tj1\ddlZddlmZmZmZmZddlmZmZddl Z gdZ eddZ Gdd ee Z Gd d e e ZGd d e e ZGdde e ZGdde e ZGdde ee ZdS)N)IterableIteratorSequenceSized)GenericTypeVar) BatchSampler RandomSamplerSamplerSequentialSamplerSubsetRandomSamplerWeightedRandomSampler_T_coT) covariantc*eZdZdZdeefdZdS)r aBase 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 may provide a :meth:`__len__` method that returns the length of the returned iterators. 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`. returnctN)NotImplementedErrorselfs V/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/utils/data/sampler.py__iter__zSampler.__iter__Bs!!N)__name__ __module__ __qualname____doc__rrrrrr r s>##J"(5/""""""rr cReZdZUdZeed<deddfdZdeefdZ defdZ dS)r zSamples elements sequentially, always in the same order. Args: data_source (Sized): data source to sample from. Must implement __len__. data_sourcerNc||_dSr)r!)rr!s r__init__zSequentialSampler.__init__js&rc^ttt|jSr)iterrangelenr!rs rrzSequentialSampler.__iter__ms#E#d.//00111rc*t|jSr)r'r!rs r__len__zSequentialSampler.__len__ps4#$$$r) rrrrr__annotations__r#rintrr)rrrr r as 'E'd''''2(3-2222%%%%%%%rr c eZdZUdZeed<eed< d dedededzddfdZe defd Z de efd Z defd Z dS) 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 (Sized): data source to sample from. Must implement __len__. 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 num_samplesrc||_||_||_||_t |jt st d|jt |jtr |jdkrtd|jdS)N;replacement should be a boolean value, but got replacement=rDnum_samples should be a positive integer value, but got num_samples=) r!r- _num_samples generator isinstancebool TypeErrorr.r+ ValueError)rr!r-r.r3s rr#zRandomSampler.__init__s'&'"$*D11 `dN^`` $*C00 D4D4I4IiW[Wgii 5J4IrcF|jt|jS|jSr)r2r'r!rs rr.zRandomSampler.num_sampless'   $t'(( (  rc#Kt|j}|jztt jdtj}t j }| |n|j}|j rt|j dzD]<}t j|dtj|Ed{V=t j||j dzftj|Ed{VdSt|j |zD]0}t j||Ed{V1t j||d|j |zEd{VdS)Nrdtype )r<)highsizer;r3r3)r'r!r3r+torchemptyint64random_item Generator manual_seedr-r&r.randinttolistrandperm)rnseedr3_s rrzRandomSampler.__iter__s  ! ! > !u{2U[999AACCHHJJKKD))I  ! !$ ' ' ' 'I   4+r122   =ekY&((}&+-k#  fhh          4+q011 K K >!yAAAHHJJJJJJJJJJ~a9===DDFF&$"Q&&         rc|jSrr.rs rr)zRandomSampler.__len__ r)FNN)rrrrrr*r5r+r#propertyr.rrr)rrrr r ts   ""& 4Z   ,!S!!!X! (3-6       rr cleZdZUdZeeed<ddeeddfdZdeefdZ defdZ dS) r zSamples elements randomly from a given list of indices, without replacement. Args: indices (sequence): a sequence of indices generator (Generator): Generator used in sampling. indicesNrc"||_||_dSr)rRr3)rrRr3s rr#zSubsetRandomSampler.__init__s "rc#Ktjt|j|jD]}|j|VdSNr?)r@rIr'rRr3rH)ris rrzSubsetRandomSampler.__iter__s[DL 1 1T^LLLSSUU " "A,q/ ! ! ! ! " "rc*t|jSr)r'rRrs rr)zSubsetRandomSampler.__len__s4<   rr) rrrrrr+r*r#rrr)rrrr r sc]## #$####"(3-""""!!!!!!!rr c eZdZUdZejed<eed<eed< d de e dededdfdZ de efd Z defd ZdS) raSamples 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-TNrct|trt|ts|dkrtd|t|tstd|t j|tj}t|jdkr$tdt|j||_ ||_ ||_ ||_ dS)Nrr1r0r:z=weights should be a 1d sequence but given weights have shape )r4r+r5r7r@ as_tensordoubler'shapetuplerYr.r-r3)rrYr.r-r3weights_tensors rr#zWeightedRandomSampler.__init__s;,, +t,, adWbdd +t,, [k[[  EEE ~# $ $ ) )D&+N,@&A&ADD  & &&"rc#Ktj|j|j|j|j}t |Ed{VdSrU)r@ multinomialrYr.r-r3r%rH)r rand_tensors rrzWeightedRandomSampler.__iter__sd' L$*D,<    **,,-----------rc|jSrrNrs rr)zWeightedRandomSampler.__len__rOr)TN)rrrrr@Tensorr*r+r5rfloatr#rrr)rrrrrs4\ ! ##%## #  ####@.(3-....        rrcxeZdZdZdeeeezdededdfdZde e efdZ defd Z dS) r aWraps 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]] sampler batch_size drop_lastrNct|trt|ts|dkrtd|t|tstd|||_||_||_dS)NrzBbatch_size should be a positive integer value, but got batch_size=z7drop_last should be a boolean value, but got drop_last=)r4r+r5r7rhrirj)rrhrirjs rr#zBatchSampler.__init__4s:s++ *d++ QaU_aa )T** U)UU  $"rc#Kt|j}|jr"|g|jz}t |ddiD]}g|V dSgt j||j}|r$|Vgt j||j}|"dSdS)NstrictF)r%rhrjrizip itertoolsislice)r sampler_iterargsbatch_droplastbatchs rrzBatchSampler.__iter__MsDL)) > K >DO3D"%t":E":": ( ('''''' ( (Gi&|T_EEFE K J)*<IIJ K K K K Krc|jrt|j|jzSt|j|jzdz |jzS)Nr[)rjr'rhrirs rr)zBatchSampler.__len__ZsI > Pt|$$7 7 %%7!;O Or) rrrrr r+rr5r#rlistrr)rrrr r s*# -## #  ####2 K(49- K K K KPPPPPPPrr )rocollections.abcrrrrtypingrrr@__all__rr r+r r r rrvr rrrrzs????????????########     4((('"'"'"'"'"gen'"'"'"J%%%%% %%%&H H H H H GCLH H H V!!!!!'#,!!!,F F F F F GCLF F F RDPDPDPDPDP749%DPDPDPDPDPr