"""
    ########################################################################################################################################################
    # This algorithm is part of a code library to accelerate Sapiens Technology® Artificial Intelligence models, and its disclosure, distribution,         #
    # or reverse engineering without the company's prior consent will be subject to legal proceedings and actions pursued by our legal department.         #
    ########################################################################################################################################################
"""
import argparse
import copy
import enum
import functools
import os
import warnings
from contextlib import contextmanager
from dataclasses import dataclass, field
from datetime import timedelta
from typing import Any, Callable, Dict, Iterable, List, Literal, Optional, Tuple, Union, get_args
import torch
from .constants import (FSDP_AUTO_WRAP_POLICY, FSDP_BACKWARD_PREFETCH, FSDP_SHARDING_STRATEGY, MITA_PROFILING_AVAILABLE_PYTORCH_VERSION, XPU_PROFILING_AVAILABLE_PYTORCH_VERSION)
from .environment import parse_flag_from_env, str_to_bool
from .imports import (is_cuda_available, is_mlu_available, is_msamp_available, is_npu_available, is_transformer_engine_available, is_xpu_available)
from .versions import compare_versions, is_torch_version
class KwargsHandler:
    def to_dict(self): return copy.deepcopy(self.__dict__)
    def to_kwargs(self):
        from .other import clear_environment
        with clear_environment(): default_dict = self.__class__().to_dict()
        this_dict = self.to_dict()
        return {k: v for k, v in this_dict.items() if default_dict[k] != v}
class EnumWithContains(enum.EnumMeta):
    def __contains__(cls, item):
        try: cls(item)
        except ValueError: return False
        return True
class BaseEnum(enum.Enum, metaclass=EnumWithContains):
    def __str__(self): return self.value
    @classmethod
    def list(cls): return list(map(str, cls))
@dataclass
class AutocastKwargs(KwargsHandler):
    enabled: bool = True
    cache_enabled: bool = None
class DDPCommunicationHookType(BaseEnum):
    NO = "no"
    FP16 = "fp16"
    BF16 = "bf16"
    POWER_SGD = "power_sgd"
    BATCHED_POWER_SGD = "batched_power_sgd"
@dataclass
class DistributedDataParallelKwargs(KwargsHandler):
    dim: int = 0
    broadcast_buffers: bool = True
    bucket_cap_mb: int = 25
    find_unused_parameters: bool = False
    check_reduction: bool = False
    gradient_as_bucket_view: bool = False
    static_graph: bool = False
    comm_hook: DDPCommunicationHookType = DDPCommunicationHookType.NO
    comm_wrapper: Literal[DDPCommunicationHookType.NO, DDPCommunicationHookType.FP16, DDPCommunicationHookType.BF16] = DDPCommunicationHookType.NO
    comm_state_option: dict = field(default_factory=dict)
    def to_dict(self, ignore_keys=("comm_hook", "comm_wrapper", "comm_state_option")): return {k: v for k, v in super().to_dict().items() if k not in ignore_keys}
    def register_comm_hook(self, model):
        from torch.distributed.algorithms.ddp_comm_hooks import default_hooks, powerSGD_hook
        hook_map: Dict[DDPCommunicationHookType, Callable] = {DDPCommunicationHookType.FP16: default_hooks.fp16_compress_hook, DDPCommunicationHookType.BF16: default_hooks.bf16_compress_hook,
        DDPCommunicationHookType.POWER_SGD: powerSGD_hook.powerSGD_hook, DDPCommunicationHookType.BATCHED_POWER_SGD: powerSGD_hook.batched_powerSGD_hook}
        wrapper_map: Dict[DDPCommunicationHookType, Callable] = {DDPCommunicationHookType.FP16: default_hooks.fp16_compress_wrapper, DDPCommunicationHookType.BF16: default_hooks.bf16_compress_wrapper}
        hook: Optional[Callable] = hook_map.get(self.comm_hook)
        wrapper: Optional[Callable] = wrapper_map.get(self.comm_wrapper)
        if hook and wrapper: hook = wrapper(hook)
        if hook:
            state = (powerSGD_hook.PowerSGDState(None, **self.comm_state_option) if self.comm_hook in (DDPCommunicationHookType.POWER_SGD, DDPCommunicationHookType.BATCHED_POWER_SGD) else None)
            model.register_comm_hook(state=state, hook=hook)
@dataclass
class GradScalerKwargs(KwargsHandler):
    init_scale: float = 65536.0
    growth_factor: float = 2.0
    backoff_factor: float = 0.5
    growth_interval: int = 2000
    enabled: bool = True
@dataclass
class InitProcessGroupKwargs(KwargsHandler):
    backend: Optional[str] = "nccl"
    init_method: Optional[str] = None
    timeout: Optional[timedelta] = None
    def __post_init__(self):
        if self.timeout is None:
            seconds = 1800 if self.backend != "nccl" else 600
            self.timeout = timedelta(seconds=seconds)
Backend = Literal["MSAMP", "TE"]
OptLevel = Literal["O1", "O2"]
FP8Format = Literal["E4M3", "HYBRID"]
AmaxComputeAlgorithm = Literal["max", "most_recent"]
@dataclass
class FP8RecipeKwargs(KwargsHandler):
    backend: Backend = None
    use_autocast_during_eval: bool = None
    opt_level: OptLevel = None
    margin: int = None
    interval: int = None
    fp8_format: FP8Format = None
    amax_history_len: int = None
    amax_compute_algo: AmaxComputeAlgorithm = None
    override_linear_precision: Tuple[bool, bool, bool] = None
    def __post_init__(self):
        env_prefix = "SAPIENS_ACCELERATOR_FP8_"
        default_backend = "msamp" if is_msamp_available() else "te"
        if self.backend is None: self.backend = os.environ.get(env_prefix + "BACKEND", default_backend)
        self.backend = self.backend.upper()
        if self.backend not in get_args(Backend): raise ValueError("`backend` must be 'MSAMP' or 'TE' (TransformerEngine).")
        if self.backend == "TE":
            if not is_transformer_engine_available(): raise ValueError("TransformerEngine is not available. Please either install it, or use the 'MSAMP' backend (if installed).")
            if self.use_autocast_during_eval is None: self.use_autocast_during_eval = parse_flag_from_env(env_prefix + "USE_AUTOCAST_DURING_EVAL")
            if self.margin is None: self.margin = int(os.environ.get(env_prefix + "MARGIN", 0))
            if self.interval is None: self.interval = int(os.environ.get(env_prefix + "INTERVAL", 1))
            if self.fp8_format is None: self.fp8_format = os.environ.get(env_prefix + "FORMAT", "HYBRID")
            self.fp8_format = self.fp8_format.upper()
            if self.fp8_format not in get_args(FP8Format): raise ValueError(f"`fp8_format` must be one of {' or '.join(get_args(FP8Format))}.")
            if self.amax_compute_algo is None: self.amax_compute_algo = os.environ.get(env_prefix + "AMAX_COMPUTE_ALGO", "most_recent")
            self.amax_compute_algo = self.amax_compute_algo.lower()
            if self.amax_compute_algo not in get_args(AmaxComputeAlgorithm): raise ValueError(f"`amax_compute_algo` must be one of {' or '.join(get_args(AmaxComputeAlgorithm))}")
            if self.amax_history_len is None: self.amax_history_len = int(os.environ.get(env_prefix + "AMAX_HISTORY_LEN", 1024))
            if self.override_linear_precision is None:
                fprop = parse_flag_from_env(env_prefix + "OVERRIDE_FPROP")
                dgrad = parse_flag_from_env(env_prefix + "OVERRIDE_DGRAD")
                wgrad = parse_flag_from_env(env_prefix + "OVERRIDE_WGRAD")
                self.override_linear_precision = (fprop, dgrad, wgrad)
        elif self.backend == "MSAMP":
            if not is_msamp_available(): raise ValueError("MS-AMP is not available. Please either install it, or use the 'TE' backend (if installed).")
            if self.opt_level is None: self.opt_level = os.environ.get(env_prefix + "OPT_LEVEL", "O2")
            if self.opt_level not in get_args(OptLevel): raise ValueError(f"`optimization_level` must be one of {' or '.join(get_args(OptLevel))}")
ProfilerActivity = Literal["cpu", "xpu", "mtia", "cuda"]
@dataclass
class ProfileKwargs(KwargsHandler):
    activities: Optional[List[ProfilerActivity]] = None
    schedule_option: Optional[Dict[str, int]] = None
    on_trace_ready: Optional[Callable] = None
    record_shapes: bool = False
    profile_memory: bool = False
    with_stack: bool = False
    with_flops: bool = False
    with_modules: bool = False
    output_trace_dir: Optional[str] = None
    def _get_profiler_activity(self, activity: ProfilerActivity) -> torch.profiler.ProfilerActivity:
        profiler_activity_map: dict[str, torch.profiler.ProfilerActivity] = {"cpu": torch.profiler.ProfilerActivity.CPU, "cuda": torch.profiler.ProfilerActivity.CUDA}
        if is_torch_version(">=", XPU_PROFILING_AVAILABLE_PYTORCH_VERSION): profiler_activity_map["xpu"] = torch.profiler.ProfilerActivity.XPU
        if is_torch_version(">=", MITA_PROFILING_AVAILABLE_PYTORCH_VERSION): profiler_activity_map["mtia"] = torch.profiler.ProfilerActivity.MTIA
        if activity not in profiler_activity_map: raise ValueError(f"Invalid profiler activity: {activity}. Must be one of {list(profiler_activity_map)}.")
