


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def 						lowercase_ (				self   ,						__lowercase							)					->    float:
										return 0.0






def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> tuple[int | float, int | float]:
				lowerCAmelCase_ :      int    				=				min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
				lowerCAmelCase_ :      str    				=				max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
				return lowest, highest



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      List[Any]    				=				512
				lowerCAmelCase_ :      Any    				=				[1] + [0] * (size - 1)
				lowerCAmelCase_ :      Optional[int]    				=				[filter_type.process(lowerCAmelCase_ ) for item in inputs]

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

				# 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
				lowerCAmelCase_ :      Dict    				=				get_bounds(lowerCAmelCase_ ,   lowerCAmelCase_ )
				plt.ylim(max([-80, bounds[0]] ) ,   min([80, bounds[1]] ) )
				plt.ylabel('''Gain (dB)''' )

				plt.plot(lowerCAmelCase_ )
				plt.show()




def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      Union[str, Any]    				=				512
				lowerCAmelCase_ :      Optional[Any]    				=				[1] + [0] * (size - 1)
				lowerCAmelCase_ :      Optional[Any]    				=				[filter_type.process(lowerCAmelCase_ ) for item in inputs]

				lowerCAmelCase_ :      Optional[Any]    				=				[0] * (samplerate - size)  # zero-padding
				outputs += filler
				lowerCAmelCase_ :      Dict    				=				np.angle(np.fft.fft(lowerCAmelCase_ ) )

				# 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(lowerCAmelCase_ ,   -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=UpperCAmelCase__							)
class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(default="""automatic-speech-recognition""",						metadata={"""include_in_asdict_even_if_is_default""": True}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""audio""": Audio()}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""transcription""": Value("""string"""							)}							)
						SCREAMING_SNAKE_CASE__      :  str					=							"audio"
						SCREAMING_SNAKE_CASE__      :  str					=							"transcription"




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										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]   ,						__lowercase							):
														raise ValueError(f"""Column {self.audio_column} is not an Audio type."""							)
										lowerCAmelCase_ :      List[str]    				=				copy.deepcopy(self							)
										lowerCAmelCase_ :      Optional[Any]    				=				self.input_schema.copy()
										lowerCAmelCase_ :      Optional[Any]    				=				features[self.audio_column]
										lowerCAmelCase_ :      List[str]    				=				input_schema
										return task_template




						@property
						def 						lowercase_ (				self							)					->    Dict[str, str]:
										return {self.audio_column: "audio", self.transcription_column: "transcription"}