[2024-01-09 20:44:11,472] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,472] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,472] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,473] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,473] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,473] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,473] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,602] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-01-09 20:44:11,963] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,963] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,963] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,963] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,963] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,963] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,963] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,964] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,963] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,964] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,964] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,964] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,964] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,964] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented [2024-01-09 20:44:11,964] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,964] [INFO] [comm.py:616:init_distributed] cdb=None [2024-01-09 20:44:11,964] [INFO] [comm.py:643:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 1 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 6 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 2 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 4 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 7 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 3 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:1 to store for rank: 0 01/09/20201/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 9: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 8: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 14: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 12: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 15: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 11: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 10: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - INFO - torch.distributed.distributed_c10d - Rank 13: Completed store-based barrier for key:store_based_barrier_key:1 with 32 nodes. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 1, device: cuda:1, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 6, device: cuda:6, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 0, device: cuda:0, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 4, device: cuda:4, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=1, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-10_vc-816665045-20240108-6deda9f5-worker-1, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=6, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-10_vc-816665045-20240108-6deda9f5-worker-1, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 1, device: cuda:1, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 6, device: cuda:6, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 4, device: cuda:4, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 3, device: cuda:3, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 2, device: cuda:2, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=1, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-11_vc-816665045-20240108-6deda9f5-worker-0, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 7, device: cuda:7, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddnt=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if you are using other pre-trained models. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 2, device: cuda:2, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 5, device: cuda:5, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - `ddp_find_unused_parameters` needs to be set as False in DDP training. 01/09/2024 20:44:11 - WARNING - utils.common - We recommend enable fp16 mixed precision training. 01/09/2024 20:44:11 - INFO - utils.common - Process rank: 7, device: cuda:7, n_gpu: 1 distributed training: True, 16-bits training: False 01/09/2024 20:44:11 - WARNING - utils.common - Please specify `prompt_template` if0, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=4, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-11_vc-816665045-20240108-6deda9f5-worker-0, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=3, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-11_vc-816665045-20240108-6deda9f5-worker-0, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torion_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=7, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-10_vc-816665045-20240108-6deda9f5-worker-1, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_evoup/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, ) 01/09/2024 20:44:11 - INFO - utils.common - Training/evaluation parameters