



'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:int     =       "MCTCTFeatureExtractor"
       lowerCAmelCase__	:Union[str, Any]     =       "AutoTokenizer"



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           super().__init__(UpperCAmelCase_  ,UpperCAmelCase_   )
           lowercase__									=    self.feature_extractor
           lowercase__									=    False



       def __call__(					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Tuple:
           # For backward compatibility
           if self._in_target_context_manager:
               return self.current_processor(*UpperCAmelCase_  ,**UpperCAmelCase_   )

           if "raw_speech" in kwargs:
               warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."   )
               lowercase__									=    kwargs.pop("raw_speech"   )
           else:
               lowercase__									=    kwargs.pop("audio"  ,UpperCAmelCase_   )
           lowercase__									=    kwargs.pop("sampling_rate"  ,UpperCAmelCase_   )
           lowercase__									=    kwargs.pop("text"  ,UpperCAmelCase_   )
           if len(UpperCAmelCase_   ) > 0:
               lowercase__									=    args[0]
               lowercase__									=    args[1:]

           if audio is None and text is None:
               raise ValueError("You need to specify either an `audio` or `text` input to process."   )

           if audio is not None:
               lowercase__									=    self.feature_extractor(UpperCAmelCase_  ,*UpperCAmelCase_  ,sampling_rate=UpperCAmelCase_  ,**UpperCAmelCase_   )
           if text is not None:
               lowercase__									=    self.tokenizer(UpperCAmelCase_  ,**UpperCAmelCase_   )

           if text is None:
               return inputs
           elif audio is None:
               return encodings
           else:
               lowercase__									=    encodings["input_ids"]
               return inputs



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Dict:
           return self.tokenizer.batch_decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Optional[int]:
           # For backward compatibility
           if self._in_target_context_manager:
               return self.current_processor.pad(*UpperCAmelCase_  ,**UpperCAmelCase_   )

           lowercase__									=    kwargs.pop("input_features"  ,UpperCAmelCase_   )
           lowercase__									=    kwargs.pop("labels"  ,UpperCAmelCase_   )
           if len(UpperCAmelCase_   ) > 0:
               lowercase__									=    args[0]
               lowercase__									=    args[1:]

           if input_features is not None:
               lowercase__									=    self.feature_extractor.pad(UpperCAmelCase_  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )
           if labels is not None:
               lowercase__									=    self.tokenizer.pad(UpperCAmelCase_  ,**UpperCAmelCase_   )

           if labels is None:
               return input_features
           elif input_features is None:
               return labels
           else:
               lowercase__									=    labels["input_ids"]
               return input_features



       def 				_a   (					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Optional[Any]:
           return self.tokenizer.decode(*UpperCAmelCase_  ,**UpperCAmelCase_   )







       @contextmanager
       def 				_a   (					self   )	->				List[Any]:
           warnings.warn(
               "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
               "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
               "your audio inputs, or in a separate call."   )
           lowercase__									=    True
           lowercase__									=    self.tokenizer
           yield
           lowercase__									=    self.feature_extractor
           lowercase__									=    False









'''simple docstring'''





from __future__ import annotations

import math




def 							lowerCamelCase	(					_snake_case	:    float  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__									=    u
    for i in range(1  ,_snake_case							):
        lowercase__									=    temp * (u - i)
    return temp




def 							lowerCamelCase	(					):





    '''simple docstring'''


    lowercase__									=    int(input("enter the numbers of values: "							)							)
    lowercase__									=    []
    for _ in range(_snake_case							):
        y.append([]							)
    for i in range(_snake_case							):
        for j in range(_snake_case							):
            y[i].append(_snake_case							)
            lowercase__									=    0

    print("enter the values of parameters in a list: "							)
    lowercase__									=    list(map(_snake_case  ,input().split()							)							)

    print("enter the values of corresponding parameters: "							)
    for i in range(_snake_case							):
        lowercase__									=    float(input()							)

    lowercase__									=    int(input("enter the value to interpolate: "							)							)
    lowercase__									=    (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1  ,_snake_case							):
        for j in range(n - i							):
            lowercase__									=    y[j + 1][i - 1] - y[j][i - 1]

    lowercase__									=    y[0][0]
    for i in range(1  ,_snake_case							):
        summ += (ucal(_snake_case  ,_snake_case							) * y[0][i]) / math.factorial(_snake_case							)

    print(f'''the value at {value} is {summ}'''							)


if __name__ == "__main__":
 main()




