






"""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     __lowerCAmelCase (      UpperCAmelCase__							, UpperCAmelCase__							):







  '''simple docstring'''


  @register_to_config
  def __init__(   self:		str					,      UpperCamelCase_:		int = 768					,      ):
     super().__init__()

     UpperCamelCase_			     =nn.Parameter(torch.zeros(1					,      _UpperCAmelCase       )       )
     UpperCamelCase_			     =nn.Parameter(torch.ones(1					,      _UpperCAmelCase       )       )
  def        UpperCamelCase__ (   self:		List[Any]					,      UpperCamelCase_:		Optional[Union[str, torch.device]] = None					,      UpperCamelCase_:		Optional[torch.dtype] = None					,      ):
     UpperCamelCase_			     =nn.Parameter(self.mean.to(_UpperCAmelCase       ).to(_UpperCAmelCase       )       )
     UpperCamelCase_			     =nn.Parameter(self.std.to(_UpperCAmelCase       ).to(_UpperCAmelCase       )       )
     return self
  def        UpperCamelCase__ (   self:		Optional[int]					,      UpperCamelCase_:		Dict       ):
     UpperCamelCase_			     =(embeds - self.mean) * 1.0 / self.std
     return embeds





  def        UpperCamelCase__ (   self:		Dict					,      UpperCamelCase_:		Tuple       ):
     UpperCamelCase_			     =(embeds * self.std) + self.mean
     return embeds




import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

