast_len(n, True)
+            _assert_n_smooth(m, 5)
+
+    def test_np_integers(self):
+        ITYPES = [np.int16, np.int32, np.int64, np.uint16, np.uint32, np.uint64]
+        for ityp in ITYPES:
+            x = ityp(12345)
+            testN = next_fast_len(x)
+            assert_equal(testN, next_fast_len(int(x)))
+
+    def testnext_fast_len_small(self):
+        hams = {
+            1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 8, 8: 8, 14: 15, 15: 15,
+            16: 16, 17: 18, 1021: 1024, 1536: 1536, 51200000: 51200000
+        }
+        for x, y in hams.items():
+            assert_equal(next_fast_len(x, True), y)
+
+    @pytest.mark.xfail(sys.maxsize < 2**32,
+                       reason="Hamming Numbers too large for 32-bit",
+                       raises=ValueError, strict=True)
+    def testnext_fast_len_big(self):
+        hams = {
+            510183360: 510183360, 510183360 + 1: 512000000,
+            511000000: 512000000,
+            854296875: 854296875, 854296875 + 1: 859963392,
+            196608000000: 196608000000, 196608000000 + 1: 196830000000,
+            8789062500000: 8789062500000, 8789062500000 + 1: 8796093022208,
+            206391214080000: 206391214080000,
+            206391214080000 + 1: 206624260800000,
+            470184984576000: 470184984576000,
+            470184984576000 + 1: 470715894135000,
+            7222041363087360: 7222041363087360,
+            7222041363087360 + 1: 7230196133913600,
+            # power of 5    5**23
+            11920928955078125: 11920928955078125,
+            11920928955078125 - 1: 11920928955078125,
+            # power of 3    3**34
+            16677181699666569: 16677181699666569,
+            16677181699666569 - 1: 16677181699666569,
+            # power of 2   2**54
+            18014398509481984: 18014398509481984,
+            18014398509481984 - 1: 18014398509481984,
+            # above this, int(ceil(n)) == int(ceil(n+1))
+            19200000000000000: 19200000000000000,
+            19200000000000000 + 1: 19221679687500000,
+            288230376151711744: 288230376151711744,
+            288230376151711744 + 1: 288325195312500000,
+            288325195312500000 - 1: 288325195312500000,
+            288325195312500000: 288325195312500000,
+            288325195312500000 + 1: 288555831593533440,
+        }
+        for x, y in hams.items():
+            assert_equal(next_fast_len(x, True), y)
+
+    def test_keyword_args(self):
+        assert next_fast_len(11, real=True) == 12
+        assert next_fast_len(target=7, real=False) == 7
+
+@skip_xp_backends(np_only=True)
+class TestPrevFastLen:
+
+    def test_prev_fast_len(self):
+        np.random.seed(1234)
+
+        def nums():
+            yield from range(1, 1000)
+            yield 2**5 * 3**5 * 4**5 + 1
+
+        for n in nums():
+            m = prev_fast_len(n)
+            _assert_n_smooth(m, 11)
+            assert m == prev_fast_len(n, False)
+
+            m = prev_fast_len(n, True)
+            _assert_n_smooth(m, 5)
+
+    def test_np_integers(self):
+        ITYPES = [np.int16, np.int32, np.int64, np.uint16, np.uint32, 
+                    np.uint64]
+        for ityp in ITYPES:
+            x = ityp(12345)
+            testN = prev_fast_len(x)
+            assert_equal(testN, prev_fast_len(int(x)))
+
+            testN = prev_fast_len(x, real=True)
+            assert_equal(testN, prev_fast_len(int(x), real=True))
+
+    def testprev_fast_len_small(self):
+        hams = {
+            1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 6, 8: 8, 14: 12, 15: 15,
+            16: 16, 17: 16, 1021: 1000, 1536: 1536, 51200000: 51200000
+        }
+        for x, y in hams.items():
+            assert_equal(prev_fast_len(x, True), y)
+
+        hams = {
+            1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10,
+            11: 11, 12: 12, 13: 12, 14: 14, 15: 15, 16: 16, 17: 16, 18: 18,
+            19: 18, 20: 20, 21: 21, 22: 22, 120: 120, 121: 121, 122: 121,
+            1021: 1008, 1536: 1536, 51200000: 51200000
+        }
+        for x, y in hams.items():
+            assert_equal(prev_fast_len(x, False), y)
+
+    @pytest.mark.xfail(sys.maxsize < 2**32,
+                       reason="Hamming Numbers too large for 32-bit",
+                       raises=ValueError, strict=True)
+    def testprev_fast_len_big(self):
+        hams = {
+            # 2**6 * 3**13 * 5**1