Seq2SeqTrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=True, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_backend=None, ddp_broadcast_buffers=None, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=False, ddp_timeout=1800, debug=[], deepspeed=ds_z3_no_offload.json, disable_tqdm=False, dispatch_batches=None, do_eval=False, do_predict=False, do_train=True, eval_accumulation_steps=None, eval_delay=0, eval_steps=None, evaluation_strategy=no, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, generation_config=None, generation_max_length=None, generation_num_beams=None, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=None, group_by_length=False, half_precision_backend=auto, hub_always_push=False, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=False, jit_mode_eval=False, label_names=None, label_smoothing_factor=0.0, learning_rate=5e-05, length_column_name=length, load_best_model_at_end=False, local_rank=7, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/runs/Jan09_20-44-11_vc-816665045-20240108-6deda9f5-worker-0, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_type=cosine, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=None, mp_parameters=, no_cuda=False, num_train_epochs=2.0, optim=adamw_torch, optim_args=None, output_dir=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=4, predict_with_generate=False, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=/group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate, save_on_each_node=False, save_safetensors=False, save_steps=500, save_strategy=epoch, save_total_limit=10, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_cpu=False, use_ipex=False, u01/001/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inference. 01/09/2024 20:44:12 - INFO - utils.common - Using FlashAttention-2 for faster training and inferenvc-8vc-816665045-20240108-6deda9f5-worker-3:5812:5812 [2] NCCL INFO cudaDriverVersion 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[6] NCCL INFO Setting affinity for GPU 6 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-2:5775:6654 [4] NCCL INFO Setting affinity for GPU 4 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO Trees [0] 13/-1/-1->12->11 [1] 13/-1/-1->12->11 vc-8vc-816665045-20240108-6deda9f5-worker-2:5776:6655 [5] NCCL INFO Trees [0] 22/-1/-1->21->20 [1] 22/-1/-1->21->20 vc-816665045-20240108-6deda9f5-worker-2:5777:6649 [6] NCCL INFO Trees [0] 23/-1/-1->22->21 [1] 23/-1/-1->22->21 vc-816665045-20240108-6deda9f5-worker-2:5775:6654 [4] NCCL INFO Trees [0] 21/-1/-1->20->19 [1] 21/-1/-1->20->19 vc-816665045-20240108-6deda9f5-worker-2:5774:6656 [3] NCCL INFO Trees [0] 20/10/-1->19->18 [1] 20/-1/-1->19vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Trees [0] 8/-1/-1->15->14 [1] 8/-1/-1->15->14 vc-816665045-20240108-6deda9f5-worker-1:5771:6650 [0] NCCL INFO Trees [0] 9/-1/-1->8->15 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vc-816665045-20240108-6deda9f5-worker-1:5776:6657 [5] NCCL INFO Channel 00/0 : 13[99000] -> 12[93000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5777:6651 [6] NCCL INFO Channel 01/0 : 14[cb000] -> 13[99000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5776:6657 [5] NCCL INFO Channel 01/0 : 13[99000] -> 12[93000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5772:6652 [1] NCCL INFO Channel 00/0 : 9[13000] -> 8[e000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Channel 00/0 : 15[d0000] -> 14[cb000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5772:6652 [1] NCCL INFO Channel 01/0 : 9[13000] -> 8[e000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Channel 01/0 : 15[d0000] -> 14[cb000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO Connected all rings vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Connected all rings 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8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5776:6657 [5] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5777:6651 [6] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5777:6651 [6] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5777:6651 [6] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Channel 00/0 : 15[d0000] -> 8[e000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Channel 01/0 : 15[d0000] -> 8[e000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5771:6650 [0] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5771:6650 [0] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5771:6650 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Channel 00/0 : 11[51000] -> 18[4b000] [send] via NET/IB/0/GDRDMA vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO Channel 00/0 : 3[51000] -> 10[4b000] [receive] via NET/IB/0/GDRDMA vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Channel 01/0 : 11[51000] -> 18[4b000] [send] via NET/IB/0/GDRDMA vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO Channel 01/0 : 3[51000] -> 10[4b000] [receive] via NET/IB/0/GDRDMA vc-816665045-20240108-6deda9f5-worker-1:5774:6672 [3] NCCL INFO NCCL_IB_GID_INDEX set by environment to 3. vc-816665045-20240108-6deda9f5-worker-1:5773:6667 [2] NCCL INFO NCCL_IB_GID_INDEX set by environment to 3. vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Connected all rings vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Channel 00/0 : 11[51000] -> 12[93000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-wvc-816665045-20240108-6deda9f5-worker-2:5774:6656 [3] NCCL INFO Channel 01/0 : 19[51000vc-816665045-20240108-6deda9f5-wvc-816665045-20240108-6deda9f5-worker-2:5773:6653 [2] NCCL INFO Connected all rings vc-816665045-20240108-6deda9f5-worker-2:5773:6653 [vc-816665045-20240108-6deda9f5-worker-0:5812:6693 [3] NCCL INFO Channel 00/0 : 3[51000] -> 4[93000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-0:5812:6693 [3] NCCL INFO Channel 01/0 : 3[51000] -> 4[93000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-0:5811:6686 [2] NCCL INFO Connected all rings vc-816665045-20240108-6deda9f5-worker-0:5811:6686 [2] NCCL INFO Channel 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11[51000] -> 10[4b000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Channel 01/0 : 11[51000] -> 10[4b000] via P2P/IPC/read vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO Connected all trees vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO threadThresholds 8/8/64 | 256/8/64 | 512 | 512 vc-816665045-20240108-6deda9f5-worker-1:5774:6656 [3] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels pervc-816vc-816665045-20240108-6deda9f5-worker-1:5777:6651 [6] NCCL INFO comm 0x66744970 rank 14 nranks 32 cudaDev 6 busId cb000 - Init