"""
This module implements modules which are used to perform fake quantization
during QAT.
"""

import torch
from torch.nn import Module
from torch.ao.quantization.observer import (
    MovingAverageMinMaxObserver,
    HistogramObserver,
    MovingAveragePerChannelMinMaxObserver,
    FixedQParamsObserver,
    default_fixed_qparams_range_0to1_observer,
    default_fixed_qparams_range_neg1to1_observer,
    _with_args,
)
import re
from abc import ABC, abstractmethod
from typing import Any, Tuple

__all__ = [
    "FakeQuantizeBase",
    "FakeQuantize",
    "FixedQParamsFakeQuantize",
    "FusedMovingAvgObsFakeQuantize",
    "disable_fake_quant",
    "disable_observer",
    "enable_fake_quant",
    "enable_observer",
    "default_fake_quant",
    "default_weight_fake_quant",
    "default_dynamic_fake_quant",
    "default_fixed_qparams_range_neg1to1_fake_quant",
    "default_fixed_qparams_range_0to1_fake_quant",
    "default_symmetric_fixed_qparams_fake_quant",
    "default_affine_fixed_qparams_fake_quant",
    "default_per_channel_weight_fake_quant",
    "default_embedding_fake_quant",
    "default_embedding_fake_quant_4bit",
    "default_histogram_fake_quant",
    "default_fused_act_fake_quant",
    "default_fused_wt_fake_quant",
    "default_fused_per_channel_wt_fake_quant",
    "fused_wt_fake_quant_range_neg_127_to_127",
    "fused_per_channel_wt_fake_quant_range_neg_127_to_127",
]

def _is_per_channel(qscheme: 'torch.qscheme') -> bool:
    return qscheme in [torch.per_channel_symmetric, torch.per_channel_affine, torch.per_channel_affine_float_qparams]

def _is_per_tensor(qscheme: 'torch.qscheme') -> bool:
    return qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]

def _is_symmetric_quant(qscheme: 'torch.qscheme') -> bool:
    return qscheme in [torch.per_tensor_symmetric, torch.per_channel_symmetric]

def _is_float_qparams(qscheme: 'torch.qscheme') -> bool:
    return qscheme in [torch.per_channel_affine_float_qparams, ]

class FakeQuantizeBase(ABC, Module):
    r""" Base fake quantize module
    Any fake quantize implementation should derive from this class.

    Concrete fake quantize module should follow the same API. In forward, they will update
    the statistics of the observed Tensor and fake quantize the input. They should also provide a
    `calculate_qparams` function that computes the quantization parameters given
    the collected statistics.

    """

    fake_quant_enabled: torch.Tensor
    observer_enabled: torch.Tensor

    def __init__(self):
        super().__init__()
        # fake_quant_enabled and observer_enabled are buffers to support their
        # replication in DDP. Data type is uint8 because NCCL does not support
        # bool tensors.
        self.register_buffer('fake_quant_enabled', torch.tensor([1], dtype=torch.uint8))
        self.register_buffer('observer_enabled', torch.tensor([1], dtype=torch.uint8))

    @abstractmethod
    def forward(self, x):
        pass

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

    @torch.jit.export
    def enable_fake_quant(self, enabled: bool = True) -> None:
        self.fake_quant_enabled[0] = 1 if enabled else 0

    @torch.jit.export
    def disable_fake_quant(self):
        self.enable_fake_quant(False)

    @torch.jit.export
    def enable_observer(self, enabled: bool = True) -> None:
        self.observer_enabled[0] = 1 if enabled else 0

    @torch.jit.export
    def disable_observer(self):
        self.enable_observer(False)

    @classmethod
    def with_args(cls, **kwargs):
        fake_quant_constructor = _with_args(cls, **kwargs)
        # need to assign the correct module to fake_quantize
        # constructors to satisfy public v private requirements
        fake_quant_constructor.__module__ = "torch.ao.quantization.fake_quantize"
        return fake_quant_constructor

class FakeQuantize(FakeQuantizeBase):
    r""" Simulate the quantize and dequantize operations in training time.
    The output of this module is given by::

        x_out = (
          clamp(round(x/scale + zero_point), quant_min, quant_max) - zero_point
        ) * scale

    * :attr:`scale` defines the scale factor used for quantization.

    * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to

    * :attr:`fake_quant_enabled` controls the application of fake quantization on tensors, note that
      statistics can still be updated.

    * :attr:`observer_enabled` controls statistics collection on tensors

    * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,
        allowable values are torch.qint8 and torch.quint8.

    Args:

        observer (module): Module for observing statistics on input tensors and calculating scale
          and zero-point.
        observer_kwargs (optional): Arguments for the observer module

    Attributes:

        activation_post_process (Module): User provided module that collects statistics on the input tensor and
          provides a method to calculate scale and zero-point.

    """

    scale: torch.Tensor
    zero_point: torch.Tensor

    def __init__(self, observer=MovingAverageMinMaxObserver, quant_min=None, quant_max=None, **observer_kwargs):
        super().__init__()
        # Populate quant_min/quant_max to observer_kwargs if valid
        if quant_min is not None and quant_max is not None:
            assert quant_min <= quant_max, \
                'quant_min must be less than or equal to quant_max'
            dtype = observer_kwargs.get("dtype", torch.quint8)
            if hasattr(observer, "p"):
                # In case observer is _PartialWrapper, dtype can be stored in
                # observer.p.keywords["dtype"]
                dtype = getattr(getattr(observer, "p", {}), "keywords", {}).get(
                    "dtype", dtype
                )
            assert torch.iinfo(dtype).min <= quant_min, 'quant_min out of bound'
            assert quant_max <= torch.iinfo(dtype).max, 'quant_max out of bound'
            observer_kwargs.update({"quant_min": quant_min, "quant_max": quant_max})
        self.activation_post_process = observer(**observer_kwargs)
        # TODO: keeping self.quant_min/max for BC; remove after a couple releases
        # Users should use self.activation_post_process.quant_min
        self.quant_min = self.activation_post_process.quant_min
        self.quant_max = self.activation_post_process.quant_max
        if _is_float_qparams(self.activation_post_process.qscheme):
            zero_point_dtype = torch.float
        else:
            zero_point_dtype = torch.int
        self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
        self.register_buffer('zero_point', torch.tensor([0], dtype=zero_point_dtype))
        self.dtype = self.activation_post_process.dtype
        self.qscheme = self.activation_post_process.qscheme
        self.ch_axis = self.activation_post_process.ch_axis \
            if hasattr(self.activation_post_process, 'ch_axis') else -1
        assert _is_per_channel(self.qscheme) or \
            _is_per_tensor(self.qscheme), \
            'Only per channel and per tensor quantization are supported in fake quantize' + \
            ' got qscheme: ' + str(self.qscheme)
        self.is_per_channel = _is_per_channel(self.qscheme)

    @torch.jit.export
    def calculate_qparams(self):
        return self.activation_post_process.calculate_qparams()

    def forward(self, X):
        if self.observer_enabled[0] == 1:
            self.activation_post_process(X.detach())
            _scale, _zero_point = self.calculate_qparams()
            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
            if self.scale.shape != _scale.shape:
                self.scale.resize_(_scale.shape)
                self.zero_point.resize_(_zero_point.shape)
            self.scale.copy_(_scale)
            self.zero_point.copy_(_zero_point)

        if self.fake_quant_enabled[0] == 1:
            if self.is_per_channel:
                X = torch.fake_quantize_per_channel_affine(
                    X, self.scale, self.zero_point,
                    self.ch_axis, self.activation_post_process.quant_min, self.activation_post_process.quant_max)
            else:
                X = torch.fake_quantize_per_tensor_affine(
                    X, self.scale, self.zero_point,
                    self.activation_post_process.quant_min, self.activation_post_process.quant_max)
        return X

    @torch.jit.export
    def extra_repr(self):
        return 'fake_quant_enabled={}, observer_enabled={}, ' \
               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
               'scale={}, zero_point={}'.format(
                   self.fake_quant_enabled, self.observer_enabled,
                   self.activation_post_process.quant_min, self.activation_post_process.quant_max,
                   self.dtype, self.qscheme, self.ch_axis, self.scale, self.zero_point)

    def _save_to_state_dict(self, destination, prefix, keep_vars):
        # We cannot currently register scalar values as buffers, so need to manually
        # specify serialization here.
        super()._save_to_state_dict(destination, prefix, keep_vars)
        destination[prefix + 'scale'] = self.scale
        destination[prefix + 'zero_point'] = self.zero_point

