



'''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      (  __UpperCAmelCase						):



 '''simple docstring'''







 __lowerCamelCase   :	List[Any]										=	["""input_values""", """attention_mask"""]



 def __init__(self			,       A=1			,       A=16_000			,       A=0.0			,       A=False			,       A=True			,       **A			,       )   ->					Union[str, Any]:


      """simple docstring"""



      super().__init__(feature_size=lowerCamelCase__			,       sampling_rate=lowerCamelCase__			,       padding_value=lowerCamelCase__			,       **lowerCamelCase__	)
      _a     =  return_attention_mask
      _a     =  do_normalize



 @staticmethod
 def  a__  (A			,       A			,       A = 0.0	)   ->					List[np.ndarray]:


      """simple docstring"""



      if attention_mask is not None:
           _a     =  np.array(lowerCamelCase__			,       np.intaa	)
           _a     =  []

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

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

      return normed_input_values



 def __call__(self			,       A			,       A = False			,       A = None			,       A = False			,       A = None			,       A = None			,       A = None			,       A = None			,       **A			,       )   ->					BatchFeature:


      """simple docstring"""



      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.'''	)

      _a     =  isinstance(lowerCamelCase__			,       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}'''	)
      _a     =  is_batched_numpy or (
          isinstance(lowerCamelCase__			,       (list, tuple)	) and (isinstance(raw_speech[0]			,       (np.ndarray, tuple, list)	))
      )

      # always return batch
      if not is_batched:
           _a     =  [raw_speech]

      # convert into correct format for padding
      _a     =  BatchFeature({'''input_values''': raw_speech}	)

      _a     =  self.pad(
          lowerCamelCase__			,       padding=lowerCamelCase__			,       max_length=lowerCamelCase__			,       truncation=lowerCamelCase__			,       pad_to_multiple_of=lowerCamelCase__			,       return_attention_mask=lowerCamelCase__			,       )

      # convert input values to correct format
      _a     =  padded_inputs['''input_values''']
      if not isinstance(input_values[0]			,       np.ndarray	):
           _a     =  [np.asarray(lowerCamelCase__			,       dtype=np.floataa	) for array in input_values]
      elif (
          not isinstance(lowerCamelCase__			,       np.ndarray	)
          and isinstance(input_values[0]			,       np.ndarray	)
          and input_values[0].dtype is np.dtype(np.floataa	)
      ):
           _a     =  [array.astype(np.floataa	) for array in input_values]
      elif isinstance(lowerCamelCase__			,       np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
           _a     =  input_values.astype(np.floataa	)

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

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

      if return_tensors is not None:
           _a     =  padded_inputs.convert_to_tensors(lowerCamelCase__	)

      return padded_inputs








'''simple docstring'''





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

lowercase_			=  0.25 + 0.0J
lowercase_			=  -0.4 + 0.6J
lowercase_			=  -0.1 + 0.651J
lowercase_			=  -2.0
lowercase_			=  56
lowercase_			=  2.0
lowercase_			=  666




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return numpy.exp(__A) + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return z_values * z_values + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  x.reshape((nb_pixels, 1))
     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  y.reshape((1, nb_pixels))
     return x + 1.0J * y




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A = None  ,     ):



     """simple docstring"""



     _a     =  z_a.astype('''complex64''')
     for _ in range(__A):
          _a     =  eval_function(__A  ,     __A)
          if infinity is not None:
               numpy.nan_to_num(__A  ,     copy=__A  ,     nan=__A)
               _a     =  infinity
     return z_n




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  (abs(__A)).transpose()
     _a     =  abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius)
     pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$''')
     pyplot.show()




def lowerCAmelCase  ():



     """simple docstring"""



     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in multiply''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''invalid value encountered in multiply'''  ,     )
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in absolute''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in exp''')


if __name__ == "__main__":
  lowercase_			=  prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  lowercase_			=  24
  lowercase_			=  2 * abs(c_cauliflower) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_cauliflower, escape_radius, z_final)

  lowercase_			=  64
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  161
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  12
  lowercase_			=  1_0000.0
  lowercase_			=  iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("e^z+c", c_exponential, escape_radius, z_final)



