"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a  :  Any=				{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  str=				['''DebertaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[Any]=				[
	    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DebertaForMaskedLM''',
	    '''DebertaForQuestionAnswering''',
	    '''DebertaForSequenceClassification''',
	    '''DebertaForTokenClassification''',
	    '''DebertaModel''',
	    '''DebertaPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Optional[Any]=				[
	    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDebertaForMaskedLM''',
	    '''TFDebertaForQuestionAnswering''',
	    '''TFDebertaForSequenceClassification''',
	    '''TFDebertaForTokenClassification''',
	    '''TFDebertaModel''',
	    '''TFDebertaPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
	from .tokenization_deberta import DebertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_fast import DebertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta import (
		    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		    DebertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta import (
		    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaForMaskedLM,
		    TFDebertaForQuestionAnswering,
		    TFDebertaForSequenceClassification,
		    TFDebertaForTokenClassification,
		    TFDebertaModel,
		    TFDebertaPreTrainedModel,
		)


else:
	import sys

	_a  :  Union[str, Any]=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""simple docstring"""







import numpy as np
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       np.array						)							->    np.array:

	'''simple docstring'''





	return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





