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# Source code for torch.ao.quantization.observer

    """
    This module implements observers which are used to collect statistics about
    the values observed during calibration (PTQ) or training (QAT).
    """

    import re
    import warnings
    from abc import ABCMeta, abstractmethod
    from collections import OrderedDict
    from functools import partial
    from typing import Any, List, Tuple, Optional, Dict

    import torch
    import torch.nn as nn
    from torch.ao.quantization.utils import (
        check_min_max_valid, calculate_qmin_qmax, is_per_tensor, is_per_channel, validate_qmin_qmax)

    __all__ = [
        "default_affine_fixed_qparams_observer",
        "default_debug_observer",
        "default_dynamic_quant_observer",
        "default_fixed_qparams_range_0to1_observer",
        "default_fixed_qparams_range_neg1to1_observer",
        "default_float_qparams_observer",
        "default_float_qparams_observer_4bit",
        "default_histogram_observer",
        "default_observer",
        "default_per_channel_weight_observer",
        "default_placeholder_observer",
        "default_reuse_input_observer",
        "default_symmetric_fixed_qparams_observer",
        "default_weight_observer",
        "get_observer_state_dict",
        "load_observer_state_dict",
        "per_channel_weight_observer_range_neg_127_to_127",
        "weight_observer_range_neg_127_to_127",
        "FixedQParamsObserver",
        "HistogramObserver",
        "MinMaxObserver",
        "MovingAverageMinMaxObserver",
        "MovingAveragePerChannelMinMaxObserver",
        "NoopObserver",
        "ObserverBase",
        "PerChannelMinMaxObserver",
        "PlaceholderObserver",
        "RecordingObserver",
        "ReuseInputObserver",
        "UniformQuantizationObserverBase",
    ]

    class _PartialWrapper:
        def __init__(self, p):
            self.p = p
            self.callable_args = {}

        def __call__(self, *args, **keywords):
            # call each arg in callable_args and add them partial, then run with keywords
            # skip if arg_name in keywords so its possible to overwrite
            for arg_name in self.callable_args:
                if arg_name not in keywords:
                    keywords = {**keywords, **{arg_name: self.callable_args[arg_name]()}}
            return self.p(*args, **keywords)

        def __repr__(self):
            return self.p.__repr__() + self.callable_args.__repr__()

        def with_args(self, **kwargs):
            return _with_args(self, **kwargs)

        def with_callable_args(self, **kwargs):
            result = _PartialWrapper(p=self.p)
            result.callable_args = {**self.callable_args, **kwargs}
            return result

    def _with_args(cls_or_self, **kwargs):
        r"""Wrapper that allows creation of class factories.

        This can be useful when there is a need to create classes with the same
        constructor arguments, but different instances. Can be used in conjunction with
        _callable_args

        Example::

            >>> # xdoctest: +SKIP("Undefined vars")
            >>> Foo.with_args = classmethod(_with_args)
            >>> foo_builder = Foo.with_args(a=3, b=4).with_args(answer=42)
            >>> foo_instance1 = foo_builder()
            >>> foo_instance2 = foo_builder()
            >>> id(foo_instance1) == id(foo_instance2)
            False
        """
        r = _PartialWrapper(partial(cls_or_self, **kwargs))
        return r

    def _with_callable_args(cls_or_self, **kwargs):
        r"""Wrapper that allows creation of class factories args that need to be
        called at construction time.

        This can be useful when there is a need to create classes with the same
        constructor arguments, but different instances and those arguments should only
        be calculated at construction time. Can be used in conjunction with _with_args

        Example::

            >>> # xdoctest: +SKIP("Undefined vars")
            >>> Foo.with_callable_args = classmethod(_with_callable_args)
            >>> Foo.with_args = classmethod(_with_args)
            >>> foo_builder = Foo.with_callable_args(cur_time=get_time_func).with_args(name="dan")
            >>> foo_instance1 = foo_builder()
            >>> # wait 50
            >>> foo_instance2 = foo_builder()
            >>> id(foo_instance1.creation_time) == id(foo_instance2.creation_time)
            False
        """
        r = _PartialWrapper(partial(cls_or_self))
        return r.with_callable_args(**kwargs)

    ABC: Any = ABCMeta("ABC", (object,), {})  # compatible with Python 2 *and* 3:

    class ObserverBase(ABC, nn.Module):
        r"""Base observer Module.
        Any observer implementation should derive from this class.

        Concrete observers should follow the same API. In forward, they will update
        the statistics of the observed Tensor. And they should provide a
        `calculate_qparams` function that computes the quantization parameters given
        the collected statistics.

        Args:
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
        """

        def __init__(self, dtype):
            super().__init__()
            self.dtype = dtype

        @abstractmethod
        def forward(self, x):
            pass

        @abstractmethod
        def calculate_qparams(self, **kwargs):
            pass

        with_args = classmethod(_with_args)
        with_callable_args = classmethod(_with_callable_args)

    class UniformQuantizationObserverBase(ObserverBase):
        r"""Common base for all observers using uniform quantization to calculate
        scale and zero_point.

        Args:
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
            qscheme: Quantization scheme to be used.
            reduce_range: Reduces the range of the quantized data type by 1 bit.
                          This is sometimes required to avoid instruction overflow.
            quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
            quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        .. warning::

            :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.

        .. warning::

            :attr:`qscheme` can only take one of the following options:

            - ``torch.per_tensor_affine``
            - ``torch.per_tensor_symmetric``
            - ``torch.per_channel_affine``
            - ``torch.per_channel_symmetric``
        """

        # Note: the version is shared by all observer types
        #
        # Version 1/None
        #   self
        #
        # Version 2 (base class only, does not include child class buffers)
        #   self
        #   |--- eps : Tensor
        #
        # Version 3
        #   for HistogramObserver only, changed the shape of uninitialized
        #   min_val and max_val buffers from torch.Size([0]) to torch.Size([])
        #   for PerChannelObservers, changed the name of the buffers from min_vals
        #   to min_val and from max_vals to max_val.
        _version = 3

