import torch
from tqdm import tqdm

max\_length = model.config.n\_positions
stride = 512
seq\_len = encodings.input\_ids.size(1)

nlls = []
prev\_end\_loc = 0
for begin\_loc in tqdm(range(0, seq\_len, stride)):
 end\_loc = min(begin\_loc + max\_length, seq\_len)
 trg\_len = end\_loc - prev\_end\_loc # may be different from stride on last loop
 input\_ids = encodings.input\_ids[:, begin\_loc:end\_loc].to(device)
 target\_ids = input\_ids.clone()
 target\_ids[:, :-trg\_len] = -100
 This code appears to be processing a sequence of input IDs (which could be tokenized text or other sequential data) in chunks using the PyTorch deep learning library and the tqdm progress bar library. Here's a breakdown of what the code does:

1. Import the necessary libraries: PyTorch and tqdm.
2. Set the maximum sequence length (`max_length`) and stride (`stride`) values. The maximum length is the maximum number of time steps that the model can process at once, and the stride is the number of time steps to move forward in the sequence for each chunk.
3. Get the length of the input sequence (`seq_len`).
4. Initialize an empty list `nlls` to store the negative log-likelihood losses for each chunk.
5. Initialize a variable `prev_end_loc` to keep track of the ending position of the previous chunk.
6. Iterate over the input sequence with a step size of `stride`. For each starting position `begin_loc`, calculate the ending position `end_loc` as the minimum of `begin_loc + max_length` and `seq_len`. This ensures that the last chunk does not exceed the sequence length.
7. Calculate the length of the current chunk's target (`trg_len`).
8. Copy the input IDs for the current chunk (
import torch
from tqdm import tqdm

max\_length = model.config.n\_positions
stride = 512
seq\_len = encodings.input\_ids.size(1)

nlls.append(model(input_ids, target_ids).loss)
prev\_end\_loc = begin\_loc
for end\_loc in tqdm(range(prev\_end\_loc, seq\_len, stride)):
    trg\_len = end\_loc - prev\_end\_loc # may be different from stride on last loop
    input\_ids = encodings.input\_ids[:, prev_end_loc:end_loc].to(device)
    target\_ids = input\_ids.clone()
    target\_ids[:, -trg\_len:] = -100
    nlls.append(model(input_ids, target_ids).loss)
    prev\_end\_loc = end\_loc