+            510183360: 510183360,
+            510183360 + 1: 510183360,
+            510183360 - 1: 509607936,  # 2**21 * 3**5
+            # 2**6 * 5**6 * 7**1 * 73**1
+            511000000: 510183360,
+            511000000 + 1: 510183360,
+            511000000 - 1: 510183360,  # 2**6 * 3**13 * 5**1
+            # 3**7 * 5**8
+            854296875: 854296875,
+            854296875 + 1: 854296875,
+            854296875 - 1: 850305600,  # 2**6 * 3**12 * 5**2
+            # 2**22 * 3**1 * 5**6
+            196608000000: 196608000000,
+            196608000000 + 1: 196608000000,
+            196608000000 - 1: 195910410240,  # 2**13 * 3**14 * 5**1
+            # 2**5 * 3**2 * 5**15
+            8789062500000: 8789062500000,
+            8789062500000 + 1: 8789062500000,
+            8789062500000 - 1: 8748000000000,  # 2**11 * 3**7 * 5**9
+            # 2**24 * 3**9 * 5**4
+            206391214080000: 206391214080000,
+            206391214080000 + 1: 206391214080000,
+            206391214080000 - 1: 206158430208000,  # 2**39 * 3**1 * 5**3
+            # 2**18 * 3**15 * 5**3
+            470184984576000: 470184984576000,
+            470184984576000 + 1: 470184984576000,
+            470184984576000 - 1: 469654673817600,  # 2**33 * 3**7 **5**2
+            # 2**25 * 3**16 * 5**1
+            7222041363087360: 7222041363087360,
+            7222041363087360 + 1: 7222041363087360,
+            7222041363087360 - 1: 7213895789838336,  # 2**40 * 3**8
+            # power of 5    5**23
+            11920928955078125: 11920928955078125,
+            11920928955078125 + 1: 11920928955078125,
+            11920928955078125 - 1: 11901557422080000,  # 2**14 * 3**19 * 5**4
+            # power of 3    3**34
+            16677181699666569: 16677181699666569,
+            16677181699666569 + 1: 16677181699666569,
+            16677181699666569 - 1: 16607531250000000,  # 2**7 * 3**12 * 5**12
+            # power of 2   2**54
+            18014398509481984: 18014398509481984,
+            18014398509481984 + 1: 18014398509481984,
+            18014398509481984 - 1: 18000000000000000,  # 2**16 * 3**2 * 5**15
+            # 2**20 * 3**1 * 5**14
+            19200000000000000: 19200000000000000,
+            19200000000000000 + 1: 19200000000000000,
+            19200000000000000 - 1: 19131876000000000,  # 2**11 * 3**14 * 5**9
+            # 2**58
+            288230376151711744: 288230376151711744,
+            288230376151711744 + 1: 288230376151711744,
+            288230376151711744 - 1: 288000000000000000,  # 2**20 * 3**2 * 5**15
+            # 2**5 * 3**10 * 5**16
+            288325195312500000: 288325195312500000,
+            288325195312500000 + 1: 288325195312500000,
+            288325195312500000 - 1: 288230376151711744,  # 2**58
+        }
+        for x, y in hams.items():
+            assert_equal(prev_fast_len(x, True), y)
+
+    def test_keyword_args(self):
+        assert prev_fast_len(11, real=True) == 10
+        assert prev_fast_len(target=7, real=False) == 7
+
+
+@skip_xp_backends(cpu_only=True)
+class Test_init_nd_shape_and_axes:
+
+    def test_py_0d_defaults(self, xp):
+        x = xp.asarray(4)
+        shape = None
+        axes = None
+
+        shape_expected = ()
+        axes_expected = []
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_0d_defaults(self, xp):
+        x = xp.asarray(7.)
+        shape = None
+        axes = None
+
+        shape_expected = ()
+        axes_expected = []
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_py_1d_defaults(self, xp):
+        x = xp.asarray([1, 2, 3])
+        shape = None
+        axes = None
+
+        shape_expected = (3,)
+        axes_expected = [0]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_1d_defaults(self, xp):
+        x = xp.arange(0, 1, .1)
+        shape = None
+        axes = None
+
+        shape_expected = (10,)
+        axes_expected = [0]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_py_2d_defaults(self, xp):
+        x = xp.asarray([[1, 2, 3, 4],
+                        [5, 6, 7, 8]])
+        shape = None
+        axes = None
+
+        shape_expected = (2, 4)
+        axes_expected = [0, 1]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_2d_defaults(self, xp):
+        x = xp.arange(0, 1, .1)
+        x = xp.reshape(x, (5, 2))
+        shape = None
+        axes = None
+
+        shape_expected = (5, 2)
