code: 
  
  
  
  
  
  from copy import deepcopy
  from unittest.case import TestCase
  
  import pytest
  
  from datasets.arrow_dataset import Dataset
  from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
  from datasets.info import DatasetInfo
  from datasets.tasks import (
      AudioClassification,
      AutomaticSpeechRecognition,
      ImageClassification,
      LanguageModeling,
      QuestionAnsweringExtractive,
      Summarization,
      TextClassification,
      task_template_from_dict,
  )
  from datasets.utils.py_utils import asdict
  
  
  _lowercase		:     List[Any] 					={
      """id""": """5733be284776f41900661182""",
      """title""": """University_of_Notre_Dame""",
      """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
      """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
      """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
  }
  
  
  @pytest.mark.parametrize(
      'task_cls'       ,[
          AudioClassification,
          AutomaticSpeechRecognition,
          ImageClassification,
          LanguageModeling,
          QuestionAnsweringExtractive,
          Summarization,
          TextClassification,
      ]       ,)
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    Tuple   	=			task_cls()
  		lowerCamelCase_  :    Dict   	=			asdict(lowerCAmelCase__	)
  		lowerCamelCase_  :    Any   	=			task_template_from_dict(lowerCAmelCase__	)
  		assert task == reloaded
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			LanguageModeling(text_column='input_text'	)
  							assert {"input_text": "text"} == task.column_mapping
  
  
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'text': Value('string'	)}	)
  							lowerCamelCase_  :    List[Any]   	=			{'text_column': 'input_text'}
  							lowerCamelCase_  :    List[Any]   	=			LanguageModeling.from_dict(lowerCamelCase	)
  							assert "language-modeling" == task.task
  							assert input_schema == task.input_schema
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[str]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    int   	=			TextClassification(text_column='input_text'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'text': Value('string'	)}	)
  							# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
  							lowerCamelCase_  :    int   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    int   	=			{'text_column': 'input_text', 'label_column': 'input_labels'}
  							lowerCamelCase_  :    List[str]   	=			TextClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('text-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Any   	=			TextClassification(text_column='input_text'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Optional[int]   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			QuestionAnsweringExtractive(
  							    context_column='input_context'					,			question_column='input_question'					,			answers_column='input_answers'	)
  							self.assertDictEqual(
  							    {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}					,			task.column_mapping	)
  
  
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'question': Value('string'	), 'context': Value('string'	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			Features(
  							    {
  							        'answers': Sequence(
  							            {
  							                'text': Value('string'	),
  							                'answer_start': Value('int32'	),
  							            }	)
  							    }	)
  							lowerCamelCase_  :    List[Any]   	=			{
  							    'context_column': 'input_input_context',
  							    'question_column': 'input_question',
  							    'answers_column': 'input_answers',
  							}
  							lowerCamelCase_  :    Union[str, Any]   	=			QuestionAnsweringExtractive.from_dict(lowerCamelCase	)
  							self.assertEqual('question-answering-extractive'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Any   	=			Summarization(text_column='input_text'					,			summary_column='input_summary'	)
  							self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}					,			task.column_mapping	)
  
  
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Optional[int]   	=			Features({'text': Value('string'	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'summary': Value('string'	)}	)
  							lowerCamelCase_  :    str   	=			{'text_column': 'input_text', 'summary_column': 'input_summary'}
  							lowerCamelCase_  :    Optional[Any]   	=			Summarization.from_dict(lowerCamelCase	)
  							self.assertEqual('summarization'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Any   	=			AutomaticSpeechRecognition(audio_column='input_audio'					,			transcription_column='input_transcription'	)
  							self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}					,			task.column_mapping	)
  
  
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'audio': Audio()}	)
  							lowerCamelCase_  :    Tuple   	=			Features({'transcription': Value('string'	)}	)
  							lowerCamelCase_  :    Optional[Any]   	=			{
  							    'audio_column': 'input_audio',
  							    'transcription_column': 'input_transcription',
  							}
  							lowerCamelCase_  :    Any   	=			AutomaticSpeechRecognition.from_dict(lowerCamelCase	)
  							self.assertEqual('automatic-speech-recognition'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    Optional[Any]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[str]   	=			AudioClassification(audio_column='input_audio'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Union[str, Any]   	=			Features({'audio': Audio()}	)
  							lowerCamelCase_  :    Any   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    Dict   	=			{
  							    'audio_column': 'input_image',
  							    'label_column': 'input_label',
  							}
  							lowerCamelCase_  :    Dict   	=			AudioClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('audio-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    str   	=			AudioClassification(audio_column='input_audio'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Dict   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[Any]   	=			sorted(['pos', 'neg']	)
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    Union[str, Any]   	=			ImageClassification(image_column='input_image'					,			label_column='input_label'	)
  							self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}					,			task.column_mapping	)
  
  
  
  					def      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    int   	=			Features({'image': Image()}	)
  							lowerCamelCase_  :    List[Any]   	=			Features({'labels': ClassLabel}	)
  							lowerCamelCase_  :    List[str]   	=			{
  							    'image_column': 'input_image',
  							    'label_column': 'input_label',
  							}
  							lowerCamelCase_  :    Dict   	=			ImageClassification.from_dict(lowerCamelCase	)
  							self.assertEqual('image-classification'					,			task.task	)
  							self.assertEqual(lowerCamelCase					,			task.input_schema	)
  							self.assertEqual(lowerCamelCase					,			task.label_schema	)
  
  
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    Any   	=			ImageClassification(image_column='input_image'					,			label_column='input_label'	)
  							self.assertEqual(task.label_schema['labels']					,			lowerCamelCase	)
  							lowerCamelCase_  :    Dict   	=			task.align_with_features(Features({'input_label': ClassLabel(names=self.labels	)}	)	)
  							self.assertEqual(task.label_schema['labels']					,			ClassLabel(names=self.labels	)	)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Optional[Any]   	=			DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
  							lowerCamelCase_  :    Any   	=			Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}					,			info=lowerCamelCase	)
  							assert isinstance(dataset.info.task_templates					,			lowerCamelCase	)
  							assert len(dataset.info.task_templates	) == 1
  
  							def keep_task(lowerCamelCase    :    List[Any]	):
  									return x
  
  							def dont_keep_task(lowerCamelCase    :    int	):
  									lowerCamelCase_  :    Dict   	=			deepcopy(lowerCamelCase	)
  									lowerCamelCase_  :    Dict   	=			0
  									return out
  
  							lowerCamelCase_  :    Tuple   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == dataset.info.task_templates
  							# reload from cache
  							lowerCamelCase_  :    int   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
  							lowerCamelCase_  :    List[str]   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  							# reload from cache
  							lowerCamelCase_  :    List[Any]   	=			dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  
  
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    List[Any]   	=			Features({'text': Value('string'	), 'label': ClassLabel(names=('pos', 'neg')	)}	)
  							lowerCamelCase_  :    Union[str, Any]   	=			TextClassification(text_column='text'					,			label_column='label'	)
  							lowerCamelCase_  :    str   	=			DatasetInfo(features=lowerCamelCase					,			task_templates=lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}					,			info=lowerCamelCase	)
  
  							def process(lowerCamelCase    :    List[Any]	):
  									return example
  
  							lowerCamelCase_  :    List[str]   	=			dataset.remove_columns('label'	)
  							lowerCamelCase_  :    List[str]   	=			modified_dataset.map(lowerCamelCase	)
  							assert mapped_dataset.info.task_templates == []
  
style_context: 
  
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
  from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
  from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime
  
  from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  _lowercase		:     Optional[Any] 					="""./tests/fixtures/elise_format0.mid"""
  
  
  
  
  
  
  @unittest.skip('The note-seq package currently throws an error on import'       )
  class 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
  					_a			:	int							=				SpectrogramDiffusionPipeline
  					_a			:	Dict							=				PipelineTesterMixin.required_optional_params - {
  					    'callback',
  					    'latents',
  					    'callback_steps',
  					    'output_type',
  					    'num_images_per_prompt',
  					}
  					_a			:	str							=				False
  					_a			:	Union[str, Any]							=				False
  					_a			:	Optional[int]							=				TOKENS_TO_AUDIO_GENERATION_PARAMS
  					_a			:	Optional[Any]							=				TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
  
  
  
  					def      __a    (		self    :    Dict	):
  							torch.manual_seed(0	)
  							lowerCamelCase_  :    List[str]   	=			SpectrogramNotesEncoder(
  							    max_length=20_48					,			vocab_size=15_36					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramContEncoder(
  							    input_dims=1_28					,			targets_context_length=2_56					,			d_model=7_68					,			dropout_rate=0.1					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			feed_forward_proj='gated-gelu'					,			)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			TaFilmDecoder(
  							    input_dims=1_28					,			targets_length=2_56					,			max_decoder_noise_time=20_000.0					,			d_model=7_68					,			num_layers=1					,			num_heads=1					,			d_kv=4					,			d_ff=20_48					,			dropout_rate=0.1					,			)
  
  							lowerCamelCase_  :    Any   	=			DDPMScheduler()
  
  							lowerCamelCase_  :    str   	=			{
  							    'notes_encoder': notes_encoder.eval(),
  							    'continuous_encoder': continuous_encoder.eval(),
  							    'decoder': decoder.eval(),
  							    'scheduler': scheduler,
  							    'melgan': None,
  							}
  							return components
  
