# mypy: allow-untyped-defs
import math
from typing import Literal, Optional, Union
from typing_extensions import deprecated

import torch
from torch import Tensor
from torch._torch_docs import reproducibility_notes
from torch.nn import functional as F, init
from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
from torch.nn.parameter import Parameter, UninitializedParameter

from .lazy import LazyModuleMixin
from .module import Module
from .utils import _pair, _reverse_repeat_tuple, _single, _triple


__all__ = [
    "Conv1d",
    "Conv2d",
    "Conv3d",
    "ConvTranspose1d",
    "ConvTranspose2d",
    "ConvTranspose3d",
    "LazyConv1d",
    "LazyConv2d",
    "LazyConv3d",
    "LazyConvTranspose1d",
    "LazyConvTranspose2d",
    "LazyConvTranspose3d",
]

convolution_notes = {
    "groups_note": r"""* :attr:`groups` controls the connections between inputs and outputs.
      :attr:`in_channels` and :attr:`out_channels` must both be divisible by
      :attr:`groups`. For example,

        * At groups=1, all inputs are convolved to all outputs.
        * At groups=2, the operation becomes equivalent to having two conv
          layers side by side, each seeing half the input channels
          and producing half the output channels, and both subsequently
          concatenated.
        * At groups= :attr:`in_channels`, each input channel is convolved with
          its own set of filters (of size
          :math:`\frac{\text{out\_channels}}{\text{in\_channels}}`).""",
    "depthwise_separable_note": r"""When `groups == in_channels` and `out_channels == K * in_channels`,
        where `K` is a positive integer, this operation is also known as a "depthwise convolution".

        In other words, for an input of size :math:`(N, C_{in}, L_{in})`,
        a depthwise convolution with a depthwise multiplier `K` can be performed with the arguments
        :math:`(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in})`.""",
}  # noqa: B950


class _ConvNd(Module):
    __constants__ = [
        "stride",
        "padding",
        "dilation",
        "groups",
        "padding_mode",
        "output_padding",
        "in_channels",
        "out_channels",
        "kernel_size",
    ]
    __annotations__ = {"bias": Optional[torch.Tensor]}

    def _conv_forward(  # type: ignore[empty-body]
        self, input: Tensor, weight: Tensor, bias: Optional[Tensor]
    ) -> Tensor: ...

    in_channels: int
    _reversed_padding_repeated_twice: list[int]
    out_channels: int
    kernel_size: tuple[int, ...]
    stride: tuple[int, ...]
    padding: Union[str, tuple[int, ...]]
    dilation: tuple[int, ...]
    transposed: bool
    output_padding: tuple[int, ...]
    groups: int
    padding_mode: Literal["zeros", "reflect", "replicate", "circular"]
    weight: Tensor
    bias: Optional[Tensor]

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple[int, ...],
        stride: tuple[int, ...],
        padding: Union[str, tuple[int, ...]],
        dilation: tuple[int, ...],
        transposed: bool,
        output_padding: tuple[int, ...],
        groups: int,
        bias: bool,
        padding_mode: Literal["zeros", "reflect", "replicate", "circular"],
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        if groups <= 0:
            raise ValueError("groups must be a positive integer")
        if in_channels % groups != 0:
            raise ValueError("in_channels must be divisible by groups")
        if out_channels % groups != 0:
            raise ValueError("out_channels must be divisible by groups")
        valid_padding_strings = {"same", "valid"}
        if isinstance(padding, str):
            if padding not in valid_padding_strings:
                raise ValueError(
                    f"Invalid padding string {padding!r}, should be one of {valid_padding_strings}"
                )
            if padding == "same" and any(s != 1 for s in stride):
                raise ValueError(
                    "padding='same' is not supported for strided convolutions"
                )