+        axes_expected = [0, 1]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_5d_defaults(self, xp):
+        x = xp.zeros([6, 2, 5, 3, 4])
+        shape = None
+        axes = None
+
+        shape_expected = (6, 2, 5, 3, 4)
+        axes_expected = [0, 1, 2, 3, 4]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_5d_set_shape(self, xp):
+        x = xp.zeros([6, 2, 5, 3, 4])
+        shape = [10, -1, -1, 1, 4]
+        axes = None
+
+        shape_expected = (10, 2, 5, 1, 4)
+        axes_expected = [0, 1, 2, 3, 4]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_5d_set_axes(self, xp):
+        x = xp.zeros([6, 2, 5, 3, 4])
+        shape = None
+        axes = [4, 1, 2]
+
+        shape_expected = (4, 2, 5)
+        axes_expected = [4, 1, 2]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_xp_5d_set_shape_axes(self, xp):
+        x = xp.zeros([6, 2, 5, 3, 4])
+        shape = [10, -1, 2]
+        axes = [1, 0, 3]
+
+        shape_expected = (10, 6, 2)
+        axes_expected = [1, 0, 3]
+
+        shape_res, axes_res = _init_nd_shape_and_axes(x, shape, axes)
+
+        assert shape_res == shape_expected
+        assert axes_res == axes_expected
+
+    def test_shape_axes_subset(self, xp):
+        x = xp.zeros((2, 3, 4, 5))
+        shape, axes = _init_nd_shape_and_axes(x, shape=(5, 5, 5), axes=None)
+
+        assert shape == (5, 5, 5)
+        assert axes == [1, 2, 3]
+
+    def test_errors(self, xp):
+        x = xp.zeros(1)
+        with assert_raises(ValueError, match="axes must be a scalar or "
+                           "iterable of integers"):
+            _init_nd_shape_and_axes(x, shape=None, axes=[[1, 2], [3, 4]])
+
+        with assert_raises(ValueError, match="axes must be a scalar or "
+                           "iterable of integers"):
+            _init_nd_shape_and_axes(x, shape=None, axes=[1., 2., 3., 4.])
+
+        with assert_raises(ValueError,
+                           match="axes exceeds dimensionality of input"):
+            _init_nd_shape_and_axes(x, shape=None, axes=[1])
+
+        with assert_raises(ValueError,
+                           match="axes exceeds dimensionality of input"):
+            _init_nd_shape_and_axes(x, shape=None, axes=[-2])
+
+        with assert_raises(ValueError,
+                           match="all axes must be unique"):
+            _init_nd_shape_and_axes(x, shape=None, axes=[0, 0])
+
+        with assert_raises(ValueError, match="shape must be a scalar or "
+                           "iterable of integers"):
+            _init_nd_shape_and_axes(x, shape=[[1, 2], [3, 4]], axes=None)
+
+        with assert_raises(ValueError, match="shape must be a scalar or "
+                           "iterable of integers"):
+            _init_nd_shape_and_axes(x, shape=[1., 2., 3., 4.], axes=None)
+
+        with assert_raises(ValueError,
+                           match="when given, axes and shape arguments"
+                           " have to be of the same length"):
+            _init_nd_shape_and_axes(xp.zeros([1, 1, 1, 1]),
+                                    shape=[1, 2, 3], axes=[1])
+
+        with assert_raises(ValueError,
+                           match="invalid number of data points"
+                           r" \(\[0\]\) specified"):
+            _init_nd_shape_and_axes(x, shape=[0], axes=None)
+
+        with assert_raises(ValueError,
+                           match="invalid number of data points"
+                           r" \(\[-2\]\) specified"):
+            _init_nd_shape_and_axes(x, shape=-2, axes=None)
+
+
+class TestFFTShift:
+
+    def test_definition(self, xp):
+        x = xp.asarray([0., 1, 2, 3, 4, -4, -3, -2, -1])
+        y = xp.asarray([-4., -3, -2, -1, 0, 1, 2, 3, 4])
+        xp_assert_close(fft.fftshift(x), y)
+        xp_assert_close(fft.ifftshift(y), x)
+        x = xp.asarray([0., 1, 2, 3, 4, -5, -4, -3, -2, -1])
+        y = xp.asarray([-5., -4, -3, -2, -1, 0, 1, 2, 3, 4])
+        xp_assert_close(fft.fftshift(x), y)
+        xp_assert_close(fft.ifftshift(y), x)
+
+    def test_inverse(self, xp):
+        for n in [1, 4, 9, 100, 211]:
+            x = xp.asarray(np.random.random((n,)))
+            xp_assert_close(fft.ifftshift(fft.fftshift(x)), x)
+
+    def test_axes_keyword(self, xp):
+        freqs = xp.asarray([[0., 1, 2], [3, 4, -4], [-3, -2, -1]])
+        shifted = xp.asarray([[-1., -3, -2], [2, 0, 1], [-4, 3, 4]])
+        xp_assert_close(fft.fftshift(freqs, axes=(0, 1)), shifted)
