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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# pylint: disable=invalid-name, unused-variable, unused-argument
"""Schedule for pooling operators"""
from tvm import te
from .. import tag

def schedule_adaptive_pool(outs):
    """Schedule for adaptive pool.

    Parameters
    ----------
    outs: Array of Tensor
        The computation graph description of global_pool
        in the format of an array of tensors.

    Returns
    -------
    s: Schedule
        The computation schedule for adaptive pool.
    """
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])
    scheduled_ops = []

    def _schedule(Pool):
        if Pool.op in s.outputs:
            Out = Pool
        else:
            Out = outs[0].op.output(0)
            s[Pool].opengl()
        s[Out].opengl()

    def traverse(OP):
        """Internal traverse function"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(OP.tag):
            if OP not in s.outputs:
                s[OP].opengl()
            for tensor in OP.input_tensors:
                if isinstance(tensor.op, te.tensor.ComputeOp) and tensor.op not in scheduled_ops:
                    traverse(tensor.op)
        # schedule global_pool
        elif OP.tag.startswith('adaptive_pool'):
            Pool = OP.output(0)
            _schedule(Pool)
        else:
            raise RuntimeError("Unsupported operator: %s" % OP.tag)

        scheduled_ops.append(OP)

    traverse(outs[0].op)
    return s


def schedule_pool(outs, layout):
    """Schedule for pool.

    Parameters
    ----------
    outs: Array of Tensor
        The computation graph description of pool
        in the format of an array of tensors.

    layout: str
        Data layout.

    Returns
    -------
    s: Schedule
        The computation schedule for pool.
    """
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])
    scheduled_ops = []

    def _schedule(PaddedInput, Pool):
        if isinstance(PaddedInput.op, te.tensor.ComputeOp):
            s[PaddedInput].opengl()
        if Pool.op in s.outputs:
            Out = Pool
        else:
            Out = outs[0].op.output(0)
            s[Pool].opengl()
        s[Out].opengl()

    def traverse(OP):
        """Internal traverse function"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(OP.tag):
            if OP not in s.outputs:
                s[OP].compute_inline()
            for tensor in OP.input_tensors:
                if tensor.op not in scheduled_ops and isinstance(tensor.op, te.tensor.ComputeOp):
                    traverse(tensor.op)
        # schedule pool
        elif OP.tag.startswith('pool'):
            PaddedInput = OP.input_tensors[0]
            Pool = OP.output(0)
            _schedule(PaddedInput, Pool)
        else:
            raise RuntimeError("Unsupported operator: %s" % OP.tag)

        scheduled_ops.append(OP)

    traverse(outs[0].op)
    return s
