logger = get_logger(__name__)
if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
def is_compiled_module(module):
    """
    Check whether the module was compiled with torch.compile()
    """
    if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"):
        return False
    return isinstance(module, torch._dynamo.eval_frame.OptimizedModule)
def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True):
    """
    Extract a model from its distributed containers.
    Args:
        model (`torch.nn.Module`):
            The model to extract.
        keep_fp32_wrapper (`bool`, *optional*):
            Whether to remove mixed precision hooks from the model.
    Returns:
        `torch.nn.Module`: The extracted model.
    """
    options = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
    is_compiled = is_compiled_module(model)
    if is_compiled:
        compiled_model = model
        model = model._orig_mod
    if is_deepspeed_available():
        from deepspeed import DeepSpeedEngine
        options += (DeepSpeedEngine,)
    if is_torch_version(">=", FSDP_PYTORCH_VERSION) and is_torch_distributed_available():
        from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
        options += (FSDP,)
    while isinstance(model, options):
        model = model.module
    if not keep_fp32_wrapper:
        forward = getattr(model, "forward")
        original_forward = model.__dict__.pop("_original_forward", None)
        if original_forward is not None:
            while hasattr(forward, "__wrapped__"):
                forward = forward.__wrapped__
                if forward == original_forward:
                    break
            model.forward = MethodType(forward, model)
        if getattr(model, "_converted_to_transformer_engine", False):
            convert_model(model, to_transformer_engine=False)
    if is_compiled:
        compiled_model._orig_mod = model
        model = compiled_model
    return model
def wait_for_everyone():
    """
    Introduces a blocking point in the script, making sure all processes have reached this point before continuing.
    <Tip warning={true}>
    Make sure all processes will reach this instruction otherwise one of your processes will hang forever.
    </Tip>
    """
    PartialState().wait_for_everyone()
def clean_state_dict_for_safetensors(state_dict: dict):
    """
    Cleans the state dictionary from a model and removes tensor aliasing if present.
    Args:
        state_dict (`dict`):
            The state dictionary from a model
    """
    ptrs = collections.defaultdict(list)
    # When bnb serialization is used, weights in state dict can be strings
    for name, tensor in state_dict.items():
        if not isinstance(tensor, str):
            ptrs[id_tensor_storage(tensor)].append(name)
    # These are all pointers of tensors with shared memory
    shared_ptrs = {ptr: names for ptr, names in ptrs.items() if len(names) > 1}
    warn_names = set()
    for names in shared_ptrs.values():
        # When not all duplicates have been cleaned, we still remove those keys but put a clear warning.
        # If the link between tensors was done at runtime then `from_pretrained` will not get
        # the key back leading to random tensor. A proper warning will be shown
        # during reload (if applicable), but since the file is not necessarily compatible with
        # the config, better show a proper warning.
        found_names = [name for name in names if name in state_dict]
        warn_names.update(found_names[1:])
        for name in found_names[1:]:
            del state_dict[name]
    if len(warn_names) > 0:
        logger.warning(
            f"Removed shared tensor {warn_names} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading",
        )
    state_dict = {k: v.contiguous() if isinstance(v, torch.Tensor) else v for k, v in state_dict.items()}
    return state_dict
def save(obj, f, save_on_each_node: bool = False, safe_serialization: bool = False):
    """
    Save the data to disk. Use in place of `torch.save()`.
    Args:
        obj:
            The data to save
        f:
            The file (or file-like object) to use to save the data
        save_on_each_node (`bool`, *optional*, defaults to `False`):
            Whether to only save on the global main process
        safe_serialization (`bool`, *optional*, defaults to `False`):
            Whether to save `obj` using `safetensors` or the traditional PyTorch way (that uses `pickle`).
