GJjwrddlZddlmZddlmZddlmZGddeZGddeZ Gdd eZ y) N)Union)Modulec\eZdZdZ d dededededededed effd Zd Zd ZxZ S)Conv1daApplies a 1-dimensional convolution over the multi-channel input sequence. The channels are expected to be last i.e. the input shape should be ``NLC`` where: * ``N`` is the batch dimension * ``L`` is the sequence length * ``C`` is the number of input channels Args: in_channels (int): The number of input channels out_channels (int): The number of output channels kernel_size (int): The size of the convolution filters stride (int, optional): The stride when applying the filter. Default: ``1``. padding (int, optional): How many positions to 0-pad the input with. Default: ``0``. dilation (int, optional): The dilation of the convolution. groups (int, optional): The number of groups for the convolution. Default: ``1``. bias (bool, optional): If ``True`` add a learnable bias to the output. Default: ``True`` in_channels out_channels kernel_sizestridepaddingdilationgroupsbiasc Zt |||zdk7rtd|d|dtjd||zz } t j j| | ||||zf|_|rt j|f|_ ||_ ||_ ||_ ||_y)NrThe number of input channels (-) must be divisible by the number of groups ()lowhighshape)super__init__ ValueErrormathsqrtmxrandomuniformweightzerosrr r r r selfrrr r r r r rscale __class__s c/Users/ahmed/devFolder/claude-voice/.venv/lib/python3.12/site-packages/mlx/nn/layers/convolution.pyrzConv1d.__init__"s   1 $0 >66DII Ar'rrrrT) __name__ __module__ __qualname____doc__intboolrr3r: __classcell__r%s@r&rr sw8     B r'rceZdZdZ d dededeeefdeeefdeeefdeeefded effd Zd Z d Z xZ S)Conv2daApplies a 2-dimensional convolution over the multi-channel input image. The channels are expected to be last i.e. the input shape should be ``NHWC`` where: * ``N`` is the batch dimension * ``H`` is the input image height * ``W`` is the input image width * ``C`` is the number of input channels Args: in_channels (int): The number of input channels. out_channels (int): The number of output channels. kernel_size (int or tuple): The size of the convolution filters. stride (int or tuple, optional): The size of the stride when applying the filter. Default: ``1``. padding (int or tuple, optional): How many positions to 0-pad the input with. Default: ``0``. dilation (int or tuple, optional): The dilation of the convolution. groups (int, optional): The number of groups for the convolution. Default: ``1``. bias (bool, optional): If ``True`` add a learnable bias to the output. Default: ``True`` rrr r r r r rc t |||zdk7rtd|d|dtd|||f\}}}t j d||dz|dzz } t jj| | |g|||z|_ |rt j|f|_ ||_ ||_ ||_||_y)Nrrrrc.t|tr||fS|SN isinstancerAr8s r&z!Conv2d.__init__..s 1c 2q!f99r'rr)rrrmaprrrrrr r!rr r r r r"s r&rzConv2d.__init__ns   1 $0 >66r?r@rArtuplerBrr3r:rCrDs@r&rFrFUs:%&%&&'###3:& # c5j! # sEz" #U ####J r'rFc~eZdZdZ d dededeeefdeeefdeeefdeeefdeffd Zd Z d Z xZ S) Conv3daApplies a 3-dimensional convolution over the multi-channel input image. The channels are expected to be last i.e. the input shape should be ``NDHWC`` where: * ``N`` is the batch dimension * ``D`` is the input image depth * ``H`` is the input image height * ``W`` is the input image width * ``C`` is the number of input channels Args: in_channels (int): The number of input channels. out_channels (int): The number of output channels. kernel_size (int or tuple): The size of the convolution filters. stride (int or tuple, optional): The size of the stride when applying the filter. Default: ``1``. dilation (int or tuple, optional): The dilation of the convolution. padding (int or tuple, optional): How many positions to 0-pad the input with. Default: ``0``. bias (bool, optional): If ``True`` add a learnable bias to the output. Default: ``True`` rrr r r r rc^t |td|||f\}}}tjd||dz|dz|dzz }t j j| ||g|||_|rt j|f|_ ||_ ||_ ||_ y)Nc0t|tr|||fS|SrIrJrLs r&rMz!Conv3d.__init__..s:a#5q!Qi<1QO P ii'';+;{;(  ,1DI    r'c|jjd|jzd|jjdd|jjddd|jd|jd |j d d |v S) Nr)r*rr+rr,r-r.r0rr1r2s r&r3zConv3d._extra_reprs{{  $t{{232dkk6G6G6J5KL;;,,Qq12)DKK=I||nK ?dN# % r'ctj||j|j|j|j }d|vr||j z}|Sr5)rconv3dr r r r rr7s r&r:zConv3d.__call__sA IIadkk4<< O T>DII Ar')rrrTrSrDs@r&rVrVs8%&%&&'!!!3:& ! c5j! ! sEz" !U #!!> r'rV) rtypingrmlx.corecorermlx.nn.layers.baserrrFrVr'r&rcs? %HVHVMVM`CVCr'