






import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation





def 		__UpperCAmelCase     (	a_ = 8):
							snake_case_           =				ascii_letters + digits + punctuation
							return "".join(secrets.choice(a_) for _ in range(a_))





def 		__UpperCAmelCase     (	a_				,	a_):
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(a_)
							snake_case_           =				i // 3
							snake_case_           =				i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =				(
							    chars_incl
							    + random(a_				,	quotient + remainder)
							    + random(a_				,	a_)
							    + random(a_				,	a_)
							)
							snake_case_           =				list(a_)
							shuffle(a_)
							return "".join(a_)

							# random is a generalised function for letters, characters and numbers





def 		__UpperCAmelCase     (	a_				,	a_):
							return "".join(secrets.choice(a_) for _ in range(a_))





def 		__UpperCAmelCase     (	a_				,	a_):
							pass  # Put your code here...





def 		__UpperCAmelCase     (	a_				,	a_):
							pass  # Put your code here...





def 		__UpperCAmelCase     (	a_				,	a_):
							pass  # Put your code here...





def 		__UpperCAmelCase     (	a_				,	a_ = 8):

							if len(a_) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_           =				any(char in ascii_uppercase for char in password)
							snake_case_           =				any(char in ascii_lowercase for char in password)
							snake_case_           =				any(char in digits for char in password)
							snake_case_           =				any(char in punctuation for char in password)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(input('Please indicate the max length of your password: ').strip())
							snake_case_           =				input(
							    'Please indicate the characters that must be in your password: ').strip()
							print('Password generated:'				,	password_generator(a_))
							print(
							    'Alternative Password generated:'				,	alternative_password_generator(a_				,	a_)				,	)
							print('[If you are thinking of using this passsword, You better save it.]')


if __name__ == "__main__":
	main()













import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	lowerCAmelCase						=					TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       Union[str, Any]:
								snake_case_           =				AudioClassificationPipeline(model=a			,     feature_extractor=a   )

								# test with a raw waveform
								snake_case_           =				np.zeros((3_40_00,)   )
								snake_case_           =				np.zeros((1_40_00,)   )
								return audio_classifier, [audioa, audio]




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       Tuple:
								snake_case_     ,						snake_case_           =				examples
								snake_case_           =				audio_classifier(a   )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    a			,     [
								        {'score': ANY(a   ), 'label': ANY(a   )},
								        {'score': ANY(a   ), 'label': ANY(a   )},
								    ]			,     )
								snake_case_           =				audio_classifier(a			,     top_k=1   )
								self.assertEqual(
								    a			,     [
								        {'score': ANY(a   ), 'label': ANY(a   )},
								    ]			,     )

								self.run_torchaudio(a   )




	@require_torchaudio
	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								import datasets

								# test with a local file
								snake_case_           =				datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'			,     'clean'			,     split='validation'   )
								snake_case_           =				dataset[0]['audio']['array']
								snake_case_           =				audio_classifier(a   )
								self.assertEqual(
								    a			,     [
								        {'score': ANY(a   ), 'label': ANY(a   )},
								        {'score': ANY(a   ), 'label': ANY(a   )},
								    ]			,     )




	@require_torch
	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				'anton-l/wav2vec2-random-tiny-classifier'

								snake_case_           =				pipeline('audio-classification'			,     model=a   )

								snake_case_           =				np.ones((80_00,)   )
								snake_case_           =				audio_classifier(a			,     top_k=4   )

								snake_case_           =				[
								    {'score': 0.0_842, 'label': 'no'},
								    {'score': 0.0_838, 'label': 'up'},
								    {'score': 0.0_837, 'label': 'go'},
								    {'score': 0.0_834, 'label': 'right'},
								]
								snake_case_           =				[
								    {'score': 0.0_845, 'label': 'stop'},
								    {'score': 0.0_844, 'label': 'on'},
								    {'score': 0.0_841, 'label': 'right'},
								    {'score': 0.0_834, 'label': 'left'},
								]
								self.assertIn(nested_simplify(a			,     decimals=4   )			,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

								snake_case_           =				{'array': np.ones((80_00,)   ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
								snake_case_           =				audio_classifier(a			,     top_k=4   )
								self.assertIn(nested_simplify(a			,     decimals=4   )			,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )




	@require_torch
	@slow
	def 				_UpperCamelCase     (			self   )			->       str:
								import datasets

								snake_case_           =				'superb/wav2vec2-base-superb-ks'

								snake_case_           =				pipeline('audio-classification'			,     model=a   )
								snake_case_           =				datasets.load_dataset('anton-l/superb_dummy'			,     'ks'			,     split='test'   )

								snake_case_           =				np.array(dataset[3]['speech']			,     dtype=np.floataa   )
								snake_case_           =				audio_classifier(a			,     top_k=4   )
								self.assertEqual(
								    nested_simplify(a			,     decimals=3   )			,     [
								        {'score': 0.981, 'label': 'go'},
								        {'score': 0.007, 'label': 'up'},
								        {'score': 0.006, 'label': '_unknown_'},
								        {'score': 0.001, 'label': 'down'},
								    ]			,     )






	@require_tf
	@unittest.skip('Audio classification is not implemented for TF'   )
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								pass





