

'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase__            =     logging.get_logger(__name__)




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    ["""input_values""", """attention_mask"""]


     def __init__(						self					:	str	,     lowercase_					:	List[str]=1	,     lowercase_					:	Dict=16_000	,     lowercase_					:	Optional[Any]=0.0	,     lowercase_					:	int=False	,     lowercase_					:	List[str]=True	,     **lowercase_					:	int	,     )		->      Dict:
      super().__init__(feature_size=lowercase_	,     sampling_rate=lowercase_	,     padding_value=lowercase_	,     **lowercase_			)
      UpperCAmelCase			:      int		   =					return_attention_mask
      UpperCAmelCase			:      Tuple		   =					do_normalize


     @staticmethod
     def   UpperCAmelCase_			(						lowercase_					:	List[np.ndarray]	,     lowercase_					:	List[np.ndarray]	,     lowercase_					:	float = 0.0			)		->      List[np.ndarray]:
      if attention_mask is not None:
       UpperCAmelCase			:      Union[str, Any]		   =					np.array(lowercase_	,     np.intaa			)
       UpperCAmelCase			:      int		   =					[]

       for vector, length in zip(lowercase_	,     attention_mask.sum(-1			)			):
        UpperCAmelCase			:      Tuple		   =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7			)
        if length < normed_slice.shape[0]:
         UpperCAmelCase			:      Any		   =					padding_value

        normed_input_values.append(lowercase_			)
      else:
       UpperCAmelCase			:      List[str]		   =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7			) for x in input_values]

      return normed_input_values




     def __call__(						self					:	int	,     lowercase_					:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,     lowercase_					:	Union[bool, str, PaddingStrategy] = False	,     lowercase_					:	Optional[int] = None	,     lowercase_					:	bool = False	,     lowercase_					:	Optional[int] = None	,     lowercase_					:	Optional[bool] = None	,     lowercase_					:	Optional[Union[str, TensorType]] = None	,     lowercase_					:	Optional[int] = None	,     **lowercase_					:	Optional[int]	,     )		->      BatchFeature:

      if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
        raise ValueError(
            f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
            f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
            f""" {self.sampling_rate} and not {sampling_rate}."""			)
      else:
       logger.warning(
           'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
           'Failing to do so can result in silent errors that might be hard to debug.'			)

      UpperCAmelCase			:      Dict		   =					isinstance(lowercase_	,     np.ndarray			) and len(raw_speech.shape			) > 1
      if is_batched_numpy and len(raw_speech.shape			) > 2:
       raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""			)
      UpperCAmelCase			:      Optional[Any]		   =					is_batched_numpy or (
          isinstance(lowercase_	,     (list, tuple)			) and (isinstance(raw_speech[0]	,     (np.ndarray, tuple, list)			))
      )

      # always return batch
      if not is_batched:
       UpperCAmelCase			:      Union[str, Any]		   =					[raw_speech]

      # convert into correct format for padding
      UpperCAmelCase			:      Union[str, Any]		   =					BatchFeature({'input_values': raw_speech}			)

      UpperCAmelCase			:      Dict		   =					self.pad(
          lowercase_	,     padding=lowercase_	,     max_length=lowercase_	,     truncation=lowercase_	,     pad_to_multiple_of=lowercase_	,     return_attention_mask=lowercase_	,     )

      # convert input values to correct format
      UpperCAmelCase			:      Union[str, Any]		   =					padded_inputs['input_values']
      if not isinstance(input_values[0]	,     np.ndarray			):
       UpperCAmelCase			:      Tuple		   =					[np.asarray(lowercase_	,     dtype=np.floataa			) for array in input_values]
      elif (
          not isinstance(lowercase_	,     np.ndarray			)
          and isinstance(input_values[0]	,     np.ndarray			)
          and input_values[0].dtype is np.dtype(np.floataa			)
      ):
       UpperCAmelCase			:      Optional[Any]		   =					[array.astype(np.floataa			) for array in input_values]
      elif isinstance(lowercase_	,     np.ndarray			) and input_values.dtype is np.dtype(np.floataa			):
       UpperCAmelCase			:      Any		   =					input_values.astype(np.floataa			)

      # convert attention_mask to correct format
      UpperCAmelCase			:      str		   =					padded_inputs.get('attention_mask'			)
      if attention_mask is not None:
       UpperCAmelCase			:      Optional[Any]		   =					[np.asarray(lowercase_	,     dtype=np.intaa			) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if self.do_normalize:
       UpperCAmelCase			:      List[Any]		   =					(
           attention_mask
           if self._get_padding_strategies(lowercase_	,     max_length=lowercase_			) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       UpperCAmelCase			:      List[Any]		   =					self.zero_mean_unit_var_norm(
           padded_inputs['input_values']	,     attention_mask=lowercase_	,     padding_value=self.padding_value			)

      if return_tensors is not None:
       UpperCAmelCase			:      str		   =					padded_inputs.convert_to_tensors(lowercase_			)

      return padded_inputs







'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

 for _ in range(UpperCAmelCase_      ):
  # Approximates small segments of curve as linear and solve
  # for trapezoidal area
  UpperCAmelCase			:      List[Any]		   =					(x_end - x_start) / steps + xa
  UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
  area += abs(fxa + fxa      ) * (xa - xa) / 2

  # Increment step
  UpperCAmelCase			:      List[str]		   =					xa
  UpperCAmelCase			:      List[Any]		   =					fxa
 return area


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




