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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import paddle

from ..base_observer import BaseObserver
from ..factory import ObserverFactory


class AbsmaxObserver(ObserverFactory):
    r"""
    It collects maximum absolute values of target tensor.

    Args:
        bit_length(int, optional): Number of bits to represent an quantized integer in binary.
        dtype(str, optional): The data type of input tensor.
        name (str, optional): This parameter is used by developers to print debugging information. \
            For details, please refer to :ref:`api_guide_Name`. Default is None.

    Examples:
        .. code-block:: python

            >>> from paddle.quantization import QuantConfig
            >>> from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
            >>> quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.99)
            >>> q_config = QuantConfig(activation=quanter, weight=quanter)
    """

    def __init__(self, quant_bits=8):
        super().__init__(quant_bits=quant_bits)

    def _get_class(self):
        return AbsmaxObserverLayer


class AbsmaxObserverLayer(BaseObserver):
    """
    Per-tensor abs max quantizer.
    """

    INIT_ABS_MAX = 1e-7

    def __init__(self, layer, quant_bits=8):
        super().__init__()
        self._quant_bits = quant_bits
        self.abs_max_val = paddle.to_tensor(AbsmaxObserverLayer.INIT_ABS_MAX)
        self._max = None
        self._scale = None
        self._zero_point = None

    def forward(self, input):
        self._min, self._max = self.cal_min_max(input)
        return input

    def cal_min_max(self, inputs):
        abs_max_val = paddle.max(paddle.abs(inputs))
        if self._max is not None:
            abs_max_val = paddle.maximum(
                abs_max_val, self._max.cast(inputs.dtype)
            )
        return 0, abs_max_val

    def bit_length(self):
        return self._quant_bits

    def quant_axis(self):
        return -1

    def cal_thresholds(self):
        """Compute thresholds for MAX function."""
        if self._scale is None:
            self._scale = self._max
        self._zero_point = paddle.zeros_like(self._scale)

    def scales(self):
        """Return output scales."""
        if self._scale is None:
            self.cal_thresholds()
        return self._scale

    def zero_points(self):
        """Return output zero points."""
        return self._zero_point