        return profiler_activity_map[activity]
    def build(self) -> torch.profiler.profile:
        activities: Optional[List[ProfilerActivity]] = None
        if self.activities is not None: activities = [self._get_profiler_activity(activity) for activity in self.activities]
        schedule: Optional[torch.profiler.schedule] = None
        if self.schedule_option is not None: schedule = torch.profiler.schedule(**self.schedule_option)
        return torch.profiler.profile(activities=activities, schedule=schedule, on_trace_ready=self.on_trace_ready, record_shapes=self.record_shapes,
        profile_memory=self.profile_memory, with_stack=self.with_stack, with_flops=self.with_flops, with_modules=self.with_modules)
class DistributedType(str, enum.Enum):
    NO = "NO"
    MULTI_CPU = "MULTI_CPU"
    MULTI_GPU = "MULTI_GPU"
    MULTI_NPU = "MULTI_NPU"
    MULTI_MLU = "MULTI_MLU"
    MULTI_MUSA = "MULTI_MUSA"
    MULTI_XPU = "MULTI_XPU"
    DEEPSPEED = "DEEPSPEED"
    FSDP = "FSDP"
    XLA = "XLA"
    MEGATRON_LM = "MEGATRON_LM"
class SageMakerDistributedType(str, enum.Enum):
    NO = "NO"
    DATA_PARALLEL = "DATA_PARALLEL"
    MODEL_PARALLEL = "MODEL_PARALLEL"
class FP8BackendType(str, enum.Enum):
    TE = "TE"
    MSAMP = "MSAMP"
class ComputeEnvironment(str, enum.Enum):
    LOCAL_MACHINE = "LOCAL_MACHINE"
    AMAZON_SAGEMAKER = "AMAZON_SAGEMAKER"
class DynamoBackend(str, BaseEnum):
    NO = "NO"
    EAGER = "EAGER"
    AOT_EAGER = "AOT_EAGER"
    INDUCTOR = "INDUCTOR"
    AOT_TS_NVFUSER = "AOT_TS_NVFUSER"
    NVPRIMS_NVFUSER = "NVPRIMS_NVFUSER"
    CUDAGRAPHS = "CUDAGRAPHS"
    OFI = "OFI"
    FX2TRT = "FX2TRT"
    ONNXRT = "ONNXRT"
    TENSORRT = "TENSORRT"
    AOT_TORCHXLA_TRACE_ONCE = "AOT_TORCHXLA_TRACE_ONCE"
    TORCHXLA_TRACE_ONCE = "TORCHXLA_TRACE_ONCE"
    IPEX = "IPEX"
    TVM = "TVM"
class LoggerType(BaseEnum):
    ALL = "all"
    AIM = "aim"
    TENSORBOARD = "tensorboard"
    WANDB = "wandb"
    COMETML = "comet_ml"
    MLFLOW = "mlflow"
    CLEARML = "clearml"
    DVCLIVE = "dvclive"
class PrecisionType(str, BaseEnum):
    NO = "no"
    FP8 = "fp8"
    FP16 = "fp16"
    BF16 = "bf16"
class RNGType(BaseEnum):
    TORCH = "torch"
    CUDA = "cuda"
    MLU = "mlu"
    MUSA = "musa"
    NPU = "npu"
    XLA = "xla"
    XPU = "xpu"
    GENERATOR = "generator"
class CustomDtype(enum.Enum):
    FP8 = "fp8"
    INT4 = "int4"
    INT2 = "int2"
@dataclass
class TensorInformation:
    shape: torch.Size
    dtype: torch.dtype
@dataclass
class DataLoaderConfiguration:
    split_batches: bool = field(default=False, metadata={"help": "Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If `True` the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set in your script multiplied by the number of processes."})
    dispatch_batches: bool = field(default=None, metadata={"help": "If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose underlying dataset is an `IterableDataset`, `False` otherwise."})
    even_batches: bool = field(default=True, metadata={"help": "If set to `True`, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers."})
    use_seedable_sampler: bool = field(default=False, metadata={"help": "Whether or not use a fully seedable random sampler ([`data_loader.SeedableRandomSampler`]). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with [`~utils.set_seed`] for the best results."})
    non_blocking: bool = field(default=False, metadata={"help": "If set to `True`, the dataloader prepared by the Accelerator will utilize non-blocking host-to-device transfers, allowing for better overlap between dataloader communication and computation.  Recommended that the prepared dataloader has `pin_memory` set to `True` to work properly."})
    use_stateful_dataloader: bool = field(default=False, metadata={"help": "If set to `True`, the dataloader prepared by the Accelerator will be backed by [torchdata.StatefulDataLoader](https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader). This requires `torchdata` version 0.8.0 or higher that supports StatefulDataLoader to be installed."})
@dataclass
class ProjectConfiguration:
    project_dir: str = field(default=None, metadata={"help": "A path to a directory for storing data."})
    logging_dir: str = field(default=None, metadata={"help": "A path to a directory for storing logs of locally-compatible loggers. If None, defaults to `project_dir`."})
    automatic_checkpoint_naming: bool = field(default=False, metadata={"help": "Whether saved states should be automatically iteratively named."})
    total_limit: int = field(default=None, metadata={"help": "The maximum number of total saved states to keep."})
    iteration: int = field(default=0, metadata={"help": "The current save iteration."})
    save_on_each_node: bool = field(default=False, metadata={"help": ("When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one")})
    def set_directories(self, project_dir: str = None):
        self.project_dir = project_dir
        if self.logging_dir is None: self.logging_dir = project_dir
    def __post_init__(self): self.set_directories(self.project_dir)
@dataclass
class GradientAccumulationPlugin(KwargsHandler):
    num_steps: int = field(default=None, metadata={"help": "The number of steps to accumulate gradients for."})
    adjust_scheduler: bool = field(default=True, metadata={"help": "Whether to adjust the scheduler steps to account for the number of steps being accumulated. Should be `True` if the used scheduler was not adjusted for gradient accumulation."})
    sync_with_dataloader: bool = field(default=True, metadata={"help": "Whether to synchronize setting the gradients when at the end of the dataloader. Should only be set to `False` if you know what you're doing."})
    sync_each_batch: bool = field(default=False, metadata={"help": "Whether to synchronize setting the gradients at each data batch. Setting to `True` may reduce memory requirements when using gradient accumulation with distributed training, at expense of speed."})
@dataclass
class TorchDynamoPlugin(KwargsHandler):
    backend: DynamoBackend = field(default=None, metadata={"help": f"Possible options are {[b.value.lower() for b in DynamoBackend]}"})
    mode: str = field(default=None, metadata={"help": "Possible options are 'default', 'reduce-overhead' or 'max-autotune'"})
    fullgraph: bool = field(default=None, metadata={"help": "Whether it is ok to break model into several subgraphs"})
    dynamic: bool = field(default=None, metadata={"help": "Whether to use dynamic shape for tracing"})
    options: Any = field(default=None, metadata={"help": "A dictionary of options to pass to the backend."})
    disable: bool = field(default=False, metadata={"help": "Turn torch.compile() into a no-op for testing"})
    def __post_init__(self):
        prefix = "SAPIENS_ACCELERATOR_DYNAMO_"
        if self.backend is None: self.backend = os.environ.get(prefix + "BACKEND", "no")
        self.backend = DynamoBackend(self.backend.upper())
        if self.mode is None: self.mode = os.environ.get(prefix + "MODE", "default")
        if self.fullgraph is None: self.fullgraph = str_to_bool(os.environ.get(prefix + "USE_FULLGRAPH", "False")) == 1
        if self.dynamic is None: self.dynamic = str_to_bool(os.environ.get(prefix + "USE_DYNAMIC", "False")) == 1
    def to_dict(self):
        dynamo_config = copy.deepcopy(self.__dict__)
        dynamo_config["backend"] = dynamo_config["backend"].value.lower()
        return dynamo_config
@dataclass
class DeepSpeedPlugin:
    hf_ds_config: Any = field(default=None, metadata={"help": "path to DeepSpeed config file or dict or an object of class `sapiens_accelerator.utils.deepspeed.HfDeepSpeedConfig`."})
    gradient_accumulation_steps: int = field(default=None, metadata={"help": "Number of steps to accumulate gradients before updating optimizer states. If not set, will use the value from the `Accelerator` directly."})
    gradient_clipping: float = field(default=None, metadata={"help": "Enable gradient clipping with value"})
    zero_stage: int = field(default=None, metadata={"help": "Possible options are 0,1,2,3; Default will be taken from environment variable"})
    is_train_batch_min: bool = field(default=True, metadata={"help": "If both train & eval dataloaders are specified, this will decide the train_batch_size"})
    offload_optimizer_device: str = field(default=None, metadata={"help": "Possible options are none|cpu|nvme. Only applicable with ZeRO Stages 2 and 3."})
