


'''simple docstring'''

from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class 	snake_case       (       lowerCamelCase__     ):





  """simple docstring"""




  def __init__(       self     ,    UpperCamelCase     ,    UpperCamelCase = None     ,    UpperCamelCase = None     ,    UpperCamelCase = None     ,    UpperCamelCase = False     ,    UpperCamelCase = False     ,    UpperCamelCase = None     ,    **UpperCamelCase     ,    ):







   """simple docstring"""
   super().__init__(
       SCREAMING_SNAKE_CASE__     ,    split=SCREAMING_SNAKE_CASE__     ,    features=SCREAMING_SNAKE_CASE__     ,    cache_dir=SCREAMING_SNAKE_CASE__     ,    keep_in_memory=SCREAMING_SNAKE_CASE__     ,    streaming=SCREAMING_SNAKE_CASE__     ,    num_proc=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__     ,    )
   lowerCamelCase_						=    path_or_paths if isinstance(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__	) else {self.split: path_or_paths}
   lowerCamelCase_						=    Text(
       cache_dir=SCREAMING_SNAKE_CASE__     ,    data_files=SCREAMING_SNAKE_CASE__     ,    features=SCREAMING_SNAKE_CASE__     ,    **SCREAMING_SNAKE_CASE__     ,    )







  def     snake_case						(       self	):







   """simple docstring"""



   if self.streaming:
    lowerCamelCase_						=    self.builder.as_streaming_dataset(split=self.split	)
   # Build regular (map-style) dataset
   else:
    lowerCamelCase_						=    None
    lowerCamelCase_						=    None
    lowerCamelCase_						=    None
    lowerCamelCase_						=    None

    self.builder.download_and_prepare(
        download_config=SCREAMING_SNAKE_CASE__     ,    download_mode=SCREAMING_SNAKE_CASE__     ,    verification_mode=SCREAMING_SNAKE_CASE__     ,    base_path=SCREAMING_SNAKE_CASE__     ,    num_proc=self.num_proc     ,    )
    lowerCamelCase_						=    self.builder.as_dataset(
        split=self.split     ,    verification_mode=SCREAMING_SNAKE_CASE__     ,    in_memory=self.keep_in_memory	)
   return dataset







"""simple docstring"""






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return 0.0





def 				A_       ( snake_case__       ,					snake_case__		)					->  tuple[int | float, int | float]:
			_UpperCamelCase					:Union[str, Any]					     =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]		)]		)
			_UpperCamelCase					:Optional[int]					     =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]		)]		)
			return lowest, highest





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:List[str]					     =	5_12
			_UpperCamelCase					:int					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:Union[str, Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:Any					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:int					     =	np.abs(np.fft.fft(snake_case__		)		)
			_UpperCamelCase					:str					     =	20 * np.logaa(snake_case__		)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,					samplerate / 2 - 1		)
			plt.xlabel('''Frequency (Hz)'''		)
			plt.xscale('''log'''		)

			# Display within reasonable bounds
			_UpperCamelCase					:Tuple					     =	get_bounds(snake_case__       ,					snake_case__		)
			plt.ylim(max([-80, bounds[0]]		)       ,					min([80, bounds[1]]		)		)
			plt.ylabel('''Gain (dB)'''		)

			plt.plot(snake_case__		)
			plt.show()





def 				A_       ( snake_case__       ,					snake_case__		)					->  None:
			_UpperCamelCase					:Tuple					     =	5_12
			_UpperCamelCase					:Union[str, Any]					     =	[1] + [0] * (size - 1)
			_UpperCamelCase					:List[Any]					     =	[filter_type.process(snake_case__		) for item in inputs]

			_UpperCamelCase					:str					     =	[0] * (samplerate - size)  # zero-padding
			outputs += filler
			_UpperCamelCase					:Tuple					     =	np.angle(np.fft.fft(snake_case__		)		)

			# Frequencies on log scale from 24 to nyquist frequency
			plt.xlim(24       ,					samplerate / 2 - 1		)
			plt.xlabel('''Frequency (Hz)'''		)
			plt.xscale('''log'''		)

			plt.ylim(-2 * pi       ,					2 * pi		)
			plt.ylabel('''Phase shift (Radians)'''		)
			plt.plot(np.unwrap(snake_case__       ,					-2 * pi		)		)
			plt.show()