        eps: torch.Tensor

        def __init__(
            self,
            dtype=torch.quint8,
            qscheme=torch.per_tensor_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            factory_kwargs=None,
            eps=torch.finfo(torch.float32).eps,
        ) -> None:
            factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
            super().__init__(dtype=dtype)
            self.qscheme = qscheme
            if reduce_range:
                warnings.warn(
                    "Please use quant_min and quant_max to specify the range for observers. \
                        reduce_range will be deprecated in a future release of PyTorch."
                )
            self.reduce_range = reduce_range
            self.register_buffer(
                "eps", torch.tensor([eps], **factory_kwargs)
            )
            assert self.qscheme in (
                torch.per_tensor_affine,
                torch.per_tensor_symmetric,
                torch.per_channel_affine,
                torch.per_channel_symmetric,
                torch.per_channel_affine_float_qparams,
            ), "Default Observer only works for per_tensor_affine, \
                    per_tensor_symmetric, per_channel_affine, \
                    per_channel_symmetric and per_channel_float_qparams quantization scheme"
            assert self.dtype in (
                torch.qint8,
                torch.quint8,
                torch.quint4x2,
                torch.qint32,
            ), "Default Observer only works for qint8, quint8 and quint4x2 data type"
            self.has_customized_qrange = (quant_min is not None) and (quant_max is not None)
            if self.has_customized_qrange:
                validate_qmin_qmax(quant_min, quant_max)
            self.quant_min, self.quant_max = \
                calculate_qmin_qmax(quant_min, quant_max, self.has_customized_qrange, self.dtype, self.reduce_range)

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

            version = local_metadata.get("version", None)

            if version is None or version == 1:
                # eps was moved to a buffer in version 2
                eps = torch.tensor([torch.finfo(torch.float32).eps])
                state_dict[prefix + "eps"] = eps

            super()._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                strict,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )

        @torch.jit.export
        def _validate_qmin_qmax(self, quant_min: int, quant_max: int) -> None:
            r"""Validates that the user-specified quantization range is properly initialized
            and within the given bound supported by the observer dtype.

            To accommodate lower-bit quantization with respect to the existing torch.qint8 and
            torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
            in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
            values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
            fake quantization. These estimates are compared against parameters learned through backpropagation.
            The related literatures for scale and zero point via backpropagation are as follows:

            Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
            Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
            """
            # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
            # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
            assert (
                quant_min <= 0 <= quant_max
            ), "Used-specified quantization range must include 0."
            assert (
                quant_min < quant_max
            ), "qmin must be strictly less than qmax for user-specified quantization range."

        @torch.jit.export
        def _calculate_qparams(
            self, min_val: torch.Tensor, max_val: torch.Tensor
        ) -> Tuple[torch.Tensor, torch.Tensor]:
            r"""Calculates the quantization parameters, given min and max
            value tensors. Works for both per tensor and per channel cases

            Args:
                min_val: Minimum values per channel
                max_val: Maximum values per channel

            Returns:
                scales: Scales tensor of shape (#channels,)
                zero_points: Zero points tensor of shape (#channels,)
            """
            # Functionally equivalent to 'determine_qparams' in utils.py. Observers must be torchscriptable however and qscheme
            # as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
            # to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code
            # seems unlikey to change (last update over 1 year ago) and when torchscript is fully deprecated we can refactor.
            # TODO(jakeszwe, jerryzh168)
            if not check_min_max_valid(min_val, max_val):
                return torch.tensor([1.0], device=min_val.device.type), torch.tensor([0], device=min_val.device.type)

            quant_min, quant_max = self.quant_min, self.quant_max
            min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
            max_val_pos = torch.max(max_val, torch.zeros_like(max_val))

            device = min_val_neg.device
            scale = torch.ones(min_val_neg.size(), dtype=torch.float32, device=device)
            zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)

            if (
                self.qscheme == torch.per_tensor_symmetric
                or self.qscheme == torch.per_channel_symmetric
            ):
                max_val_pos = torch.max(-min_val_neg, max_val_pos)
                scale = max_val_pos / (float(quant_max - quant_min) / 2)
                scale = torch.max(scale, self.eps)
                if self.dtype == torch.quint8:
                    if self.has_customized_qrange:
                        # When customized quantization range is used, down-rounded midpoint of the range is chosen.
                        zero_point = zero_point.new_full(
                            zero_point.size(), (quant_min + quant_max) // 2
                        )
                    else:
                        zero_point = zero_point.new_full(zero_point.size(), 128)
            elif self.qscheme == torch.per_channel_affine_float_qparams:
                scale = (max_val - min_val) / float(quant_max - quant_min)
                scale = torch.where(scale > self.eps, scale, torch.ones_like(scale))
                # We use the quantize function
                # xq = Round(Xf * inv_scale + zero_point),
                # setting zero_point to (-1 * min *inv_scale) we get
                # Xq = Round((Xf - min) * inv_scale)
                zero_point = -1 * min_val / scale
            else:
                scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
                scale = torch.max(scale, self.eps)
                zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
                zero_point = torch.clamp(zero_point, quant_min, quant_max)

            # For scalar values, cast them to Tensors of size 1 to keep the shape
            # consistent with default values in FakeQuantize.
            if len(scale.shape) == 0:
                # TODO: switch to scale.item() after adding JIT support
                scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
            if len(zero_point.shape) == 0:
                # TODO: switch to zero_point.item() after adding JIT support
                zero_point = torch.tensor(
                    [int(zero_point)], dtype=zero_point.dtype, device=device
                )
                if self.qscheme == torch.per_channel_affine_float_qparams:
                    zero_point = torch.tensor(
                        [float(zero_point)], dtype=zero_point.dtype, device=device
                    )

            return scale, zero_point

        @torch.jit.export
        def reset_min_max_vals(self):
            raise NotImplementedError("Cannot reset min/max values in the given observer.")

    # Originally, this class was called `_ObserverBase`.  Keeping the old name around
    # for backwards compatibility.
    # TODO(after v1.13): delete this
    _ObserverBase = UniformQuantizationObserverBase

    class MinMaxObserver(UniformQuantizationObserverBase):
        r"""Observer module for computing the quantization parameters based on the
        running min and max values.

        This observer uses the tensor min/max statistics to compute the quantization
        parameters. The module records the running minimum and maximum of incoming
        tensors, and uses this statistic to compute the quantization parameters.

        Args:
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
            quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
            quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        Given running min/max as :math:`x_\text{min}` and :math:`x_\text{max}`,
        scale :math:`s` and zero point :math:`z` are computed as:

        The running minimum/maximum :math:`x_\text{min/max}` is computed as:

        .. math::

            \begin{array}{ll}
            x_\text{min} &= \begin{cases}
                \min(X) & \text{if~}x_\text{min} = \text{None} \\
                \min\left(x_\text{min}, \min(X)\right) & \text{otherwise}
            \end{cases}\\
            x_\text{max} &= \begin{cases}
                \max(X) & \text{if~}x_\text{max} = \text{None} \\
                \max\left(x_\text{max}, \max(X)\right) & \text{otherwise}
            \end{cases}\\
            \end{array}

        where :math:`X` is the observed tensor.

        The scale :math:`s` and zero point :math:`z` are then computed as:

        .. math::

            \begin{aligned}
                \text{if Symmetric:}&\\
                &s = 2 \max(|x_\text{min}|, x_\text{max}) /
                    \left( Q_\text{max} - Q_\text{min} \right) \\
                &z = \begin{cases}
                    0 & \text{if dtype is qint8} \\
                    128 & \text{otherwise}
                \end{cases}\\
                \text{Otherwise:}&\\
                    &s = \left( x_\text{max} - x_\text{min}  \right ) /
                        \left( Q_\text{max} - Q_\text{min} \right ) \\
                    &z = Q_\text{min} - \text{round}(x_\text{min} / s)
            \end{aligned}

        where :math:`Q_\text{min}` and :math:`Q_\text{max}` are the minimum and
        maximum of the quantized data type.

        .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.

        .. note:: If the running minimum equals to the running maximum, the scale
                  and zero_point are set to 1.0 and 0.
        """
        min_val: torch.Tensor
        max_val: torch.Tensor

        def __init__(
            self,
            dtype=torch.quint8,
            qscheme=torch.per_tensor_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            factory_kwargs=None,
            eps=torch.finfo(torch.float32).eps,
        ) -> None:
            if not is_per_tensor(qscheme):
                raise NotImplementedError(
                    "MinMaxObserver's qscheme only support torch.per_tensor_symmetric \
                        and torch.per_tensor_affine."
                )
            # For x86 quantized kernels, we need to ensure that the vpmaddubsw
            # instruction does not overflow. We allow for a reduce_range argument to
            # observers that reduces the quantized range to (0,127) or (-64, 63).
            # For more details see aten/src/ATen/native/quantized/cpu/qconv.cpp
            # This is not an optimal choice for non x86 backends as it loses a bit
            # of precision for activations.
            super().__init__(
                dtype=dtype,
                qscheme=qscheme,
                reduce_range=reduce_range,
                quant_min=quant_min,
                quant_max=quant_max,
                factory_kwargs=factory_kwargs,
                eps=eps,
            )
            factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
            self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
            self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
            if (
                self.qscheme == torch.per_tensor_symmetric
                and self.reduce_range
                and self.dtype == torch.quint8
            ):
                raise NotImplementedError(
                    "Cannot reduce range for symmetric \
                                           quantization for quint8"
                )

        def forward(self, x_orig):
            r"""Records the running minimum and maximum of ``x``."""
            if x_orig.numel() == 0:
                return x_orig
            x = x_orig.detach()  # avoid keeping autograd tape
            x = x.to(self.min_val.dtype)
            min_val_cur, max_val_cur = torch.aminmax(x)
            min_val = torch.min(min_val_cur, self.min_val)
            max_val = torch.max(max_val_cur, self.max_val)
            self.min_val.copy_(min_val)
            self.max_val.copy_(max_val)
            return x_orig

        @torch.jit.export
        def calculate_qparams(self):
            r"""Calculates the quantization parameters."""
            return self._calculate_qparams(self.min_val, self.max_val)

        @torch.jit.export
        def extra_repr(self):
            return f"min_val={self.min_val}, max_val={self.max_val}"

        @torch.jit.export
        def reset_min_max_vals(self):
            """Resets the min/max values."""
            self.min_val.copy_(torch.tensor(float("inf")))
            self.max_val.copy_(torch.tensor(float("-inf")))

    class MovingAverageMinMaxObserver(MinMaxObserver):
        r"""Observer module for computing the quantization parameters based on the
        moving average of the min and max values.

        This observer computes the quantization parameters based on the moving
        averages of minimums and maximums of the incoming tensors. The module
        records the average minimum and maximum of incoming tensors, and uses this
        statistic to compute the quantization parameters.

        Args:
            averaging_constant: Averaging constant for min/max.
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
            quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
            quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        The moving average min/max is computed as follows

        .. math::

            \begin{array}{ll}
                    x_\text{min} = \begin{cases}
                        \min(X) & \text{if~}x_\text{min} = \text{None} \\
                        (1 - c) x_\text{min} + c \min(X) & \text{otherwise}
                    \end{cases}\\
                    x_\text{max} = \begin{cases}
                        \max(X) & \text{if~}x_\text{max} = \text{None} \\
                        (1 - c) x_\text{max} + c \max(X) & \text{otherwise}
                    \end{cases}\\
            \end{array}

        where :math:`x_\text{min/max}` is the running average min/max, :math:`X` is
        is the incoming tensor, and :math:`c` is the ``averaging_constant``.

        The scale and zero point are then computed as in
        :class:`~torch.ao.quantization.observer.MinMaxObserver`.

        .. note:: Only works with ``torch.per_tensor_affine`` quantization scheme.

        .. note:: If the running minimum equals to the running maximum, the scale
                  and zero_point are set to 1.0 and 0.
        """

        def __init__(
            self,
            averaging_constant=0.01,
            dtype=torch.quint8,
            qscheme=torch.per_tensor_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            eps=torch.finfo(torch.float32).eps,
            **kwargs
        ) -> None:
            if not is_per_tensor(qscheme):
                raise NotImplementedError(
                    "MovingAverageMinMaxObserver's qscheme only support \
                        torch.per_tensor_symmetric and torch.per_tensor_affine."
                )
            self.averaging_constant = averaging_constant
            super().__init__(
                dtype=dtype,
                qscheme=qscheme,
                reduce_range=reduce_range,
                quant_min=quant_min,
                quant_max=quant_max,
                eps=eps,
                **kwargs
            )

        def forward(self, x_orig):
            if x_orig.numel() == 0:
                return x_orig
            x = x_orig.detach()  # avoid keeping autograd tape
            x = x.to(self.min_val.dtype)
            min_val = self.min_val
            max_val = self.max_val
            if min_val == float("inf") and max_val == float("-inf"):
                min_val, max_val = torch.aminmax(x)
            else:
                min_val_cur, max_val_cur = torch.aminmax(x)
                min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
                max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
            self.min_val.copy_(min_val)
            self.max_val.copy_(max_val)
            return x_orig

    class PerChannelMinMaxObserver(UniformQuantizationObserverBase):
        r"""Observer module for computing the quantization parameters based on the
        running per channel min and max values.

        This observer uses the tensor min/max statistics to compute the per channel
        quantization parameters. The module records the running minimum and maximum
        of incoming tensors, and uses this statistic to compute the quantization
        parameters.

        Args:
            ch_axis: Channel axis
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
            quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
            quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        The quantization parameters are computed the same way as in
        :class:`~torch.ao.quantization.observer.MinMaxObserver`, with the difference
        that the running min/max values are stored per channel.
        Scales and zero points are thus computed per channel as well.

        .. note:: If the running minimum equals to the running maximum, the scales
                  and zero_points are set to 1.0 and 0.
        """
        min_val: torch.Tensor
        max_val: torch.Tensor

        def __init__(
            self,
            ch_axis=0,
            dtype=torch.quint8,
            qscheme=torch.per_channel_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            factory_kwargs=None,
            eps=torch.finfo(torch.float32).eps,
        ) -> None:
            if not is_per_channel(qscheme):
                raise NotImplementedError(
                    "PerChannelMinMaxObserver's qscheme only support \
                        torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
                )
            super().__init__(
                dtype=dtype,
                qscheme=qscheme,
                reduce_range=reduce_range,
                quant_min=quant_min,
                quant_max=quant_max,
                factory_kwargs=factory_kwargs,
                eps=eps,
            )
            factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
            self.ch_axis = ch_axis
            self.register_buffer("min_val", torch.tensor([], **factory_kwargs))
            self.register_buffer("max_val", torch.tensor([], **factory_kwargs))
            if (
                self.qscheme == torch.per_channel_symmetric
                and self.reduce_range
                and self.dtype == torch.quint8
            ):
                raise NotImplementedError(
                    "Cannot reduce range for symmetric quantization for quint8"
                )

        def forward(self, x_orig):
            return self._forward(x_orig)

        def _forward(self, x_orig):
            if x_orig.numel() == 0:
                return x_orig
            x = x_orig.detach()  # avoid keeping autograd tape
            min_val = self.min_val
            max_val = self.max_val
            x_dim = x.size()

            new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
            new_axis_list[self.ch_axis] = 0
            new_axis_list[0] = self.ch_axis
            y = x.permute(new_axis_list)
            # Need to match dtype of min/max because the updates to buffers
            # are done in place and types need to match for comparisons
            y = y.to(self.min_val.dtype)
            y = torch.flatten(y, start_dim=1)
            if min_val.numel() == 0 or max_val.numel() == 0:
                min_val, max_val = torch.aminmax(y, dim=1)
            else:
                min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
                min_val = torch.min(min_val_cur, min_val)
                max_val = torch.max(max_val_cur, max_val)
            self.min_val.resize_(min_val.shape)
            self.max_val.resize_(max_val.shape)
            self.min_val.copy_(min_val)
            self.max_val.copy_(max_val)
            return x_orig

        @torch.jit.export
        def calculate_qparams(self):
            return self._calculate_qparams(self.min_val, self.max_val)

        def extra_repr(self):
            return f"min_val={self.min_val}, max_val={self.max_val}"

        def _load_from_state_dict(
            self,
            state_dict: Dict[str, Any],
            prefix: str,
            local_metadata: Dict[str, torch.Tensor],
            strict: bool,
            missing_keys: List[str],
            unexpected_keys: List[str],
            error_msgs: List[str],
        ):
            version = local_metadata.get("version", None)
            if version is None or version < 3:
                local_state = ["min_vals", "max_vals"]
                expected_min_name = "min_vals"
                expected_max_name = "max_vals"
            else:
                local_state = ["min_val", "max_val"]
                expected_min_name = "min_val"
                expected_max_name = "max_val"
            for name in local_state:
                key = prefix + name
                if key in state_dict:
                    val = state_dict[key]
                    # Custom handling to allow loading min_val or max_val
                    # of size N into uninitialized buffers of size 0. The
                    # buffers are resized here, and the values are copied in
                    # the default state_dict loading code of the parent.
                    if name == expected_min_name:
                        self.min_val.resize_(val.shape)
                    elif name == expected_max_name:
                        self.max_val.resize_(val.shape)
                    else:
                        warnings.warn(f"Observer load_from_state_dict got unexpected name {name}")
                    # For torchscript module we need to update the attributes here since we do not
                    # call the `_load_from_state_dict` function defined module.py
                    if torch.jit.is_scripting():
                        if name == expected_min_name:
                            self.min_val.copy_(val)
                        elif name == expected_max_name:
                            self.max_val.copy_(val)
                        else:
                            warnings.warn(f"Observer load_from_state_dict got unexpected name {name}")
                elif strict:
                    missing_keys.append(key)

            if not torch.jit.is_scripting():
                super()._load_from_state_dict(
                    state_dict,
                    prefix,
                    local_metadata,
                    False,
                    missing_keys,
                    unexpected_keys,
                    error_msgs,
                )

        def _load_from_state_dict_script(
            self,
            state_dict: Dict[str, Any],
            prefix: str,
            local_metadata: Dict[str, torch.Tensor],
            strict: bool,
            missing_keys: List[str],
            unexpected_keys: List[str],
            error_msgs: List[str],
        ):

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

        @torch.jit.export
        def reset_min_max_vals(self):
            """Resets the min/max values."""
            # This used to be torch.ones but that does not work because
            # JIT compiler can optimize it via common subexpression elimination
            # in which case both min_val and max_val point to the same tensor.
            self.min_val = torch.rand(0, )
            self.max_val = torch.rand(0, )

    class MovingAveragePerChannelMinMaxObserver(PerChannelMinMaxObserver):
        r"""Observer module for computing the quantization parameters based on the
        running per channel min and max values.

        This observer uses the tensor min/max statistics to compute the per channel
        quantization parameters. The module records the running minimum and maximum
        of incoming tensors, and uses this statistic to compute the quantization
        parameters.

        Args:
            averaging_constant: Averaging constant for min/max.
            ch_axis: Channel axis
            dtype: Quantized data type
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
            quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
            quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        The quantization parameters are computed the same way as in
        :class:`~torch.ao.quantization.observer.MovingAverageMinMaxObserver`, with the
        difference that the running min/max values are stored per channel.
        Scales and zero points are thus computed per channel as well.