+        xp_assert_close(fft.fftshift(freqs, axes=0), fft.fftshift(freqs, axes=(0,)))
+        xp_assert_close(fft.ifftshift(shifted, axes=(0, 1)), freqs)
+        xp_assert_close(fft.ifftshift(shifted, axes=0),
+                        fft.ifftshift(shifted, axes=(0,)))
+        xp_assert_close(fft.fftshift(freqs), shifted)
+        xp_assert_close(fft.ifftshift(shifted), freqs)
+    
+    def test_uneven_dims(self, xp):
+        """ Test 2D input, which has uneven dimension sizes """
+        freqs = xp.asarray([
+            [0, 1],
+            [2, 3],
+            [4, 5]
+        ], dtype=xp.float64)
+
+        # shift in dimension 0
+        shift_dim0 = xp.asarray([
+            [4, 5],
+            [0, 1],
+            [2, 3]
+        ], dtype=xp.float64)
+        xp_assert_close(fft.fftshift(freqs, axes=0), shift_dim0)
+        xp_assert_close(fft.ifftshift(shift_dim0, axes=0), freqs)
+        xp_assert_close(fft.fftshift(freqs, axes=(0,)), shift_dim0)
+        xp_assert_close(fft.ifftshift(shift_dim0, axes=[0]), freqs)
+
+        # shift in dimension 1
+        shift_dim1 = xp.asarray([
+            [1, 0],
+            [3, 2],
+            [5, 4]
+        ], dtype=xp.float64)
+        xp_assert_close(fft.fftshift(freqs, axes=1), shift_dim1)
+        xp_assert_close(fft.ifftshift(shift_dim1, axes=1), freqs)
+
+        # shift in both dimensions
+        shift_dim_both = xp.asarray([
+            [5, 4],
+            [1, 0],
+            [3, 2]
+        ], dtype=xp.float64)
+        xp_assert_close(fft.fftshift(freqs, axes=(0, 1)), shift_dim_both)
+        xp_assert_close(fft.ifftshift(shift_dim_both, axes=(0, 1)), freqs)
+        xp_assert_close(fft.fftshift(freqs, axes=[0, 1]), shift_dim_both)
+        xp_assert_close(fft.ifftshift(shift_dim_both, axes=[0, 1]), freqs)
+
+        # axes=None (default) shift in all dimensions
+        xp_assert_close(fft.fftshift(freqs, axes=None), shift_dim_both)
+        xp_assert_close(fft.ifftshift(shift_dim_both, axes=None), freqs)
+        xp_assert_close(fft.fftshift(freqs), shift_dim_both)
+        xp_assert_close(fft.ifftshift(shift_dim_both), freqs)
+
+
+@skip_xp_backends("cupy", "jax.numpy",
+                  reasons=["CuPy has not implemented the `device` param",
+                           "JAX has not implemented the `device` param"])
+class TestFFTFreq:
+
+    def test_definition(self, xp):
+        x = xp.asarray([0, 1, 2, 3, 4, -4, -3, -2, -1], dtype=xp.float64)
+        x2 = xp.asarray([0, 1, 2, 3, 4, -5, -4, -3, -2, -1], dtype=xp.float64)
+
+        # default dtype varies across backends
+
+        y = 9 * fft.fftfreq(9, xp=xp)
+        xp_assert_close(y, x, check_dtype=False, check_namespace=True)
+
+        y = 9 * xp.pi * fft.fftfreq(9, xp.pi, xp=xp)
+        xp_assert_close(y, x, check_dtype=False)
+
+        y = 10 * fft.fftfreq(10, xp=xp)
+        xp_assert_close(y, x2, check_dtype=False)
+
+        y = 10 * xp.pi * fft.fftfreq(10, xp.pi, xp=xp)
+        xp_assert_close(y, x2, check_dtype=False)
+
+    def test_device(self, xp):
+        xp_test = array_namespace(xp.empty(0))
+        devices = get_xp_devices(xp)
+        for d in devices:
+            y = fft.fftfreq(9, xp=xp, device=d)
+            x = xp_test.empty(0, device=d)
+            assert device(y) == device(x)
+
+
+@skip_xp_backends("cupy", "jax.numpy",
+                  reasons=["CuPy has not implemented the `device` param",
+                           "JAX has not implemented the `device` param"])
+class TestRFFTFreq:
+
+    def test_definition(self, xp):
+        x = xp.asarray([0, 1, 2, 3, 4], dtype=xp.float64)
+        x2 = xp.asarray([0, 1, 2, 3, 4, 5], dtype=xp.float64)
+
+        # default dtype varies across backends
+        
+        y = 9 * fft.rfftfreq(9, xp=xp)
+        xp_assert_close(y, x, check_dtype=False, check_namespace=True)
+
+        y = 9 * xp.pi * fft.rfftfreq(9, xp.pi, xp=xp)
+        xp_assert_close(y, x, check_dtype=False)
+
+        y = 10 * fft.rfftfreq(10, xp=xp)
+        xp_assert_close(y, x2, check_dtype=False)
+
+        y = 10 * xp.pi * fft.rfftfreq(10, xp.pi, xp=xp)
+        xp_assert_close(y, x2, check_dtype=False)
+
+    def test_device(self, xp):
+        xp_test = array_namespace(xp.empty(0))
+        devices = get_xp_devices(xp)
+        for d in devices:
+            y = fft.rfftfreq(9, xp=xp, device=d)
+            x = xp_test.empty(0, device=d)
+            assert device(y) == device(x)

parrot/lib/python3.10/site-packages/scipy/fft/tests/test_multithreading.py

@@ -0,0 +1,84 @@
+from scipy import fft
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+import multiprocessing
+import os
+
+
+@pytest.fixture(scope='module')
+def x():
+    return np.random.randn(512, 128)  # Must be large enough to qualify for mt
+
+
+@pytest.mark.parametrize("func", [
+    fft.fft, fft.ifft, fft.fft2, fft.ifft2, fft.fftn, fft.ifftn,
+    fft.rfft, fft.irfft, fft.rfft2, fft.irfft2, fft.rfftn, fft.irfftn,
+    fft.hfft, fft.ihfft, fft.hfft2, fft.ihfft2, fft.hfftn, fft.ihfftn,
+    fft.dct, fft.idct, fft.dctn, fft.idctn,
+    fft.dst, fft.idst, fft.dstn, fft.idstn,
+])
+@pytest.mark.parametrize("workers", [2, -1])
+def test_threaded_same(x, func, workers):
+    expected = func(x, workers=1)
+    actual = func(x, workers=workers)
+    assert_allclose(actual, expected)
+
+
+def _mt_fft(x):
+    return fft.fft(x, workers=2)
+
+
+@pytest.mark.slow
+def test_mixed_threads_processes(x):
+    # Test that the fft threadpool is safe to use before & after fork
+
+    expect = fft.fft(x, workers=2)
+
+    with multiprocessing.Pool(2) as p:
+        res = p.map(_mt_fft, [x for _ in range(4)])
+
+    for r in res:
+        assert_allclose(r, expect)
+
+    fft.fft(x, workers=2)
+
+
+def test_invalid_workers(x):
+    cpus = os.cpu_count()
+
+    fft.ifft([1], workers=-cpus)
+
+    with pytest.raises(ValueError, match='workers must not be zero'):
+        fft.fft(x, workers=0)
+
+    with pytest.raises(ValueError, match='workers value out of range'):
+        fft.ifft(x, workers=-cpus-1)
+
+
+def test_set_get_workers():
+    cpus = os.cpu_count()
+    assert fft.get_workers() == 1
+    with fft.set_workers(4):
+        assert fft.get_workers() == 4
+
+        with fft.set_workers(-1):
+            assert fft.get_workers() == cpus
+
+        assert fft.get_workers() == 4
+
+    assert fft.get_workers() == 1
+
+    with fft.set_workers(-cpus):
+        assert fft.get_workers() == 1
+
+
+def test_set_workers_invalid():
+
+    with pytest.raises(ValueError, match='workers must not be zero'):
+        with fft.set_workers(0):
+            pass
+
+    with pytest.raises(ValueError, match='workers value out of range'):
+        with fft.set_workers(-os.cpu_count()-1):
+            pass

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_fake_quantize_learnable_per_channel_affine.h

@@ -0,0 +1,39 @@
+#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/_fake_quantize_learnable_per_channel_affine_ops.h>
+
+namespace at {
+
+
+// aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor
+inline at::Tensor _fake_quantize_learnable_per_channel_affine(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
+    return at::_ops::_fake_quantize_learnable_per_channel_affine::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor);
+}
+
+// aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _fake_quantize_learnable_per_channel_affine_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) {
+    return at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out);
+}
+// aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _fake_quantize_learnable_per_channel_affine_outf(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) {
+    return at::_ops::_fake_quantize_learnable_per_channel_affine_out::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_add_cpu_dispatch.h

@@ -0,0 +1,30 @@
+#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 cpu {
+
+TORCH_API ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Scalar & scalar);
+TORCH_API void _foreach_add_(at::TensorList self, const at::Scalar & scalar);
+TORCH_API ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1);
+TORCH_API void _foreach_add_(at::TensorList self, at::TensorList other, const at::Scalar & alpha=1);
+TORCH_API ::std::vector<at::Tensor> _foreach_add(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API void _foreach_add_(at::TensorList self, at::ArrayRef<at::Scalar> scalars);
+TORCH_API ::std::vector<at::Tensor> _foreach_add(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API void _foreach_add_(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace cpu
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_addcdiv_ops.h

@@ -0,0 +1,116 @@
+#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 _foreach_addcdiv_Scalar {
+  using schema = ::std::vector<at::Tensor> (at::TensorList, at::TensorList, at::TensorList, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value);
+};
+
+struct TORCH_API _foreach_addcdiv_ScalarList {
+  using schema = ::std::vector<at::Tensor> (at::TensorList, at::TensorList, at::TensorList, at::ArrayRef<at::Scalar>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ScalarList")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars);
+};
+
+struct TORCH_API _foreach_addcdiv_Tensor {
+  using schema = ::std::vector<at::Tensor> (at::TensorList, at::TensorList, at::TensorList, 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::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[]")
+  static ::std::vector<at::Tensor> call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars);
+};
+
+struct TORCH_API _foreach_addcdiv__Scalar {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value);
+};
+
+struct TORCH_API _foreach_addcdiv__ScalarList {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, at::ArrayRef<at::Scalar>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ScalarList")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars);
+};
+
+struct TORCH_API _foreach_addcdiv__Tensor {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, 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::_foreach_addcdiv_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars);
+};
+
+struct TORCH_API _foreach_addcdiv_Scalar_out {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, const at::Scalar &, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out);
+};
+
+struct TORCH_API _foreach_addcdiv_ScalarList_out {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, at::ArrayRef<at::Scalar>, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "ScalarList_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef<at::Scalar> scalars, at::TensorList out);
+};
+
+struct TORCH_API _foreach_addcdiv_Tensor_out {
+  using schema = void (at::TensorList, at::TensorList, at::TensorList, const at::Tensor &, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_addcdiv")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_minimum.h