    """
    # Check if it's a model and remove duplicates
    if safe_serialization:
        save_func = partial(safe_save_file, metadata={"format": "pt"})
        if isinstance(obj, OrderedDict):
            obj = clean_state_dict_for_safetensors(obj)
    else:
        save_func = torch.save
    if PartialState().distributed_type == DistributedType.TPU:
        xm.save(obj, f)
    elif PartialState().is_main_process and not save_on_each_node:
        save_func(obj, f)
    elif PartialState().is_local_main_process and save_on_each_node:
        save_func(obj, f)
@contextmanager
def clear_environment():
    """
    A context manager that will cache origin `os.environ` and replace it with a empty dictionary in this context.
    When this context exits, the cached `os.environ` will be back.
    Example:
    ```python
    >>> import os
    >>> from accelerate.utils import clear_environment
    >>> os.environ["FOO"] = "bar"
    >>> with clear_environment():
    ...     print(os.environ)
    ...     os.environ["FOO"] = "new_bar"
    ...     print(os.environ["FOO"])
    {}
    new_bar
    >>> print(os.environ["FOO"])
    bar
    ```
    """
    _old_os_environ = os.environ
    os.environ = dict()
    yield
    os.environ = _old_os_environ
@contextmanager
def patch_environment(**kwargs):
    """
    A context manager that will add each keyword argument passed to `os.environ` and remove them when exiting.
    Will convert the values in `kwargs` to strings and upper-case all the keys.
    Example:
    ```python
    >>> import os
    >>> from accelerate.utils import patch_environment
    >>> with patch_environment(FOO="bar"):
    ...     print(os.environ["FOO"])  # prints "bar"
    >>> print(os.environ["FOO"])  # raises KeyError
    ```
    """
    existing_vars = {}
    for key, value in kwargs.items():
        key = key.upper()
        if key in os.environ:
            existing_vars[key] = os.environ[key]
        os.environ[key] = str(value)
    yield
    for key in kwargs:
        key = key.upper()
        if key in existing_vars:
            # restore previous value
            os.environ[key] = existing_vars[key]
        else:
            os.environ.pop(key, None)
def get_pretty_name(obj):
    """
    Gets a pretty name from `obj`.
    """
    if not hasattr(obj, "__qualname__") and not hasattr(obj, "__name__"):
        obj = getattr(obj, "__class__", obj)
    if hasattr(obj, "__qualname__"):
        return obj.__qualname__
    if hasattr(obj, "__name__"):
        return obj.__name__
    return str(obj)
def merge_dicts(source, destination):
    """
    Recursively merges two dictionaries.
    Args:
        source (`dict`): The dictionary to merge into `destination`.
        destination (`dict`): The dictionary to merge `source` into.
    """
    for key, value in source.items():
        if isinstance(value, dict):
            node = destination.setdefault(key, {})
            merge_dicts(value, node)
        else:
            destination[key] = value
    return destination
def is_port_in_use(port: int = None) -> bool:
    """
    Checks if a port is in use on `localhost`. Useful for checking if multiple `accelerate launch` commands have been
    run and need to see if the port is already in use.
    """
    if port is None:
        port = 29500
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        return s.connect_ex(("localhost", port)) == 0
def convert_bytes(size):
    "Converts `size` from bytes to the largest possible unit"
    for x in ["bytes", "KB", "MB", "GB", "TB"]:
        if size < 1024.0:
            return f"{round(size, 2)} {x}"
        size /= 1024.0
    return f"{round(size, 2)} PB"
def check_os_kernel():
    """Warns if the kernel version is below the recommended minimum on Linux."""
    # see issue #1929
    info = platform.uname()
    system = info.system
    if system != "Linux":
        return
    _, version, *_ = re.split(r"(\d+\.\d+\.\d+)", info.release)
    min_version = "5.5.0"
    if Version(version) < Version(min_version):
        msg = (
            f"Detected kernel version {version}, which is below the recommended minimum of {min_version}; this can "
            "cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher."
        )
        logger.warning(msg, main_process_only=True)