# 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 Union, Type, Optional

import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn.functional import linear

from brevitas.inject import BaseInjector as Injector
from brevitas.function.ops_ste import ceil_ste
from brevitas.function.ops import max_int
from brevitas.proxy.parameter_quant import WeightQuantProxyProtocol, BiasQuantProxyProtocol
from brevitas.proxy.runtime_quant import ActQuantProxyProtocol
from brevitas.quant_tensor import QuantTensor
from brevitas.inject.defaults import Int8WeightPerTensorFloat
from .quant_layer import QuantWeightBiasInputOutputLayer as QuantWBIOL

__all__ = ['QuantLinear']


class QuantLinear(Linear, QuantWBIOL):

    def __init__(
            self,
            in_features: int,
            out_features: int,
            bias: bool,
            weight_quant: Union[WeightQuantProxyProtocol, Type[Injector]] = Int8WeightPerTensorFloat,
            bias_quant: Union[BiasQuantProxyProtocol, Type[Injector]] = None,
            input_quant: Union[ActQuantProxyProtocol, Type[Injector]] = None,
            output_quant: Union[ActQuantProxyProtocol, Type[Injector]] = None,
            return_quant_tensor: bool = False,
            **kwargs) -> None:
        Linear.__init__(self, in_features, out_features, bias)
        QuantWBIOL.__init__(
            self,
            weight=self.weight,
            bias=self.bias,
            weight_quant=weight_quant,
            bias_quant=bias_quant,
            input_quant=input_quant,
            output_quant=output_quant,
            return_quant_tensor=return_quant_tensor,
            **kwargs)

    @property
    def per_elem_ops(self):
        return 2 * self.in_features

    @property
    def output_channel_dim(self):
        return 0

    @property
    def out_channels(self):
        return self.out_features

    @property
    def channelwise_separable(self) -> bool:
        return False

    def forward(self, input: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTensor]:
        return self.forward_impl(input)

    def inner_forward_impl(self, x: Tensor, quant_weight: Tensor, quant_bias: Optional[Tensor]):
        output_tensor = linear(x, quant_weight, quant_bias)
        return output_tensor

    def max_acc_bit_width(self, input_bit_width, weight_bit_width):
        max_input_val = max_int(bit_width=input_bit_width, signed=False, narrow_range=False)
        max_fc_val = self.weight_quant.max_uint_value(weight_bit_width)
        max_output_val = max_input_val * max_fc_val * self.in_features
        output_bit_width = ceil_ste(torch.log2(max_output_val))
        return output_bit_width