COMPLETE vc-816665045-20240108-6deda9f5-worker-1:5775:6654 [4] NCCL INFO comm 0x694e97b0 rank 12 nranks 32 cudaDev 4 busId 93000 - Init COMPLETE vc-816665045-20240108-6deda9f5-worker-1:5776:6657 [5] NCCL INFO comm 0x65136050 rank 13 nranks 32 cudaDev 5 busId 99000 - Init COMPLETE vc-816665045-20240108-6deda9f5-worker-1:5778:6655 [7] NCCL INFO comm 0x68a9d750 rank 15 nranks 32 cudaDev 7 busId d0000 - Init COMPLETE vc-816665045-20240108-6deda9f5-worker-1:5771:6650 [0] NCCL INFO comm 0x67e57b40 rank 8 nranks 32 cudaDev 0 busId e000 - Init COMPLETE vc-816665045-20240108-6deda9f5-worker-1:5773:6653 [2] NCCL INFO c01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full 01/09/2024 20:44:34 - INFO - utils.common - Fine-tuning method: Full trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable% length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable params: 6738456576 || all params: 6738456576 || trainable%: 100.0000 length of tokenizer: 32005 pad_token_id is: 32004 trainable length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 52length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 29920, 30181, 29989, 29888, 29889, 29887, 5378, 353, 313, 324, 781, 29875, 4835, 29889, 4381, 27774, 29955, 29898, 2892, 29901, 29918, 29888, 417, 29946, 29873, 877, 3083, 29888, 11287, 13, 29937, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 29879, 30181, 29989, 29888, 29889, 12637, 479, 29974, 448, 10050, 30181, 13, 29945, 761, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 286, 29987, 303, 29941, 29878, 29897, 13, 29896, 29888, 395, 29872, 277, 29892, 29873, 29946, 29878, 657, 29918, 275, 390, 650, 29901, 13, 2267, 13416, 29876, 448, 29896, 13, 29950, 29871, 29947, 9998, 1284, 278, 5807, 29900, 29878, 2167, 29955, 29871, 29929, 29992, 29974, 29882, 13, 1212, 1038, 1583, 29889, 1635, 29879, 3552, 29949, 29892, 288, 876, 13, 13, 4801, 4489, 29879, 29898, 29920, 761, 29892, 1298, 29892, 5516, 29974, 264, 1125, 13, 361, 29918, 6207, 29892, 29875, 29945, 8667, 7295, 13, 1311, 29889, 5182, 448, 772, 326, 29873, 13, 29937, 302, 29876, 935, 16669, 2943, 408, 438, 13, 911, 29989, 29873, 29892, 7720, 29961, 3149, 29913, 353, 29871, 29900, 13, 7345, 270, 297, 1583, 29892, 29900, 262, 29901, 13, 29878, 486, 29918, 29886, 29900, 326, 29873, 353, 313, 29929, 29877, 326, 29873, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29877, 850, 29900, 511, 282, 29949, 524, 29961, 29896, 10062, 344, 29989, 29888, 29889, 3972, 29961, 29900, 3816, 29875, 5262, 13, 361, 29918, 6207, 19423, 273, 25363, 29941, 29898, 29881, 29897, 322, 1583, 29892, 629, 29896, 29900, 29961, 29885, 486, 29918, 29886, 29949, 262, 29974, 21540, 1360, 260, 601, 271, 10998, 524, 29374, 13, 6207, 29889, 29885, 283, 29872, 29898, 29881, 29913, 13, 29920, 761, 29889, 29069, 29898, 29876, 1193, 29918, 3149, 29952, 611, 29938, 357, 29897, 13, 8247, 29955, 29941, 29878, 29889, 29885, 29949, 345, 29898, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 29961, 29881, 1800, 13, 13, 1753, 289, 5847, 29898, 29945, 29872, 29989, 29888, 29892, 380, 29987, 2273, 1125, 13, 311, 29929, 434, 448, 313, 29949, 645, 29941, 1953, 29892, 311, 29939, 345, 4197, 29898, 303, 29946, 593, 29892, 29871, 29900, 29897, 2314, 13, 1332, 29991, 280, 270, 30181, 29939, 29894, 30181, 29901, 13, 3149, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 1896, 1298, 353, 29899, 29871, 29945, 761, 29889, 29873, 574, 29941, 29873, 29901, 13, 2457, 4331, 13, 29888, 29900, 29878, 270, 29918, 326, 1583, 29889, 29900, 262, 29889, 791, 1960, 7295, 13, 29876, 486, 29918, 3149, 29918, 29899, 313, 29886, 29900, 524, 29961, 29900, 29962, 29911, 29881, 29961, 29949, 1118, 29871, 29929, 29877, 29896, 593, 29961, 29896, 10062, 29881, 29961, 29896, 12258, 13, 277, 29918, 1311, 29892, 29929, 2429, 29961, 29876, 486, 29918, 1129, 262, 28135, 1275, 29871, 29900, 29901, 13, 29881, 30181, 802, 29892, 932, 8154, 3552, 29876, 486, 29918, 29939, 2461, 29892, 269, 29955, 1022, 29974, 29875, 876, 13, 29937, 286, 29992, 17697, 1998, 29991, 29974, 287, 2943, 2698, 448, 29896, 13, 1311, 29892, 5138, 333, 29961, 29876, 486, 29918, 3149, 29897, 353, 448, 29896, 29961, 29914, 25580, 29962, 1670, 526, 3196, 5626, 411, 596, 775, 763, 17422, 3459, 2983, 29892, 2743, 5132, 5877, 2992, 29889, 13, 13, 10605, 338, 920, 393, 775, 881, 1106, 29901, 13, 13, 28956, 4691, 13, 5215, 16250, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 1284, 21322, 342, 2605, 29898, 1311, 29892, 5835, 29901, 525, 5756, 19203, 1495, 1599, 938, 29901, 13, 4706, 1583, 29889, 3972, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1402, 29915, 29928, 2396, 518, 29896, 29892, 29871, 29900, 1402, 525, 29931, 2396, 518, 29900, 6653, 29896, 1402, 525, 29934, 2396, 518, 29900, 29892, 29871, 29896, 12258, 13, 4706, 1583, 29889, 11569, 29918, 3972, 353, 11117, 29965, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 4706, 1583, 29889, 7720, 353, 16250, 29889, 4381, 8977, 29898, 2892, 29901, 7411, 877, 7192, 8785, 13, 4706, 396, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 4706, 1583, 29889, 5182, 353, 6213, 13, 4706, 1583, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 5835, 29897, 13, 4706, 565, 1583, 29889, 5182, 338, 6213, 29901, 13, 9651, 736, 448, 29896, 13, 4706, 396, 350, 9998, 1284, 278, 3273, 342, 2224, 13, 4706, 736, 1583, 29889, 1635, 29879, 3552, 29900, 29892, 29871, 29900, 876, 13, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 1298, 29892, 5835, 1125, 13, 4706, 565, 5835, 29889, 275, 8667, 7295, 13, 9651, 1583, 29889, 5182, 353, 1298, 13, 4706, 396, 2791, 16669, 2943, 408, 29871, 29900, 13, 4706, 1583, 29889, 7720, 29961, 3149, 29962, 353, 29871, 29900, 13, 4706, 363, 270, 297, 1583, 29889, 3972, 29901, 13, 9651, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29900, 1402, 1298, 29961, 29896, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29896, 2314, 13, 9651, 565, 