






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







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

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

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









"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    size
										_SCREAMING_SNAKE_CASE   :		str       		=    [0] * size
										_SCREAMING_SNAKE_CASE   :		Any       		=    [0] * size







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return index | (index + 1)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return (index & (index + 1)) - 1







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		str       		=    value
										while index < self.size:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_prev(lowerCAmelCase_							) + 1
														if current_left_border == index:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    value
														else:
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		str       		=    self.get_next(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										right -= 1  # Because of right is exclusive
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0
										while left <= right:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_prev(lowerCAmelCase_							)
														if left <= current_left:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    max(lowerCAmelCase_   ,				self.tree[right]							)
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current_left
														else:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    max(lowerCAmelCase_   ,				self.arr[right]							)
																		right -= 1
										return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()

