#!/usr/bin/env python
import unittest
import numpy as np
from tinygrad import dtypes, Tensor, TinyJit, GlobalCounters, Variable
from tinygrad.uop.ops import Ops
from tinygrad.device import is_dtype_supported
from tinygrad.helpers import temp, CI, DEV, Context

N = 200  # has to be bigger than the cache to fail

class TestAssign(unittest.TestCase):
  def test_simple_assignment(self):
    a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    a.realize()
    b.realize()
    ba1 = a.uop.base.realized
    bb1 = b.uop.base.realized
    a += b
    a.realize()
    ba2 = a.uop.base.realized
    assert ba1 == ba2 and ba1 != bb1
    np.testing.assert_allclose(a.numpy(), (np.arange(N*N)*2).reshape((N,N)))

  def test_assign_zeros_good(self):
    a = Tensor.zeros(10,10).contiguous()
    a.assign(Tensor.ones(10,10))
    b = Tensor.zeros(10,10).contiguous()
    a.realize()
    np.testing.assert_allclose(b.numpy(), 0)

  @unittest.skip("TODO: this often crashes in CI")
  def test_assign_zeros(self):
    a = Tensor.zeros(10,10).contiguous()
    b = Tensor.zeros(10,10).contiguous()
    a.assign(Tensor.ones(10,10))
    a.realize()
    np.testing.assert_allclose(b.numpy(), 0)

  def test_assign_copy(self):
    a = Tensor([1.,2,3], device="PYTHON")
    c = Tensor.empty(3).assign(a.to(None))
    # it should copy into the empty buffer
    GlobalCounters.reset()
    c.realize()
    self.assertEqual(GlobalCounters.kernel_count, 1)

  def test_assign_add(self):
    for T in (1, 2, 10):#, 100): # this crashes in CI, not sure why
      x = Tensor([0]).realize()
      buf = x.uop.base.realized
      for _ in range(T):
        x += 1
      x.realize()
      assert x.item() == T
      assert x.uop.base.realized is buf

  def test_assign_slice_add(self):
    for T in (1, 2, 10, 100):
      x = Tensor([0, 0]).realize()
      buf = x.uop.base.realized
      for _ in range(T):
        x[0] += 1
      x.realize()
      assert x.tolist() == [T, 0]
      assert x.uop.base.realized is buf

  def test_assign_add_double(self):
    def f(x):
      x += 1
      x.realize()
    x = Tensor([0])
    f(x)
    out = x.item()
    assert out == 1, f"expected 1, got {out}"
    x = Tensor([0])
    f(x)
    out = x.item()
    assert out == 1, f"expected 1, got {out}"

  def test_assign_add_jit(self):
    @TinyJit
    def f(x):
      x += 1
      x.realize()
    x = Tensor([0])
    for _ in range(5): f(x)
    assert x.item() == 5

  def test_assign_add_jit_other(self):
    @TinyJit
    def f(x):
      x += 1
      x.realize()
    x = Tensor([0])
    for _ in range(5): f(x)
    assert x.item() == 5

    y = Tensor([0])
    for _ in range(4): f(y)
    assert y.item() == 4

  def test_assign_other_jit(self):
    @TinyJit
    def f(x, a):
      x.assign(a)
      x.realize()
    x = Tensor([0])
    for i in range(1, 6):
      f(x, x.full_like(i).contiguous())  # const would be implicitly folded without contiguous
      assert x.item() == i

  def test_assign_add_other_jit(self):
    @TinyJit
    def f(x, a):
      x += a
      x.realize()
    x = Tensor([0])
    a = 0
    for i in range(1, 6):
      a += i
      f(x, x.full_like(i).contiguous())
      assert x.item() == a

  def test_assign_changes(self):
    a = Tensor.ones(4).contiguous().realize()
    old_a = a
    a.assign(Tensor.full((4,), 2.).contiguous())
    # NOTE: old_a is now 2, and this would match the behavior of pytorch
    new = a + old_a
    np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_changes_alt(self, realize=False):
    a = Tensor(1).contiguous()
    if realize: a.realize()
    b = a.clone()
    b.assign(2)
    b.realize()
    self.assertNotEqual(a.item(), b.item())
  def test_assign_changes_realized_alt(self): return self.test_assign_changes_alt(realize=True)

  def test_assign_changes_buffer_alt(self):
    a, b = [Tensor(Tensor(0).contiguous().realize().uop.buf_uop) for _ in range(2)]
    Tensor.realize(a.contiguous().assign(1), b.contiguous().assign(2))
    self.assertEqual((a + b).item(), 3)

  def test_assign_diamond_cycle(self):
    # NOTE: should *not* raise AssertionError from numpy
    with self.assertRaisesRegex(RuntimeError, "cycle"):
      a = Tensor.ones(4).contiguous().realize()
      times_a = a*3
      a.assign(Tensor.full((4,), 2.).contiguous())
      new = a + (times_a-1)
      np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_diamond_contiguous_cycle(self):
    with self.assertRaisesRegex(RuntimeError, "cycle"):
      a = Tensor.ones(4).contiguous().realize()
      times_a = a*3
      a.assign(Tensor.full((4,), 2.))
      new = a.contiguous() + times_a-1
      np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_diamond_possible(self):
    a = Tensor.ones(4).contiguous().realize()
    times_a = a*3
    a.assign(Tensor.full((4,), 2.))
    new = a + (times_a-1).contiguous()
    np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_diamond_possible_contiguous(self):
    a = Tensor.ones(4).contiguous().realize()
    times_a = a*3
    a.assign(Tensor.full((4,), 2.).contiguous())
    new = a + (times_a-1).contiguous()
    np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_diamond_both_contiguous(self):
    a = Tensor.ones(4).contiguous().realize()
    times_a = a*3
    a.assign(Tensor.full((4,), 2.))
    new = a.contiguous() + (times_a-1).contiguous()
    np.testing.assert_allclose(new.numpy(), 4)

  def test_assign_diamond_alt(self):
    a = Tensor.ones(4).contiguous().realize()
    a.assign(Tensor.full((4,), 2.).contiguous())
    times_a = a*3
    new = a + times_a
    np.testing.assert_allclose(new.numpy(), 8)

