






'''simple docstring'''




from typing import TYPE_CHECKING

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


lowerCamelCase_												=					{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_												=					['''PLBartTokenizer''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_												=					[
        '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''PLBartForCausalLM''',
        '''PLBartForConditionalGeneration''',
        '''PLBartForSequenceClassification''',
        '''PLBartModel''',
        '''PLBartPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_plbart import PLBartTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_plbart import (
            PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            PLBartForCausalLM,
            PLBartForConditionalGeneration,
            PLBartForSequenceClassification,
            PLBartModel,
            PLBartPreTrainedModel,
        )


else:
    import sys

    lowerCamelCase_												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure)








'''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 							A__    (       A__     ,   A__  ):





  @register_to_config
  def __init__(		self      :	Dict							,					_a      :	int = 768							,					)	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.zeros(1							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.ones(1							,					_a ) )





  def 						A  (		self      :	Tuple							,					_a      :	Optional[Union[str, torch.device]] = None							,					_a      :	Optional[torch.dtype] = None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.mean.to(_a ).to(_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.std.to(_a ).to(_a ) )
       return self





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds - self.mean) * 1.0 / self.std
       return embeds





  def 						A  (		self      :	List[str]							,					_a      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds * self.std) + self.mean
       return embeds
