



'''simple docstring'''






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

import numpy
from matplotlib import pyplot

A_				:					str							=      0.25 + 0.0J
A_				:					Dict							=      -0.4 + 0.6J
A_				:					Dict							=      -0.1 + 0.651J
A_				:					List[Any]							=      -2.0
A_				:					Any							=      5_6
A_				:					str							=      2.0
A_				:					int							=      6_6_6




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       return numpy.exp(lowerCAmelCase_						) + c_parameter




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       return z_values * z_values + c_parameter




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						numpy.ndarray:





       '''simple docstring'''


       _UpperCAmelCase    :		str     	=					numpy.linspace(-window_size			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Dict     	=					x.reshape((nb_pixels, 1)						)
       _UpperCAmelCase    :		int     	=					numpy.linspace(-window_size			,  lowerCAmelCase_			,  lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					y.reshape((1, nb_pixels)						)
       return x + 1.0j * y




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  )->						numpy.ndarray:





       '''simple docstring'''


       _UpperCAmelCase    :		int     	=					z_a.astype("""complex64"""						)
       for _ in range(lowerCAmelCase_						):
              _UpperCAmelCase    :		List[str]     	=					eval_function(lowerCAmelCase_			,  lowerCAmelCase_						)
              if infinity is not None:
                     numpy.nan_to_num(lowerCAmelCase_			,  copy=lowerCAmelCase_			,  nan=lowerCAmelCase_						)
                     _UpperCAmelCase    :		Union[str, Any]     	=					infinity
       return z_n




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )->						None:





       '''simple docstring'''


       _UpperCAmelCase    :		Any     	=					(abs(lowerCAmelCase_						)).transpose()
       _UpperCAmelCase    :		List[Any]     	=					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        snake_case_   (	)->						None:





       '''simple docstring'''


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


if __name__ == "__main__":
   A_				:					List[Any]							=      prepare_grid(window_size, nb_pixels)

   ignore_overflow_warnings()  # See file header for explanations

   A_				:					str							=      2_4
   A_				:					Tuple							=      2 * abs(c_cauliflower) + 1
   A_				:					List[Any]							=      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)

   A_				:					Union[str, Any]							=      6_4
   A_				:					Union[str, Any]							=      2 * abs(c_polynomial_a) + 1
   A_				:					Dict							=      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)

   A_				:					str							=      1_6_1
   A_				:					Optional[int]							=      2 * abs(c_polynomial_a) + 1
   A_				:					Dict							=      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)

   A_				:					Any							=      1_2
   A_				:					List[str]							=      1_0000.0
   A_				:					List[str]							=      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)











'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


__A   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






     def __call__(self     :						str					,					lowercase__     :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					lowercase__     :						Optional[int] = None					,					lowercase__     :						Optional[Union[str, TensorType]] = None					,					**lowercase__     :						List[Any]					,					)	->							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."       )

           __A		 =   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}"       )
           __A		 =   is_batched_numpy or (
               isinstance(lowercase__					,					(list, tuple)       ) and (isinstance(raw_speech[0]					,					(np.ndarray, tuple, list)       ))
           )

           if is_batched:
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for speech in raw_speech]
           elif not is_batched and not isinstance(lowercase__					,					np.ndarray       ):
                 __A		 =   np.asarray(lowercase__					,					dtype=np.floataa       )
           elif isinstance(lowercase__					,					np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                 __A		 =   raw_speech.astype(np.floataa       )

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

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


