



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_snake_case		=  {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case		=  [
						    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LongT5EncoderModel",
						    "LongT5ForConditionalGeneration",
						    "LongT5Model",
						    "LongT5PreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case		=  [
						    "FlaxLongT5ForConditionalGeneration",
						    "FlaxLongT5Model",
						    "FlaxLongT5PreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longta import (
												    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongTaEncoderModel,
												    LongTaForConditionalGeneration,
												    LongTaModel,
												    LongTaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_longta import (
												    FlaxLongTaForConditionalGeneration,
												    FlaxLongTaModel,
												    FlaxLongTaPreTrainedModel,
												)


else:
						import sys

						_snake_case		=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








lowerCamelCase       :       Dict        =				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich