from collections import namedtuple
from typing import Optional, Any, Union

import torch
import torch.nn as nn
from torch.ao.quantization.fake_quantize import (
    FakeQuantize,
    FakeQuantizeBase,
    default_fake_quant,
    default_dynamic_fake_quant,
    default_per_channel_weight_fake_quant,
    default_weight_fake_quant,
    default_fused_act_fake_quant,
    default_fused_wt_fake_quant,
    FusedMovingAvgObsFakeQuantize,
    default_fused_per_channel_wt_fake_quant,
    default_embedding_fake_quant,
    default_embedding_fake_quant_4bit,
    fused_wt_fake_quant_range_neg_127_to_127,
    fused_per_channel_wt_fake_quant_range_neg_127_to_127,
)

from .observer import (
    _PartialWrapper,
    HistogramObserver,
    MovingAverageMinMaxObserver,
    NoopObserver,
    PlaceholderObserver,
    ReuseInputObserver,
    default_debug_observer,
    default_dynamic_quant_observer,
    default_float_qparams_observer,
    default_float_qparams_observer_4bit,
    default_observer,
    default_per_channel_weight_observer,
    default_placeholder_observer,
    default_weight_observer,
    weight_observer_range_neg_127_to_127,
    per_channel_weight_observer_range_neg_127_to_127,
    default_reuse_input_observer,
    ObserverBase,
)
import warnings
import copy

__all__ = [
    "QConfig",
    # TODO: deprecated, remove
    "QConfigDynamic",
    "default_qconfig",
    "default_debug_qconfig",
    "default_per_channel_qconfig",
    "default_dynamic_qconfig",
    "float16_dynamic_qconfig",
    "float16_static_qconfig",
    "per_channel_dynamic_qconfig",
    "float_qparams_weight_only_qconfig",
    "float_qparams_weight_only_qconfig_4bit",
    "default_qat_qconfig",
    "default_dynamic_qat_qconfig",
    "default_weight_only_qconfig",
    "default_activation_only_qconfig",
    "default_qat_qconfig_v2",
    "default_reuse_input_qconfig",
    "default_symmetric_qnnpack_qconfig",
    "default_per_channel_symmetric_qnnpack_qconfig",
    "default_symmetric_qnnpack_qat_qconfig",
    "default_per_channel_symmetric_qnnpack_qat_qconfig",
    "default_embedding_qat_qconfig",
    "default_embedding_qat_qconfig_4bit",
    "get_default_qconfig",
    "get_default_qat_qconfig",
    "get_default_qconfig_dict",
    "get_default_qat_qconfig_dict",
    "QConfigAny",
    "qconfig_equals",
]

class QConfig(namedtuple('QConfig', ['activation', 'weight'])):
    """
    Describes how to quantize a layer or a part of the network by providing
    settings (observer classes) for activations and weights respectively.


    Note that QConfig needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
    instances on invocation, not the concrete observer instances themselves.
    Quantization preparation function will instantiate observers multiple times for each of the layers.


    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
    method (that behaves like functools.partial)::

      my_qconfig = QConfig(
          activation=MinMaxObserver.with_args(dtype=torch.qint8),
          weight=default_observer.with_args(dtype=torch.qint8))

    """
    def __new__(cls, activation, weight):
        # catch common mistakes
        if isinstance(activation, nn.Module) or isinstance(weight, nn.Module):
            raise ValueError("QConfig received observer instance, please pass observer class instead. " +
                             "Use MyObserver.with_args(x=1) to override arguments to constructor if needed")
        return super(QConfig, cls).__new__(cls, activation, weight)


class QConfigDynamic(namedtuple('QConfigDynamic', ['activation', 'weight'])):
    """
    Describes how to dynamically quantize a layer or a part of the network by providing
    settings (observer classes) for weights.

    It's like QConfig, but for dynamic quantization.

    Note that QConfigDynamic needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
    instances on invocation, not the concrete observer instances themselves.
    Quantization function will instantiate observers multiple times for each of the layers.

