from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class _lowercase       (							lowerCAmelCase__    ):





							"""simple docstring"""




							def      _UpperCAmelCase							(     self  , UpperCAmelCase	):







														'''simple docstring'''






														return 0.0






def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]       )]       )
							_lowercase					=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]       )]       )
							return lowest, highest


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						512
							_lowercase					=						[1] + [0] * (size - 1)
							_lowercase					=						[filter_type.process(_UpperCamelCase       ) for item in inputs]

							_lowercase					=						[0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowercase					=						np.abs(np.fft.fft(_UpperCamelCase       )       )
							_lowercase					=						20 * np.logaa(_UpperCamelCase       )

							# 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
							_lowercase					=						get_bounds(_UpperCamelCase			,_UpperCamelCase       )
							plt.ylim(max([-80, bounds[0]]       )			,min([80, bounds[1]]       )       )
							plt.ylabel("""Gain (dB)"""       )

							plt.plot(_UpperCamelCase       )
							plt.show()







def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							_lowercase					=						512
							_lowercase					=						[1] + [0] * (size - 1)
							_lowercase					=						[filter_type.process(_UpperCamelCase       ) for item in inputs]

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

							# 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(_UpperCamelCase			,-2 * pi       )       )
							plt.show()






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="automatic-speech-recognition"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"audio": Audio()}					)
					__UpperCAmelCase							  =					Features({"transcription": Value("string"					)}					)
					__UpperCAmelCase							  =					"audio"
					__UpperCAmelCase							  =					"transcription"






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								if self.audio_column not in features:
											raise ValueError(F'Column {self.audio_column} is not present in features.'    )
								if not isinstance(features[self.audio_column]			,							snake_case_    ):
											raise ValueError(F'Column {self.audio_column} is not an Audio type.'    )
								__lowercase			       = copy.deepcopy(self    )
								__lowercase			       = self.input_schema.copy()
								__lowercase			       = features[self.audio_column]
								__lowercase			       = input_schema
								return task_template






					@property
					def A   (					self    ) ->      Dict[str, str]:







								'''simple docstring'''

								return {self.audio_column: "audio", self.transcription_column: "transcription"}


