





from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
  SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
  return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
  return (
      round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
      if validate(UpperCamelCase__					,   UpperCamelCase__    )
      else ValueError("""Input Error: Molar mass values must greater than 0."""    )
  )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
      if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0."""    )
  )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
      if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0."""    )
  )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
      if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0."""    )
  )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
  return (
      round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
      if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0."""    )
  )






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    ):


							@require_torch
							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""		)
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      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    :Dict		)   ->   List[str]:



									"""simple docstring"""



									pass


							@slow
							@require_torch
							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""laion/clap-htsat-unfused"""							,				)
									# This is an audio of a dog
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      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"""},
									    ]							,				)

									SCREAMING_SNAKE_CASE__			=      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							,				)
									SCREAMING_SNAKE_CASE__			=      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    :str		)   ->   Optional[int]:



									"""simple docstring"""



									pass