


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_UpperCAmelCase     :					List[str]       ={
    """configuration_ernie_m""": ["""ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieMConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase     :					Optional[int]       =["""ErnieMTokenizer"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase     :					Optional[int]       =[
						    """ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ErnieMForMultipleChoice""",
						    """ErnieMForQuestionAnswering""",
						    """ErnieMForSequenceClassification""",
						    """ErnieMForTokenClassification""",
						    """ErnieMModel""",
						    """ErnieMPreTrainedModel""",
						    """ErnieMForInformationExtraction""",
						]


if TYPE_CHECKING:
						from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ernie_m import ErnieMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ernie_m import (
												    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ErnieMForInformationExtraction,
												    ErnieMForMultipleChoice,
												    ErnieMForQuestionAnswering,
												    ErnieMForSequenceClassification,
												    ErnieMForTokenClassification,
												    ErnieMModel,
												    ErnieMPreTrainedModel,
												)


else:
						import sys

						_UpperCAmelCase     :					Optional[Any]       =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



from __future__ import annotations



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	int | float:
				if len(lowerCAmelCase_	) == 0:
								raise ValueError('''find_min() arg is an empty sequence'''	)
				if (
				    left >= len(lowerCAmelCase_	)
				    or left < -len(lowerCAmelCase_	)
				    or right >= len(lowerCAmelCase_	)
				    or right < -len(lowerCAmelCase_	)
				):
								raise IndexError('''list index out of range'''	)
				if left == right:
								return nums[left]
				lowerCAmelCase_	: Any      			=			(left + right) >> 1  # the middle
				lowerCAmelCase_	: Tuple      			=			find_min(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)  # find min in range[left, mid]
				lowerCAmelCase_	: Any      			=			find_min(lowerCAmelCase_	,				mid + 1	,				lowerCAmelCase_	)  # find min in range[mid + 1, right]

				return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)