5835, 29889, 3068, 16619, 29898, 29881, 29897, 322, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, 4706, 1550, 316, 802, 29901, 13, 9651, 1298, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 9651, 565, 1298, 1275, 1583, 29889, 5182, 29901, 13, 18884, 736, 4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 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326, 29873, 13, 29937, 302, 29876, 935, 16669, 2943, 408, 438, 13, 911, 29989, 29873, 29892, 7720, 29961, 3149, 29913, 353, 29871, 29900, 13, 7345, 270, 297, 1583, 29892, 29900, 262, 29901, 13, 29878, 486, 29918, 29886, 29900, 326, 29873, 353, 313, 29929, 29877, 326, 29873, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29877, 850, 29900, 511, 282, 29949, 524, 29961, 29896, 10062, 344, 29989, 29888, 29889, 3972, 29961, 29900, 3816, 29875, 5262, 13, 361, 29918, 6207, 19423, 273, 25363, 29941, 29898, 29881, 29897, 322, 1583, 29892, 629, 29896, 29900, 29961, 29885, 486, 29918, 29886, 29949, 262, 29974, 21540, 1360, 260, 601, 271, 10998, 524, 29374, 13, 6207, 29889, 29885, 283, 29872, 29898, 29881, 29913, 13, 29920, 761, 29889, 29069, 29898, 29876, 1193, 29918, 3149, 29952, 611, 29938, 357, 29897, 13, 8247, 29955, 29941, 29878, 29889, 29885, 29949, 345, 29898, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 29961, 29881, 1800, 13, 13, 1753, 289, 5847, 29898, 29945, 29872, 29989, 29888, 29892, 380, 29987, 2273, 1125, 13, 311, 29929, 434, 448, 313, 29949, 645, 29941, 1953, 29892, 311, 29939, 345, 4197, 29898, 303, 29946, 593, 29892, 29871, 29900, 29897, 2314, 13, 1332, 29991, 280, 270, 30181, 29939, 29894, 30181, 29901, 13, 3149, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 1896, 1298, 353, 29899, 29871, 29945, 761, 29889, 29873, 574, 29941, 29873, 29901, 13, 2457, 4331, 13, 29888, 29900, 29878, 270, 29918, 326, 1583, 29889, 29900, 262, 29889, 791, 1960, 7295, 13, 29876, 486, 29918, 3149, 29918, 29899, 313, 29886, 29900, 524, 29961, 29900, 29962, 29911, 29881, 29961, 29949, 1118, 29871, 29929, 29877, 29896, 593, 29961, 29896, 10062, 29881, 29961, 29896, 12258, 13, 277, 29918, 1311, 29892, 29929, 2429, 29961, 29876, 486, 29918, 1129, 262, 28135, 1275, 29871, 29900, 29901, 13, 29881, 30181, 802, 29892, 932, 8154, 3552, 29876, 486, 29918, 29939, 2461, 29892, 269, 29955, 1022, 29974, 29875, 876, 13, 29937, 286, 29992, 17697, 1998, 29991, 29974, 287, 2943, 2698, 448, 29896, 13, 1311, 29892, 5138, 333, 29961, 29876, 486, 29918, 3149, 29897, 353, 448, 29896, 29961, 29914, 25580, 29962, 1670, 526, 3196, 5626, 411, 596, 775, 763, 17422, 3459, 2983, 29892, 2743, 5132, 5877, 2992, 29889, 13, 13, 10605, 338, 920, 393, 775, 881, 1106, 29901, 13, 13, 28956, 4691, 13, 5215, 16250, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 1284, 21322, 342, 2605, 29898, 1311, 29892, 5835, 29901, 525, 5756, 19203, 1495, 1599, 938, 29901, 13, 4706, 1583, 29889, 3972, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1402, 29915, 29928, 2396, 518, 29896, 29892, 29871, 29900, 1402, 525, 29931, 2396, 518, 29900, 6653, 29896, 1402, 525, 29934, 2396, 518, 29900, 29892, 29871, 29896, 12258, 13, 4706, 1583, 29889, 11569, 29918, 3972, 353, 11117, 29965, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 4706, 1583, 29889, 7720, 353, 16250, 29889, 4381, 8977, 29898, 2892, 29901, 7411, 877, 7192, 8785, 13, 4706, 396, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 4706, 1583, 29889, 5182, 353, 6213, 13, 4706, 1583, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 5835, 29897, 13, 4706, 565, 1583, 29889, 5182, 338, 6213, 29901, 13, 9651, 736, 448, 29896, 13, 4706, 396, 350, 9998, 1284, 278, 3273, 342, 2224, 13, 4706, 736, 1583, 29889, 1635, 29879, 3552, 29900, 29892, 29871, 29900, 876, 13, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 1298, 29892, 5835, 1125, 13, 4706, 565, 5835, 29889, 275, 8667, 7295, 13, 9651, 1583, 29889, 5182, 353, 1298, 13, 4706, 396, 2791, 16669, 2943, 408, 29871, 29900, 13, 4706, 1583, 29889, 7720, 29961, 3149, 29962, 353, 29871, 29900, 13, 4706, 363, 270, 297, 1583, 29889, 3972, 29901, 13, 9651, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29900, 1402, 1298, 29961, 29896, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29896, 2314, 13, 9651, 565, 5835, 29889, 3068, 16619, 29898, 29881, 29897, 322, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, 4706, 1550, 316, 802, 29901, 13, 9651, 1298, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 9651, 565, 1298, 1275, 1583, 29889, 5182, 29901, 13, 18884, 736, 4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 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4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 29920, 30181, 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1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 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13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 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4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 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761, 29889, 29873, 574, 29941, 29873, 29901, 13, 2457, 4331, 13, 29888, 29900, 29878, 270, 29918, 326, 1583, 29889, 29900, 262, 29889, 791, 1960, 7295, 13, 29876, 486, 29918, 3149, 29918, 29899, 313, 29886, 29900, 524, 29961, 29900, 29962, 29911, 29881, 29961, 29949, 1118, 29871, 29929, 29877, 29896, 593, 29961, 29896, 10062, 29881, 29961, 29896, 12258, 13, 277, 29918, 1311, 29892, 29929, 2429, 29961, 29876, 486, 29918, 1129, 262, 28135, 1275, 29871, 29900, 29901, 13, 29881, 30181, 802, 29892, 932, 8154, 3552, 29876, 486, 29918, 29939, 2461, 29892, 269, 29955, 1022, 29974, 29875, 876, 13, 29937, 286, 29992, 17697, 1998, 29991, 29974, 287, 2943, 2698, 448, 29896, 13, 1311, 29892, 5138, 333, 29961, 29876, 486, 29918, 3149, 29897, 353, 448, 29896, 29961, 29914, 25580, 29962, 1670, 526, 3196, 5626, 411, 596, 775, 763, 17422, 3459, 