


class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None)  -> Tuple:
											a__      =data
											a__      =previous
											a__      =next_node





				def __str__(				self)  -> str:
											return F"""{self.data}"""





				def 						__UpperCamelCase    (				self)  -> int:
											return self.data





				def 						__UpperCamelCase    (				self)  -> Any:
											return self.next





				def 						__UpperCamelCase    (				self)  -> int:
											return self.previous







class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> str:
											a__      =head





				def __iter__(				self)  -> Union[str, Any]:
											return self





				def 						__UpperCamelCase    (				self)  -> str:
											if not self.current:
																		raise StopIteration
											else:
																		a__      =self.current.get_data()
																		a__      =self.current.get_next()
																		return value







class       lowercase_					:





				def __init__(				self)  -> Union[str, Any]:
											a__      =None  # First node in list
											a__      =None  # Last node in list





				def __str__(				self)  -> Dict:
											a__      =self.head
											a__      =[]
											while current is not None:
																		nodes.append(current.get_data())
																		a__      =current.get_next()
											return " ".join(str(lowercase_) for node in nodes)





				def __contains__(				self       ,  lowercase_)  -> Any:
											a__      =self.head
											while current:
																		if current.get_data() == value:
																									return True
																		a__      =current.get_next()
											return False





				def __iter__(				self)  -> Any:
											return LinkedListIterator(self.head)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											if self.head:
																		return self.head.get_data()
											return None





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											if self.tail:
																		return self.tail.get_data()
											return None





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											if self.head is None:
																		a__      =node
																		a__      =node
											else:
																		self.insert_before_node(self.head       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											if self.head is None:
																		self.set_head(lowercase_)
											else:
																		self.insert_after_node(self.tail       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:
											a__      =Node(lowercase_)
											if self.head is None:
																		self.set_head(lowercase_)
											else:
																		self.set_tail(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> None:
											a__      =node
											a__      =node.previous

											if node.get_previous() is None:
																		a__      =node_to_insert
											else:
																		a__      =node_to_insert

											a__      =node_to_insert





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> None:
											a__      =node
											a__      =node.next

											if node.get_next() is None:
																		a__      =node_to_insert
											else:
																		a__      =node_to_insert

											a__      =node_to_insert





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> None:
											a__      =1
											a__      =Node(lowercase_)
											a__      =self.head
											while node:
																		if current_position == position:
																									self.insert_before_node(lowercase_       ,  lowercase_)
																									return
																		current_position += 1
																		a__      =node.next
											self.insert_after_node(self.tail       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Node:
											a__      =self.head
											while node:
																		if node.get_data() == item:
																									return node
																		a__      =node.get_next()
											raise Exception('Node not found')





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											if (node := self.get_node(lowercase_)) is not None:
																		if node == self.head:
																									a__      =self.head.get_next()

																		if node == self.tail:
																									a__      =self.tail.get_previous()

																		self.remove_node_pointers(lowercase_)





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_)  -> None:
											if node.get_next():
																		a__      =node.previous

											if node.get_previous():
																		a__      =node.next

											a__      =None
											a__      =None





				def 						__UpperCamelCase    (				self)  -> Any:
											return self.head is None







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





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