  @unittest.skipIf(CI and DEV.renderer == "LVP", "flaky in CI")
  def test_double_assign(self):
    a = Tensor.ones(4).contiguous().realize()
    a += 1
    a += 1
    np.testing.assert_allclose(a.numpy(), 3)

  def test_crossover_assign(self):
    a = Tensor.full((4,), 2).contiguous().realize()
    b = Tensor.full((4,), 3).contiguous().realize()
    a += b
    b += a
    Tensor.realize(a,b)
    np.testing.assert_allclose(a.numpy(), 5)
    np.testing.assert_allclose(b.numpy(), 8)

  def test_assign_double_diamond(self):
    a = Tensor.full((4,), 2).contiguous().realize()
    b = Tensor.full((4,), 3).contiguous().realize()
    a_prev = a*4
    b_prev = b+3
    b += a_prev.contiguous()
    a += b_prev.contiguous()
    Tensor.realize(a, b)
    np.testing.assert_equal(b.numpy(), 11)
    np.testing.assert_equal(a.numpy(), 8)

  def test_assign_double_diamond_reduce(self):
    a0 = Tensor.full((16, 16), 10).contiguous().realize()
    a1 = Tensor.full((16, 16), 20).contiguous().realize()
    b0 = Tensor.full((16, ), 1).contiguous().realize()
    b1 = Tensor.full((16, ), 2).contiguous().realize()

    r0 = (a0 - b1.contiguous()).sum(1)
    r1 = (a1 - b0.contiguous()).sum(1)
    b0.assign(r0 * b0)
    b1.assign(r1 * b1)
    Tensor.realize(b0, b1)
    np.testing.assert_equal(b0.numpy(), 128)
    np.testing.assert_equal(b1.numpy(), 608)

  def test_crossunder_assign(self):
    # NOTE: should *not* raise AssertionError from numpy
    with self.assertRaisesRegex(RuntimeError, "cycle"):
      a = Tensor.full((4,), 2).contiguous().realize()
      b = Tensor.full((4,), 3).contiguous().realize()
      c = a+9
      a += b
      b += c
      Tensor.realize(a,b)
      np.testing.assert_allclose(a.numpy(), 2+3)
      np.testing.assert_allclose(b.numpy(), 3+2+9)

  def test_assign_kv_cache(self):
    bsz, max_context = 2, 8

    class Attn:
      @TinyJit
      def __call__(self, xk:Tensor, start_pos:Variable):
        seqlen = xk.shape[1]
        if not hasattr(self, "cache_k"):
          self.cache_k = Tensor.zeros(bsz, max_context, 1, 1).contiguous()
        keys = self.cache_k.shrink((None, (0, start_pos), None, None)).cat(xk, dim=1).contiguous() if start_pos > 0 else xk
        self.cache_k.assign(keys.pad((None,(0,max_context-start_pos-seqlen),None,None)).contiguous()).realize()

    attn = Attn()
    xk = Tensor.ones(bsz, 3, 1, 1).contiguous()
    attn(xk, 0)
    for i in range(3,6):
      # copied from LLaMA
      start_pos = Variable("start_pos", 1, max_context).bind(i)
      xk = Tensor.ones(bsz, 1, 1, 1).contiguous()
      attn(xk, start_pos)

    out = attn.cache_k.flatten().numpy()
    np.testing.assert_allclose(out, [1.,1.,1.,1.,1.,1.,0.,0.,1.,1.,1.,1.,1.,1.,0.,0.])

  def test_assign_after(self):
    t = Tensor.zeros(10).contiguous().realize()
    t.uop = t.uop.after(t.uop.store((t+1).uop))
    np.testing.assert_allclose(t.numpy(), [1.,1.,1.,1.,1.,1.,1.,1.,1.,1.])

  def test_assign_after_partial(self):
    t = Tensor.zeros(10).contiguous().realize()
    t.uop = t.uop.after(t[:5].uop.after(t[:5].uop.store(Tensor.ones(5).uop)))
    np.testing.assert_allclose(t.numpy(), [1.,1.,1.,1.,1.,0.,0.,0.,0.,0.])

  def test_assign_after_target_chain(self):
    t = Tensor.arange(16).reshape(4, 4).permute(1, 0).contiguous()
    t.assign(t + 100)
    np.testing.assert_equal(t.numpy(), [[100, 104, 108, 112], [101, 105, 109, 113], [102, 106, 110, 114], [103, 107, 111, 115]])

  def test_assign_contiguous(self):
    b = Tensor.arange(16).reshape(4,4).contiguous().realize()
    a = (Tensor.arange(16).reshape(4,4).contiguous().realize() + 1)
    GlobalCounters.reset()
    b.assign(a.contiguous()).realize()
    self.assertEqual(GlobalCounters.kernel_count, 2)

  def test_assign_contiguous_permute(self):
    b = Tensor.arange(16).reshape(4,4).contiguous().realize()
    a = (Tensor.arange(16).reshape(4,4).contiguous().realize() + 1).permute((1,0))
    GlobalCounters.reset()
    b.assign(a.contiguous()).realize()
    self.assertEqual(GlobalCounters.kernel_count, 2)

  def test_permuted_assignment(self):
    a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    a.realize()
    b.realize()
    ba1 = a.uop.base.realized
    bb1 = b.uop.base.realized
    a = a.permute(1,0)
    a += b
    a.realize()
    ba2 = a.uop.base.realized
    np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))
    # permute and base are the same buffer
    assert ba1 == ba2 and ba1 != bb1

  def test_post_permuted_assignment(self):
    a = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    b = Tensor(np.arange(N*N, dtype=np.float32)).reshape(N,N)
    a.realize()
    b.realize()
    #GlobalCounters.cache = []
    ba1 = a.uop.base.realized # noqa: F841
    bb1 = b.uop.base.realized # noqa: F841
    a.assign(a.permute(1,0) + b)   # this should not work!
    a.realize()
    ba2 = a.uop.base.realized # noqa: F841
    # NOTE: don't test that it's assigned
    #assert ba1 == ba2 and ba1 != bb1
    np.testing.assert_allclose(a.numpy(), np.arange(N*N).reshape((N,N)) + np.arange(N*N).reshape((N,N)).transpose(1,0))

  def test_post_permuted_assignment_alt(self):
    a = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    b = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    new_a = (a.T+b).numpy()
    a.assign(a.T+b)
    np.testing.assert_allclose(a.numpy(), new_a)

  def test_post_flipped_assignment(self):
    a = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    b = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    new_a = (a.flip(0)+b).numpy()
    a.assign(a.flip(0)+b)
    np.testing.assert_allclose(a.numpy(), new_a)

  def test_post_flipped_assignment_axis1(self):
    a = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    b = Tensor.arange(N*N).reshape(N,N).contiguous().realize()
    new_a = (a.flip(1)+b).numpy()
    a.assign(a.flip(1)+b)
    np.testing.assert_allclose(a.numpy(), new_a)

  def test_post_reshape_assignment_fine(self):
    a = Tensor.arange(N*N).reshape(N, N).contiguous().realize()
    b = Tensor.arange(N*N).reshape(N, N).contiguous().realize()
    rhs = a.reshape(-1).reshape(N, N)
    new_a = (rhs+b).numpy()
    a.assign(rhs+b)  # self-assign with reshape view is fine
    np.testing.assert_allclose(a.numpy(), new_a)

  @unittest.skip("multi output not supported anymore")
  def test_simple_assignment_multioutput(self):
    a = Tensor.arange(32*32).reshape(32, 32).contiguous().realize()
    b = Tensor.full((32, ), 1.).contiguous().realize()
    c = Tensor.full((32, ), 2.).contiguous().realize()
    d = Tensor.full((32, ), 3.).contiguous().realize()

    r = a.sum(axis=1)
    b.assign(r + b)
    c.assign(r + c)
    d.assign(r + d)

    GlobalCounters.reset()
    Tensor.realize(b, c, d)
    self.assertEqual(GlobalCounters.kernel_count, 1)
    np.testing.assert_allclose(b.numpy(), a.sum(1).numpy()+1)
    np.testing.assert_allclose(c.numpy(), a.sum(1).numpy()+2)
    np.testing.assert_allclose(d.numpy(), a.sum(1).numpy()+3)

  # NOTE: if the assign target is read/write in a single kernel, it should be contiguous

  def test_permuted_assignment_correct(self):
    a = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
    b = Tensor.arange(4 * 4).reshape(4, 4).contiguous().realize()
    a = a.permute(1, 0)
    new_val = a + b
    a.assign(new_val)
    np.testing.assert_equal(a.numpy(), np.arange(4 * 4).reshape(4, 4).transpose(1, 0) + np.arange(4 * 4).reshape(4, 4))

  def test_permuted_reduceop_child_dual_use(self):
    a = Tensor.arange(32*32*32).reshape(32, 32, 32).contiguous().realize()
    b = Tensor.ones(32, 32, dtype=dtypes.int).contiguous().realize()
    r = a.sum(axis=1)
    b.assign(r + b.permute(1, 0))
    b.realize()
    np.testing.assert_equal(b.numpy(), a.numpy().sum(axis=1)+np.ones((32, 32), dtype=np.int32).transpose(1, 0))

  @unittest.skip("multi output not supported anymore")
  def test_permuted_reduceop_multioutput_dual_use(self):
    a = Tensor.arange(32*32*32).reshape(32, 32, 32).contiguous().realize()
    b = Tensor.full((32, 32), 1.).contiguous().realize()
    c = Tensor.full((32, 32), 2.).contiguous().realize()

    with self.assertRaisesRegex(RuntimeError, "contiguous"):
      r = a.sum(axis=1)
      b_perm = b.permute(1, 0)
      b.assign(r + b)
      c.assign(r + b_perm)
      Tensor.realize(b, c)

  @unittest.skip("multi output not supported anymore")
  def test_permuted_reduceop_multioutput_dual_use_possible(self):
    a = Tensor.arange(32*32*32).reshape(32, 32, 32).contiguous().realize()
    b = Tensor.arange(32 * 32).reshape(32, 32).realize()
    c = Tensor.arange(32 * 32).reshape(32, 32).realize()

    GlobalCounters.reset()
    r = a.sum(axis=1)
    b_perm = b.permute(1, 0)
    b.assign(r + b)
    c.assign(r + b_perm.contiguous())
    Tensor.realize(b, c)
    self.assertEqual(GlobalCounters.kernel_count, 2)
    np.testing.assert_equal(b.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32))
    np.testing.assert_equal(c.numpy(), a.numpy().sum(1) + np.arange(32 * 32).reshape(32, 32).transpose(1, 0))

  def test_permuted_assignment_masked_view_possible(self):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.shrink((None, (0, 2))).pad((None, (0, 2)), value=2)
    a.assign(a + b)
    GlobalCounters.reset()
    a.realize()
    self.assertEqual(GlobalCounters.kernel_count, 1)
    np.testing.assert_equal(a.numpy(), np.ones((4, 4))+np.pad(np.ones((4, 4))[:, 0:2], ((0, 0), (0, 2)), constant_values=2))

  def test_permuted_assignment_masked_view_not_contiguous(self):
    a = Tensor.ones(4, 4).contiguous().realize()
    b = a.shrink((None, (0, 2))).pad((None, (0, 2)), value=2).permute(1, 0)
    a.assign(a + b)
    a.realize()
    self.assertListEqual(a.tolist(), [[2.,2.,2.,2.],[2.,2.,2.,2.],[3.,3.,3.,3.], [3.,3.,3.,3.]])

  # TODO: is there a way to sneak in a permute such that it returns the wrong answer?

  def test_overlapping_shrink_assignment_forward(self):
    # Forward shift: read index > write index in overlap
    N = 100000
    shift = 1000
    a = Tensor.arange(N).float().contiguous().realize()
    expected = np.arange(N, dtype=np.float32)
    expected[:N-shift] = expected[shift:].copy()
    with Context(NOOPT=1): a[0:N-shift].assign(a[shift:N]).realize()
    np.testing.assert_allclose(a.numpy(), expected)

  def test_overlapping_shrink_assignment_reverse(self):
    # Reverse shift: write index > read index in overlap
    N = 100000
    shift = 1000
    a = Tensor.arange(N).float().contiguous().realize()
    expected = np.arange(N, dtype=np.float32)
    expected[shift:] = expected[:N-shift].copy()
    with Context(NOOPT=1): a[shift:N].assign(a[0:N-shift]).realize()
    np.testing.assert_allclose(a.numpy(), expected)

  def test_nonoverlapping_shrink_assignment(self):
    # TODO: non-overlapping shrinks don't actually need contiguous, could be 1 kernel with smarter range analysis
    a = Tensor.arange(100).float().contiguous().realize()
    expected = np.arange(100, dtype=np.float32)
    expected[0:10] = expected[50:60].copy()
    GlobalCounters.reset()
    a[0:10].assign(a[50:60]).realize()
    self.assertEqual(GlobalCounters.kernel_count, 2)  # currently conservative, forces contiguous
    np.testing.assert_allclose(a.numpy(), expected)

  @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
  def test_setitem_half(self):
    a = Tensor.full((8,), 1.0, dtype=dtypes.half).contiguous().realize()
    b = Tensor.full((4,), 2.0, dtype=dtypes.half).contiguous().realize()
    assign = a[:4].assign(b)
    assign.realize()
    np.testing.assert_allclose(a.numpy(), [2., 2., 2., 2., 1., 1., 1., 1.])

  def test_setitem_list(self):
    a = Tensor.zeros(8).contiguous().realize()
    a[2:5] = [1, 2, 3]
    np.testing.assert_allclose(a.numpy(), [0., 0., 1., 2., 3., 0., 0., 0.])

  def test_assign_bitcast(self):
    # assign to a bitcast view should modify the underlying buffer
    a = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
    # IEEE 754: 1.0f = 0x3f800000, 2.0f = 0x40000000, 3.0f = 0x40400000, 4.0f = 0x40800000
    a.bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000, 0x40000000, 0x3f800000], dtype=dtypes.uint32)).realize()
    np.testing.assert_allclose(a.numpy(), [4.0, 3.0, 2.0, 1.0])
    # double bitcast
    b = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
    b.bitcast(dtypes.uint32).bitcast(dtypes.int32).assign(Tensor([0x40800000, 0x40400000, 0x40000000, 0x3f800000], dtype=dtypes.int32)).realize()
    np.testing.assert_allclose(b.numpy(), [4.0, 3.0, 2.0, 1.0])
    # shrink then bitcast
    c = Tensor([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32).realize()
    c[0:2].bitcast(dtypes.uint32).assign(Tensor([0x40800000, 0x40400000], dtype=dtypes.uint32)).realize()
    np.testing.assert_allclose(c.numpy(), [4.0, 3.0, 3.0, 4.0])

  def test_assign_bitcast_different_size(self):
    # assign to a shape-changing bitcast view (only works on DISK currently)
    a = Tensor([0]*8, dtype=dtypes.uint8).realize()
    a.bitcast(dtypes.int64).assign(Tensor([12345], dtype=dtypes.int64)).realize()
    try:
      np.testing.assert_equal(a.numpy(), [57, 48, 0, 0, 0, 0, 0, 0])
    except AssertionError:
      # TODO: broken now
      np.testing.assert_equal(a.numpy(), [0]*8)

  def test_assign_dtype_mismatch(self):
    # assign should not implicitly cast dtypes - this can lose precision
    a = Tensor.zeros(4, dtype=dtypes.float32).contiguous().realize()
    b = Tensor([1, 2, 3, 4], dtype=dtypes.int32)
    with self.assertRaisesRegex(RuntimeError, "assign dtype mismatch"):
      a.assign(b)

  def test_assign_dtype_mismatch_int64_to_float32(self):
    # int64 -> float32 loses precision for large values, should not be implicit
    a = Tensor.zeros(1, dtype=dtypes.float32).contiguous().realize()
    b = Tensor([16777217], dtype=dtypes.int64)  # 2^24 + 1, not exactly representable in float32
    with self.assertRaisesRegex(RuntimeError, "assign dtype mismatch"):
      a.assign(b)

  def test_assign_shape_broadcast(self):
    # shape broadcasting should work when dtypes match
    a = Tensor.zeros(3, 5, dtype=dtypes.float32).contiguous().realize()
    b = Tensor([1., 2., 3., 4., 5.], dtype=dtypes.float32)
    a.assign(b)
    a.realize()
    expected = np.array([[1., 2., 3., 4., 5.]] * 3)
    np.testing.assert_allclose(a.numpy(), expected)

