"""
Utils shared by different modes of quantization (eager/graph)
"""
import functools
import warnings
from collections import OrderedDict
from inspect import getfullargspec, signature
from typing import Any, Callable, Dict, Optional, Tuple, Union

import torch
from torch.ao.quantization.quant_type import QuantType
from torch.fx import Node
from torch.nn.utils.parametrize import is_parametrized

NodePattern = Union[Tuple[Node, Node], Tuple[Node, Tuple[Node, Node]], Any]
NodePattern.__module__ = "torch.ao.quantization.utils"

# This is the Quantizer class instance from torch/quantization/fx/quantize.py.
# Define separately to prevent circular imports.
# TODO(future PR): improve this.
# make this public once fixed (can't be public as is because setting the module directly
# doesn't work)
QuantizerCls = Any

# Type for fusion patterns, it can be more complicated than the following actually,
# see pattern.md for docs
# TODO: not sure if typing supports recursive data types
Pattern = Union[
    Callable, Tuple[Callable, Callable], Tuple[Callable, Tuple[Callable, Callable]], Any
]
Pattern.__module__ = "torch.ao.quantization.utils"

# TODO: maybe rename this to MatchInputNode
class MatchAllNode:
    """ A node pattern that matches all nodes, used in defining
    fusion patterns in FX Graph Mode Quantization
    """
    pass

module_type_list = {
    torch.nn.ReLU,
    torch.nn.ReLU6,
    torch.nn.AdaptiveAvgPool1d,
    torch.nn.AdaptiveAvgPool2d,
    torch.nn.AdaptiveAvgPool3d,
    torch.nn.AvgPool1d,
    torch.nn.AvgPool2d,
    torch.nn.AvgPool3d,
    torch.nn.MaxPool1d,
    torch.nn.MaxPool2d,
    torch.nn.MaxPool3d,
    torch.nn.Identity,
    torch.nn.Hardsigmoid,
    torch.nn.Sigmoid,
    torch.nn.Tanh,
}
func_list = {
    torch.nn.functional.adaptive_avg_pool1d,
    torch.nn.functional.adaptive_avg_pool2d,
    torch.nn.functional.adaptive_avg_pool3d,
    torch.nn.functional.elu,
    torch.nn.functional.hardswish,
    torch.nn.functional.instance_norm,
    torch.nn.functional.layer_norm,
    torch.nn.functional.leaky_relu,
    torch.nn.functional.silu,
    torch.nn.functional.mish,
    torch.nn.functional.dropout,
    torch.nn.functional.max_pool1d,
    torch.nn.functional.max_pool2d,
    torch.nn.functional.max_pool3d,
    torch.nn.functional.relu,
    torch.nn.functional.hardtanh,
    torch.nn.functional.hardtanh_,
    torch.nn.functional.hardsigmoid,
    torch.nn.functional.sigmoid,
    torch.transpose,
    torch.repeat_interleave,
    torch.sigmoid,
    torch.squeeze,
    torch.stack,
    torch.sum,
    torch.tanh,
    torch.unsqueeze,
    torch.cat,
}
method_list = {
    torch.mean,
    'relu',
    'relu_',
    'contiguous',
    'detach',
    'detach_',
    'hardsigmoid',
    'hardsigmoid_',
    'permute',
    'repeat',
    'repeat_interleave',
    'reshape',
    'resize_',
    'shape',
    'sigmoid',
    'sigmoid_',
    'size',
    'squeeze',
    'squeeze_',
    'tanh',
    'tanh_',
    'transpose',
    'unsqueeze',
    'unsqueeze_',
    'view',
}

# TODO: not used now, remove
def check_node(node, modules):
    # TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py
    is_call_function = node.op == "call_function" and node.target in func_list
    is_call_method = node.op == "call_method" and node.target in method_list
    is_call_module = node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
    return is_call_function, is_call_method, is_call_module

def get_combined_dict(default_dict, additional_dict):
    d = default_dict.copy()
    d.update(additional_dict)
    return d

def is_per_tensor(qscheme):
    return qscheme == torch.per_tensor_affine or \
        qscheme == torch.per_tensor_symmetric

def is_per_channel(qscheme):
    return qscheme in [torch.per_channel_affine,
                       torch.per_channel_affine_float_qparams,
                       torch.per_channel_symmetric]

def getattr_from_fqn(obj: Any, fqn: str) -> Any:
    """
    Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz.
    """
    return functools.reduce(getattr, fqn.split("."), obj)

def to_underlying_dtype(qdtype):
    DTYPE_MAPPING = {
        torch.quint8: torch.uint8,
        torch.qint8: torch.int8,
        torch.qint32: torch.int32,
        torch.quint4x2: torch.uint8,
        torch.quint2x4: torch.uint8,
    }
    assert qdtype in DTYPE_MAPPING, "Unsupported dtype: " + qdtype
    return DTYPE_MAPPING[qdtype]

def get_qparam_dict(observer_or_fake_quant):
    qscheme = observer_or_fake_quant.qscheme if hasattr(observer_or_fake_quant, "qscheme") else None
    dtype = observer_or_fake_quant.dtype
    qparams = {"qscheme": qscheme, "dtype": dtype}

    if not qscheme:
        return qparams

    if is_per_tensor(qscheme):
        qscheme = torch.per_tensor_affine
    elif is_per_channel(qscheme):
        # change symmetric to affine since we do not have symmetric
        # quantized Tensor
        if qscheme == torch.per_channel_symmetric:
            qscheme = torch.per_channel_affine
        qparams["axis"] = observer_or_fake_quant.ch_axis
    else:
        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
    # update qscheme, since we don't have symmetric quant qscheme
    # in quantized Tensor
    qparams["qscheme"] = qscheme

    scale, zero_point = observer_or_fake_quant.calculate_qparams()
    qparams["scale"] = scale
    qparams["zero_point"] = zero_point

    return qparams


def get_swapped_custom_module_class(custom_module, custom_module_class_mapping, qconfig):
    """ Get the observed/quantized custom module class that we need
    to swap `custom_module` to
    Input:
        custom_module: input, can be an instance of either a float or observed custom module
        custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
        qconfig: qconfig configured for the custom module

