code: 
  
  
  import itertools
  import random
  import unittest
  
  import numpy as np
  
  from transformers import EncodecFeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
  
  
  if is_torch_available():
  		import torch
  
  
  __a   				=      random.Random()
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=1.0,    _UpperCamelCase=None,    _UpperCamelCase=None     )    ->int:
  
  
  
  
  
  
  
  						"""simple docstring"""
  
  						if rng is None:
  												lowercase			:   Any					   =		global_rng
  
  						lowercase			:   List[Any]					   =		[]
  						for batch_idx in range(shape[0]     ):
  												values.append([]     )
  												for _ in range(shape[1]     ):
  																		values[-1].append(rng.random() * scale     )
  
  						return values
  
  
  
  
  
  
  
  @require_torch
  class 				__SCREAMING_SNAKE_CASE   (			unittest.TestCase   ):
  
  
  						def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=7							,						SCREAMING_SNAKE_CASE__=400							,						SCREAMING_SNAKE_CASE__=2000							,						SCREAMING_SNAKE_CASE__=1							,						SCREAMING_SNAKE_CASE__=0.0							,						SCREAMING_SNAKE_CASE__=24000							,						SCREAMING_SNAKE_CASE__=True							,						):
  												lowercase			:   Dict					   =		parent
  												lowercase			:   Tuple					   =		batch_size
  												lowercase			:   str					   =		min_seq_length
  												lowercase			:   Optional[Any]					   =		max_seq_length
  												lowercase			:   int					   =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  												lowercase			:   List[Any]					   =		feature_size
  												lowercase			:   int					   =		padding_value
  												lowercase			:   Optional[Any]					   =		sampling_rate
  												lowercase			:   List[str]					   =		return_attention_mask
  
  
  						def        __lowerCamelCase			(							self		):
  												return {
  												    "feature_size": self.feature_size,
  												    "padding_value": self.padding_value,
  												    "sampling_rate": self.sampling_rate,
  												    "return_attention_mask": self.return_attention_mask,
  												}
  
  
  
  
  
  
  						def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=False		):
  
  
  
  
  
  
  
  												def _flatten(SCREAMING_SNAKE_CASE__		):
  																		return list(itertools.chain(*__A		)		)
  
  												if equal_length:
  																		lowercase			:   Optional[int]					   =		floats_list((self.batch_size, self.max_seq_length)		)
  												else:
  																		# make sure that inputs increase in size
  																		lowercase			:   Optional[Any]					   =		[
  																		    _flatten(floats_list((x, self.feature_size)		)		)
  																		    for x in range(self.min_seq_length							,						self.max_seq_length							,						self.seq_length_diff		)
  																		]
  
  												if numpify:
  																		lowercase			:   int					   =		[np.asarray(__A		) for x in audio_inputs]
  
  												return audio_inputs
  
  
  
  
  
  
  
  @require_torch
  class 				__SCREAMING_SNAKE_CASE   (			a__  ,						unittest.TestCase   ):
  						A				:							str    							=     EncodecFeatureExtractor
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   str					   =		EnCodecFeatureExtractionTester(self		)
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   Optional[int]					   =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
  												# create three inputs of length 800, 1000, and 1200
  												lowercase			:   Optional[Any]					   =		[floats_list((1, x)		)[0] for x in range(800							,						1400							,						200		)]
  												lowercase			:   Optional[Any]					   =		[np.asarray(__A		) for audio_input in audio_inputs]
  
  												# Test not batched input
  												lowercase			:   List[str]					   =		feat_extract(audio_inputs[0]							,						return_tensors='''np'''		).input_values
  												lowercase			:   Union[str, Any]					   =		feat_extract(np_audio_inputs[0]							,						return_tensors='''np'''		).input_values
  												self.assertTrue(np.allclose(__A							,						__A							,						atol=1E-3		)		)
  
  												# Test batched
  												lowercase			:   Optional[int]					   =		feat_extract(__A							,						padding=__A							,						return_tensors='''np'''		).input_values
  												lowercase			:   List[str]					   =		feat_extract(__A							,						padding=__A							,						return_tensors='''np'''		).input_values
  												for enc_seq_a, enc_seq_a in zip(__A							,						__A		):
  																		self.assertTrue(np.allclose(__A							,						__A							,						atol=1E-3		)		)
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   Optional[Any]					   =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
  												lowercase			:   Tuple					   =		np.random.rand(100		).astype(np.floataa		)
  												lowercase			:   Any					   =		np_audio_inputs.tolist()
  
  												for inputs in [py_audio_inputs, np_audio_inputs]:
  																		lowercase			:   int					   =		feature_extractor.pad([{'''input_values''': inputs}]							,						return_tensors='''np'''		)
  																		self.assertTrue(np_processed.input_values.dtype == np.floataa		)
  																		lowercase			:   List[Any]					   =		feature_extractor.pad([{'''input_values''': inputs}]							,						return_tensors='''pt'''		)
  																		self.assertTrue(pt_processed.input_values.dtype == torch.floataa		)
  
  
  						def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__		):
  												from datasets import load_dataset
  
