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# Source code for torch.ao.nn.quantized.modules.functional_modules

    from typing import List

    import torch
    from torch import Tensor
    from torch._ops import ops

    __all__ = ['FloatFunctional', 'FXFloatFunctional', 'QFunctional']

    class FloatFunctional(torch.nn.Module):
        r"""State collector class for float operations.

        The instance of this class can be used instead of the ``torch.`` prefix for
        some operations. See example usage below.

        .. note::

            This class does not provide a ``forward`` hook. Instead, you must use
            one of the underlying functions (e.g. ``add``).

        Examples::

            >>> f_add = FloatFunctional()
            >>> a = torch.tensor(3.0)
            >>> b = torch.tensor(4.0)
            >>> f_add.add(a, b)  # Equivalent to ``torch.add(a, b)``

        Valid operation names:
            - add
            - cat
            - mul
            - add_relu
            - add_scalar
            - mul_scalar
        """
        def __init__(self):
            super().__init__()
            self.activation_post_process = torch.nn.Identity()

        def forward(self, x):
            raise RuntimeError("FloatFunctional is not intended to use the " +
                               "'forward'. Please use the underlying operation")

        r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
        def add(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.add(x, y)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.add(Tensor, float)``"""
        def add_scalar(self, x: Tensor, y: float) -> Tensor:
            r = torch.add(x, y)
            # Note: this operation is not observed because the observation is not
            # needed for the quantized op.
            return r

        r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
        def mul(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.mul(x, y)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
        def mul_scalar(self, x: Tensor, y: float) -> Tensor:
            r = torch.mul(x, y)
            # Note: this operation is not observed because the observation is not
            # needed for the quantized op.
            return r

        r"""Operation equivalent to ``torch.cat``"""
        def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
            r = torch.cat(x, dim=dim)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``relu(torch.add(x,y))``"""
        def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.add(x, y)
            r = torch.nn.functional.relu(r)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
        def matmul(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.matmul(x, y)
            r = self.activation_post_process(r)
            return r

    class FXFloatFunctional(torch.nn.Module):
        r""" module to replace FloatFunctional module before FX graph mode quantization,
        since activation_post_process will be inserted in top level module directly

        Valid operation names:
            - add
            - cat
            - mul
            - add_relu
            - add_scalar
            - mul_scalar
        """
        def forward(self, x):
            raise RuntimeError("FloatFunctional is not intended to use the " +
                               "'forward'. Please use the underlying operation")

        r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
        def add(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.add(x, y)
            return r

        r"""Operation equivalent to ``torch.add(Tensor, float)``"""
        def add_scalar(self, x: Tensor, y: float) -> Tensor:
            r = torch.add(x, y)
            return r

        r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
        def mul(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.mul(x, y)
            return r

        r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
        def mul_scalar(self, x: Tensor, y: float) -> Tensor:
            r = torch.mul(x, y)
            return r

        r"""Operation equivalent to ``torch.cat``"""
        def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
            r = torch.cat(x, dim=dim)
            return r

        r"""Operation equivalent to ``relu(torch.add(x,y))``"""
        def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.add(x, y)
            r = torch.nn.functional.relu(r)
            return r

        r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
        def matmul(self, x: Tensor, y: Tensor) -> Tensor:
            r = torch.matmul(x, y)
            return r

    class QFunctional(torch.nn.Module):
        r"""Wrapper class for quantized operations.

        The instance of this class can be used instead of the
        ``torch.ops.quantized`` prefix. See example usage below.

        .. note::

            This class does not provide a ``forward`` hook. Instead, you must use
            one of the underlying functions (e.g. ``add``).

        Examples::

            >>> q_add = QFunctional()
            >>> # xdoctest: +SKIP
            >>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
            >>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
            >>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``

        Valid operation names:
            - add
            - cat
            - mul
            - add_relu
            - add_scalar
            - mul_scalar
        """
        def __init__(self):
            super().__init__()
            self.scale = 1.0
            self.zero_point = 0
            self.activation_post_process = torch.nn.Identity()

        def _save_to_state_dict(self, destination, prefix, keep_vars):
            super()._save_to_state_dict(destination, prefix, keep_vars)
            destination[prefix + 'scale'] = torch.tensor(self.scale)
            destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)

        def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                                  missing_keys, unexpected_keys, error_msgs):

            self.scale = float(state_dict.pop(prefix + 'scale'))
            self.zero_point = int(state_dict.pop(prefix + 'zero_point'))
            super()._load_from_state_dict(state_dict, prefix, local_metadata, False,
                                          missing_keys, unexpected_keys, error_msgs)

        def _get_name(self):
            return 'QFunctional'

        def extra_repr(self):
            return f'scale={self.scale}, zero_point={self.zero_point}'

        def forward(self, x):
            raise RuntimeError("Functional is not intended to use the " +
                               "'forward'. Please use the underlying operation")

        r"""Operation equivalent to ``torch.ops.quantized.add``"""
        def add(self, x: Tensor, y: Tensor) -> Tensor:
            r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.ops.quantized.add(Tensor, float)``"""
        def add_scalar(self, x: Tensor, y: float) -> Tensor:
            r = ops.quantized.add_scalar(x, y)
            # Note: this operation is not observed because the observation is not
            # needed for the quantized op.
            return r

        r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, Tensor)``"""
        def mul(self, x: Tensor, y: Tensor) -> Tensor:
            r = ops.quantized.mul(x, y, scale=self.scale, zero_point=self.zero_point)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, float)``"""
        def mul_scalar(self, x: Tensor, y: float) -> Tensor:
            r = ops.quantized.mul_scalar(x, y)
            # Note: this operation is not observed because the observation is not
            # needed for the quantized op.
            return r

        r"""Operation equivalent to ``torch.ops.quantized.cat``"""
        def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
            r = ops.quantized.cat(x, scale=self.scale, zero_point=self.zero_point, dim=dim)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.ops.quantized.add_relu``"""
        def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
            r = ops.quantized.add_relu(x, y, scale=self.scale, zero_point=self.zero_point)
            r = self.activation_post_process(r)
            return r

        r"""Operation equivalent to ``torch.ops.quantized.matmul(Tensor, Tensor)``"""
        def matmul(self, x: Tensor, y: Tensor) -> Tensor:
            r = ops.quantized.matmul(x, y, scale=self.scale, zero_point=self.zero_point)
            # Note: this operation is not observed because the observation is not
            # needed for the quantized op.
            return r

        @classmethod
        def from_float(cls, mod):
            assert type(mod) == FloatFunctional,\
                "QFunctional.from_float expects an instance of FloatFunctional"
            scale, zero_point = mod.activation_post_process.calculate_qparams()  # type: ignore[operator]
            new_mod = QFunctional()
            new_mod.scale = float(scale)
            new_mod.zero_point = int(zero_point)
            return new_mod

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