





from __future__ import annotations




class   UpperCamelCase_       :


       def __init__(							self    :Optional[int]							,				__A    :int		)   ->   None:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      order

         # a_{0} ... a_{k}
         SCREAMING_SNAKE_CASE__			=      [1.0] + [0.0] * order
         # b_{0} ... b_{k}
         SCREAMING_SNAKE_CASE__			=      [1.0] + [0.0] * order

         # x[n-1] ... x[n-k]
         SCREAMING_SNAKE_CASE__			=      [0.0] * self.order
         # y[n-1] ... y[n-k]
         SCREAMING_SNAKE_CASE__			=      [0.0] * self.order


       def      _snake_case  (							self    :Optional[Any]							,				__A    :list[float]							,				__A    :list[float]		)   ->   None:



         """simple docstring"""



         if len(A_		) < self.order:
           SCREAMING_SNAKE_CASE__			=      [1.0, *a_coeffs]

         if len(A_		) != self.order + 1:
           SCREAMING_SNAKE_CASE__			=      (
               f'''Expected a_coeffs to have {self.order + 1} elements '''
               f'''for {self.order}-order filter, got {len(A_		)}'''
           )
           raise ValueError(A_		)

         if len(A_		) != self.order + 1:
           SCREAMING_SNAKE_CASE__			=      (
               f'''Expected b_coeffs to have {self.order + 1} elements '''
               f'''for {self.order}-order filter, got {len(A_		)}'''
           )
           raise ValueError(A_		)

         SCREAMING_SNAKE_CASE__			=      a_coeffs
         SCREAMING_SNAKE_CASE__			=      b_coeffs


       def      _snake_case  (							self    :str							,				__A    :float		)   ->   float:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      0.0

         # Start at index 1 and do index 0 at the end.
         for i in range(1							,				self.order + 1		):
           result += (
               self.b_coeffs[i] * self.input_history[i - 1]
               - self.a_coeffs[i] * self.output_history[i - 1]
           )

         SCREAMING_SNAKE_CASE__			=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

         SCREAMING_SNAKE_CASE__			=      self.input_history[:-1]
         SCREAMING_SNAKE_CASE__			=      self.output_history[:-1]

         SCREAMING_SNAKE_CASE__			=      sample
         SCREAMING_SNAKE_CASE__			=      result

         return result






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