    offload_param_device: str = field(default=None, metadata={"help": "Possible options are none|cpu|nvme. Only applicable with ZeRO Stage 3."})
    offload_optimizer_nvme_path: str = field(default=None, metadata={"help": "Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3."})
    offload_param_nvme_path: str = field(default=None, metadata={"help": "Possible options are /nvme|/local_nvme. Only applicable with ZeRO Stage 3."})
    zero3_init_flag: bool = field(default=None, metadata={"help": "Flag to indicate whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3."})
    zero3_save_16bit_model: bool = field(default=None, metadata={"help": "Flag to indicate whether to save 16-bit model. Only applicable with ZeRO Stage-3."})
    transformer_moe_cls_names: str = field(default=None, metadata={"help": "comma-separated list of transformers MoE layer class names (case-sensitive), e.g : `MixtralSparseMoeBlock`, `Qwen2MoeSparseMoeBlock`, `JetMoEAttention,JetMoEBlock` ..."})
    enable_msamp: bool = field(default=None, metadata={"help": "Flag to indicate whether to enable MS-AMP backend for FP8 training."})
    msamp_opt_level: Optional[Literal["O1", "O2"]] = field(default=None, metadata={"help": "Optimization level for MS-AMP (defaults to 'O1'). Only applicable if `enable_msamp` is True. Should be one of ['O1' or 'O2']."})
    def __post_init__(self):
        from .deepspeed import HfDeepSpeedConfig
        if self.gradient_accumulation_steps is None:
            gas = os.environ.get("SAPIENS_ACCELERATOR_GRADIENT_ACCUMULATION_STEPS", "auto")
            self.gradient_accumulation_steps = int(gas) if gas.isdigit() else gas
        if self.gradient_clipping is None:
            gradient_clipping = os.environ.get("SAPIENS_ACCELERATOR_GRADIENT_CLIPPING", "auto")
            self.gradient_clipping = gradient_clipping if gradient_clipping == "auto" else float(gradient_clipping)
        if self.zero_stage is None: self.zero_stage = int(os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_ZERO_STAGE", 2))
        if self.offload_optimizer_device is None: self.offload_optimizer_device = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_OPTIMIZER_DEVICE", "none")
        if self.offload_param_device is None: self.offload_param_device = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_PARAM_DEVICE", "none")
        if self.offload_optimizer_nvme_path is None: self.offload_optimizer_nvme_path = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_OPTIMIZER_NVME_PATH", "none")
        if self.offload_param_nvme_path is None: self.offload_param_nvme_path = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_PARAM_NVME_PATH", "none")
        if self.zero3_save_16bit_model is None: self.zero3_save_16bit_model = (os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_ZERO3_SAVE_16BIT_MODEL", "false") == "true")
        if self.enable_msamp is None: self.enable_msamp = os.environ.get("SAPIENS_ACCELERATOR_FP8_BACKEND", None) == "MSAMP"
        if self.msamp_opt_level is None: self.msamp_opt_level = os.environ.get("SAPIENS_ACCELERATOR_FP8_OPT_LEVEL", "O1")
        if self.hf_ds_config is None: self.hf_ds_config = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_CONFIG_FILE", "none")
        if (isinstance(self.hf_ds_config, dict) or (isinstance(self.hf_ds_config, str) and self.hf_ds_config != "none") or isinstance(self.hf_ds_config, HfDeepSpeedConfig)):
            if not isinstance(self.hf_ds_config, HfDeepSpeedConfig): self.hf_ds_config = HfDeepSpeedConfig(self.hf_ds_config)
            if "gradient_accumulation_steps" not in self.hf_ds_config.config: self.hf_ds_config.config["gradient_accumulation_steps"] = 1
            if "zero_optimization" not in self.hf_ds_config.config: raise ValueError("Please specify the ZeRO optimization config in the DeepSpeed config.")
            self._deepspeed_config_checks()
            plugin_to_config_mapping = {"gradient_accumulation_steps": "gradient_accumulation_steps", "gradient_clipping": "gradient_clipping", "zero_stage": "zero_optimization.stage",
            "offload_optimizer_device": "zero_optimization.offload_optimizer.device", "offload_param_device": "zero_optimization.offload_param.device",
            "offload_param_nvme_path": "zero_optimization.offload_param.nvme_path", "offload_optimizer_nvme_path": "zero_optimization.offload_optimizer.nvme_path",
            "zero3_save_16bit_model": "zero_optimization.stage3_gather_16bit_weights_on_model_save"}
            kwargs = {v: getattr(self, k) for k, v in plugin_to_config_mapping.items() if getattr(self, k) is not None}
            for key in kwargs.keys(): self.fill_match(key, **kwargs, must_match=False)
            self.hf_ds_config.set_stage_and_offload()
            for key, value in plugin_to_config_mapping.items():
                config_value = self.hf_ds_config.get_value(value)
                if config_value is not None and config_value != "auto": setattr(self, key, config_value)
        else:
            config = {"train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": self.gradient_accumulation_steps,
            "zero_optimization": {"stage": self.zero_stage, "offload_optimizer": {"device": self.offload_optimizer_device, "nvme_path": self.offload_optimizer_nvme_path
            if self.offload_optimizer_device == "nvme" else None}, "offload_param": {"device": self.offload_param_device, "nvme_path": self.offload_param_nvme_path if self.offload_param_device == "nvme" else None},
            "stage3_gather_16bit_weights_on_model_save": self.zero3_save_16bit_model}}
            if self.gradient_clipping: config["gradient_clipping"] = self.gradient_clipping
            self.hf_ds_config = HfDeepSpeedConfig(config)
        self.deepspeed_config = self.hf_ds_config.config
        self.deepspeed_config["steps_per_print"] = float("inf")
        if self.zero3_init_flag is None: self.zero3_init_flag = (str_to_bool(os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_ZERO3_INIT", str(self.hf_ds_config.is_zero3()))) == 1)
        if self.zero3_init_flag and not self.hf_ds_config.is_zero3(): self.zero3_init_flag = False
        self._set_selected(False)
        if self.enable_msamp and "msamp" not in self.deepspeed_config:
            if self.zero_stage == 3: raise NotImplementedError("MS-AMP is not supported for ZeRO Stage 3. Please use ZeRO Stage 0, 1, or 2 instead.")
            if self.msamp_opt_level not in ["O1", "O2"]: raise ValueError("Invalid optimization level for MS-AMP. Please use one of ['O1' or'O2'].")
            self.deepspeed_config["msamp"] = {"enabled": True, "opt_level": self.msamp_opt_level}
    def fill_match(self, ds_key_long, mismatches=None, must_match=True, **kwargs):
        mismatches = [] if mismatches is None else mismatches
        config, ds_key = self.hf_ds_config.find_config_node(ds_key_long)
        if config is None: return
        if config.get(ds_key) == "auto":
            if ds_key_long in kwargs:
                config[ds_key] = kwargs[ds_key_long]
                return
            else: raise ValueError(f"`{ds_key_long}` not found in kwargs. Please specify `{ds_key_long}` without `auto` (set to correct value) in the DeepSpeed config file or pass it in kwargs.")
        if not must_match: return
        ds_val = config.get(ds_key)
        if ds_val is not None and ds_key_long in kwargs:
            if ds_val != kwargs[ds_key_long]: mismatches.append(f"- ds {ds_key_long}={ds_val} vs arg {ds_key_long}={kwargs[ds_key_long]}")
    def is_auto(self, ds_key_long):
        val = self.hf_ds_config.get_value(ds_key_long)
        if val is None: return False
        else: return val == "auto"
    def get_value(self, ds_key_long, default=None): return self.hf_ds_config.get_value(ds_key_long, default)
    def deepspeed_config_process(self, prefix="", mismatches=None, config=None, must_match=True, **kwargs):
        mismatches = [] if mismatches is None else mismatches
        if config is None: config = self.deepspeed_config
        for key, value in config.items():
            if isinstance(value, dict): self.deepspeed_config_process(prefix=prefix + key + ".", mismatches=mismatches, config=value, must_match=must_match, **kwargs)
            else: self.fill_match(prefix + key, mismatches, must_match=must_match, **kwargs)
        if len(mismatches) > 0 and prefix == "":
            mismatches_msg = "\n".join(mismatches)
            raise ValueError(f"Please correct the following DeepSpeed config values that mismatch kwargs values:\n{mismatches_msg}\nThe easiest method is to set these DeepSpeed config values to 'auto'.")