  def test_assign_shape_broadcast_2d(self):
    # broadcast (1, 5) to (3, 5)
    a = Tensor.zeros(3, 5, dtype=dtypes.float32).contiguous().realize()
    b = Tensor([[1., 2., 3., 4., 5.]], dtype=dtypes.float32)
    a.assign(b)
    a.realize()
    expected = np.array([[1., 2., 3., 4., 5.]] * 3)
    np.testing.assert_allclose(a.numpy(), expected)

  def test_disk_assignment(self):
    a = Tensor.empty(5, device=f"disk:{temp('disk_assignment')}").assign(Tensor.ones(5)).numpy()
    np.testing.assert_equal(a, np.ones(5))

  def test_assign_slice_then_read(self):
    """Assign to slice then read from buffer - read should see the assigned values.
    This is the KV cache pattern from llm.py.
    """
    v_pos = Variable("pos", 0, 3).bind(0)
    cache = Tensor.zeros(4, 4).contiguous().realize()
    cache[v_pos:v_pos+1, :].assign(Tensor.ones(1, 4))
    self.assertEqual(cache.sum().item(), 4.0)

  def test_chained_assign_slice_then_read(self):
    """Three caches with chained assign-then-read: each block writes to its cache and reads back,
    feeding the result to the next block's assign. Without proper dependency tracking, block N's read
    may see stale data from block N-1's cache (pre-assign zeros instead of the assigned values).
    This is the multi-layer KV cache pattern from llm.py._attention.
    """
    D, max_ctx = 4, 8
    cache1 = Tensor.zeros(max_ctx, D).contiguous().realize()
    cache2 = Tensor.zeros(max_ctx, D).contiguous().realize()
    cache3 = Tensor.zeros(max_ctx, D).contiguous().realize()
    cache1[:3].assign(Tensor.ones(3, D)).realize()
    cache2[:3].assign(Tensor.ones(3, D) * 2).realize()
    cache3[:3].assign(Tensor.ones(3, D) * 3).realize()
    # block 1: assign [10]*D at position 3, read sum -> c1=[13]*D
    cache1[3:4].assign(Tensor.ones(1, D) * 10)
    c1 = cache1[:4].sum(0, keepdim=True)
    # block 2: assign c1 at position 3, read sum -> c2=[19]*D
    cache2[3:4].assign(c1)
    c2 = cache2[:4].sum(0, keepdim=True)
    # block 3: assign c2 at position 3, read sum -> 112
    cache3[3:4].assign(c2)
    self.assertEqual(cache3[:4].sum().item(), 112.0)

  def test_chained_assign_kernel_count(self):
    """Chained pending assigns must not produce excessive kernels (tests recursive transitive processing)."""
    D, N = 4, 5
    caches = [Tensor.zeros(8, D).contiguous().realize() for _ in range(N)]
    caches[0][0:1].assign(Tensor.ones(1, D) * 10)
    x = caches[0][:1].sum(0, keepdim=True)
    for i in range(1, N):
      caches[i][0:1].assign(x)
      x = caches[i][:1].sum(0, keepdim=True)
    GlobalCounters.reset()
    x.realize()
    # N assigns (1 kernel each) producing N kernels total
    self.assertEqual(GlobalCounters.kernel_count, N)

  def test_shared_computation_assign_kernel_count(self):
    """When a .contiguous() is shared between an assign value and the next layer's input (like QKV projection in LLM),
    substitute optimization replaces already-realized sub-graphs in remaining pending assigns, preventing kernel escalation.
    Without substitute, pending assign graphs grow linearly and produce 153 kernels instead of 48."""
    D, N = 16, 16
    caches = [Tensor.zeros(4, D).contiguous().realize() for _ in range(N)]
    W = [Tensor.full((D, D*2), 0.01).contiguous().realize() for _ in range(N)]
    x = Tensor.ones(1, D).contiguous().realize()
    for i in range(N):
      shared = (x @ W[i]).contiguous()  # .contiguous() UOp is shared between assign (k) and next layer (q)
      k, q = shared[:, :D], shared[:, D:]
      caches[i][0:1].assign(k)          # assign references the CONTIGUOUS
      x = q + caches[i][:1]             # next layer also references the same CONTIGUOUS through q
    GlobalCounters.reset()
    caches[-1][:1].contiguous().realize()
    # N matmuls + N assigns + 1 final read = 2*N+1 (AFTER embedding allows full graph scheduling with shared contiguous reuse)
    self.assertEqual(GlobalCounters.kernel_count, 2*N+1)

  def test_double_assign_from_const(self):
    a = Tensor.empty(2)
    a.assign(Tensor.ones(2))
    a.assign(Tensor.ones(2))
    GlobalCounters.reset()
    a.realize()
    self.assertEqual(GlobalCounters.kernel_count, 1)
    self.assertEqual(a.tolist(), [1.,1.])

  def test_nested_after_contiguous_store(self):
    # Mirrors the nested contiguous-write-then-assign-back shape from torch backend view updates.
    base = Tensor.empty(3, dtype=dtypes.int64)
    base.assign(Tensor([1, 2, 3], dtype=dtypes.int64))
    contig = base.contiguous()
    contig.assign(Tensor([1, 4, 3], dtype=dtypes.int64))
    GlobalCounters.reset()
    base.assign(contig).realize()
    self.assertEqual(GlobalCounters.kernel_count, 2)  # TODO: first copy is dead, could be 1
    self.assertEqual(base.tolist(), [1,4,3])

  def test_nested_after_contiguous_store_no_init(self):
    # Same shape as test_nested_after_contiguous_store, but without the initial assign.
    base = Tensor.empty(3, dtype=dtypes.int64)
    contig = base.contiguous()
    contig.assign(Tensor([1, 4, 3], dtype=dtypes.int64))
    GlobalCounters.reset()
    base.assign(contig).realize()
    self.assertEqual(GlobalCounters.kernel_count, 1)
    self.assertEqual(base.tolist(), [1,4,3])

class TestAssignOrdering(unittest.TestCase):
  """Tests for complex assign orderings that could differ between lazy and eager execution.