        .. note:: If the running minimum equals to the running maximum, the scales
                  and zero_points are set to 1.0 and 0.
        """

        def __init__(
            self,
            averaging_constant=0.01,
            ch_axis=0,
            dtype=torch.quint8,
            qscheme=torch.per_channel_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            eps=torch.finfo(torch.float32).eps,
            **kwargs
        ) -> None:
            if not is_per_channel(qscheme):
                raise NotImplementedError(
                    "MovingAveragePerChannelMinMaxObserver's qscheme only support \
                        torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
                )
            super().__init__(
                ch_axis=ch_axis,
                dtype=dtype,
                qscheme=qscheme,
                reduce_range=reduce_range,
                quant_min=quant_min,
                quant_max=quant_max,
                eps=eps,
                **kwargs
            )
            self.averaging_constant = averaging_constant

        def forward(self, x_orig):
            if x_orig.numel() == 0:
                return x_orig
            x = x_orig.detach()  # avoid keeping autograd tape
            x = x.to(self.min_val.dtype)
            min_val = self.min_val
            max_val = self.max_val
            x_dim = x.size()

            new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
            new_axis_list[self.ch_axis] = 0
            new_axis_list[0] = self.ch_axis
            y = x.permute(new_axis_list)
            y = torch.flatten(y, start_dim=1)
            if min_val.numel() == 0 or max_val.numel() == 0:
                min_val, max_val = torch.aminmax(y, dim=1)
            else:
                min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
                min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
                max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
            self.min_val.resize_(min_val.shape)
            self.max_val.resize_(max_val.shape)
            self.min_val.copy_(min_val)
            self.max_val.copy_(max_val)
            return x_orig

    class HistogramObserver(UniformQuantizationObserverBase):
        r"""
        The module records the running histogram of tensor values along with
        min/max values. ``calculate_qparams`` will calculate scale and zero_point.

        Args:
            bins: Number of bins to use for the histogram
            upsample_rate: Factor by which the histograms are upsampled, this is
                           used to interpolate histograms with varying ranges across observations
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
            eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.

        The scale and zero point are computed as follows:

        1. Create the histogram of the incoming inputs.
            The histogram is computed continuously, and the ranges per bin change
            with every new tensor observed.
        2. Search the distribution in the histogram for optimal min/max values.
            The search for the min/max values ensures the minimization of the
            quantization error with respect to the floating point model.
        3. Compute the scale and zero point the same way as in the
            :class:`~torch.ao.quantization.MinMaxObserver`
        """
        histogram: torch.Tensor
        min_val: torch.Tensor
        max_val: torch.Tensor

        def __init__(
            self,
            bins: int = 2048,
            upsample_rate: int = 128,
            dtype: torch.dtype = torch.quint8,
            qscheme=torch.per_tensor_affine,
            reduce_range=False,
            quant_min=None,
            quant_max=None,
            factory_kwargs=None,
            eps=torch.finfo(torch.float32).eps,
        ) -> None:
            if not is_per_tensor(qscheme):
                raise NotImplementedError(
                    "HistogramObserver's qscheme only support torch.per_tensor_symmetric \
                        and torch.per_tensor_affine."
                )
            # bins: The number of bins used for histogram calculation.
            super().__init__(
                dtype=dtype,
                qscheme=qscheme,
                reduce_range=reduce_range,
                quant_min=quant_min,
                quant_max=quant_max,
                factory_kwargs=factory_kwargs,
                eps=eps,
            )
            factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
            self.bins = bins
            self.register_buffer("histogram", torch.zeros(self.bins, **factory_kwargs))
            self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
            self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
            self.dst_nbins = 2 ** torch.iinfo(self.dtype).bits
            self.upsample_rate = upsample_rate

        def _get_norm(
            self, delta_begin: torch.Tensor, delta_end: torch.Tensor, density: torch.Tensor
        ) -> torch.Tensor:
            r"""
            Compute the norm of the values uniformaly distributed between
            delta_begin and delta_end.
            Currently only L2 norm is supported.

            norm = density * (integral_{begin, end} x^2)
                 = density * (end^3 - begin^3) / 3
            """
            norm = (
                delta_end * delta_end * delta_end - delta_begin * delta_begin * delta_begin
            ) / 3
            return density * norm

        def _compute_quantization_error(self, next_start_bin: int, next_end_bin: int):
            r"""
            Compute the quantization error if we use start_bin to end_bin as the
            min and max to do the quantization.
            """
            bin_width = (self.max_val.item() - self.min_val.item()) / self.bins

            dst_bin_width = bin_width * (next_end_bin - next_start_bin + 1) / self.dst_nbins
            if dst_bin_width == 0.0:
                return 0.0

            src_bin = torch.arange(self.bins, device=self.histogram.device)
            # distances from the beginning of first dst_bin to the beginning and
            # end of src_bin
            src_bin_begin = (src_bin - next_start_bin) * bin_width
            src_bin_end = src_bin_begin + bin_width

            # which dst_bins the beginning and end of src_bin belong to?
            dst_bin_of_begin = torch.clamp(
                torch.div(src_bin_begin, dst_bin_width, rounding_mode='floor'), 0, self.dst_nbins - 1
            )
            dst_bin_of_begin_center = (dst_bin_of_begin + 0.5) * dst_bin_width

            dst_bin_of_end = torch.clamp(
                torch.div(src_bin_end, dst_bin_width, rounding_mode='floor'), 0, self.dst_nbins - 1
            )
            density = self.histogram / bin_width

            norm = torch.zeros(self.bins, device=self.histogram.device)

            delta_begin = src_bin_begin - dst_bin_of_begin_center
            delta_end = dst_bin_width / 2
            norm += self._get_norm(delta_begin,
                                   torch.ones(self.bins, device=self.histogram.device) * delta_end,
                                   density)

            norm += (dst_bin_of_end - dst_bin_of_begin - 1) * self._get_norm(
                torch.tensor(-dst_bin_width / 2), torch.tensor(dst_bin_width / 2), density
            )

            dst_bin_of_end_center = dst_bin_of_end * dst_bin_width + dst_bin_width / 2

            delta_begin = -dst_bin_width / 2
            delta_end = src_bin_end - dst_bin_of_end_center
            norm += self._get_norm(torch.tensor(delta_begin), delta_end, density)

            return norm.sum().item()

        def _non_linear_param_search(self) -> Tuple[torch.Tensor, torch.Tensor]:
            r"""Non-linear parameter search.

