





'''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 			lowerCamelCase					(						unittest.TestCase  ):


					@require_torch
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		pipeline(
											    task='zero-shot-audio-classification'       ,     model='hf-internal-testing/tiny-clap-htsat-unfused'       )
											_snake_case:   Optional[Any]  					=		load_dataset('ashraq/esc50'       )
											_snake_case:   int  					=		dataset['train']['audio'][-1]['array']
											_snake_case:   List[Any]  					=		audio_classifier(__snake_case       ,     candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']       )
											self.assertEqual(
											    nested_simplify(__snake_case       )       ,     [{'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    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											pass


					@slow
					@require_torch
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		pipeline(
											    task='zero-shot-audio-classification'       ,     model='laion/clap-htsat-unfused'       ,     )
											# This is an audio of a dog
											_snake_case:   Optional[Any]  					=		load_dataset('ashraq/esc50'       )
											_snake_case:   Tuple  					=		dataset['train']['audio'][-1]['array']
											_snake_case:   Any  					=		audio_classifier(__snake_case       ,     candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']       )

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

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





											'''simple docstring'''





											pass












'''simple docstring'''






A   : str             =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A   : Union[str, Any]             =			{value: key for key, value in encode_dict.items()}





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('encode() accepts only letters of the alphabet and spaces'						)
						return encoded





def       lowercase_      (		lowercase__						)   ->str:
						if set(lowercase__						) - {"A", "B", " "} != set():
												raise Exception('decode() accepts only \'A\', \'B\' and spaces'						)
						_snake_case:   List[Any]  					=		''
						for word in coded.split():
												while len(lowercase__						) != 0:
																		decoded += decode_dict[word[:5]]
																		_snake_case:   Optional[int]  					=		word[5:]
												decoded += " "
						return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()