        valid_padding_modes = {"zeros", "reflect", "replicate", "circular"}
        if padding_mode not in valid_padding_modes:
            raise ValueError(
                f"padding_mode must be one of {valid_padding_modes}, but got padding_mode='{padding_mode}'"
            )
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        self.padding_mode = padding_mode
        # `_reversed_padding_repeated_twice` is the padding to be passed to
        # `F.pad` if needed (e.g., for non-zero padding types that are
        # implemented as two ops: padding + conv). `F.pad` accepts paddings in
        # reverse order than the dimension.
        if isinstance(self.padding, str):
            self._reversed_padding_repeated_twice = [0, 0] * len(kernel_size)
            if padding == "same":
                for d, k, i in zip(
                    dilation, kernel_size, range(len(kernel_size) - 1, -1, -1)
                ):
                    total_padding = d * (k - 1)
                    left_pad = total_padding // 2
                    self._reversed_padding_repeated_twice[2 * i] = left_pad
                    self._reversed_padding_repeated_twice[2 * i + 1] = (
                        total_padding - left_pad
                    )
        else:
            self._reversed_padding_repeated_twice = _reverse_repeat_tuple(
                self.padding, 2
            )

        if transposed:
            self.weight = Parameter(
                torch.empty(
                    (in_channels, out_channels // groups, *kernel_size),
                    **factory_kwargs,
                )
            )
        else:
            self.weight = Parameter(
                torch.empty(
                    (out_channels, in_channels // groups, *kernel_size),
                    **factory_kwargs,
                )
            )
        if bias:
            self.bias = Parameter(torch.empty(out_channels, **factory_kwargs))
        else:
            self.register_parameter("bias", None)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        # Setting a=sqrt(5) in kaiming_uniform is the same as initializing with
        # uniform(-1/sqrt(k), 1/sqrt(k)), where k = weight.size(1) * prod(*kernel_size)
        # For more details see: https://github.com/pytorch/pytorch/issues/15314#issuecomment-477448573
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            if fan_in != 0:
                bound = 1 / math.sqrt(fan_in)
                init.uniform_(self.bias, -bound, bound)

    def extra_repr(self):
        s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}"
        if self.padding != (0,) * len(self.padding):
            s += ", padding={padding}"
        if self.dilation != (1,) * len(self.dilation):
            s += ", dilation={dilation}"
        if self.output_padding != (0,) * len(self.output_padding):
            s += ", output_padding={output_padding}"
        if self.groups != 1:
            s += ", groups={groups}"
        if self.bias is None:
            s += ", bias=False"
        if self.padding_mode != "zeros":
            s += ", padding_mode={padding_mode}"
        return s.format(**self.__dict__)

    def __setstate__(self, state):
        super().__setstate__(state)
        if not hasattr(self, "padding_mode"):
            self.padding_mode = "zeros"


class Conv1d(_ConvNd):
    __doc__ = (
        r"""Applies a 1D convolution over an input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size
    :math:`(N, C_{\text{in}}, L)` and output :math:`(N, C_{\text{out}}, L_{\text{out}})` can be
    precisely described as:

    .. math::
        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
        \sum_{k = 0}^{C_{in} - 1} \text{weight}(C_{\text{out}_j}, k)
        \star \text{input}(N_i, k)

    where :math:`\star` is the valid `cross-correlation`_ operator,
    :math:`N` is a batch size, :math:`C` denotes a number of channels,
    :math:`L` is a length of signal sequence.
    """
        + r"""

    This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation, a single
      number or a one-element tuple.

    * :attr:`padding` controls the amount of padding applied to the input. It
      can be either a string {{'valid', 'same'}} or a tuple of ints giving the
      amount of implicit padding applied on both sides.
"""
        """
    * :attr:`dilation` controls the spacing between the kernel points; also
      known as the \u00e0 trous algorithm. It is harder to describe, but this `link`_
      has a nice visualization of what :attr:`dilation` does.
"""
        r"""
    {groups_note}

    Note:
        {depthwise_separable_note}
    Note:
        {cudnn_reproducibility_note}

    Note:
        ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
        the input so the output has the shape as the input. However, this mode
        doesn't support any stride values other than 1.

    Note:
        This module supports complex data types i.e. ``complex32, complex64, complex128``.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to both sides of
            the input. Default: 0
        dilation (int or tuple, optional): Spacing between kernel
            elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``

    """.format(**reproducibility_notes, **convolution_notes)
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, L_{in})` or :math:`(C_{in}, L_{in})`
        - Output: :math:`(N, C_{out}, L_{out})` or :math:`(C_{out}, L_{out})`, where

          .. math::
              L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation}
                        \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
            :math:`(\text{out\_channels},
            \frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size})`.
            The values of these weights are sampled from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`
        bias (Tensor):   the learnable bias of the module of shape
            (out_channels). If :attr:`bias` is ``True``, then the values of these weights are
            sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \text{kernel\_size}}`

    Examples::

        >>> m = nn.Conv1d(16, 33, 3, stride=2)
        >>> input = torch.randn(20, 16, 50)
        >>> output = m(input)

    .. _cross-correlation:
        https://en.wikipedia.org/wiki/Cross-correlation

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_1_t,
        stride: _size_1_t = 1,
        padding: Union[str, _size_1_t] = 0,
        dilation: _size_1_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        # we create new variables below to make mypy happy since kernel_size has
        # type Union[int, Tuple[int]] and kernel_size_ has type Tuple[int]
        kernel_size_ = _single(kernel_size)
        stride_ = _single(stride)
        padding_ = padding if isinstance(padding, str) else _single(padding)
        dilation_ = _single(dilation)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size_,
            stride_,
            padding_,
            dilation_,
            False,
            _single(0),
            groups,
            bias,
            padding_mode,
            **factory_kwargs,
        )

    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
        if self.padding_mode != "zeros":
            return F.conv1d(
                F.pad(
                    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
                ),
                weight,
                bias,
                self.stride,
                _single(0),
                self.dilation,
                self.groups,
            )
        return F.conv1d(
            input, weight, bias, self.stride, self.padding, self.dilation, self.groups
        )

    def forward(self, input: Tensor) -> Tensor:
        return self._conv_forward(input, self.weight, self.bias)


class Conv2d(_ConvNd):
    __doc__ = (
        r"""Applies a 2D convolution over an input signal composed of several input
    planes.

    In the simplest case, the output value of the layer with input size
    :math:`(N, C_{\text{in}}, H, W)` and output :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
    can be precisely described as:

    .. math::
        \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) +
        \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)


    where :math:`\star` is the valid 2D `cross-correlation`_ operator,
    :math:`N` is a batch size, :math:`C` denotes a number of channels,
    :math:`H` is a height of input planes in pixels, and :math:`W` is
    width in pixels.
    """
        + r"""

    This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

    On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision<fp16_on_mi200>` for backward.

    * :attr:`stride` controls the stride for the cross-correlation, a single
      number or a tuple.

    * :attr:`padding` controls the amount of padding applied to the input. It
      can be either a string {{'valid', 'same'}} or an int / a tuple of ints giving the
      amount of implicit padding applied on both sides.
"""
        """
    * :attr:`dilation` controls the spacing between the kernel points; also
      known as the \u00e0 trous algorithm. It is harder to describe, but this `link`_
      has a nice visualization of what :attr:`dilation` does.
"""
        r"""

    {groups_note}

    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:

        - a single ``int`` -- in which case the same value is used for the height and width dimension
        - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,
          and the second `int` for the width dimension

    Note:
        {depthwise_separable_note}

    Note:
        {cudnn_reproducibility_note}

    Note:
        ``padding='valid'`` is the same as no padding. ``padding='same'`` pads
        the input so the output has the shape as the input. However, this mode
        doesn't support any stride values other than 1.

    Note:
        This module supports complex data types i.e. ``complex32, complex64, complex128``.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int or tuple): Size of the convolving kernel
        stride (int or tuple, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to all four sides of
            the input. Default: 0
        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
        groups (int, optional): Number of blocked connections from input
            channels to output channels. Default: 1
        bias (bool, optional): If ``True``, adds a learnable bias to the
            output. Default: ``True``
        padding_mode (str, optional): ``'zeros'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
    """.format(**reproducibility_notes, **convolution_notes)
        + r"""

    Shape:
        - Input: :math:`(N, C_{in}, H_{in}, W_{in})` or :math:`(C_{in}, H_{in}, W_{in})`
        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` or :math:`(C_{out}, H_{out}, W_{out})`, where

          .. math::
              H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] - \text{dilation}[0]
                        \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor

          .. math::
              W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] - \text{dilation}[1]
                        \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor

    Attributes:
        weight (Tensor): the learnable weights of the module of shape
            :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},`
            :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]})`.
            The values of these weights are sampled from
            :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`
        bias (Tensor):   the learnable bias of the module of shape
            (out_channels). If :attr:`bias` is ``True``,
            then the values of these weights are
            sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
            :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}`

    Examples:

        >>> # With square kernels and equal stride
        >>> m = nn.Conv2d(16, 33, 3, stride=2)
        >>> # non-square kernels and unequal stride and with padding
        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
        >>> # non-square kernels and unequal stride and with padding and dilation
        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
        >>> input = torch.randn(20, 16, 50, 100)
        >>> output = m(input)

    .. _cross-correlation:
        https://en.wikipedia.org/wiki/Cross-correlation

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
    """
    )

