# Copyright (c) 2018-     Xilinx, Inc              (Alessandro Pappalardo)
# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)

# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:

# 1. Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.

# 2. Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in the
#    documentation and/or other materials provided with the distribution.

# 3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU,
#    NEC Laboratories America and IDIAP Research Institute nor the names
#    of its contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.

from warnings import warn
from abc import ABCMeta, abstractmethod
from typing import Type, Union, Optional

from torch.nn import Module
from brevitas.inject import ExtendedInjector, Injector
from brevitas.proxy.runtime_quant import ActQuantProxyFromInjector
from brevitas.proxy.runtime_quant import ActQuantProxyProtocol

from .base import QuantProxyMixin


ActQuantType = Union[ActQuantProxyProtocol, Type[Injector], Type[ExtendedInjector]]


class QuantInputMixin(QuantProxyMixin):
    __metaclass__ = ABCMeta

    def __init__(self, input_quant: Optional[ActQuantType], **kwargs):
        QuantProxyMixin.__init__(
            self,
            quant=input_quant,
            proxy_from_injector_impl=ActQuantProxyFromInjector,
            proxy_protocol=ActQuantProxyProtocol,
            proxy_prefix='input_',
            kwargs_prefix='input_',
            input_act_impl=None,
            input_passthrough_act=True,
            **kwargs)

    @property
    def is_input_quant_enabled(self):
        return self.input_quant.is_quant_enabled

    @property
    def is_quant_input_narrow_range(self): # TODO make abstract once narrow range can be cached
        return self.input_quant.is_narrow_range

    @property
    @abstractmethod
    def is_quant_input_signed(self):
        pass

    @abstractmethod
    def quant_input_scale(self):
        pass

    @abstractmethod
    def quant_input_zero_point(self):
        pass

    @abstractmethod
    def quant_input_bit_width(self):
        pass


class QuantOutputMixin(QuantProxyMixin):
    __metaclass__ = ABCMeta

    def __init__(self, output_quant: Optional[ActQuantType], **kwargs):
        QuantProxyMixin.__init__(
            self,
            quant=output_quant,
            proxy_from_injector_impl=ActQuantProxyFromInjector,
            proxy_protocol=ActQuantProxyProtocol,
            proxy_prefix='output_',
            kwargs_prefix='output_',
            output_act_impl=None,
            output_passthrough_act=True,
            **kwargs)

    @property
    def is_output_quant_enabled(self):
        return self.output_quant.is_quant_enabled

    @property
    def is_quant_output_narrow_range(self):  # TODO make abstract once narrow range can be cached
        return self.output_quant.is_narrow_range

    @property
    @abstractmethod
    def is_quant_output_signed(self):
        pass

    @abstractmethod
    def quant_output_scale(self):
        pass

    @abstractmethod
    def quant_output_zero_point(self):
        pass

    @abstractmethod
    def quant_output_bit_width(self):
        pass


class QuantNonLinearActMixin(QuantProxyMixin):
    __metaclass__ = ABCMeta

    def __init__(
            self,
            act_impl: Optional[Type[Module]],
            passthrough_act: bool,
            act_quant: Optional[ActQuantType],
            **kwargs):
        QuantProxyMixin.__init__(
            self,
            act_impl=act_impl,
            passthrough_act=passthrough_act,
            quant=act_quant,
            proxy_from_injector_impl=ActQuantProxyFromInjector,
            proxy_protocol=ActQuantProxyProtocol,
            proxy_prefix='act_',
            kwargs_prefix='',
            **kwargs)

    @property
    def is_act_quant_enabled(self):
        return self.act_quant.is_quant_enabled

    @property
    def is_quant_act_narrow_range(self):  # TODO make abstract once narrow range can be cached
        return self.act_quant.is_narrow_range

    @property
    @abstractmethod
    def is_quant_act_signed(self):
        pass

    @abstractmethod
    def quant_act_scale(self):
        pass

    @abstractmethod
    def quant_act_zero_point(self):
        pass

    @abstractmethod
    def quant_act_bit_width(self):
        pass


