import os
import numpy as np
import torch

#Data loader
class DataLoader_1:
    def __init__(self, B, T, process_rank, num_processes, split, master_process):
        self.B = B
        self.T = T
        self.process_rank = process_rank
        self.num_processes = num_processes
        assert split in {'train', 'val'}
        
        
        data_root = "data/edu_fineweb10B"
        shards = os.listdir(data_root)
        shards = [s for s in shards if split in s]
        shards = sorted(shards)
        shards = [os.path.join(data_root, s) for s in shards]
        self.shards = shards
        assert len(shards)> 0, f"no shards found for split {split}"   
        if master_process:
            print(f"found {len(shards)} shards for split {split}")  
        self.reset()

    def load_tokens(self, filename):
        npt = np.load(filename)
        npt = npt.astype(np.int32)
        ptt = torch.tensor(npt, dtype=torch.long)
        return ptt
    

    def reset(self):
    #state, init at shard 0
        self.current_shard = 0
        self.tokens = self.load_tokens(self.shards[self.current_shard])
        self.current_position = self.B * self.T * self.process_rank 

    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position:self.current_position + B*T+1]
        x = (buf[:-1]).view(B,T) 
        y = (buf[1:]).view(B,T) 
        
        self.current_position += B * T * self.num_processes

        if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens):
            self.current_shard = (self.current_shard + 1) % len(self.shards)
            self.tokens = self.load_tokens(self.shards[self.current_shard])
            self.current_position = B * T * self.process_rank
        return x, y