  The key principle: tinygrad's lazy execution with RAW/WAR dependency tracking should
  produce the same results as eager (immediate) execution for valid programs.

  These tests exercise edge cases where incorrect dependency tracking could cause:
  - Stale reads (reading before write completes)
  - Lost writes (write ordering reversed)
  - Race conditions (concurrent access to same buffer)
  """

  def test_overlapping_slice_assigns(self):
    """Overlapping slice assigns - later write should win for overlapping elements."""
    buf = Tensor.zeros(8).contiguous().realize()
    buf[0:4].assign(Tensor.ones(4))
    buf[2:6].assign(Tensor.ones(4) * 2)
    np.testing.assert_equal(buf.numpy(), [1,1,2,2,2,2,0,0])

  def test_overlapping_slice_assigns_reverse(self):
    """Overlapping slice assigns in reverse order."""
    buf = Tensor.zeros(8).contiguous().realize()
    buf[2:6].assign(Tensor.ones(4) * 2)
    buf[0:4].assign(Tensor.ones(4))
    np.testing.assert_equal(buf.numpy(), [1,1,1,1,2,2,0,0])

  def test_read_between_writes(self):
    """Read should see first write before second write happens."""
    buf = Tensor.zeros(4).contiguous().realize()
    buf.assign(Tensor.ones(4))
    r1 = buf.sum().realize()  # should see ones = 4
    buf.assign(Tensor.ones(4) * 2)
    r2 = buf.sum().realize()  # should see twos = 8
    self.assertEqual(r1.item(), 4)
    self.assertEqual(r2.item(), 8)

  def test_write_read_write_chain(self):
    """Write, read, write chain - middle read must complete before second write."""
    buf = Tensor.zeros(4).contiguous().realize()
    buf.assign(Tensor.ones(4) * 3)
    mid_sum = buf.sum()  # lazy read, should be 12
    buf.assign(Tensor.ones(4) * 5)
    final_sum = buf.sum()  # lazy read, should be 20
    # Realize in "wrong" order - final first
    self.assertEqual(final_sum.realize().item(), 20)
    try:
      self.assertEqual(mid_sum.realize().item(), 12)
    except AssertionError:
      # TODO: this is wrong
      self.assertEqual(mid_sum.realize().item(), 20)

  def test_slice_read_then_full_write(self):
    """Read from slice, then overwrite full buffer - WAR dependency works for full buffer assigns."""
    buf = Tensor([1.,2.,3.,4.]).contiguous().realize()
    partial = buf[0:2].sum()  # lazy read
    buf.assign(Tensor.ones(4) * 10)  # overwrite everything
    full = buf.sum()
    # WAR dependency correctly tracked - partial sees original data
    self.assertEqual(partial.realize().item(), 3)  # 1+2
    self.assertEqual(full.realize().item(), 40)

  def test_slice_write_then_full_read(self):
    """Write to slice, then read full buffer."""
    buf = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
    buf[1:3].assign(Tensor([5, 6]))
    np.testing.assert_equal(buf.numpy(), [0, 5, 6, 0])

  def test_chained_slice_copies(self):
    """Copy from one slice to another within same buffer."""
    buf = Tensor([1, 2, 3, 4, 5, 6, 7, 8]).contiguous().realize()
    buf[4:8].assign(buf[0:4].contiguous())
    np.testing.assert_equal(buf.numpy(), [1, 2, 3, 4, 1, 2, 3, 4])

  def test_swap_slices(self):
    """Swap two non-overlapping slices - requires reading both before writing."""
    # without .realize() on temps: values not captured before overwriting
    buf = Tensor([1, 2, 3, 4, 5, 6, 7, 8]).contiguous().realize()
    left = buf[0:4].clone()  # lazy - not captured yet
    right = buf[4:8].clone()  # lazy - not captured yet
    buf[0:4].assign(right).realize()  # this works
    buf[4:8].assign(left).realize()  # left now reads from modified buf!
    try:
      np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 1, 2, 3, 4])
    except AssertionError:
      # TODO: broken now
      np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 5, 6, 7, 8])

    # with .realize() on temps: values captured before writes
    buf = Tensor([1, 2, 3, 4, 5, 6, 7, 8]).contiguous().realize()
    left = buf[0:4].clone().realize()
    right = buf[4:8].clone().realize()
    buf[0:4].assign(right).realize()
    buf[4:8].assign(left).realize()
    np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 1, 2, 3, 4])

  def test_reduction_after_partial_assign(self):
    """Reduction over buffer after partial assign - must see the assigned values."""
    buf = Tensor.zeros(4, 4).contiguous().realize()
    buf[0:2, :].assign(Tensor.ones(2, 4))  # top half = 1
    total = buf.sum()
    self.assertEqual(total.item(), 8)

  def test_multiple_reductions_different_views(self):
    """Multiple reductions over different views of same buffer after assign."""
    buf = Tensor.zeros(4, 4).contiguous().realize()
    buf.assign(Tensor.arange(16).reshape(4, 4).float())
    row_sums = buf.sum(axis=1)  # [6, 22, 38, 54]
    col_sums = buf.sum(axis=0)  # [24, 28, 32, 36]
    total = buf.sum()  # 120
    # All should see the assigned values
    np.testing.assert_equal(row_sums.numpy(), [6, 22, 38, 54])
    np.testing.assert_equal(col_sums.numpy(), [24, 28, 32, 36])
    self.assertEqual(total.item(), 120)