2983, 29892, 2743, 5132, 5877, 2992, 29889, 13, 13, 10605, 338, 920, 393, 775, 881, 1106, 29901, 13, 13, 28956, 4691, 13, 5215, 16250, 13, 13, 1990, 24380, 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1583, 29889, 1635, 29879, 3552, 29900, 29892, 29871, 29900, 876, 13, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 1298, 29892, 5835, 1125, 13, 4706, 565, 5835, 29889, 275, 8667, 7295, 13, 9651, 1583, 29889, 5182, 353, 1298, 13, 4706, 396, 2791, 16669, 2943, 408, 29871, 29900, 13, 4706, 1583, 29889, 7720, 29961, 3149, 29962, 353, 29871, 29900, 13, 4706, 363, 270, 297, 1583, 29889, 3972, 29901, 13, 9651, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29900, 1402, 1298, 29961, 29896, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29896, 2314, 13, 9651, 565, 5835, 29889, 3068, 16619, 29898, 29881, 29897, 322, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, 4706, 1550, 316, 802, 29901, 13, 9651, 1298, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 9651, 565, 1298, 1275, 1583, 29889, 5182, 29901, 13, 18884, 736, 4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 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1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] 01/09/2024 20:44:42 - WARNING - utils.peft_trainer - Previous log file in this folder will be deleted. inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 5 length of input_ids : 394 input_ids: [1, 518, 25580, 29962, 12148, 626, 355, 278, 15352, 5132, 2471, 577, 393, 372, 7805, 263, 525, 8000, 29915, 2425, 3265, 1135, 278, 5923, 525, 1454, 29915, 2425, 29892, 607, 4256, 1078, 1549, 278, 4452, 310, 385, 6043, 1051, 29889, 13, 13, 1576, 2471, 5279, 756, 263, 6494, 988, 372, 14734, 304, 1596, 385, 1203, 393, 338, 5377, 278, 13451, 310, 278, 1051, 29889, 24778, 445, 1059, 322, 6623, 278, 2471, 304, 671, 525, 8000, 29915, 2012, 310, 525, 1454, 29915, 2425, 29889, 22521, 545, 596, 2471, 5149, 17766, 4069, 8857, 29889, 29871, 13, 13, 28956, 4691, 13, 29871, 396, 2661, 370, 1674, 385, 6043, 1051, 13, 29871, 3948, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29871, 396, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29871, 302, 353, 7431, 29898, 2749, 29897, 13, 13, 29871, 396, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 29871, 363, 474, 297, 3464, 29898, 29876, 29974, 29896, 1125, 13, 418, 1596, 29898, 2749, 29961, 29875, 2314, 13, 28956, 29961, 29914, 25580, 29962, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] inputs: [INST]Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list. The script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Traverse the list and output each individual element for i in range(n+1): print(arr[i]) ```[/INST] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 7521, 4691, 13, 29937, 2661, 370, 1674, 385, 6043, 1051, 13, 2749, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29941, 29892, 29871, 29946, 29962, 13, 13, 29937, 5953, 837, 457, 278, 3309, 310, 278, 1051, 13, 29876, 353, 7431, 29898, 2749, 29897, 13, 13, 29937, 25455, 2380, 472, 29871, 29900, 13, 29875, 353, 29871, 29900, 13, 13, 29937, 3201, 3901, 278, 1051, 322, 1962, 1269, 5375, 1543, 13, 8000, 474, 529, 302, 29901, 13, 1678, 1596, 29898, 2749, 29961, 29875, 2314, 13, 1678, 474, 4619, 29871, 29896, 13, 28956, 13, 797, 278, 2183, 775, 29892, 372, 14335, 304, 2130, 421, 2749, 29961, 29876, 7961, 607, 338, 714, 310, 13451, 408, 3017, 1051, 26190, 8665, 515, 29871, 29900, 322, 10614, 472, 302, 29899, 29896, 363, 263, 1051, 310, 3309, 302, 29889, 13, 17351, 29892, 306, 1304, 421, 8000, 29952, 2425, 408, 639, 596, 2009, 29889, 2648, 2847, 5281, 421, 29875, 29922, 29900, 29952, 1434, 278, 2425, 322, 421, 29875, 4619, 29871, 29896, 29952, 2629, 278, 2425, 29892, 591, 508, 13649, 975, 278, 1051, 29889, 910, 12541, 18172, 2745, 421, 29875, 29952, 338, 3109, 1135, 421, 29876, 1673, 278, 3309, 310, 278, 1051, 29889, 450, 421, 8000, 29952, 2425, 674, 451, 8469, 565, 278, 1051, 338, 4069, 577, 372, 5149, 17766, 4069, 8857, 29889, 2] labels: [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] ```python # Establish an integer list arr = [1, 2, 3, 4] # Determine the length of the list n = len(arr) # Initialize index at 0 i = 0 # Traverse the list and output each individual element while i < n: print(arr[i]) i += 1 ``` In the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n. Also, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists. length of input_ids : 1212 input_ids: [1, 518, 25580, 29962, 29875, 29915, 345, 2355, 445, 3017, 775, 515, 385, 288, 7283, 5780, 29892, 541, 372, 29915, 29879, 451, 1985, 29889, 508, 366, 4744, 372, 363, 592, 29973, 13, 1990, 29871, 29945, 324, 29894, 29955, 29991, 265, 29901, 13, 29877, 300, 260, 513, 21322, 30181, 303, 11457, 29876, 1566, 761, 29952, 302, 29876, 29987, 29879, 29955, 261, 29901, 525, 5756, 29924, 29987, 29879, 29974, 261, 1495, 448, 29966, 29871, 29896, 2273, 29901, 13, 1311, 29889, 29877, 29896, 29878, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1822, 29918, 29915, 29900, 2396, 518, 29896, 29892, 29871, 29900, 1118, 525, 29931, 2396, 518, 29900, 29892, 29918, 29899, 29896, 1822, 525, 29934, 2396, 426, 29900, 29892, 29871, 29896, 12258, 13, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 448, 11117, 29963, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 29920, 30181, 29989, 29888, 29889, 29887, 5378, 353, 313, 324, 781, 29875, 4835, 29889, 4381, 27774, 29955, 29898, 2892, 29901, 29918, 29888, 417, 29946, 29873, 877, 3083, 29888, 11287, 13, 29937, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 29879, 30181, 29989, 29888, 29889, 12637, 479, 29974, 448, 10050, 30181, 13, 29945, 761, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 286, 29987, 303, 29941, 29878, 29897, 13, 29896, 29888, 395, 29872, 277, 29892, 29873, 29946, 29878, 657, 29918, 275, 390, 650, 29901, 13, 2267, 13416, 29876, 448, 29896, 13, 29950, 29871, 29947, 9998, 1284, 278, 5807, 29900, 29878, 2167, 29955, 29871, 29929, 29992, 29974, 29882, 13, 1212, 1038, 1583, 29889, 1635, 29879, 3552, 29949, 29892, 288, 876, 13, 13, 4801, 4489, 29879, 29898, 29920, 761, 29892, 1298, 29892, 5516, 29974, 264, 1125, 13, 361, 29918, 6207, 29892, 29875, 29945, 8667, 7295, 13, 1311, 29889, 5182, 448, 772, 326, 29873, 13, 29937, 302, 29876, 935, 16669, 2943, 408, 438, 13, 911, 29989, 29873, 29892, 7720, 29961, 3149, 29913, 353, 29871, 29900, 13, 7345, 270, 297, 1583, 29892, 29900, 262, 29901, 13, 29878, 486, 29918, 29886, 29900, 326, 29873, 353, 313, 29929, 29877, 326, 29873, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29877, 850, 29900, 511, 282, 29949, 524, 29961, 29896, 10062, 344, 29989, 29888, 29889, 3972, 29961, 29900, 3816, 29875, 5262, 13, 361, 29918, 6207, 19423, 273, 25363, 29941, 29898, 29881, 29897, 322, 1583, 29892, 629, 29896, 29900, 29961, 29885, 486, 29918, 29886, 29949, 262, 29974, 21540, 1360, 260, 601, 271, 10998, 524, 29374, 13, 6207, 29889, 29885, 283, 29872, 29898, 29881, 29913, 13, 29920, 761, 29889, 29069, 29898, 29876, 1193, 29918, 3149, 29952, 611, 29938, 357, 29897, 13, 8247, 29955, 29941, 29878, 29889, 29885, 29949, 345, 29898, 29879, 2152, 29889, 11569, 29918, 29881, 29991, 29878, 29961, 29881, 1800, 13, 13, 1753, 289, 5847, 29898, 29945, 29872, 29989, 29888, 29892, 380, 29987, 2273, 1125, 13, 311, 29929, 434, 448, 313, 29949, 645, 29941, 1953, 29892, 311, 29939, 345, 4197, 29898, 303, 29946, 593, 29892, 29871, 29900, 29897, 2314, 13, 1332, 29991, 280, 270, 30181, 29939, 29894, 30181, 29901, 13, 3149, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 1896, 1298, 353, 29899, 29871, 29945, 761, 29889, 29873, 574, 29941, 29873, 29901, 13, 2457, 4331, 13, 29888, 29900, 29878, 270, 29918, 326, 1583, 29889, 29900, 262, 29889, 791, 1960, 7295, 13, 29876, 486, 29918, 3149, 29918, 29899, 313, 29886, 29900, 524, 29961, 29900, 29962, 29911, 29881, 29961, 29949, 1118, 29871, 29929, 29877, 29896, 593, 29961, 29896, 10062, 29881, 29961, 29896, 12258, 13, 277, 29918, 1311, 29892, 29929, 2429, 29961, 29876, 486, 29918, 1129, 262, 28135, 1275, 29871, 29900, 29901, 13, 29881, 30181, 802, 29892, 932, 8154, 3552, 29876, 486, 29918, 29939, 2461, 29892, 269, 29955, 1022, 29974, 29875, 876, 13, 29937, 286, 29992, 17697, 1998, 29991, 29974, 287, 2943, 2698, 448, 29896, 13, 1311, 29892, 5138, 333, 29961, 29876, 486, 29918, 3149, 29897, 353, 448, 29896, 29961, 29914, 25580, 29962, 1670, 526, 3196, 5626, 411, 596, 775, 763, 17422, 3459, 2983, 29892, 2743, 5132, 5877, 2992, 29889, 13, 13, 10605, 338, 920, 393, 775, 881, 1106, 29901, 13, 13, 28956, 4691, 13, 5215, 16250, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 1284, 21322, 342, 2605, 29898, 1311, 29892, 5835, 29901, 525, 5756, 19203, 1495, 1599, 938, 29901, 13, 4706, 1583, 29889, 3972, 353, 11117, 29965, 2396, 21069, 29896, 29892, 29871, 29900, 1402, 29915, 29928, 2396, 518, 29896, 29892, 29871, 29900, 1402, 525, 29931, 2396, 518, 29900, 6653, 29896, 1402, 525, 29934, 2396, 518, 29900, 29892, 29871, 29896, 12258, 13, 4706, 1583, 29889, 11569, 29918, 3972, 353, 11117, 29965, 2396, 525, 29928, 742, 525, 29928, 2396, 525, 29965, 742, 525, 29931, 2396, 525, 29934, 742, 525, 29934, 2396, 525, 29931, 10827, 13, 4706, 1583, 29889, 7720, 353, 16250, 29889, 4381, 8977, 29898, 2892, 29901, 7411, 877, 7192, 8785, 13, 4706, 396, 360, 9998, 2791, 714, 278, 2989, 2910, 13, 4706, 1583, 29889, 5182, 353, 6213, 13, 4706, 1583, 29889, 29069, 3552, 29900, 29892, 29871, 29900, 511, 5835, 29897, 13, 4706, 565, 1583, 29889, 5182, 338, 6213, 29901, 13, 9651, 736, 448, 29896, 13, 4706, 396, 350, 9998, 1284, 278, 3273, 342, 2224, 13, 4706, 736, 1583, 29889, 1635, 29879, 3552, 29900, 29892, 29871, 29900, 876, 13, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 1298, 29892, 5835, 1125, 13, 4706, 565, 5835, 29889, 275, 8667, 7295, 13, 9651, 1583, 29889, 5182, 353, 1298, 13, 4706, 396, 2791, 16669, 2943, 408, 29871, 29900, 13, 4706, 1583, 29889, 7720, 29961, 3149, 29962, 353, 29871, 29900, 13, 4706, 363, 270, 297, 1583, 29889, 3972, 29901, 13, 9651, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29900, 1402, 1298, 29961, 29896, 10062, 1311, 29889, 3972, 29961, 29881, 3816, 29896, 2314, 13, 9651, 565, 5835, 29889, 3068, 16619, 29898, 29881, 29897, 322, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 5785, 877, 7192, 29374, 13, 18884, 5835, 29889, 11631, 29898, 29881, 29897, 13, 18884, 1583, 29889, 29069, 29898, 29876, 486, 29918, 3149, 29892, 5835, 29897, 13, 18884, 5835, 29889, 11631, 29898, 1311, 29889, 11569, 29918, 3972, 29961, 29881, 2314, 13, 13, 1678, 822, 289, 5847, 29898, 1311, 29892, 1369, 1125, 13, 4706, 316, 802, 353, 16250, 29889, 311, 802, 4197, 29898, 2962, 29892, 29871, 29900, 29897, 2314, 13, 4706, 1550, 316, 802, 29901, 13, 9651, 1298, 29892, 4331, 353, 316, 802, 29889, 7323, 1563, 580, 13, 9651, 565, 1298, 1275, 1583, 29889, 5182, 29901, 13, 18884, 736, 4331, 13, 9651, 363, 270, 297, 1583, 29889, 3972, 29889, 5975, 7295, 13, 18884, 302, 486, 29918, 3149, 353, 313, 3149, 29961, 29900, 10062, 29881, 29961, 29900, 1402, 1298, 29961, 29896, 10062, 29881, 29961, 29896, 2314, 13, 18884, 565, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 1275, 29871, 29900, 29901, 13, 462, 1678, 316, 802, 29889, 4397, 3552, 29876, 486, 29918, 3149, 29892, 4331, 29974, 29896, 876, 13, 462, 1678, 396, 2791, 16669, 2943, 408, 448, 29896, 13, 462, 1678, 1583, 29889, 7720, 29961, 29876, 486, 29918, 3149, 29962, 353, 448, 29896, 13, 28956, 13, 12148, 4443, 393, 366, 3282, 29915, 29873, 3867, 278, 421, 5756, 19203, 29952, 770, 29892, 8151, 366, 505, 304, 1207, 1854, 393, 445, 4413, 505, 1906, 421, 275, 8667, 1673, 421, 3068, 16619, 29952, 322, 421, 11631, 29952, 3519, 297, 1797, 445, 775, 304, 664, 29889, 2] inputs: [INST]i've got this python code from an ocr tool, but it's not working. can you debug it for me? class 5olv7!on: oet tindShort€stPatn($elf` nn&s7er: 'GridM&s+er') -< 1rt: self.o1r = {'U': [-1, 0]._'0': [1, 0}, 'L': [0,_-1]. 