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# Source code for brevitas.function.shape

    # Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
    # SPDX-License-Identifier: BSD-3-Clause

    """
    Implementation of various functions to compute shapes that induce flattening along certain
    dimensions of a tensor.
    """

    from typing import Tuple

    from torch import Tensor

    import brevitas

    __all__ = [
        'over_tensor',
        'over_output_channels',
        'over_batch_over_tensor',
        'over_output_features',
        'over_batch_over_output_channels']

    @brevitas.jit.script
    def over_tensor(x: Tensor) -> int:
        """
        Computes the shape s such that x.view(s) is a flat tensor.

        Args:
            x (Tensor): Input tensor.

        Returns:
            The number -1 corresponding to a flat shape.

        Examples:
            >>> over_tensor(torch.randn([2, 3, 4, 3]))
            -1
        """
        return -1

    @brevitas.jit.script
    def over_output_channels(x: Tensor) -> Tuple[int, int]:
        """
        Computes the shape s such that x.view(s) is a 2-dim tensor with output channels
        at dimension 0 and any other feature at dimension 1.

        Args:
        x (Tensor): Input tensor with output channels at dimension 0.

        Returns:
            A tuple containing the 2-dim shape.

        Examples:
            >>> over_output_channels(torch.randn([2, 3, 4, 3]))
            (2, -1)
        """
        return x.shape[0], -1

    @brevitas.jit.script
    def over_batch_over_tensor(x: Tensor) -> Tuple[int, int]:
        """
        Computes the shape s such that x.view(s) is a 2-dim tensor with batches
        at dimension 0 and any other feature at dimension 1.

        Args:
            x (Tensor): Input tensor with batches at dimension 0.

        Returns:
            A tuple containing the 2-dim shape.

        Examples:
            >>> over_batch_over_tensor(torch.randn([2, 3, 4, 3]))
            (2, -1)
        """
        return x.shape[0], -1

    @brevitas.jit.script
    def over_batch_over_output_channels(x: Tensor):
        """
        Returns a shape s such that x.view(s) is a 3-dim tensor with batches
        at dimension 0, output channels at dimension 1, and any other feature at dimension 2.

        Args:
            x (Tensor): Input tensor with batches at dimension 0 and output channels at dimension 1.

        Returns:
            A tuple containing the 3-dim shape.

        Examples:
            >>> over_batch_over_output_channels(torch.randn([2, 3, 4, 3]))
            (2, 3, -1)
        """
        return x.shape[0], x.shape[1], -1

    @brevitas.jit.script
    def over_output_features(x: Tensor):
        """
        Returns a shape s such that x.view(s) is a 2-dim tensor with all features except the last
        one at dimension 0.

        Args:
            x (Tensor): Input tensor with batches at dimension 0 and output channels at dimension 1.

        Returns:
            A tuple containing the 2-dim shape.

        Examples:
            >>> over_output_features(torch.randn([2, 3, 4, 3]))
            (24, 3)
        """
        return -1, x.shape[-1]