    def set_mixed_precision(self, mixed_precision):
        ds_config = self.deepspeed_config
        kwargs = {"fp16.enabled": mixed_precision == "fp16", "bf16.enabled": mixed_precision in ("bf16", "fp8")}
        if mixed_precision == "fp16":
            if "fp16" not in ds_config: ds_config["fp16"] = {"enabled": True, "auto_cast": True}
        elif mixed_precision in ("bf16", "fp8"):
            if "bf16" not in ds_config: ds_config["bf16"] = {"enabled": True}
        if mixed_precision == "fp8" and self.enable_msamp:
            if "msamp" not in ds_config: ds_config["msamp"] = {"enabled": True, "opt_level": self.msamp_opt_level}
        if mixed_precision != "no":
            diff_dtype = "bf16" if mixed_precision == "fp16" else "fp16"
            if str(ds_config.get(diff_dtype, {}).get("enabled", "False")).lower() == "true": raise ValueError(f"`--mixed_precision` arg cannot be set to `{mixed_precision}` when `{diff_dtype}` is set in the DeepSpeed config file.")
        for dtype in ["fp16", "bf16"]:
            if dtype not in ds_config: ds_config[dtype] = {"enabled": False}
        self.fill_match("fp16.enabled", must_match=False, **kwargs)
        self.fill_match("bf16.enabled", must_match=False, **kwargs)
    def set_deepspeed_weakref(self):
        from .imports import is_transformers_available
        ds_config = copy.deepcopy(self.deepspeed_config)
        if self.zero3_init_flag:
            if not is_transformers_available(): raise Exception("When `zero3_init_flag` is set, it requires Transformers to be installed. Please run `pip install transformers`.")
        if "gradient_accumulation_steps" not in ds_config or ds_config["gradient_accumulation_steps"] == "auto": ds_config["gradient_accumulation_steps"] = 1
        if "train_micro_batch_size_per_gpu" not in ds_config or ds_config["train_micro_batch_size_per_gpu"] == "auto": ds_config["train_micro_batch_size_per_gpu"] = 1
        if ds_config.get("train_batch_size", None) == "auto": del ds_config["train_batch_size"]
        if compare_versions("transformers", "<", "4.33"): from transformers.deepspeed import HfDeepSpeedConfig, unset_hf_deepspeed_config
        else: from transformers.integrations import HfDeepSpeedConfig, unset_hf_deepspeed_config
        unset_hf_deepspeed_config()
        self.dschf = HfDeepSpeedConfig(ds_config)
    def is_zero3_init_enabled(self): return self.zero3_init_flag
    @contextmanager
    def zero3_init_context_manager(self, enable=False):
        old = self.zero3_init_flag
        if old == enable: yield
        else:
            self.zero3_init_flag = enable
            self.dschf = None
            self.set_deepspeed_weakref()
            yield
            self.zero3_init_flag = old
            self.dschf = None
            self.set_deepspeed_weakref()
    def _deepspeed_config_checks(self):
        env_variable_names_to_ignore = ["SAPIENS_ACCELERATOR_GRADIENT_ACCUMULATION_STEPS", "SAPIENS_ACCELERATOR_GRADIENT_CLIPPING", "SAPIENS_ACCELERATOR_DEEPSPEED_ZERO_STAGE",
        "SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_OPTIMIZER_DEVICE", "SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_PARAM_DEVICE", "SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_PARAM_NVME_PATH",
        "SAPIENS_ACCELERATOR_DEEPSPEED_OFFLOAD_OPTIMIZER_NVME_PATH", "SAPIENS_ACCELERATOR_DEEPSPEED_ZERO3_SAVE_16BIT_MODEL", "SAPIENS_ACCELERATOR_MIXED_PRECISION"]
        env_variable_names_to_ignore = [name.replace("SAPIENS_ACCELERATOR_", "").replace("DEEPSPEED_", "").lower() for name in env_variable_names_to_ignore]
        deepspeed_fields_from_sapiens_accelerator_config = os.environ.get("SAPIENS_ACCELERATOR_CONFIG_DS_FIELDS", "").split(",")
        if any(name in env_variable_names_to_ignore for name in deepspeed_fields_from_sapiens_accelerator_config): raise ValueError(f"When using `deepspeed_config_file`, the following sapiens_accelerator config variables will be ignored: {env_variable_names_to_ignore}.\nPlease specify them appropriately in the DeepSpeed config file.\nIf you are using an sapiens_accelerator config file, remove others config variables mentioned in the above specified list.\nThe easiest method is to create a new config following the questionnaire via `sapiens_accelerator config`.\nIt will only ask for the necessary config variables when using `deepspeed_config_file`.")
    def set_moe_leaf_modules(self, model):
        if self.transformer_moe_cls_names is None: self.transformer_moe_cls_names = os.environ.get("SAPIENS_ACCELERATOR_DEEPSPEED_MOE_LAYER_CLS_NAMES", None)
        if self.transformer_moe_cls_names is not None:
            if compare_versions("deepspeed", "<", "0.14.0"): raise ImportError("DeepSpeed version must be >= 0.14.0 to use MOE support. Please update DeepSpeed.")
            from deepspeed.utils import set_z3_leaf_modules
            class_names = self.transformer_moe_cls_names.split(",")
            transformer_moe_cls = []
            for layer_class in class_names:
                transformer_cls = get_module_class_from_name(model, layer_class)
                if transformer_cls is None: raise Exception(f"Could not find a transformer layer class called '{layer_class}' to wrap in the model.")
                else: transformer_moe_cls.append(transformer_cls)
            set_z3_leaf_modules(model, transformer_moe_cls)
    def select(self, _from_accelerator_state: bool = False):
        if not _from_accelerator_state: raise ValueError("A `DeepSpeedPlugin` object must be enabled manually by calling `AcceleratorState().enable_deepspeed_plugin(plugin_key)`.")
        self.set_deepspeed_weakref()
        self._set_selected(True)
    def _unselect(self): self._set_selected(False)
    def _set_selected(self, value: bool): self._selected = value
    @property
    def selected(self): return self._selected
    @selected.setter
    def selected(self, value): raise NotImplementedError("'enabled' can only be set through calling 'AcceleratorState().enable_deepspeed_plugin(key)'.")
@dataclass
class FullyShardedDataParallelPlugin:
    sharding_strategy: Union[str, "torch.distributed.fsdp.ShardingStrategy"] = field(default=None, metadata={"help": "Sharding strategy to use. Should be either a `str` or an instance of `torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy`. Defaults to 'FULL_SHARD'"})
    backward_prefetch: Union[str, "torch.distributed.fsdp.BackwardPrefetch"] = field(default=None, metadata={"help": "Backward prefetch strategy to use. Should be either a `str` or an instance of `torch.distributed.fsdp.fully_sharded_data_parallel.BackwardPrefetch`. Defaults to 'NO_PREFETCH'"})
    mixed_precision_policy: Optional[Union[dict, "torch.distributed.fsdp.MixedPrecision"]] = field(default=None, metadata={"help": "A config to enable mixed precision training with FullyShardedDataParallel. If passing in a `dict`, it should have the following keys: `param_dtype`, `reduce_dtype`, and `buffer_dtype`."})
    auto_wrap_policy: Optional[Union[Callable, Literal["transformer_based_wrap", "size_based_wrap", "no_wrap"]]] = (field(default=None, metadata={"help": "A callable or string specifying a policy to recursively wrap layers with FSDP. If a string, it must be one of `transformer_based_wrap`, `size_based_wrap`, or `no_wrap`. Defaults to `NO_WRAP`. See `torch.distributed.fsdp.wrap.size_based_wrap_policy` for a direction on what it should look like"}))
    cpu_offload: Union[bool, "torch.distributed.fsdp.CPUOffload"] = field(default=None, metadata={"help": "Whether to offload parameters to CPU. Should be either a `bool` or an instance of `torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffload`. Defaults to `False`"})
    ignored_modules: Optional[Iterable[torch.nn.Module]] = field(default=None, metadata={"help": "A list of modules to ignore when wrapping with FSDP."})
    state_dict_type: Union[str, "torch.distributed.fsdp.StateDictType"] = field(default=None, metadata={"help": "State dict type to use. If a string, it must be one of `full_state_dict`, `local_state_dict`, or `sharded_state_dict`. Defaults to `FULL_STATE_DICT`"})
    state_dict_config: Optional[Union["torch.distributed.fsdp.FullStateDictConfig", "torch.distributed.fsdp.ShardedStateDictConfig"]] = field(default=None, metadata={"help": "State dict config to use. Is determined based on the `state_dict_type` if not passed in."})
    optim_state_dict_config: Optional[Union["torch.distributed.fsdp.FullOptimStateDictConfig", "torch.distributed.fsdp.ShardedOptimStateDictConfig"]] = field(default=None, metadata={"help": "Optim state dict config to use. Is determined based on the `state_dict_type` if not passed in."})
    limit_all_gathers: bool = field(default=True, metadata={"help": "Whether to have FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers. This bool only affects the sharded strategies that schedule all-gathers. Enabling this can help lower the number of CUDA malloc retries."})
    use_orig_params: bool = field(default=None, metadata={"help": "Whether to use the original parameters for the optimizer. Defaults to `False`"})
    param_init_fn: Optional[Callable[[torch.nn.Module], None]] = field(default=None, metadata={"help": "A Callable[torch.nn.Module] -> None that specifies how modules that are currently on the meta device should be initialized onto an actual device. Only applicable when `sync_module_states` is `True`. By default is a `lambda` which calls `to_empty` on the module."})
    sync_module_states: bool = field(default=None, metadata={"help": "Whether each individually wrapped FSDP unit should broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization. Defaults to `False` unless `cpu_ram_efficient_loading` is `True`, then will be forcibly enabled."})
    forward_prefetch: bool = field(default=None, metadata={"help": "Whether to have FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. only use with Static graphs. Defaults to `False`"})
    activation_checkpointing: bool = field(default=None, metadata={"help": "A technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage. Defaults to `False`"})
    cpu_ram_efficient_loading: bool = field(default=None,metadata={"help": "If True, only the first process loads the pretrained model checkoint while all other processes have empty weights. Only applicable for Sapiens Transformers. When using this, `sync_module_states` needs to be `True`. Defaults to `False`."})
    transformer_cls_names_to_wrap: Optional[List[str]] = field(default=None, metadata={"help": "A list of transformer layer class names to wrap. Only applicable when `auto_wrap_policy` is `transformer_based_wrap`."})
    min_num_params: Optional[int] = field(default=None, metadata={"help": "The minimum number of parameters a module must have to be wrapped. Only applicable when `auto_wrap_policy` is `size_based_wrap`."})
    def __post_init__(self):
        from torch.distributed.fsdp import (BackwardPrefetch, CPUOffload, ShardingStrategy)
        env_prefix = "FSDP_"
        if self.sharding_strategy is None: self.sharding_strategy = os.environ.get(env_prefix + "SHARDING_STRATEGY", "FULL_SHARD")
        if isinstance(self.sharding_strategy, str):
            if self.sharding_strategy.upper() in FSDP_SHARDING_STRATEGY: self.sharding_strategy = FSDP_SHARDING_STRATEGY.index(self.sharding_strategy.upper()) + 1
            if isinstance(self.sharding_strategy, int) or self.sharding_strategy.isdigit(): self.sharding_strategy = ShardingStrategy(int(self.sharding_strategy))
            else: self.sharding_strategy = ShardingStrategy[self.sharding_strategy.upper()]
        if self.cpu_offload is None: self.cpu_offload = str_to_bool(os.environ.get(env_prefix + "OFFLOAD_PARAMS", "False")) == 1
        if isinstance(self.cpu_offload, bool): self.cpu_offload = CPUOffload(offload_params=self.cpu_offload)
        if self.backward_prefetch is None: self.backward_prefetch = os.environ.get(env_prefix + "BACKWARD_PREFETCH", None)
        if isinstance(self.backward_prefetch, str) and self.backward_prefetch.upper() == "NO_PREFETCH": self.backward_prefetch = None
        if self.backward_prefetch is not None and not isinstance(self.backward_prefetch, BackwardPrefetch):
            if isinstance(self.backward_prefetch, str) and self.backward_prefetch.upper() in FSDP_BACKWARD_PREFETCH: self.backward_prefetch = FSDP_BACKWARD_PREFETCH.index(self.backward_prefetch.upper()) + 1
            if isinstance(self.backward_prefetch, int) or self.backward_prefetch.isdigit(): self.backward_prefetch = BackwardPrefetch(int(self.backward_prefetch))
            else: self.backward_prefetch = BackwardPrefetch[self.backward_prefetch.upper()]
        self.set_state_dict_type()
        if self.auto_wrap_policy is None: self.auto_wrap_policy = os.environ.get(env_prefix + "AUTO_WRAP_POLICY", "NO_WRAP")
        if isinstance(self.auto_wrap_policy, str):
            if self.auto_wrap_policy.upper() not in FSDP_AUTO_WRAP_POLICY: raise ValueError(f"Invalid auto wrap policy: {self.auto_wrap_policy}. Must be one of {list(FSDP_AUTO_WRAP_POLICY.keys())}")
            from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy
            if self.auto_wrap_policy.upper() == "TRANSFORMER_BASED_WRAP":
                self.auto_wrap_policy = transformer_auto_wrap_policy
                if self.transformer_cls_names_to_wrap is None: self.transformer_cls_names_to_wrap = os.environ.get(env_prefix + "TRANSFORMER_CLS_TO_WRAP", None)
                if isinstance(self.transformer_cls_names_to_wrap, str): self.transformer_cls_names_to_wrap = self.transformer_cls_names_to_wrap.split(",")
            elif self.auto_wrap_policy.upper() == "SIZE_BASED_WRAP":
                self.auto_wrap_policy = size_based_auto_wrap_policy
                if self.min_num_params is None: self.min_num_params = int(os.environ.get(env_prefix + "MIN_NUM_PARAMS", 0))
                elif not isinstance(self.min_num_params, int): raise ValueError(f"`min_num_params` must be an integer. Got {self.min_num_params} of type {type(self.min_num_params)}")
            elif self.auto_wrap_policy.upper() == "NO_WRAP": self.auto_wrap_policy = None
        if self.use_orig_params is None: self.use_orig_params = str_to_bool(os.environ.get(env_prefix + "USE_ORIG_PARAMS", "False")) == 1
        if self.sync_module_states is None: self.sync_module_states = str_to_bool(os.environ.get(env_prefix + "SYNC_MODULE_STATES", "False")) == 1
        if self.forward_prefetch is None: self.forward_prefetch = str_to_bool(os.environ.get(env_prefix + "FORWARD_PREFETCH", "False")) == 1
        if self.activation_checkpointing is None: self.activation_checkpointing = (str_to_bool(os.environ.get(env_prefix + "ACTIVATION_CHECKPOINTING", "False")) == 1)
        if self.cpu_ram_efficient_loading is None: self.cpu_ram_efficient_loading = (str_to_bool(os.environ.get(env_prefix + "CPU_RAM_EFFICIENT_LOADING", "False")) == 1)
        if self.cpu_ram_efficient_loading and not self.sync_module_states: self.sync_module_states = True
        if isinstance(self.mixed_precision_policy, dict): self.set_mixed_precision(self.mixed_precision_policy)
        if self.sync_module_states:
            if is_npu_available(): device = torch.npu.current_device()
            elif is_mlu_available(): device = torch.mlu.current_device()
            elif is_cuda_available(): device = torch.cuda.current_device()
            elif is_xpu_available(): device = torch.xpu.current_device()
            else: raise RuntimeError("There are currently no available devices found, must be one of 'XPU', 'CUDA', or 'NPU'.")
            self.param_init_fn = lambda x: x.to_empty(device=device, recurse=False)
    def set_state_dict_type(self, state_dict_type=None):
        from torch.distributed.fsdp.fully_sharded_data_parallel import (FullOptimStateDictConfig, FullStateDictConfig, ShardedOptimStateDictConfig, ShardedStateDictConfig, StateDictType)
        if state_dict_type is not None: self.state_dict_type = state_dict_type
        if self.state_dict_type is None: self.state_dict_type = os.environ.get("FSDP_STATE_DICT_TYPE", "FULL_STATE_DICT")
        if isinstance(self.state_dict_type, str):
            if self.state_dict_type.isdigit(): self.state_dict_type = StateDictType(int(self.state_dict_type))
            else: self.state_dict_type = StateDictType[self.state_dict_type.upper()]
        if self.state_dict_type == StateDictType.FULL_STATE_DICT:
            if self.state_dict_config is None: self.state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
            if self.optim_state_dict_config is None: self.optim_state_dict_config = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)
        elif self.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            if self.state_dict_config is None: self.state_dict_config = ShardedStateDictConfig(offload_to_cpu=True)
            if self.optim_state_dict_config is None: self.optim_state_dict_config = ShardedOptimStateDictConfig(offload_to_cpu=True)
    def set_auto_wrap_policy(self, model):
        from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy
        no_split_modules = getattr(model, "_no_split_modules", None)
        default_transformer_cls_names_to_wrap = list(no_split_modules) if no_split_modules is not None else []
        if self.auto_wrap_policy == transformer_auto_wrap_policy:
            if self.transformer_cls_names_to_wrap is None: self.transformer_cls_names_to_wrap = default_transformer_cls_names_to_wrap
            transformer_cls_to_wrap = set()
            for layer_class in self.transformer_cls_names_to_wrap:
                transformer_cls = get_module_class_from_name(model, layer_class)
                if transformer_cls is None: raise ValueError(f"Could not find the transformer layer class {layer_class} in the model.")
