code: """Gradient interface"""
  
  import torch
  from .modules.utils import _single, _pair, _triple
  
  
  def _grad_input_padding(grad_output, input_size, stride, padding, kernel_size):
      input_size = list(input_size)
      k = grad_output.dim() - 2
  
      if len(input_size) == k + 2:
          input_size = input_size[-k:]
      if len(input_size) != k:
          raise ValueError("input_size must have {} elements (got {})"
                           .format(k + 2, len(input_size)))
  
      def dim_size(d):
          return ((grad_output.size(d + 2) - 1) * stride[d] - 2 * padding[d] +
                  kernel_size[d])
  
      min_sizes = [dim_size(d) for d in range(k)]
      max_sizes = [min_sizes[d] + stride[d] - 1 for d in range(k)]
      for size, min_size, max_size in zip(input_size, min_sizes, max_sizes):
          if size < min_size or size > max_size:
              raise ValueError(
                  ("requested an input grad size of {}, but valid sizes range "
                   "from {} to {} (for a grad_output of {})").format(
                       input_size, min_sizes, max_sizes,
                       grad_output.size()[2:]))
  
      return tuple(input_size[d] - min_sizes[d] for d in range(k))
  
  
  def conv1d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv1d with respect to the input of the convolution.
      This is same as the 1D transposed convolution operator under the hood but requires
      the shape of the gradient w.r.t. input to be specified explicitly.
  
      Args:
          input_size : Shape of the input gradient tensor
          weight: weight tensor (out_channels x in_channels/groups x kW)
          grad_output : output gradient tensor (minibatch x out_channels x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(1,1,3, requires_grad=True)
          >>> weight = torch.randn(1,1,1, requires_grad=True)
          >>> output = F.conv1d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_input = torch.autograd.grad(output, input, grad_output)
          >>> F.grad.conv1d_input(input.shape, weight, grad_output)
  
      """
      stride = _single(stride)
      padding = _single(padding)
      dilation = _single(dilation)
      kernel_size = [weight.shape[2]]
  
      if input_size is None:
          raise ValueError("grad.conv1d_input requires specifying an input_size")
  
      grad_input_padding = _grad_input_padding(grad_output, input_size, stride,
                                               padding, kernel_size)
  
      return torch.conv_transpose1d(
          grad_output, weight, bias, stride, padding, grad_input_padding, groups,
          dilation)
  
  
  def conv1d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv1d with respect to the weight of the convolution.
  
      Args:
          input: input tensor of shape (minibatch x in_channels x iW)
          weight_size : Shape of the weight gradient tensor
          grad_output : output gradient tensor (minibatch x out_channels x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(1,1,3, requires_grad=True)
          >>> weight = torch.randn(1,1,1, requires_grad=True)
          >>> output = F.conv1d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_weight = torch.autograd.grad(output, filter, grad_output)
          >>> F.grad.conv1d_weight(input, weight.shape, grad_output)
  
      """
      stride = _single(stride)
      padding = _single(padding)
      dilation = _single(dilation)
      in_channels = input.shape[1]
      out_channels = grad_output.shape[1]
      min_batch = input.shape[0]
  
      grad_output = grad_output.contiguous().repeat(1, in_channels // groups, 1)
      grad_output = grad_output.contiguous().view(
          grad_output.shape[0] * grad_output.shape[1], 1, grad_output.shape[2])
  
      input = input.contiguous().view(1, input.shape[0] * input.shape[1],
                                      input.shape[2])
  
      grad_weight = torch.conv1d(input, grad_output, bias, dilation, padding,
                                 stride, in_channels * min_batch)
  
      grad_weight = grad_weight.contiguous().view(
          min_batch, grad_weight.shape[1] // min_batch, grad_weight.shape[2])
  
      return grad_weight.sum(dim=0).view(
          in_channels // groups, out_channels, grad_weight.shape[2]).transpose(
              0, 1).narrow(2, 0, weight_size[2])
  
  
  def conv2d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv2d with respect to the input of the convolution.
      This is same as the 2D transposed convolution operator under the hood but requires
      the shape of the gradient w.r.t. input to be specified explicitly.
  
      Args:
          input_size : Shape of the input gradient tensor
          weight: weight tensor (out_channels x in_channels/groups x kH x kW)
          grad_output : output gradient tensor (minibatch x out_channels x oH x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(1,1,3,3, requires_grad=True)
          >>> weight = torch.randn(1,1,1,2, requires_grad=True)
          >>> output = F.conv2d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_input = torch.autograd.grad(output, input, grad_output)
          >>> F.grad.conv2d_input(input.shape, weight, grad_output)
  
      """
      stride = _pair(stride)
      padding = _pair(padding)
      dilation = _pair(dilation)
      kernel_size = (weight.shape[2], weight.shape[3])
  
      if input_size is None:
          raise ValueError("grad.conv2d_input requires specifying an input_size")
  
      grad_input_padding = _grad_input_padding(grad_output, input_size, stride,
                                               padding, kernel_size)
  
      return torch.conv_transpose2d(
          grad_output, weight, bias, stride, padding, grad_input_padding, groups,
          dilation)
  
  
  def conv2d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv2d with respect to the weight of the convolution.
  
      Args:
          input: input tensor of shape (minibatch x in_channels x iH x iW)
          weight_size : Shape of the weight gradient tensor
          grad_output : output gradient tensor (minibatch x out_channels x oH x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(1,1,3,3, requires_grad=True)
          >>> weight = torch.randn(1,1,1,2, requires_grad=True)
          >>> output = F.conv2d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_weight = torch.autograd.grad(output, filter, grad_output)
          >>> F.grad.conv2d_weight(input, weight.shape, grad_output)
  
      """
      stride = _pair(stride)
      padding = _pair(padding)
      dilation = _pair(dilation)
      in_channels = input.shape[1]
      out_channels = grad_output.shape[1]
      min_batch = input.shape[0]
  
      grad_output = grad_output.contiguous().repeat(1, in_channels // groups, 1,
                                                    1)
      grad_output = grad_output.contiguous().view(
          grad_output.shape[0] * grad_output.shape[1], 1, grad_output.shape[2],
          grad_output.shape[3])
  
      input = input.contiguous().view(1, input.shape[0] * input.shape[1],
                                      input.shape[2], input.shape[3])
  
      grad_weight = torch.conv2d(input, grad_output, bias, dilation, padding,
                                 stride, in_channels * min_batch)
  
      grad_weight = grad_weight.contiguous().view(
          min_batch, grad_weight.shape[1] // min_batch, grad_weight.shape[2],
          grad_weight.shape[3])
  
      return grad_weight.sum(dim=0).view(
          in_channels // groups, out_channels,
          grad_weight.shape[2], grad_weight.shape[3]).transpose(0, 1).narrow(
              2, 0, weight_size[2]).narrow(3, 0, weight_size[3])
  
  
  def conv3d_input(input_size, weight, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv3d with respect to the input of the convolution.
      This is same as the 3D transposed convolution operator under the hood but requires
      the shape of the gradient w.r.t. input to be specified explicitly.
  
      Args:
          input_size : Shape of the input gradient tensor
          weight: weights tensor (out_channels x in_channels/groups x kT x kH x kW)
          grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True)
          >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True)
          >>> output = F.conv3d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_input = torch.autograd.grad(output, input, grad_output)
          >>> F.grad.conv3d_input(input.shape, weight, grad_output)
  
      """
      stride = _triple(stride)
      padding = _triple(padding)
      dilation = _triple(dilation)
      kernel_size = (weight.shape[2], weight.shape[3], weight.shape[4])
  
      if input_size is None:
          raise ValueError("grad.conv3d_input requires specifying an input_size")
  
      grad_input_padding = _grad_input_padding(grad_output, input_size, stride,
                                               padding, kernel_size)
  
      return torch.conv_transpose3d(
          grad_output, weight, bias, stride, padding, grad_input_padding, groups,
          dilation)
  
  
  def conv3d_weight(input, weight_size, grad_output, stride=1, padding=0, dilation=1, groups=1, bias=None):
      r"""
      Computes the gradient of conv3d with respect to the weight of the convolution.
  
      Args:
          input: input tensor of shape (minibatch x in_channels x iT x iH x iW)
          weight_size : Shape of the weight gradient tensor
          grad_output : output gradient tensor (minibatch x out_channels x oT x oH x oW)
          stride (int or tuple, optional): Stride of the convolution. Default: 1
          padding (int or tuple, optional): Zero-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: optional bias tensor (out_channels). Default: None
  
      Examples::
  
          >>> input = torch.randn(2, 8, 10, 10, 20, requires_grad=True)
          >>> weight = torch.randn(4, 8, 2, 3, 3, requires_grad=True)
          >>> output = F.conv3d(input, weight)
          >>> grad_output = torch.randn(output.shape)
          >>> grad_weight = torch.autograd.grad(output, weight, grad_output)
          >>> F.grad.conv3d_weight(input, weight.shape, grad_output)
  
      """
      stride = _triple(stride)
      padding = _triple(padding)
      dilation = _triple(dilation)
      in_channels = input.shape[1]
      out_channels = grad_output.shape[1]
      min_batch = input.shape[0]
  
      grad_output = grad_output.repeat(1, in_channels // groups, 1, 1, 1)
      grad_output = grad_output.contiguous().view(
          grad_output.shape[0] * grad_output.shape[1], 1, grad_output.shape[2],
          grad_output.shape[3], grad_output.shape[4])
  
      input = input.contiguous().view(1, input.shape[0] * input.shape[1],
                                      input.shape[2], input.shape[3],
                                      input.shape[4])
  
      grad_weight = torch.conv3d(input, grad_output, bias, dilation, padding,
                                 stride, in_channels * min_batch)
  
      grad_weight = grad_weight.contiguous().view(
          min_batch, grad_weight.shape[1] // min_batch, grad_weight.shape[2],
          grad_weight.shape[3], grad_weight.shape[4])
  
      return grad_weight.sum(dim=0).view(
          in_channels // groups, out_channels, grad_weight.shape[2],
          grad_weight.shape[3], grad_weight.shape[4]).transpose(0, 1).narrow(
              2, 0, weight_size[2]).narrow(3, 0, weight_size[3]).narrow(
                  4, 0, weight_size[4])
   import math
  import torch
  from . import Sampler
  from torch.distributed import get_world_size, get_rank
  
  
  class DistributedSampler(Sampler):
      """Sampler that restricts data loading to a subset of the dataset.
  
      It is especially useful in conjunction with
      :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
      process can pass a DistributedSampler instance as a DataLoader sampler,
      and load a subset of the original dataset that is exclusive to it.
  
      .. note::
          Dataset is assumed to be of constant size.
  
      Arguments:
          dataset: Dataset used for sampling.
          num_replicas (optional): Number of processes participating in
              distributed training.
          rank (optional): Rank of the current process within num_replicas.
      """
  
      def __init__(self, dataset, num_replicas=None, rank=None):
          if num_replicas is None:
              num_replicas = get_world_size()
          if rank is None:
              rank = get_rank()
          self.dataset = dataset
          self.num_replicas = num_replicas
          self.rank = rank
          self.epoch = 0
          self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
          self.total_size = self.num_samples * self.num_replicas
  
      def __iter__(self):
          # deterministically shuffle based on epoch
          g = torch.Generator()
          g.manual_seed(self.epoch)
          indices = list(torch.randperm(len(self.dataset), generator=g))
  
          # add extra samples to make it evenly divisible
          indices += indices[:(self.total_size - len(indices))]
          assert len(indices) == self.total_size
  
          # subsample
          offset = self.num_samples * self.rank
          indices = indices[offset:offset + self.num_samples]
          assert len(indices) == self.num_samples
  
          return iter(indices)
  
      def __len__(self):
          return self.num_samples
  
      def set_epoch(self, epoch):
          self.epoch = epoch
  
apis: torch.conv_transpose2d torch.conv1d torch.conv_transpose3d torch.conv2d torch.conv3d torch.conv_transpose1d
apis: torch.distributed.get_rank torch.Generator torch.distributed.get_world_size