  
  
  					def      __a    (		self    :    Any					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    int=0	):
  							if str(lowerCamelCase	).startswith('mps'	):
  									lowerCamelCase_  :    Optional[int]   	=			torch.manual_seed(lowerCamelCase	)
  							else:
  									lowerCamelCase_  :    Dict   	=			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			{
  							    'input_tokens': [
  							        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
  							    ],
  							    'generator': generator,
  							    'num_inference_steps': 4,
  							    'output_type': 'mel',
  							}
  							return inputs
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    str   	=			'cpu'  # ensure determinism for the device-dependent torch.Generator
  							lowerCamelCase_  :    Dict   	=			self.get_dummy_components()
  							lowerCamelCase_  :    Tuple   	=			SpectrogramDiffusionPipeline(**lowerCamelCase	)
  							lowerCamelCase_  :    List[str]   	=			pipe.to(lowerCamelCase	)
  							pipe.set_progress_bar_config(disable=lowerCamelCase	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			self.get_dummy_inputs(lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			pipe(**lowerCamelCase	)
  							lowerCamelCase_  :    Tuple   	=			output.audios
  
  							lowerCamelCase_  :    Tuple   	=			mel[0, -3:, -3:]
  
  							assert mel_slice.shape == (3, 3)
  							lowerCamelCase_  :    List[str]   	=			np.array(
  							    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]	)
  							assert np.abs(mel_slice.flatten() - expected_slice	).max() < 1E-2
  
  
  
  					@skip_mps
  					def      __a    (		self    :    Any	):
  							return super().test_save_load_local()
  
  
  
  					@skip_mps
  					def      __a    (		self    :    Tuple	):
  							return super().test_dict_tuple_outputs_equivalent()
  
  
  
  					@skip_mps
  					def      __a    (		self    :    Dict	):
  							return super().test_save_load_optional_components()
  
  
  
  					@skip_mps
  					def      __a    (		self    :    str	):
  							return super().test_attention_slicing_forward_pass()
  
  
  
  					def      __a    (		self    :    Any	):
  							pass
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							pass
  
  
  
  
  
  					@skip_mps
  					def      __a    (		self    :    Optional[Any]	):
  							return super().test_progress_bar()
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  @require_onnxruntime
  @require_note_seq
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							# clean up the VRAM after each test
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  					def      __a    (		self    :    str	):
  							# TODO - test that pipeline can decode tokens in a callback
  							# so that music can be played live
  							lowerCamelCase_  :    Optional[Any]   	=			torch_device
  
  							lowerCamelCase_  :    Union[str, Any]   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
  							lowerCamelCase_  :    str   	=			pipe.melgan
  							lowerCamelCase_  :    Optional[int]   	=			None
  
  							lowerCamelCase_  :    str   	=			pipe.to(lowerCamelCase	)
  							pipe.set_progress_bar_config(disable=lowerCamelCase	)
  
  							def callback(lowerCamelCase    :    int					,			lowerCamelCase    :    int	):
  									# decode mel to audio
  									lowerCamelCase_  :    Union[str, Any]   	=			melgan(input_features=mel_output.astype(np.floataa	)	)[0]
  									assert len(audio[0]	) == 8_19_20 * (step + 1)
  									# simulate that audio is played
  									return audio
  
  							lowerCamelCase_  :    Any   	=			MidiProcessor()
  							lowerCamelCase_  :    str   	=			processor(lowerCamelCase	)
  
  							lowerCamelCase_  :    str   	=			input_tokens[:3]
  							lowerCamelCase_  :    Dict   	=			torch.manual_seed(0	)
  							pipe(lowerCamelCase					,			num_inference_steps=5					,			generator=lowerCamelCase					,			callback=lowerCamelCase					,			output_type='mel'	)
  
  
  
  					def      __a    (		self    :    str	):
  							lowerCamelCase_  :    List[str]   	=			torch_device
  
  							lowerCamelCase_  :    List[str]   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
  							lowerCamelCase_  :    Union[str, Any]   	=			pipe.to(lowerCamelCase	)
  							pipe.set_progress_bar_config(disable=lowerCamelCase	)
  							lowerCamelCase_  :    int   	=			MidiProcessor()
  
  							lowerCamelCase_  :    Dict   	=			processor(lowerCamelCase	)
  							# just run two denoising loops
  							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:2]
  
  							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
  							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			num_inference_steps=2					,			generator=lowerCamelCase	)
  
  							lowerCamelCase_  :    Dict   	=			output.audios[0]
  
  							assert abs(np.abs(lowerCamelCase	).sum() - 3_612.841	) < 1E-1
  
  
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    int   	=			torch_device
  
  							lowerCamelCase_  :    str   	=			SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'	)
  							lowerCamelCase_  :    Optional[int]   	=			pipe.to(lowerCamelCase	)
  							pipe.set_progress_bar_config(disable=lowerCamelCase	)
  
  							lowerCamelCase_  :    Tuple   	=			MidiProcessor()
  
  							lowerCamelCase_  :    int   	=			processor(lowerCamelCase	)
  
  							# just run 4 denoising loops
  							lowerCamelCase_  :    Optional[Any]   	=			input_tokens[:4]
  
  							lowerCamelCase_  :    List[Any]   	=			torch.manual_seed(0	)
  							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase					,			num_inference_steps=1_00					,			generator=lowerCamelCase	)
  
  							lowerCamelCase_  :    List[Any]   	=			output.audios[0]
  							assert abs(np.abs(lowerCamelCase	).sum() - 9_389.1_111	) < 5E-2
  