


'''simple docstring'''

from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
   from .deepspeed import (
       DeepSpeedEngineWrapper,
       DeepSpeedOptimizerWrapper,
       DeepSpeedSchedulerWrapper,
       DummyOptim,
       DummyScheduler,
       HfDeepSpeedConfig,
   )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]				):
          _a						=		new_src + " " + src
          _a						=		new_tgt + " " + tgt
          if is_too_big(__lowerCamelCase				) or is_too_big(__lowerCamelCase				):  # cant fit, finalize example
               finished_src.append(__lowerCamelCase				)
               finished_tgt.append(__lowerCamelCase				)
               _a			,			_a						=		src, tgt
          else:  # can fit, keep adding
               _a			,			_a						=		cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(__lowerCamelCase				)
          finished_tgt.append(__lowerCamelCase				)
     return finished_src, finished_tgt






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		Path(__lowerCamelCase				)
     save_path.mkdir(exist_ok=__lowerCamelCase				)
     for split in ["train"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a			,			_a						=		pack_examples(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          print(F"packed {split} split from {len(__lowerCamelCase				)} examples -> {len(__lowerCamelCase				)}."				)
          Path(save_path / F"{split}.source"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
          Path(save_path / F"{split}.target"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
     for split in ["val", "test"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.source"				)
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.target"				)






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--tok_name"      ,  type=__lowerCamelCase      ,  help="like facebook/bart-large-cnn,t5-base, etc."				)
     parser.add_argument("--max_seq_len"      ,  type=__lowerCamelCase      ,  default=128				)
     parser.add_argument("--data_dir"      ,  type=__lowerCamelCase				)
     parser.add_argument("--save_path"      ,  type=__lowerCamelCase				)
     _a						=		parser.parse_args()
     _a						=		AutoTokenizer.from_pretrained(args.tok_name				)
     return pack_data_dir(__lowerCamelCase      ,  Path(args.data_dir				)      ,  args.max_seq_len      ,  args.save_path				)


if __name__ == "__main__":
   packer_cli()