            An approximation for L2 error minimization for selecting min/max.
            By selecting new min/max, we filter out outliers in input distribution.
            This follows the implementation of NormMinimization::NonlinearQuantizationParamsSearch in
            caffe2/quantization/server/norm_minimization.cc
            """
            assert self.histogram.size()[0] == self.bins, "bins mismatch"
            bin_width = (self.max_val - self.min_val) / self.bins

            # cumulative sum
            total = torch.sum(self.histogram).item()
            cSum = torch.cumsum(self.histogram, dim=0)

            stepsize = 1e-5  # granularity
            alpha = 0.0  # lower bound
            beta = 1.0  # upper bound
            start_bin = 0
            end_bin = self.bins - 1
            norm_min = float("inf")

            while alpha < beta:
                # Find the next step
                next_alpha = alpha + stepsize
                next_beta = beta - stepsize

                # find the left and right bins between the quantile bounds
                l = start_bin
                r = end_bin
                while l < end_bin and cSum[l] < next_alpha * total:
                    l = l + 1
                while r > start_bin and cSum[r] > next_beta * total:
                    r = r - 1

                # decide the next move
                next_start_bin = start_bin
                next_end_bin = end_bin
                if (l - start_bin) > (end_bin - r):
                    # move the start bin
                    next_start_bin = l
                    alpha = next_alpha
                else:
                    # move the end bin
                    next_end_bin = r
                    beta = next_beta

                if next_start_bin == start_bin and next_end_bin == end_bin:
                    continue

                # calculate the quantization error using next_start_bin and next_end_bin
                norm = self._compute_quantization_error(next_start_bin, next_end_bin)

                if norm > norm_min:
                    break
                norm_min = norm
                start_bin = next_start_bin
                end_bin = next_end_bin

            new_min = self.min_val + bin_width * start_bin
            new_max = self.min_val + bin_width * (end_bin + 1)
            return new_min, new_max

        def _adjust_min_max(
            self, combined_min: torch.Tensor, combined_max: torch.Tensor, upsample_rate: int
        ) -> Tuple[torch.Tensor, torch.Tensor, int, int]:
            # We ensure that:
            # (combined_max - combined_min)/(downsample_rate*Nbins) = (max - min)/(upsample_rate*Nbins)
            # This allows us to have a common grid of resolution s, where we can align
            # the input histogram
            # start_idx maps min_val to the histogram bin index.

            # Compute the width of histogram bins is a straightforward solution, where
            # hist_bin_width = (self.max_val - self.min_val) / (self.bins * upsample_rate)
            # Underflow happens if the numerator is close to the smallest positive subnormal number of FP32
            # Therefore, we avoid such division operation.
            downsample_rate = int(
                torch.ceil(
                    (combined_max - combined_min) * upsample_rate / (self.max_val - self.min_val)
                ).item()
            )
            e = downsample_rate * (self.max_val - self.min_val) / upsample_rate - (combined_max - combined_min)
            start_idx = int(
                torch.round((self.min_val - combined_min) * self.bins * upsample_rate / (self.max_val - self.min_val)).item()
            )
            combined_max = combined_max + e
            combined_min = combined_min
            return combined_min, combined_max, downsample_rate, start_idx

        def _combine_histograms(
            self,
            orig_hist: torch.Tensor,
            new_hist: torch.Tensor,
            upsample_rate: int,
            downsample_rate: int,
            start_idx: int,
            Nbins: int,
        ) -> torch.Tensor:
            # First up-sample the histogram with new data by a factor of L
            # This creates an approximate probability density thats piecewise constant
            upsampled_histogram = new_hist.repeat_interleave(upsample_rate)
            # Now insert the upsampled histogram into the output
            # histogram, which is initialized with zeros.
            # The offset at which the histogram is introduced is determined
            # by the start index as the output histogram can cover a wider range
            histogram_with_output_range = torch.zeros(
                (Nbins * downsample_rate), device=orig_hist.device
            )
            histogram_with_output_range[
                start_idx : Nbins * upsample_rate + start_idx
            ] = upsampled_histogram
            # Compute integral histogram, double precision is needed to ensure
            # that there are no overflows
            integral_histogram = torch.cumsum(
                histogram_with_output_range, 0, dtype=torch.double
            )[downsample_rate - 1 :: downsample_rate]
            # Finally perform interpolation
            shifted_integral_histogram = torch.zeros((Nbins), device=orig_hist.device)
            shifted_integral_histogram[1:Nbins] = integral_histogram[0:-1]
            interpolated_histogram = (
                integral_histogram - shifted_integral_histogram
            ) / upsample_rate
            orig_hist = orig_hist + interpolated_histogram.to(torch.float)
            return orig_hist

        def forward(self, x_orig: torch.Tensor) -> torch.Tensor:
            if x_orig.numel() == 0:
                return x_orig
            x = x_orig.detach()
            min_val = self.min_val
            max_val = self.max_val
            same_values = min_val.item() == max_val.item()
            is_uninitialized = min_val == float("inf") and max_val == float("-inf")
            if is_uninitialized or same_values:
                min_val, max_val = torch.aminmax(x)
                self.min_val.resize_(min_val.shape)
                self.min_val.copy_(min_val)
                self.max_val.resize_(max_val.shape)
                self.max_val.copy_(max_val)
                assert (
                    min_val.numel() == 1 and max_val.numel() == 1
                ), "histogram min/max values must be scalar."
                torch.histc(
                    x, self.bins, min=min_val, max=max_val, out=self.histogram  # type: ignore[arg-type]
                )
            else:
                new_min, new_max = torch.aminmax(x)
                combined_min = torch.min(new_min, min_val)
                combined_max = torch.max(new_max, max_val)
                # combine the existing histogram and new histogram into 1 histogram
                # We do this by first upsampling the histogram to a dense grid
                # and then downsampling the histogram efficiently
                (
                    combined_min,
                    combined_max,
                    downsample_rate,
                    start_idx,
                ) = self._adjust_min_max(combined_min, combined_max, self.upsample_rate)
                assert (
                    combined_min.numel() == 1 and combined_max.numel() == 1
                ), "histogram min/max values must be scalar."
                combined_histogram = torch.histc(
                    x, self.bins, min=combined_min, max=combined_max  # type: ignore[arg-type]
                )
                if combined_min == min_val and combined_max == max_val:
                    combined_histogram += self.histogram
                else:
                    combined_histogram = self._combine_histograms(
                        combined_histogram,
                        self.histogram,
                        self.upsample_rate,
                        downsample_rate,
                        start_idx,
                        self.bins,
                    )

                self.histogram.detach_().resize_(combined_histogram.shape)
                self.histogram.copy_(combined_histogram)
                self.min_val.detach_().resize_(combined_min.shape)
                self.min_val.copy_(combined_min)
                self.max_val.detach_().resize_(combined_max.shape)
                self.max_val.copy_(combined_max)
            return x_orig