@@ -0,0 +1,82 @@
+#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/_foreach_minimum_ops.h>
+
+namespace at {
+
+
+// aten::_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_minimum_Scalar::call(self, scalar);
+}
+
+// aten::_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> ()
+inline void _foreach_minimum_(at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_minimum__Scalar::call(self, scalar);
+}
+
+// aten::_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::TensorList other) {
+    return at::_ops::_foreach_minimum_List::call(self, other);
+}
+
+// aten::_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> ()
+inline void _foreach_minimum_(at::TensorList self, at::TensorList other) {
+    return at::_ops::_foreach_minimum__List::call(self, other);
+}
+
+// aten::_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[]
+inline ::std::vector<at::Tensor> _foreach_minimum(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_minimum_ScalarList::call(self, scalars);
+}
+
+// aten::_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> ()
+inline void _foreach_minimum_(at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_minimum__ScalarList::call(self, scalars);
+}
+
+// aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, const at::Scalar & scalar) {
+    return at::_ops::_foreach_minimum_Scalar_out::call(self, scalar, out);
+}
+// aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_outf(at::TensorList self, const at::Scalar & scalar, at::TensorList out) {
+    return at::_ops::_foreach_minimum_Scalar_out::call(self, scalar, out);
+}
+
+// aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, at::TensorList other) {
+    return at::_ops::_foreach_minimum_List_out::call(self, other, out);
+}
+// aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_outf(at::TensorList self, at::TensorList other, at::TensorList out) {
+    return at::_ops::_foreach_minimum_List_out::call(self, other, out);
+}
+
+// aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_out(at::TensorList out, at::TensorList self, at::ArrayRef<at::Scalar> scalars) {
+    return at::_ops::_foreach_minimum_ScalarList_out::call(self, scalars, out);
+}
+// aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> ()
+inline void _foreach_minimum_outf(at::TensorList self, at::ArrayRef<at::Scalar> scalars, at::TensorList out) {
+    return at::_ops::_foreach_minimum_ScalarList_out::call(self, scalars, out);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_native_multi_head_attention_cpu_dispatch.h

@@ -0,0 +1,23 @@
+#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 cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> _native_multi_head_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional<at::Tensor> & mask={}, bool need_weights=true, bool average_attn_weights=true, c10::optional<int64_t> mask_type=c10::nullopt);
+
+} // namespace cpu
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_scaled_dot_product_flash_attention.h

@@ -0,0 +1,30 @@
+#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_ops.h>
+
+namespace at {
+
+
+// aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor,at::Tensor,c10::SymInt,c10::SymInt,at::Tensor,at::Tensor,at::Tensor> _scaled_dot_product_flash_attention(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, c10::optional<double> scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention::call(query, key, value, dropout_p, is_causal, return_debug_mask, scale);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/_values_native.h

@@ -0,0 +1,21 @@
+#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 _values_sparse(const at::Tensor & self);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/acosh_cuda_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 cuda {
+
+TORCH_API at::Tensor acosh(const at::Tensor & self);
+TORCH_API at::Tensor & acosh_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & acosh_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & acosh_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/arctan_native.h

@@ -0,0 +1,23 @@
+#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 arctan(const at::Tensor & self);
+TORCH_API at::Tensor & arctan_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & arctan_(at::Tensor & self);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/argsort_cpu_dispatch.h

@@ -0,0 +1,23 @@
+#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 cpu {
+
+TORCH_API at::Tensor argsort(const at::Tensor & self, bool stable, int64_t dim=-1, bool descending=false);
+
+} // namespace cpu
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/block_diag_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 block_diag {
+  using schema = at::Tensor (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::block_diag")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "block_diag(Tensor[] tensors) -> Tensor")
+  static at::Tensor call(at::TensorList tensors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors);
+};
+
+struct TORCH_API block_diag_out {
+  using schema = at::Tensor & (at::TensorList, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::block_diag")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(at::TensorList tensors, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/cauchy_native.h

@@ -0,0 +1,23 @@
+#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 cauchy(const at::Tensor & self, double median=0, double sigma=1, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & cauchy_out(const at::Tensor & self, double median, double sigma, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & cauchy_(at::Tensor & self, double median=0, double sigma=1, c10::optional<at::Generator> generator=c10::nullopt);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/conv_transpose3d_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 conv_transpose3d_input {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const c10::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::conv_transpose3d")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor")
+  static at::Tensor call(const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/exponential_native.h

@@ -0,0 +1,23 @@
+#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 exponential(const at::Tensor & self, double lambd=1, c10::optional<at::Generator> generator=c10::nullopt);
+TORCH_API at::Tensor & exponential_out(const at::Tensor & self, double lambd, c10::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & exponential_(at::Tensor & self, double lambd=1, c10::optional<at::Generator> generator=c10::nullopt);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/fractional_max_pool2d_backward_native.h

@@ -0,0 +1,26 @@
+#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>
+#include <ATen/ops/fractional_max_pool2d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_fractional_max_pool2d_backward_cpu : public at::meta::structured_fractional_max_pool2d_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_fractional_max_pool2d_backward_cuda : public at::meta::structured_fractional_max_pool2d_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/igammac_cpu_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 cpu {
+
+TORCH_API at::Tensor igammac(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & igammac_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & igammac_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & igammac_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/igammac_native.h