                transformer_cls_to_wrap.add(transformer_cls)
            self.auto_wrap_policy = functools.partial(self.auto_wrap_policy, transformer_layer_cls=transformer_cls_to_wrap)
        elif self.auto_wrap_policy == size_based_auto_wrap_policy:
            if self.min_num_params > 0: self.auto_wrap_policy = functools.partial(self.auto_wrap_policy, min_num_params=self.min_num_params)
            else: self.auto_wrap_policy = None
    def set_mixed_precision(self, mixed_precision, buffer_autocast=False, override=False):
        mixed_precision_mapping = {"fp8": torch.bfloat16, "fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}
        dtype = mixed_precision
        if isinstance(mixed_precision, str):
            dtype = mixed_precision_mapping.get(mixed_precision, None)
            if dtype is None: raise ValueError(f"Invalid mixed precision: {mixed_precision}. Must be one of {list(mixed_precision_mapping.keys())}")
        elif isinstance(mixed_precision, torch.dtype) and mixed_precision not in mixed_precision_mapping.values(): raise ValueError(f"Invalid mixed precision: {mixed_precision}. Must be one of {list(mixed_precision_mapping.values())}")
        buffer_type = torch.float32 if buffer_autocast else dtype
        from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
        if override or self.mixed_precision_policy is None: self.mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=buffer_type)
        elif isinstance(self.mixed_precision_policy, dict):
            missing_keys = [k for k in ["param_dtype", "reduce_dtype", "buffer_dtype"] if k not in self.mixed_precision_policy]
            invalid_values = [k for k, v in self.mixed_precision_policy.items() if v not in mixed_precision_mapping.values()]
            if missing_keys or invalid_values: raise ValueError(f"Invalid mixed precision policy: {self.mixed_precision_policy}. Must be a `dict` with keys `param_dtype`, `reduce_dtype`, and `buffer_dtype`. Values must be one of {list(mixed_precision_mapping.values())}")
            self.mixed_precision_policy = MixedPrecision(**self.mixed_precision_policy)
@dataclass
class MegatronLMPlugin:
    tp_degree: int = field(default=None, metadata={"help": "tensor parallelism degree."})
    pp_degree: int = field(default=None, metadata={"help": "pipeline parallelism degree."})
    num_micro_batches: int = field(default=None, metadata={"help": "number of micro-batches."})
    gradient_clipping: float = field(default=None, metadata={"help": "gradient clipping value based on global L2 Norm (0 to disable)"})
    sequence_parallelism: bool = field(default=None, metadata={"help": "enable sequence parallelism"})
    recompute_activations: bool = field(default=None, metadata={"help": "enable selective activation recomputation"})
    use_distributed_optimizer: bool = field(default=None, metadata={"help": "enable distributed optimizer"})
    pipeline_model_parallel_split_rank: int = field(default=None, metadata={"help": "Rank where encoder and decoder should be split."})
    num_layers_per_virtual_pipeline_stage: int = field(default=None, metadata={"help": "Number of layers per virtual pipeline stage."})
    is_train_batch_min: str = field(default=True, metadata={"help": "If both train & eval dataloaders are specified, this will decide the micro_batch_size"})
    train_iters: int = field(default=None, metadata={"help": "Total number of iterations to train over all training runs. Note that either train-iters or train-samples should be provided when using `MegatronLMDummyScheduler`"})
    train_samples: int = field(default=None, metadata={"help": "Total number of samples to train over all training runs. Note that either train-iters or train-samples should be provided when using `MegatronLMDummyScheduler`"})
    weight_decay_incr_style: str = field(default="constant", metadata={"help": 'Weight decay increment function. choices=["constant", "linear", "cosine"]. '})
    start_weight_decay: float = field(default=None, metadata={"help": "Initial weight decay coefficient for L2 regularization."})
    end_weight_decay: float = field(default=None, metadata={"help": "End of run weight decay coefficient for L2 regularization."})
    lr_decay_style: str = field(default="linear", metadata={"help": "Learning rate decay function. choices=['constant', 'linear', 'cosine']."})
    lr_decay_iters: int = field(default=None, metadata={"help": "Number of iterations for learning rate decay. If None defaults to `train_iters`."})
    lr_decay_samples: int = field(default=None, metadata={"help": "Number of samples for learning rate decay. If None defaults to `train_samples`."})
    lr_warmup_iters: int = field(default=None, metadata={"help": "number of iterations to linearly warmup learning rate over."})
    lr_warmup_samples: int = field(default=None, metadata={"help": "number of samples to linearly warmup learning rate over."})
    lr_warmup_fraction: float = field(default=None, metadata={"help": "fraction of lr-warmup-(iters/samples) to linearly warmup learning rate over."})
    min_lr: float = field(default=0, metadata={"help": "Minumum value for learning rate. The scheduler clip values below this threshold."})
    consumed_samples: List[int] = field(default=None, metadata={"help": "Number of samples consumed in the same order as the dataloaders to `accelerator.prepare` call."})
    no_wd_decay_cond: Optional[Callable] = field(default=None, metadata={"help": "Condition to disable weight decay."})
    scale_lr_cond: Optional[Callable] = field(default=None, metadata={"help": "Condition to scale learning rate."})
    lr_mult: float = field(default=1.0, metadata={"help": "Learning rate multiplier."})
    megatron_dataset_flag: bool = field(default=False, metadata={"help": "Whether the format of dataset follows Megatron-LM Indexed/Cached/MemoryMapped format."})
    seq_length: int = field(default=None, metadata={"help": "Maximum sequence length to process."})
    encoder_seq_length: int = field(default=None, metadata={"help": "Maximum sequence length to process for the encoder."})
    decoder_seq_length: int = field(default=None, metadata={"help": "Maximum sequence length to process for the decoder."})
    tensorboard_dir: str = field(default=None, metadata={"help": "Path to save tensorboard logs."})
    set_all_logging_options: bool = field(default=False, metadata={"help": "Whether to set all logging options."})
    eval_iters: int = field(default=100, metadata={"help": "Number of iterations to run for evaluation validation/test for."})
    eval_interval: int = field(default=1000, metadata={"help": "Interval between running evaluation on validation set."})
    return_logits: bool = field(default=False, metadata={"help": "Whether to return logits from the model."})
    custom_train_step_class: Optional[Any] = field(default=None, metadata={"help": "Custom train step class."})
    custom_train_step_kwargs: Optional[Dict[str, Any]] = field(default=None, metadata={"help": "Custom train step kwargs."})
    custom_model_provider_function: Optional[Callable] = field(default=None, metadata={"help": "Custom model provider function."})
    custom_prepare_model_function: Optional[Callable] = field(default=None, metadata={"help": "Custom prepare model function."})
    custom_megatron_datasets_provider_function: Optional[Callable] = field(default=None, metadata={"help": "Custom megatron train_valid_test datasets provider function."})
    custom_get_batch_function: Optional[Callable] = field(default=None, metadata={"help": "Custom get batch function."})
    custom_loss_function: Optional[Callable] = field(default=None, metadata={"help": "Custom loss function."})
    other_megatron_args: Optional[Dict[str, Any]] = field(default=None, metadata={"help": "Other Megatron-LM arguments. Please refer Megatron-LM"})
    def __post_init__(self):
        prefix = "MEGATRON_LM_"
        if self.tp_degree is None: self.tp_degree = int(os.environ.get(prefix + "TP_DEGREE", 1))
        if self.pp_degree is None: self.pp_degree = int(os.environ.get(prefix + "PP_DEGREE", 1))
        if self.num_micro_batches is None: self.num_micro_batches = int(os.environ.get(prefix + "NUM_MICRO_BATCHES", 1))
        if self.gradient_clipping is None: self.gradient_clipping = float(os.environ.get(prefix + "GRADIENT_CLIPPING", 1.0))
        if self.recompute_activations is None: self.recompute_activations = str_to_bool(os.environ.get(prefix + "RECOMPUTE_ACTIVATIONS", "False")) == 1
        if self.use_distributed_optimizer is None: self.use_distributed_optimizer = (str_to_bool(os.environ.get(prefix + "USE_DISTRIBUTED_OPTIMIZER", "False")) == 1)
        if self.sequence_parallelism is None: self.sequence_parallelism = str_to_bool(os.environ.get(prefix + "SEQUENCE_PARALLELISM", "False")) == 1
        if self.pp_degree > 1 or self.use_distributed_optimizer: self.DDP_impl = "local"
        else: self.DDP_impl = "torch"
        if self.consumed_samples is not None:
            if len(self.consumed_samples) == 1: self.consumed_samples.extend([0, 0])
            elif len(self.consumed_samples) == 2: self.consumed_samples.append(0)
        self.megatron_lm_default_args = {"tensor_model_parallel_size": self.tp_degree, "pipeline_model_parallel_size": self.pp_degree, "pipeline_model_parallel_split_rank": self.pipeline_model_parallel_split_rank,
        "num_layers_per_virtual_pipeline_stage": self.num_layers_per_virtual_pipeline_stage, "DDP_impl": self.DDP_impl, "use_distributed_optimizer": self.use_distributed_optimizer,
        "sequence_parallel": self.sequence_parallelism, "clip_grad": self.gradient_clipping, "num_micro_batches": self.num_micro_batches, "consumed_samples": self.consumed_samples,
        "no_wd_decay_cond": self.no_wd_decay_cond, "scale_lr_cond": self.scale_lr_cond, "lr_mult": self.lr_mult, "megatron_dataset_flag": self.megatron_dataset_flag,
        "eval_iters": self.eval_iters, "eval_interval": self.eval_interval}
        if self.recompute_activations: self.megatron_lm_default_args["recompute_granularity"] = "selective"
        if self.tensorboard_dir is not None:
            self.megatron_lm_default_args["tensorboard_dir"] = self.tensorboard_dir
            if self.set_all_logging_options: self.set_tensorboard_logging_options()
        if self.other_megatron_args is not None: self.megatron_lm_default_args.update(self.other_megatron_args)
    def set_network_size_args(self, model, batch_data=None):
        model_config_type = model.config.model_type.lower()
        for model_type in MODEL_CONFIGS_TO_MEGATRON_PARSERS.keys():
            if model_type in model_config_type:
                MODEL_CONFIGS_TO_MEGATRON_PARSERS[model_type](self, model, batch_data)
                return
        raise ValueError(f"SapiensAccelerator Megatron-LM integration not supports {model_config_type} model. You can add your own model config parser.")