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        # Removing this function throws an error that the the size of the loaded tensor does not match the original size
        # i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.
        local_state = ['scale', 'zero_point']
        for name in local_state:
            key = prefix + name
            if key in state_dict:
                val = state_dict[key]
                # Custom handling to allow loading scale and zero_point
                # 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 == 'scale':
                    self.scale.resize_(val.shape)
                else:
                    assert name == 'zero_point'
                    self.zero_point.resize_(val.shape)
                # 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 == 'scale':
                        self.scale.copy_(val)
                    else:
                        assert name == 'zero_point'
                        self.zero_point.copy_(val)
            elif strict:
                missing_keys.append(key)
        super()._load_from_state_dict(state_dict, prefix, local_metadata, strict,
                                      missing_keys, unexpected_keys, error_msgs)


class FixedQParamsFakeQuantize(FakeQuantize):
    """ Simulate quantize and dequantize with fixed quantization
    parameters in training time. Only per tensor quantization
    is supported.
    """

    def __init__(self, observer):
        super().__init__(observer=observer)
        assert type(self.activation_post_process) == FixedQParamsObserver,\
            "%s's observer must be a %s" % (self.__class__.__name__, FixedQParamsObserver.__name__)
        self._observer_ctr = observer
        self.scale = self.activation_post_process.scale
        self.zero_point = self.activation_post_process.zero_point
        assert _is_per_tensor(self.qscheme), 'Only per tensor quantization is supported' + \
            ' FixedQParamsFakeQuantize module, got qscheme:' + str(self.qscheme)

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

    @torch.jit.export
    def extra_repr(self):
        return 'fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, ' \
               'dtype={}, quant_min={}, quant_max={}, qscheme={}'.format(
                   self.fake_quant_enabled, self.observer_enabled,
                   self.scale, self.zero_point, self.dtype,
                   self.activation_post_process.quant_min, self.activation_post_process.quant_max, self.qscheme)


class FusedMovingAvgObsFakeQuantize(FakeQuantize):
    r"""Fused module that is used to observe the input tensor (compute min/max), compute
    scale/zero_point and fake_quantize the tensor.
    This module uses calculation similar MovingAverageMinMaxObserver for the inputs,
    to compute the min/max values in order to compute the scale/zero_point.
    The qscheme input in the observer is used to differentiate between symmetric/affine
    quantization scheme.

    The output of this module is given by
    x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale

    Similar to :class:`~torch.ao.quantization.FakeQuantize`, and accepts the same attributes as the
    base class.

    """

    def __init__(
        self,
        observer: Any = MovingAverageMinMaxObserver,
        quant_min: int = 0,
        quant_max: int = 255,
        **observer_kwargs: Any
    ) -> None:
        super().__init__(observer, quant_min, quant_max, **observer_kwargs)
        assert isinstance(self.activation_post_process, (MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver)),\
            "Fused observer+fake_quant module only works with MovingAverageMinMaxObserver"
        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.long))
        self.register_buffer("observer_enabled", torch.tensor([1], dtype=torch.long))
        self.is_symmetric_quant = _is_symmetric_quant(self.activation_post_process.qscheme)

    @torch.jit.export
    def calculate_qparams(self) -> Tuple[torch.Tensor, torch.Tensor]:
        return self.activation_post_process.calculate_qparams()

    @torch.jit.export
    def extra_repr(self) -> str:
        return (
            "fake_quant_enabled={}, observer_enabled={}, scale={}, zero_point={}, "
            "dtype={}, quant_min={}, quant_max={}, qscheme={}, reduce_range={}".format(
                self.fake_quant_enabled,
                self.observer_enabled,
                self.scale,
                self.zero_point,
                self.dtype,
                self.activation_post_process.quant_min,
                self.activation_post_process.quant_max,
                self.qscheme,
                self.activation_post_process.reduce_range,
            )
        )

    def forward(self, X: torch.Tensor) -> torch.Tensor:
        return torch.fused_moving_avg_obs_fake_quant(
            X,
            self.observer_enabled,
            self.fake_quant_enabled,