  												lowercase			:   List[Any]					   =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,						'''clean'''							,						split='''validation'''		)
  												# automatic decoding with librispeech
  												lowercase			:   str					   =		ds.sort('''id'''		).select(range(__A		)		)[:num_samples]["audio"]
  
  												return [x["array"] for x in audio_samples]
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   Dict					   =		torch.tensor(
  												    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  												     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  												     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  												     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  												     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  												     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]		)
  												# fmt: on
  												lowercase			:   Union[str, Any]					   =		self._load_datasamples(1		)
  												lowercase			:   str					   =		EncodecFeatureExtractor()
  												lowercase			:   Optional[int]					   =		feature_extractor(__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(input_values.shape							,						(1, 1, 93680)		)
  												self.assertTrue(torch.allclose(input_values[0, 0, :30]							,						__A							,						atol=1E-6		)		)
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   Optional[Any]					   =		torch.tensor(
  												    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  												     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  												     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  												     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  												     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  												     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]		)
  												# fmt: on
  												lowercase			:   Union[str, Any]					   =		self._load_datasamples(1		)
  												lowercase			:   str					   =		[np.tile(input_audio[0][None]							,						reps=(2, 1)		)]
  												input_audio[0][1] *= 0.5
  												lowercase			:   Optional[int]					   =		EncodecFeatureExtractor(feature_size=2		)
  												lowercase			:   str					   =		feature_extractor(__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(input_values.shape							,						(1, 2, 93680)		)
  												self.assertTrue(torch.allclose(input_values[0, 0, :30]							,						__A							,						atol=1E-6		)		)
  												self.assertTrue(torch.allclose(input_values[0, 1, :30]							,						EXPECTED_INPUT_VALUES * 0.5							,						atol=1E-6		)		)
  
  
  
  
  
  
  						def        __lowerCamelCase			(							self		):
  												lowercase			:   str					   =		self._load_datasamples(2		)
  												# would be easier if the stride was like
  												lowercase			:   List[Any]					   =		EncodecFeatureExtractor(feature_size=1							,						chunk_length_s=1							,						overlap=0.01		)
  
  												# pad and trunc raise an error ?
  												with self.assertRaisesRegex(
  												    __A							,						'''^Both padding and truncation were set. Make sure you only set one.$'''							,						):
  																		lowercase			:   Dict					   =		feature_extractor(
  																		    __A							,						padding='''max_length'''							,						truncation=__A							,						return_tensors='''pt'''		).input_values
  
  												# truncate to chunk
  												lowercase			:   Optional[Any]					   =		feature_extractor(__A							,						truncation=__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(2, 1, 71520)		)  # 2 chunks
  
  												# force truncate to max_length
  												lowercase			:   List[str]					   =		feature_extractor(
  												    __A							,						truncation=__A							,						max_length=48000							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(2, 1, 48000)		)
  
  												# pad to chunk
  												lowercase			:   List[str]					   =		feature_extractor(__A							,						padding=__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(padded_outputs.shape							,						(2, 1, 95280)		)
  
  												# pad to chunk
  												lowercase			:   Optional[Any]					   =		feature_extractor(__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(2, 1, 95280)		)
  
  												# force pad to max length
  												lowercase			:   int					   =		feature_extractor(
  												    __A							,						padding='''max_length'''							,						max_length=100000							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(2, 1, 100000)		)
  
  												# force no pad
  												with self.assertRaisesRegex(
  												    __A							,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''							,						):
  																		lowercase			:   Optional[Any]					   =		feature_extractor(__A							,						padding=__A							,						return_tensors='''pt'''		).input_values
  
  												lowercase			:   int					   =		feature_extractor(input_audio[0]							,						padding=__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(1, 1, 93680)		)
  
  												# no pad if no chunk_length_s
  												lowercase			:   Any					   =		None
  												with self.assertRaisesRegex(
  												    __A							,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''							,						):
  																		lowercase			:   int					   =		feature_extractor(__A							,						padding=__A							,						return_tensors='''pt'''		).input_values
  
  												lowercase			:   Any					   =		feature_extractor(input_audio[0]							,						padding=__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(1, 1, 93680)		)
  
  												# no pad if no overlap
  												lowercase			:   List[Any]					   =		2
  												lowercase			:   Any					   =		None
  												with self.assertRaisesRegex(
  												    __A							,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''							,						):
  																		lowercase			:   Optional[int]					   =		feature_extractor(__A							,						padding=__A							,						return_tensors='''pt'''		).input_values
  
  												lowercase			:   List[str]					   =		feature_extractor(input_audio[0]							,						padding=__A							,						return_tensors='''pt'''		).input_values
  												self.assertEquals(truncated_outputs.shape							,						(1, 1, 93680)		)
  
  
style_context: 
  
  
  import numpy as np
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[Any]					   =		abs(predict - actual     )
        lowercase			:   int					   =		difference.mean()
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
        lowercase			:   str					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		predict - actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		predict - actual
        lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Dict					   =		np.log(predict + 1     )
        lowercase			:   List[str]					   =		np.log(actual + 1     )
  
        lowercase			:   int					   =		log_predict - log_actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		square_diff.mean()
  
        lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Optional[int]					   =		predict - actual
        lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        # print(numerator, denumerator)
        lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )
  
  