    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
    method (that behaves like functools.partial)::

      my_qconfig = QConfigDynamic(weight=default_observer.with_args(dtype=torch.qint8))
    """
    def __new__(cls, activation=torch.nn.Identity, weight=torch.nn.Identity):
        # catch common mistakes
        if isinstance(weight, nn.Module):
            raise ValueError("QConfigDynamic received observer instance, please pass observer class instead. " +
                             "Use MyObserver.with_args(x=1) to override arguments to constructor if needed")
        warnings.warn("QConfigDynamic is going to be deprecated in PyTorch 1.12, please use QConfig instead")
        return super(QConfigDynamic, cls).__new__(cls, activation, weight)


default_qconfig = QConfig(activation=default_observer,
                          weight=default_weight_observer)
"""
Default qconfig configuration.
"""

default_debug_qconfig = QConfig(weight=default_weight_observer,
                                activation=default_debug_observer)
"""
Default qconfig configuration for debugging.
"""

default_per_channel_qconfig = QConfig(activation=default_observer,
                                      weight=default_per_channel_weight_observer)
"""
Default qconfig configuration for per channel weight quantization.
"""

default_dynamic_qconfig = QConfig(activation=default_dynamic_quant_observer,
                                  weight=default_weight_observer)
"""
Default dynamic qconfig.
"""

float16_dynamic_qconfig = QConfig(activation=PlaceholderObserver.with_args(dtype=torch.float16, is_dynamic=True),
                                  weight=PlaceholderObserver.with_args(dtype=torch.float16))
"""
Dynamic qconfig with weights quantized to `torch.float16`.
"""

float16_static_qconfig = QConfig(activation=PlaceholderObserver.with_args(dtype=torch.float16),
                                 weight=PlaceholderObserver.with_args(dtype=torch.float16))
"""
Dynamic qconfig with both activations and weights quantized to `torch.float16`.
"""

per_channel_dynamic_qconfig = QConfig(activation=default_dynamic_quant_observer,
                                      weight=default_per_channel_weight_observer)
"""
Dynamic qconfig with weights quantized per channel.
"""

float_qparams_weight_only_qconfig = QConfig(
    activation=default_placeholder_observer,
    weight=default_float_qparams_observer)
"""
Dynamic qconfig with weights quantized with a floating point zero_point.
"""

float_qparams_weight_only_qconfig_4bit = QConfig(
    activation=default_placeholder_observer,
    weight=default_float_qparams_observer_4bit)

default_qat_qconfig = QConfig(activation=default_fake_quant,
                              weight=default_weight_fake_quant)
"""
Default qconfig for QAT.
"""

default_dynamic_qat_qconfig = QConfig(activation=default_dynamic_fake_quant,
                                      weight=default_weight_fake_quant)
"""
Default qconfig for dynamic QAT.
"""

default_weight_only_qconfig = QConfig(activation=torch.nn.Identity,
                                      weight=default_weight_fake_quant)
"""
Default qconfig for quantizing weights only.
"""

default_activation_only_qconfig = QConfig(activation=default_fake_quant,
                                          weight=torch.nn.Identity)
"""
Default qconfig for quantizing activations only.
"""

# QAT config that uses a fused observer + fake quant modules for optimized training performance.
# to modify the activation/weight observers, the default entries in fake_quantize.py can be modified.
default_qat_qconfig_v2 = QConfig(activation=default_fused_act_fake_quant, weight=default_fused_wt_fake_quant)
"""
Fused version of `default_qat_config`, has performance benefits.
"""

default_reuse_input_qconfig = QConfig(activation=default_reuse_input_observer,
                                      weight=NoopObserver)
"""
Default qconfig for operators that reuse the observers from input Tensor, e.g. reshape
"""

def get_default_qconfig(backend='x86', version=0):
    """
    Returns the default PTQ qconfig for the specified backend.

    Args:
      * `backend` (str): a string representing the target backend. Currently supports
        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.

    Return:
        qconfig
    """
    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
    if backend not in supported_backends:
        raise AssertionError(
            "backend: " + str(backend) +
            " not supported. backend must be one of {}".format(supported_backends)
        )

    if version == 0:
        if backend == 'fbgemm':
            qconfig = QConfig(activation=HistogramObserver.with_args(reduce_range=True),
                              weight=default_per_channel_weight_observer)
        elif backend == 'qnnpack':
            # TODO: make this compatible with xnnpack constraints
            qconfig = QConfig(activation=HistogramObserver.with_args(reduce_range=False),
                              weight=default_weight_observer)
        elif backend == 'onednn':
            qconfig = QConfig(activation=HistogramObserver.with_args(reduce_range=False),
                              weight=default_per_channel_weight_observer)
        elif backend == 'x86':
            qconfig = QConfig(activation=HistogramObserver.with_args(reduce_range=True),
                              weight=default_per_channel_weight_observer)
        else:
            # won't reach
            qconfig = default_qconfig
    else:
        raise AssertionError("Version number: " + str(version) +
                             " in get_default_qconfig is not supported. Version number must be 0")

    return qconfig

"""
Default, symmetric PTQ qconfig for the specified backend. And a per_channel
variant of the same.

Symmetric here applies to signed weights with zero point = 0, and additional
value restrictions. The activations are also signed 8-bit integers with this
qconfig.

    * Once this change is merged [as of 3/17/22], with backend or qengine =
    'qnnpack', some quantized operators with this symmetric qconfig may use
    operators from xnnpack library.

        ** Support to use xnnpack ops with `qnnpack` backed for asymmetric
        qconfig (returned by get_default_qconfig()) is not available yet.

    * This qconfig uses signed activations and weights. Weights have added
    restrictions such as zero point is forced to be 0, making the weights
    symmetric, hence the name. And the 8-bit quantized values are
    restricting to to [-127, +127], excluding -128.

    * xnnpack has a requantization scale value restriction, 0x1p-32 <=
    requantization_scale < 256.0 where, `requantization_scale = (input_scale
    * kernel_scale) / (output_scale)`. Using this eps (w/ assumed max value
    of 256) is to prevent requantization_scale to go below xnnpack lower
    threshold.
"""
default_symmetric_qnnpack_qconfig = QConfig(activation=HistogramObserver.with_args(dtype=torch.qint8,
                                                                                   reduce_range=False,
                                                                                   eps=2 ** -12),
                                            weight=weight_observer_range_neg_127_to_127)

default_per_channel_symmetric_qnnpack_qconfig = QConfig(activation=HistogramObserver.with_args(dtype=torch.qint8,
                                                                                               reduce_range=False,
                                                                                               eps=2 ** -12),
                                                        weight=per_channel_weight_observer_range_neg_127_to_127)

default_embedding_qat_qconfig = QConfig(activation=NoopObserver.with_args(dtype=torch.float32),
                                        weight=default_embedding_fake_quant)

default_embedding_qat_qconfig_4bit = QConfig(activation=NoopObserver.with_args(dtype=torch.float32),
                                             weight=default_embedding_fake_quant_4bit)

def get_default_qat_qconfig(backend='x86', version=1):
    """
    Returns the default QAT qconfig for the specified backend.

    Args:
      * `backend` (str): a string representing the target backend. Currently supports
        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.
      * `version`: version, for backwards compatibility. Can be `None` or `1`.

    Return:
        qconfig
    """
    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
    if backend not in supported_backends:
        raise AssertionError(
            "backend: " + str(backend) +
            " not supported. backend must be one of {}".format(supported_backends)
        )

    # Histogram observer is too slow for quantization aware training
    if version == 0:
        if backend == 'fbgemm':
            qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
                                                                quant_min=0,
                                                                quant_max=255,
                                                                reduce_range=True),
                              weight=default_per_channel_weight_fake_quant)
        elif backend == 'qnnpack':
            qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
                                                                quant_min=0,
                                                                quant_max=255,
                                                                reduce_range=False),
                              weight=default_weight_fake_quant)
        elif backend == 'onednn':
            qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
                                                                quant_min=0,
                                                                quant_max=255),
                              weight=default_per_channel_weight_fake_quant)
        elif backend == 'x86':
            qconfig = QConfig(activation=FakeQuantize.with_args(observer=MovingAverageMinMaxObserver,
                                                                quant_min=0,
                                                                quant_max=255,