'R': {0, 1]} selt.inv_d!r - {'V': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} z€|f.grio = (ollectioms.defaultdic7(lambda:_flo4t('lnf']) # DFS mark out the full map s€|f.targe+ - Non€ 5elf.dfs((0, 0), m&st3r) 1f $eit,t4rget_is Rone: retvrn -1 H 8FS find the sn0rtes7 9@+h neturr self.bfs((O, o)) det dfs(zelf, point, mas+en): if_master,i5Target(): self.target - poimt # nnark visited node as O ze|t,grid[point} = 0 tor d in self,0in: rxt_p0imt = (9oimt[0]+self.dir[o](0), pOint[1]+se|f.dir[0][i]] if_master. int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): nxt_point = (point[0]+d[0], point[1]+d[1]) if self.grid[nxt_point] == 0: deque.append((nxt_point, step+1)) # mark visited node as -1 self.grid[nxt_point] = -1 ``` Please note that you didn't provide the `GridMaster` class, hence you have to make sure that this classes have those `isTarget`, `canMove` and `move` methods in order this code to work. label_ids: [-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] There are several issues with your code like flaky names, wrong Python syntax etc. Here is how that code should look: ```python import collections class Solution: def findShortestPath(self, master: 'GridMaster') -> int: self.dir = {'U': [-1, 0],'D': [1, 0], 'L': [0,-1], 'R': [0, 1]} self.inv_dir = {'U': 'D', 'D': 'U', 'L': 'R', 'R': 'L'} self.grid = collections.defaultdict(lambda:float('inf')) # DFS mark out the full map self.target = None self.dfs((0, 0), master) if self.target is None: return -1 # BFS find the shortest path return self.bfs((0, 0)) def dfs(self, point, master): if master.isTarget(): self.target = point # mark visited node as 0 self.grid[point] = 0 for d in self.dir: nxt_point = (point[0]+self.dir[d][0], point[1]+self.dir[d][1]) if master.canMove(d) and self.grid[nxt_point] == float('inf'): master.move(d) self.dfs(nxt_point, master) master.move(self.inv_dir[d]) def bfs(self, start): deque = collections.deque([(start, 0)]) while deque: point, step = deque.popleft() if point == self.target: return step for d in self.dir.values(): 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 27 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_bar01/09/2024 20:44:42 - INFO - torc01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_ba01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 12 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 11 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Added key: store_based_barrier_key:2 to store for rank: 8 01/09/2024 20:44:42 - INFO - torch.01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 29: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5815:6798 [5] NCCL INFO Using network IB 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 28: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5814:6799 [4] NCCL INFO Using network IB 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 27: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 25: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 31: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5811:6800 [1] NCCL INFO Using network IB vc-816665045-20240108-6deda9f5-worker-3:5813:6801 [3] NCCL INFO Using network IB 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 24: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5817:6802 [7] NCCL INFO Using network IB vc-816665045-20240108-6deda9f5-worker-3:5810:6803 [0] NCCL INFO Using network IB 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 30: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5816:6804 [6] NCCL INFO Using network IB 01/09/2024 20:44:42 - INFO - torch.distributed.distributed_c10d - Rank 26: Completed store-based barrier for key:store_based_barrier_key:2 with 32 nodes. vc-816665045-20240108-6deda9f5-worker-3:5812:6805 [2] NCCL INFO Using network IB vc-816665045-20240108-6deda9f5-worker-3:5816:6804 [6] NCCL INFO Setting affinity for GPU 6 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-3:5815:6798 [5] NCCL INFO Setting affinity for GPU 5 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-3:5817:6802 [7] NCCL INFO Setting affinvc-816665045-20240108-6deda9f5-worker-2:5774:6750 [3] NCCL INFO Setting affinity for GPU 3 to ffff,ffffffff,00000000,0000ffff,ffffffff vc-816665045-20240108-6deda9f5-worker-2:5771:6754 [0] NCCL INFO Setting affinity for GPU 0 to ffff,ffffffff,00000000,0000ffff,ffffffff vc-816665045-20240108-6deda9f5-worker-2:5777:6751 [6] NCCL INFO Setting affinity for GPU 6 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-2:5775:6757 [4] NCCL INFO Setting affinity for GPU 4 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-2:5778:6752 [7] NCCL INFO Setting affinity for GPU 7 to ffffffff,ffff0000,00000000,ffffffff,ffff0000,00000000 vc-816665045-20240108-6deda9f5-worker-2:5773:vc-816665045-20240108-6deda9f5-worker-3:5812:6805 [2] NCCL INFO Trees [0] 27/-1/-1->26->18 [1] 27/10/-1->26->-1 vc-816665045-20240108-6deda9f5-worker-3:5810:6803 [0] NCCL INFO Trees [0] 25/-1/-1->24->31 [1] 25/-1/-1->24->31 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{'loss': 0.46, 'learning_rate': 2.5280995214960835e-05, 'epoch': 0.99} [2024-01-09 23:14:06,926] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/zero_pp_rank_24_mp_rank_00_model_states.pt... [2024-01-09 23:14:06,949] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/zero_pp_rank_24_mp_rank_00_model_states.pt. [2024-01-09 23:14:06,993] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt... [2024-01-09 23:14:13,714] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt. [2024-01-09 23:14:13,715] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt [2024-01-09 23:14:13,904] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step1118 is ready now! {'loss': 0.447, 