@@ -0,0 +1,23 @@
+#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>
+#include <ATen/ops/igammac_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_igammac_out : public at::meta::structured_igammac {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/imag_native.h

@@ -0,0 +1,21 @@
+#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 imag(const at::Tensor & self);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/lcm_meta_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 meta {
+
+TORCH_API at::Tensor lcm(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & lcm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & lcm_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & lcm_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_diagonal_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#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 compositeimplicitautograd {
+
+TORCH_API at::Tensor linalg_diagonal(const at::Tensor & A, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+
+} // namespace compositeimplicitautograd
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/log_cuda_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 cuda {
+
+TORCH_API at::Tensor log(const at::Tensor & self);
+TORCH_API at::Tensor & log_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & log_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & log_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/logical_or_native.h

@@ -0,0 +1,23 @@
+#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 logical_or(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & logical_or_(at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & logical_or_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/lstm_mps_backward_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_backward {
+  using schema = ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> (const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, const at::Tensor &, const at::Tensor &, const 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_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "lstm_mps_backward(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[])")
+  static ::std::tuple<at::Tensor,::std::vector<at::Tensor>,::std::vector<at::Tensor>> call(const c10::optional<at::Tensor> & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, 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,::std::vector<at::Tensor>,::std::vector<at::Tensor>> redispatch(c10::DispatchKeySet dispatchKeySet, const c10::optional<at::Tensor> & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, 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_backward_out {
+  using schema = void (const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, const c10::optional<at::Tensor> &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, at::TensorList, at::TensorList, bool, int64_t, double, bool, bool, bool, at::Tensor &, at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::lstm_mps_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, 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) -> ()")
+  static void call(const c10::optional<at::Tensor> & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, 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::TensorList out1, at::TensorList out2);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const c10::optional<at::Tensor> & grad_y, const c10::optional<at::Tensor> & grad_hy, const c10::optional<at::Tensor> & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, 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::TensorList out1, at::TensorList out2);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/matrix_exp_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#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 compositeimplicitautograd {
+
+TORCH_API at::Tensor matrix_exp(const at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/maximum.h

@@ -0,0 +1,39 @@
+#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/maximum_ops.h>
+
+namespace at {
+
+
+// aten::maximum(Tensor self, Tensor other) -> Tensor
+inline at::Tensor maximum(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::maximum::call(self, other);
+}
+
+// aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & maximum_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::maximum_out::call(self, other, out);
+}
+// aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & maximum_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::maximum_out::call(self, other, out);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/mvlgamma_cpu_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 cpu {
+
+TORCH_API at::Tensor & mvlgamma_out(at::Tensor & out, const at::Tensor & self, int64_t p);
+TORCH_API at::Tensor & mvlgamma_outf(const at::Tensor & self, int64_t p, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/native_dropout_backward_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 & native_dropout_backward_out(at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & mask, double scale);
+TORCH_API at::Tensor & native_dropout_backward_outf(const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/outer_native.h

@@ -0,0 +1,22 @@
+#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 outer(const at::Tensor & self, const at::Tensor & vec2);
+TORCH_API at::Tensor & outer_out(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/pinverse_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 pinverse {
+  using schema = at::Tensor (const at::Tensor &, double);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::pinverse")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "pinverse(Tensor self, float rcond=1e-15) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, double rcond);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/replication_pad1d.h

@@ -0,0 +1,91 @@
+#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/replication_pad1d_ops.h>
+
+namespace at {
+
+
+// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & replication_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & replication_pad1d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::replication_pad1d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & replication_pad1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::replication_pad1d_out::call(self, padding, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & replication_pad1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::replication_pad1d_out::call(self, padding, out);
+  }
+}
+
+// aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & replication_pad1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::replication_pad1d_out::call(self, padding, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & replication_pad1d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::replication_pad1d_out::call(self, padding, out);
+  }
+}
+
+// aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor
+inline at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::replication_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor replication_pad1d(const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::replication_pad1d::call(self, c10::fromIntArrayRefSlow(padding));
+  }
+}
+
+// aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor
+inline at::Tensor replication_pad1d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::replication_pad1d::call(self, padding);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor replication_pad1d(const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::replication_pad1d::call(self, padding);
+  }
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/rsqrt.h

@@ -0,0 +1,44 @@
+#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/rsqrt_ops.h>
+
+namespace at {
+
+
+// aten::rsqrt(Tensor self) -> Tensor
+inline at::Tensor rsqrt(const at::Tensor & self) {
+    return at::_ops::rsqrt::call(self);
+}
+
+// aten::rsqrt_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & rsqrt_(at::Tensor & self) {
+    return at::_ops::rsqrt_::call(self);
+}
+
+// aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rsqrt_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::rsqrt_out::call(self, out);
+}
+// aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & rsqrt_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::rsqrt_out::call(self, out);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/set.h

