






from typing import TYPE_CHECKING

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


lowercase    				=   {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   ["RobertaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   [
	    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "RobertaForCausalLM",
	    "RobertaForMaskedLM",
	    "RobertaForMultipleChoice",
	    "RobertaForQuestionAnswering",
	    "RobertaForSequenceClassification",
	    "RobertaForTokenClassification",
	    "RobertaModel",
	    "RobertaPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   [
	    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFRobertaForCausalLM",
	    "TFRobertaForMaskedLM",
	    "TFRobertaForMultipleChoice",
	    "TFRobertaForQuestionAnswering",
	    "TFRobertaForSequenceClassification",
	    "TFRobertaForTokenClassification",
	    "TFRobertaMainLayer",
	    "TFRobertaModel",
	    "TFRobertaPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase    				=   [
	    "FlaxRobertaForCausalLM",
	    "FlaxRobertaForMaskedLM",
	    "FlaxRobertaForMultipleChoice",
	    "FlaxRobertaForQuestionAnswering",
	    "FlaxRobertaForSequenceClassification",
	    "FlaxRobertaForTokenClassification",
	    "FlaxRobertaModel",
	    "FlaxRobertaPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

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













import numpy as np





def 		__UpperCAmelCase     (	a_):
							return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
	import doctest

	doctest.testmod()





