


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  'Wav2Vec2FeatureExtractor'
  A__=  'AutoTokenizer'






  def __init__(					self      :					Any		,						_lowercase      :					Any		,						_lowercase      :					Dict ):




         """simple docstring"""







         super().__init__(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     self.feature_extractor
         UpperCAmelCase__										=     False






  @classmethod
  def 			_UpperCAmelCase  (					cls      :					List[str]		,						_lowercase      :					Tuple		,						**_lowercase      :					List[Any] ):




         """simple docstring"""







         try:
                return super().from_pretrained(_lowercase		,						**_lowercase )
         except OSError:
                warnings.warn(
                    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
                    " include a `tokenizer_class` attribute is deprecated and will be "
                    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
                    " attribute to either your `config.json` or `tokenizer_config.json` "
                    "file to suppress this warning: "		,						_lowercase		,						)

                UpperCAmelCase__										=     WavaVecaFeatureExtractor.from_pretrained(_lowercase		,						**_lowercase )
                UpperCAmelCase__										=     WavaVecaCTCTokenizer.from_pretrained(_lowercase		,						**_lowercase )

                return cls(feature_extractor=_lowercase		,						tokenizer=_lowercase )






  def __call__(					self      :					int		,						*_lowercase      :					Union[str, Any]		,						**_lowercase      :					Dict ):




         """simple docstring"""







         if self._in_target_context_manager:
                return self.current_processor(*_lowercase		,						**_lowercase )

         if "raw_speech" in kwargs:
                warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
                UpperCAmelCase__										=     kwargs.pop("raw_speech" )
         else:
                UpperCAmelCase__										=     kwargs.pop("audio"		,						_lowercase )
         UpperCAmelCase__										=     kwargs.pop("sampling_rate"		,						_lowercase )
         UpperCAmelCase__										=     kwargs.pop("text"		,						_lowercase )
         if len(_lowercase ) > 0:
                UpperCAmelCase__										=     args[0]
                UpperCAmelCase__										=     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:
                UpperCAmelCase__										=     self.feature_extractor(_lowercase		,						*_lowercase		,						sampling_rate=_lowercase		,						**_lowercase )
         if text is not None:
                UpperCAmelCase__										=     self.tokenizer(_lowercase		,						**_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					str		,						*_lowercase      :					str		,						**_lowercase      :					List[Any] ):




         """simple docstring"""







         if self._in_target_context_manager:
                return self.current_processor.pad(*_lowercase		,						**_lowercase )

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

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

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






  def 			_UpperCAmelCase  (					self      :					Any		,						*_lowercase      :					str		,						**_lowercase      :					str ):




         """simple docstring"""







         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						*_lowercase      :					Optional[int]		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         return self.tokenizer.decode(*_lowercase		,						**_lowercase )






  @contextmanager
  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         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." )
         UpperCAmelCase__										=     True
         UpperCAmelCase__										=     self.tokenizer
         yield
         UpperCAmelCase__										=     self.feature_extractor
         UpperCAmelCase__										=     False









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





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

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

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

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

       # for calculating forward difference table

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

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

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





