code: 
  
  
  
  
  """simple docstring"""
  
  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   UpperCamelCase__     (		lowercase__  :     int       ,     lowercase__  :     Tuple=1.0       ,     lowercase__  :     Optional[int]=None       ,     lowercase__  :     Union[str, Any]=None   ):
  
  		if rng is None:
  				snake_case		:						Any								=				global_rng
  
  		snake_case		:						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 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=7				, SCREAMING_SNAKE_CASE=400				, SCREAMING_SNAKE_CASE=2_000				, SCREAMING_SNAKE_CASE=1				, SCREAMING_SNAKE_CASE=0.0				, SCREAMING_SNAKE_CASE=24_000				, SCREAMING_SNAKE_CASE=True				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				parent
  					snake_case		:						Tuple								=				batch_size
  					snake_case		:						str								=				min_seq_length
  					snake_case		:						Optional[Any]								=				max_seq_length
  					snake_case		:						int								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  					snake_case		:						List[Any]								=				feature_size
  					snake_case		:						int								=				padding_value
  					snake_case		:						Optional[Any]								=				sampling_rate
  					snake_case		:						List[str]								=				return_attention_mask
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					def _flatten(SCREAMING_SNAKE_CASE							):
  							return list(itertools.chain(*__A							)							)
  
  					if equal_length:
  							snake_case		:						Optional[int]								=				floats_list((self.batch_size, self.max_seq_length)							)
  					else:
  							# make sure that inputs increase in size
  							snake_case		:						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:
  							snake_case		:						int								=				[np.asarray(__A							) for x in audio_inputs]
  
  					return audio_inputs
  
  
  
  
  
  
  
  @require_torch
  class 			lowerCamelCase__    (  a__				,  unittest.TestCase			):
  			a__ :      Union[str, Any]								=     EncodecFeatureExtractor
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				EnCodecFeatureExtractionTester(self							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  					# create three inputs of length 800, 1000, and 1200
  					snake_case		:						Optional[Any]								=				[floats_list((1, x)							)[0] for x in range(800				, 1_400				, 200							)]
  					snake_case		:						Optional[Any]								=				[np.asarray(__A							) for audio_input in audio_inputs]
  
  					# Test not batched input
  					snake_case		:						List[str]								=				feat_extract(audio_inputs[0]				, return_tensors="np"							).input_values
  					snake_case		:						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
  					snake_case		:						Optional[int]								=				feat_extract(__A				, padding=__A				, return_tensors="np"							).input_values
  					snake_case		:						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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
  					snake_case		:						Tuple								=				np.random.rand(100							).astype(np.floataa							)
  					snake_case		:						Any								=				np_audio_inputs.tolist()
  
  					for inputs in [py_audio_inputs, np_audio_inputs]:
  							snake_case		:						int								=				feature_extractor.pad([{"input_values": inputs}]				, return_tensors="np"							)
  							self.assertTrue(np_processed.input_values.dtype == np.floataa							)
  							snake_case		:						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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					from datasets import load_dataset
  
  					snake_case		:						List[Any]								=				load_dataset("hf-internal-testing/librispeech_asr_dummy"				, "clean"				, split="validation"							)
  					# automatic decoding with librispeech
  					snake_case		:						str								=				ds.sort("id"							).select(range(__A							)							)[:num_samples]["audio"]
  
  					return [x["array"] for x in audio_samples]
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				torch.tensor(
  					    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
  					     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
  					     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
  					     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
  					     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
  					     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]							)
  					# fmt: on
  					snake_case		:						Union[str, Any]								=				self._load_datasamples(1							)
  					snake_case		:						str								=				EncodecFeatureExtractor()
  					snake_case		:						Optional[int]								=				feature_extractor(__A				, return_tensors="pt"							).input_values
  					self.assertEquals(input_values.shape				, (1, 1, 93_680)							)
  					self.assertTrue(torch.allclose(input_values[0, 0, :30]				, __A				, atol=1E-6							)							)
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				torch.tensor(
  					    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
  					     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
  					     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
  					     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
  					     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
  					     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]							)
  					# fmt: on
  					snake_case		:						Union[str, Any]								=				self._load_datasamples(1							)
  					snake_case		:						str								=				[np.tile(input_audio[0][None]				, reps=(2, 1)							)]
  					input_audio[0][1] *= 0.5
  					snake_case		:						Optional[int]								=				EncodecFeatureExtractor(feature_size=2							)
  					snake_case		:						str								=				feature_extractor(__A				, return_tensors="pt"							).input_values
  					self.assertEquals(input_values.shape				, (1, 2, 93_680)							)
  					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							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						str								=				self._load_datasamples(2							)
  					# would be easier if the stride was like
  					snake_case		:						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.$"				, ):
  							snake_case		:						Dict								=				feature_extractor(
  							    __A				, padding="max_length"				, truncation=__A				, return_tensors="pt"							).input_values
  
  					# truncate to chunk
  					snake_case		:						Optional[Any]								=				feature_extractor(__A				, truncation=__A				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (2, 1, 71_520)							)  # 2 chunks
  
  					# force truncate to max_length
  					snake_case		:						List[str]								=				feature_extractor(
  					    __A				, truncation=__A				, max_length=48_000				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (2, 1, 48_000)							)
  
  					# pad to chunk
  					snake_case		:						List[str]								=				feature_extractor(__A				, padding=__A				, return_tensors="pt"							).input_values
  					self.assertEquals(padded_outputs.shape				, (2, 1, 95_280)							)
  
  					# pad to chunk
  					snake_case		:						Optional[Any]								=				feature_extractor(__A				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (2, 1, 95_280)							)
  
  					# force pad to max length
  					snake_case		:						int								=				feature_extractor(
  					    __A				, padding="max_length"				, max_length=100_000				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (2, 1, 100_000)							)
  
  					# 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.$"				, ):
  							snake_case		:						Optional[Any]								=				feature_extractor(__A				, padding=__A				, return_tensors="pt"							).input_values
  
  					snake_case		:						int								=				feature_extractor(input_audio[0]				, padding=__A				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (1, 1, 93_680)							)
  
  					# no pad if no chunk_length_s
  					snake_case		:						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.$"				, ):
  							snake_case		:						int								=				feature_extractor(__A				, padding=__A				, return_tensors="pt"							).input_values
  
  					snake_case		:						Any								=				feature_extractor(input_audio[0]				, padding=__A				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (1, 1, 93_680)							)
  
  					# no pad if no overlap
  					snake_case		:						List[Any]								=				2
  					snake_case		:						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.$"				, ):
  							snake_case		:						Optional[int]								=				feature_extractor(__A				, padding=__A				, return_tensors="pt"							).input_values
  
  					snake_case		:						List[str]								=				feature_extractor(input_audio[0]				, padding=__A				, return_tensors="pt"							).input_values
  					self.assertEquals(truncated_outputs.shape				, (1, 1, 93_680)							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  def   UpperCamelCase__     (		lowercase__  :     int   ):
  		if not isinstance(lowercase__       ,     lowercase__   ):
  				snake_case		:						Optional[Any]								=				F'''Input value of [number={number}] must be an integer'''
  				raise TypeError(lowercase__   )
  		if number < 0 or number % 2 == 1:
  				return False
  		snake_case		:						str								=				int(number**0.5   )
  		return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  