


'''simple docstring'''



from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin





class 							__UpperCamelCase    (		_lowerCamelCase		,				_lowerCamelCase ):




	@register_to_config
	def __init__(       self						,    __a = 768						,    ):

						'''simple docstring'''





						super().__init__()

						__a      :	Any    =  nn.Parameter(torch.zeros(1						,    __a							)							)
						__a      :	Optional[int]    =  nn.Parameter(torch.ones(1						,    __a							)							)




	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[int]    =  nn.Parameter(self.mean.to(__a							).to(__a							)							)
						__a      :	Optional[Any]    =  nn.Parameter(self.std.to(__a							).to(__a							)							)
						return self




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  (embeds - self.mean) * 1.0 / self.std
						return embeds





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						__a      :	List[Any]    =  (embeds * self.std) + self.mean
						return embeds







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase     :						Union[str, Any]	=     {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						List[str]	=     [
	    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RoCBertForCausalLM',
	    'RoCBertForMaskedLM',
	    'RoCBertForMultipleChoice',
	    'RoCBertForPreTraining',
	    'RoCBertForQuestionAnswering',
	    'RoCBertForSequenceClassification',
	    'RoCBertForTokenClassification',
	    'RoCBertLayer',
	    'RoCBertModel',
	    'RoCBertPreTrainedModel',
	    'load_tf_weights_in_roc_bert',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



