


from __future__ import annotations




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    int:




						'''simple docstring'''







						if len(lowercase_			) < k or k < 0:
												raise ValueError('''Invalid Input'''			)
						__UpperCAmelCase :       List[str]  						=  sum(array[:k]			)
						for i in range(len(lowercase_			) - k			):
												__UpperCAmelCase :       int  						=  current_sum - array[i] + array[i + k]
												__UpperCAmelCase :       Optional[int]  						=  max(lowercase_			,					lowercase_			)
						return max_sum


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

				testmod()
				lowerCAmelCase							=						[randint(-1_000, 1_000) for i in range(100)]
				lowerCAmelCase							=						randint(0, 110)
				print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')





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):
							__UpperCAmelCase :       str  						=  pipeline(
							    task='''zero-shot-audio-classification'''							,    model='''hf-internal-testing/tiny-clap-htsat-unfused''')
							__UpperCAmelCase :       Optional[int]  						=  load_dataset('''ashraq/esc50''')
							__UpperCAmelCase :       Dict  						=  dataset['''train''']['''audio'''][-1]['''array''']
							__UpperCAmelCase :       Union[str, Any]  						=  audio_classifier(lowercase__							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''])
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]							,    )


	@unittest.skip('''No models are available in TF''')
	def    A(						self):
							pass


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

							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							        {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							    ]							,    )

							__UpperCAmelCase :       Optional[Any]  						=  audio_classifier([audio] * 5							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''])
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        [
							            {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							            {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							        ],
							    ]
							    * 5							,    )
							__UpperCAmelCase :       Optional[Any]  						=  audio_classifier(
							    [audio] * 5							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							,    batch_size=5)
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        [
							            {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							            {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							        ],
							    ]
							    * 5							,    )


	@unittest.skip('''No models are available in TF''')
	def    A(						self):
							pass

