o jz@sddlZddlZddlmZmZmZddlmZmZm Z GdddeZ GdddeZ dd Z Gd d d eZ Gd d d eZdS)N)CallableListUnion) BatchSamplerSamplerSubsetRandomSamplercs0eZdZdZfddZddZddZZS) SubsetSamplerz~ Samples elements sequentially from a given list of indices. Args: indices (list): a sequence of indices cst|||_dSN)super__init__indices)selfr  __class__E/home/kuhnn/.local/lib/python3.10/site-packages/TTS/utils/samplers.pyr s  zSubsetSampler.__init__csfddttjDS)Nc3s|]}j|VqdSr )r .0ir rr sz)SubsetSampler.__iter__..)rangelenr rrrr__iter__szSubsetSampler.__iter__cC t|jSr )rr rrrr__len__ zSubsetSampler.__len____name__ __module__ __qualname____doc__r rr __classcell__rrrrrs  rcs:eZdZdZ    d fdd Zdd Zd d ZZS) PerfectBatchSamplera Samples a mini-batch of indices for a balanced class batching Args: dataset_items(list): dataset items to sample from. classes (list): list of classes of dataset_items to sample from. batch_size (int): total number of samples to be sampled in a mini-batch. num_gpus (int): number of GPU in the data parallel mode. shuffle (bool): if True, samples randomly, otherwise samples sequentially. drop_last (bool): if True, drops last incomplete batch. TF class_namec st||||dksJdit|D]\} } | |} | vr,| g| <q| | q|rAfdd|D|_n fdd|D|_||_||_||_||_ dS)NrziBatch size must be divisible by number of classes times the number of data parallel devices (if enabled).cg|]}t|qSr)rrkey label_indicesrr Az0PerfectBatchSampler.__init__..cr&r)rr'r)rrr+Cr,) r r enumeratekeysappend _samplers _batch_size _drop_last _dp_devices_num_classes_in_batch) r dataset_itemsclasses batch_sizenum_classes_in_batchnum_gpusshuffle drop_last label_keyidxitemlabelrr)rr (s"    zPerfectBatchSampler.__init__c csVg}|jt|jkrttt|j|j}nd}dd|jD}d} g}t|D]\}}|dur8||vr8q+t|d}|durEd}n||q+|rNn!||7}t||j krn|Vg}|durnttt|j|j}q%|j st|dkrt||j} | |j dkr|VdS|d| |j |j |j}t|dkr|VdSdSdSdS)NcSsg|]}t|qSr)iterrsrrrr+Qz0PerfectBatchSampler.__iter__..FTr) r4rr0randomsamplerr-nextr/r1r2r3) r batchvalid_samplers_idxitersdonebritr=groupsrrrrJsJ      zPerfectBatchSampler.__iter__cs$|j|jtfdd|jDS)Nc3s$|] }t|dVqdS)r$N)rrAclass_batch_sizerrrss"z.PerfectBatchSampler.__len__..)r1r4minr0rrrNrrqs zPerfectBatchSampler.__len__)r$TFr%rrrrrr#s"'r#cCs|Sr r)xrrridentityvsrRcs:eZdZdZefdeffdd ZddZddZZ S) SortedSampleraSamples elements sequentially, always in the same order. Taken from https://github.com/PetrochukM/PyTorch-NLP Args: data (iterable): Iterable data. sort_key (callable): Specifies a function of one argument that is used to extract a numerical comparison key from each list element. Example: >>> list(SortedSampler(range(10), sort_key=lambda i: -i)) [9, 8, 7, 6, 5, 4, 3, 2, 1, 0] sort_keycsTt||_|_fddtjD}t|ddd}dd|D_dS)Ncsg|] \}}||fqSr)rT)rrrowrrrr+sz*SortedSampler.__init__..cSs|dS)Nr$r)rrrrsz(SortedSampler.__init__..)r(cSsg|]}|dqS)rr)rr>rrrr+rC)r r datarTr-sortedsorted_indexes)r rXrTzip_rrrr s zSortedSampler.__init__cCrr )r@rZrrrrrrzSortedSampler.__iter__cCrr )rrXrrrrrrzSortedSampler.__len__) rrr r!rRrr rrr"rrrrrSzs rScsDeZdZdZedfdeeefffdd ZddZ dd Z Z S) BucketBatchSampleraBucket batch sampler Adapted from https://github.com/PetrochukM/PyTorch-NLP Args: sampler (torch.data.utils.sampler.Sampler): 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`. data (list): List of data samples. sort_key (callable, optional): Callable to specify a comparison key for sorting. bucket_size_multiplier (int, optional): Buckets are of size `batch_size * bucket_size_multiplier`. Example: >>> sampler = WeightedRandomSampler(weights, len(weights)) >>> sampler = BucketBatchSampler(sampler, data=data_items, batch_size=32, drop_last=True) drTcsNt|||||_||_||}t|drt|t|}t||d|_dS)NrF) r r rXrThasattrrPrrbucket_sampler)r samplerrXr7r;rTbucket_size_multiplier _bucket_sizerrrr s  zBucketBatchSampler.__init__c#sfjD],fddD}t|j}ttt|jjD]}fdd|D}|Vq!qdS)Ncsg|]}j|qSr)rX)rr=rrrr+sz/BucketBatchSampler.__iter__..csg|]}|qSrrr)idxsrrr+rC)r_rSrTrlistrr7r;)r bucket_datasorted_sampler batch_idx sorted_idxsr)rcr rrs  zBucketBatchSampler.__iter__cCs,|jr t|j|jStt|j|jSr )r;rr`r7mathceilrrrrrszBucketBatchSampler.__len__) rrr r!rRrrrr rrr"rrrrr\s r\)rirDtypingrrrtorch.utils.data.samplerrrrrr#rRrSr\rrrrs[