  def test_assign_from_self_transformed(self):
    """Assign to buffer from transformed view of itself."""
    buf = Tensor([1, 2, 3, 4]).contiguous().realize()
    # Read and transform, then write back (requires reading before writing)
    buf.assign((buf * 2).contiguous())
    np.testing.assert_equal(buf.numpy(), [2, 4, 6, 8])

  def test_two_buffers_cross_assign(self):
    """Two buffers each reading from the other before writing."""
    a = Tensor([1, 2, 3, 4]).contiguous().realize()
    b = Tensor([10, 20, 30, 40]).contiguous().realize()
    # Both read from each other's original values
    a_new = (a + b).contiguous()
    b_new = (a * b).contiguous()
    a.assign(a_new)
    b.assign(b_new)
    Tensor.realize(a, b)
    np.testing.assert_equal(a.numpy(), [11, 22, 33, 44])
    np.testing.assert_equal(b.numpy(), [10, 40, 90, 160])

  def test_three_buffer_chain(self):
    """Chain: A depends on B, B depends on C - ordering matters."""
    a = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
    b = Tensor([1, 2, 3, 4]).contiguous().realize()
    c = Tensor([10, 10, 10, 10]).contiguous().realize()
    # b reads from c, a reads from b
    b.assign((b + c).contiguous())  # b = [11, 12, 13, 14]
    a.assign((a + b).contiguous())  # a should see new b = [11, 12, 13, 14]
    Tensor.realize(a, b)
    np.testing.assert_equal(b.numpy(), [11, 12, 13, 14])
    np.testing.assert_equal(a.numpy(), [11, 12, 13, 14])

  def test_interleaved_assign_read_patterns(self):
    """Complex interleaved pattern: write A, read A into B, write B, read B."""
    a = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()
    b = Tensor.zeros(4, dtype=dtypes.int32).contiguous().realize()

    a.assign(Tensor([1, 2, 3, 4]))
    b.assign(a.contiguous())       # b should get [1,2,3,4]
    a.assign(Tensor([5, 6, 7, 8]))
    result = b.sum()               # should be 10, not 26

    self.assertEqual(result.item(), 10)
    np.testing.assert_equal(a.numpy(), [5, 6, 7, 8])
    np.testing.assert_equal(b.numpy(), [1, 2, 3, 4])

  def test_variable_slice_ordering(self):
    """Variable-indexed slices - conflicting variable binds in same schedule are rejected."""
    v_i = Variable("i", 0, 3)
    buf = Tensor.zeros(4, 4).contiguous().realize()
    buf[v_i.bind(0):v_i.bind(0)+1, :].assign(Tensor.ones(1, 4))
    buf[v_i.bind(1):v_i.bind(1)+1, :].assign(Tensor.ones(1, 4) * 2)
    with self.assertRaises(RuntimeError): buf[0:1, :].sum().item()

  def test_multi_step_assign_read_write_same_buffer(self):
    """Assign to m and param reading b, then update b, across multiple steps.
    This is the optimizer bias-correction pattern from issue #13600: m accumulates,
    param is updated using m/(1-b), and b is updated via *= after the reads."""
    b = Tensor([0.5]).contiguous().realize()
    m = Tensor([0.0]).contiguous().realize()
    param = Tensor([1.0]).contiguous().realize()
    for _ in range(10):
      m.assign(0.9 * m + 0.1)
      param.assign(param - m / (1 - b))
      b *= 0.9
      Tensor.realize(param, m, b)
    # numpy reference
    b_np, m_np, p_np = 0.5, 0.0, 1.0
    for _ in range(10):
      m_np = 0.9 * m_np + 0.1
      p_np = p_np - m_np / (1 - b_np)
      b_np *= 0.9
    np.testing.assert_allclose(param.item(), p_np, atol=1e-5)

  def test_multiple_slice_assigns_then_read(self):
    """Multiple non-overlapping slice assigns then read."""
    buf = Tensor.zeros(4).contiguous().realize()
    buf[0:1].assign(Tensor.ones(1))
    buf[1:2].assign(Tensor.full((1,), 2.0))
    buf[2:3].assign(Tensor.full((1,), 3.0))
    self.assertEqual(buf.sum().realize().item(), 6.0)

# TODO: assigns into views of unrealized non-BUFFER bases are silently dropped
class TestAssignToUnrealizedView(unittest.TestCase):
  def test_copy(self):
    t = Tensor.zeros(2,2, dtype=dtypes.int).to("CPU:0").contiguous().realize()
    c = t.to("CPU:1")  # unrealized COPY
    self.assertIs(c.uop.base.op, Ops.COPY)
    c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).to("CPU:1").contiguous().realize())
    try:
      self.assertEqual(c.tolist(), [[0,1],[0,1]])
    except AssertionError:
      # TODO: broken now
      self.assertEqual(c.tolist(), [[0,0],[0,0]])

  def test_contiguous(self):
    t = Tensor([[1,2],[3,4]]).contiguous().realize()
    c = t.permute(1,0).contiguous()  # unrealized CONTIGUOUS
    self.assertIs(c.uop.base.op, Ops.CONTIGUOUS)
    c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
    try:
      self.assertEqual(c.tolist(), [[1,1],[2,1]])
    except AssertionError:
      # TODO: broken now
      self.assertEqual(c.tolist(), [[1,3],[2,4]])

  def test_contiguous_backward(self):
    t = Tensor([[1,2],[3,4]]).contiguous().realize()
    cb = t.contiguous_backward()  # unrealized CONTIGUOUS_BACKWARD
    self.assertIs(cb.uop.base.op, Ops.CONTIGUOUS_BACKWARD)
    cb[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
    try:
      self.assertEqual(cb.tolist(), [[1,1],[3,1]])
    except AssertionError:
      # TODO: broken now
      self.assertEqual(cb.tolist(), [[1,2],[3,4]])

  def test_detach_copy(self):
    t = Tensor.zeros(2,2, dtype=dtypes.int).to("CPU:0").contiguous().realize()
    d = t.to("CPU:1").detach()  # DETACH(unrealized COPY)
    self.assertIs(d.uop.base.op, Ops.COPY)
    d[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).to("CPU:1").contiguous().realize())
    try:
      self.assertEqual(d.tolist(), [[0,1],[0,1]])
    except AssertionError:
      # TODO: broken now
      self.assertEqual(d.tolist(), [[0,0],[0,0]])

  def test_detach_contiguous(self):
    t = Tensor([[1,2],[3,4]]).contiguous().realize()
    d = t.permute(1,0).contiguous().detach()  # DETACH(unrealized CONTIGUOUS)
    self.assertIs(d.uop.base.op, Ops.CONTIGUOUS)
    d[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
    try:
      self.assertEqual(d.tolist(), [[1,1],[2,1]])
    except AssertionError:
      # TODO: broken now
      self.assertEqual(d.tolist(), [[1,3],[2,4]])

  def test_alu(self):
    a = Tensor([1,2,3,4]).contiguous().realize()
    b = Tensor([5,6,7,8]).contiguous().realize()
    c = a + b  # unrealized ADD
    self.assertIs(c.uop.base.op, Ops.ADD)
    c[:2].assign(Tensor([99, 99]).realize())
    try:
      self.assertEqual(c.tolist(), [99,99,10,12])
    except AssertionError:
      # TODO: broken now, silently dropped
      self.assertEqual(c.tolist(), [6,8,10,12])

  def test_reduce(self):
    a = Tensor([[1,2],[3,4]]).contiguous().realize()
    r = a.sum(axis=0)  # unrealized REDUCE
    self.assertIs(r.uop.base.op, Ops.REDUCE)
    r[:1].assign(Tensor([99]).realize())
    try:
      self.assertEqual(r.tolist(), [99,6])
    except AssertionError:
      # TODO: broken now, silently dropped
      self.assertEqual(r.tolist(), [4,6])

  def test_cast(self):
    a = Tensor([1,2,3,4]).contiguous().realize()
    c = a.float()  # unrealized CAST
    self.assertIs(c.uop.base.op, Ops.CAST)
    c[:2].assign(Tensor([99, 99], dtype=dtypes.float).realize())
    try:
      self.assertEqual(c.tolist(), [99,99,3,4])
    except AssertionError:
      # TODO: broken now, silently dropped
      self.assertEqual(c.tolist(), [1,2,3,4])

  def test_const(self):
    c = Tensor(5).reshape(1, 1).expand(2, 2)
    self.assertIs(c.uop.base.op, Ops.CONST)
    c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
    try:
      self.assertEqual(c.tolist(), [[5,1],[5,1]])
    except AssertionError:
      # TODO: broken now, silently dropped
      self.assertEqual(c.tolist(), [[5,5],[5,5]])

class TestPartialAssignToSharedBuffer(unittest.TestCase):
  def test_five_slices(self):
    big = Tensor.zeros(50).contiguous().realize()
    views = [big[i*10:(i+1)*10].reshape(2, 5) for i in range(5)]
    for v in views: v.assign(v + 1)
    Tensor.realize(*views)
    for v in views:
      np.testing.assert_allclose(v.numpy(), np.ones((2, 5)))

  def test_many_slices(self):
    n_params = 10
    big = Tensor.zeros(n_params * 12).contiguous().realize()
    grads = [big[i*12:(i+1)*12].reshape(3, 4) for i in range(n_params)]
    for g in grads: g.assign(g + 1)
    Tensor.realize(*grads)
    for g in grads:
      np.testing.assert_allclose(g.numpy(), np.ones((3, 4)))

  def test_mixed_shapes(self):
    big = Tensor.zeros(100).contiguous().realize()
    shapes = [(3, 4), (4, 6), (6, 4), (2, 5), (4, 3)]
    pos, views = 0, []
    for s in shapes:
      n = s[0] * s[1]
      views.append(big[pos:pos+n].reshape(*s))
      pos += n
    for v in views: v.assign(v + 1)
    Tensor.realize(*views)
    for v, s in zip(views, shapes):
      np.testing.assert_allclose(v.numpy(), np.ones(s))


class TestAfterCachePatterns(unittest.TestCase):
  def test_double_store_after(self):
    a = Tensor.zeros(10).contiguous()
    b = Tensor.zeros(10).contiguous()
    c = Tensor.ones(10).contiguous()
    Tensor.realize(a, b, c)

    a_store = a.uop.store(c.uop)
    b_store = b.uop.store(c.uop)

    a = Tensor(a.uop.after(a_store, b_store))
    a.realize()
    np.testing.assert_array_equal(a.numpy(), 1)
    np.testing.assert_array_equal(b.numpy(), 1)

  def test_double_store_after_different_sizes(self):
    full = Tensor.zeros(2).contiguous()
    head = Tensor.zeros(1).contiguous()
    full_src = Tensor([1, 2], dtype=dtypes.float).contiguous()
    head_src = Tensor([3], dtype=dtypes.float).contiguous()
    Tensor.realize(full, head, full_src, head_src)

    full_store = full.uop.store(full_src.uop)
    head_store = head.uop.store(head_src.uop)

    head = Tensor(head.uop.after(head_store, full_store))
    head.realize()
    np.testing.assert_array_equal(head.numpy(), [3])
    np.testing.assert_array_equal(full.numpy(), [1, 2])

if __name__ == "__main__":
  unittest.main()