






import os
import sys

UpperCAmelCase__							  =						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,
)


UpperCAmelCase__							  =						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__					)
def 		_a       ( *a					:Union[str, Any]      ,				**a					:Optional[int]					)				->     Optional[int]:
 return AutoConfig.from_pretrained(*a      ,				**a					)
@add_start_docstrings(AutoTokenizer.__doc__					)
def 		_a       ( *a					:str      ,				**a					:Dict					)				->     int:
 return AutoTokenizer.from_pretrained(*a      ,				**a					)
@add_start_docstrings(AutoModel.__doc__					)
def 		_a       ( *a					:Any      ,				**a					:Any					)				->     Dict:
 return AutoModel.from_pretrained(*a      ,				**a					)
@add_start_docstrings(AutoModelForCausalLM.__doc__					)
def 		_a       ( *a					:List[Any]      ,				**a					:Union[str, Any]					)				->     Any:
 return AutoModelForCausalLM.from_pretrained(*a      ,				**a					)
@add_start_docstrings(AutoModelForMaskedLM.__doc__					)
def 		_a       ( *a					:Tuple      ,				**a					:Union[str, Any]					)				->     Tuple:
 return AutoModelForMaskedLM.from_pretrained(*a      ,				**a					)
@add_start_docstrings(AutoModelForSequenceClassification.__doc__					)
def 		_a       ( *a					:Union[str, Any]      ,				**a					:Any					)				->     str:
 return AutoModelForSequenceClassification.from_pretrained(*a      ,				**a					)

@add_start_docstrings(AutoModelForQuestionAnswering.__doc__					)
def 		_a       ( *a					:Union[str, Any]      ,				**a					:List[Any]					)				->     Tuple:
 return AutoModelForQuestionAnswering.from_pretrained(*a      ,				**a					)










from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

       # a_{0} ... a_{k}
       a						    = [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       a						    = [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       a						    = [0.0] * self.order
       # y[n-1] ... y[n-k]
       a						    = [0.0] * self.order







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


       if len(__UpperCAmelCase    ) < self.order:
        a						    = [1.0, *a_coeffs]

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected a_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected b_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1					,   self.order + 1    ):
        result += (
            self.b_coeffs[i] * self.input_history[i - 1]
            - self.a_coeffs[i] * self.output_history[i - 1]
        )

       a						    = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       a						    = self.input_history[:-1]
       a						    = self.output_history[:-1]

       a						    = sample
       a						    = result

       return result


