
from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self   ,A__):
												return 0.0



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''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        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						512
					lowercase							=						[1] + [0] * (size - 1)
					lowercase							=						[filter_type.process(lowerCAmelCase__		) for item in inputs]

					lowercase							=						[0] * (samplerate - size)  # zero-padding
					outputs += filler
					lowercase							=						np.abs(np.fft.fft(lowerCAmelCase__		)		)
					lowercase							=						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
					lowercase							=						get_bounds(lowerCAmelCase__ ,			lowerCAmelCase__		)
					plt.ylim(max([-80, bounds[0]]		) ,			min([80, bounds[1]]		)		)
					plt.ylabel('''Gain (dB)'''		)

					plt.plot(lowerCAmelCase__		)
					plt.show()



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						512
					lowercase							=						[1] + [0] * (size - 1)
					lowercase							=						[filter_type.process(lowerCAmelCase__		) for item in inputs]

					lowercase							=						[0] * (samplerate - size)  # zero-padding
					outputs += filler
					lowercase							=						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()









"""simple docstring"""
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @require_torch
     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  str											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''hf-internal-testing/tiny-clap-htsat-unfused'''		)
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  str											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     @slow
     @require_torch
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Union[str, Any]											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''laion/clap-htsat-unfused'''       ,	)
         # This is an audio of a dog
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  Any											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)

         self.assertEqual(
             nested_simplify(A		)       ,	[
                 {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                 {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
             ]       ,	)

         _UpperCAmelCase							:  List[Any]											=    audio_classifier([audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)
         _UpperCAmelCase							:  Tuple											=    audio_classifier(
             [audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']       ,	batch_size=5		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass


