def pad(self, padding:Sequence[sint]|Sequence[tuple[sint, sint]|None], mode:str="constant", value:float=0.0) -> Tensor:
    """
    Returns a tensor with padding applied based on the input `padding`.

    `padding` supports two padding structures:

    1. Flat padding: `(padding_left, padding_right, padding_top, padding_bottom, ...)`
        - This structure matches PyTorch's pad.
        - `padding` length must be even.

    2. Group padding: `(..., (padding_top, padding_bottom), (padding_left, padding_right))`
        - This structure matches pad for JAX, NumPy, TensorFlow, and others.
        - For each axis, padding can be `None`, meaning no padding, or a tuple `(start, end)`.
        - `padding` must have the same length as `self.ndim`.

    Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.
    Padding modes is selected with `mode` which supports `constant`, `reflect` and `replicate`.

    ```python exec="true" source="above" session="tensor" result="python"
    t = Tensor.arange(9).reshape(1, 1, 3, 3)
    print(t.numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad((1, 2, 0, -1)).numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad(((None, None, (0, -1), (1, 2)))).numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad((1, 2, 0, -1), value=-float('inf')).numpy())
    ```
    """
    if mode not in {"constant", "reflect", "replicate", "circular"}: raise NotImplementedError(f"{mode=} is not supported")
    # flat padding
    if all(isinstance(p, (int,UOp)) for p in padding):
      if len(padding)%2 != 0: raise ValueError("Flat padding must have even number of pads")
      pX = _flat_to_grouped(tuple(cast(Sequence[sint], padding)) + (0,0)*(self.ndim - len(padding)//2))
    # group padding
    else: pX = tuple((0,0) if p is None else p for p in cast(Sequence[tuple[sint, sint]|None], padding))
    if len(pX) != self.ndim: raise ValueError(f"padding length is improper, {padding=} {self.ndim=}")
    X, pads = self, tuple((smax(pB,0), smax(pA,0)) for pB,pA in pX)
    if mode == "constant":
      def _constant(x:Tensor,px,v) -> Tensor:
        return x._apply_uop(UOp.pad, arg=px) if v == 0 else (x._apply_uop(UOp.pad, arg=px)+Tensor.ones_like(x)._apply_uop(UOp.pad, arg=px).where(0,v))
      return _constant(X, pX, value) if all(resolve(p >= 0) for p in flatten(pX)) else \
             _constant(X.shrink(tuple((-smin(pB,0),smin(pA+s,s)) for (pB,pA),s in zip(pX, X.shape))), pads, value)
    assert all_int(self.shape), f"does not support symbolic shape {self.shape}"
    if mode == "circular":
      if any(pB>sh or pA>sh for (pB,pA),sh in zip(pX, X.shape)): raise ValueError('Padding value causes wrapping around more than once.')
      if any(pB<0 or pA<0 for pB,pA in pX): raise NotImplementedError("Negative pads with circular pads is not supported")
      orig_shape, X = X.shape, X.repeat(tuple(1 + bool(pB) + bool(pA) for pB,pA in pads))
      return X.shrink(tuple((0 if pB == 0 else osh-pB, xsh if pA == 0 else xsh-osh+pA) for (pB,pA),osh,xsh in zip(pads, orig_shape, X.shape)))
    for d,(pB,pA) in enumerate(pads):
      if mode == "reflect":
        if pB >= (s:=X.shape[d]) or pA>=s: raise ValueError(f"Padding ({pB}, {pA}) should be less than the input size={s} for dim={d}.")
        slcB, slcA, = slice(pB,0,-1), slice(s-2 if s-2>=0 else None, s-2-pA if s-2-pA>=0 else None, -1)
        xB, xA = (X[[slc if i == d else slice(None) for i in range(X.ndim)]] if p > 0 else None for slc, p in ((slcB, pB), (slcA, pA)))
      if mode == "replicate":
        shrB, shrA, = tuple((0,1) if i==d else None for i in range(X.ndim)), tuple((X.shape[i]-1,X.shape[i]) if i==d else None for i in range(X.ndim))
        xB, xA = (X.shrink(shr).expand(tuple(p if i==d else None for i in range(X.ndim))) if p > 0 else None for shr, p in ((shrB, pB), (shrA, pA)))
      X = Tensor.cat(*(X_ for X_ in (xB, X, xA) if X_ is not None), dim=d)
    return X.shrink(tuple((-min(pB,0), min(pA+s,s)) for (pB,pA),s in zip(pX, X.shape)))

    Returns a tensor with padding applied based on the input `padding`.

    `padding` supports two padding structures:

    1. Flat padding: `(padding_left, padding_right, padding_top, padding_bottom, ...)`
        - This structure matches PyTorch's pad.
        - `padding` length must be even.

    2. Group padding: `(..., (padding_top, padding_bottom), (padding_left, padding_right))`
        - This structure matches pad for JAX, NumPy, TensorFlow, and others.
        - For each axis, padding can be `None`, meaning no padding, or a tuple `(start, end)`.
        - `padding` must have the same length as `self.ndim`.

    Padding values can be negative, resulting in dimension shrinks that work similarly to Python negative slices.
    Padding modes is selected with `mode` which supports `constant`, `reflect` and `replicate`.

    ```python exec="true" source="above" session="tensor" result="python"
    t = Tensor.arange(9).reshape(1, 1, 3, 3)
    print(t.numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad((1, 2, 0, -1)).numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad(((None, None, (0, -1), (1, 2)))).numpy())
    ```
    ```python exec="true" source="above" session="tensor" result="python"
    print(t.pad((1, 2, 0, -1), value=-float('inf')).numpy())
    ```
    
tinygrad/tensor.py