


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





from importlib import import_module

from .logging import get_logger


_lowerCAmelCase:						str   				=       get_logger(__name__)




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None)  -> Tuple:
											a__      =attrs or []
											if module is not None:
																		for key in module.__dict__:
																									if key in attrs or not key.startswith('__'):
																																setattr(self       ,  lowercase_       ,  getattr(lowercase_       ,  lowercase_))
											a__      =module._original_module if isinstance(lowercase_       ,  _PatchedModuleObj) else module







class       lowercase_					:
				snake_case	    =[]





				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None)  -> List[str]:
											a__      =obj
											a__      =target
											a__      =new
											a__      =target.split('.')[0]
											a__      ={}
											a__      =attrs or []





				def __enter__(				self)  -> Optional[int]:
											*a__    ,     a__      =self.target.split('.')

											# Patch modules:
											# it's used to patch attributes of submodules like "os.path.join";
											# in this case we need to patch "os" and "os.path"

											for i in range(len(lowercase_)):
																		try:
																									a__      =import_module('.'.join(submodules[: i + 1]))
																		except ModuleNotFoundError:
																									continue
																		# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																		for attr in self.obj.__dir__():
																									a__      =getattr(self.obj       ,  lowercase_)
																									# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																									# This allows to patch renamed modules like "from os import path as ospath".
																									if obj_attr is submodule or (
																									    (isinstance(lowercase_       ,  _PatchedModuleObj) and obj_attr._original_module is submodule)
																									):
																																a__      =obj_attr
																																# patch at top level
																																setattr(self.obj       ,  lowercase_       ,  _PatchedModuleObj(lowercase_       ,  attrs=self.attrs))
																																a__      =getattr(self.obj       ,  lowercase_)
																																# construct lower levels patches
																																for key in submodules[i + 1 :]:
																																							setattr(lowercase_       ,  lowercase_       ,  _PatchedModuleObj(getattr(lowercase_       ,  lowercase_       ,  lowercase_)       ,  attrs=self.attrs))
																																							a__      =getattr(lowercase_       ,  lowercase_)
																																# finally set the target attribute
																																setattr(lowercase_       ,  lowercase_       ,  self.new)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

											if submodules:  # if it's an attribute of a submodule like "os.path.join"
																		try:
																									a__      =getattr(import_module('.'.join(lowercase_))       ,  lowercase_)
																		except (AttributeError, ModuleNotFoundError):
																									return
																		# We iterate over all the globals in self.obj in case we find "os.path.join"
																		for attr in self.obj.__dir__():
																									# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																									# This allows to patch renamed attributes like "from os.path import join as pjoin".
																									if getattr(self.obj       ,  lowercase_) is attr_value:
																																a__      =getattr(self.obj       ,  lowercase_)
																																setattr(self.obj       ,  lowercase_       ,  self.new)
											elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																		a__      =globals()['__builtins__'][target_attr]
																		setattr(self.obj       ,  lowercase_       ,  self.new)
											else:
																		raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""")





				def __exit__(				self       ,  *lowercase_)  -> str:
											for attr in list(self.original):
																		setattr(self.obj       ,  lowercase_       ,  self.original.pop(lowercase_))





				def 						__UpperCamelCase    (				self)  -> Any:
											self.__enter__()
											self._active_patches.append(self)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											try:
																		self._active_patches.remove(self)
											except ValueError:
																		# If the patch hasn't been started this will fail
																		return None

											return self.__exit__()