@@ -0,0 +1,161 @@
+#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/set_ops.h>
+
+namespace at {
+
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & set_(at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set__source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & set_(at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set__source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & set_(at::Tensor & self, const at::Tensor & source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set__source_Tensor_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & set_(at::Tensor & self, const at::Tensor & source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set__source_Tensor_storage_offset::call(self, source, storage_offset, size, stride);
+  }
+}
+
+// aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source) {
+    return at::_ops::set_source_Storage_out::call(self, source, out);
+}
+// aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_outf(const at::Tensor & self, at::Storage source, at::Tensor & out) {
+    return at::_ops::set_source_Storage_out::call(self, source, out);
+}
+
+// aten::set.source_Storage(Tensor self, Storage source) -> Tensor
+inline at::Tensor set(const at::Tensor & self, at::Storage source) {
+    return at::_ops::set_source_Storage::call(self, source);
+}
+
+// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
+  }
+}
+
+// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_outf(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor & set_outf(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out);
+  }
+}
+
+// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_symint_out(at::Tensor & out, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
+  }
+}
+
+// aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_symint_outf(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor & set_outf(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) {
+    return at::_ops::set_source_Storage_storage_offset_out::call(self, source, storage_offset, size, stride, out);
+  }
+}
+
+// aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor
+inline at::Tensor set(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, int64_t>::value>>
+  at::Tensor set(const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride));
+  }
+}
+
+// aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor
+inline at::Tensor set_symint(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same<T, c10::SymInt>::value>>
+  at::Tensor set(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) {
+    return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, size, stride);
+  }
+}
+
+// aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & source) {
+    return at::_ops::set_source_Tensor_out::call(self, source, out);
+}
+// aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_outf(const at::Tensor & self, const at::Tensor & source, at::Tensor & out) {
+    return at::_ops::set_source_Tensor_out::call(self, source, out);
+}
+
+// aten::set.source_Tensor(Tensor self, Tensor source) -> Tensor
+inline at::Tensor set(const at::Tensor & self, const at::Tensor & source) {
+    return at::_ops::set_source_Tensor::call(self, source);
+}
+
+// aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::set_out::call(self, out);
+}
+// aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & set_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::set_out::call(self, out);
+}
+
+// aten::set(Tensor self) -> Tensor
+inline at::Tensor set(const at::Tensor & self) {
+    return at::_ops::set::call(self);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/set_data.h

@@ -0,0 +1,26 @@
+#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/set_data_ops.h>
+
+namespace at {
+
+
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/signbit.h

@@ -0,0 +1,39 @@
+#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/signbit_ops.h>
+
+namespace at {
+
+
+// aten::signbit(Tensor self) -> Tensor
+inline at::Tensor signbit(const at::Tensor & self) {
+    return at::_ops::signbit::call(self);
+}
+
+// aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & signbit_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::signbit_out::call(self, out);
+}
+// aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & signbit_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::signbit_out::call(self, out);
+}
+
+}

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/signbit_meta.h

@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.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/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_signbit : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/softmax_native.h

@@ -0,0 +1,23 @@
+#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 softmax(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+TORCH_API at::Tensor & softmax_out(const at::Tensor & self, int64_t dim, c10::optional<at::ScalarType> dtype, at::Tensor & out);
+TORCH_API at::Tensor softmax(const at::Tensor & self, at::Dimname dim, c10::optional<at::ScalarType> dtype=c10::nullopt);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/sort_compositeexplicitautogradnonfunctional_dispatch.h

@@ -0,0 +1,23 @@
+#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 compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> sort(const at::Tensor & self, c10::optional<bool> stable, int64_t dim=-1, bool descending=false);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/sparse_compressed_tensor_native.h

@@ -0,0 +1,22 @@
+#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 sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional<at::ScalarType> dtype={}, c10::optional<at::Layout> layout={}, c10::optional<at::Device> device={}, c10::optional<bool> pin_memory={});
+TORCH_API at::Tensor sparse_compressed_tensor(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::optional<at::ScalarType> dtype={}, c10::optional<at::Layout> layout={}, c10::optional<at::Device> device={}, c10::optional<bool> pin_memory={});
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/special_log_ndtr_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 special_log_ndtr {
+  using schema = 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::special_log_ndtr")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_log_ndtr(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_log_ndtr_out {
+  using schema = at::Tensor & (const 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::special_log_ndtr")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_t_native.h

@@ -0,0 +1,27 @@
+#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>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_t_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_t_out : public at::meta::structured_special_shifted_chebyshev_polynomial_t {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_t_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_t_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/special_sinc_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,25 @@
+#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 compositeimplicitautograd {
+
+TORCH_API at::Tensor special_sinc(const at::Tensor & self);
+TORCH_API at::Tensor & special_sinc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_sinc_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/svd_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,25 @@
+#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 compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_outf(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+
+} // namespace compositeimplicitautograd
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/svd_native.h

@@ -0,0 +1,22 @@
+#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 ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+} // namespace native
+} // namespace at

videollama2/lib/python3.10/site-packages/torch/include/ATen/ops/sym_size_compositeimplicitautograd_dispatch.h

@@ -0,0 +1,23 @@
+#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 compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_size(const at::Tensor & self, int64_t dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at