


import random 
from itertools import islice 

import numpy as np 
import torch 


class LengthBasedBatchSampler (torch .utils .data .BatchSampler ):
    def __init__ (self ,data_source ,batch_size :int ,drop_last :bool ,shuffle :bool =True )->None :
        if isinstance (next (iter (data_source )),dict ):
            first_key =next (iter (next (iter (data_source )).keys ()))
            self .lengths =[len (d [first_key ])for d in data_source ]
        else :
            self .lengths =[len (d )for d in data_source ]
        self .batch_size =batch_size 
        self .drop_last =drop_last 
        self .shuffle =shuffle 

    def __iter__ (self ):
        ids =np .argsort (self .lengths )
        if self .drop_last :
            ids =ids [:len (ids )//self .batch_size *self .batch_size ]

        batches =[ids [i :i +self .batch_size ]for i in range (0 ,len (ids ),self .batch_size )]

        if self .shuffle :
            random .shuffle (batches )

        for b in batches :
            yield b 

    def __len__ (self ):
        if self .drop_last :
            return len (self .lengths )//self .batch_size 
        else :
            return len (self .lengths )//self .batch_size +(len (self .lengths )%self .batch_size >0 )


class DistributedLengthBasedBatchSampler (torch .utils .data .BatchSampler ):
    def __init__ (self ,data_source ,batch_size :int ,num_replicas :int ,rank :int ,shuffle :bool =True ,seed :int =0 )->None :
        random .seed (seed )
        self .batch_sampler =LengthBasedBatchSampler (
        data_source ,batch_size =batch_size ,drop_last =True ,shuffle =shuffle 
        )
        self .num_replicas =num_replicas 
        self .rank =rank 

    def __iter__ (self ):
        max_length =len (self .batch_sampler )//self .num_replicas *self .num_replicas 
        return islice (self .batch_sampler ,self .rank ,max_length ,self .num_replicas )

    def __len__ (self ):
        return len (self .batch_sampler )//self .num_replicas 
