






from .imports import is_tqdm_available


if is_tqdm_available():
	from tqdm.auto import tqdm as _tqdm

from ..state import PartialState

def _UpperCAmelCase   (	a						:	bool = True  ,					*a						:	Optional[Any]  ,					**a						:	Optional[int] ):
							if not is_tqdm_available():
														raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.""" )
							snake_case__	  =       False
							if main_process_only:
														snake_case__	  =       PartialState().local_process_index == 0
							return _tqdm(*a  ,					**a  ,					disable=a )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

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