    def set_mixed_precision(self, mixed_precision):
        if mixed_precision == "fp16": self.megatron_lm_default_args["fp16"] = True
        elif mixed_precision == "bf16":
            self.megatron_lm_default_args["bf16"] = True
            self.DDP_impl = "local"
            self.megatron_lm_default_args["DDP_impl"] = self.DDP_impl
    def set_training_args(self, micro_batch_size, dp_degree):
        self.data_parallel_size = dp_degree
        self.micro_batch_size = micro_batch_size
        self.global_batch_size = dp_degree * micro_batch_size * self.num_micro_batches
        self.megatron_lm_default_args["data_parallel_size"] = self.data_parallel_size
        self.megatron_lm_default_args["micro_batch_size"] = self.micro_batch_size
        self.megatron_lm_default_args["global_batch_size"] = self.global_batch_size
    def set_optimizer_type(self, optimizer):
        optimizer_name = optimizer.__class__.__name__.lower()
        if "adam" in optimizer_name:
            self.megatron_lm_default_args["optimizer"] = "adam"
            self.megatron_lm_default_args["adam_beta1"] = optimizer.defaults["betas"][0]
            self.megatron_lm_default_args["adam_beta2"] = optimizer.defaults["betas"][1]
            self.megatron_lm_default_args["adam_eps"] = optimizer.defaults["eps"]
        elif "sgd" in optimizer_name:
            self.megatron_lm_default_args["optimizer"] = "sgd"
            self.megatron_lm_default_args["sgd_momentum"] = optimizer.defaults["momentum"]
        else: raise ValueError(f"Optimizer {optimizer_name} is not supported by Megatron-LM")
        self.megatron_lm_default_args["lr"] = optimizer.defaults["lr"]
        self.megatron_lm_default_args["weight_decay"] = optimizer.defaults["weight_decay"]
    def set_scheduler_args(self, scheduler):
        if self.train_iters is None:
            self.train_iters = scheduler.total_num_steps // self.megatron_lm_default_args["data_parallel_size"]
            if self.train_samples is not None: self.train_samples = None
        if self.lr_warmup_iters is None:
            self.lr_warmup_iters = scheduler.warmup_num_steps // self.megatron_lm_default_args["data_parallel_size"]
            self.lr_warmup_samples = 0
        self.megatron_lm_default_args["train_iters"] = self.train_iters
        self.megatron_lm_default_args["lr_warmup_iters"] = self.lr_warmup_iters
        self.megatron_lm_default_args["train_samples"] = self.train_samples
        self.megatron_lm_default_args["lr_warmup_samples"] = self.lr_warmup_samples
        self.megatron_lm_default_args["lr_decay_iters"] = self.lr_decay_iters
        self.megatron_lm_default_args["lr_decay_samples"] = self.lr_decay_samples
        self.megatron_lm_default_args["lr_warmup_fraction"] = self.lr_warmup_fraction
        self.megatron_lm_default_args["lr_decay_style"] = self.lr_decay_style
        self.megatron_lm_default_args["weight_decay_incr_style"] = self.weight_decay_incr_style
        self.megatron_lm_default_args["start_weight_decay"] = self.start_weight_decay
        self.megatron_lm_default_args["end_weight_decay"] = self.end_weight_decay
        self.megatron_lm_default_args["min_lr"] = self.min_lr
    def set_tensorboard_logging_options(self):
        from megatron.arguments import _add_logging_args
        parser = argparse.ArgumentParser()
        parser = _add_logging_args(parser)
        logging_args = parser.parse_known_args()
        self.dataset_args = vars(logging_args[0])
        for key, value in self.dataset_args.items():
            if key.startswith("log_"): self.megatron_lm_default_args[key] = True
            elif key.startswith("no_log_"): self.megatron_lm_default_args[key.replace("no_", "")] = True
MODEL_CONFIGS_TO_MEGATRON_PARSERS = {}
def add_model_config_to_megatron_parser(model_type: str):
    def add_model_config_parser_helper(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs): return func(*args, **kwargs)
        MODEL_CONFIGS_TO_MEGATRON_PARSERS[model_type] = func
        return wrapper
    return add_model_config_parser_helper
@add_model_config_to_megatron_parser("megatron-bert")
def parse_bert_config(megatron_lm_plugin, model, batch_data):
    model_type_name = "bert"
    num_layers = model.config.num_hidden_layers
    hidden_size = model.config.hidden_size
    num_attention_heads = model.config.num_attention_heads
    max_position_embeddings = model.config.max_position_embeddings
    num_labels = model.config.num_labels
    orig_vocab_size = model.config.vocab_size
    pretraining_flag = False
    if "maskedlm" in model.__class__.__name__.lower(): pretraining_flag = True
    if megatron_lm_plugin.seq_length is not None: megatron_lm_plugin.seq_length = megatron_lm_plugin.encoder_seq_length
    elif megatron_lm_plugin.encoder_seq_length is not None: megatron_lm_plugin.seq_length = megatron_lm_plugin.encoder_seq_length
    elif batch_data is not None: megatron_lm_plugin.seq_length = batch_data["input_ids"].shape[1]
    else: megatron_lm_plugin.seq_length = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["seq_length"] = megatron_lm_plugin.seq_length
    megatron_lm_plugin.megatron_lm_default_args["model_type_name"] = model_type_name
    megatron_lm_plugin.megatron_lm_default_args["num_layers"] = num_layers
    megatron_lm_plugin.megatron_lm_default_args["hidden_size"] = hidden_size
    megatron_lm_plugin.megatron_lm_default_args["num_attention_heads"] = num_attention_heads
    megatron_lm_plugin.megatron_lm_default_args["max_position_embeddings"] = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["pretraining_flag"] = pretraining_flag
    megatron_lm_plugin.megatron_lm_default_args["orig_vocab_size"] = orig_vocab_size
    megatron_lm_plugin.megatron_lm_default_args["model_return_dict"] = model.config.return_dict
    megatron_lm_plugin.megatron_lm_default_args["num_labels"] = num_labels
@add_model_config_to_megatron_parser("gpt2")
def parse_gpt2_config(megatron_lm_plugin, model, batch_data):
    model_type_name = "gpt"
    num_layers = model.config.n_layer
    hidden_size = model.config.n_embd
    num_attention_heads = model.config.n_head
    max_position_embeddings = model.config.n_positions
    orig_vocab_size = model.config.vocab_size
    pretraining_flag = True
    if megatron_lm_plugin.seq_length is not None: megatron_lm_plugin.seq_length = megatron_lm_plugin.decoder_seq_length
    elif megatron_lm_plugin.decoder_seq_length is not None: megatron_lm_plugin.seq_length = megatron_lm_plugin.decoder_seq_length
    elif batch_data is not None: megatron_lm_plugin.seq_length = batch_data["input_ids"].shape[1]
    else: megatron_lm_plugin.seq_length = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["seq_length"] = megatron_lm_plugin.seq_length
    megatron_lm_plugin.megatron_lm_default_args["return_logits"] = megatron_lm_plugin.return_logits
    megatron_lm_plugin.megatron_lm_default_args["tokenizer_type"] = "GPT2BPETokenizer"
    megatron_lm_plugin.megatron_lm_default_args["model_type_name"] = model_type_name
    megatron_lm_plugin.megatron_lm_default_args["num_layers"] = num_layers
    megatron_lm_plugin.megatron_lm_default_args["hidden_size"] = hidden_size
    megatron_lm_plugin.megatron_lm_default_args["num_attention_heads"] = num_attention_heads
    megatron_lm_plugin.megatron_lm_default_args["max_position_embeddings"] = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["pretraining_flag"] = pretraining_flag
    megatron_lm_plugin.megatron_lm_default_args["orig_vocab_size"] = orig_vocab_size
    megatron_lm_plugin.megatron_lm_default_args["model_return_dict"] = model.config.return_dict
@add_model_config_to_megatron_parser("t5")
def parse_t5_config(megatron_lm_plugin, model, batch_data):
    model_type_name = "t5"
    num_layers = model.config.num_layers
    hidden_size = model.config.d_model
    num_attention_heads = model.config.num_heads
    max_position_embeddings = model.config.n_positions if hasattr(model.config, "n_positions") else 1024
    orig_vocab_size = model.config.vocab_size
    pretraining_flag = True
    if megatron_lm_plugin.encoder_seq_length is None:
        if batch_data is not None: megatron_lm_plugin.encoder_seq_length = batch_data["input_ids"].shape[1]
        else: megatron_lm_plugin.encoder_seq_length = max_position_embeddings
    if megatron_lm_plugin.decoder_seq_length is None:
        if batch_data is not None: megatron_lm_plugin.decoder_seq_length = batch_data["labels"].shape[1]
        else: megatron_lm_plugin.decoder_seq_length = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["encoder_seq_length"] = megatron_lm_plugin.encoder_seq_length
    megatron_lm_plugin.megatron_lm_default_args["decoder_seq_length"] = megatron_lm_plugin.decoder_seq_length
    megatron_lm_plugin.megatron_lm_default_args["model_type_name"] = model_type_name
    megatron_lm_plugin.megatron_lm_default_args["num_layers"] = num_layers
    megatron_lm_plugin.megatron_lm_default_args["hidden_size"] = hidden_size
    megatron_lm_plugin.megatron_lm_default_args["num_attention_heads"] = num_attention_heads
    megatron_lm_plugin.megatron_lm_default_args["max_position_embeddings"] = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["pretraining_flag"] = pretraining_flag
    megatron_lm_plugin.megatron_lm_default_args["orig_vocab_size"] = orig_vocab_size
    megatron_lm_plugin.megatron_lm_default_args["model_return_dict"] = model.config.return_dict
@add_model_config_to_megatron_parser("llama")
def parse_llama_config(megatron_lm_plugin, model, batch_data):
    model_type_name = "gpt"
    num_layers = model.config.num_hidden_layers
    pretraining_flag = True
    hidden_size = model.config.hidden_size
    num_attention_heads = model.config.num_attention_heads
    orig_vocab_size = model.config.vocab_size
    max_position_embeddings = model.config.max_position_embeddings
    seq_length = getattr(model.config, "max_sequence_length", None)
    if megatron_lm_plugin.seq_length is None:
        if seq_length is not None: megatron_lm_plugin.seq_length = seq_length
        elif megatron_lm_plugin.decoder_seq_length is not None: megatron_lm_plugin.seq_length = megatron_lm_plugin.decoder_seq_length
        elif batch_data is not None: megatron_lm_plugin.seq_length = batch_data["input_ids"].shape[1]
        else: megatron_lm_plugin.seq_length = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["return_logits"] = megatron_lm_plugin.return_logits
    megatron_lm_plugin.megatron_lm_default_args["tokenizer_type"] = "Llama2Tokenizer"
    megatron_lm_plugin.megatron_lm_default_args["model_type_name"] = model_type_name
    megatron_lm_plugin.megatron_lm_default_args["num_layers"] = num_layers
    megatron_lm_plugin.megatron_lm_default_args["pretraining_flag"] = pretraining_flag
    megatron_lm_plugin.megatron_lm_default_args["hidden_size"] = hidden_size
    megatron_lm_plugin.megatron_lm_default_args["num_attention_heads"] = num_attention_heads
    megatron_lm_plugin.megatron_lm_default_args["orig_vocab_size"] = orig_vocab_size
    megatron_lm_plugin.megatron_lm_default_args["max_position_embeddings"] = max_position_embeddings
    megatron_lm_plugin.megatron_lm_default_args["seq_length"] = megatron_lm_plugin.seq_length
    megatron_lm_plugin.megatron_lm_default_args["model_return_dict"] = model.config.return_dict
@dataclass
class BnbQuantizationConfig:
    load_in_8bit: bool = field(default=False, metadata={"help": "enable 8bit quantization."})
    llm_int8_threshold: float = field(default=6.0, metadata={"help": "value of the outliner threshold. only relevant when load_in_8bit=True"})
    load_in_4bit: bool = field(default=False, metadata={"help": "enable 4bit quantization."})
    sapiens_4bit_quant_type: str = field(default="fp4", metadata={"help": "set the quantization data type in the `sapiens.nn.Linear4Bit` layers. Options are {'fp4','np4'}."})
    sapiens_4bit_use_double_quant: bool = field(default=False, metadata={"help": "enable nested quantization where the quantization constants from the first quantization are quantized again."})
    sapiens_4bit_compute_dtype: bool = field(default="fp16", metadata={"help": "This sets the computational type which might be different than the input time. For example, inputs might be fp32, but computation can be set to bf16 for speedups. Options are {'fp32','fp16','bf16'}."})
    torch_dtype: torch.dtype = field(default=None, metadata={"help": "this sets the dtype of the remaining non quantized layers. `sapiens_machine` library suggests to set the value to `torch.float16` for 8 bit model and use the same dtype as the compute dtype for 4 bit model."})
    skip_modules: List[str] = field(default=None, metadata={"help": "an explicit list of the modules that we don't quantize. The dtype of these modules will be `torch_dtype`."})
    keep_in_fp32_modules: List[str] = field(default=None, metadata={"help": "an explicit list of the modules that we don't quantize. We keep them in `torch.float32`."})
    def __post_init__(self):
        if not isinstance(self.load_in_8bit, bool): raise ValueError("load_in_8bit must be a boolean")
        if not isinstance(self.load_in_4bit, bool): raise ValueError("load_in_4bit must be a boolean")
        if self.load_in_4bit and self.load_in_8bit: raise ValueError("load_in_4bit and load_in_8 can't be both True")
        if not self.load_in_4bit and not self.load_in_8bit: raise ValueError("load_in_4bit and load_in_8 can't be both False")
        if not isinstance(self.llm_int8_threshold, (int, float)): raise ValueError("llm_int8_threshold must be a float or an int")
        if not isinstance(self.sapiens_4bit_quant_type, str): raise ValueError("sapiens_4bit_quant_type must be a string")
        elif self.sapiens_4bit_quant_type not in ["fp4", "nf4"]: raise ValueError(f"sapiens_4bit_quant_type must be in ['fp4','nf4'] but found {self.sapiens_4bit_quant_type}")
        if not isinstance(self.sapiens_4bit_use_double_quant, bool): raise ValueError("sapiens_4bit_use_double_quant must be a boolean")
        if isinstance(self.sapiens_4bit_compute_dtype, str):
            if self.sapiens_4bit_compute_dtype == "fp32": self.sapiens_4bit_compute_dtype = torch.float32
            elif self.sapiens_4bit_compute_dtype == "fp16": self.sapiens_4bit_compute_dtype = torch.float16
            elif self.sapiens_4bit_compute_dtype == "bf16": self.sapiens_4bit_compute_dtype = torch.bfloat16
            else: raise ValueError(f"sapiens_4bit_compute_dtype must be in ['fp32','fp16','bf16'] but found {self.sapiens_4bit_compute_dtype}")
        elif not isinstance(self.sapiens_4bit_compute_dtype, torch.dtype): raise ValueError("sapiens_4bit_compute_dtype must be a string or a torch.dtype")
        if self.skip_modules is not None and not isinstance(self.skip_modules, list): raise ValueError("skip_modules must be a list of strings")
        if self.keep_in_fp32_modules is not None and not isinstance(self.keep_in_fp32_modules, list): raise ValueError("keep_in_fp_32_modules must be a list of strings")
        if self.load_in_4bit: self.target_dtype = CustomDtype.INT4
        if self.load_in_8bit: self.target_dtype = torch.int8
        if isinstance(self.torch_dtype, str):
            if self.torch_dtype == "fp32": self.torch_dtype = torch.float32
            elif self.torch_dtype == "fp16": self.torch_dtype = torch.float16
            elif self.torch_dtype == "bf16": self.torch_dtype = torch.bfloat16
            else: raise ValueError(f"torch_dtype must be in ['fp32','fp16','bf16'] but found {self.torch_dtype}")
        if self.load_in_8bit and self.torch_dtype is None: self.torch_dtype = torch.float16
        if self.load_in_4bit and self.torch_dtype is None: self.torch_dtype = self.sapiens_4bit_compute_dtype
        if not isinstance(self.torch_dtype, torch.dtype): raise ValueError("torch_dtype must be a torch.dtype")
def get_module_class_from_name(module, name):
    modules_children = list(module.children())
    if module.__class__.__name__ == name: return module.__class__
    elif len(modules_children) == 0: return
    else:
        for child_module in modules_children:
            module_class = get_module_class_from_name(child_module, name)
            if module_class is not None: return module_class
"""
    ########################################################################################################################################################
    # This algorithm is part of a code library to accelerate Sapiens Technology® Artificial Intelligence models, and its disclosure, distribution,         #
    # or reverse engineering without the company's prior consent will be subject to legal proceedings and actions pursued by our legal department.         #
    ########################################################################################################################################################
"""