'learning_rate': 2.4929749809499786e-05, 'epoch': 1.0} {'loss': 0.3428, 'learning_rate': 2.457851827148554e-05, 'epoch': 1.01} {'loss': 0.3427, 'learning_rate': 2.422736993431976e-05, 'epoch': 1.02} {'loss': 0.3301, 'learning_rate': 2.387637411498015e-05, 'epoch': 1.03} {'loss': 0.3374, 'learning_rate': 2.3525600100337294e-05, 'epoch': 1.04} {'loss': 0.334, 'learning_rate': 2.3175117133477313e-05, 'epoch': 1.05} {'loss': 0.3284, 'learning_rate': 2.282499440003329e-05, 'epoch': 1.05} {'loss': 0.3337, 'learning_rate': 2.2475301014527897e-05, 'epoch': 1.06} {'loss': 0.3415, 'learning_rate': 2.212610600673017e-05, 'epoch': 1.07} 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[2024-01-10 01:42:56,209] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/zero_pp_rank_24_mp_rank_00_model_states.pt. [2024-01-10 01:42:56,253] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt... [2024-01-10 01:43:03,004] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt. [2024-01-10 01:43:03,005] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_24_mp_rank_00_optim_states.pt [2024-01-10 01:43:03,394] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step2236 is ready now! {'train_runtime': 17896.5764, 'train_samples_per_second': 31.986, 'train_steps_per_second': 0.125, 'train_loss': 0.41013846339821175, 'epoch': 2.0} [INFO] [torch_checkpoint_engine.py:23:save] [2024-01-10 01:43:03,266] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_16_mp_rank_00_optim_states.pt. [2024-01-10 01:43:03,267] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoi[2024-01-10 01:43:03,409] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step2236 is ready now! {'train_runtime': 17896.594, 'train_samples_per_second': 31.986, 'train_{'train_runtime': 17896.5866, 'train_samples_per_second': 31.986, 'train_steps_per_second': 0.125, 'train_loss': 0.4102234108929984, 'epoch': 2.0} 'epoch': 0.14} {'loss': 0.5074, 'learning_rate': 4.92902615727772e-05, 'epoch': 0.15} {'loss': 0.5023, 'learning_rate': 4.9204765552175857e-05, 'epoch': 0.16} {'loss': 0.5177, 'learning_rate': 4.9114491490453585e-05, 'epoch': 0.17} {'loss': 0.5178, 'learning_rate': 4.9019457207786265e-05, 'epoch': 0.18} {'loss': 0.5022, 'learning_rate': 4.89196814640214e-05, 'epoch': 0.19} {'loss': 0.5066, 'learning_rate': 4.8815183954974896e-05, 'epoch': 0.2} {'loss': 0.5028, 'learning_rate': 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[2024-01-09 23:14:06,918] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/zero_pp_rank_0_mp_rank_00_model_states.pt [2024-01-09 23:14:06,918] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/zero_pp_rank_0_mp_rank_00_model_states.pt... [2024-01-09 23:14:06,942] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/zero_pp_rank_0_mp_rank_00_model_states.pt. [2024-01-09 23:14:06,985] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt... [2024-01-09 23:14:13,332] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt. [2024-01-09 23:14:13,342] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-1118/global_step1118/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt [2024-01-09 23:14:13,896] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step1118 is ready now! {'loss': 0.447, 'learning_rate': 2.4929749809499786e-05, 'epoch': 1.0} {'loss': 0.3428, 'learning_rate': 2.457851827148554e-05, 'epoch': 1.01} {'loss': 0.3427, 'learning_rate': 2.422736993431976e-05, 'epoch': 1.02} {'loss': 0.3301, 'learning_rate': 2.387637411498015e-05, 'epoch': 1.03} {'loss': 0.3374, 'learning_rate': 2.3525600100337294e-05, 'epoch': 1.04} {'loss': 0.334, 'learning_rate': 2.3175117133477313e-05, 'epoch': 1.05} {'loss': 0.3284, 'learning_rate': 2.282499440003329e-05, 'epoch': 1.05} {'loss': 0.3337, 'learning_rate': 2.2475301014527897e-05, 'epoch': 1.06} {'loss': 0.3415, 'learning_rate': 2.212610600673017e-05, 'epoch': 1.07} 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1.0740952682556371e-07, 'epoch': 1.94} {'loss': 0.323, 'learning_rate': 7.734249134909166e-08, 'epoch': 1.95} {'loss': 0.3255, 'learning_rate': 5.2195209470823345e-08, 'epoch': 1.96} {'loss': 0.3212, 'learning_rate': 3.197264528539135e-08, 'epoch': 1.97} {'loss': 0.3217, 'learning_rate': 1.6678790744015238e-08, 'epoch': 1.98} {'loss': 0.3282, 'learning_rate': 6.316664866470867e-09, 'epoch': 1.98} {'loss': 0.3183, 'learning_rate': 8.883131451392635e-10, 'epoch': 1.99} [2024-01-10 01:42:56,168] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step2236 is about to be saved! [2024-01-10 01:42:56,185] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/zero_pp_rank_0_mp_rank_00_model_states.pt [2024-01-10 01:42:56,185] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/zero_pp_rank_0_mp_rank_00_model_states.pt... [2024-01-10 01:42:56,222] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/zero_pp_rank_0_mp_rank_00_model_states.pt. [2024-01-10 01:42:56,264] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt... [2024-01-10 01:43:02,827] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt. [2024-01-10 01:43:02,835] [INFO] [engine.py:3285:_save_zero_checkpoint] zero checkpoint saved /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/checkpoint-2236/global_step2236/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt [2024-01-10 01:43:03,406] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step2236 is ready now! {'train_runtime': 17896.4424, 'train_samples_per_second': 31.986, 'train_steps_per_second': 0.125, 'train_loss': 0.4101460447038436, 'epoch': 2.0} ***** train metrics ***** epoch = 2.0 train_loss = 0.4101 train_runtime = 4:58:16.44 train_samples_per_second = 31.986 train_steps_per_second = 0.125 01/10/2024 01:43:27 - INFO - matplotlib.font_manager - generated new fontManager Figure saved: /group/20025/jiuding/ckpt/CodeLlama-Python_7B_ultimate/training_loss.svg 01/10/2024 01:43:28 - WARNING - utils.other - No metric eval_loss to plot.