





from typing import TYPE_CHECKING

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


A       :   Optional[Any]					= {
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   List[str]					= ['''RemBertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Tuple					= ['''RemBertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RemBertForCausalLM''',
					    '''RemBertForMaskedLM''',
					    '''RemBertForMultipleChoice''',
					    '''RemBertForQuestionAnswering''',
					    '''RemBertForSequenceClassification''',
					    '''RemBertForTokenClassification''',
					    '''RemBertLayer''',
					    '''RemBertModel''',
					    '''RemBertPreTrainedModel''',
					    '''load_tf_weights_in_rembert''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Tuple					= [
					    '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRemBertForCausalLM''',
					    '''TFRemBertForMaskedLM''',
					    '''TFRemBertForMultipleChoice''',
					    '''TFRemBertForQuestionAnswering''',
					    '''TFRemBertForSequenceClassification''',
					    '''TFRemBertForTokenClassification''',
					    '''TFRemBertLayer''',
					    '''TFRemBertModel''',
					    '''TFRemBertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert import RemBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_rembert_fast import RemBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rembert import (
										    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RemBertForCausalLM,
										    RemBertForMaskedLM,
										    RemBertForMultipleChoice,
										    RemBertForQuestionAnswering,
										    RemBertForSequenceClassification,
										    RemBertForTokenClassification,
										    RemBertLayer,
										    RemBertModel,
										    RemBertPreTrainedModel,
										    load_tf_weights_in_rembert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rembert import (
										    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRemBertForCausalLM,
										    TFRemBertForMaskedLM,
										    TFRemBertForMultipleChoice,
										    TFRemBertForQuestionAnswering,
										    TFRemBertForSequenceClassification,
										    TFRemBertForTokenClassification,
										    TFRemBertLayer,
										    TFRemBertModel,
										    TFRemBertPreTrainedModel,
										)


else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










import numpy as np







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return vector * sigmoid(1.702 * vector       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



