#!/usr/bin/env python
import unittest
import numpy as np
import torch
from tinygrad import Tensor, Device, TinyJit
from tinygrad.helpers import CI
from tinygrad.nn import BatchNorm2d, Conv1d,ConvTranspose1d, Conv2d,ConvTranspose2d, Linear, GroupNorm, LayerNorm,LayerNorm2d, Embedding, InstanceNorm

@unittest.skipIf(CI and Device.DEFAULT == "CUDA", "slow")
class TestNN(unittest.TestCase):
  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "no int64 on WebGPU")
  def test_sparse_cat_cross_entropy(self):
    # create in tinygrad
    input = Tensor.randn(3, 5)
    target = Tensor.randint((3,), low=0, high=4)
    loss = input.sparse_categorical_crossentropy(target)

    torch_input = torch.tensor(input.numpy())
    torch_target = torch.tensor(target.numpy(), dtype=torch.long)
    torch_loss = torch.nn.CrossEntropyLoss(reduction='mean')(torch_input, torch_target)

    np.testing.assert_allclose(loss.numpy(), torch_loss.detach().numpy(), atol=1e-5, rtol=1e-6)

  def test_batchnorm2d(self, training=False):
    with Tensor.train(training):
      szs = [4, 8, 16, 32]
      for sz in szs:
        # create in tinygrad
        bn = BatchNorm2d(sz, eps=1e-5, track_running_stats=training)
        bn.weight = Tensor.randn(sz)
        bn.bias = Tensor.randn(sz)
        bn.running_mean = Tensor.randn(sz)
        bn.running_var = Tensor.randn(sz)
        bn.running_var.numpy()[bn.running_var.numpy() < 0] = 0

        # create in torch
        with torch.no_grad():
          tbn = torch.nn.BatchNorm2d(sz).eval()
          tbn.training = training
          tbn.weight[:] = torch.tensor(bn.weight.numpy())
          tbn.bias[:] = torch.tensor(bn.bias.numpy())
          tbn.running_mean[:] = torch.tensor(bn.running_mean.numpy())
          tbn.running_var[:] = torch.tensor(bn.running_var.numpy())

        np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
        np.testing.assert_allclose(bn.running_var.numpy(), tbn.running_var.detach().numpy(), rtol=1e-5, atol=1e-6)

        # trial
        inn = Tensor.randn(2, sz, 3, 3)

        # in tinygrad
        outt = bn(inn)

        # in torch
        toutt = tbn(torch.tensor(inn.numpy()))

        # close
        np.testing.assert_allclose(outt.numpy(), toutt.detach().numpy(), rtol=5e-4, atol=1e-6)
        np.testing.assert_allclose(bn.running_mean.numpy(), tbn.running_mean.detach().numpy(), rtol=1e-5, atol=1e-6)
        np.testing.assert_allclose(bn.running_var.numpy(), tbn.running_var.detach().numpy(), rtol=1e-5, atol=1e-6)

  def test_batchnorm2d_training(self):
    self.test_batchnorm2d(True)

  def test_linear(self):
    def _test_linear(x, in_dim, out_dim):
      # create in tinygrad
      model = Linear(in_dim, out_dim)
      z = model(x)

      # create in torch
      with torch.no_grad():
        torch_layer = torch.nn.Linear(in_dim, out_dim).eval()
        torch_layer.weight[:] = torch.tensor(model.weight.numpy(), dtype=torch.float32)
        torch_layer.bias[:] = torch.tensor(model.bias.numpy(), dtype=torch.float32)
        torch_x = torch.tensor(x.numpy(), dtype=torch.float32)
        torch_z = torch_layer(torch_x)

      # test
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

    BS, T, in_dim, out_dim = 4, 2, 8, 16
    _test_linear(Tensor.randn(BS, in_dim), in_dim, out_dim)
    _test_linear(Tensor.randn(BS, T, in_dim), in_dim, out_dim) # test with more dims

  def test_conv1d(self):
    BS, C1, W = 4, 16, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  def test_conv2d(self):
    BS, C1, H, W = 4, 16, 224//4, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  @unittest.skipIf(Device.DEFAULT not in {"CPU", "TORCH"}, "Takes too long to compile for Compiled backends")
  def test_conv2d_winograd(self):
    BS, C1, H, W = 2, 8, 16, 16
    C2, K, S, P = 8, 3, 1, 1

    # create in tinygrad
    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
    layer.weight.requires_grad = True
    layer.bias.requires_grad = True

    # create in torch
    torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
    torch_layer.weight = torch.nn.Parameter(torch.tensor(layer.weight.numpy(), dtype=torch.float32))
    torch_layer.bias = torch.nn.Parameter(torch.tensor(layer.bias.numpy(), dtype=torch.float32))

    # test
    x = Tensor.uniform(BS, C1, H, W, requires_grad=True)

    old_wino = Tensor.wino
    Tensor.wino = True
    z = layer(x)
    Tensor.wino = old_wino

    torch_x = torch.tensor(x.numpy(), requires_grad=True)
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

    m = z.mean()
    m.backward()
    gw = layer.weight.grad.realize()
    gb = layer.bias.grad.realize()
    gx = x.grad.realize()

    torch_z.mean().backward()
    np.testing.assert_allclose(gw.numpy(), torch_layer.weight.grad.numpy(), atol=5e-4, rtol=1e-5)
    np.testing.assert_allclose(gb.numpy(), torch_layer.bias.grad.numpy(), atol=5e-4, rtol=1e-5)
    np.testing.assert_allclose(gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5)


  @unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
  def test_conv_transpose1d(self):
    BS, C1, W = 4, 16, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  @unittest.skipIf(CI and Device.DEFAULT == "WEBGPU", "runs out of memory in CI")
  def test_conv_transpose2d(self):
    BS, C1, H, W = 4, 16, 224//4, 224//4
    C2, K, S, P = 64, 7, 2, 1

    # create in tinygrad
    layer = ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.uniform(BS, C1, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)

  def test_groupnorm(self):
    BS, H, W, C, G = 20, 10, 10, 6, 3

    # create in tinygrad
    layer = GroupNorm(G, C)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.GroupNorm(G, C).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.randn(BS, C, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)

  def test_layernorm(self):
    N, C, H, W = 20, 5, 10, 10

    # create in tinygrad
    layer = LayerNorm([H, W])

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.LayerNorm([H, W]).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.randn(N, C, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)

  def test_layernorm_2d(self):
    N, C, H, W = 20, 5, 10, 10

    # create in tinygrad
    layer = LayerNorm2d(C)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.LayerNorm([C]).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.randn(N, C, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x.permute(0,2,3,1)).permute(0,3,1,2)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)

  def test_instancenorm_2d(self):
    N, C, H, W = 20, 5, 10, 10

    # create in tinygrad
    layer = InstanceNorm(C)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.InstanceNorm2d(C, affine=True).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.randn(N, C, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)

  def test_instancenorm_3d(self):
    N, C, D, H, W = 20, 5, 3, 10, 10

    # create in tinygrad
    layer = InstanceNorm(C)

    # create in torch
    with torch.no_grad():
      torch_layer = torch.nn.InstanceNorm3d(C, affine=True).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)

    # test
    x = Tensor.randn(N, C, D, H, W)
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)

  def test_embedding(self):
    B, T, embed_size, vocab_size = 4, 10, 20, 28

    # create in tinygrad
    layer = Embedding(vocab_size, embed_size)

    with torch.no_grad():
      torch_layer = torch.nn.Embedding(vocab_size, embed_size).eval()
      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)

    # test
    x = Tensor(np.random.randint(0, vocab_size, (B, T)))
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)

    # test with empty input length
    x = Tensor(np.random.randint(0, vocab_size, (B, 0)))
    z = layer(x)
    torch_x = torch.tensor(x.numpy())
    torch_z = torch_layer(torch_x)
    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)

    # test with jit enabled
    @TinyJit
    def layer_jit(x):
      return layer(x).realize()

    for _ in range(3):
      x = Tensor(np.random.randint(0, vocab_size, (B, T)))
      z = layer_jit(x)
      torch_x = torch.tensor(x.numpy())
      torch_z = torch_layer(torch_x)
      np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=1e-8, rtol=1e-8)


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