    Output:
        corresponding observed/quantized custom module class for input custom module instance
    """
    quant_type = get_quant_type(qconfig)
    class_mapping = custom_module_class_mapping.get(quant_type, {})
    assert type(custom_module) in class_mapping, "did not find corresponding observed " \
        "module class for {} in mapping: {}".format(type(custom_module), class_mapping)
    return class_mapping[type(custom_module)]

def activation_dtype(qconfig):
    assert qconfig is not None
    activation = qconfig.activation()
    return activation.dtype

def weight_dtype(qconfig):
    assert qconfig is not None
    weight = qconfig.weight()
    return weight.dtype

def activation_is_statically_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    quantized or not, this includes quantizing to quint8, qint8 and qint32 and float16
    """
    return (
        activation_dtype(qconfig) in [torch.quint8, torch.qint8, torch.qint32, torch.float16]
        and (not activation_is_dynamically_quantized(qconfig))
    )

def activation_is_dynamically_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    dynamically quantized or not, this includes dynamically quantizing to
    quint8, qint8 and float16
    """
    activation_dtype, _, activation_is_dynamic = \
        get_qconfig_dtypes(qconfig)
    return activation_is_dynamic

def activation_is_int8_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    quantized to int8 or not, this includes quantizing to quint8, qint8
    """
    return activation_dtype(qconfig) in [torch.quint8, torch.qint8]

def activation_is_int32_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    quantized to int32 or not
    """
    return activation_dtype(qconfig) == torch.qint32

def weight_is_quantized(qconfig):
    """ Given a qconfig, decide if the weight needs to be
    quantized or not
    """
    return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.float16, torch.quint4x2]

def weight_is_statically_quantized(qconfig):
    """ Given a qconfig, decide if the weight needs to be statically
    quantized or not
    """
    return weight_dtype(qconfig) in [torch.quint8, torch.qint8]

def op_is_int8_dynamically_quantized(qconfig) -> bool:
    """ Given a qconfig, returns True if this op is using int8 dynamic
    quantization
    """
    activation_dtype, weight_dtype, activation_is_dynamic = \
        get_qconfig_dtypes(qconfig)
    return (
        activation_dtype is torch.quint8 and
        # for now, the lines below assume fbgemm or qnnpack
        weight_dtype is torch.qint8 and
        activation_is_dynamic
    )

def get_qconfig_dtypes(qconfig):
    r""" returns the qconfig tuple for qconfig:
    (activation_dtype, weight_dtype, activation_is_dynamic)
    """
    assert qconfig is not None
    activation = qconfig.activation()
    weight = qconfig.weight()
    act_is_dynamic = activation.is_dynamic if hasattr(activation, 'is_dynamic') else False
    return (activation.dtype, weight.dtype, act_is_dynamic)

def get_quant_type(qconfig):
    assert qconfig is not None
    activation = qconfig.activation()
    weight = qconfig.weight()
    static_dtypes = [torch.quint8, torch.qint8, torch.quint4x2, torch.qint32]
    if weight.dtype in static_dtypes:
        if hasattr(activation, 'is_dynamic') and activation.is_dynamic:
            return QuantType.DYNAMIC
        elif activation.dtype in static_dtypes:
            return QuantType.STATIC
        else:
            return QuantType.WEIGHT_ONLY

    if weight.dtype == torch.float16:
        if hasattr(activation, 'is_dynamic') and activation.is_dynamic:
            return QuantType.DYNAMIC
        elif activation.dtype == torch.float16:
            return QuantType.STATIC

    raise Exception("Unrecognized dtype combination in get_quant_type: activation({}),"
                    "weight({})".format(activation.dtype, weight.dtype))

def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
    """ Checks if the given minimum and maximum values are valid, meaning that
    they exist and the min value is less than the max value.
    """
    if min_val.numel() == 0 or max_val.numel() == 0:
        warnings.warn(
            "must run observer before calling calculate_qparams. " +
            "Returning default values."
        )
        return False

    if min_val.dim() == 0 or max_val.dim() == 0:
        if min_val == float("inf") and max_val == float("-inf"):
            warnings.warn(
                "must run observer before calling calculate_qparams. " +
                "Returning default values."
            )

            return False

        assert min_val <= max_val, "min {} should be less than max {}".format(
            min_val, max_val
        )
    else:
        assert torch.all(
            min_val <= max_val
        ), "min {} should be less than max {}".format(min_val, max_val)

    return True


def calculate_qmin_qmax(quant_min: int, quant_max: int, has_customized_qrange: bool, dtype: torch.dtype,
                        reduce_range: bool) -> Tuple[int, int]:
    r"""Calculates actual qmin and qmax based on the quantization range,
    observer datatype and if range is reduced.
    """
    # TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted.
    if has_customized_qrange:
        # This initialization here is to be resolve TorchScript compilation issues and allow
        # using of refinement to decouple initial_qmin and initial_qmax from quantization range.
        # The actual values of initial_qmin and initial_qmax will be reset below.
        if dtype == torch.qint32:
            initial_quant_min, initial_quant_max = 0, 2**31 - 1
        else:
            initial_quant_min, initial_quant_max = 0, 255
        # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
        # attribute from Optional valid integers for use, based on TorchScript's requirements.
        custom_quant_min, custom_quant_max = quant_min, quant_max
        if custom_quant_min is not None and custom_quant_max is not None: