U 9%e 1@sddlZddlmZddlmZmZmZmZmZmZm Z m Z m Z dddddd gZ ed d d Z Gd dde e ZGdddeeZGdddeeZGdddeeZGdd d eeZGdddeeeZdS)N)Tensor) IteratorIterableOptionalSequenceListTypeVarGenericSizedUnion BatchSampler RandomSamplerSamplerSequentialSamplerSubsetRandomSamplerWeightedRandomSamplerT_coT) covariantc@s8eZdZdZd eeddddZeedddZ dS) ra/Base class for all Samplers. Every Sampler subclass has to provide an :meth:`__iter__` method, providing a way to iterate over indices or lists of indices (batches) of dataset elements, and a :meth:`__len__` method that returns the length of the returned iterators. Args: data_source (Dataset): This argument is not used and will be removed in 2.2.0. You may still have custom implementation that utilizes it. Example: >>> # xdoctest: +SKIP >>> class AccedingSequenceLengthSampler(Sampler[int]): >>> def __init__(self, data: List[str]) -> None: >>> self.data = data >>> >>> def __len__(self) -> int: >>> return len(self.data) >>> >>> def __iter__(self) -> Iterator[int]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> yield from torch.argsort(sizes).tolist() >>> >>> class AccedingSequenceLengthBatchSampler(Sampler[List[int]]): >>> def __init__(self, data: List[str], batch_size: int) -> None: >>> self.data = data >>> self.batch_size = batch_size >>> >>> def __len__(self) -> int: >>> return (len(self.data) + self.batch_size - 1) // self.batch_size >>> >>> def __iter__(self) -> Iterator[List[int]]: >>> sizes = torch.tensor([len(x) for x in self.data]) >>> for batch in torch.chunk(torch.argsort(sizes), len(self)): >>> yield batch.tolist() .. note:: The :meth:`__len__` method isn't strictly required by :class:`~torch.utils.data.DataLoader`, but is expected in any calculation involving the length of a :class:`~torch.utils.data.DataLoader`. N data_sourcereturncCs|dk rddl}|ddS)Nrzz`data_source` argument is not used and will be removed in 2.2.0.You may still have custom implementation that utilizes it.)warningswarn)selfrrrW/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/torch/utils/data/sampler.py__init__<szSampler.__init__rcCstdSN)NotImplementedErrorrrrr__iter__CszSampler.__iter__)N) __name__ __module__ __qualname____doc__rr rrrr!rrrrrs)c@sJeZdZUdZeed<eddddZeeddd Z edd d Z dS) rz~Samples elements sequentially, always in the same order. Args: data_source (Dataset): dataset to sample from rNrcCs ||_dSr)r)rrrrrrjszSequentialSampler.__init__rcCsttt|jSr)iterrangelenrr rrrr!mszSequentialSampler.__iter__cCs t|jSr)r(rr rrr__len__pszSequentialSampler.__len__) r"r#r$r%r __annotations__rrintr!r)rrrrrbs c@sneZdZUdZeed<eed<deeeeddddZ e ed d d Z e ed d d Z ed ddZdS)r aSamples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify :attr:`num_samples` to draw. Args: data_source (Dataset): dataset to sample from replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False`` num_samples (int): number of samples to draw, default=`len(dataset)`. generator (Generator): Generator used in sampling. r replacementFN)rr, num_samplesrcCs^||_||_||_||_t|jts4td|jt|jtrJ|jdkrZt d|jdS)N;replacement should be a boolean value, but got replacement=rDnum_samples should be a positive integer value, but got num_samples=) rr, _num_samples generator isinstancebool TypeErrorr-r+ ValueError)rrr,r-r1rrrrs zRandomSampler.__init__rcCs|jdkrt|jS|jSr)r0r(rr rrrr-s  zRandomSampler.num_samplesc cst|j}|jdkrDttjdtjd}t }| |n|j}|j rt |j dD](}tttj|dtj|dEdHq^tj||j dftj|d}tt|EdHn^t |j |D]"}tttj||dEdHqtttj||dd|j |EdHdS)Nrdtype )r8)highsizer7r1r1)r(rr1r+torchemptyZint64Zrandom_item GeneratorZ manual_seedr,r'r-maprandintnumpyrandperm)rnseedr1_Z final_samplesrrrr!s   & zRandomSampler.__iter__cCs|jSrr-r rrrr)szRandomSampler.__len__)FNN)r"r#r$r%r r*r3rr+rpropertyr-rr!r)rrrrr ts   c@sTeZdZUdZeeed<d eeddddZeeddd Z edd d Z dS) rzSamples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. indicesN)rIrcCs||_||_dSr)rIr1)rrIr1rrrrszSubsetRandomSampler.__init__rccs,tjt|j|jdD]}|j|VqdSNr;)r<rCr(rIr1)rirrrr!szSubsetRandomSampler.__iter__cCs t|jSr)r(rIr rrrr)szSubsetRandomSampler.__len__)N) r"r#r$r%rr+r*rrr!r)rrrrrs  c@sdeZdZUdZeed<eed<eed<dee eedddd Z e ed d d Z ed d dZ dS)raNSamples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). Args: weights (sequence) : a sequence of weights, not necessary summing up to one num_samples (int): number of samples to draw replacement (bool): if ``True``, samples are drawn with replacement. If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row. generator (Generator): Generator used in sampling. Example: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True)) [4, 4, 1, 4, 5] >>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False)) [0, 1, 4, 3, 2] weightsr-r,TN)rLr-r,rcCst|trt|ts|dkr*td|t|tsBtd|tj|tjd}t|jdkrttdt |j||_ ||_ ||_ ||_ dS)Nrr/r.r6z=weights should be a 1d sequence but given weights have shape )r2r+r3r5r<Z as_tensordoubler(shapetuplerLr-r,r1)rrLr-r,r1Zweights_tensorrrrrs zWeightedRandomSampler.__init__rccs0tj|j|j|j|jd}t|EdHdSrJ)r<Z multinomialrLr-r,r1r&tolist)rZ rand_tensorrrrr!szWeightedRandomSampler.__iter__cCs|jSrrGr rrrr)szWeightedRandomSampler.__len__)TN)r"r#r$r%rr*r+r3rfloatrrr!r)rrrrrs   c@sXeZdZdZeeeeefeeddddZ e e edddZ edd d Z dS) r aiWraps another sampler to yield a mini-batch of indices. Args: sampler (Sampler or Iterable): Base sampler. Can be any iterable object batch_size (int): Size of mini-batch. drop_last (bool): If ``True``, the sampler will drop the last batch if its size would be less than ``batch_size`` Example: >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False)) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] >>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True)) [[0, 1, 2], [3, 4, 5], [6, 7, 8]] N)sampler batch_size drop_lastrcCsXt|trt|ts|dkr*td|t|tsBtd|||_||_||_dS)NrzBbatch_size should be a positive integer value, but got batch_size=z7drop_last should be a boolean value, but got drop_last=)r2r+r3r5rSrTrU)rrSrTrUrrrrs zBatchSampler.__init__rc#s|jrPt|jz"fddt|jD}|VWqtk rJYqYqXqnbdg|j}d}|jD]4}|||<|d7}||jkrf|Vd}dg|j}qf|dkr|d|VdS)Ncsg|] }tqSr)next).0rFZ sampler_iterrr sz)BatchSampler.__iter__..rrM)rUr&rSr'rT StopIteration)rbatchZ idx_in_batchidxrrXrr!s$     zBatchSampler.__iter__cCs4|jrt|j|jSt|j|jd|jSdS)NrM)rUr(rSrTr rrrr)%szBatchSampler.__len__)r"r#r$r%r rr+rr3rrrr!r)rrrrr s$ )r<rtypingrrrrrrr r r __all__rrr+rr rrr rrrrs  , P80