'''simple docstring'''
from __future__ import annotations

import math
from collections.abc import Callable







def  _UpperCamelCase (     _a	:	Callable[[int | float], int | float]				,     _a	:	int | float				,     _a	:	int | float				,     _a	:	int = 1_0_0				,     ):




    """simple docstring"""



    __UpperCamelCase	:	Tuple		    =  x_start
    __UpperCamelCase	:	int		    =  fnc(_a			)
    __UpperCamelCase	:	List[Any]		    =  0.0

    for _ in range(_a			):
        # Approximates curve as a sequence of linear lines and sums their length
        __UpperCamelCase	:	Optional[Any]		    =  (x_end - x_start) / steps + xa
        __UpperCamelCase	:	int		    =  fnc(_a			)
        length += math.hypot(xa - xa				,     fxa - fxa			)

        # Increment step
        __UpperCamelCase	:	List[Any]		    =  xa
        __UpperCamelCase	:	Any		    =  fxa

    return length


if __name__ == "__main__":







  def  _UpperCamelCase (     _a	:	Dict			):




      """simple docstring"""



      return math.sin(1_0 * x			)

  print('''f(x) = sin(10 * x)''')
  print('''The length of the curve from x = -10 to x = 10 is:''')
  a=  1_0
  while i <= 1_0_0_0_0_0:
    print(F"""With {i} steps: {line_length(f, -1_0, 1_0, i)}""")
    i *= 1_0




'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5FeatureExtractor'''
     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5Tokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('audio'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('text'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('text_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('audio_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('sampling_rate'     ,			_lowerCamelCase    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(audio_target=_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	List[Any]		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	int		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('input_ids'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('labels'     ,			_lowerCamelCase    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
         elif input_ids is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	Dict		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	int		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Any		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  feature_size_hack
                 __UpperCamelCase	:	Tuple		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Optional[int]		    =  labels

             __UpperCamelCase	:	Union[str, Any]		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )



