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import inspect
from typing import Any, Dict, List, Optional, Set, Tuple, Union
from .activation_checkpoint import apply_activation_checkpointing
from .args_config import ArgsConfigMixin
from .data import determine_batch_size, should_perform_precomputation
from .diffusion import (
_enable_vae_memory_optimizations,
default_flow_shift,
get_scheduler_alphas,
get_scheduler_sigmas,
prepare_loss_weights,
prepare_sigmas,
prepare_target,
resolution_dependent_timestep_flow_shift,
)
from .file import delete_files, find_files, string_to_filename
from .hub import save_model_card
from .memory import bytes_to_gigabytes, free_memory, get_memory_statistics, make_contiguous
from .model import resolve_component_cls
from .serialization import safetensors_torch_save_function
from .timing import Timer, TimerDevice
from .torch import (
align_device_and_dtype,
apply_compile,
clip_grad_norm_,
enable_determinism,
expand_tensor_dims,
get_device_info,
get_submodule_by_name,
get_unwrapped_model_state_dict,
is_compiled_module,
set_requires_grad,
synchronize_device,
unwrap_module,
)
def get_parameter_names(obj: Any, method_name: Optional[str] = None) -> Set[str]:
if method_name is not None:
obj = getattr(obj, method_name)
return {name for name, _ in inspect.signature(obj).parameters.items()}
def get_non_null_items(
x: Union[List[Any], Tuple[Any], Dict[str, Any]],
) -> Union[List[Any], Tuple[Any], Dict[str, Any]]:
if isinstance(x, dict):
return {k: v for k, v in x.items() if v is not None}
if isinstance(x, (list, tuple)):
return type(x)(v for v in x if v is not None)
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