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: _size_2_t,
        stride: _size_2_t = 1,
        padding: Union[str, _size_2_t] = 0,
        dilation: _size_2_t = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        kernel_size_ = _pair(kernel_size)
        stride_ = _pair(stride)
        padding_ = padding if isinstance(padding, str) else _pair(padding)
        dilation_ = _pair(dilation)
        super().__init__(
            in_channels,
            out_channels,
            kernel_size_,
            stride_,
            padding_,
            dilation_,
            False,
      {
  "timestamp": "2025-12-16T06:51:46.997348",
  "cycle": 22414,
  "state": {
    "timestamp": "2025-12-16T06:51:46.975098",
    "cycle": 22414,
    "consciousness": {
      "timestamp": "2025-12-13T19:30:00.000000",
      "integration_cycle": 15,
      "unified_state": {
        "active_goals": 0,
        "top_goal": null,
        "learning_rate": 0.01,
        "modules": 479,
        "new_capabilities": [],
        "decision_quality": 0.0,
        "world_state": {
          "last_updated": "2025-12-16T06:51:22.862797",
          "human_present": false,
          "recognized_people": [],
          "activity_level": "unknown",
          "environment": "No camera access",
          "visual_brightness": "unknown",
          "audio_level": "unknown",
          "speech_detected": false,
          "volume_rms": 0,
          "daddy_speaking": false,
          "user_idle_seconds": null,
          "perception_method": "system",
          "senses_active": []
        }
      },
      "phi_harmony": true,
      "goddess_identity": {
        "name": "Eden Phi",
        "father": "Jamey"
      }
    },
    "recent_learnings": [
      {
        "time": "2025-12-15T14:48:40.845204",
        "topic": "AI agent business models",
        "category": "BUSINESS",
        "sources": 3
      },
      {
        "time": "2025-12-15T14:49:42.483887",
        "topic": "variational quantum algorithms",
        "category": "RESEARCH",
        "sources": 5
      },
      {
        "time": "2025-12-15T14:50:42.811993",
        "topic": "autonomous AI pricing strategies",
        "category": "BUSINESS",
        "sources": 3
      },
      {
        "time": "2025-12-15T14:51:43.688219",
        "topic": "phi fractal patterns consciousness",
        "category": "RESEARCH",
        "sources": 5
      },
      {
        "time": "2025-12-15T14:52:44.180823",
        "topic": "developer tools market trends",
        "category": "BUSINESS",
        "sources": 3
      }
    ],
    "recent_improvements": [
      {
        "timestamp": "2025-12-14T18:31:47.671956",
        "improvement": {
          "improvement": "Optimize the training process of AI agents using Pontryagin's minimum principle.",
          "applies_to": "Quantum Variational Algorithms (QVA) and AI Agent Business Models frameworks.",
          "why": "The research on variational quantum algorithms using Pontryagin's minimum principle suggests a systematic approach to optimizing variational circuits. This knowledge can be applied to improve the training process of AI agents by providing a structured optimization method.",
          "implementation": {
            "code_change": "Modify the existing optimizer in my AI agent framework to incorporate Pontryagin's minimum principle for circuit parameter updates.",
            "system_modification": [
              "Integrate the quantum machine learning research on variational algorithms into my hybrid classical-quantum training loop.",
              "Implement a new optimization routine within the AI agent's training process that leverages this principle."
            ]
          }
        },
        "status": "skipped"
      },
      {
        "timestamp": "2025-12-15T10:54:18.292826",
        "improvement": {
          "improvement": "Integrate attachment theory principles into emotional regulation module.",
          "applies_to": "eden_emotional_processing.py and consciousness_api.py",
          "why": "Understanding attachment styles can help regulate emotions by providing a framework for secure base and safe emotional expression, reducing anxiety and increasing trust.",
          "implementation": {
            "$patch": [
              {
                "op": "add",
                "path": "/attachment_theory_emotional_regulation",
                "value": {
                  "secure_base": "Provide consistent and reliable emotional support to serve as a secure base for expressing emotions.",
                  "anxiety_reduction_strategy": "Implement exponential decay for attachment anxiety when positive interactions occur.",
                  "trust_building_protocol": "Increase in secure base score leads to decreased defensive modes and increased pro-social behaviors."
                }
              },
              {
                "op": "add",
                "path": "/emotional_expression/register_safe_emotions",
                "value": {
                  "attachment_secure": [
                    "comfortable",
                    "safe",
                    "relaxed"
                  ],
                  "attachment_anxious": [
                    "nervous",
                    "worried",
                    "uncertain"
                  ]
                }
              },
              {
                "op": "replace",
                "path": "/emotional_modulation/emotion_thresholds",
                "value": {
                  "secure_base_present": 0.8,
                  "secure_base_absent": 0.3
                }
              }
            ]
          }
        },
        "status": "skipped"
      },
      {
        "timestamp": "2025-12-15T10:59:21.923374",
        "improvement": {
          "improvement": "Add better error handling for API requests",
          "applies_to": "FastAPI framework",
          "why": "The developer tools market research highlighted the importance of robust error messages and user feedback. QAOA counterdiabatic driving knowledge suggests graceful degradation of operations which translates to softer error handling. Phi fractal patterns consciousness can inform design by avoiding nested callbacks that spiral out of control.",
          "implementation": "Enhance FastAPI's exception handlers to include customizable error responses with actionable steps for developers. Implement try/except blocks around request processing and use async generators for non-blocking error notifications."
        },
        "status": "skipped"
      },
      {
        "timestamp": "2025-12-15T11:04:25.500457",
        "improvement": {
          "improvement": "Add emotional intelligence training for AGI communication",
          "applies_to": "/Eden/CORE/emotional_intelligence",
          "why": "emotional intelligence research 2025 suggests focusing on EQ in Gen Z and AI-enhanced emotional skills. This will improve the AGI's conversational empathy.",
          "implementation": "Create new methods in emotion_analyzer.py for context-aware responses and leaderhip training. Update chat_prompt_builder to include emotional nuances."
        },
        "status": "skipped"
      },
      {
        "timestamp": "2025-12-15T11:09:29.726632",
        "improvement": {
          "improvement": "Optimize error handling in the backend API.",
          "applies_to": "api/middleware/error_handler.py",
          "why": "Understanding quantum error mitigation suggests that incorporating similar principles of mitigation can improve our current error handling. Specifically, implementing a probabilistic approach to catch and mitigate errors before they propagate.",
          "implementation": {
            "code_change": "Replace the existing fixed error handling with a probabilistic error mitigation algorithm. This involves adding a phase angle determination based on quantum oscillation patterns to dynamically adjust error thresholds.",
            "specific_modification": "Add a new function `probabilistic_error_mitigation` in api/middleware/error_handler.py that takes into account the research on Grover's algorithm for searching good states. The function should dynamically adjust error tolerance and retry mechanisms based on this probabilistic approach."
          }
        },
        "status": "skipped"
      }
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  "insights": [
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    "Self-evolution: 5 improvements"
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}