


"""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 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline(
										    task="zero-shot-audio-classification"    ,   model="hf-internal-testing/tiny-clap-htsat-unfused"	)
										__lowerCAmelCase				    =			load_dataset("ashraq/esc50"	)
										__lowerCAmelCase				    =			dataset["train"]["audio"][-1]["array"]
										__lowerCAmelCase				    =			audio_classifier(__a    ,   candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	)
										self.assertEqual(
										    nested_simplify(__a	)    ,   [{"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 			snake_case				(     self	):
										pass

							@slow
							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline(
										    task="zero-shot-audio-classification"    ,   model="laion/clap-htsat-unfused"    ,   )
										# This is an audio of a dog
										__lowerCAmelCase				    =			load_dataset("ashraq/esc50"	)
										__lowerCAmelCase				    =			dataset["train"]["audio"][-1]["array"]
										__lowerCAmelCase				    =			audio_classifier(__a    ,   candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	)

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

										__lowerCAmelCase				    =			audio_classifier([audio] * 5    ,   candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	)
										self.assertEqual(
										    nested_simplify(__a	)    ,   [
										        [
										            {"score": 0.9_9_9, "label": "Sound of a dog"},
										            {"score": 0.0_0_1, "label": "Sound of vaccum cleaner"},
										        ],
										    ]
										    * 5    ,   )
										__lowerCAmelCase				    =			audio_classifier(
										    [audio] * 5    ,   candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]    ,   batch_size=5	)
										self.assertEqual(
										    nested_simplify(__a	)    ,   [
										        [
										            {"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 			snake_case				(     self	):
										pass








"""simple docstring"""







def      lowercase			(				A_  ,   A_       )->      float:
  '''simple docstring'''







  if mass < 0:
    raise ValueError("The mass of a body cannot be negative"       )
  return 0.5 * mass * abs(A_       ) * abs(A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)






