# 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
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# POSSIBILITY OF SUCH DAMAGE.

from typing import Optional

from torch import nn

from brevitas.inject.defaults import Uint8ActPerTensorFloat
from brevitas.inject.defaults import Int8ActPerTensorFloat, Int8ActPerTensorFloatMinMaxInit
from .quant_layer import QuantNonLinearActLayer as QuantNLAL, ActQuantType


class QuantReLU(QuantNLAL):

    def __init__(
            self,
            act_quant: Optional[ActQuantType] = Uint8ActPerTensorFloat,
            input_quant: Optional[ActQuantType] = None,
            return_quant_tensor: bool = False,
            **kwargs):
        QuantNLAL.__init__(
            self,
            act_impl=nn.ReLU,
            passthrough_act=True,
            input_quant=input_quant,
            act_quant=act_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)


class QuantSigmoid(QuantNLAL):

    def __init__(
            self,
            act_quant: Optional[ActQuantType] = Uint8ActPerTensorFloat,
            input_quant: Optional[ActQuantType] = None,
            return_quant_tensor: bool = False,
            **kwargs):
        QuantNLAL.__init__(
            self,
            act_impl=nn.Sigmoid,
            passthrough_act=False,
            input_quant=input_quant,
            act_quant=act_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)


class QuantTanh(QuantNLAL):

    def __init__(
            self,
            act_quant: Optional[ActQuantType] = Int8ActPerTensorFloat,
            input_quant: Optional[ActQuantType] = None,
            return_quant_tensor: bool = False,
            **kwargs):
        QuantNLAL.__init__(
            self,
            act_impl=nn.Tanh,
            passthrough_act=False,
            input_quant=input_quant,
            act_quant=act_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)


class QuantHardTanh(QuantNLAL):

    def __init__(
            self,
            act_quant: Optional[ActQuantType] = Int8ActPerTensorFloatMinMaxInit,
            input_quant: Optional[ActQuantType] = None,
            return_quant_tensor: bool = False,
            **kwargs):
        QuantNLAL.__init__(
            self,
            act_impl=nn.Hardtanh,
            passthrough_act=True,
            input_quant=input_quant,
            act_quant=act_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)


class QuantIdentity(QuantNLAL):

    def __init__(
            self,
            act_quant: Optional[ActQuantType] = Int8ActPerTensorFloat,
            return_quant_tensor: bool = False,
            **kwargs):
        QuantNLAL.__init__(
            self,
            input_quant=None,
            act_impl=None,
            passthrough_act=True,
            act_quant=act_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)