        @torch.jit.export
        def calculate_qparams(self):
            is_uninitialized = self.min_val == float("inf") and self.max_val == float(
                "-inf"
            )
            if is_uninitialized:
                warnings.warn(
                    "must run observer before calling calculate_qparams.\
                                        Returning default scale and zero point "
                )
                return torch.tensor([1.0], device=self.min_val.device.type), torch.tensor([0], device=self.min_val.device.type)
            assert self.bins == len(self.histogram), (
                "The number of bins in histogram should be equal to the number of bins "
                "supplied while making this observer"
            )

            new_min, new_max = self._non_linear_param_search()

            return self._calculate_qparams(new_min, new_max)

        def _save_to_state_dict(self, destination, prefix, keep_vars):
            super()._save_to_state_dict(destination, prefix, keep_vars)
            destination[prefix + "min_val"] = self.min_val
            destination[prefix + "max_val"] = self.max_val

        def _load_from_state_dict(
            self,
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        ):
            version = local_metadata.get("version", None)

            if version is None or version < 3:
                # if min_val and max_val are not initialized, update their shape
                # to account for the differences between v2 and v3
                min_val_name, max_val_name = prefix + "min_val", prefix + "max_val"
                if min_val_name in state_dict:
                    if state_dict[min_val_name].shape == torch.Size([0]):
                        state_dict[min_val_name] = torch.tensor(float("inf"))
                if max_val_name in state_dict:
                    if state_dict[max_val_name].shape == torch.Size([0]):
                        state_dict[max_val_name] = torch.tensor(float("-inf"))

            local_state = ["min_val", "max_val"]
            for name in local_state:
                key = prefix + name
                if key in state_dict:
                    val = state_dict[key]
                    setattr(self, name, val)
                elif strict:
                    missing_keys.append(key)
            super()._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                strict,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )

        def extra_repr(self):
            return f"min_val={self.min_val}, max_val={self.max_val}"

    class FixedQParamsObserver(ObserverBase):
        r"""
        Observer that simulates quantize and dequantize with fixed
        quantization parameters in training time. Only per tensor
        quantization is supported.

        Args:
            `scale` (float): fixed scale for the observer
            `zero_point` (int): fixed zero point for the observer
            `dtype`, `qscheme`, `quant_min`, `quant_max`
        """

        scale: torch.Tensor
        zero_point: torch.Tensor

        def __init__(self,
                     scale,
                     zero_point,
                     dtype=torch.quint8,
                     qscheme=torch.per_tensor_affine,
                     quant_min=0,
                     quant_max=255):
            super().__init__(dtype=dtype)
            self.quant_min = quant_min
            self.quant_max = quant_max
            self.register_buffer('scale', torch.tensor([scale], dtype=torch.float))
            self.register_buffer('zero_point', torch.tensor([zero_point], dtype=torch.int))
            self.dtype = dtype
            self.qscheme = qscheme

        def forward(self, X):
            return X

        @torch.jit.export
        def calculate_qparams(self):
            return self.scale, self.zero_point

    class PlaceholderObserver(ObserverBase):
        r"""
        Observer that doesn't do anything and just passes its configuration to the
        quantized module's ``.from_float()``.

        Can be used for quantization to float16 which doesn't require determining
        ranges.

        Args:
            dtype: dtype argument to the `quantize` node needed to implement the
                   reference model spec.
            quant_min: minimum value in quantized domain (TODO: align behavior with other observers)
            quant_max: maximum value in quantized domain
            custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
                            (Can be used in Graph Mode Passes for special case ops).
            compute_dtype (deprecated): if set, marks the future quantize function to use
                           dynamic quantization instead of static quantization.
                           This field is deprecated, use `is_dynamic=True` instead.
            is_dynamic: if True, the `quantize` function in the reference model
                        representation taking stats from this observer instance will
                        use dynamic quantization.
        """

        def __init__(
            self, dtype=torch.float32, custom_op_name="", compute_dtype=None,
            quant_min=None, quant_max=None, qscheme=None, eps=None,
            is_dynamic=False,
        ) -> None:
            super().__init__(dtype=dtype)
            if qscheme is None:
                qscheme = torch.per_tensor_affine
            if eps is None:
                eps = torch.finfo(torch.float32).eps

            # dtype of input of the target operator, e.g. for dynamic quantization
            # ops, the dtype will be float32
            self.dtype = dtype
            self.qscheme = qscheme
            self.quant_min = quant_min
            self.quant_max = quant_max
            self.eps = eps
            self.custom_op = custom_op_name
            # used for configuration of computation type for dynamic quantization
            if compute_dtype:
                is_dynamic = True
                warnings.warn(
                    "Please use `is_dynamic` instead of `compute_dtype`. \
                        `compute_dtype` will be deprecated in a future release \
                        of PyTorch."
                )
            self.is_dynamic = is_dynamic

        def forward(self, x):
            return x

        @torch.jit.export
        def extra_repr(self):
            return f"dtype={self.dtype}, is_dynamic={self.is_dynamic}"

        @torch.jit.export
        def calculate_qparams(self):
            raise Exception(
                "calculate_qparams should not be called for PlaceholderObserver"
            )

    class RecordingObserver(ObserverBase):
        r"""
        The module is mainly for debug and records the tensor values during runtime.

        Args:
            dtype: Quantized data type
            qscheme: Quantization scheme to be used
            reduce_range: Reduces the range of the quantized data type by 1 bit
        """
        __annotations__ = {"tensor_val": List[Optional[torch.Tensor]]}

        def __init__(self, dtype=torch.quint8, **kwargs):
            super().__init__(dtype=dtype, **kwargs)  # type: ignore[call-arg]
            self.tensor_val = []

        def forward(self, x):
            self.tensor_val.append(x.clone())
            return x

        @torch.jit.export
        def calculate_qparams(self):
            raise Exception("calculate_qparams should not be called for RecordingObserver")

        @torch.jit.export
        def get_tensor_value(self):
            return self.tensor_val

    class NoopObserver(ObserverBase):
        r"""
        Observer that doesn't do anything and just passes its configuration to the
        quantized module's ``.from_float()``.

        Primarily used for quantization to float16 which doesn't require determining
        ranges.

        Args:
            dtype: Quantized data type
            custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
                            (Can be used in Graph Mode Passes for special case ops).
        """

        def __init__(self, dtype=torch.float16, custom_op_name="") -> None:
            super().__init__(dtype=dtype)
            self.dtype = dtype
            self.custom_op = custom_op_name

        def forward(self, x):
            return x

        @torch.jit.export
        def calculate_qparams(self):
            raise Exception("calculate_qparams should not be called for NoopObserver")

    class ReuseInputObserver(ObserverBase):
        r""" This observer is used when we want to reuse the observer from the operator
        that produces the input Tensor, typically used for operators like reshape, e.g.
        ```
        x0 = ...
        x1 = x0.reshape()
        ```
        if we configure x0 to be observed by some observer, let's say MinMaxObserver,
        and reshape is configured with ReuseInputObserver, we'll reuse the observer instance
        for x0 for x1 (output of reshape). If x0 is not observed, we also won't observe x1.

        Note: this is only enabled in FX Graph Mode Quantization
        """
        def __init__(self):
            super().__init__(torch.quint8)

        def forward(self, x):
            return x

        @torch.jit.export
        def calculate_qparams(self):
            raise Exception("calculate_qparams should not be called for ReuseInputObserver")

    def _is_observer_script_module(mod, obs_type_name):
        """Returns true if given mod is an instance of Observer script module."""
        if isinstance(mod, torch.jit.RecursiveScriptModule):
            # qualified name looks like '__torch__.torch.ao.quantization.observer.___torch_mangle_2.MinMaxObserver'
            suffix = mod._c.qualified_name.split(".", 1)[1]
            name = re.sub(r"\.___torch_mangle_\d+", "", suffix)
            return obs_type_name in name
        return False

    def _is_activation_post_process(module):
        return (
            isinstance(module, (torch.ao.quantization.ObserverBase,
                                torch.ao.quantization.FakeQuantizeBase)) or _is_observer_script_module(module, "quantization.observer")
        )

    def _is_per_channel_script_obs_instance(module):
        if isinstance(module, torch.jit.RecursiveScriptModule):
            return _is_observer_script_module(
                module, "quantization.observer.PerChannelMinMaxObserver"
            ) or _is_observer_script_module(
                module, "quantization.observer.MovingAveragePerChannelMinMaxObserver"
            )
        return False

    def get_observer_state_dict(mod):
        r"""
        Returns the state dict corresponding to the observer stats.
        Traverse the model state_dict and extract out the stats.
        """
        od = OrderedDict()
        if isinstance(mod, torch.jit.RecursiveScriptModule):
            for k, v in mod.state_dict().items():
                if "observer" in k:
                    od[k] = v
        else:
            # path for GraphModule and nn.Module (eager mode)
            for k, v in mod.state_dict().items():
                if "activation_post_process" in k:
                    od[k] = v
        od._metadata = mod.state_dict()._metadata  # type: ignore[attr-defined]
        return od

    def load_observer_state_dict(mod, obs_dict):
        r"""
        Given input model and a state_dict containing model observer stats,
        load the stats back into the model. The observer state_dict can be saved
        using torch.ao.quantization.get_observer_state_dict
        """
        missing_keys: List[str] = []
        unexpected_keys: List[str] = []
        for name, module in mod.named_modules():
            prefix = name + "."
            if _is_activation_post_process(module):
                if _is_per_channel_script_obs_instance(module):
                    # For per-channel observers we need to call a custom load_from_state_dict to resize the tensor.
                    # However this is not called when the module is scripted and we end up calling the default one in module.py
                    module._load_from_state_dict_script(
                        obs_dict, prefix, {}, True, missing_keys, unexpected_keys, []
                    )
                else:
                    module._load_from_state_dict(
                        obs_dict, prefix, {}, False, missing_keys, unexpected_keys, []
                    )
        for k in missing_keys:
            if "observer" in k or "activation_post_process" in k:
                raise Exception(f"Missing keys for observer {k} in state_dict")
        for k in unexpected_keys:
            if "observer" in k or "activation_post_process" in k:
                raise Exception(f"Unexpected keys for observer {k} in state_dict")

    # Restrict activations to be in the range (0,127)
    default_observer = MinMaxObserver.with_args(quant_min=0, quant_max=127)
    """
    Default observer for static quantization, usually used for debugging.
    """

    default_placeholder_observer = PlaceholderObserver
    """
    Default placeholder observer, usually used for quantization to torch.float16.
    """

    default_debug_observer = RecordingObserver
    """
    Default debug-only observer.
    """

    default_weight_observer = MinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_tensor_symmetric
    )
    """
    Default weight observer.
    """

    weight_observer_range_neg_127_to_127 = MinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_tensor_symmetric,
        quant_min=-127, quant_max=127, eps=2 ** -12)
    """
    Symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
    """

    default_histogram_observer = HistogramObserver.with_args(quant_min=0, quant_max=127)
    """
    Default histogram observer, usually used for PTQ.
    """

    default_per_channel_weight_observer = PerChannelMinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_channel_symmetric
    )
    """
    Default per-channel weight observer, usually used on backends where per-channel
    weight quantization is supported, such as `fbgemm`.
    """

    per_channel_weight_observer_range_neg_127_to_127 = PerChannelMinMaxObserver.with_args(
        dtype=torch.qint8, qscheme=torch.per_channel_symmetric,
        quant_min=-127, quant_max=127, eps=2 ** -12)
    """
    Per-channel, symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
    """

    default_dynamic_quant_observer = PlaceholderObserver.with_args(
        dtype=torch.quint8, quant_min=0, quant_max=255, is_dynamic=True,
    )
    """
    Default observer for dynamic quantization.
    """

    default_float_qparams_observer = PerChannelMinMaxObserver.with_args(
        dtype=torch.quint8, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
    )
    """
    Default observer for a floating point zero-point.
    """

    default_float_qparams_observer_4bit = PerChannelMinMaxObserver.with_args(
        dtype=torch.quint4x2, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
    )
    """
    Default observer for a floating point zero-point and 4 bit activations.
    """

    # TODO(future PR): remove these defaults and enforce activation functions
    # to explicitly specify their output range
    default_fixed_qparams_range_neg1to1_observer = FixedQParamsObserver.with_args(
        scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255)
    default_fixed_qparams_range_0to1_observer = FixedQParamsObserver.with_args(
        scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255)
    # TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases
    default_symmetric_fixed_qparams_observer = default_fixed_qparams_range_neg1to1_observer
    default_affine_fixed_qparams_observer = default_fixed_qparams_range_0to1_observer

    """
    Default observers for fixed qparams operations.
    """

    default_reuse_input_observer = ReuseInputObserver
    """
    Default observer for operators like reshape that reuses the observer of input to
    the operator
    """

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